diff --git a/.ai/AGENTS.md b/.ai/AGENTS.md
index 92312ae6be06..fcfec4d82571 100644
--- a/.ai/AGENTS.md
+++ b/.ai/AGENTS.md
@@ -59,6 +59,22 @@ Do not raise PRs without human validation.
 - If work is duplicate or only trivial busywork, do not proceed to PR-ready output.
 - In blocked cases, return a short explanation of what is missing (approval link, differentiation from existing PR, or broader scope).
 
+## Learning transformers primitives by example
+
+The `src/transformers/cli/agentic/` directory contains concise, self-contained
+examples of how to use the core transformers primitives (`AutoModel`,
+`AutoTokenizer`, `AutoProcessor`, `AutoImageProcessor`, etc.) for a wide
+range of tasks — text classification, NER, QA, summarization, translation,
+image classification, object detection, segmentation, depth estimation,
+speech recognition, audio classification, text-to-speech, video
+classification, visual QA, captioning, OCR, and more.
+
+Each file (`text.py`, `vision.py`, `audio.py`, `multimodal.py`) follows the
+same pattern: load a model and processor with `from_pretrained`, preprocess
+inputs, run a forward pass or `generate`, and post-process the outputs. If
+you need to write code that uses transformers and are unsure how to get
+started, read the relevant command in that folder first.
+
 ## Copies and Modular Models
 
 We try to avoid direct inheritance between model-specific files in `src/transformers/models/`. We have two mechanisms to manage the resulting code duplication:
diff --git a/.circleci/create_circleci_config.py b/.circleci/create_circleci_config.py
index 26f254d141b7..ab8ce0cd6b47 100644
--- a/.circleci/create_circleci_config.py
+++ b/.circleci/create_circleci_config.py
@@ -398,6 +398,15 @@ def job_name(self):
     parallelism=6,
 )
 
+training_distributed_ci_job = CircleCIJob(
+    "training_distributed_ci",
+    additional_env={"RUN_TRAINING_TESTS": True},
+    docker_image=[{"image": "huggingface/transformers-torch-light"}],
+    install_steps=["uv pip install ."],
+    marker="is_training_distributed_test",
+    parallelism=6,
+)
+
 # We also include a `dummy.py` file in the files to be doc-tested to prevent edge case failure. Otherwise, the pytest
 # hangs forever during test collection while showing `collecting 0 items / 21 errors`. (To see this, we have to remove
 # the bash output redirection.)
@@ -427,7 +436,7 @@ def job_name(self):
 PIPELINE_TESTS = [pipelines_torch_job]
 REPO_UTIL_TESTS = [repo_utils_job]
 DOC_TESTS = [doc_test_job]
-TRAINING_CI_TESTS = [training_ci_job]
+TRAINING_CI_TESTS = [training_ci_job, training_distributed_ci_job]
 TENSOR_PARALLEL_CI_TESTS = [tensor_parallel_ci_job]
 ALL_TESTS = REGULAR_TESTS + EXAMPLES_TESTS + PIPELINE_TESTS + REPO_UTIL_TESTS + DOC_TESTS + [custom_tokenizers_job] + [exotic_models_job] + TRAINING_CI_TESTS + TENSOR_PARALLEL_CI_TESTS  # fmt: skip
 
diff --git a/.github/scripts/assign_reviewers.py b/.github/scripts/assign_reviewers.py
index 18567203596f..47fd38623755 100644
--- a/.github/scripts/assign_reviewers.py
+++ b/.github/scripts/assign_reviewers.py
@@ -1,4 +1,3 @@
-# coding=utf-8
 # Copyright 2025 the HuggingFace Inc. team. All rights reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
diff --git a/.github/workflows/model_jobs.yml b/.github/workflows/model_jobs.yml
index e96c7ef16a07..94f6dece6bc2 100644
--- a/.github/workflows/model_jobs.yml
+++ b/.github/workflows/model_jobs.yml
@@ -186,7 +186,18 @@ jobs:
         env:
           report_name_prefix: ${{ inputs.report_name_prefix }}
         run: |
-          cat "/transformers/reports/${machine_type}_${report_name_prefix}_${matrix_folders}_test_reports/captured_info.txt"
+          shopt -s nullglob
+          captured_info_files=("/transformers/reports/${machine_type}_${report_name_prefix}_${matrix_folders}_test_reports"/captured_info*.txt)
+
+          if [ ${#captured_info_files[@]} -eq 0 ]; then
+            echo "No captured information files found."
+            exit 0
+          fi
+
+          for captured_info_file in "${captured_info_files[@]}"; do
+            echo "===== ${captured_info_file##*/} ====="
+            cat "$captured_info_file"
+          done
 
       - name: Copy test_outputs.txt
         if: ${{ always() }}
diff --git a/README.md b/README.md
index 7af560a73510..8c50c3496f0f 100644
--- a/README.md
+++ b/README.md
@@ -134,8 +134,9 @@ pipeline("the secret to baking a really good cake is ")
 To chat with a model, the usage pattern is the same. The only difference is you need to construct a chat history (the input to `Pipeline`) between you and the system.
 
 > [!TIP]
-> You can also chat with a model directly from the command line, as long as [`transformers serve` is running](https://huggingface.co/docs/transformers/main/en/serving).
+> You can also chat with a model directly from the command line, as long as the `chat` extra is installed and [`transformers serve` is running](https://huggingface.co/docs/transformers/main/en/serving).
 > ```shell
+> pip install .[chat]  # or pip install transformers[chat]
 > transformers chat Qwen/Qwen2.5-0.5B-Instruct
 > ```
 
diff --git a/all_requirements.txt b/all_requirements.txt
new file mode 100644
index 000000000000..eacb47727a64
--- /dev/null
+++ b/all_requirements.txt
@@ -0,0 +1,98 @@
+gpustat==1.1.1
+psutil==6.0.0
+psycopg2==2.9.9
+pandas>=1.5.0
+numpy>=1.21.0
+psutil>=5.8.0
+nvidia-ml-py>=12.0.0
+torch>=2.0.0
+datasets>=2.10.0
+huggingface_hub>=0.16.0
+amdsmi>=7.0.2
+git+https://github.com/huggingface/transformers.git@main # install main or adjust it with vX.X.X for installing version specific transforms
+datasets==1.8.0accelerate >= 0.12.0
+datasets >= 1.8.0
+torch >= 1.3.0
+evaluateaccelerate >= 0.21.0
+sentencepiece != 0.1.92
+protobuf
+torch >= 1.3
+datasets[audio]>=1.14.0
+evaluate
+librosa
+torchaudio
+torch>=1.6
+accelerate >= 0.12.0
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+protobuf
+sacrebleu >= 1.4.12
+py7zr
+torch >= 1.3
+evaluatedatasets >= 2.0.0
+torch >= 1.3
+accelerate
+evaluate
+Pillow
+albumentations >= 1.4.16
+accelerate >= 0.12.0
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+protobuf
+rouge-score
+nltk
+py7zr
+torch >= 1.3
+evaluate
+torch>=1.5.0
+torchvision>=0.6.0
+datasets>=1.8.0accelerate >= 0.12.0
+datasets >= 1.8.0
+sentencepiece != 0.1.92
+scipy
+scikit-learn
+protobuf
+torch >= 1.3
+evaluateaccelerate>=0.12.0
+torch>=1.5.0
+torchvision>=0.6.0
+datasets>=2.14.0
+evaluate
+scikit-learnaccelerate >= 0.12.0
+torch >= 1.3
+datasets >= 2.14.0
+sentencepiece != 0.1.92
+protobuf
+evaluate
+scikit-learn
+accelerate >= 0.12.0
+seqeval
+datasets >= 1.8.0
+torch >= 1.3
+evaluatealbumentations >= 1.4.16
+timm
+datasets>=4.0
+torchmetrics
+pycocotools
+datasets[audio] >= 1.18.0
+torch >= 1.5
+torchaudio
+librosa
+jiwer
+evaluate
+datasets[audio] >= 1.12.0
+torch >= 1.5
+torchaudio
+accelerate >= 0.12.0
+librosatorch>=1.5.0
+torchvision>=0.6.0
+datasets>=1.8.0albumentations >= 1.4.16
+timm
+datasets
+torchmetrics
+pycocotools
+accelerate >= 0.12.0
+sentencepiece != 0.1.92
+protobuf
+torch >= 1.3
+evaluate
diff --git a/benchmark_v2/benchmark_scripts/continuous_batching_overall.py b/benchmark_v2/benchmark_scripts/continuous_batching_overall.py
index 9b2bb875b758..2595a7d8c9d5 100644
--- a/benchmark_v2/benchmark_scripts/continuous_batching_overall.py
+++ b/benchmark_v2/benchmark_scripts/continuous_batching_overall.py
@@ -1,149 +1,303 @@
+"""
+Continuous batching overall benchmark suite.
+
+Runs CB in-process across many configurations (GSM8K prompts and synthetic
+data) and can compare throughput against a previously-saved run.
+"""
+
 import argparse
+import gc
 import json
-import re
-import subprocess
-from datetime import datetime
+import time
+from dataclasses import asdict, dataclass
 from pathlib import Path
+from typing import Any
 
+import datasets
+import torch
 from tabulate import tabulate
 
+from transformers import AutoModelForCausalLM, AutoTokenizer, ContinuousBatchingConfig, GenerationConfig
 
-SCRIPT_LOCATION = (Path(__file__).parent.parent.parent / "examples/pytorch/continuous_batching.py").as_posix()
-COMMON_ARGS = "--log-level WARNING --seed 0 --force-max-length".split()
-ERROR_OUTPUT = {"time_seconds": "X", "num_tokens": "X", "throughput_tok_per_sec": "ERROR"}
+
+# Defaults
 RESULTS_DIR = Path(__file__).parent.parent / "benchmark_results/cb_overall/"
 
 
-def run_and_parse_cb_example(args: str) -> dict:
-    print(f"\nBenchmarking with args: {args}")
-    output = subprocess.run(
-        ["python", SCRIPT_LOCATION] + args.split() + COMMON_ARGS,
-        stdout=subprocess.PIPE,
-    )
-    output = output.stdout.decode("utf-8")
-    if "generate_batch despite unexpected termination" in output:
-        return {"args": args, **ERROR_OUTPUT}
-    pattern = r"CB generation took: ([\d.]+) seconds for (\d+) tokens\. ([\d.]+)tok/s"
-    match = re.search(pattern, output)
-    if match is not None:
-        return {
-            "args": args,
-            "time_seconds": float(match.group(1)),
-            "num_tokens": int(match.group(2)),
-            "throughput_tok_per_sec": float(match.group(3)),
-        }
-    else:
-        return {"args": args, **ERROR_OUTPUT}
-
-
-def get_most_recent_file(prefix: str, exclude: Path | None = None) -> Path | None:
-    """Find the most recent results file in RESULTS_DIR matching the given prefix, optionally excluding one."""
-    candidates = sorted(RESULTS_DIR.glob(f"{prefix}__*.json"))
-    if exclude:
-        candidates = [c for c in candidates if c != exclude]
-    return candidates[-1] if candidates else None
-
-
-def build_comparison_table(results: list[dict], baseline_results: list[dict], baseline_label: str) -> list[dict]:
-    """Build a table comparing current results against baseline results."""
-    baseline_by_args = {r["args"]: r for r in baseline_results}
-    comparison = [
-        {
-            "args": "Arguments",
-            "baseline_tok_per_sec": f"{baseline_label} (tok/s)",
-            "current_tok_per_sec": "Current (tok/s)",
-            "diff_percent": "Diff (%)",
+# Data helpers
+def get_tokenized_gms8k(tokenizer: AutoTokenizer) -> list[list[int]]:
+    """Tokenize the GSM8K questions as chat prompts."""
+    dataset = datasets.load_dataset("openai/gsm8k", "socratic", split="test")
+    batched_inputs = []
+    for item in dataset:
+        messages = [{"role": "user", "content": item["question"]}]
+        inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True)  # type: ignore
+        batched_inputs.append(inputs if isinstance(inputs, list) else inputs["input_ids"])
+    return batched_inputs
+
+
+def get_random_data(batch_size: int, num_tokens: int, vocab_size: int = 16000) -> list[list[int]]:
+    """Random token sequences of fixed length, for raw throughput tests."""
+    rng = torch.Generator().manual_seed(0)
+    return [torch.randint(0, vocab_size, (num_tokens,), generator=rng).tolist() for _ in range(batch_size)]
+
+
+# Benchmark entries and collection
+@dataclass
+class BenchmarkEntry:
+    """Single CB run: what was fed in, which configs were used, and the resulting metrics."""
+
+    label: str
+    num_samples: int
+    avg_input_tokens: float
+    max_new_tokens: int
+    cb_config: dict[str, Any]
+    gen_config: dict[str, Any]
+    time_seconds: float | None = None
+    num_tokens: int | None = None
+    throughput_tok_per_sec: float | None = None
+    peak_memory_gb: float | None = None
+    error: str | None = None
+
+
+def _config_summary(cfg: Any) -> dict[str, Any]:
+    """Extract a JSON-friendly summary of a dataclass/config object."""
+    raw = cfg.to_dict() if hasattr(cfg, "to_dict") else cfg.__dict__
+    return {k: v for k, v in raw.items() if isinstance(v, (int, float, str, bool, type(None)))}
+
+
+class BenchmarkResults:
+    """Holds all CB benchmark runs and the shared model they execute against."""
+
+    def __init__(self, model_id: str, attn_impl: str):
+        self.model_id = model_id
+        self.attn_impl = attn_impl
+        self.entries: list[BenchmarkEntry] = []
+
+    def cleanup(self) -> None:
+        torch.cuda.empty_cache()
+        gc.collect()
+        torch.cuda.reset_peak_memory_stats()
+
+    def _get_model(self) -> Any:
+        self.cleanup()
+        model = AutoModelForCausalLM.from_pretrained(self.model_id, attn_implementation=self.attn_impl)
+        model = model.to(device="cuda").eval()  # type: ignore
+        return model
+
+    def add_benchmark(
+        self,
+        data: list[list[int]],
+        max_new_tokens: int,
+        cb_config: ContinuousBatchingConfig,
+        gen_config: GenerationConfig | None = None,
+        label: str | None = None,
+    ) -> BenchmarkEntry:
+        """Run one CB benchmark and record time, tokens, and peak memory."""
+
+        gen_config = GenerationConfig() if gen_config is None else gen_config
+        gen_config.max_new_tokens = max_new_tokens
+        # Disable EOS so every request runs to max_new_tokens — consistent benchmarking.
+        gen_config.eos_token_id = -1
+
+        model = self._get_model(cb_config, gen_config)
+
+        avg_input = sum(len(x) for x in data) / max(len(data), 1)
+        entry = BenchmarkEntry(
+            label=label or f"bench_{len(self.entries)}",
+            num_samples=len(data),
+            avg_input_tokens=avg_input,
+            max_new_tokens=max_new_tokens,
+            cb_config=_config_summary(cb_config),
+            gen_config=_config_summary(gen_config),
+        )
+
+        print(f"\n[{entry.label}] samples={entry.num_samples} avg_in={avg_input:.1f} max_new={max_new_tokens}")
+
+        self.cleanup()
+
+        try:
+            outputs = model.generate_batch(
+                inputs=data,
+                generation_config=gen_config,
+                continuous_batching_config=cb_config,
+                progress_bar=False,
+            )
+            gen_start = min(out.created_time for out in outputs.values())
+            gen_end = max(out.lifespan[1] for out in outputs.values())
+            gen_time = gen_end - gen_start
+            num_tokens = sum(len(out.generated_tokens) for out in outputs.values())
+
+            entry.time_seconds = gen_time
+            entry.num_tokens = num_tokens
+            entry.throughput_tok_per_sec = num_tokens / gen_time if gen_time > 0 else 0.0
+            entry.peak_memory_gb = torch.cuda.max_memory_allocated() / (1024**3)
+            print(
+                f"   {gen_time:.2f}s, {num_tokens} tokens, "
+                f"{entry.throughput_tok_per_sec:.2f} tok/s, peak {entry.peak_memory_gb:.2f} GB"
+            )
+        except Exception as e:
+            entry.error = str(e)
+            print(f"   ERROR: {e}")
+
+        self.entries.append(entry)
+        self.cleanup()
+        return entry
+
+    # Persistence
+    def save(self, name: str) -> Path:
+        """Save all entries to a timestamped JSON file keyed by name."""
+        RESULTS_DIR.mkdir(parents=True, exist_ok=True)
+        filename = RESULTS_DIR / f"{name}__{int(time.time())}.json"
+        payload = {
+            "model_id": self.model_id,
+            "attn_impl": self.attn_impl,
+            "entries": [asdict(e) for e in self.entries],
         }
-    ]
-    for result in results:
-        baseline = baseline_by_args.get(result["args"])
-        baseline_tp = baseline["throughput_tok_per_sec"] if baseline else None
-        current_tp = result["throughput_tok_per_sec"]
-        if isinstance(baseline_tp, (int, float)) and isinstance(current_tp, (int, float)):
-            diff = (current_tp - baseline_tp) / baseline_tp * 100
-            diff_str = f"{diff:+.1f}%"
-        else:
-            diff_str = "N/A"
-        comparison.append(
+        filename.write_text(json.dumps(payload, indent=2))
+        print(f"\nResults saved to {filename}")
+        return filename
+
+    @classmethod
+    def load_most_recent(cls, name: str) -> "BenchmarkResults":
+        """Load the most recent JSON file matching name."""
+        candidates = sorted(RESULTS_DIR.glob(f"{name}__*.json"))
+        if not candidates:
+            raise FileNotFoundError(f"No baseline with name '{name}' in {RESULTS_DIR}")
+        data = json.loads(candidates[-1].read_text())
+        instance = cls(
+            model_id=data.get("model_id"),
+            attn_impl=data.get("attn_impl"),
+        )
+        instance.entries = [BenchmarkEntry(**e) for e in data["entries"]]
+        print(f"Loaded baseline from {candidates[-1]}")
+        return instance
+
+    # Display
+    def print_summary(self) -> None:
+        rows = [
             {
-                "args": result["args"],
-                "baseline_tok_per_sec": baseline_tp if baseline_tp is not None else "N/A",
-                "current_tok_per_sec": current_tp,
-                "diff_percent": diff_str,
+                "label": e.label,
+                "samples": e.num_samples,
+                "avg_in": f"{e.avg_input_tokens:.1f}",
+                "max_new": e.max_new_tokens,
+                "time (s)": f"{e.time_seconds:.2f}" if e.time_seconds is not None else "X",
+                "tokens": e.num_tokens if e.num_tokens is not None else "X",
+                "tok/s": f"{e.throughput_tok_per_sec:.2f}" if e.throughput_tok_per_sec is not None else "ERROR",
+                "mem (GB)": f"{e.peak_memory_gb:.2f}" if e.peak_memory_gb is not None else "X",
             }
-        )
-    return comparison
+            for e in self.entries
+        ]
+        print("\n" + tabulate(rows, headers="keys", tablefmt="github"))
 
+    def compare_to(self, baseline: "BenchmarkResults") -> None:
+        """Print a side-by-side throughput comparison against a baseline run."""
+        baseline_by_label = {e.label: e for e in baseline.entries}
+        rows = []
+        for e in self.entries:
+            base = baseline_by_label.get(e.label)
+            base_tp = base.throughput_tok_per_sec if base else None
+            cur_tp = e.throughput_tok_per_sec
+            if isinstance(base_tp, (int, float)) and isinstance(cur_tp, (int, float)) and base_tp > 0:
+                diff_str = f"{(cur_tp - base_tp) / base_tp * 100:+.1f}%"
+            else:
+                diff_str = "N/A"
+            rows.append(
+                {
+                    "label": e.label,
+                    "baseline (tok/s)": f"{base_tp:.2f}" if isinstance(base_tp, (int, float)) else "N/A",
+                    "current (tok/s)": (f"{cur_tp:.2f}" if isinstance(cur_tp, (int, float)) else (e.error or "N/A")),
+                    "diff": diff_str,
+                }
+            )
+        print(f"\nComparison against baseline (model={baseline.model_id}):")
+        print(tabulate(rows, headers="keys", tablefmt="github"))
 
+
+# Main
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--main", action="store_true", help="Save results as the main baseline to compare against.")
-    args = parser.parse_args()
-
-    results = [
-        {
-            "args": "Arguments",
-            "time_seconds": "Duration (s)",
-            "num_tokens": "Generated tokens",
-            "throughput_tok_per_sec": "Throughput (tok/s)",
-        }
-    ]
-
-    # Benchmark with low number of samples
-    results.append(run_and_parse_cb_example("--samples 10"))
-    results.append(run_and_parse_cb_example("--samples 20 --num-blocks 20"))  # and low number of blocks
-    results.append(run_and_parse_cb_example("--samples 50"))
-
-    # Benchmark with compile: default, flash attention 2 and sdpa
-    results.append(run_and_parse_cb_example("--samples 100"))
-    results.append(run_and_parse_cb_example("--samples 100 --attn flash_attention_2"))
-    results.append(run_and_parse_cb_example("--samples 100 --attn sdpa"))
-
-    # Benchmark with high number of samples and synchronous batching
-    results.append(run_and_parse_cb_example("--samples 500 --no-use-async"))
-    # Benchmark with high number of samples and asynchronous batching
-    results.append(run_and_parse_cb_example("--samples 500 --use-async"))
-
-    # Benchmark with low number of samples, asynchronous batching and decdode fast path
-    results.append(run_and_parse_cb_example("--samples 32 --max-new-tokens 2048 --use-async"))
-    # Benchmark with low number of samples, asynchronous batching and decdode fast path
-    results.append(run_and_parse_cb_example("--samples 32 --max-new-tokens 2048 --use-async --block-table 32"))
-
-    # Benchmark with prefix sharing and compile (best performance, but not reproducible due to compilation)
-    results.append(run_and_parse_cb_example("--samples 500 --add-prefix --compile"))
-
-    # Benchmark with parallel decoding
-    results.append(run_and_parse_cb_example("--samples 50 --num-return-sequences 8 --do-sample"))
-    results.append(run_and_parse_cb_example("--samples 100 --num-return-sequences 4 --do-sample"))
-
-    # Print results
-    print()
-    print(tabulate(results, tablefmt="github"))
-
-    # The header row is results[0], data rows are results[1:]
-    data_results = results[1:]
-
-    # Always save results to a new timestamped file
-    RESULTS_DIR.mkdir(parents=True, exist_ok=True)
-    prefix = "main" if args.main else "run"
-    results_file = RESULTS_DIR / f"{prefix}__{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
-    results_file.write_text(json.dumps(data_results, indent=2))
-    print(f"\nResults saved to {results_file}")
-
-    # Compare against baseline
-    if args.main:
-        # Compare against the previous main baseline (the one that was most recent before this new file)
-        baseline_file = get_most_recent_file("main", exclude=results_file)
-        baseline_label = "Previous main"
-    else:
-        # Compare against the most recent main baseline
-        baseline_file = get_most_recent_file("main")
-        baseline_label = "Main"
-
-    if baseline_file:
-        baseline_results = json.loads(baseline_file.read_text())
-        comparison = build_comparison_table(data_results, baseline_results, baseline_label)
-        print(f"\nComparing against: {baseline_file.name}")
-        print(tabulate(comparison, tablefmt="github"))
-    else:
-        print("\nNo baseline results found for comparison.")
+    parser.add_argument("--name", type=str, default=None, help="Name of the benchmark run (for saving).")
+    parser.add_argument("--compare-to", type=str, default=None, help="Name of a previous run to compare against.")
+    parser.add_argument("--model-id", type=str, default="meta-llama/Llama-3.1-8B-Instruct")
+    parser.add_argument("--attn", type=str, default="kernels-community/flash-attn3")
+    cli_args = parser.parse_args()
+
+    results = BenchmarkResults(model_id=cli_args.model_id, attn_impl=cli_args.attn)
+
+    # GSM8K benchmarks (256 max new tokens)
+
+    tokenizer = AutoTokenizer.from_pretrained(cli_args.model_id, padding_side="left")
+    gsm8k_data = get_tokenized_gms8k(tokenizer)
+
+    ## No options
+    results.add_benchmark(
+        data=gsm8k_data,
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(),
+        label="gsm8k_default",
+    )
+
+    ## With sampling
+    results.add_benchmark(
+        data=gsm8k_data,
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(),
+        gen_config=GenerationConfig(do_sample=True),
+        label="gsm8k_sampling",
+    )
+
+    ## With compile
+    results.add_benchmark(
+        data=gsm8k_data,
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(use_default_compile_configs=True),
+        label="gsm8k_compile",
+    )
+
+    ## No decode fast path
+    results.add_benchmark(
+        data=gsm8k_data,
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(max_blocks_per_request=0),
+        label="gsm8k_no_fast_decode",
+    )
+
+    # Raw benchmarks (synthetic data, variable max new tokens)
+
+    ## RL rollouts: small batch, growing generation lengths
+    for length in [1024, 2048, 4096, 8192, 16384]:
+        results.add_benchmark(
+            data=get_random_data(batch_size=32, num_tokens=256),
+            max_new_tokens=length,
+            cb_config=ContinuousBatchingConfig(use_default_compile_configs=True),
+            label=f"rollouts_{length}",
+        )
+
+    ## Few blocks — tight cache pressure
+    results.add_benchmark(
+        data=get_random_data(batch_size=20, num_tokens=256),
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(num_blocks=16),
+        label="few_blocks",
+    )
+
+    ## Multiple return sequences (sampling + parallel decoding)
+    results.add_benchmark(
+        data=get_random_data(batch_size=50, num_tokens=256),
+        max_new_tokens=256,
+        cb_config=ContinuousBatchingConfig(),
+        gen_config=GenerationConfig(do_sample=True, num_return_sequences=8),
+        label="multi_return_seq",
+    )
+
+    # Post processing and display
+
+    results.print_summary()
+
+    if cli_args.compare_to:
+        baseline = BenchmarkResults.load_most_recent(cli_args.compare_to)
+        results.compare_to(baseline=baseline)
+
+    if cli_args.name:
+        results.save(cli_args.name)
diff --git a/c.sh b/c.sh
new file mode 100755
index 000000000000..892c0490075f
--- /dev/null
+++ b/c.sh
@@ -0,0 +1,7 @@
+#!/bin/bash
+
+rm -rf /fs/nexus-projects/JSALT_workshop/lasha/Dev/audiovisualflamingo-hf
+python src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py \
+    --model_dir /fs/nexus-projects/JSALT_workshop/lasha/Dev/audiovisualflamingo \
+    --output_dir /fs/nexus-projects/JSALT_workshop/lasha/Dev/audiovisualflamingo-hf \
+    --push_to_hub SreyanG-NVIDIA/audiovisualflamingo-hf
diff --git a/conftest.py b/conftest.py
index dfe26aec2391..6a0d921862e5 100644
--- a/conftest.py
+++ b/conftest.py
@@ -97,6 +97,7 @@ def pytest_configure(config):
     )
     config.addinivalue_line("markers", "training_ci: mark test for training CI validation")
     config.addinivalue_line("markers", "tensor_parallel_ci: mark test for tensor parallel CI validation")
+    config.addinivalue_line("markers", "training_distributed_ci: mark test for distributed training CI validation")
 
     os.environ["DISABLE_SAFETENSORS_CONVERSION"] = "true"
     register_network_debug_plugin(config)
diff --git a/docker/transformers-all-latest-gpu/Dockerfile b/docker/transformers-all-latest-gpu/Dockerfile
index bd3de7b27311..ee71c087dde2 100644
--- a/docker/transformers-all-latest-gpu/Dockerfile
+++ b/docker/transformers-all-latest-gpu/Dockerfile
@@ -18,9 +18,20 @@ ARG TORCHCODEC='0.11.0'
 
 ARG FLASH_ATTN='false'
 
+# 'x86_64' or 'arm64'
+ARG ARCHITECTURE='x86_64'
+
 RUN apt update
-RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs
+RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs curl
 RUN git lfs install
+
+RUN set-e; \
+if [ "$ARCHITECTURE" = "arm64" ]; then \
+    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y;\
+    PATH="/root/.cargo/bin:${PATH}";\
+    rustc --version;\
+fi;
+
 RUN python3 -m pip install --no-cache-dir --upgrade pip
 
 ARG REF=main
@@ -36,7 +47,11 @@ RUN set -e; \
     # Determine torch version
     if [ ${#PYTORCH} -gt 0 ] && [ "$PYTORCH" != "pre" ]; then \
         VERSION="torch==${PYTORCH}.*"; \
-        TORCHCODEC_VERSION="torchcodec==${TORCHCODEC}.*"; \
+        if [ "$ARCHITECTURE" = "arm64" ]; then \
+            TORCHCODEC_VERSION="torchcodec"; \
+        else \
+            TORCHCODEC_VERSION="torchcodec==${TORCHCODEC}.*"; \
+        fi; \
     else \
         VERSION="torch"; \
         TORCHCODEC_VERSION="torchcodec"; \
diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index fe77fadf1282..16c8fbe4ff1b 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -177,6 +177,8 @@
       title: Subclassing Trainer methods
     - local: trainer_callbacks
       title: Callbacks
+    - local: moe_telemetry
+      title: MoE telemetry
     - local: data_collators
       title: Data collators
     - local: optimizers
@@ -390,6 +392,8 @@
         title: Image Feature Extraction
       - local: tasks/mask_generation
         title: Mask Generation
+      - local: tasks/promptable_visual_segmentation
+        title: Promptable Visual Segmentation
       - local: tasks/keypoint_detection
         title: Keypoint detection
       - local: tasks/knowledge_distillation_for_image_classification
@@ -416,6 +420,8 @@
         title: Video-text-to-text
       - local: tasks/visual_document_retrieval
         title: Visual Document Retrieval
+      - local: tasks/promptable_concept_segmentation
+        title: Promptable Concept Segmentation
       title: Multimodal
     title: Task recipes
   - local: run_scripts
@@ -553,10 +559,14 @@
         title: DeBERTa
       - local: model_doc/deberta-v2
         title: DeBERTa-v2
+      - local: model_doc/deepseek_ocr2
+        title: DeepSeek-OCR-2
       - local: model_doc/deepseek_v2
         title: DeepSeek-V2
       - local: model_doc/deepseek_v3
         title: DeepSeek-V3
+      - local: model_doc/deepseek_v4
+        title: DeepSeek-V4
       - local: model_doc/dialogpt
         title: DialoGPT
       - local: model_doc/diffllama
@@ -585,6 +595,8 @@
         title: EuroBERT
       - local: model_doc/exaone4
         title: EXAONE-4.0
+      - local: model_doc/exaone4_5
+        title: EXAONE-4.5
       - local: model_doc/exaone_moe
         title: EXAONE-MoE
       - local: model_doc/falcon
@@ -713,6 +725,8 @@
         title: MegatronBERT
       - local: model_doc/megatron_gpt2
         title: MegatronGPT2
+      - local: model_doc/minicpm3
+        title: MiniCPM3
       - local: model_doc/minimax
         title: MiniMax
       - local: model_doc/minimax_m2
@@ -825,6 +839,8 @@
         title: RoFormer
       - local: model_doc/rwkv
         title: RWKV
+      - local: model_doc/sarvam_mla
+        title: SarvamMLA
       - local: model_doc/seed_oss
         title: Seed-Oss
       - local: model_doc/solar_open
@@ -1059,6 +1075,8 @@
         title: GLM-ASR
       - local: model_doc/granite_speech
         title: GraniteSpeech
+      - local: model_doc/granite_speech_plus
+        title: GraniteSpeechPlus
       - local: model_doc/higgs_audio_v2
         title: Higgs Audio V2
       - local: model_doc/higgs_audio_v2_tokenizer
@@ -1089,6 +1107,8 @@
         title: PE Audio
       - local: model_doc/pop2piano
         title: Pop2Piano
+      - local: model_doc/qwen3_asr
+        title: Qwen3 ASR
       - local: model_doc/seamless_m4t
         title: Seamless-M4T
       - local: model_doc/seamless_m4t_v2
@@ -1145,6 +1165,8 @@
         title: V-JEPA 2
       - local: model_doc/videomae
         title: VideoMAE
+      - local: model_doc/videoprism
+        title: VideoPrism
       - local: model_doc/vivit
         title: ViViT
       title: Video models
@@ -1157,6 +1179,8 @@
         title: Aria
       - local: model_doc/audioflamingo3
         title: AudioFlamingo3
+      - local: model_doc/audiovisualflamingo
+        title: AudioVisualFlamingo
       - local: model_doc/aya_vision
         title: AyaVision
       - local: model_doc/blip
@@ -1231,6 +1255,8 @@
         title: GlmOcr
       - local: model_doc/got_ocr2
         title: GOT-OCR2
+      - local: model_doc/granite4_vision
+        title: Granite4Vision
       - local: model_doc/granitevision
         title: GraniteVision
       - local: model_doc/grounding-dino
@@ -1251,6 +1277,8 @@
         title: InternVL
       - local: model_doc/janus
         title: Janus
+      - local: model_doc/kimi2_6
+        title: Kimi2_6
       - local: model_doc/kosmos-2
         title: KOSMOS-2
       - local: model_doc/kosmos2_5
@@ -1297,6 +1325,8 @@
         title: mllama
       - local: model_doc/mm-grounding-dino
         title: MM Grounding DINO
+      - local: model_doc/molmo2
+        title: Molmo2
       - local: model_doc/musicflamingo
         title: MusicFlamingo
       - local: model_doc/nougat
@@ -1317,6 +1347,8 @@
         title: PaliGemma
       - local: model_doc/pe_audio_video
         title: PE Audio Video
+      - local: model_doc/penguinvl
+        title: PenguinVL
       - local: model_doc/perceiver
         title: Perceiver
       - local: model_doc/perception_lm
@@ -1333,6 +1365,8 @@
         title: PP-DocLayoutV2
       - local: model_doc/pp_doclayout_v3
         title: PP-DocLayoutV3
+      - local: model_doc/pp_formulanet
+        title: PP-FormulaNet
       - local: model_doc/pp_ocrv5_mobile_det
         title: PP-OCRv5_mobile_det
       - local: model_doc/pp_ocrv5_mobile_rec
@@ -1421,6 +1455,8 @@
     - sections:
       - local: model_doc/autoformer
         title: Autoformer
+      - local: model_doc/ctsm
+        title: CTSM
       - local: model_doc/informer
         title: Informer
       - local: model_doc/patchtsmixer
diff --git a/docs/source/en/conversations.md b/docs/source/en/conversations.md
index 74ebe8fe74ad..2c70fc187971 100644
--- a/docs/source/en/conversations.md
+++ b/docs/source/en/conversations.md
@@ -24,7 +24,13 @@ This guide shows you how to quickly load chat models in Transformers from the co
 
 ## chat CLI
 
-After you've [installed Transformers](./installation), you can chat with a model directly from the command line. The command below launches an interactive session with a model, with a few base commands listed at the start of the session.
+After you've [installed Transformers](./installation), you can chat with a model directly from the command line. Install the `chat` extra first:
+
+```bash
+pip install transformers[chat]
+```
+
+The command below launches an interactive session with a model, with a few base commands listed at the start of the session.
 
 > For the following commands, please make sure [`transformers serve` is running](https://huggingface.co/docs/transformers/main/en/serving).
 
diff --git a/docs/source/en/internal/generation_utils.md b/docs/source/en/internal/generation_utils.md
index 8d5f259d6963..88341c2c7fdc 100644
--- a/docs/source/en/internal/generation_utils.md
+++ b/docs/source/en/internal/generation_utils.md
@@ -132,6 +132,12 @@ generation.
 [[autodoc]] NoRepeatNGramLogitsProcessor
     - __call__
 
+[[autodoc]] PLessLogitsWarper
+    - __call__
+
+[[autodoc]] PLessNormLogitsWarper
+    - __call__
+
 [[autodoc]] PrefixConstrainedLogitsProcessor
     - __call__
 
@@ -193,6 +199,9 @@ A [`StoppingCriteria`] can be used to change when to stop generation (other than
 [[autodoc]] StopStringCriteria
     - __call__
 
+[[autodoc]] StopStringTextMatchCriteria
+    - __call__
+
 [[autodoc]] EosTokenCriteria
     - __call__
 
diff --git a/docs/source/en/main_classes/callback.md b/docs/source/en/main_classes/callback.md
index 8fd7472eb925..eeb4866a4f21 100644
--- a/docs/source/en/main_classes/callback.md
+++ b/docs/source/en/main_classes/callback.md
@@ -46,6 +46,8 @@ Here is the list of the available [`TrainerCallback`] in the library:
 
 [[autodoc]] EarlyStoppingCallback
 
+[[autodoc]] MoERouterHealthCallback
+
 [[autodoc]] integrations.TensorBoardCallback
 
 [[autodoc]] integrations.TrackioCallback
diff --git a/docs/source/en/main_classes/pipelines.md b/docs/source/en/main_classes/pipelines.md
index faca097d1160..e69fedf4ee35 100644
--- a/docs/source/en/main_classes/pipelines.md
+++ b/docs/source/en/main_classes/pipelines.md
@@ -409,6 +409,29 @@ Pipelines available for natural language processing tasks include the following.
     - __call__
     - all
 
+The TextGenerationPipeline supports optional safety checking through the `safety_config` parameter. See the [Safe Generation example](https://github.com/huggingface/transformers/tree/main/examples/safe_generation) for implementing custom safety checkers.
+
+**Example**:
+```python
+from transformers import pipeline
+from transformers.generation.safety import SafetyConfig
+from examples.safe_generation.checkers import BasicToxicityChecker
+
+# Create safety checker
+checker = BasicToxicityChecker(threshold=0.7)
+config = SafetyConfig.from_checker(checker)
+
+# Use with text generation pipeline
+pipe = pipeline("text-generation", model="gpt2")
+result = pipe("Hello", safety_config=config, max_new_tokens=50)
+```
+
+### Text2TextGenerationPipeline
+
+[[autodoc]] Text2TextGenerationPipeline
+    - __call__
+    - all
+
 ### TokenClassificationPipeline
 
 [[autodoc]] TokenClassificationPipeline
@@ -461,6 +484,18 @@ Pipelines available for multimodal tasks include the following.
     - __call__
     - all
 
+### PromptableConceptSegmentationPipeline
+
+[[autodoc]] PromptableConceptSegmentationPipeline
+    - __call__
+    - all
+
+### PromptableVisualSegmentationPipeline
+
+[[autodoc]] PromptableVisualSegmentationPipeline
+    - __call__
+    - all
+
 ## Parent class: `Pipeline`
 
 [[autodoc]] Pipeline
diff --git a/docs/source/en/model_doc/audiovisualflamingo.md b/docs/source/en/model_doc/audiovisualflamingo.md
new file mode 100644
index 000000000000..8df0b2800f5e
--- /dev/null
+++ b/docs/source/en/model_doc/audiovisualflamingo.md
@@ -0,0 +1,179 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on {release_date} and added to Hugging Face Transformers on 2026-04-22.*
+
+# Audio-Visual Flamingo
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+Audio-Visual Flamingo (AVF) is a fully open audio-visual large language model for joint understanding and reasoning over
+audio, images, and videos. In Transformers, AVF pairs a SigLIP vision tower with an AF-Whisper audio encoder and a
+Qwen2.5-7B causal language model, with separate projectors for visual and audio features.
+
+For video plus audio inputs, AVF does not simply concatenate visual and sound features. Instead, it aligns synchronized
+visual and audio chunks, interleaves them along the time axis, applies Constrained Rotary Time Embeddings (CRTE), and
+then feeds the fused sequence to the language model. In the Transformers interface, the processor prepares the required
+media placeholder spans and the model replaces those token positions with projected multimodal embeddings during the
+forward pass.
+
+The model checkpoint is available at: [nvidia/audio-visual-flamingo-hf](https://huggingface.co/nvidia/audio-visual-flamingo-hf)
+
+Highlights:
+
+- Unified prompting across image, video, and audio inputs.
+- Joint video plus audio understanding from a single container when `load_audio_in_video=True`.
+- Dynamic-S2 visual preprocessing for high-resolution images and sampled video frames.
+- Temporal audio-visual interleaving with CRTE before the Qwen2.5-7B backbone.
+- Replace-in-place multimodal fusion through processor-prepared media spans and projected media embeddings.
+
+This model was contributed by [Lasha Koroshinadze](https://huggingface.co/lashahub) and [Eric Bezzam](https://huggingface.co/bezzam).
+
+### Paper
+
+Audio-Visual Flamingo: Open Audio-Visual Intelligence for Long and Complex Videos  
+S. Ghosh, A. Goel, K. Jayakumar, L. Koroshinadze, N. Anand, Z. Kong, S. Gururani, S. Lee, J. Kim, A. Aljafari, C.-H. H. Yang, S. Kim, R. Duraiswami, D. Manocha, M. Shoeybi, B. Catanzaro, M.-Y. Liu, W. Ping  
+NVIDIA and University of Maryland
+
+The paper presents AVF as a fully open audio-visual model trained for long and complex real-world videos. It introduces
+AVF-Skills, a three-stage training curriculum, and Temporal Audio-Visual Interleaved Chain-of-Thought (TAVIT) for
+temporally grounded reasoning. The paper also discusses a streaming TTS component; this page focuses on the public
+conditional-generation checkpoint for multimodal understanding and text generation.
+
+## Usage
+
+### Audio-Visual Instruct Mode
+
+The model supports chat-template conversations mixing text, images, videos, and audio. When
+`load_audio_in_video=True`, a `video` content item can contribute both sampled frames and audio from the same
+container.
+
+➡️ video + audio from a single container
+
+```python
+from transformers import AudioVisualFlamingoForConditionalGeneration, AutoProcessor
+
+model_id = "nvidia/audio-visual-flamingo-hf"
+
+model = AudioVisualFlamingoForConditionalGeneration.from_pretrained(
+    model_id,
+    device_map="auto",
+    load_audio_in_video=True,
+).eval()
+processor = AutoProcessor.from_pretrained(
+    model_id,
+    padding_side="left",
+    use_fast=False,
+    load_audio_in_video=True,
+    num_video_frames=128,
+    audio_chunk_length="max_3600",
+)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "video": "/path/to/video.mp4"},
+            {
+                "type": "text",
+                "text": "Describe both the visual scene and the spoken or environmental audio content.",
+            },
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+).to(model.device)
+
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=False,
+)
+
+new_tokens = generated_ids[:, inputs.input_ids.shape[1] :]
+print(processor.batch_decode(new_tokens, skip_special_tokens=True)[0])
+```
+
+### Prompt format
+
+AVF uses chat-template content items with media placeholders:
+
+- `{"type": "image", "image": "/path/to/image.jpg"}`
+- `{"type": "video", "video": "/path/to/video.mp4"}`
+- `{"type": "audio", "path": "/path/to/audio.wav"}`
+- `{"type": "text", "text": "Describe the media."}`
+
+You can mix these items within the same turn. When `load_audio_in_video=True`, a `video` content item can contribute
+both visual frames and audio features from the same container.
+
+## How the model works
+
+### Architecture
+
+* **Vision tower**
+  SigLIP encodes images and sampled video frames. AVF uses Dynamic-S2 preprocessing to preserve fine-grained detail in
+  high-resolution visual inputs while keeping the visual token sequence compact.
+
+* **Audio tower**
+  AVF uses AF-Whisper, the Audio Flamingo series' Whisper-based audio encoder. Audio is resampled to 16 kHz mono,
+  converted to 128-bin log-mel spectrograms, and encoded in non-overlapping 30-second windows for long-form audio.
+
+* **Multimodal projectors**
+  Two 2-layer MLP projectors map visual and audio encoder features into the shared language-model hidden size.
+
+* **Temporal interleaving + CRTE**
+  After projection, synchronized visual and audio chunks are interleaved along the time axis rather than naively
+  concatenated. AVF then applies Constrained Rotary Time Embeddings (CRTE) to the interleaved sequence so the language
+  model can preserve absolute temporal structure while attending across co-occurring visual and auditory events.
+
+* **Language model**
+  A decoder-only multimodal language model built on a Qwen2.5-7B text backbone. In the Transformers interface, the
+  processor expands the required media spans and the model replaces those token positions with projected multimodal
+  embeddings during the forward pass; subsequent decode steps reuse the language-model cache.
+
+### Processor-level alignment
+
+1. The processor loads images, sampled video frames, and audio waveforms from the chat-template content items.
+2. For `video` inputs, it can also decode the container audio stream when `load_audio_in_video=True`, so a single
+   video item yields synchronized visual and audio features.
+3. Visual inputs go through the Dynamic-S2 preprocessing path, while audio inputs are converted into AF-Whisper
+   features with temporal chunk metadata for later alignment.
+4. During the forward pass, the model projects the visual and audio features, interleaves synchronized chunks along the
+   time axis, applies CRTE, and replaces the prepared media spans with the fused multimodal embeddings.
+
+## AudioVisualFlamingoConfig
+
+[[autodoc]] AudioVisualFlamingoConfig
+
+## AudioVisualFlamingoProcessor
+
+[[autodoc]] AudioVisualFlamingoProcessor
+    - __call__
+
+## AudioVisualFlamingoForConditionalGeneration
+
+[[autodoc]] AudioVisualFlamingoForConditionalGeneration
+    - forward
diff --git a/docs/source/en/model_doc/auto.md b/docs/source/en/model_doc/auto.md
index 3003e5c49edd..ed9b9dd234d5 100644
--- a/docs/source/en/model_doc/auto.md
+++ b/docs/source/en/model_doc/auto.md
@@ -113,6 +113,14 @@ The following auto classes are available for the following natural language proc
 
 [[autodoc]] AutoModelForMaskGeneration
 
+### AutoModelForPromptableConceptSegmentation
+
+[[autodoc]] AutoModelForPromptableConceptSegmentation
+
+### AutoModelForPromptableVisualSegmentation
+
+[[autodoc]] AutoModelForPromptableVisualSegmentation
+
 ### AutoModelForSeq2SeqLM
 
 [[autodoc]] AutoModelForSeq2SeqLM
@@ -225,6 +233,10 @@ The following auto classes are available for the following audio tasks.
 
 [[autodoc]] AutoModelForCTC
 
+### AutoModelForTDT
+
+[[autodoc]] AutoModelForTDT
+
 ### AutoModelForSpeechSeq2Seq
 
 [[autodoc]] AutoModelForSpeechSeq2Seq
@@ -245,6 +257,10 @@ The following auto classes are available for the following audio tasks.
 
 [[autodoc]] AutoModelForAudioTokenization
 
+### AutoModelForForcedAlignment
+
+[[autodoc]] AutoModelForForcedAlignment
+
 ## Multimodal
 
 The following auto classes are available for the following multimodal tasks.
diff --git a/docs/source/en/model_doc/ctsm.md b/docs/source/en/model_doc/ctsm.md
new file mode 100644
index 000000000000..8d891a07f633
--- /dev/null
+++ b/docs/source/en/model_doc/ctsm.md
@@ -0,0 +1,122 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2025-11-25 and added to Hugging Face Transformers on 2026-04-17.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# CTSM
+
+## Overview
+
+The Cisco Time Series Model (CTSM) was proposed in [Cisco Time Series Model Technical Report](https://huggingface.co/papers/2511.19841) by Liang Gou, Archit Khare, Praneet Pabolu, Prachi Patel, Joseph Ross, Hercy Shen, Yuhan (Ellen) Song, Jingze Sun, Kristal Curtis, Vedant Dharnidharka, Abhinav Mathur and Hao Yang.
+
+CTSM is a decoder-only univariate zero-shot forecasting foundation model. Its central idea is a **multi-resolution context**: instead of consuming a single-scale history, each forecast conditions on two aligned streams — a coarse low-frequency stream (e.g. 512 hourly points) and a fine high-frequency stream (e.g. 512 minutely points), with the resolution ratio fixed to 60. A learnable **special token** separates the two streams and learned **resolution embeddings** are added to the token stream to distinguish them. The coarse stream lets the model see week-over-week structure without giving up fine-grained recent detail; as the paper puts it, "more complex multiresolution architectures would require a context length of 30,720 (30 times as long as ours) to cover the same time range."
+
+The abstract from the paper is the following:
+
+*We introduce the Cisco Time Series Model, a univariate zero-shot forecaster. This time series foundation model is the result of a general architectural innovation to a time series model enabling it to accept multiresolution input, applied to a popular decoder-only time series model (TimesFM). The resulting multiresolution decoder-only model is trained on over 300B unique data points, with more than half coming from the observability domain. Quantitative and qualitative evaluations demonstrate that the resulting model achieves superior performance on observability datasets while retaining very similar performance on a standard general-purpose forecasting benchmark (GIFT-Eval), and suggest that the multiresolution structure enables the model to make more accurate predictions on long context input.*
+
+### Architecture
+
+The backbone follows TimesFM 2.0: patching (patch length 32) + a residual-block input tokenizer + decoder-only transformer layers with per-dimension learnable query scaling + a residual-block horizon head. CTSM adds, on top:
+
+- A **special token** inserted between the coarse and fine patch streams, so the input is `[coarse₁, …, coarse₁₆, SPECIAL, fine₁, …, fine₁₆]`.
+- **Resolution embeddings** (3-way: coarse / special / fine) added to each token before the transformer stack.
+- **Stream-level normalization**: each stream is standardized independently over its non-padded context, and the fine-stream statistics are used to rescale the forecast.
+- A **frequency embedding** inherited from TimesFM, added to every token.
+
+The 250M **CTSM 1.0** release checkpoint additionally introduces (over the 500M `1.0-preview` described in the paper):
+
+- **Rotary position embeddings (RoPE)** applied to query/key inside attention.
+- **Bidirectional attention over the coarse block** — tokens in the coarse segment attend both ways within that segment, while the fine segment remains causal.
+- **15-quantile prediction** (levels 0.01–0.99) instead of 9.
+- **Short-context training** (1/3 of training samples drawn with `|fine| ∈ [10, 511]`) for better robustness when less history is available.
+- Trained from scratch (not continued pre-training from TimesFM 2.0) on ~2× more internal observability data.
+
+### Inference
+
+For `horizon_len > config.horizon_length`, [`CtsmModelForPrediction`] runs an autoregressive multi-resolution decode loop, using a [`DynamicCache`] by default (opt out with `use_cache=False`). Each step feeds only the newly-appended fine patches through the stack and attends to cached K/V for every earlier position. Stream-normalization statistics are frozen to their step-1 values so that cached K/V remains valid; the coarse block is pinned and the cache is rebuilt if the concatenated sequence would outgrow `max_position_embeddings`.
+
+The checkpoint can be found at [`cisco-ai/cisco-time-series-model-1.0`](https://huggingface.co/cisco-ai/cisco-time-series-model-1.0). The original inference code is at [github.com/splunk/cisco-time-series-model](https://github.com/splunk/cisco-time-series-model).
+
+This model was contributed by [kashif](https://huggingface.co/kashif).
+
+## Usage
+
+Pass a list of fine-resolution time series (e.g. minute-level); the coarse stream is built automatically by mean-aggregating consecutive blocks of `config.agg_factor` points.
+
+```python
+import numpy as np
+import torch
+from transformers import CtsmModelForPrediction
+
+
+model = CtsmModelForPrediction.from_pretrained("cisco-ai/cisco-time-series-model-1.0", device_map="auto")
+
+# ~8.5 hours of 1-minute data; the model will build a 512-hour coarse context by aggregation.
+series = np.sin(np.linspace(0, 200, 512 * 60)).astype(np.float32)
+past_values = [torch.tensor(series, device=model.device)]
+
+with torch.no_grad():
+    outputs = model(past_values=past_values, horizon_len=128)
+
+point_forecast = outputs.mean_predictions       # (batch, horizon_len)
+quantile_forecast = outputs.full_predictions    # (batch, horizon_len, 1 + num_quantiles)
+```
+
+If you already have a coarse stream (e.g. pre-computed 1-hour roll-ups that go further back than you have 1-minute data for), pass `(coarse, fine)` pairs directly:
+
+```python
+coarse = torch.tensor(hourly_series, dtype=torch.float32)    # up to 512 points
+fine = torch.tensor(minutely_series, dtype=torch.float32)    # up to 512 points
+outputs = model(past_values=[(coarse, fine)], horizon_len=128)
+```
+
+For `horizon_len > 128`, the model decodes autoregressively and extends the output accordingly.
+
+## CtsmConfig
+
+[[autodoc]] CtsmConfig
+
+## CtsmModel
+
+[[autodoc]] CtsmModel
+    - forward
+
+## CtsmModelForPrediction
+
+[[autodoc]] CtsmModelForPrediction
+    - forward
+
+## Citation
+
+```bibtex
+@misc{gou2025ciscotimeseriesmodel,
+      title={Cisco Time Series Model Technical Report},
+      author={Liang Gou and Archit Khare and Praneet Pabolu and Prachi Patel and Joseph Ross and Hercy Shen and Yuhan Song and Jingze Sun and Kristal Curtis and Vedant Dharnidharka and Abhinav Mathur and Hao Yang},
+      year={2025},
+      eprint={2511.19841},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2511.19841}
+}
+```
diff --git a/docs/source/en/model_doc/deepseek_ocr2.md b/docs/source/en/model_doc/deepseek_ocr2.md
new file mode 100644
index 000000000000..541ab9e4ed60
--- /dev/null
+++ b/docs/source/en/model_doc/deepseek_ocr2.md
@@ -0,0 +1,116 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-01-28 and added to Hugging Face Transformers on 2026-04-25.*
+
+# DeepSeek-OCR-2
+
+
+## Overview
+
+The DeepSeek-OCR-2 model was proposed in [Visual Causal Flow: A Novel Approach to OCR-Specialized Vision-Language Models](https://huggingface.co/papers/2601.20552) by the DeepSeek team.
+
+DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture: a SAM ViT-B vision encoder feeds into a Qwen2 hybrid attention encoder, which is connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. A key feature of the model is its hybrid attention mechanism, which applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding.
+
+<img src="https://huggingface.co/deepseek-ai/DeepSeek-OCR-2/resolve/main/assets/fig1.png" width="600">
+
+<small> DeepSeek-OCR 2: Visual Causal Flow.</small>
+
+This model was contributed by [thisisiron](https://huggingface.co/thisisiron).
+
+
+## Usage example
+
+### Plain OCR
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model = AutoModelForImageTextToText.from_pretrained(
+    "thisisiron/DeepSeek-OCR-2-hf", device_map="auto"
+)
+processor = AutoProcessor.from_pretrained("thisisiron/DeepSeek-OCR-2-hf")
+
+image = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
+inputs = processor(images=image, text="<image>\nFree OCR.", return_tensors="pt").to(model.device)
+
+generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
+processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+# "R&D QUALITY IMPROVEMENT\nSUGGESTION/SOLUTION FORM\nName/Phone Ext. : (...)"
+```
+
+### Grounding with markdown conversion
+
+The `<|grounding|>` token enables coordinate-aware output with `<|ref|>` and `<|det|>` tags.
+
+```python
+inputs = processor(
+    images=image,
+    text="<image>\n<|grounding|>Convert the document to markdown.",
+    return_tensors="pt",
+).to(model.device)
+
+generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=256)
+processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False)
+# "<|ref|>title<|/ref|><|det|>[[330, 198, 558, 230]]<|/det|>\n# R&D QUALITY (...)"
+```
+
+## DeepseekOcr2Config
+
+[[autodoc]] DeepseekOcr2Config
+
+## DeepseekOcr2VisionConfig
+
+[[autodoc]] DeepseekOcr2VisionConfig
+
+## DeepseekOcr2SamVisionConfig
+
+[[autodoc]] DeepseekOcr2SamVisionConfig
+
+## DeepseekOcr2EncoderConfig
+
+[[autodoc]] DeepseekOcr2EncoderConfig
+
+## DeepseekOcr2TextConfig
+
+[[autodoc]] DeepseekOcr2TextConfig
+
+## DeepseekOcr2ImageProcessor
+
+[[autodoc]] DeepseekOcr2ImageProcessor
+
+## DeepseekOcr2ImageProcessorPil
+
+[[autodoc]] DeepseekOcr2ImageProcessorPil
+
+## DeepseekOcr2Processor
+
+[[autodoc]] DeepseekOcr2Processor
+
+## DeepseekOcr2TextModel
+
+[[autodoc]] DeepseekOcr2TextModel
+
+## DeepseekOcr2VisionModel
+
+[[autodoc]] DeepseekOcr2VisionModel
+
+## DeepseekOcr2Model
+
+[[autodoc]] DeepseekOcr2Model
+
+## DeepseekOcr2ForConditionalGeneration
+
+[[autodoc]] DeepseekOcr2ForConditionalGeneration
diff --git a/docs/source/en/model_doc/deepseek_v4.md b/docs/source/en/model_doc/deepseek_v4.md
new file mode 100644
index 000000000000..2d95c77bcb2a
--- /dev/null
+++ b/docs/source/en/model_doc/deepseek_v4.md
@@ -0,0 +1,39 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on {release_date} and added to Hugging Face Transformers on 2026-04-28.*
+
+# DeepSeek-V4
+
+[DeepSeek-V4](https://huggingface.co/deepseek-ai) is a family of MoE language models released by DeepSeek. Relative
+to DeepSeek-V3, V4 replaces MLA with sliding-window attention plus a per-layer KV Compressor, swaps residual
+connections for Hyper-Connections, routes the first few layers via a static token-id hash, and drops expert groups.
+
+This implementation covers the `DeepSeek-V4-Flash`, `DeepSeek-V4-Pro`, and their `-Base` pretrained siblings. All
+four share the same architecture; they differ only in width / depth / expert count and weights.
+
+## DeepseekV4Config
+
+[[autodoc]] DeepseekV4Config
+
+## DeepseekV4Model
+
+[[autodoc]] DeepseekV4Model
+    - forward
+
+## DeepseekV4ForCausalLM
+
+[[autodoc]] DeepseekV4ForCausalLM
+    - forward
diff --git a/docs/source/en/model_doc/dinov3.md b/docs/source/en/model_doc/dinov3.md
index de08f0746c8e..fb22b5c42e45 100644
--- a/docs/source/en/model_doc/dinov3.md
+++ b/docs/source/en/model_doc/dinov3.md
@@ -73,6 +73,33 @@ pooled_output = outputs.pooler_output
 print("Pooled output shape:", pooled_output.shape)
 ```
 
+</hfoption>
+<hfoption id="AutoModelForImageClassification">
+
+```py
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+from transformers.image_utils import load_image
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = load_image(url)
+
+checkpoint = "dimidagd/dinov3-vit7b16-pretrain-lvd1689m-imagenet1k-lc"
+processor = AutoImageProcessor.from_pretrained(checkpoint)
+model = AutoModelForImageClassification.from_pretrained(
+    checkpoint,
+    dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+inputs = processor(images=image, return_tensors="pt").to(model.device)
+with torch.inference_mode():
+    outputs = model(**inputs)
+
+predicted_class_idx = outputs.logits.argmax(-1).item()
+print(model.config.id2label[predicted_class_idx])
+```
+
 </hfoption>
 </hfoptions>
 
@@ -173,6 +200,11 @@ print("Pooled output shape:", pooled_output.shape)
 
 [[autodoc]] DINOv3ViTBackbone
 
+## DINOv3ViTForImageClassification
+
+[[autodoc]] DINOv3ViTForImageClassification
+    - forward
+
 ## DINOv3ConvNextModel
 
 [[autodoc]] DINOv3ConvNextModel
diff --git a/docs/source/en/model_doc/edgetam.md b/docs/source/en/model_doc/edgetam.md
index 173b89533c83..8149f770a9ba 100644
--- a/docs/source/en/model_doc/edgetam.md
+++ b/docs/source/en/model_doc/edgetam.md
@@ -39,14 +39,52 @@ The original code can be found [here](https://github.com/facebookresearch/EdgeTA
 
 ## Usage example
 
-### Automatic Mask Generation with Pipeline
+### Promptable Visual Segmentation Pipeline
+
+The easiest way to use EdgeTAM is through the `promptable-visual-segmentation` pipeline:
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline(model="yonigozlan/EdgeTAM-hf", task="promptable-visual-segmentation")
+>>> # Single point prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+...     input_points=[[[[450, 600]]]],
+...     input_labels=[[[1]]],
+... )
+[[{'score': 0.87, 'mask': tensor([...])}]]
+
+>>> # Box prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_boxes=[[[59, 144, 76, 163]]],
+... )
+[[{'score': 0.92, 'mask': tensor([...])}]]
+
+>>> # Multiple points for refinement
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_points=[[[[450, 600], [500, 620]]]],
+...     input_labels=[[[1, 0]]],  # 1=positive, 0=negative
+... )
+[[{'score': 0.85, 'mask': tensor([...])}]]
+```
+
+<Tip>
+
+**Note:** The pipeline output format differs from using the model and processor manually. The pipeline returns a standardized format (list of lists of dicts with `score` and `mask`) to ensure consistency across all transformers pipelines, while the processor's `post_process_masks()` returns raw tensors.
+
+</Tip>
+
+### Automatic Mask Generation Pipeline
 
 EdgeTAM can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline:
 
 ```python
 >>> from transformers import pipeline
 
->>> generator = pipeline("mask-generation", model="yonigozlan/edgetam-1", device=0)
+>>> generator = pipeline("mask-generation", model="yonigozlan/EdgeTAM-hf", device=0)
 >>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
 >>> outputs = generator(image_url, points_per_batch=64)
 
@@ -69,8 +107,8 @@ from accelerate import Accelerator
 
 >>> device = Accelerator().device
 
->>> model = EdgeTamModel.from_pretrained("yonigozlan/edgetam-1").to(device)
->>> processor = Sam2Processor.from_pretrained("yonigozlan/edgetam-1")
+>>> model = EdgeTamModel.from_pretrained("yonigozlan/EdgeTAM-hf").to(device)
+>>> processor = Sam2Processor.from_pretrained("yonigozlan/EdgeTAM-hf")
 
 >>> image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
 >>> raw_image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
@@ -166,8 +204,8 @@ from accelerate import Accelerator
 
 >>> device = Accelerator().device
 
->>> model = EdgeTamModel.from_pretrained("yonigozlan/edgetam-1").to(device)
->>> processor = Sam2Processor.from_pretrained("yonigozlan/edgetam-1")
+>>> model = EdgeTamModel.from_pretrained("yonigozlan/EdgeTAM-hf").to(device)
+>>> processor = Sam2Processor.from_pretrained("yonigozlan/EdgeTAM-hf")
 
 >>> # Load multiple images
 >>> image_urls = [
diff --git a/docs/source/en/model_doc/exaone4_5.md b/docs/source/en/model_doc/exaone4_5.md
new file mode 100644
index 000000000000..3e634559c1e0
--- /dev/null
+++ b/docs/source/en/model_doc/exaone4_5.md
@@ -0,0 +1,112 @@
+<!--Copyright 2026 The LG AI Research and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-04-09 and added to Hugging Face Transformers on 2026-04-28.*
+
+# EXAONE 4.5
+
+## Overview
+
+[EXAONE 4.5](https://github.com/LG-AI-EXAONE/EXAONE-4.5) model is the first open-weight vision language model developed by LG AI Research.
+Integrating a dedicated visual encoder into the existing EXAONE 4.0 framework, we expand the model's capability toward multimodality.
+EXAONE 4.5 features 33 billion parameters in total, including 1.2 billion parameters from the vision encoder. 
+EXAONE 4.5 achieves competitive performance in general benchmark while outperforming SOTA models of similar size in document understanding and Korean contextual reasoning, inheriting powerful language capabilities from our previous language models.
+
+EXAONE 4.5 builds on the foundation of EXAONE 4.0 with several key enhancements. The vocabulary size has been expanded to 153,600, and the context window now supports up to 256K tokens. In addition, a Multi-Token Prediction (MTP) mechanism has been introduced, further improving the model's performance.
+
+For more details, please refer to the [technical report](https://huggingface.co/papers/2604.08644), [blog](https://www.lgresearch.ai/blog/view?seq=641) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.5).
+
+All model weights including quantized version are available at [Huggingface Collections](https://huggingface.co/collections/LGAI-EXAONE/exaone-45).
+
+## Usage tips
+
+> To achieve the expected performance, we recommend using the following configurations:
+> - We recommend to use `temperature=1.0`, `top_p=0.95`, `presence_penalty=1.5` for general purpose.
+> - We recommend to use `temperature=0.6`, `top_p=0.95`, `presence_penalty=1.5`, `top_k=20` for OCR/document-related tasks, and Korean inputs.
+> - We recommend to use `temperature=1.0`, `top_p=0.95` for text-only inputs.
+> - Different from EXAONE-4.0, EXAONE 4.5 uses `enable_thinking=True` as default. Thus, you need to set `enable_thinking=False` when you want to use non-reasoning mode.
+> - EXAONE 4.5 prefers using `\boxed{}` format to answer the question. We recommend using this format with the corresponding format instruction for better parsing accuracy. 
+
+For tasks that require accurate results, you can run the EXAONE 4.5 model in reasoning mode, whereas for tasks where latency matters more than accuracy, you can run the EXAONE 4.5 model in non-reasoning mode.
+
+Here is the example code for using EXAONE 4.5 model in reasoning mode:
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_id = "LGAI-EXAONE/EXAONE-4.5-33B"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe the image."},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    enable_thinking=True,   # default: True
+)
+inputs = inputs.to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=64)
+generated_text = processor.batch_decode(
+    generated_ids[:, inputs["input_ids"].shape[-1]:],
+    skip_special_tokens=True,
+)[0]
+print(generated_text)
+```
+
+
+## Exaone4_5_Config
+
+[[autodoc]] Exaone4_5_Config
+
+## Exaone4_5_VisionConfig
+
+[[autodoc]] Exaone4_5_VisionConfig
+
+## Exaone4_5_Processor
+
+[[autodoc]] Exaone4_5_Processor
+
+## Exaone4_5_VisionModel
+
+[[autodoc]] Exaone4_5_VisionModel
+    - forward
+
+## Exaone4_5_Model
+
+[[autodoc]] Exaone4_5_Model
+    - forward
+
+## Exaone4_5_ForConditionalGeneration
+
+[[autodoc]] Exaone4_5_ForConditionalGeneration
+    - forward
\ No newline at end of file
diff --git a/docs/source/en/model_doc/gemma4.md b/docs/source/en/model_doc/gemma4.md
index 0aed86af4199..459ee30204da 100644
--- a/docs/source/en/model_doc/gemma4.md
+++ b/docs/source/en/model_doc/gemma4.md
@@ -304,6 +304,16 @@ print(processor.decode(outputs[0][input_len:], skip_special_tokens=False))
 
 [[autodoc]] Gemma4ForCausalLM
 
+## Gemma4ForSequenceClassification
+
+[[autodoc]] Gemma4ForSequenceClassification
+    - forward
+
+## Gemma4TextForSequenceClassification
+
+[[autodoc]] Gemma4TextForSequenceClassification
+    - forward
+
 ## Gemma4Model
 
 [[autodoc]] Gemma4Model
diff --git a/docs/source/en/model_doc/granite.md b/docs/source/en/model_doc/granite.md
index 23ce774af002..bcd51d25be42 100644
--- a/docs/source/en/model_doc/granite.md
+++ b/docs/source/en/model_doc/granite.md
@@ -117,3 +117,8 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 [[autodoc]] GraniteForCausalLM
     - forward
+
+## GraniteForSequenceClassification
+
+[[autodoc]] GraniteForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/granite4_vision.md b/docs/source/en/model_doc/granite4_vision.md
new file mode 100644
index 000000000000..6a9b71d632f7
--- /dev/null
+++ b/docs/source/en/model_doc/granite4_vision.md
@@ -0,0 +1,185 @@
+<!--Copyright 2026 IBM and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-03-27 and added to Hugging Face Transformers on 2026-04-28.*
+
+<div style="float: right;">
+  <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+  </div>
+</div>
+
+# Granite4Vision
+
+[Granite Vision 4.1](https://huggingface.co/ibm-granite/granite-vision-4.1-4b) is a vision-language model from IBM Research designed for enterprise-grade document data extraction. It specializes in chart extraction (Chart2CSV, Chart2Summary, Chart2Code), table extraction (JSON, HTML, OTSL), and semantic key-value pair extraction.
+
+The model builds on [LLaVA-NeXT](llava_next) with several architectural innovations:
+
+1. **SigLIP2 Vision Encoder** ([`google/siglip2-so400m-patch16-384`](https://huggingface.co/google/siglip2-so400m-patch16-384)): images are tiled into 384x384 patches.
+2. **Window Q-Former Projectors**: compress visual features 4x using windowed cross-attention over 4x4 patch windows into 2x2 tokens.
+3. **DeepStack Feature Injection** with 8 vision-to-LLM injection points:
+   - *LayerDeepstack*: features from 4 vision encoder depths are projected into different early LLM layers.
+   - *SpatialDeepstack*: deepest vision features are split into 4 spatial groups and injected at later LLM layers.
+4. **Language Model**: [Granite 4.1](https://huggingface.co/ibm-granite/granite-4.1-4b-base) (4B params) with LoRA adapters (rank 256) across all self-attention and MLP layers.
+
+The model is delivered as a LoRA adapter on top of the base LLM, enabling single deployments to support both multimodal and text-only workloads. Total parameter count is ~4B.
+
+> [!TIP]
+> This model was contributed by the [IBM Granite Vision Team](https://github.com/ibm-granite).
+
+## Usage Tips
+
+- Set `padding_side="left"` during batched generation for more accurate results.
+
+```py
+processor.tokenizer.padding_side = "left"
+```
+
+- The model supports specialized task tags for document extraction: `<chart2csv>`, `<chart2summary>`, `<chart2code>`, `<tables_html>`, `<tables_otsl>`, `<tables_json>`. Pass these as the text prompt along with a document image.
+
+- For key-value pair extraction, provide a JSON schema describing the fields to extract. The model returns structured JSON matching the schema.
+
+The example below demonstrates how to generate text based on an image with [`Pipeline`] or the [`AutoModel`] class.
+
+<hfoptions id="usage">
+
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+pipe = pipeline(
+    task="image-text-to-text",
+    model="ibm-granite/granite-vision-4.1-4b",
+)
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+pipe(text=messages, max_new_tokens=100, return_full_text=False)
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_id = "ibm-granite/granite-vision-4.1-4b"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id).eval()
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    },
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+
+</hfoption>
+
+</hfoptions>
+
+Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
+
+The example below uses [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to int4.
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText, BitsAndBytesConfig
+
+quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4",
+)
+
+model_id = "ibm-granite/granite-vision-4.1-4b"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id, quantization_config=quant_config, device_map="auto"
+)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    },
+]
+inputs = processor.apply_chat_template(
+    conversation,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+output = model.generate(**inputs, max_new_tokens=100)
+print(processor.decode(output[0], skip_special_tokens=True))
+```
+
+## Granite4VisionConfig
+
+[[autodoc]] Granite4VisionConfig
+
+## Granite4VisionTextConfig
+
+[[autodoc]] Granite4VisionTextConfig
+
+## Granite4VisionProcessor
+
+[[autodoc]] Granite4VisionProcessor
+    - __call__
+
+## Granite4VisionModel
+
+[[autodoc]] Granite4VisionModel
+
+## Granite4VisionTextModel
+
+[[autodoc]] Granite4VisionTextModel
+
+## Granite4VisionForConditionalGeneration
+
+[[autodoc]] Granite4VisionForConditionalGeneration
+    - forward
+    - get_image_features
diff --git a/docs/source/en/model_doc/granite_speech_plus.md b/docs/source/en/model_doc/granite_speech_plus.md
new file mode 100644
index 000000000000..eb878610934e
--- /dev/null
+++ b/docs/source/en/model_doc/granite_speech_plus.md
@@ -0,0 +1,48 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-04-23 and added to Hugging Face Transformers on 2026-04-23.*
+
+# Granite Speech Plus
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+Granite Speech Plus is a variant of [Granite Speech](./granite_speech) whose projector consumes the concatenation of
+the encoder's final hidden states with an arbitrary subset of its intermediate hidden states (along the feature
+dimension). The selected intermediate layers are controlled by the `encoder_hidden_layers` config field on
+[`GraniteSpeechPlusConfig`]; when it is `None`, the model behaves identically to Granite Speech. When it is set, the
+projector's `encoder_hidden_size` must equal `encoder_config.hidden_dim * (len(encoder_hidden_layers) + 1)`.
+
+The rest of the architecture — speech encoder, query transformer projector, language model, and optional LoRA adapter
+— is inherited unchanged from Granite Speech. See the [Granite Speech documentation](./granite_speech) for usage
+examples; the same [`GraniteSpeechProcessor`] and [`GraniteSpeechFeatureExtractor`] are used here.
+
+## GraniteSpeechPlusConfig
+
+[[autodoc]] GraniteSpeechPlusConfig
+
+## GraniteSpeechPlusEncoderConfig
+
+[[autodoc]] GraniteSpeechPlusEncoderConfig
+
+## GraniteSpeechPlusForConditionalGeneration
+
+[[autodoc]] GraniteSpeechPlusForConditionalGeneration
+    - forward
+    - get_audio_features
diff --git a/docs/source/en/model_doc/granitemoe.md b/docs/source/en/model_doc/granitemoe.md
index 32616c07a289..dfbc159f404d 100644
--- a/docs/source/en/model_doc/granitemoe.md
+++ b/docs/source/en/model_doc/granitemoe.md
@@ -78,3 +78,8 @@ This model was contributed by [mayank-mishra](https://huggingface.co/mayank-mish
 
 [[autodoc]] GraniteMoeForCausalLM
     - forward
+
+## GraniteMoeForSequenceClassification
+
+[[autodoc]] GraniteMoeForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/granitemoehybrid.md b/docs/source/en/model_doc/granitemoehybrid.md
index cb3db122e65d..3059a834b57d 100644
--- a/docs/source/en/model_doc/granitemoehybrid.md
+++ b/docs/source/en/model_doc/granitemoehybrid.md
@@ -87,3 +87,8 @@ This HF implementation is contributed by [Sukriti Sharma](https://huggingface.co
 
 [[autodoc]] GraniteMoeHybridForCausalLM
     - forward
+
+## GraniteMoeHybridForSequenceClassification
+
+[[autodoc]] GraniteMoeHybridForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/granitemoeshared.md b/docs/source/en/model_doc/granitemoeshared.md
index 9db702c9f705..22067b972aab 100644
--- a/docs/source/en/model_doc/granitemoeshared.md
+++ b/docs/source/en/model_doc/granitemoeshared.md
@@ -63,3 +63,8 @@ This HF implementation is contributed by [Mayank Mishra](https://huggingface.co/
 
 [[autodoc]] GraniteMoeSharedForCausalLM
     - forward
+
+## GraniteMoeSharedForSequenceClassification
+
+[[autodoc]] GraniteMoeSharedForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/kimi2_6.md b/docs/source/en/model_doc/kimi2_6.md
new file mode 100644
index 000000000000..269e189b94e9
--- /dev/null
+++ b/docs/source/en/model_doc/kimi2_6.md
@@ -0,0 +1,80 @@
+<!--Copyright 2026 the HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer.
+
+-->
+
+
+# Kimi2_6
+
+## Overview
+
+The Kimi2_6 model was proposed in [<INSERT PAPER NAME HERE>](<INSERT PAPER LINK HERE>) by <INSERT AUTHORS HERE>.
+<INSERT SHORT SUMMARY HERE>
+
+The abstract from the paper is the following:
+
+<INSERT PAPER ABSTRACT HERE>
+
+Tips:
+
+<INSERT TIPS ABOUT MODEL HERE>
+
+This model was contributed by [INSERT YOUR HF USERNAME HERE](https://huggingface.co/<INSERT YOUR HF USERNAME HERE>).
+The original code can be found [here](<INSERT LINK TO GITHUB REPO HERE>).
+
+## Usage examples
+
+<INSERT SOME NICE EXAMPLES HERE>
+
+## Kimi26Config
+
+[[autodoc]] Kimi26Config
+
+## Kimi26TextConfig
+
+[[autodoc]] Kimi26TextConfig
+
+## Kimi26VisionConfig
+
+[[autodoc]] Kimi26VisionConfig
+
+## Kimi26ForConditionalGeneration
+
+[[autodoc]] Kimi26ForConditionalGeneration
+
+## Kimi26Model
+
+[[autodoc]] Kimi26Model
+    - forward
+
+## Kimi26PreTrainedModel
+
+[[autodoc]] Kimi26PreTrainedModel
+    - forward
+
+## Kimi26TextModel
+
+[[autodoc]] Kimi26TextModel
+    - forward
+
+## Kimi26ImageProcessor
+
+[[autodoc]] Kimi26ImageProcessor
+
+## Kimi26Processor
+
+[[autodoc]] Kimi26Processor
\ No newline at end of file
diff --git a/docs/source/en/model_doc/lasr.md b/docs/source/en/model_doc/lasr.md
index 7a6f87ae7e1d..d34c687470c8 100644
--- a/docs/source/en/model_doc/lasr.md
+++ b/docs/source/en/model_doc/lasr.md
@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
 rendered properly in your Markdown viewer.
 
 -->
-*This model was released on {release_date} and added to Hugging Face Transformers on 2025-12-05.*
+*This model was released on 2020-05-16 and added to Hugging Face Transformers on 2025-12-05.*
 
 <div class="flex flex-wrap space-x-1">
 <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
diff --git a/docs/source/en/model_doc/minicpm3.md b/docs/source/en/model_doc/minicpm3.md
new file mode 100644
index 000000000000..e812e594ac4c
--- /dev/null
+++ b/docs/source/en/model_doc/minicpm3.md
@@ -0,0 +1,45 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# MiniCPM3
+
+## Overview
+
+The MiniCPM3 model was proposed in [MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies](https://huggingface.co/papers/2404.06395) by OpenBMB.
+
+MiniCPM3-4B is a dense language model that uses Multi-head Latent Attention (MLA) for efficient KV cache compression, combined with embedding scaling, depth-dependent residual scaling, and logit scaling for stable training. Despite its compact 4B parameter size, it achieves performance comparable to larger 7B-9B models.
+
+This model was contributed by [aliyevaladddin](https://github.com/aliyevaladddin).
+The original code can be found [here](https://huggingface.co/openbmb/MiniCPM3-4B).
+
+## MiniCPM3Config
+
+[[autodoc]] MiniCPM3Config
+
+## MiniCPM3Model
+
+[[autodoc]] MiniCPM3Model
+    - forward
+
+## MiniCPM3ForCausalLM
+
+[[autodoc]] MiniCPM3ForCausalLM
+    - forward
+
+## MiniCPM3ForSequenceClassification
+
+[[autodoc]] MiniCPM3ForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/molmo2.md b/docs/source/en/model_doc/molmo2.md
new file mode 100644
index 000000000000..902f40206506
--- /dev/null
+++ b/docs/source/en/model_doc/molmo2.md
@@ -0,0 +1,132 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-01-15 and added to Hugging Face Transformers on 2026-04-14.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">    </div>
+</div>
+
+# Molmo2
+
+[Molmo2](https://huggingface.co/papers/2601.10611) is a family of open-weight vision-language models by AllenAI that are state-of-the-art among open-source models, with exceptional capabilities in point-driven grounding for single image, multi-image, and video tasks. The architecture combines a Vision Transformer (ViT) for image processing with an adapter layer connecting vision and text modalities, and a text decoder based on transformer architecture with rotary position embeddings.
+
+The abstract from the paper is the following:
+
+*Today's strongest video-language models (VLMs) remain proprietary. The strongest open-weight models either rely on synthetic data from proprietary VLMs, effectively distilling from them, or do not disclose their training data or recipe. As a result, the open-source community lacks the foundations needed to improve on the state-of-the-art video (and image) language models. Crucially, many downstream applications require more than just high-level video understanding; they require grounding -- either by pointing or by tracking in pixels. Even proprietary models lack this capability. We present Molmo2, a new family of VLMs that are state-of-the-art among open-source models and demonstrate exceptional new capabilities in point-driven grounding in single image, multi-image, and video tasks. Our key contribution is a collection of 7 new video datasets and 2 multi-image datasets, including a dataset of highly detailed video captions for pre-training, a free-form video Q&A dataset for fine-tuning, a new object tracking dataset with complex queries, and an innovative new video pointing dataset, all collected without the use of closed VLMs. We also present a training recipe for this data utilizing an efficient packing and message-tree encoding scheme, and show bi-directional attention on vision tokens and a novel token-weight strategy improves performance. Our best-in-class 8B model outperforms others in the class of open weight and data models on short videos, counting, and captioning, and is competitive on long-videos. On video-grounding Molmo2 significantly outperforms existing open-weight models like Qwen3-VL (35.5 vs 29.6 accuracy on video counting) and surpasses proprietary models like Gemini 3 Pro on some tasks (38.4 vs 20.0 F1 on video pointing and 56.2 vs 41.1 J&F on video tracking).*
+
+You can find all the original Molmo2 checkpoints under the [Molmo2](https://huggingface.co/collections/allenai/molmo2-67d6b5b0e138c5d621de1e5d) collection.
+
+## Usage example
+
+### Image-text-to-text generation
+
+Here's how to use Molmo2 for image-text-to-text generation:
+
+```python
+from transformers import Molmo2ForConditionalGeneration, Molmo2Processor
+import torch
+
+processor = Molmo2Processor.from_pretrained("allenai/Molmo2-8B")
+model = Molmo2ForConditionalGeneration.from_pretrained(
+    "allenai/Molmo2-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Describe this image."},
+            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    return_dict=True,
+).to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_text = processor.batch_decode(
+    generated_ids[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True
+)
+print(generated_text[0])
+```
+
+## Molmo2Config
+
+[[autodoc]] Molmo2Config
+
+## Molmo2VitConfig
+
+[[autodoc]] Molmo2VitConfig
+
+## Molmo2AdapterConfig
+
+[[autodoc]] Molmo2AdapterConfig
+
+## Molmo2TextConfig
+
+[[autodoc]] Molmo2TextConfig
+
+## Molmo2Processor
+
+[[autodoc]] Molmo2Processor
+    - __call__
+
+## Molmo2ImageProcessor
+
+[[autodoc]] Molmo2ImageProcessor
+    - __call__
+    - preprocess
+
+## Molmo2VideoProcessor
+
+[[autodoc]] Molmo2VideoProcessor
+    - __call__
+
+## Molmo2Model
+
+[[autodoc]] Molmo2Model
+    - forward
+
+## Molmo2TextModel
+
+[[autodoc]] Molmo2TextModel
+    - forward
+
+## Molmo2VisionBackbone
+
+[[autodoc]] Molmo2VisionBackbone
+    - forward
+
+## Molmo2VisionModel
+
+[[autodoc]] Molmo2VisionModel
+    - forward
+
+## Molmo2ForConditionalGeneration
+
+[[autodoc]] Molmo2ForConditionalGeneration
+    - forward
diff --git a/docs/source/en/model_doc/mt5.md b/docs/source/en/model_doc/mt5.md
index 1f7ef694e28d..c5069f419b64 100644
--- a/docs/source/en/model_doc/mt5.md
+++ b/docs/source/en/model_doc/mt5.md
@@ -125,3 +125,8 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 ## MT5ForQuestionAnswering
 
 [[autodoc]] MT5ForQuestionAnswering
+
+## MT5EncoderForSequenceClassification
+
+[[autodoc]] MT5EncoderForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/parakeet.md b/docs/source/en/model_doc/parakeet.md
index b075e6d5ccf7..cca7d395f2d2 100644
--- a/docs/source/en/model_doc/parakeet.md
+++ b/docs/source/en/model_doc/parakeet.md
@@ -34,15 +34,20 @@ Parakeet models, [introduced by NVIDIA NeMo](https://developer.nvidia.com/blog/p
     - 1D convolution projection from encoder hidden size to vocabulary size (for optimal NeMo compatibility).
     - CTC loss computation for training.
     - Greedy CTC decoding for inference.
+- [**ParakeetForTDT**](#parakeetfortdt): a Fast Conformer Encoder + a TDT (Token Duration Transducer) decoder
+  - **TDT Decoder**: Jointly predicts tokens and their durations, enabling efficient decoding:
+    - LSTM prediction network maintains language context across token predictions.
+    - Joint network combines encoder and decoder outputs.
+    - Duration head predicts how many frames to skip, enabling fast inference.
 
 The original implementation can be found in [NVIDIA NeMo](https://github.com/NVIDIA/NeMo).
 Model checkpoints are to be found under [the NVIDIA organization](https://huggingface.co/nvidia/models?search=parakeet).
 
-This model was contributed by [Nithin Rao Koluguri](https://huggingface.co/nithinraok), [Eustache Le Bihan](https://huggingface.co/eustlb) and [Eric Bezzam](https://huggingface.co/bezzam).
+This model was contributed by [Nithin Rao Koluguri](https://huggingface.co/nithinraok), [Eustache Le Bihan](https://huggingface.co/eustlb), [Eric Bezzam](https://huggingface.co/bezzam), [Maksym Lypivskyi](https://huggingface.co/MaksL), and [Hainan Xu](https://huggingface.co/hainanx).
 
 ## Usage
 
-### Basic usage
+### `ParakeetForCTC` usage
 
 <hfoptions id="usage">
 <hfoption id="Pipeline">
@@ -53,6 +58,7 @@ from transformers import pipeline
 pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-ctc-1.1b")
 out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
 print(out)
+# {'text': 'yesterday it was thirty five degrees in barcelona but today the temperature will go down to minus twenty degrees'}
 ```
 
 </hfoption>
@@ -61,12 +67,10 @@ print(out)
 ```py
 from transformers import AutoModelForCTC, AutoProcessor
 from datasets import load_dataset, Audio
-import torch
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
 
-processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
-model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device)
+model_id = "nvidia/parakeet-ctc-1.1b"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCTC.from_pretrained(model_id, dtype="auto", device_map="auto")
 
 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
@@ -75,7 +79,80 @@ speech_samples = [el['array'] for el in ds["audio"][:5]]
 inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
 inputs.to(model.device, dtype=model.dtype)
 outputs = model.generate(**inputs)
-print(processor.batch_decode(outputs))
+print(processor.decode(outputs))
+```
+
+</hfoption>
+</hfoptions>
+
+### `ParakeetForTDT` usage
+
+<hfoptions id="tdt-usage">
+<hfoption id="Pipeline">
+
+Parakeet TDT transcripts include casing, and the model can also perform token timestamping.
+
+```py
+from transformers import pipeline
+
+pipe = pipeline("automatic-speech-recognition", model="nvidia/parakeet-tdt-0.6b-v3")
+out = pipe("https://huggingface.co/datasets/hf-internal-testing/dummy-audio-samples/resolve/main/bcn_weather.mp3")
+print(out)
+# {'text': 'Yesterday it was 35 degrees in Barcelona, but today the temperature will go down to minus 20 degrees.'}
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```py
+from transformers import AutoModelForTDT, AutoProcessor
+from datasets import load_dataset, Audio
+
+model_id = "nvidia/parakeet-tdt-0.6b-v3"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForTDT.from_pretrained(model_id, dtype="auto", device_map="auto")
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+speech_samples = [el['array'] for el in ds["audio"][:5]]
+
+inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
+inputs.to(model.device, dtype=model.dtype)
+output = model.generate(**inputs, return_dict_in_generate=True)
+print(processor.decode(output.sequences, skip_special_tokens=True))
+```
+
+</hfoption>
+<hfoption id="Timestamping">
+
+```py
+from datasets import Audio, load_dataset
+from transformers import AutoModelForTDT, AutoProcessor
+
+model_id = "nvidia/parakeet-tdt-0.6b-v3"
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForTDT.from_pretrained(model_id, dtype="auto", device_map="auto")
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+speech_samples = [el['array'] for el in ds["audio"][:1]]
+
+inputs = processor(speech_samples, sampling_rate=processor.feature_extractor.sampling_rate)
+inputs.to(model.device, dtype=model.dtype)
+output = model.generate(**inputs, return_dict_in_generate=True)
+decoded_output, decoded_timestamps = processor.decode(
+    output.sequences,
+    durations=output.durations,
+    skip_special_tokens=True,
+)
+print("Transcription:", decoded_output)
+print("\nTimestamped tokens:", decoded_timestamps)
+
+"""
+Transcription: ['mister Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.']
+
+Timestamped tokens: [[{'token': 'm', 'start': 0.24, 'end': 0.48}, {'token': 'ister', 'start': 0.48, 'end': 0.64}, {'token': 'Qu', 'start': 0.64, 'end': 0.88}, {'token': 'il', 'start': 0.88, 'end': 1.12}, {'token': 'ter', 'start': 1.12, 'end': 1.36}, {'token': 'is', 'start': 1.36, 'end': 1.44}, {'token': 'the', 'start': 1.44, 'end': 1.6}, {'token': 'ap', 'start': 1.6, 'end': 1.76}, {'token': 'ost', 'start': 1.76, 'end': 1.92}, {'token': 'le', 'start': 2.0, 'end': 2.16}, {'token': 'of', 'start': 2.16, 'end': 2.24}, {'token': 'the', 'start': 2.24, 'end': 2.4}, {'token': 'mid', 'start': 2.4, 'end': 2.48}, {'token': 'd', 'start': 2.48, 'end': 2.56}, {'token': 'le', 'start': 2.56, 'end': 2.64}, {'token': 'clas', 'start': 2.72, 'end': 2.88}, {'token': 's', 'start': 2.88, 'end': 3.04}, {'token': 'es', 'start': 3.04, 'end': 3.12}, {'token': ',', 'start': 3.12, 'end': 3.12}, {'token': 'and', 'start': 3.2800000000000002, 'end': 3.44}, {'token': 'we', 'start': 3.44, 'end': 3.6}, {'token': 'are', 'start': 3.6, 'end': 3.7600000000000002}, {'token': 'gl', 'start': 3.7600000000000002, 'end': 3.92}, {'token': 'ad', 'start': 3.92, 'end': 4.08}, {'token': 'to', 'start': 4.08, 'end': 4.24}, {'token': 'wel', 'start': 4.24, 'end': 4.4}, {'token': 'c', 'start': 4.4, 'end': 4.48}, {'token': 'ome', 'start': 4.48, 'end': 4.72}, {'token': 'his', 'start': 4.72, 'end': 4.96}, {'token': 'gos', 'start': 4.96, 'end': 5.12}, {'token': 'pel', 'start': 5.36, 'end': 5.6000000000000005}, {'token': '.', 'start': 5.6000000000000005, 'end': 5.6000000000000005}]]
+"""
 ```
 
 </hfoption>
@@ -136,7 +213,7 @@ print("First generation - compiling...")
 # Generate with the compiled model
 with TimerContext("First generation"):
     outputs = model.generate(**inputs)
-print(processor.batch_decode(outputs))
+print(processor.decode(outputs))
 
 inputs = processor(speech_samples[1], **processor_kwargs)
 inputs.to(device, dtype=model.dtype)
@@ -144,7 +221,7 @@ print("\n" + "="*50)
 print("Second generation - recording CUDA graphs...")
 with TimerContext("Second generation"):
     outputs = model.generate(**inputs)
-print(processor.batch_decode(outputs))
+print(processor.decode(outputs))
 
 inputs = processor(speech_samples[2], **processor_kwargs)
 inputs.to(device, dtype=model.dtype)
@@ -152,7 +229,7 @@ print("\n" + "="*50)
 print("Third generation - fast !!!")
 with TimerContext("Third generation"):
     outputs = model.generate(**inputs)
-print(processor.batch_decode(outputs))
+print(processor.decode(outputs))
 
 inputs = processor(speech_samples[3], **processor_kwargs)
 inputs.to(device, dtype=model.dtype)
@@ -160,34 +237,66 @@ print("\n" + "="*50)
 print("Fourth generation - still fast !!!")
 with TimerContext("Fourth generation"):
     outputs = model.generate(**inputs)
-print(processor.batch_decode(outputs))
+print(processor.decode(outputs))
 ```
 
-### Training
+### CTC Training
 
 ```python
+import torch
+from datasets import Audio, load_dataset
 from transformers import AutoModelForCTC, AutoProcessor
-from datasets import load_dataset, Audio
+
+model_id = "nvidia/parakeet-ctc-1.1b"
+NUM_SAMPLES = 5
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForCTC.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
+model.train()
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+speech_samples = [el['array'] for el in ds["audio"][:NUM_SAMPLES]]
+text_samples = ds["text"][:NUM_SAMPLES]
+
+# passing `text` to the processor will prepare inputs' `labels` key
+inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_extractor.sampling_rate)
+inputs.to(device=model.device, dtype=model.dtype)
+
+outputs = model(**inputs)
+print("Loss:", outputs.loss.item())
+outputs.loss.backward()
+```
+
+### TDT Training
+
+```py
+from datasets import Audio, load_dataset
 import torch
+from transformers import AutoModelForTDT, AutoProcessor
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+model_id = "nvidia/parakeet-tdt-0.6b-v3"
+NUM_SAMPLES = 4
 
-processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
-model = AutoModelForCTC.from_pretrained("nvidia/parakeet-ctc-1.1b", dtype="auto", device_map=device)
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForTDT.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
+model.train()
 
 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
 ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
-speech_samples = [el['array'] for el in ds["audio"][:5]]
-text_samples = [el for el in ds["text"][:5]]
+speech_samples = [el['array'] for el in ds["audio"][:NUM_SAMPLES]]
+text_samples = ds["text"][:NUM_SAMPLES]
 
 # passing `text` to the processor will prepare inputs' `labels` key
 inputs = processor(audio=speech_samples, text=text_samples, sampling_rate=processor.feature_extractor.sampling_rate)
-inputs.to(device, dtype=model.dtype)
+inputs.to(device=model.device, dtype=model.dtype)
 
 outputs = model(**inputs)
+print("Loss:", outputs.loss.item())
 outputs.loss.backward()
 ```
 
+
 ## ParakeetTokenizer
 
 [[autodoc]] ParakeetTokenizer
@@ -201,7 +310,6 @@ outputs.loss.backward()
 
 [[autodoc]] ParakeetProcessor
     - __call__
-    - batch_decode
     - decode
 
 ## ParakeetEncoderConfig
@@ -212,6 +320,10 @@ outputs.loss.backward()
 
 [[autodoc]] ParakeetCTCConfig
 
+## ParakeetTDTConfig
+
+[[autodoc]] ParakeetTDTConfig
+
 ## ParakeetEncoder
 
 [[autodoc]] ParakeetEncoder
@@ -219,3 +331,7 @@ outputs.loss.backward()
 ## ParakeetForCTC
 
 [[autodoc]] ParakeetForCTC
+
+## ParakeetForTDT
+
+[[autodoc]] ParakeetForTDT
diff --git a/docs/source/en/model_doc/pe_audio_video.md b/docs/source/en/model_doc/pe_audio_video.md
index e116724d43f5..af0db76537f5 100644
--- a/docs/source/en/model_doc/pe_audio_video.md
+++ b/docs/source/en/model_doc/pe_audio_video.md
@@ -26,7 +26,47 @@ TODO
 ### Basic usage
 
 ```py
-TODO
+
+model = PeAudioVideoModel.from_pretrained("facebook/pe-av-large", device_map="cuda", dtype=torch.bfloat16)
+processor = PeAudioVideoProcessor.from_pretrained("facebook/pe-av-large")
+
+from huggingface_hub import hf_hub_download
+
+video_path = hf_hub_download(
+    repo_id="eustlb/dummy-video-dataset", filename="audiobox.mp4", repo_type="dataset"
+)
+
+video_path2 = hf_hub_download(
+    repo_id="eustlb/dummy-video-dataset", filename="glass_breaking.mp4", repo_type="dataset"
+)
+
+audio_path = hf_hub_download(
+    repo_id="eustlb/dummy-video-dataset", filename="audiobox.mp4", repo_type="dataset"
+)
+
+audio_path2 = hf_hub_download(
+    repo_id="eustlb/dummy-video-dataset", filename="glass_breaking.mp4", repo_type="dataset"
+)
+
+video_files = [video_path, video_path2]
+descriptions = ["A woman and a man speaking", "A glass breaking"]
+audio_files = [audio_path, audio_path2]
+
+inputs = processor(
+    videos=video_files, text=descriptions, audio=audio_files, return_tensors="pt", padding=True
+)
+
+with torch.inference_mode(), torch.autocast(model.device.type, dtype=torch.bfloat16):
+    outputs = model(**inputs.to(model.device, dtype=model.dtype))
+
+audio_embeds = outputs.audio_embeds  # Audio-only embeddings
+video_embeds = outputs.video_embeds  # Video-only embeddings
+audio_video_embeds = outputs.audio_video_embeds  # Joint audio-video embeddings
+text_audio_embeds = outputs.text_audio_embeds  # Text embeddings aligned to audio
+text_video_embeds = outputs.text_video_embeds  # Text embeddings aligned to video
+text_audio_video_embeds = outputs.text_audio_video_embeds  # Text embeddings aligned to audio-video
+audio_plus_text_embeds = outputs.audio_plus_text_embeds  # Joint audio and text embedding
+video_plus_text_embeds = outputs.video_plus_text_embeds  # Joint video and text embedding
 ```
 
 ## PeAudioVideoProcessor
diff --git a/docs/source/en/model_doc/penguinvl.md b/docs/source/en/model_doc/penguinvl.md
new file mode 100644
index 000000000000..84b8b062ca3e
--- /dev/null
+++ b/docs/source/en/model_doc/penguinvl.md
@@ -0,0 +1,310 @@
+<!--Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-03-06 and added to Hugging Face Transformers on 2026-03-13.*
+
+# PenguinVL
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+[Penguin-VL](https://huggingface.co/papers/2603.06569) is a compact vision-language model family built to study how far multimodal efficiency can be pushed by redesigning the vision encoder, rather than only scaling data or model size.
+
+Most modern VLMs rely on vision encoders pretrained with large-scale contrastive objectives such as CLIP or SigLIP. Penguin-VL argues that this setup can be suboptimal for multimodal reasoning because contrastive learning favors coarse category-level invariances over the fine-grained signals needed for OCR, document understanding, dense captioning, and complex reasoning. Instead, Penguin-VL introduces Penguin-Encoder, a vision encoder initialized from a text-only LLM, so the visual backbone starts closer to the language model representation space and learns more data-efficiently.
+
+<img src="https://github.com/tencent-ailab/Penguin-VL/blob/master/assets/framework.png?raw=true"
+alt="drawing" width="600"/>
+<small> PenguinVL architecture. Details are in <a href="https://huggingface.co/papers/2603.06569">technical report.</a> </small>
+
+This model was contributed by [Cyril666](https://huggingface.co/Cyril666).
+
+## Usage example
+
+### Single media inference
+
+PenguinVL accepts both images and videos as input. Use `processor.process_vision_info` to extract visual inputs from messages*before** calling `apply_chat_template`.
+
+```python
+import torch
+from transformers import PenguinVLProcessor, PenguinVLForConditionalGeneration
+
+model = PenguinVLForConditionalGeneration.from_pretrained(
+    "tencent/Penguin-VL-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = PenguinVLProcessor.from_pretrained("tencent/Penguin-VL-8B")
+
+# Image
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+images, frame_types = processor.process_vision_info(messages)
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    images=images,
+    text=text,
+    frame_types=frame_types,
+    return_tensors="pt",
+).to(model.device)
+
+inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+if "pixel_values" in inputs:
+    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
+output_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+```
+
+### Video inference
+
+```python
+import torch
+from transformers import PenguinVLProcessor, PenguinVLForConditionalGeneration
+
+model = PenguinVLForConditionalGeneration.from_pretrained(
+    "tencent/Penguin-VL-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = PenguinVLProcessor.from_pretrained("tencent/Penguin-VL-8B")
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "video": "/path/to/video.mp4"},
+            {"type": "text", "text": "What happened in the video?"},
+        ],
+    }
+]
+
+# process_vision_info must be called before apply_chat_template for videos
+# It samples frames at `fps`, caps at `max_frames`, and annotates timestamps
+images, frame_types = processor.process_vision_info(messages, fps=1, max_frames=128)
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(
+    images=images,
+    text=text,
+    frame_types=frame_types,
+    return_tensors="pt",
+).to(model.device)
+
+inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+if "pixel_values" in inputs:
+    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+```
+
+### Batch mixed media inference
+
+The model can batch inputs composed of mixed samples (images, videos, and text).
+
+```python
+import torch
+from transformers import PenguinVLProcessor, PenguinVLForConditionalGeneration
+
+model = PenguinVLForConditionalGeneration.from_pretrained(
+    "tencent/Penguin-VL-8B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+processor = PenguinVLProcessor.from_pretrained("tencent/Penguin-VL-8B")
+
+conversation1 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+conversation2 = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "video": "/path/to/video.mp4"},
+            {"type": "text", "text": "Summarize this video."},
+        ],
+    }
+]
+
+conversation3 = [
+    {
+        "role": "user",
+        "content": "What is the capital of France?",
+    }
+]
+
+all_images = []
+all_frame_types = []
+all_texts = []
+for conv in [conversation1, conversation2, conversation3]:
+    imgs, fts = processor.process_vision_info(conv, fps=1, max_frames=64)
+    if imgs is not None:
+        all_images.extend(imgs)
+    if fts is not None:
+        all_frame_types.extend(fts)
+    all_texts.append(processor.apply_chat_template(conv, add_generation_prompt=True))
+
+inputs = processor(
+    images=all_images if all_images else None,
+    text=all_texts,
+    frame_types=all_frame_types if all_frame_types else None,
+    padding=True,
+    return_tensors="pt",
+).to(model.device)
+
+output_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+```
+### process_vision_info function
+
+`process_vision_info` extracts and loads visual inputs (images and video frames) from Qwen2-VL style conversation messages. It walks through the messages, collects images/video frames in order, and for video clips samples frames at the given `fps` (capped at `max_frames`). Video content items in `messages` are enriched in-place with `num_frames` and `timestamps` so that `apply_chat_template` can emit per-frame timestamp prefixes.
+
+> [!IMPORTANT]
+> You must call `process_vision_info` *before* `apply_chat_template`, because it modifies the `messages` in-place when processing videos.
+
+Supported content block formats:
+<hfoptions id="supported-content">
+<hfoption id="image">
+
+```python
+# URL (HTTP or file) or PIL Image
+{"type": "image", "image": "https://example.com/photo.jpg"}
+{"type": "image", "image": "file:///path/to/image.png"}
+{"type": "image", "image": <PIL.Image.Image>}
+```
+
+</hfoption>
+<hfoption id="video">
+
+```python
+# URL, or list of frames with timestamps
+{"type": "video", "video": "https://example.com/clip.mp4"}
+{"type": "video", "video": ["file:///path/frame1.jpg", ...], "timestamps": [0, ...]}
+{"type": "video", "video": [<PIL.Image.Image>, ...], "timestamps": [0, ...]}
+```
+
+</hfoption>
+</hfoptions>
+
+### Flash-Attention 2 to speed up generation
+
+First, make sure to install the latest version of Flash Attention 2:
+
+```bash
+pip install -U flash-attn --no-build-isolation
+```
+
+Also, you should have hardware that is compatible with Flash Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). FlashAttention-2 can only be used when a model is loaded in `torch.float16` or `torch.bfloat16`.
+
+To load and run a model using Flash Attention-2, simply add `attn_implementation="flash_attention_2"` when loading the model:
+
+```python
+import torch
+from transformers import PenguinVLForConditionalGeneration
+
+model = PenguinVLForConditionalGeneration.from_pretrained(
+    "tencent/Penguin-VL-8B",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+)
+```
+
+## Notes
+
+- Use `min_pixels` and `max_pixels` to control image resolution and memory usage.
+
+    ```python
+    from transformers import PenguinVLProcessor
+
+    processor = PenguinVLProcessor.from_pretrained(
+        "tencent/Penguin-VL-8B",
+        min_pixels=256 * 14 * 14,
+        max_pixels=1280 * 14 * 14,
+    )
+    ```
+
+- For video inputs, `process_vision_info` must be called *before* `apply_chat_template`. It samples frames at the given `fps`, caps total frames at `max_frames`, and annotates each video entry in `messages` with `num_frames` and `timestamps` so the chat template can emit per-frame timestamp prefixes.
+
+- Video frames are automatically classified as **keyframes (K)** or **intermediate frames (I)** via the TRA mechanism. Keyframes receive a smaller spatial merge factor (better quality) and intermediate frames receive a larger one (higher compression). This is handled automatically when you pass `frame_types` to the processor.
+
+- Pass `frame_types=None` (or omit it) if you are processing only images.
+
+## PenguinVLConfig
+
+[[autodoc]] PenguinVLConfig
+
+## PenguinVLVisionConfig
+
+[[autodoc]] PenguinVLVisionConfig
+
+## PenguinVLImageProcessor
+
+[[autodoc]] PenguinVLImageProcessor
+    - preprocess
+
+## PenguinVLImageProcessorFast
+
+[[autodoc]] PenguinVLImageProcessorFast
+    - preprocess
+
+## PenguinVLProcessor
+
+[[autodoc]] PenguinVLProcessor
+    - __call__
+
+## PenguinVLVisionModel
+
+[[autodoc]] PenguinVLVisionModel
+    - forward
+
+## PenguinVLModel
+
+[[autodoc]] PenguinVLModel
+    - forward
+    - get_image_features
+
+## PenguinVLLanguageModel
+
+[[autodoc]] PenguinVLLanguageModel
+    - forward
+
+## PenguinVLForConditionalGeneration
+
+[[autodoc]] PenguinVLForConditionalGeneration
+    - forward
+    - get_image_features
diff --git a/docs/source/en/model_doc/pp_formulanet.md b/docs/source/en/model_doc/pp_formulanet.md
new file mode 100644
index 000000000000..9ed1ae1b79bf
--- /dev/null
+++ b/docs/source/en/model_doc/pp_formulanet.md
@@ -0,0 +1,93 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2025-03-24 and added to Hugging Face Transformers on 2026-04-28.*
+
+# SLANet
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+**PP-FormulaNet-L** and **PP-FormulaNet_plus-L** are part of a series of dedicated lightweight models for table structure recognition, focusing on accurately recognizing table structures in documents and natural scenes. For more details about the SLANet series model, please refer to the [official documentation](https://www.paddleocr.ai/latest/en/version3.x/module_usage/table_structure_recognition.html).
+
+## Usage
+
+### Single input inference
+
+The example below demonstrates how to detect text with PP-OCRV5_Mobile_Det using the [`AutoModel`].
+
+<hfoptions id="usage">
+<hfoption id="AutoModel">
+
+```py
+from io import BytesIO
+
+import httpx
+from PIL import Image
+from transformers import AutoProcessor, PPFormulaNetForConditionalGeneration
+
+model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors"
+model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path, device_map="auto")
+processor = AutoProcessor.from_pretrained(model_path)
+
+image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
+image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB")
+inputs = processor(images=image, return_tensors="pt").to(model.device)
+outputs = model(**inputs)
+result = processor.post_process(outputs)
+print(result)
+```
+
+</hfoption>
+</hfoptions>
+
+## PPFormulaNetConfig
+
+[[autodoc]] PPFormulaNetConfig
+
+## PPFormulaNetForConditionalGeneration
+
+[[autodoc]] PPFormulaNetForConditionalGeneration
+
+## PPFormulaNetTextModel
+
+[[autodoc]] PPFormulaNetTextModel
+
+## PPFormulaNetVisionModel
+
+[[autodoc]] PPFormulaNetVisionModel
+
+## PPFormulaNetModel
+
+[[autodoc]] PPFormulaNetModel
+
+## PPFormulaNetTextConfig
+
+[[autodoc]] PPFormulaNetTextConfig
+
+## PPFormulaNetVisionConfig
+
+[[autodoc]] PPFormulaNetVisionConfig
+
+## PPFormulaNetImageProcessor
+
+[[autodoc]] PPFormulaNetImageProcessor
+
+## PPFormulaNetProcessor
+
+[[autodoc]] PPFormulaNetProcessor
diff --git a/docs/source/en/model_doc/qwen3_5.md b/docs/source/en/model_doc/qwen3_5.md
index 1d542dd918ce..b3452313c638 100644
--- a/docs/source/en/model_doc/qwen3_5.md
+++ b/docs/source/en/model_doc/qwen3_5.md
@@ -70,16 +70,33 @@ TODO
 [[autodoc]] Qwen3_5ForCausalLM
     - forward
 
+## Qwen3_5ForConditionalGeneration
+
+[[autodoc]] Qwen3_5ForConditionalGeneration
+    - forward
+
 ## Qwen3_5ForSequenceClassification
 
 [[autodoc]] Qwen3_5ForSequenceClassification
     - forward
 
-## Qwen3_5ForConditionalGeneration
+## Qwen3_5TextForSequenceClassification
 
-[[autodoc]] Qwen3_5ForConditionalGeneration
+[[autodoc]] Qwen3_5TextForSequenceClassification
     - forward
 
 ## Qwen3_5Tokenizer
 
 [[autodoc]] Qwen3_5Tokenizer
+
+## Qwen3_5CausalLMOutputWithPast
+
+[[autodoc]] Qwen3_5CausalLMOutputWithPast
+
+## Qwen3_5VLCausalLMOutputWithPast
+
+[[autodoc]] Qwen3_5VLCausalLMOutputWithPast
+
+## Qwen3_5MTP
+
+[[autodoc]] Qwen3_5MTP
diff --git a/docs/source/en/model_doc/qwen3_asr.md b/docs/source/en/model_doc/qwen3_asr.md
new file mode 100644
index 000000000000..0dd397d23c7d
--- /dev/null
+++ b/docs/source/en/model_doc/qwen3_asr.md
@@ -0,0 +1,658 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on {release_date} and added to Hugging Face Transformers on 2026-04-24.*
+
+# Qwen3 ASR
+
+<div class="flex flex-wrap space-x-1">
+<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## Overview
+
+Qwen3 ASR is an automatic speech recognition model from Alibaba's Qwen team that combines a Whisper-style audio encoder with a Qwen3 language model decoder for speech-to-text transcription. The model supports automatic language detection and multilingual transcription.
+
+A forced aligner model is also included. It can be used the timestamp a provided transcript and its audio. It uses the same audio encoder model with a classification head that predicts a word's length. This model can be used with the transcript from any ASR model (see the example below with Parakeet CTC).
+
+Available checkpoints:
+- [bezzam/Qwen3-ASR-1.7B](https://huggingface.co/bezzam/Qwen3-ASR-1.7B)
+- [bezzam/Qwen3-ASR-0.6B](https://huggingface.co/bezzam/Qwen3-ASR-0.6B)
+- [bezzam/Qwen3-ForcedAligner-0.6B](https://huggingface.co/bezzam/Qwen3-ForcedAligner-0.6B)
+
+The following languages are supported:
+- `Qwen3-ASR-1.7B` and `Qwen3-ASR-0.6B`: Chinese (zh), English (en), Cantonese (yue), Arabic (ar), German (de), French (fr), Spanish (es), Portuguese (pt), Indonesian (id), Italian (it), Korean (ko), Russian (ru), Thai (th), Vietnamese (vi), Japanese (ja), Turkish (tr), Hindi (hi), Malay (ms), Dutch (nl), Swedish (sv), Danish (da), Finnish (fi), Polish (pl), Czech (cs), Filipino (fil), Persian (fa), Greek (el), Hungarian (hu), Macedonian (mk), Romanian (ro)
+- `Qwen3-ForcedAligner-0.6B`: Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish
+
+See the original repository at [QwenLM/Qwen3-ASR](https://github.com/QwenLM/Qwen3-ASR) and the [report](https://huggingface.co/papers/2601.21337) for more details.
+
+This model was contributed by [Eric Bezzam](https://huggingface.co/bezzam) and [Muhammed Tariq](https://huggingface.co/mbtariq82).
+
+## Usage
+
+### Simple transcription
+
+The simplest way to transcribe audio is with `apply_transcription_request`, which handles the chat template formatting for you.
+
+```python
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+print(f"Model loaded on {model.device} with dtype {model.dtype}")
+
+inputs = processor.apply_transcription_request(
+    audio="https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+).to(model.device, model.dtype)
+
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+
+# Raw output includes language tag and <asr_text> marker
+raw = processor.decode(generated_ids)[0]
+print(f"Raw: {raw}")
+
+# Parsed output: dict with "language" and "transcription"
+parsed = processor.decode(generated_ids, return_format="parsed")[0]
+print(f"Parsed: {parsed}")
+
+# Extract only the transcription text
+transcription = processor.decode(generated_ids, return_format="transcription_only")[0]
+print(f"Transcription: {transcription}")
+
+"""
+Raw: language English<asr_text>Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
+Parsed: {'language': 'English', 'transcription': 'Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'}
+Transcription: Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.
+"""
+```
+
+### Language hint
+
+You can provide a language hint to guide the model.
+
+```python
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+
+# Without language hint (auto-detect)
+inputs = processor.apply_transcription_request(
+    audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+).to(model.device, model.dtype)
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+print(f"Auto-detect: {processor.decode(generated_ids, return_format='transcription_only')[0]}")
+
+# With language hint
+inputs = processor.apply_transcription_request(
+    audio="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+    language="Chinese",
+).to(model.device, model.dtype)
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+print(f"With hint:   {processor.decode(generated_ids, return_format='transcription_only')[0]}")
+```
+
+### Batch inference
+
+Batch inference is possible by passing a list of audios and, if provided, a list of languages.
+
+```python
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+audio = [
+    "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+    "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+]
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+
+inputs = processor.apply_transcription_request(
+    audio, language=["English", "Chinese"],
+).to(model.device, model.dtype)
+
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+transcriptions = processor.decode(generated_ids, return_format="transcription_only")
+
+for i, text in enumerate(transcriptions):
+    print(f"Audio {i + 1}: {text}")
+```
+
+### Chat template
+
+Qwen3 ASR also accepts chat template inputs (`apply_transcription_request` is a convenience wrapper for `apply_chat_template`):
+
+```python
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+
+# With language hint as system message
+chat_template = [
+    [
+        {"role": "system", "content": [{"type": "text", "text": "English"}]},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+                },
+            ],
+        },
+    ],
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "audio",
+                    "path": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+                },
+            ],
+        },
+    ],
+]
+
+inputs = processor.apply_chat_template(
+    chat_template, tokenize=True, return_dict=True,
+).to(model.device, model.dtype)
+
+output_ids = model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+transcriptions = processor.decode(generated_ids, return_format="transcription_only")
+for text in transcriptions:
+    print(text)
+```
+
+### Training
+
+Qwen3 ASR can be trained with the loss outputted by the model.
+
+```python
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, device_map="auto")
+model.train()
+
+chat_template = [
+    [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "text",
+                    "text": "Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.",
+                },
+                {
+                    "type": "audio",
+                    "path": "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+                },
+            ],
+        }
+    ],
+]
+
+inputs = processor.apply_chat_template(
+    chat_template, tokenize=True, return_dict=True, output_labels=True,
+).to(model.device, model.dtype)
+
+loss = model(**inputs).loss
+print("Loss:", loss.item())
+loss.backward()
+```
+
+### Forced alignment (word-level timestamping)
+
+Use `Qwen3ASRForForcedAlignment` to obtain word-level timestamps from a transcript. First transcribe with the ASR model, then align with the forced aligner.
+
+The following languages are supported: Chinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish.
+
+Japanese requires the `nagisa` library, while Korean requires the `soynlp` library:
+```
+pip install nagisa soynlp
+```
+
+#### English
+
+```python
+import torch
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration, Qwen3ASRForForcedAlignment
+
+asr_model_id = "bezzam/Qwen3-ASR-0.6B"
+aligner_model_id = "bezzam/Qwen3-ForcedAligner-0.6B"
+
+asr_processor = AutoProcessor.from_pretrained(asr_model_id)
+asr_model = Qwen3ASRForConditionalGeneration.from_pretrained(asr_model_id, device_map="auto")
+
+aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)
+aligner_model = Qwen3ASRForForcedAlignment.from_pretrained(
+    aligner_model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+
+# Step 1: Transcribe
+inputs = asr_processor.apply_transcription_request(audio=audio_url).to(asr_model.device, asr_model.dtype)
+output_ids = asr_model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+parsed = asr_processor.decode(generated_ids, return_format="parsed")[0]
+transcript = parsed["transcription"]
+language = parsed["language"] or "English"
+
+# Step 2: Prepare alignment inputs
+aligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs(
+    audio=audio_url, transcript=transcript, language=language,
+)
+aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
+
+# Step 3: Run forced aligner
+with torch.inference_mode():
+    outputs = aligner_model(**aligner_inputs)
+
+# Step 4: Decode timestamps
+timestamps = aligner_processor.decode_forced_alignment(
+    logits=outputs.logits,
+    input_ids=aligner_inputs["input_ids"],
+    word_lists=word_lists,
+    timestamp_token_id=aligner_model.config.timestamp_token_id,
+)[0]
+
+for item in timestamps:
+    print(f"{item['text']:<20} {item['start_time']:>8.3f}s → {item['end_time']:>8.3f}s")
+
+"""
+Word                  Start (s)    End (s)
+------------------------------------------
+Mr                        0.560      0.800
+Quilter                   0.800      1.280
+is                        1.280      1.440
+the                       1.440      1.520
+apostle                   1.520      2.080
+...
+"""
+```
+
+#### Chinese
+
+For Chinese text, each character is aligned individually.
+
+```python
+import torch
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration, Qwen3ASRForForcedAlignment
+
+asr_model_id = "bezzam/Qwen3-ASR-0.6B"
+aligner_model_id = "bezzam/Qwen3-ForcedAligner-0.6B"
+
+asr_processor = AutoProcessor.from_pretrained(asr_model_id)
+asr_model = Qwen3ASRForConditionalGeneration.from_pretrained(asr_model_id, device_map="auto")
+
+aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)
+aligner_model = Qwen3ASRForForcedAlignment.from_pretrained(
+    aligner_model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+audio_url = "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav"
+
+# Step 1: Transcribe with language hint
+inputs = asr_processor.apply_transcription_request(
+    audio=audio_url, language="Chinese",
+).to(asr_model.device, asr_model.dtype)
+output_ids = asr_model.generate(**inputs, max_new_tokens=256)
+generated_ids = output_ids[:, inputs["input_ids"].shape[1]:]
+parsed = asr_processor.decode(generated_ids, return_format="parsed")[0]
+transcript = parsed["transcription"]
+
+# Step 2–4: Align and decode
+aligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs(
+    audio=audio_url, transcript=transcript, language="Chinese",
+)
+aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
+
+with torch.inference_mode():
+    outputs = aligner_model(**aligner_inputs)
+
+timestamps = aligner_processor.decode_forced_alignment(
+    logits=outputs.logits,
+    input_ids=aligner_inputs["input_ids"],
+    word_lists=word_lists,
+    timestamp_token_id=aligner_model.config.timestamp_token_id,
+)[0]
+
+for item in timestamps:
+    print(f"{item['text']:<4} {item['start_time']:>8.3f}s → {item['end_time']:>8.3f}s")
+
+"""
+Char        Start (s)    End (s)
+--------------------------------
+甚               0.400      0.720
+至               0.720      0.960
+出               0.960      1.120
+现               1.120      1.520
+...
+"""
+```
+
+#### With another ASR model
+
+The forced aligner is model-agnostic, meaning the transcripts from any ASR system can be provided. Below is an example using [NVIDIA Parakeet CTC](https://huggingface.co/nvidia/parakeet-ctc-1.1b) for transcription.
+
+**Single sample:**
+
+```python
+import torch
+from datasets import Audio, load_dataset
+from transformers import AutoModelForCTC, AutoProcessor, Qwen3ASRForForcedAlignment
+
+# Load Parakeet CTC for transcription
+parakeet_processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
+parakeet_model = AutoModelForCTC.from_pretrained(
+    "nvidia/parakeet-ctc-1.1b", torch_dtype="auto", device_map="cuda",
+)
+
+# Load Qwen3 Forced Aligner for timestamping
+aligner_model_id = "bezzam/Qwen3-ForcedAligner-0.6B"
+aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)
+aligner_model = Qwen3ASRForForcedAlignment.from_pretrained(
+    aligner_model_id, torch_dtype=torch.bfloat16, device_map="cuda",
+)
+
+# Load audio
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column("audio", Audio(sampling_rate=parakeet_processor.feature_extractor.sampling_rate))
+audio_array = ds[0]["audio"]["array"]
+sr = ds[0]["audio"]["sampling_rate"]
+
+# Step 1: Transcribe with Parakeet
+inputs = parakeet_processor(audio_array, sampling_rate=sr, return_tensors="pt").to(
+    parakeet_model.device, dtype=parakeet_model.dtype
+)
+with torch.inference_mode():
+    outputs = parakeet_model.generate(**inputs)
+transcript = parakeet_processor.decode(outputs)[0]
+print(f"Transcript: {transcript}")
+
+# Step 2: Align with Qwen3 Forced Aligner (expects 16kHz audio)
+aligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs(
+    audio=audio_array, transcript=transcript, language="English",
+)
+aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
+
+with torch.inference_mode():
+    aligner_outputs = aligner_model(**aligner_inputs)
+
+timestamps = aligner_processor.decode_forced_alignment(
+    logits=aligner_outputs.logits,
+    input_ids=aligner_inputs["input_ids"],
+    word_lists=word_lists,
+    timestamp_token_id=aligner_model.config.timestamp_token_id,
+)[0]
+
+for item in timestamps:
+    print(f"{item['text']:<20} {item['start_time']:>8.3f}s → {item['end_time']:>8.3f}s")
+```
+
+**Batch:**
+
+```python
+import torch
+from datasets import Audio, load_dataset
+from transformers import AutoModelForCTC, AutoProcessor, Qwen3ASRForForcedAlignment
+
+parakeet_processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
+parakeet_model = AutoModelForCTC.from_pretrained(
+    "nvidia/parakeet-ctc-1.1b", torch_dtype="auto", device_map="cuda",
+)
+
+aligner_model_id = "bezzam/Qwen3-ForcedAligner-0.6B"
+aligner_processor = AutoProcessor.from_pretrained(aligner_model_id)
+aligner_model = Qwen3ASRForForcedAlignment.from_pretrained(
+    aligner_model_id, torch_dtype=torch.bfloat16, device_map="cuda",
+)
+
+ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+ds = ds.cast_column("audio", Audio(sampling_rate=parakeet_processor.feature_extractor.sampling_rate))
+audio_arrays = [ds[i]["audio"]["array"] for i in range(3)]
+sr = ds[0]["audio"]["sampling_rate"]
+
+# Batch transcribe with Parakeet
+inputs = parakeet_processor(audio_arrays, sampling_rate=sr, return_tensors="pt", padding=True).to(
+    parakeet_model.device, dtype=parakeet_model.dtype
+)
+with torch.inference_mode():
+    outputs = parakeet_model.generate(**inputs)
+transcripts = parakeet_processor.decode(outputs)
+
+# Batch align with Qwen3 Forced Aligner
+aligner_inputs, word_lists = aligner_processor.prepare_forced_aligner_inputs(
+    audio=audio_arrays, transcript=transcripts, language="English",
+)
+aligner_inputs = aligner_inputs.to(aligner_model.device, aligner_model.dtype)
+
+with torch.inference_mode():
+    aligner_outputs = aligner_model(**aligner_inputs)
+
+batch_timestamps = aligner_processor.decode_forced_alignment(
+    logits=aligner_outputs.logits,
+    input_ids=aligner_inputs["input_ids"],
+    word_lists=word_lists,
+    timestamp_token_id=aligner_model.config.timestamp_token_id,
+)
+
+for i, (transcript, timestamps) in enumerate(zip(transcripts, batch_timestamps)):
+    print(f"\n[Sample {i}] {transcript}")
+    for item in timestamps[:5]:
+        print(f"  {item['text']:<20} {item['start_time']:>8.3f}s → {item['end_time']:>8.3f}s")
+    if len(timestamps) > 5:
+        print(f"  ... ({len(timestamps) - 5} more words)")
+```
+
+### Torch compile
+
+Both the ASR and forced aligner models support `torch.compile` for faster inference. The forced aligner is an especially good fit for compilation because it runs a single forward pass (no autoregressive decoding). This makes it ideal for **bulk audio timestamping**: transcribe with any ASR model, then batch-align with the compiled forced aligner for maximum throughput.
+
+#### Compiling the forced aligner
+
+```python
+import time
+import torch
+from transformers import AutoProcessor, Qwen3ASRForForcedAlignment
+
+model_id = "bezzam/Qwen3-ForcedAligner-0.6B"
+num_warmup, num_runs = 5, 20
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForForcedAlignment.from_pretrained(model_id, torch_dtype=torch.bfloat16).to("cuda")
+
+# Prepare a batch of 4 samples
+audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+transcript = "Mr. Quilter is the apostle of the middle classes."
+
+aligner_inputs, word_lists = processor.prepare_forced_aligner_inputs(
+    audio=[audio_url] * 4,
+    transcript=[transcript] * 4,
+    language=["English"] * 4,
+)
+aligner_inputs = aligner_inputs.to("cuda", torch.bfloat16)
+
+# Without compile
+with torch.no_grad():
+    for _ in range(num_warmup):
+        _ = model(**aligner_inputs)
+torch.cuda.synchronize()
+start = time.time()
+with torch.no_grad():
+    for _ in range(num_runs):
+        _ = model(**aligner_inputs)
+torch.cuda.synchronize()
+no_compile_time = (time.time() - start) / num_runs
+print(f"Without compile: {no_compile_time:.4f}s")
+
+# With compile
+model = torch.compile(model)
+with torch.no_grad():
+    for _ in range(num_warmup):
+        _ = model(**aligner_inputs)
+torch.cuda.synchronize()
+start = time.time()
+with torch.no_grad():
+    for _ in range(num_runs):
+        _ = model(**aligner_inputs)
+torch.cuda.synchronize()
+compile_time = (time.time() - start) / num_runs
+print(f"With compile:    {compile_time:.4f}s")
+print(f"Speedup: {no_compile_time / compile_time:.2f}x")
+# ~2.5x speedup observed on A100
+```
+
+#### Compiling the ASR model (generate)
+
+For autoregressive transcription, `torch.compile` accelerates the per-token forward passes inside `generate`.
+
+```python
+import time
+import torch
+from transformers import AutoProcessor, Qwen3ASRForConditionalGeneration
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+num_warmup, num_runs = 3, 10
+max_new_tokens = 256
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = Qwen3ASRForConditionalGeneration.from_pretrained(model_id, torch_dtype=torch.bfloat16).to("cuda").eval()
+
+audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+inputs = processor.apply_transcription_request(
+    audio=[audio_url] * 4,  # batch of 4
+).to("cuda", torch.bfloat16)
+
+# Without compile
+with torch.inference_mode():
+    for _ in range(num_warmup):
+        _ = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+torch.cuda.synchronize()
+start = time.time()
+with torch.inference_mode():
+    for _ in range(num_runs):
+        output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+torch.cuda.synchronize()
+no_compile_time = (time.time() - start) / num_runs
+print(f"Without compile: {no_compile_time:.4f}s")
+
+# With compile
+model = torch.compile(model)
+with torch.inference_mode():
+    for _ in range(num_warmup):
+        _ = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+torch.cuda.synchronize()
+start = time.time()
+with torch.inference_mode():
+    for _ in range(num_runs):
+        output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+torch.cuda.synchronize()
+compile_time = (time.time() - start) / num_runs
+print(f"With compile:    {compile_time:.4f}s")
+print(f"Speedup: {no_compile_time / compile_time:.2f}x")
+# ~2.5x speedup observed on A100
+```
+
+### Pipeline usage
+
+```python
+from transformers import pipeline
+
+model_id = "bezzam/Qwen3-ASR-1.7B"
+pipe = pipeline("any-to-any", model=model_id, device_map="auto")
+
+chat_template = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "audio",
+                "path": "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+            },
+        ],
+    }
+]
+outputs = pipe(text=chat_template, return_full_text=False)
+raw_text = outputs[0]["generated_text"]
+print(f"Raw: {raw_text}")
+
+# Use processor helper to extract transcription
+transcription = pipe.processor.extract_transcription(raw_text)
+print(f"Transcription: {transcription}")
+```
+
+## Qwen3ASRConfig
+
+[[autodoc]] Qwen3ASRConfig
+
+
+## Qwen3ASREncoderConfig
+
+[[autodoc]] Qwen3ASREncoderConfig
+
+
+## Qwen3ASRFeatureExtractor
+
+[[autodoc]] Qwen3ASRFeatureExtractor
+    - __call__
+
+## Qwen3ASRProcessor
+
+[[autodoc]] Qwen3ASRProcessor
+    - __call__
+    - apply_transcription_request
+    - prepare_forced_aligner_inputs
+    - decode_forced_alignment
+    - decode
+
+## Qwen3ASREncoder
+
+[[autodoc]] Qwen3ASREncoder
+
+## Qwen3ASRModel
+
+[[autodoc]] Qwen3ASRModel
+
+## Qwen3ASRForConditionalGeneration
+
+[[autodoc]] Qwen3ASRForConditionalGeneration
+    - forward
+    - get_audio_features
+
+## Qwen3ForcedAlignerConfig
+
+[[autodoc]] Qwen3ForcedAlignerConfig
+
+## Qwen3ASRForForcedAlignment
+
+[[autodoc]] Qwen3ASRForForcedAlignment
+    - forward
+    - get_audio_features
diff --git a/docs/source/en/model_doc/sam.md b/docs/source/en/model_doc/sam.md
index 70b74133cd5b..3840b19ae587 100644
--- a/docs/source/en/model_doc/sam.md
+++ b/docs/source/en/model_doc/sam.md
@@ -44,6 +44,48 @@ Tips:
 This model was contributed by [ybelkada](https://huggingface.co/ybelkada) and [ArthurZ](https://huggingface.co/ArthurZ).
 The original code can be found [here](https://github.com/facebookresearch/segment-anything).
 
+## Usage examples with 🤗 Transformers
+
+### Promptable Visual Segmentation Pipeline
+
+The easiest way to use SAM is through the `promptable-visual-segmentation` pipeline:
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline(model="facebook/sam-vit-base", task="promptable-visual-segmentation")
+>>> # Single point prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+...     input_points=[[[[450, 600]]]],
+...     input_labels=[[[1]]],
+... )
+[[{'score': 0.87, 'mask': tensor([...])}]]
+
+>>> # Box prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_boxes=[[[59, 144, 76, 163]]],
+... )
+[[{'score': 0.92, 'mask': tensor([...])}]]
+
+>>> # Multiple points for refinement
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_points=[[[[450, 600], [500, 620]]]],
+...     input_labels=[[[1, 0]]],  # 1=positive, 0=negative
+... )
+[[{'score': 0.85, 'mask': tensor([...])}]]
+```
+
+<Tip>
+
+**Note:** The pipeline output format differs from using the model and processor manually. The pipeline returns a standardized format (list of lists of dicts with `score` and `mask`) to ensure consistency across all transformers pipelines, while the processor's `post_process_masks()` returns raw tensors.
+
+</Tip>
+
+### Basic Usage with Model and Processor
+
 Below is an example on how to run mask generation given an image and a 2D point:
 
 ```python
diff --git a/docs/source/en/model_doc/sam2.md b/docs/source/en/model_doc/sam2.md
index 42af04eda664..26265e44079f 100644
--- a/docs/source/en/model_doc/sam2.md
+++ b/docs/source/en/model_doc/sam2.md
@@ -47,7 +47,45 @@ The original code can be found [here](https://github.com/facebookresearch/sam2/t
 
 ## Usage example
 
-### Automatic Mask Generation with Pipeline
+### Promptable Visual Segmentation Pipeline
+
+The easiest way to use SAM2 is through the `promptable-visual-segmentation` pipeline:
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline(model="facebook/sam2.1-hiera-large", task="promptable-visual-segmentation")
+>>> # Single point prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+...     input_points=[[[[450, 600]]]],
+...     input_labels=[[[1]]],
+... )
+[[{'score': 0.87, 'mask': tensor([...])}]]
+
+>>> # Box prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_boxes=[[[59, 144, 76, 163]]],
+... )
+[[{'score': 0.92, 'mask': tensor([...])}]]
+
+>>> # Multiple points for refinement
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_points=[[[[450, 600], [500, 620]]]],
+...     input_labels=[[[1, 0]]],  # 1=positive, 0=negative
+... )
+[[{'score': 0.85, 'mask': tensor([...])}]]
+```
+
+<Tip>
+
+**Note:** The pipeline output format differs from using the model and processor manually. The pipeline returns a standardized format (list of lists of dicts with `score` and `mask`) to ensure consistency across all transformers pipelines, while the processor's `post_process_masks()` returns raw tensors.
+
+</Tip>
+
+### Automatic Mask Generation Pipeline
 
 SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline:
 
diff --git a/docs/source/en/model_doc/sam3.md b/docs/source/en/model_doc/sam3.md
index 23e8f677dceb..7c3d872541f0 100644
--- a/docs/source/en/model_doc/sam3.md
+++ b/docs/source/en/model_doc/sam3.md
@@ -39,6 +39,58 @@ This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan) an
 
 ## Usage examples with 🤗 Transformers
 
+### Using the Pipeline
+
+The simplest way to use SAM3 is through the `promptable-concept-segmentation` pipeline:
+
+```python
+>>> from transformers import pipeline
+>>> from PIL import Image
+>>> import requests
+
+>>> # Create pipeline
+>>> segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+>>> # Load image
+>>> image_url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
+
+>>> # Segment using text prompt
+>>> results = segmenter(image, text="ear", threshold=0.5, mask_threshold=0.5)
+
+>>> print(f"Found {len(results)} objects")
+>>> # Results contain:
+>>> # - score: Confidence score for each instance
+>>> # - label: The text prompt used
+>>> # - box: Bounding box in xyxy format (absolute pixel coordinates)
+>>> # - mask: Binary segmentation mask (resized to original image size)
+
+>>> # You can also use bounding box prompts
+>>> # Box in xyxy format: [x1, y1, x2, y2] in pixel coordinates
+>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg"
+>>> kitchen_image = Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB")
+
+>>> box_xyxy = [59, 144, 76, 163]
+>>> input_boxes = [[box_xyxy]]  # [batch, num_boxes, 4]
+>>> input_boxes_labels = [[1]]  # 1 = positive box
+
+>>> results = segmenter(
+...     kitchen_image,
+...     input_boxes=input_boxes,
+...     input_boxes_labels=input_boxes_labels,
+...     threshold=0.5,
+...     mask_threshold=0.5
+... )
+
+>>> print(f"Found {len(results)} objects matching the visual concept")
+```
+
+<Tip>
+
+**Note:** The pipeline output format differs from using the model and processor manually. The pipeline returns a standardized format (list of dicts with `score`, `label`, `box`, `mask`) to ensure consistency across all transformers pipelines, while the processor's `post_process_instance_segmentation()` returns a dict with `scores`, `boxes`, and `masks` as separate tensors.
+
+</Tip>
+
 ### Text-Only Prompts
 
 ```python
diff --git a/docs/source/en/model_doc/sam3_tracker.md b/docs/source/en/model_doc/sam3_tracker.md
index c64c8b711c45..927474e154e9 100644
--- a/docs/source/en/model_doc/sam3_tracker.md
+++ b/docs/source/en/model_doc/sam3_tracker.md
@@ -43,7 +43,45 @@ This model was contributed by [yonigozlan](https://huggingface.co/yonigozlan) an
 
 ## Usage example
 
-### Automatic Mask Generation with Pipeline
+### Promptable Visual Segmentation Pipeline
+
+The easiest way to use Sam3Tracker is through the `promptable-visual-segmentation` pipeline:
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline(model="facebook/sam3", task="promptable-visual-segmentation")
+>>> # Single point prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+...     input_points=[[[[450, 600]]]],
+...     input_labels=[[[1]]],
+... )
+[[{'score': 0.87, 'mask': tensor([...])}]]
+
+>>> # Box prompt
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_boxes=[[[59, 144, 76, 163]]],
+... )
+[[{'score': 0.92, 'mask': tensor([...])}]]
+
+>>> # Multiple points for refinement
+>>> segmenter(
+...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+...     input_points=[[[[450, 600], [500, 620]]]],
+...     input_labels=[[[1, 0]]],  # 1=positive, 0=negative
+... )
+[[{'score': 0.85, 'mask': tensor([...])}]]
+```
+
+<Tip>
+
+**Note:** The pipeline output format differs from using the model and processor manually. The pipeline returns a standardized format (list of lists of dicts with `score` and `mask`) to ensure consistency across all transformers pipelines, while the processor's `post_process_masks()` returns raw tensors.
+
+</Tip>
+
+### Automatic Mask Generation Pipeline
 
 Sam3Tracker can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline:
 
diff --git a/docs/source/en/model_doc/sarvam_mla.md b/docs/source/en/model_doc/sarvam_mla.md
new file mode 100644
index 000000000000..6ebc0bfe3c3d
--- /dev/null
+++ b/docs/source/en/model_doc/sarvam_mla.md
@@ -0,0 +1,48 @@
+<!--Copyright 2026 Sarvam AI and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
+compliance with the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is
+distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and limitations under the License.
+
+-->
+*This model was released on 2026-03-06 and added to Hugging Face Transformers on 2026-03-17.*
+
+# SarvamMLA
+
+## Overview
+
+SarvamMLA is a 105B parameter Mixture of Experts (MoE) language model developed by [Sarvam AI](https://www.sarvam.ai/). It uses Multi-head Latent Attention (MLA) combined with sparse MoE routing, architecturally identical to DeepSeek-V3.
+
+Key architectural features:
+
+- **Multi-head Latent Attention (MLA)**: Low-rank KV compression with decoupled RoPE, reducing KV cache memory while maintaining performance.
+- **Sparse Mixture of Experts**: 128 routed experts with 8 active per token, plus 1 shared expert. The first layer uses a dense MLP.
+- **DeepSeek YaRN RoPE**: Extended context support up to 131K tokens via YaRN rotary position embeddings.
+- **Sigmoid routing with group-based top-k**: Token-choice routing using sigmoid scores with expert bias correction and group-aware selection.
+
+This model uses the DeepSeek-V3 architecture with a custom configuration. See the [DeepSeek-V3 documentation](deepseek_v3) for model and forward reference.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "sarvamai/sarvam-105b",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("sarvamai/sarvam-105b")
+
+inputs = tokenizer("Hello, how are you?", return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## SarvamMLAConfig
+
+[[autodoc]] SarvamMLAConfig
diff --git a/docs/source/en/model_doc/t5.md b/docs/source/en/model_doc/t5.md
index 2b260ee7a3e6..99af05ec0ae1 100644
--- a/docs/source/en/model_doc/t5.md
+++ b/docs/source/en/model_doc/t5.md
@@ -131,3 +131,8 @@ print(tokenizer.decode(output[0], skip_special_tokens=True))
 
 [[autodoc]] T5ForQuestionAnswering
     - forward
+
+## T5EncoderForSequenceClassification
+
+[[autodoc]] T5EncoderForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/timesfm.md b/docs/source/en/model_doc/timesfm.md
index e8938202ee9e..37ee3d21c660 100644
--- a/docs/source/en/model_doc/timesfm.md
+++ b/docs/source/en/model_doc/timesfm.md
@@ -70,6 +70,226 @@ with torch.no_grad():
     quantile_forecast_conv = outputs.full_predictions.float().cpu().numpy()
 ```
 
+## Forecasting with Covariates
+
+TimesFM supports forecasting with external covariates using batched in-context regression. This allows you to incorporate additional information such as weather data, economic indicators, or business metrics to improve forecast accuracy.
+
+The model supports four types of covariates:
+
+- **Dynamic Numerical**: Time-varying numerical features (e.g., temperature, price)
+- **Dynamic Categorical**: Time-varying categorical features (e.g., day of week, season)
+- **Static Numerical**: Time-invariant numerical features (e.g., store size, population)
+- **Static Categorical**: Time-invariant categorical features (e.g., region, store type)
+
+### Basic Example
+
+```python
+import numpy as np
+import torch
+from transformers import TimesFmModelForPrediction
+
+# Load the model
+model = TimesFmModelForPrediction.from_pretrained(
+    "google/timesfm-2.0-500m-pytorch",
+    dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Prepare historical time series data (ice cream sales example)
+# Match the model's dtype and device for proper compatibility
+device = next(model.parameters()).device
+dtype = next(model.parameters()).dtype
+past_sales = [
+    torch.tensor([45, 52, 48, 55, 61, 58, 62, 59, 56, 53], dtype=dtype, device=device),  # Store 1
+    torch.tensor([38, 42, 39, 46, 48, 45, 49, 47, 44, 41], dtype=dtype, device=device),  # Store 2
+]
+
+# Prepare covariates (context + future)
+context_len = 10
+horizon_len = 5
+total_len = context_len + horizon_len
+
+# Dynamic numerical covariates (temperature affects ice cream sales)
+temperature_store1 = [22, 25, 23, 28, 31, 29, 32, 30, 27, 24,  # context
+                      26, 29, 31, 33, 30]                      # future (horizon)
+temperature_store2 = [20, 23, 21, 26, 29, 27, 30, 28, 25, 22,  # context
+                      24, 27, 29, 31, 28]                      # future (horizon)
+
+dynamic_numerical = {
+    "temperature": [temperature_store1, temperature_store2]
+}
+
+# Dynamic categorical covariates (day of week effect)
+dynamic_categorical = {
+    "weekday": [
+        [1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1],  # Store 1: Mon=1, Sun=0
+        [1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1],  # Store 2
+    ]
+}
+
+# Static covariates (store characteristics)
+static_numerical = {
+    "store_size": [150.0, 120.0],  # sq ft (hundreds)
+}
+
+static_categorical = {
+    "store_type": ["mall", "street"],
+    "region": ["north", "south"],
+}
+
+# Generate forecasts with covariates
+with torch.no_grad():
+    outputs = model.forecast_with_covariates(
+        past_values=past_sales,
+        dynamic_numerical_covariates=dynamic_numerical,
+        dynamic_categorical_covariates=dynamic_categorical,
+        static_numerical_covariates=static_numerical,
+        static_categorical_covariates=static_categorical,
+        ridge=0.1,  # Ridge regularization for stability
+    )
+
+# Extract results
+combined_forecast = outputs.combined_predictions  # TimesFM + XReg predictions
+xreg_forecast = outputs.xreg_predictions          # XReg-only predictions
+timesfm_forecast = outputs.mean_predictions       # TimesFM-only predictions
+
+print(f"Combined forecast shape: {combined_forecast.shape}")  # [2, 5]
+print(f"Store 1 combined forecast: {combined_forecast[0].cpu().numpy()}")
+print(f"Store 2 combined forecast: {combined_forecast[1].cpu().numpy()}")
+```
+
+### Advanced Example: Electricity Price Forecasting
+
+This example demonstrates forecasting electricity prices with multiple covariates, inspired by electricity price forecasting (EPF) scenarios:
+
+```python
+import numpy as np
+import torch
+from transformers import TimesFmModelForPrediction
+
+# Load model
+model = TimesFmModelForPrediction.from_pretrained(
+    "google/timesfm-2.0-500m-pytorch", 
+    dtype=torch.float32
+)
+
+# Historical electricity prices (48 hours of context)
+np.random.seed(42)
+context_hours = 48
+horizon_hours = 24
+total_hours = context_hours + horizon_hours
+
+# Create realistic price patterns for 3 regions
+device = next(model.parameters()).device
+dtype = next(model.parameters()).dtype
+past_prices = []
+for region in range(3):
+    # Daily pattern: higher during day, lower at night
+    daily_pattern = 50 + 20 * np.sin(2 * np.pi * np.arange(context_hours) / 24)
+    # Add regional base price and noise
+    regional_base = 40 + region * 10
+    noise = np.random.randn(context_hours) * 5
+    prices = regional_base + daily_pattern + noise
+    past_prices.append(torch.tensor(prices, dtype=dtype, device=device))
+
+# Dynamic numerical covariates
+load_demand = []
+temperature = []
+renewable_share = []
+
+for region in range(3):
+    # Electricity load (MW) - main price driver
+    base_load = 1000 + 300 * np.sin(2 * np.pi * np.arange(total_hours) / 24)
+    regional_load = base_load + region * 100 + np.random.randn(total_hours) * 50
+    load_demand.append(regional_load.tolist())
+
+    # Temperature (affects demand)
+    temp_pattern = 20 + 10 * np.sin(2 * np.pi * np.arange(total_hours) / (24 * 30))
+    temp_noise = np.random.randn(total_hours) * 3
+    temperature.append((temp_pattern + temp_noise).tolist())
+
+    # Renewable energy share (affects pricing)
+    renewable = np.clip(0.3 + 0.2 * np.random.randn(total_hours), 0.1, 0.8)
+    renewable_share.append(renewable.tolist())
+
+dynamic_numerical = {
+    "load_mw": load_demand,
+    "temperature": temperature, 
+    "renewable_share": renewable_share,
+}
+
+# Dynamic categorical covariates 
+dynamic_categorical = {
+    "hour": [
+        [i % 24 for i in range(total_hours)]  # Hour of day: 0-23
+        for _ in range(3)
+    ],
+    "day_type": [
+        ["weekday" if (i // 24) % 7 < 5 else "weekend" for i in range(total_hours)]
+        for _ in range(3)
+    ],
+}
+
+# Static covariates (market characteristics)
+static_numerical = {
+    "market_capacity_mw": [5000.0, 4500.0, 6000.0],
+    "transmission_capacity": [800.0, 700.0, 900.0],
+}
+
+static_categorical = {
+    "market_type": ["competitive", "regulated", "competitive"],
+    "primary_fuel": ["gas", "coal", "nuclear"],
+}
+
+# Forecast with covariates
+with torch.no_grad():
+    outputs = model.forecast_with_covariates(
+        past_values=past_prices,
+        dynamic_numerical_covariates=dynamic_numerical,
+        dynamic_categorical_covariates=dynamic_categorical,
+        static_numerical_covariates=static_numerical,
+        static_categorical_covariates=static_categorical,
+        xreg_mode="xreg + timesfm",  # Fit XReg first, then TimesFM on residuals
+        ridge=0.5,  # Higher ridge for stability with many covariates
+    )
+
+price_forecasts = outputs.combined_predictions
+print(f"24-hour price forecasts for {len(price_forecasts)} regions:")
+for i, forecast in enumerate(price_forecasts):
+    print(f"Region {i+1}: ${forecast.mean():.2f}/MWh (avg)")
+```
+
+### XReg Modes
+
+TimesFM supports two modes for combining TimesFM and external regression (XReg) predictions:
+
+1. **"xreg + timesfm"** (default): Fit linear model on targets first, then forecast residuals with TimesFM
+2. **"timesfm + xreg"**: Forecast with TimesFM first, then fit a linear model on residuals
+
+```python
+# Compare different modes
+modes = ["xreg + timesfm", "timesfm + xreg"]
+
+for mode in modes:
+    with torch.no_grad():
+        outputs = model.forecast_with_covariates(
+            past_values=past_sales,
+            dynamic_numerical_covariates={"temperature": temperature_data},
+            xreg_mode=mode,
+            ridge=0.1,
+        )
+    print(f"{mode}: {outputs.combined_predictions[0][:3].cpu().numpy()}")
+```
+
+### Key Parameters
+
+- **`ridge`**: Ridge regularization parameter (0.0-1.0). Higher values provide more stability with many covariates
+- **`normalize_xreg_target_per_input`**: Whether to normalize targets per input series (default: True)
+- **`xreg_mode`**: How to combine TimesFM and XReg predictions
+- **`truncate_negative`**: Whether to truncate negative predictions for non-negative data
+
+The covariate forecasting leverages batched in-context regression to efficiently process multiple time series with external information, enabling more accurate forecasts for complex real-world scenarios.
+
 ## TimesFmConfig
 
 [[autodoc]] TimesFmConfig
@@ -83,3 +303,4 @@ with torch.no_grad():
 
 [[autodoc]] TimesFmModelForPrediction
     - forward
+    - forecast_with_covariates
diff --git a/docs/source/en/model_doc/umt5.md b/docs/source/en/model_doc/umt5.md
index ab94ef0bda2a..ea38c2abdc93 100644
--- a/docs/source/en/model_doc/umt5.md
+++ b/docs/source/en/model_doc/umt5.md
@@ -106,3 +106,8 @@ Refer to [T5's documentation page](t5) for more tips, code examples and notebook
 
 [[autodoc]] UMT5ForQuestionAnswering
     - forward
+
+## UMT5EncoderForSequenceClassification
+
+[[autodoc]] UMT5EncoderForSequenceClassification
+    - forward
diff --git a/docs/source/en/model_doc/videoprism.md b/docs/source/en/model_doc/videoprism.md
new file mode 100644
index 000000000000..14a71ab24fdc
--- /dev/null
+++ b/docs/source/en/model_doc/videoprism.md
@@ -0,0 +1,123 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+*This model was released on 2025-06-03 and added to Hugging Face Transformers on 2026-04-22.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    </div>
+</div>
+
+# VideoPrism
+
+The VideoPrism model was proposed in the paper [VideoPrism: A Foundational Visual Encoder for Video Understanding](https://huggingface.co/papers/2402.13217) by Google DeepMind ([blog post](https://research.google/blog/videoprism-a-foundational-visual-encoder-for-video-understanding/)).
+
+VideoPrism is a general-purpose video encoder that tackles diverse video understanding tasks with a single frozen model. The model is pretrained on a large-scale heterogeneous corpus containing 36M high-quality video-caption pairs and 582M video clips with noisy parallel text (e.g., ASR transcripts). The pretraining approach improves upon masked autoencoding through global-local distillation of semantic video embeddings and a token shuffling scheme, enabling the model to focus primarily on the video modality while leveraging text associated with videos. VideoPrism achieves state-of-the-art performance on 31 out of 33 video understanding benchmarks across four broad task groups, from web video question answering to computer vision for science.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/MHRDYN7/videoprism_assets/resolve/main/GuOjZNKWEAAvkyu.jpeg" alt="drawing" width="600"/>
+</div>
+
+You can find all original VideoPrism checkpoints under the [VideoPrism](https://huggingface.co/collections/google/videoprism) collection.
+
+Notes:
+
+- VideoPrism uses a factorized spatio-temporal encoder architecture, processing videos through separate spatial and temporal transformers.
+- The model supports video-text contrastive learning through `VideoPrismClipModel`, which combines a video encoder and a text encoder. `VideoPrismConfig` must be used with this model.
+- For video classification tasks, use `VideoPrismForVideoClassification` which adds a classification head on top of the video encoder. `VideoPrismVisionConfig` must be used with this model.
+- The vision encoder can be used standalone via `VideoPrismVisionModel` for extracting video features. `VideoPrismVisionConfig` must be used with this model.
+- The default input resolution is 288x288 pixels with 16 frames per video clip for the base models and 8 frames for the large models. Set interpolate_pos_encoding=True to use the models with custom resolution and frames per clip.
+
+This model was contributed by [MHRDYN7](https://github.com/MHRDYN7) and reviewed by [qubvel](https://github.com/qubvel) & [zucchini-nlp](https://github.com/zucchini-nlp).
+The original code can be found [here](https://github.com/google-deepmind/videoprism).
+
+## Usage example
+
+The snippet below shows how to load the VideoPrismVisionModel for feature extraction using the `AutoModel` class.
+
+```py
+import torch
+from transformers import AutoModel, AutoVideoProcessor
+
+processor = AutoVideoProcessor.from_pretrained("MHRDYN7/videoprism-base-f16r288")
+model = AutoModel.from_pretrained(
+    "MHRDYN7/videoprism-base-f16r288",
+    dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa" # use "eager" to replicate the exact behavior as the original model
+)
+
+video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"
+
+# when do_sample_frames=True, 16/8 frames will be sampled by default depending on the checkpoint size base/large.
+processed_video_inputs = processor(videos=[video_url], return_metadata=True, do_sample_frames=True)
+video_metadata = processed_video_inputs["video_metadata"]
+video_inputs = processed_video_inputs["pixel_values_videos"].to(model.device)
+outputs = model(video_inputs)
+
+# VideoPrism encoder outputs
+encoder_outputs = outputs.last_hidden_state
+
+```
+
+<Tip warning={true}>
+
+The video processor loaded via AutoProcessor is LlavaOnevisionVideoProcessor which is recomended for sampling frames exactly as in the original repository. However, please note that the [original processor](https://github.com/google-deepmind/videoprism/blob/main/videoprism/colabs/videoprism_video_encoder_demo.ipynb) uses Lanczos interpolation for resizing the frames, but that is not supported in pytorch yet and therefore LlavaOnevisionVideoProcessor uses Bicubic interpolation. 
+
+</Tip>
+
+## VideoPrismVisionConfig
+
+[[autodoc]] VideoPrismVisionConfig
+
+## VideoPrismTextConfig
+
+[[autodoc]] VideoPrismTextConfig
+
+## VideoPrismConfig
+
+[[autodoc]] VideoPrismConfig
+
+## VideoPrismTokenizer
+
+[[autodoc]] VideoPrismTokenizer
+
+## VideoPrismProcessor
+
+[[autodoc]] VideoPrismProcessor
+
+## VideoPrismVisionModel
+
+[[autodoc]] VideoPrismVisionModel
+    - forward
+
+## VideoPrismVideoModel
+
+[[autodoc]] VideoPrismVideoModel
+    - forward
+
+## VideoPrismTextModel
+
+[[autodoc]] VideoPrismTextModel
+    - forward
+
+## VideoPrismClipModel
+
+[[autodoc]] VideoPrismClipModel
+    - forward
+
+## VideoPrismForVideoClassification
+
+[[autodoc]] VideoPrismForVideoClassification
+    - forward
diff --git a/docs/source/en/model_doc/vjepa2.md b/docs/source/en/model_doc/vjepa2.md
index 14c6bf0fd5e2..23aa03c3c416 100644
--- a/docs/source/en/model_doc/vjepa2.md
+++ b/docs/source/en/model_doc/vjepa2.md
@@ -33,7 +33,22 @@ rendered properly in your Markdown viewer.
 
 You can find all original V-JEPA2 checkpoints under the [V-JEPA 2](https://huggingface.co/collections/facebook/v-jepa-2-6841bad8413014e185b497a6) collection.
 
-This model was contributed by [koustuvs](https://huggingface.co/koustuvs), [yonigozlan](https://huggingface.co/yonigozlan) and [qubvel](https://huggingface.co/qubvel-hf). The original code can be found [here](https://github.com/facebookresearch/vjepa2).
+### V-JEPA 2.1
+
+V-JEPA 2.1 was released by Meta on 2026-03-16 with four pretrained backbones at 384 resolution:
+
+| Model | Parameters | Distilled | Checkpoint |
+|-------|-----------|-----------|------------|
+| ViT-B/16, 384 | 80M | Yes (from ViT-G) | `vjepa2.1-vitb-fpc64-384` |
+| ViT-L/16, 384 | 300M | Yes (from ViT-G) | `vjepa2.1-vitl-fpc64-384` |
+| ViT-g/16, 384 | 1B | No | `vjepa2.1-vitg-fpc64-384` |
+| ViT-G/16, 384 | 2B | No | `vjepa2.1-vitG-fpc64-384` |
+
+Key architectural differences from V-JEPA 2: corrected RoPE implementation (`repeat_interleave`), learnable modality embeddings, hierarchical feature extraction with per-layer norms, separate image patch embedding, RoPE position interpolation, and predictor context token projection (`return_all_tokens`).
+
+V-JEPA 2.1 models are loaded using the same `VJEPA2Model` class with 2.1-specific config fields set automatically by the conversion script.
+
+This model was contributed by [koustuvs](https://huggingface.co/koustuvs), [yonigozlan](https://huggingface.co/yonigozlan) and [qubvel](https://huggingface.co/qubvel-hf). V-JEPA 2.1 support was added by [davevanveen](https://huggingface.co/davevanveen). The original code can be found [here](https://github.com/facebookresearch/vjepa2).
 
 ## Usage example
 
diff --git a/docs/source/en/model_doc/voxtral_realtime.md b/docs/source/en/model_doc/voxtral_realtime.md
index 7ae8c1267bd9..08b0bf1d5048 100644
--- a/docs/source/en/model_doc/voxtral_realtime.md
+++ b/docs/source/en/model_doc/voxtral_realtime.md
@@ -77,6 +77,12 @@ for decoded_output in decoded_outputs:
     print(decoded_output)
 ```
 
+### Audio encoder precomputation
+
+By default, when the full audio is available (i.e. not streaming), the audio encoder and projector are run once before generation begins. The resulting embeddings are then simply sliced at each decoding step, which is much faster than running the encoder repeatedly.
+
+This is the default behavior (`precompute_audio_embeds=True`). You can disable it if needed. Note that the default vLLM implementation runs the encoder at every step since it relies on a different optimization paradigm.
+
 ### Streaming Transcription
 > [!NOTE]
 > This is an experimental feature and the API is subject to change.
diff --git a/docs/source/en/moe_telemetry.md b/docs/source/en/moe_telemetry.md
new file mode 100644
index 000000000000..f84a48799ac2
--- /dev/null
+++ b/docs/source/en/moe_telemetry.md
@@ -0,0 +1,82 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# MoE telemetry
+
+Use MoE telemetry to monitor router health during training without changing model outputs or exposing per-token expert assignments through the default [`Trainer`] API.
+
+The first version focuses on trainer-friendly scalar metrics:
+
+- entropy
+- normalized entropy
+- load coefficient of variation (CV)
+- max-load ratio
+- active experts
+- dead experts
+
+These metrics are logged through the standard [`Trainer`] callback path, so experiment trackers continue to receive ordinary flat scalar dictionaries. Exact expert assignments remain internal to the model unless a separate replay or debug feature explicitly exposes them.
+
+## Logging router health with a callback
+
+The intended implementation is a built-in [`TrainerCallback`] that:
+
+- reads router activity from the model without changing default model outputs
+- prefers exact selected expert indices when a router surfaces them internally
+- falls back to router-logit-derived top-k assignments when exact indices are not available
+- emits flat scalar metrics through the normal trainer logging path
+- keeps routing telemetry memory-safe by aggregating expert counts immediately instead of storing full routing tensors
+
+```python
+from transformers import MoERouterHealthCallback, Trainer
+
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    callbacks=[MoERouterHealthCallback()],
+)
+```
+
+The callback aggregates per-layer expert counts during forwards, then emits a flat `dict[str, float]` during trainer logging. That keeps the trainer interface unchanged while making the metrics usable with standard experiment trackers.
+
+The built-in callback uses a reduction policy rather than blindly reducing over all distributed ranks:
+
+- normal distributed replicas: reduce counts across the world process group so trainer logs show global metrics
+- tensor-parallel MoE models: do not implicitly world-reduce replicated router counts
+- local-only debugging: disable implicit reduction explicitly
+
+This keeps the default behavior intuitive for common `Trainer` usage while avoiding overcounting in `tp_plan`-based MoE runs.
+
+## Distributed and DeepEP-style settings
+
+The metric definitions are based on routing assignments, not transport internals.
+
+For distributed MoE systems:
+
+1. compute local expert counts from routing decisions
+2. optionally reduce those counts across the expert group
+3. derive health metrics from the reduced counts
+
+This is why the callback design reduces per-expert counts, not transport-specific state. Backends such as standard expert parallel or DeepEP can reduce those counts before computing the final scalar metrics, while keeping the metric API itself backend-agnostic.
+
+Use local metrics when you want rank-local visibility. Use reduced counts when you want trainer-facing global health metrics. In particular, tensor-parallel MoE models may replicate routing state across ranks, so global world-size reduction is not always the correct default.
+
+## Related docs
+
+- [Callbacks](./trainer_callbacks)
+- [Experts backends](./experts_interface)
+- [Expert parallelism](./expert_parallelism)
diff --git a/docs/source/en/tasks/promptable_concept_segmentation.md b/docs/source/en/tasks/promptable_concept_segmentation.md
new file mode 100644
index 000000000000..8985ac92a0d4
--- /dev/null
+++ b/docs/source/en/tasks/promptable_concept_segmentation.md
@@ -0,0 +1,302 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Promptable Concept Segmentation
+
+[[open-in-colab]]
+
+Promptable Concept Segmentation (PCS) is a computer vision task that detects and segments **all instances** of objects matching a given concept in an image. Unlike traditional instance segmentation that is limited to a fixed set of object classes, PCS can segment objects based on:
+
+- **Text prompts** (e.g., "yellow school bus", "ear", "dial")
+- **Visual prompts** (bounding boxes indicating positive or negative examples)
+- **Combined prompts** (text + visual cues)
+
+For each matching object, PCS returns:
+- Binary segmentation masks
+- Bounding boxes
+- Confidence scores
+
+> [!NOTE]
+> Currently, [SAM3](https://huggingface.co/facebook/sam3) is the primary model supporting this task on the Hub.
+
+In this guide, you will learn how to:
+
+- Use the pipeline for quick inference
+- Segment objects with text prompts
+- Segment objects with bounding box prompts
+- Combine text and visual prompts for refined segmentation
+- Process multiple images in batches
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```bash
+pip install -q transformers
+```
+
+## Promptable Concept Segmentation pipeline
+
+The simplest way to try out promptable concept segmentation is to use the [`pipeline`]. Instantiate a pipeline from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=sam3):
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+```
+
+Next, choose an image you'd like to segment objects in. Here we'll use an image from the [COCO dataset](https://cocodataset.org/):
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="http://images.cocodataset.org/val2017/000000077595.jpg" alt="Cats on a couch"/>
+</div>
+
+### Text-based segmentation
+
+Pass the image and a text prompt describing the concept you want to segment:
+
+```py
+>>> results = segmenter(image, text="ear", threshold=0.5, mask_threshold=0.5)
+>>> results
+[{'score': 0.8492,
+  'label': 'ear',
+  'box': {'xmin': 335, 'ymin': 149, 'xmax': 369, 'ymax': 186},
+  'mask': tensor([[False, False, False, ..., False, False, False],
+                  [False, False, False, ..., False, False, False],
+                  ...])},
+ {'score': 0.8415,
+  'label': 'ear',
+  'box': {'xmin': 194, 'ymin': 152, 'xmax': 227, 'ymax': 190},
+  'mask': tensor([[False, False, False, ..., False, False, False],
+                  ...])},
+ ...]
+```
+
+The results contain all detected instances of the concept:
+- `score`: Confidence score (0-1)
+- `label`: The text prompt used
+- `box`: Bounding box in `{xmin, ymin, xmax, ymax}` format (absolute pixel coordinates)
+- `mask`: Binary segmentation mask (same size as original image)
+
+### Visualizing results
+
+Let's visualize the segmentation masks:
+
+```py
+>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+>>> from matplotlib.patches import Rectangle
+
+>>> fig, ax = plt.subplots(1, 1, figsize=(10, 8))
+>>> ax.imshow(image)
+
+>>> # Create a colored overlay for all masks
+>>> overlay = np.zeros((*image.size[::-1], 4))
+>>> colors = plt.cm.rainbow(np.linspace(0, 1, len(results)))
+
+>>> for i, result in enumerate(results):
+...     mask = result["mask"].numpy()
+...     box = result["box"]
+...     score = result["score"]
+...
+...     # Add colored mask
+...     overlay[mask] = [*colors[i][:3], 0.5]
+...
+...     # Draw bounding box
+...     rect = Rectangle(
+...         (box["xmin"], box["ymin"]),
+...         box["xmax"] - box["xmin"],
+...         box["ymax"] - box["ymin"],
+...         linewidth=2,
+...         edgecolor=colors[i],
+...         facecolor="none",
+...     )
+...     ax.add_patch(rect)
+...     ax.text(box["xmin"], box["ymin"] - 5, f"{score:.2f}", color="white", fontsize=12, weight="bold")
+
+>>> ax.imshow(overlay)
+>>> ax.axis("off")
+>>> plt.tight_layout()
+>>> plt.show()
+```
+
+### Box-based segmentation
+
+You can also segment objects using bounding boxes as visual prompts. This is useful when you want to segment specific object instances:
+
+```py
+>>> # Load a different image
+>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg"
+>>> kitchen_image = Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB")
+
+>>> # Define a bounding box around a dial (xyxy format: [x1, y1, x2, y2])
+>>> box_xyxy = [59, 144, 76, 163]
+>>> input_boxes = [[box_xyxy]]  # [batch, num_boxes, 4]
+>>> input_boxes_labels = [[1]]  # 1 = positive box (include objects like this)
+
+>>> results = segmenter(
+...     kitchen_image,
+...     input_boxes=input_boxes,
+...     input_boxes_labels=input_boxes_labels,
+...     threshold=0.5,
+...     mask_threshold=0.5,
+... )
+
+>>> print(f"Found {len(results)} objects matching the visual concept")
+```
+
+Box labels can be:
+- `1`: Positive (find objects similar to this)
+- `0`: Negative (exclude objects like this)
+
+### Combined text and visual prompts
+
+For more precise segmentation, combine text prompts with visual examples:
+
+```py
+>>> # Segment "handle" but exclude the oven handle using a negative box
+>>> text = "handle"
+>>> oven_handle_box = [40, 183, 318, 204]  # Box covering oven handle
+>>> input_boxes = [[oven_handle_box]]
+>>> input_boxes_labels = [[0]]  # 0 = negative (exclude this region)
+
+>>> results = segmenter(
+...     kitchen_image,
+...     text=text,
+...     input_boxes=input_boxes,
+...     input_boxes_labels=input_boxes_labels,
+...     threshold=0.5,
+...     mask_threshold=0.5,
+... )
+>>> # This will segment pot handles but exclude the oven handle
+```
+
+## Manual inference with model and processor
+
+While the pipeline is convenient, you may want more control over the inference process. Here's how to use the model and processor directly:
+
+```py
+>>> from transformers import Sam3Processor, Sam3Model
+>>> import torch
+
+>>> device = "cuda" if torch.cuda.is_available() else "cpu"
+>>> model = Sam3Model.from_pretrained("facebook/sam3").to(device)
+>>> processor = Sam3Processor.from_pretrained("facebook/sam3")
+```
+
+Load an image:
+
+```py
+>>> url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+```
+
+Prepare inputs and run inference:
+
+```py
+>>> inputs = processor(images=image, text="ear", return_tensors="pt").to(device)
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+
+>>> # Post-process results
+>>> results = processor.post_process_instance_segmentation(
+...     outputs,
+...     threshold=0.5,
+...     mask_threshold=0.5,
+...     target_sizes=inputs.get("original_sizes").tolist(),
+... )[0]
+
+>>> print(f"Found {len(results['masks'])} objects")
+>>> # Results contain:
+>>> # - masks: List of binary masks (torch.Tensor)
+>>> # - boxes: Bounding boxes in xyxy format (torch.Tensor)
+>>> # - scores: Confidence scores (torch.Tensor)
+```
+
+> [!TIP]
+> **Pipeline vs Manual Output Format**: The pipeline returns a standardized format (list of dicts with `score`, `label`, `box`, `mask`) for consistency across transformers. The processor's `post_process_instance_segmentation()` returns separate tensors (`scores`, `boxes`, `masks`) for more flexible post-processing.
+
+## Batch processing
+
+You can process multiple images efficiently by batching them together:
+
+```py
+>>> cat_url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg"
+>>> images = [
+...     Image.open(requests.get(cat_url, stream=True).raw).convert("RGB"),
+...     Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB"),
+... ]
+
+>>> # Different text prompt for each image
+>>> text_prompts = ["ear", "dial"]
+
+>>> inputs = processor(images=images, text=text_prompts, return_tensors="pt").to(device)
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+
+>>> results = processor.post_process_instance_segmentation(
+...     outputs,
+...     threshold=0.5,
+...     mask_threshold=0.5,
+...     target_sizes=inputs.get("original_sizes").tolist(),
+... )
+
+>>> for i, result in enumerate(results):
+...     print(f"Image {i+1}: {len(result['masks'])} objects found with prompt '{text_prompts[i]}'")
+```
+
+## Efficient multi-prompt inference
+
+When running multiple prompts on the same image, pre-compute vision embeddings to avoid redundant computation:
+
+```py
+>>> # Pre-process image and compute vision embeddings once
+>>> img_inputs = processor(images=image, return_tensors="pt").to(device)
+>>> with torch.no_grad():
+...     vision_embeds = model.get_vision_features(pixel_values=img_inputs.pixel_values)
+
+>>> # Run multiple text prompts efficiently
+>>> text_prompts = ["ear", "eye", "nose"]
+>>> all_results = []
+
+>>> for prompt in text_prompts:
+...     text_inputs = processor(text=prompt, return_tensors="pt").to(device)
+...     with torch.no_grad():
+...         outputs = model(vision_embeds=vision_embeds, **text_inputs)
+...
+...     results = processor.post_process_instance_segmentation(
+...         outputs,
+...         threshold=0.5,
+...         mask_threshold=0.5,
+...         target_sizes=img_inputs.get("original_sizes").tolist(),
+...     )[0]
+...     all_results.append({"prompt": prompt, "results": results})
+
+>>> for item in all_results:
+...     print(f"Prompt '{item['prompt']}': {len(item['results']['masks'])} objects found")
+```
+
+This approach significantly speeds up inference when testing multiple concepts on the same image!
diff --git a/docs/source/en/tasks/promptable_visual_segmentation.md b/docs/source/en/tasks/promptable_visual_segmentation.md
new file mode 100644
index 000000000000..862548860ba3
--- /dev/null
+++ b/docs/source/en/tasks/promptable_visual_segmentation.md
@@ -0,0 +1,381 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Promptable Visual Segmentation
+
+[[open-in-colab]]
+
+Promptable Visual Segmentation (PVS) is a computer vision task that segments objects in an image based on interactive visual prompts. Unlike automatic segmentation methods, PVS lets you specify **exactly which objects** to segment by providing:
+
+- **Point prompts** with labels (positive points to include, negative points to exclude)
+- **Bounding box prompts** (rectangular regions around objects)
+- **Combinations** of points and boxes for refined segmentation
+
+For each prompted object, PVS returns:
+- Binary segmentation masks
+- Quality/confidence scores (IoU predictions)
+
+> [!NOTE]
+> This task is supported by the SAM-family models on the Hub: [SAM3Tracker](https://huggingface.co/facebook/sam3), [SAM2](https://huggingface.co/facebook/sam2.1-hiera-large), [SAM](https://huggingface.co/facebook/sam-vit-base), and [EdgeTAM](https://huggingface.co/yonigozlan/EdgeTAM-hf).
+
+In this guide, you will learn how to:
+
+- Use the pipeline for quick inference
+- Segment objects with single point clicks
+- Refine segmentation with multiple points
+- Use bounding boxes as prompts
+- Segment multiple objects simultaneously
+- Process batches of images efficiently
+
+Before you begin, make sure you have all the necessary libraries installed:
+
+```bash
+pip install -q transformers
+```
+
+## Promptable Visual Segmentation pipeline
+
+The simplest way to try out promptable visual segmentation is to use the [`pipeline`]. Instantiate a pipeline from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=sam2):
+
+```python
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline("promptable-visual-segmentation", model="facebook/sam2.1-hiera-large")
+```
+
+Next, choose an image you'd like to segment objects in. Here we'll use an image from the [COCO dataset](https://cocodataset.org/):
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="http://images.cocodataset.org/val2017/000000077595.jpg" alt="Cats on a couch"/>
+</div>
+
+### Single point segmentation
+
+Pass the image and a point prompt. Points are specified as `[[[x, y]]]` coordinates with corresponding labels `[[[1]]]` where `1` means "include this object":
+
+```py
+>>> # Click on a cat's body
+>>> input_points = [[[[450, 600]]]]  # [batch, objects, points_per_object, coordinates]
+>>> input_labels = [[[1]]]  # [batch, objects, points_per_object] - 1=positive click
+
+>>> results = segmenter(image, input_points=input_points, input_labels=input_labels)
+>>> results
+[[{'score': 0.8731,
+   'mask': tensor([[False, False, False, ..., False, False, False],
+                   [False, False, False, ..., False, False, False],
+                   ...])}]]
+```
+
+The results are a list of lists (one inner list per input image). Each object gets multiple mask predictions ranked by quality score:
+- `score`: Quality score (typically IoU prediction, 0-1)
+- `mask`: Binary segmentation mask (same size as original image)
+
+By default, the model returns 3 masks per prompt, ranked by quality. To get only the best mask:
+
+```py
+>>> results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+>>> print(f"Returned {len(results[0])} mask(s)")  # 1 mask
+Returned 1 mask(s)
+```
+
+### Visualizing results
+
+Let's visualize the segmentation mask:
+
+```py
+>>> import numpy as np
+>>> import matplotlib.pyplot as plt
+
+>>> fig, axes = plt.subplots(1, 2, figsize=(15, 5))
+
+>>> # Show original image with point
+>>> axes[0].imshow(image)
+>>> point_x, point_y = input_points[0][0][0]
+>>> axes[0].plot(point_x, point_y, "ro", markersize=10, markeredgewidth=2, markeredgecolor="white")
+>>> axes[0].set_title("Input: Image + Point")
+>>> axes[0].axis("off")
+
+>>> # Show segmentation result
+>>> mask = results[0][0]["mask"].numpy()
+>>> score = results[0][0]["score"]
+
+>>> axes[1].imshow(image)
+>>> # Create colored overlay
+>>> overlay = np.zeros((*mask.shape, 4))
+>>> overlay[mask] = [1, 0, 0, 0.5]  # Red with 50% transparency
+>>> axes[1].imshow(overlay)
+>>> axes[1].set_title(f"Segmentation (score: {score:.3f})")
+>>> axes[1].axis("off")
+
+>>> plt.tight_layout()
+>>> plt.show()
+```
+
+### Multiple points for refinement
+
+You can provide multiple points to refine the segmentation. Use positive points (label=1) to include regions and negative points (label=0) to exclude them:
+
+```py
+>>> # First positive point on cat body, second negative point on the couch
+>>> input_points = [[[[450, 600], [300, 400]]]]
+>>> input_labels = [[[1, 0]]]  # 1=include, 0=exclude
+
+>>> results = segmenter(
+...     image,
+...     input_points=input_points,
+...     input_labels=input_labels,
+...     multimask_output=False,
+... )
+>>> # This will segment the cat while excluding couch regions
+```
+
+### Bounding box segmentation
+
+You can also use bounding boxes as prompts. Boxes are specified in `[x1, y1, x2, y2]` format (top-left and bottom-right corners):
+
+```py
+>>> # Define a box around the left cat
+>>> input_boxes = [[[100, 200, 350, 550]]]  # [batch, objects, 4]
+
+>>> results = segmenter(image, input_boxes=input_boxes, multimask_output=False)
+>>> mask = results[0][0]["mask"]
+>>> print(f"Segmented object with box prompt, score: {results[0][0]['score']:.3f}")
+```
+
+### Multiple objects segmentation
+
+Segment multiple objects in the same image by providing multiple prompts:
+
+```py
+>>> # Points for two cats - each cat gets its own point
+>>> input_points = [
+...     [[[450, 600]], [[200, 300]]]  # Two objects, each with one point
+... ]
+>>> input_labels = [[[1], [1]]]  # Both positive
+
+>>> results = segmenter(
+...     image,
+...     input_points=input_points,
+...     input_labels=input_labels,
+...     multimask_output=False,
+... )
+
+>>> print(f"Segmented {len(results[0])} objects")
+>>> for i, obj_result in enumerate(results[0]):
+...     print(f"Object {i+1}: score={obj_result['score']:.3f}")
+```
+
+### Combining points and boxes
+
+For maximum precision, you can combine point and box prompts:
+
+```py
+>>> # Box around an object + refinement points
+>>> input_boxes = [[[100, 200, 350, 550]]]
+>>> input_points = [[[[200, 300], [150, 250]]]]  # Positive and negative points
+>>> input_labels = [[[1, 0]]]
+
+>>> results = segmenter(
+...     image,
+...     input_points=input_points,
+...     input_labels=input_labels,
+...     input_boxes=input_boxes,
+...     multimask_output=False,
+... )
+```
+
+## Manual inference with model and processor
+
+While the pipeline is convenient, you may want more control over the inference process. Here's how to use the model and processor directly:
+
+```py
+>>> from transformers import Sam2Processor, Sam2Model
+>>> import torch
+
+>>> device = "cuda" if torch.cuda.is_available() else "cpu"
+>>> model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-large").to(device)
+>>> processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-large")
+```
+
+Load an image:
+
+```py
+>>> url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
+```
+
+Prepare inputs and run inference:
+
+```py
+>>> input_points = [[[[450, 600]]]]
+>>> input_labels = [[[1]]]
+
+>>> inputs = processor(
+...     images=image,
+...     input_points=input_points,
+...     input_labels=input_labels,
+...     return_tensors="pt",
+... ).to(device)
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs, multimask_output=False)
+
+>>> # Post-process masks to original image size
+>>> masks = processor.post_process_masks(
+...     outputs.pred_masks.cpu(),
+...     inputs["original_sizes"],
+... )[0]
+
+>>> print(f"Mask shape: {masks.shape}")  # [num_objects, num_masks_per_object, height, width]
+>>> print(f"IoU scores: {outputs.iou_scores}")
+>>> # Results contain:
+>>> # - masks: Segmentation masks (torch.Tensor)
+>>> # - iou_scores: Quality predictions for each mask (torch.Tensor)
+```
+
+> [!TIP]
+> **Pipeline vs Manual Output Format**: The pipeline returns a standardized format (list of lists of dicts with `score` and `mask`) for consistency across transformers. The processor's `post_process_masks()` returns raw tensors for more flexible post-processing.
+
+## Batch processing
+
+You can process multiple images efficiently by batching them together:
+
+```py
+>>> cat_url = "http://images.cocodataset.org/val2017/000000077595.jpg"
+>>> kitchen_url = "http://images.cocodataset.org/val2017/000000136466.jpg"
+>>> images = [
+...     Image.open(requests.get(cat_url, stream=True).raw).convert("RGB"),
+...     Image.open(requests.get(kitchen_url, stream=True).raw).convert("RGB"),
+... ]
+
+>>> # Different prompts for each image
+>>> input_points = [
+...     [[[450, 600]]],  # Cat image: single point
+...     [[[300, 250]]],  # Kitchen image: single point
+... ]
+>>> input_labels = [[[1]], [[1]]]
+
+>>> inputs = processor(
+...     images=images,
+...     input_points=input_points,
+...     input_labels=input_labels,
+...     return_tensors="pt",
+... ).to(device)
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs, multimask_output=False)
+
+>>> masks = processor.post_process_masks(outputs.pred_masks.cpu(), inputs["original_sizes"])
+
+>>> for i, image_masks in enumerate(masks):
+...     print(f"Image {i+1}: {image_masks.shape[0]} object(s) segmented")
+```
+
+## Efficient multi-prompt inference
+
+When running multiple prompts on the same image, pre-compute image embeddings to avoid redundant computation:
+
+```py
+>>> # Pre-process image and compute image embeddings once
+>>> img_inputs = processor(images=image, return_tensors="pt").to(device)
+>>> with torch.no_grad():
+...     image_embeddings = model.get_image_features(pixel_values=img_inputs.pixel_values)
+
+>>> # Run multiple prompts efficiently
+>>> point_prompts = [
+...     [[[[450, 600]]]],  # Point on left cat
+...     [[[[200, 300]]]],  # Point on right cat
+...     [[[[150, 450]]]],  # Point on couch
+... ]
+>>> all_results = []
+
+>>> for points in point_prompts:
+...     labels = [[[1]]]
+...     prompt_inputs = processor(
+...         input_points=points,
+...         input_labels=labels,
+...         original_sizes=img_inputs["original_sizes"],
+...         return_tensors="pt",
+...     ).to(device)
+...
+...     with torch.no_grad():
+...         outputs = model(
+...             input_points=prompt_inputs["input_points"],
+...             input_labels=prompt_inputs["input_labels"],
+...             image_embeddings=image_embeddings,
+...             multimask_output=False,
+...         )
+...
+...     masks = processor.post_process_masks(
+...         outputs.pred_masks.cpu(),
+...         img_inputs["original_sizes"],
+...     )[0]
+...     all_results.append({"points": points, "masks": masks, "scores": outputs.iou_scores})
+
+>>> print(f"Processed {len(all_results)} prompts efficiently")
+```
+
+This approach significantly speeds up inference when testing multiple points on the same image!
+
+## Advanced usage: Interactive segmentation
+
+PVS is ideal for interactive applications where users click to segment objects. Here's a simple iterative refinement workflow:
+
+```py
+>>> def interactive_segment(image, positive_points, negative_points=None):
+...     """Segment an object with interactive point clicks."""
+...     all_points = positive_points + (negative_points or [])
+...     labels = [1] * len(positive_points) + [0] * len(negative_points or [])
+...
+...     input_points = [[all_points]]
+...     input_labels = [[labels]]
+...
+...     results = segmenter(
+...         image,
+...         input_points=input_points,
+...         input_labels=input_labels,
+...         multimask_output=False,
+...     )
+...     return results[0][0]
+
+>>> # Simulated interactive clicks
+>>> # Initial click
+>>> result = interactive_segment(image, positive_points=[[450, 600]])
+>>> print(f"Initial segmentation score: {result['score']:.3f}")
+
+>>> # Refine with additional positive click
+>>> result = interactive_segment(image, positive_points=[[450, 600], [380, 550]])
+>>> print(f"Refined segmentation score: {result['score']:.3f}")
+
+>>> # Further refine with negative click to exclude background
+>>> result = interactive_segment(
+...     image,
+...     positive_points=[[450, 600], [380, 550]],
+...     negative_points=[[300, 400]],
+... )
+>>> print(f"Final segmentation score: {result['score']:.3f}")
+```
+
+This demonstrates how PVS can be used in interactive tools where users iteratively refine segmentation masks by adding positive and negative clicks!
diff --git a/docs/source/en/tasks/zero_shot_object_detection.md b/docs/source/en/tasks/zero_shot_object_detection.md
index 8a5506939898..344b7de5a133 100644
--- a/docs/source/en/tasks/zero_shot_object_detection.md
+++ b/docs/source/en/tasks/zero_shot_object_detection.md
@@ -168,8 +168,12 @@ boxes have the correct coordinates relative to the original image:
 ...     outputs = model(**inputs)
 
 >>> results = processor.post_process_grounded_object_detection(
-...    outputs, threshold=0.50, target_sizes=[(image.height, image.width)], text_labels=text_labels,
-... )[0]
+...     outputs,
+...     threshold=0.50,
+...     target_sizes=[(image.height, image.width)],
+...     text_labels=text_labels,
+... )
+>>> results = results[0]
 
 >>> draw = ImageDraw.Draw(image)
 
diff --git a/docs/source/en/trainer_callbacks.md b/docs/source/en/trainer_callbacks.md
index 00a92e3dc7a1..fbb9893ed57c 100644
--- a/docs/source/en/trainer_callbacks.md
+++ b/docs/source/en/trainer_callbacks.md
@@ -127,7 +127,20 @@ trainer = Trainer(
 )
 ```
 
+### MoERouterHealthCallback
+
+[`MoERouterHealthCallback`] logs MoE router-health scalars through the normal trainer logging path. It is designed for MoE training telemetry, not for routing replay or transport debugging.
+
+The callback:
+
+- aggregates expert counts during router forwards instead of storing full routing tensors
+- logs flat scalar keys that work with W&B, TensorBoard, and other integrated reporters
+- uses an automatic reduction policy so distributed replica training gets global metrics by default, while tensor-parallel MoE runs avoid overcounting replicated router state
+
+See the [MoE telemetry](./moe_telemetry) guide for the metric definitions and distributed semantics.
+
 ## Next steps
 
 - See all available [integrated callbacks](./main_classes/callback#available-callbacks) for logging to experiment trackers.
+- The [MoE telemetry](./moe_telemetry) guide shows how to log router health metrics through a callback without changing model outputs.
 - The [Subclassing Trainer methods](./trainer_customize) guide covers overriding [`Trainer`] methods when you need to change what the training loop computes.
diff --git a/docs/source/ko/_toctree.yml b/docs/source/ko/_toctree.yml
index 135d9fda8270..0428c6718b4c 100644
--- a/docs/source/ko/_toctree.yml
+++ b/docs/source/ko/_toctree.yml
@@ -527,6 +527,8 @@
         title: EXAONE-4.0
       - local: model_doc/exaone_moe
         title: EXAONE-MoE
+      - local: model_doc/exaone4_5
+        title: EXAONE-4.5
       - local: in_translation
         title: Falcon
       - local: in_translation
diff --git a/docs/source/ko/model_doc/exaone4_5.md b/docs/source/ko/model_doc/exaone4_5.md
new file mode 100644
index 000000000000..6723245ef2db
--- /dev/null
+++ b/docs/source/ko/model_doc/exaone4_5.md
@@ -0,0 +1,111 @@
+<!--Copyright 2026 The LG AI Research and The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+*This model was released on 2026-04-09 and added to Hugging Face Transformers on 2026-04-28.*
+
+# EXAONE 4.5
+
+## 개요
+
+[EXAONE 4.5](https://github.com/LG-AI-EXAONE/EXAONE-4.5) 모델은 LG AI연구원에서 공개한 최초의 오픈 웨이트(open-weight) 비전-자연어 모델(vision-language model)입니다. 
+전용 비전 인코더를 기존 개발된 EXAONE 4.0 프레임워크에 통합하여 모델의 능력을 비전과 자연어를 고려한 멀티모달리티로 확장했습니다. EXAONE 4.5는 1.2B 크기의 비전 인코더를 포함해 총 33B 크기의 모델로 구성됩니다. 
+EXAONE 4.5는 이전 EXAONE 모델군으로부터 이어져 온 강력한 언어 처리 능력 덕분에 범용 벤치마크에서 경쟁력 있는 성능을 달성함과 동시에, 동등 규모의 최신 SOTA 모델을 능가하는 문서 이해 능력과 한국 문화적 추론 능력을 갖추고 있습니다.
+
+EXAONE 4.5는 EXAONE 4.0을 기반으로 몇 가지 핵심 개선 사항을 적용했습니다. 어휘 크기를 153,600으로 확장했으며, 컨텍스트 윈도우는 최대 256K 토큰까지 지원합니다. 또한 MTP(Multi-Token Prediction) 메커니즘을 도입해 모델 성능을 한층 더 높였습니다.
+
+더 자세한 정보는 [기술 보고서](https://huggingface.co/papers/2604.08644), [블로그](https://www.lgresearch.ai/blog/view?seq=641), [공식 GitHub](https://github.com/LG-AI-EXAONE/EXAONE-4.5) 페이지를 참고해 주세요.
+
+양자화된 버전을 포함한 공개된 모든 체크포인트는 [Huggingface 콜렉션](https://huggingface.co/collections/LGAI-EXAONE/exaone-45)에서 확인할 수 있습니다.
+
+## 사용 팁
+
+> 기대한 성능을 얻기 위해 다음 설정 사용을 권장합니다.
+> - 범용 용도로는 `temperature=1.0`, `top_p=0.95`, `presence_penalty=1.5`를 권장합니다.
+> - OCR/문서 관련 작업과 한국어 입력에는 `temperature=0.6`, `top_p=0.95`, `presence_penalty=1.5`, `top_k=20`을 권장합니다.
+> - 텍스트 전용 입력에는 `temperature=1.0`, `top_p=0.95`를 권장합니다.
+> - EXAONE-4.0과 달리 EXAONE 4.5는 기본값으로 `enable_thinking=True`를 사용합니다. 따라서 non-reasoning 모드를 사용할 때는 `enable_thinking=False`로 설정해야 합니다.
+> - EXAONE 4.5는 질문에 답할 때 `\boxed{}` 형식을 선호합니다. 파싱 정확도를 높이려면 해당 형식 지시문과 함께 사용하는 것을 권장합니다.
+
+정확한 결과가 중요한 작업에서는 EXAONE 4.5 모델을 reasoning 모드로 실행할 수 있습니다. 반면에 지연 시간이 정확도보다 중요한 작업에서는 EXAONE 4.5 모델을 non-reasoning 모드로 실행할 수 있습니다.
+
+다음은 EXAONE 4.5 모델을 reasoning 모드로 사용하는 예제 코드입니다.
+
+```python
+import torch
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_id = "LGAI-EXAONE/EXAONE-4.5-33B"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+            {"type": "text", "text": "이 이미지를 설명해 줘."},
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    enable_thinking=True,   # default: True
+)
+inputs = inputs.to(model.device)
+
+generated_ids = model.generate(**inputs, max_new_tokens=64)
+generated_text = processor.batch_decode(
+    generated_ids[:, inputs["input_ids"].shape[-1]:],
+    skip_special_tokens=True,
+)[0]
+print(generated_text)
+```
+
+
+## Exaone4_5_Config
+
+[[autodoc]] Exaone4_5_Config
+
+## Exaone4_5_VisionConfig
+
+[[autodoc]] Exaone4_5_VisionConfig
+
+## Exaone4_5_Processor
+
+[[autodoc]] Exaone4_5_Processor
+
+## Exaone4_5_VisionModel
+
+[[autodoc]] Exaone4_5_VisionModel
+    - forward
+
+## Exaone4_5_Model
+
+[[autodoc]] Exaone4_5_Model
+    - forward
+
+## Exaone4_5_ForConditionalGeneration
+
+[[autodoc]] Exaone4_5_ForConditionalGeneration
+    - forward
\ No newline at end of file
diff --git a/docs/source/zh/internal/generation_utils.md b/docs/source/zh/internal/generation_utils.md
index 282202cb79e1..91527651f370 100644
--- a/docs/source/zh/internal/generation_utils.md
+++ b/docs/source/zh/internal/generation_utils.md
@@ -127,6 +127,12 @@ generation_output[:2]
 [[autodoc]] NoRepeatNGramLogitsProcessor
     - __call__
 
+[[autodoc]] PLessLogitsWarper
+    - __call__
+
+[[autodoc]] PLessNormLogitsWarper
+    - __call__
+
 [[autodoc]] PrefixConstrainedLogitsProcessor
     - __call__
 
diff --git a/examples/3d_parrallel_v2.py b/examples/3d_parrallel_v2.py
new file mode 100644
index 000000000000..ccc52c27afe3
--- /dev/null
+++ b/examples/3d_parrallel_v2.py
@@ -0,0 +1,97 @@
+"""
+this script is used to test training using DDP/TP/PP in the PR #29153
+"""
+
+import argparse
+import os
+
+import torch
+import torch.distributed as dist
+import wandb
+from accelerate.logging import get_logger
+from accelerate.utils import set_seed
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
+
+
+logger = get_logger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Simple test training script.")
+    parser.add_argument("--lr", type=float, default=5e-5)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--batch_size", type=int, default=8)
+    parser.add_argument("--num_train_epochs", type=int, default=1)
+    parser.add_argument("--with_tracking", action="store_true")
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    set_seed(args.seed)
+
+    # safer: handle both DDP and non-DDP
+    if dist.is_available() and dist.is_initialized():
+        local_rank = int(os.environ["LOCAL_RANK"])
+    else:
+        local_rank = 0
+
+    device = torch.device(f"cuda:{local_rank}" if torch.cuda.is_available() else "cpu")
+
+    tokenizer = AutoTokenizer.from_pretrained("roneneldan/TinyStories-1M")
+    tokenizer.pad_token = tokenizer.eos_token
+
+    raw_datasets = load_dataset("roneneldan/TinyStories-1M")
+    # much safer num_proc (avoid 60-proc deadlock on small machines)
+    raw_datasets = raw_datasets.map(
+        lambda samples: tokenizer(samples["text"]),
+        batched=True,
+        num_proc=min(8, os.cpu_count()),
+    )
+
+    model = AutoModelForCausalLM.from_pretrained("roneneldan/TinyStories-1M").to(device)
+    model.train()
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)
+
+    train_dataloader = DataLoader(raw_datasets["train"], batch_size=args.batch_size, shuffle=True, drop_last=True)
+
+    num_training_steps = args.num_train_epochs * len(train_dataloader)
+    lr_scheduler = get_scheduler(
+        "linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
+    )
+
+    if args.with_tracking and (not dist.is_initialized() or dist.get_rank() == 0):
+        wandb.init(project="tiny-stories", config=vars(args))
+        wandb.watch(model, log="all")
+
+    for epoch in range(args.num_train_epochs):
+        for step, batch in enumerate(train_dataloader):
+            batch = {k: v.to(device) for k, v in batch.items() if isinstance(v, torch.Tensor)}
+            outputs = model(**batch)
+            loss = outputs.loss
+            loss.backward()
+
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+
+            if step % 10 == 0 and (not dist.is_initialized() or dist.get_rank() == 0):
+                logger.info(f"Epoch {epoch}, step {step}, loss {loss.item()}")
+                if args.with_tracking:
+                    wandb.log({"loss": loss.item(), "lr": lr_scheduler.get_last_lr()[0]})
+
+    # cvleanup only if distributed was initialised
+    if dist.is_available() and dist.is_initialized():
+        dist.destroy_process_group()
+
+    if args.with_tracking and (not dist.is_initialized() or dist.get_rank() == 0):
+        wandb.finish()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/modular-transformers/modeling_new_task_model.py b/examples/modular-transformers/modeling_new_task_model.py
index ff6e666a804e..a207b5d32e0f 100644
--- a/examples/modular-transformers/modeling_new_task_model.py
+++ b/examples/modular-transformers/modeling_new_task_model.py
@@ -160,10 +160,8 @@ def create_causal_mask_mapping(
     # from `forward` call. If users run a `forward` call, we have no option to infer `is_first_iteration` because users may be
     # running generation with custom loop. Thus we need to infer it in a `non-perfect` way
     # NOTE: Determining prefill in that case requires checking data values, which is not compile-compatible.
-    is_first_iteration = (
-        is_first_iteration
-        if is_first_iteration
-        else (past_key_values is None or not past_key_values.is_initialized or pixel_values is not None)
+    is_first_iteration = is_first_iteration or (
+        past_key_values is None or not past_key_values.is_initialized or pixel_values is not None
     )
 
     if is_first_iteration or not kwargs.get("use_cache", True):
@@ -256,9 +254,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/examples/pytorch/image-pretraining/run_mim_no_trainer.py b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
index f6d13078bbc6..5e539047a6b9 100644
--- a/examples/pytorch/image-pretraining/run_mim_no_trainer.py
+++ b/examples/pytorch/image-pretraining/run_mim_no_trainer.py
@@ -633,7 +633,7 @@ def preprocess_images(examples):
     )
 
     # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.TPU:
+    if accelerator.distributed_type == DistributedType.XLA:
         model.tie_weights()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
diff --git a/examples/pytorch/language-modeling/run_clm_no_trainer.py b/examples/pytorch/language-modeling/run_clm_no_trainer.py
index 0f8d2cd0d6e3..a9a0a8285bf6 100755
--- a/examples/pytorch/language-modeling/run_clm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_clm_no_trainer.py
@@ -509,10 +509,17 @@ def group_texts(examples):
 
     # DataLoaders creation:
     train_dataloader = DataLoader(
-        train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
+        train_dataset,
+        shuffle=True,
+        collate_fn=default_data_collator,
+        batch_size=args.per_device_train_batch_size,
+        pin_memory=torch.cuda.is_available(),
     )
     eval_dataloader = DataLoader(
-        eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
+        eval_dataset,
+        collate_fn=default_data_collator,
+        batch_size=args.per_device_eval_batch_size,
+        pin_memory=torch.cuda.is_available(),
     )
 
     # Optimizer
@@ -553,7 +560,7 @@ def group_texts(examples):
     )
 
     # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.TPU:
+    if accelerator.distributed_type == DistributedType.XLA:
         model.tie_weights()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
@@ -627,6 +634,7 @@ def group_texts(examples):
         model.train()
         if args.with_tracking:
             total_loss = 0
+            total_samples = 0
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We skip the first `n` batches in the dataloader when resuming from a checkpoint
             active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
@@ -638,7 +646,9 @@ def group_texts(examples):
                 loss = outputs.loss
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
-                    total_loss += loss.detach().float()
+                    batch_size = batch["input_ids"].shape[0]
+                    total_loss += loss.detach().float() * batch_size
+                    total_samples += batch_size
                 accelerator.backward(loss)
                 optimizer.step()
                 lr_scheduler.step()
@@ -665,7 +675,8 @@ def group_texts(examples):
                 outputs = model(**batch)
 
             loss = outputs.loss
-            losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
+            batch_size = batch["input_ids"].shape[0]
+            losses.append(accelerator.gather_for_metrics(loss.repeat(batch_size)))
 
         losses = torch.cat(losses)
         try:
@@ -681,7 +692,7 @@ def group_texts(examples):
                 {
                     "perplexity": perplexity,
                     "eval_loss": eval_loss,
-                    "train_loss": total_loss.item() / len(train_dataloader),
+                    "train_loss": total_loss.item() / total_samples,
                     "epoch": epoch,
                     "step": completed_steps,
                 },
diff --git a/examples/pytorch/language-modeling/run_fim_no_trainer.py b/examples/pytorch/language-modeling/run_fim_no_trainer.py
index 962e497b72e0..a0c0ff8b7da0 100644
--- a/examples/pytorch/language-modeling/run_fim_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_fim_no_trainer.py
@@ -743,7 +743,7 @@ def apply_fim(examples):
     )
 
     # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.TPU:
+    if accelerator.distributed_type == DistributedType.XLA:
         model.tie_weights()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
@@ -817,6 +817,7 @@ def apply_fim(examples):
         model.train()
         if args.with_tracking:
             total_loss = 0
+            total_samples = 0
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We skip the first `n` batches in the dataloader when resuming from a checkpoint
             active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
@@ -828,7 +829,9 @@ def apply_fim(examples):
                 loss = outputs.loss
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
-                    total_loss += loss.detach().float()
+                    batch_size = batch["input_ids"].shape[0]
+                    total_loss += loss.detach().float() * batch_size
+                    total_samples += batch_size
                 accelerator.backward(loss)
                 optimizer.step()
                 lr_scheduler.step()
@@ -855,7 +858,8 @@ def apply_fim(examples):
                 outputs = model(**batch)
 
             loss = outputs.loss
-            losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
+            batch_size = batch["input_ids"].shape[0]
+            losses.append(accelerator.gather_for_metrics(loss.repeat(batch_size)))
 
         losses = torch.cat(losses)
         try:
@@ -871,7 +875,7 @@ def apply_fim(examples):
                 {
                     "perplexity": perplexity,
                     "eval_loss": eval_loss,
-                    "train_loss": total_loss.item() / len(train_dataloader),
+                    "train_loss": total_loss.item() / total_samples,
                     "epoch": epoch,
                     "step": completed_steps,
                 },
diff --git a/examples/pytorch/language-modeling/run_mlm_no_trainer.py b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
index 981a496badad..a4ed188c0fa1 100755
--- a/examples/pytorch/language-modeling/run_mlm_no_trainer.py
+++ b/examples/pytorch/language-modeling/run_mlm_no_trainer.py
@@ -582,7 +582,7 @@ def group_texts(examples):
     )
 
     # On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
-    if accelerator.distributed_type == DistributedType.TPU:
+    if accelerator.distributed_type == DistributedType.XLA:
         model.tie_weights()
 
     # We need to recalculate our total training steps as the size of the training dataloader may have changed.
@@ -656,6 +656,7 @@ def group_texts(examples):
         model.train()
         if args.with_tracking:
             total_loss = 0
+            total_samples = 0
         if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
             # We skip the first `n` batches in the dataloader when resuming from a checkpoint
             active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
@@ -667,7 +668,9 @@ def group_texts(examples):
                 loss = outputs.loss
                 # We keep track of the loss at each epoch
                 if args.with_tracking:
-                    total_loss += loss.detach().float()
+                    batch_size = batch["input_ids"].shape[0]
+                    total_loss += loss.detach().float() * batch_size
+                    total_samples += batch_size
                 accelerator.backward(loss)
                 optimizer.step()
                 lr_scheduler.step()
@@ -695,7 +698,8 @@ def group_texts(examples):
                 outputs = model(**batch)
 
             loss = outputs.loss
-            losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
+            batch_size = batch["input_ids"].shape[0]
+            losses.append(accelerator.gather_for_metrics(loss.repeat(batch_size)))
 
         losses = torch.cat(losses)
         try:
@@ -711,7 +715,7 @@ def group_texts(examples):
                 {
                     "perplexity": perplexity,
                     "eval_loss": eval_loss,
-                    "train_loss": total_loss.item() / len(train_dataloader),
+                    "train_loss": total_loss.item() / total_samples,
                     "epoch": epoch,
                     "step": completed_steps,
                 },
diff --git a/examples/pytorch/text-classification/README.md b/examples/pytorch/text-classification/README.md
index f426824b5104..3bb1ceb507cb 100644
--- a/examples/pytorch/text-classification/README.md
+++ b/examples/pytorch/text-classification/README.md
@@ -14,6 +14,39 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
+## Simple Sentiment Analysis (Beginner-Friendly)
+
+**NEW:** For those new to transformers, we now have a simplified example perfect for learning!
+
+The script [`run_simple_sentiment.py`](./run_simple_sentiment.py) provides a beginner-friendly introduction to text classification. It fine-tunes a DistilBERT model on the IMDB movie review dataset with clear explanations at each step.
+
+### Quick Start
+```bash
+# Basic usage with full dataset
+python run_simple_sentiment.py
+
+# Quick demo with smaller dataset (faster for testing)
+python run_simple_sentiment.py --max_train_samples 1000 --max_eval_samples 200
+
+# Custom model
+python run_simple_sentiment.py --model_name_or_path bert-base-uncased
+```
+
+### Why use this example?
+- **Educational focus**: Clear comments explaining each step
+- **Quick to run**: Option to use subset of data
+- **Simple structure**: Easier to understand than production scripts
+- **Complete workflow**: Loading data → Training → Evaluation → Predictions
+
+Expected accuracy: ~90% on IMDB test set after 3 epochs.
+
+Run tests:
+```bash
+python test_simple_sentiment.py
+```
+
+---
+
 # Text classification examples
 
 ## GLUE tasks
@@ -249,3 +282,20 @@ Training with the previously defined hyper-parameters yields the following resul
 ```bash
 acc = 0.7093812375249501
 ```
+
+---
+
+## Multi-label text classification
+
+The script [`run_multilabel_classification.py`](./run_multilabel_classification.py) demonstrates a multi-label text classifier using `BCEWithLogitsLoss` via `problem_type="multi_label_classification"`. It reports F1 (micro/macro), Hamming loss, and subset accuracy, and can tune the decision threshold on the validation set.
+
+```bash
+python run_multilabel_classification.py \
+  --model_name_or_path distilbert-base-uncased \
+  --dataset_name go_emotions \
+  --text_column text \
+  --label_columns admiration,amusement,anger \
+  --do_train \
+  --do_eval \
+  --output_dir /tmp/multilabel
+```
diff --git a/examples/pytorch/text-classification/run_classification.py b/examples/pytorch/text-classification/run_classification.py
index 457ccc9001bf..573adbe46c81 100755
--- a/examples/pytorch/text-classification/run_classification.py
+++ b/examples/pytorch/text-classification/run_classification.py
@@ -412,8 +412,9 @@ def main():
 
     # Trying to have good defaults here, don't hesitate to tweak to your needs.
 
+    label_feature = raw_datasets["train"].features["label"]
     is_regression = (
-        raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        getattr(label_feature, "dtype", None) in ["float32", "float64"]
         if data_args.do_regression is None
         else data_args.do_regression
     )
@@ -439,7 +440,7 @@ def main():
                     raise error
 
     else:  # classification
-        if raw_datasets["train"].features["label"].dtype == "list":  # multi-label classification
+        if isinstance(raw_datasets["train"].features["label"], datasets.Sequence):  # multi-label classification
             is_multi_label = True
             logger.info("Label type is list, doing multi-label classification")
         # Trying to find the number of labels in a multi-label classification task
diff --git a/examples/pytorch/text-classification/run_multilabel_classification.py b/examples/pytorch/text-classification/run_multilabel_classification.py
new file mode 100644
index 000000000000..c0ae3cd02e91
--- /dev/null
+++ b/examples/pytorch/text-classification/run_multilabel_classification.py
@@ -0,0 +1,247 @@
+#!/usr/bin/env python
+import argparse
+import inspect
+import json
+import os
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+from datasets import load_dataset
+from sklearn.metrics import accuracy_score, f1_score, hamming_loss
+
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    Trainer,
+    TrainingArguments,
+    set_seed,
+)
+
+
+# Global matplotlib import (no reassignments later!)
+try:
+    import matplotlib.pyplot as plt
+except Exception:
+    plt = None
+
+
+def sigmoid(x: np.ndarray) -> np.ndarray:
+    return 1 / (1 + np.exp(-x))
+
+
+def binarize_probs(p: np.ndarray, th: float) -> np.ndarray:
+    return (p >= th).astype(np.int64)
+
+
+def multilabel_metrics(y_true: np.ndarray, y_pred: np.ndarray) -> dict[str, float]:
+    return {
+        "f1_micro": float(f1_score(y_true, y_pred, average="micro", zero_division=0)),
+        "f1_macro": float(f1_score(y_true, y_pred, average="macro", zero_division=0)),
+        "hamming_loss": float(hamming_loss(y_true, y_pred)),
+        "subset_accuracy": float(accuracy_score(y_true, y_pred)),
+    }
+
+
+@dataclass
+class DatasetColumns:
+    text: str
+    labels: str
+
+
+def build_one_hot_fn(n: int):
+    def fn(ids: list[int]) -> list[float]:
+        arr = np.zeros(n, dtype=np.float32)
+        if ids is not None:
+            for i in ids:
+                if 0 <= i < n:
+                    arr[i] = 1.0
+        return arr.tolist()
+
+    return fn
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument("--model_name_or_path", type=str, default="prajjwal1/bert-tiny")
+    p.add_argument("--dataset_name", type=str, default="go_emotions")
+    p.add_argument("--dataset_config_name", type=str, default=None)
+    p.add_argument("--train_split", type=str, default="train")
+    p.add_argument("--validation_split", type=str, default="validation")
+    p.add_argument("--test_split", type=str, default=None)
+    p.add_argument("--text_column", type=str, default="text")
+    p.add_argument("--labels_column", type=str, default="labels")
+    # training
+    p.add_argument("--output_dir", type=str, default="./mlc_out")
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--do_train", action="store_true")
+    p.add_argument("--do_eval", action="store_true")
+    p.add_argument("--do_predict", action="store_true")
+    p.add_argument("--max_train_samples", type=int, default=None)
+    p.add_argument("--max_eval_samples", type=int, default=None)
+    p.add_argument("--max_predict_samples", type=int, default=None)
+    p.add_argument("--num_train_epochs", type=float, default=1.0)
+    p.add_argument("--per_device_train_batch_size", type=int, default=16)
+    p.add_argument("--per_device_eval_batch_size", type=int, default=32)
+    p.add_argument("--learning_rate", type=float, default=5e-5)
+    p.add_argument("--weight_decay", type=float, default=0.0)
+    p.add_argument("--warmup_ratio", type=float, default=0.0)
+    p.add_argument("--lr_scheduler_type", type=str, default="linear")
+    # thresholds/plots
+    p.add_argument("--threshold", type=float, default=0.5)
+    p.add_argument("--tune_thresholds", action="store_true")
+    p.add_argument("--plot_threshold_curve", action="store_true")
+    # inference
+    p.add_argument("--predict_texts", type=str, nargs="*", default=None)
+    args = p.parse_args()
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    print("[STEP] started, output_dir:", args.output_dir, flush=True)
+    set_seed(args.seed)
+
+    print("[STEP] loading dataset…", flush=True)
+    raw = load_dataset(args.dataset_name, args.dataset_config_name)
+    if args.train_split not in raw:
+        raise ValueError("missing train split")
+    if args.do_eval and args.validation_split not in raw:
+        raise ValueError("missing validation split")
+
+    label_feature = raw[args.train_split].features[args.labels_column].feature
+    label_names: list[str] = list(label_feature.names)
+    num_labels = len(label_names)
+    print(f"[STEP] dataset ok, num_labels={num_labels}", flush=True)
+
+    cols = DatasetColumns(text=args.text_column, labels=args.labels_column)
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=True)
+    config = AutoConfig.from_pretrained(args.model_name_or_path, num_labels=num_labels)
+    config.problem_type = "multi_label_classification"
+    model = AutoModelForSequenceClassification.from_pretrained(args.model_name_or_path, config=config)
+
+    to_one_hot = build_one_hot_fn(num_labels)
+
+    def preprocess(batch):
+        enc = tokenizer(batch[cols.text], truncation=True, padding=False)
+        enc["labels"] = [to_one_hot(ids) for ids in batch[cols.labels]]
+        return enc
+
+    def maybe_select(ds, n):
+        return ds if n is None else ds.select(range(min(n, len(ds))))
+
+    train_ds = maybe_select(raw[args.train_split], args.max_train_samples) if args.do_train else None
+    eval_ds = maybe_select(raw[args.validation_split], args.max_eval_samples) if args.do_eval else None
+
+    if args.do_train:
+        train_ds = train_ds.map(preprocess, batched=True, remove_columns=train_ds.column_names)
+    if args.do_eval:
+        eval_ds = eval_ds.map(preprocess, batched=True, remove_columns=eval_ds.column_names)
+
+    # collator that casts labels to float32 for BCEWithLogitsLoss
+    _base_collator = DataCollatorWithPadding(tokenizer=tokenizer)
+
+    def data_collator(features):
+        batch = _base_collator(features)
+        if "labels" in batch:
+            batch["labels"] = batch["labels"].to(dtype=torch.float32)
+        return batch
+
+    base_threshold = args.threshold
+
+    def compute_metrics(ev):
+        logits, labels = ev
+        probs = sigmoid(logits)
+        preds = binarize_probs(probs, base_threshold)
+        return multilabel_metrics(labels.astype(int), preds)
+
+    # ---- TrainingArguments (v4/v5 compatible) ----
+    print("[STEP] building trainer…", flush=True)
+    strategy = "epoch" if args.do_eval else "no"
+    ta_kwargs = {
+        "output_dir": args.output_dir,
+        "learning_rate": args.learning_rate,
+        "per_device_train_batch_size": args.per_device_train_batch_size,
+        "per_device_eval_batch_size": args.per_device_eval_batch_size,
+        "num_train_epochs": args.num_train_epochs,
+        "weight_decay": args.weight_decay,
+        "warmup_ratio": args.warmup_ratio,
+        "lr_scheduler_type": args.lr_scheduler_type,
+        "load_best_model_at_end": bool(args.do_eval),
+        "metric_for_best_model": "f1_micro",
+        "greater_is_better": True,
+        "logging_steps": 50,
+        "report_to": "none",
+    }
+    sig = inspect.signature(TrainingArguments)
+    if "eval_strategy" in sig.parameters:
+        ta_kwargs["eval_strategy"] = strategy
+    elif "evaluation_strategy" in sig.parameters:
+        ta_kwargs["evaluation_strategy"] = strategy
+    if "save_strategy" in sig.parameters:
+        ta_kwargs["save_strategy"] = strategy
+    training_args = TrainingArguments(**ta_kwargs)
+
+    # ---- Trainer (v4/v5 compatible tokenizer kwarg) ----
+    trainer_kwargs = {
+        "model": model,
+        "args": training_args,
+        "train_dataset": train_ds if args.do_train else None,
+        "eval_dataset": eval_ds if args.do_eval else None,
+        "data_collator": data_collator,
+    }
+    if args.do_eval:
+        trainer_kwargs["compute_metrics"] = compute_metrics
+    if "tokenizer" in inspect.signature(Trainer.__init__).parameters:
+        trainer_kwargs["tokenizer"] = tokenizer
+    trainer = Trainer(**trainer_kwargs)
+
+    results: dict[str, dict] = {}
+    if args.do_train:
+        print("[STEP] training…", flush=True)
+        train_out = trainer.train()
+        trainer.save_model()
+        results["train"] = train_out.metrics
+
+    if args.do_eval:
+        print("[STEP] evaluating…", flush=True)
+        eval_out = trainer.evaluate()
+        results["eval_base_threshold"] = eval_out
+        print("[STEP] eval done.", flush=True)
+
+        if args.tune_thresholds:
+            print("[STEP] sweeping thresholds…", flush=True)
+            preds_output = trainer.predict(eval_ds)
+            logits = preds_output.predictions
+            labels = preds_output.label_ids.astype(int)
+            probs = sigmoid(logits)
+            ths = np.linspace(0.05, 0.95, 19)
+            f1s = []
+            best = {"threshold": base_threshold, "f1_micro": -1.0, "metrics": None}
+            for th in ths:
+                mets = multilabel_metrics(labels, binarize_probs(probs, th))
+                f1s.append(mets["f1_micro"])
+                if mets["f1_micro"] > best["f1_micro"]:
+                    best = {"threshold": float(th), "f1_micro": mets["f1_micro"], "metrics": mets}
+            results["threshold_tuning"] = best
+            print(f"[STEP] best threshold: {best['threshold']:.2f} f1_micro={best['f1_micro']:.4f}", flush=True)
+
+            if args.plot_threshold_curve and plt is not None:
+                print("[STEP] plotting curve…", flush=True)
+                os.makedirs(args.output_dir, exist_ok=True)
+                plt.figure(figsize=(6, 4))
+                plt.plot(ths, f1s, marker="o")
+                plt.xlabel("Threshold")
+                plt.ylabel("F1-micro")
+                plt.title("Validation F1-micro vs Threshold")
+                plt.grid(True, alpha=0.3)
+                plt.savefig(os.path.join(args.output_dir, "threshold_sweep.png"), dpi=160, bbox_inches="tight")
+                print("[STEP] plot saved.", flush=True)
+
+    os.makedirs(args.output_dir, exist_ok=True)
+    with open(os.path.join(args.output_dir, "results_multilabel.json"), "w") as f:
+        json.dump(results, f, indent=2)
+    print("[STEP] results saved to", os.path.join(args.output_dir, "results_multilabel.json"), flush=True)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/pytorch/text-classification/run_simple_sentiment.py b/examples/pytorch/text-classification/run_simple_sentiment.py
new file mode 100644
index 000000000000..750383828a1d
--- /dev/null
+++ b/examples/pytorch/text-classification/run_simple_sentiment.py
@@ -0,0 +1,322 @@
+#!/usr/bin/env python
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Simple Sentiment Analysis Example for Beginners
+
+This is a beginner-friendly introduction to text classification using transformers.
+It demonstrates the basic workflow of fine-tuning a pre-trained model on the IMDB
+movie review dataset for binary sentiment classification.
+
+This script is intentionally simpler than run_glue.py and run_classification.py
+to serve as an educational entry point for those new to transformers.
+
+Key Learning Points:
+- Loading and preprocessing datasets
+- Using pre-trained models for sequence classification
+- Fine-tuning with the Trainer API
+- Evaluating model performance
+- Making predictions on new text
+
+Requirements:
+    pip install transformers datasets torch scikit-learn
+
+Usage:
+    python run_simple_sentiment.py
+
+    # For smaller/faster demo:
+    python run_simple_sentiment.py --max_train_samples 1000 --max_eval_samples 200
+"""
+
+import argparse
+import logging
+import sys
+
+import numpy as np
+import torch
+from datasets import load_dataset
+
+from transformers import (
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    Trainer,
+    TrainingArguments,
+)
+
+
+# Setup logging
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    handlers=[logging.StreamHandler(sys.stdout)],
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    """Parse command line arguments."""
+    parser = argparse.ArgumentParser(description="Simple sentiment analysis example using IMDB dataset")
+
+    # Model arguments
+    parser.add_argument(
+        "--model_name_or_path",
+        type=str,
+        default="distilbert-base-uncased",
+        help="Path to pretrained model or model identifier from huggingface.co/models",
+    )
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=256,
+        help="Maximum sequence length for tokenization",
+    )
+
+    # Data arguments
+    parser.add_argument(
+        "--max_train_samples",
+        type=int,
+        default=None,
+        help="Limit the number of training samples (useful for quick testing)",
+    )
+    parser.add_argument(
+        "--max_eval_samples",
+        type=int,
+        default=None,
+        help="Limit the number of evaluation samples (useful for quick testing)",
+    )
+
+    # Training arguments
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="./imdb_sentiment_output",
+        help="Output directory for model checkpoints and predictions",
+    )
+    parser.add_argument(
+        "--num_train_epochs",
+        type=int,
+        default=3,
+        help="Number of training epochs",
+    )
+    parser.add_argument(
+        "--per_device_train_batch_size",
+        type=int,
+        default=16,
+        help="Batch size per device during training",
+    )
+    parser.add_argument(
+        "--per_device_eval_batch_size",
+        type=int,
+        default=16,
+        help="Batch size per device during evaluation",
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=2e-5,
+        help="Learning rate for optimizer",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for reproducibility",
+    )
+
+    return parser.parse_args()
+
+
+def compute_metrics(eval_pred):
+    """
+    Compute accuracy and F1 score for evaluation.
+
+    Args:
+        eval_pred: EvalPrediction object containing predictions and labels
+
+    Returns:
+        Dictionary with computed metrics
+    """
+    from sklearn.metrics import accuracy_score, f1_score
+
+    predictions, labels = eval_pred
+    predictions = np.argmax(predictions, axis=1)
+
+    accuracy = accuracy_score(labels, predictions)
+    f1 = f1_score(labels, predictions, average="binary")
+
+    return {
+        "accuracy": accuracy,
+        "f1": f1,
+    }
+
+
+def preprocess_function(examples, tokenizer, max_length):
+    """
+    Tokenize the text data.
+
+    Args:
+        examples: Batch of examples containing 'text' field
+        tokenizer: Tokenizer to use
+        max_length: Maximum sequence length
+
+    Returns:
+        Tokenized examples
+    """
+    return tokenizer(
+        examples["text"],
+        truncation=True,
+        padding="max_length",
+        max_length=max_length,
+    )
+
+
+def main():
+    """Main training and evaluation function."""
+
+    # Parse arguments
+    args = parse_args()
+
+    # Set seed for reproducibility
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+
+    logger.info("=" * 80)
+    logger.info("Simple Sentiment Analysis with Transformers")
+    logger.info("=" * 80)
+    logger.info(f"Model: {args.model_name_or_path}")
+    logger.info(f"Output directory: {args.output_dir}")
+
+    # Step 1: Load dataset
+    logger.info("\n Step 1: Loading IMDB dataset...")
+    dataset = load_dataset("imdb")
+
+    # Optionally limit dataset size for faster experimentation
+    if args.max_train_samples:
+        dataset["train"] = dataset["train"].select(range(args.max_train_samples))
+        logger.info(f"   Limited training samples to {args.max_train_samples}")
+
+    if args.max_eval_samples:
+        dataset["test"] = dataset["test"].select(range(args.max_eval_samples))
+        logger.info(f"   Limited test samples to {args.max_eval_samples}")
+
+    logger.info(f"   Training samples: {len(dataset['train'])}")
+    logger.info(f"   Test samples: {len(dataset['test'])}")
+
+    # Step 2: Load tokenizer and model
+    logger.info("\n Step 2: Loading model and tokenizer...")
+    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
+    model = AutoModelForSequenceClassification.from_pretrained(
+        args.model_name_or_path,
+        num_labels=2,  # Binary classification: positive/negative
+    )
+    logger.info(f"    Loaded {args.model_name_or_path}")
+
+    # Step 3: Tokenize dataset
+    logger.info("\n Step 3: Tokenizing dataset...")
+    tokenized_datasets = dataset.map(
+        lambda x: preprocess_function(x, tokenizer, args.max_length),
+        batched=True,
+        desc="Tokenizing",
+    )
+    logger.info("    Tokenization complete")
+
+    # Step 4: Setup training
+    logger.info("\n  Step 4: Setting up training configuration...")
+    training_args = TrainingArguments(
+        output_dir=args.output_dir,
+        num_train_epochs=args.num_train_epochs,
+        per_device_train_batch_size=args.per_device_train_batch_size,
+        per_device_eval_batch_size=args.per_device_eval_batch_size,
+        learning_rate=args.learning_rate,
+        weight_decay=0.01,
+        eval_strategy="epoch",
+        save_strategy="epoch",
+        load_best_model_at_end=True,
+        metric_for_best_model="accuracy",
+        logging_steps=100,
+        seed=args.seed,
+        report_to="none",  # Disable wandb/tensorboard for simplicity
+    )
+
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets["train"],
+        eval_dataset=tokenized_datasets["test"],
+        compute_metrics=compute_metrics,
+    )
+    logger.info("    Trainer initialized")
+
+    # Step 5: Train model
+    logger.info("\n Step 5: Training model...")
+    logger.info(f"   Training for {args.num_train_epochs} epochs")
+    trainer.train()
+
+    # Save model
+    trainer.save_model()
+    logger.info(f"    Model saved to {args.output_dir}")
+
+    # Step 6: Evaluate
+    logger.info("\n Step 6: Evaluating model...")
+    metrics = trainer.evaluate()
+
+    logger.info("\n" + "=" * 80)
+    logger.info("EVALUATION RESULTS")
+    logger.info("=" * 80)
+    for key, value in metrics.items():
+        logger.info(f"   {key}: {value:.4f}")
+
+    # Step 7: Example predictions
+    logger.info("\n" + "=" * 80)
+    logger.info("EXAMPLE PREDICTIONS")
+    logger.info("=" * 80)
+
+    example_texts = [
+        "This movie was absolutely fantastic! Best film I've seen all year.",
+        "Terrible waste of time. I want my money back.",
+        "An okay movie, nothing special but not terrible either.",
+    ]
+
+    for text in example_texts:
+        inputs = tokenizer(
+            text,
+            return_tensors="pt",
+            truncation=True,
+            padding=True,
+            max_length=args.max_length,
+        )
+
+        # Move to same device as model
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+            prediction = torch.argmax(probs, dim=-1).item()
+
+        sentiment = "Positive" if prediction == 1 else "Negative"
+        confidence = probs[0][prediction].item()
+
+        logger.info(f"\nText: {text}")
+        logger.info(f"Prediction: {sentiment} (confidence: {confidence:.2%})")
+
+    logger.info("\n" + "=" * 80)
+    logger.info(" Training and evaluation completed successfully!")
+    logger.info("=" * 80)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/pytorch/text-classification/test_simple_sentiment.py b/examples/pytorch/text-classification/test_simple_sentiment.py
new file mode 100644
index 000000000000..5b860c7bac3e
--- /dev/null
+++ b/examples/pytorch/text-classification/test_simple_sentiment.py
@@ -0,0 +1,101 @@
+"""
+Unit tests for simple sentiment analysis example.
+"""
+
+import os
+import sys
+import unittest
+
+import torch
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+# Add the example directory to path
+sys.path.insert(0, os.path.dirname(__file__))
+
+try:
+    from run_simple_sentiment import compute_metrics, preprocess_function
+except ImportError:
+    # If running from different directory
+    pass
+
+
+class TestSimpleSentiment(unittest.TestCase):
+    """Test cases for simple sentiment analysis."""
+
+    @classmethod
+    def setUpClass(cls):
+        """Set up test fixtures that are reused across tests."""
+        cls.model_name = "distilbert-base-uncased"
+        cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
+        cls.model = AutoModelForSequenceClassification.from_pretrained(cls.model_name, num_labels=2)
+
+    def test_tokenizer_loading(self):
+        """Test that tokenizer loads correctly."""
+        self.assertIsNotNone(self.tokenizer)
+        self.assertTrue(hasattr(self.tokenizer, "encode"))
+
+    def test_model_loading(self):
+        """Test that model loads correctly."""
+        self.assertIsNotNone(self.model)
+        self.assertEqual(self.model.config.num_labels, 2)
+
+    def test_preprocess_function(self):
+        """Test text preprocessing and tokenization."""
+        examples = {"text": ["This is a positive review.", "This is a negative review."]}
+
+        result = preprocess_function(examples, self.tokenizer, max_length=128)
+
+        self.assertIn("input_ids", result)
+        self.assertIn("attention_mask", result)
+        self.assertEqual(len(result["input_ids"]), 2)
+        self.assertEqual(len(result["input_ids"][0]), 128)
+
+    def test_model_inference(self):
+        """Test model can perform inference."""
+        text = "This movie was great!"
+        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        self.assertEqual(outputs.logits.shape, (1, 2))
+
+        # Test softmax probabilities sum to 1
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        self.assertAlmostEqual(probs.sum().item(), 1.0, places=5)
+
+    def test_compute_metrics(self):
+        """Test metrics computation."""
+        from collections import namedtuple
+
+        import numpy as np
+
+        # Create a proper EvalPrediction-like object
+        EvalPrediction = namedtuple("EvalPrediction", ["predictions", "label_ids"])
+
+        # Create mock predictions
+        # Predictions (logits for 2 classes, 4 samples)
+        predictions = np.array(
+            [
+                [0.9, 0.1],  # Predicts class 0
+                [0.2, 0.8],  # Predicts class 1
+                [0.7, 0.3],  # Predicts class 0
+                [0.1, 0.9],  # Predicts class 1
+            ]
+        )
+        # True labels
+        labels = np.array([0, 1, 0, 1])
+
+        eval_pred = EvalPrediction(predictions=predictions, label_ids=labels)
+        metrics = compute_metrics(eval_pred)
+
+        self.assertIn("accuracy", metrics)
+        self.assertIn("f1", metrics)
+        self.assertEqual(metrics["accuracy"], 1.0)  # All correct
+        self.assertEqual(metrics["f1"], 1.0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/examples/pytorch/translation/run_translation.py b/examples/pytorch/translation/run_translation.py
index a705bc94a7f3..6fb8a786dc27 100755
--- a/examples/pytorch/translation/run_translation.py
+++ b/examples/pytorch/translation/run_translation.py
@@ -446,6 +446,9 @@ def main():
         )
         model.config.forced_bos_token_id = forced_bos_token_id
 
+        if hasattr(model, "generation_config") and model.generation_config is not None:
+            model.generation_config.forced_bos_token_id = forced_bos_token_id
+
     # Get the language codes for input/target.
     source_lang = data_args.source_lang.split("_")[0]
     target_lang = data_args.target_lang.split("_")[0]
diff --git a/examples/safe_generation/README.md b/examples/safe_generation/README.md
new file mode 100644
index 000000000000..80659d87e638
--- /dev/null
+++ b/examples/safe_generation/README.md
@@ -0,0 +1,254 @@
+# Safe Generation Example Implementations
+
+This directory contains reference implementations of safety checkers for the transformers safe generation feature.
+
+## Overview
+
+The core transformers library provides **infrastructure only**:
+- `SafetyChecker` abstract base class
+- `SafetyLogitsProcessor` and `SafetyStoppingCriteria`
+- `SafetyConfig` configuration system
+- `SafetyResult` and `SafetyViolation` data structures
+
+**Concrete implementations** like `BasicToxicityChecker` are provided here as examples.
+
+This follows the same pattern as watermarking in transformers - the core provides infrastructure, users provide or choose implementations.
+
+## Usage
+
+### Basic Usage with Pipeline
+
+```python
+from examples.safe_generation import BasicToxicityChecker
+from transformers import pipeline
+from transformers.generation.safety import SafetyConfig
+
+# Create a safety checker
+checker = BasicToxicityChecker(threshold=0.7)
+
+# Option 1: Use with SafetyConfig
+config = SafetyConfig.from_checker(checker)
+pipe = pipeline("text-generation", model="gpt2", safety_config=config)
+
+# Option 2: Direct generation with model
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("gpt2")
+tokenizer = AutoTokenizer.from_pretrained("gpt2")
+
+# Attach tokenizer to model (required for safety processors)
+model.tokenizer = tokenizer
+
+inputs = tokenizer("Hello, I want to", return_tensors="pt")
+outputs = model.generate(**inputs, safety_config=config, max_new_tokens=20)
+print(tokenizer.decode(outputs[0]))
+```
+
+### Using Preset Configurations
+
+SafetyConfig provides three preset configurations for different safety/performance trade-offs:
+
+```python
+from examples.safe_generation import BasicToxicityChecker
+from transformers.generation.safety import SafetyConfig, STRICT_PRESET, MODERATE_PRESET, LENIENT_PRESET
+
+checker = BasicToxicityChecker(threshold=0.7)
+
+# STRICT preset - Maximum safety, more overhead
+# - Smaller caches (50 entries, 500 unsafe hash limit)
+# - Returns violations and metadata for debugging
+config_strict = SafetyConfig.from_checker(checker, **STRICT_PRESET)
+
+# MODERATE preset - Balanced approach (default)
+# - Medium caches (100 entries, 1000 unsafe hash limit)
+# - No extra metadata (better performance)
+config_moderate = SafetyConfig.from_checker(checker, **MODERATE_PRESET)
+
+# LENIENT preset - Performance-optimized
+# - Larger caches (200 entries, 2000 unsafe hash limit)
+# - No extra metadata
+config_lenient = SafetyConfig.from_checker(checker, **LENIENT_PRESET)
+
+# Custom preset - Mix and match
+config_custom = SafetyConfig.from_checker(
+    checker,
+    cache_size=150,
+    unsafe_hash_limit=1500,
+    return_violations=True,  # Get detailed violation info
+    return_metadata=False    # Skip extra metadata
+)
+```
+
+**Preset Comparison:**
+
+| Preset | cache_size | unsafe_hash_limit | return_violations | return_metadata | Use Case |
+|--------|-----------|-------------------|-------------------|-----------------|----------|
+| STRICT | 50 | 500 | True | True | High-risk applications, debugging |
+| MODERATE | 100 | 1000 | False | False | General use (balanced) |
+| LENIENT | 200 | 2000 | False | False | Performance-critical, trusted content |
+
+### Customizing the BasicToxicityChecker
+
+```python
+from examples.safe_generation import BasicToxicityChecker
+
+# Use different threshold
+strict_checker = BasicToxicityChecker(threshold=0.5)  # More strict
+
+# Use different model
+custom_checker = BasicToxicityChecker(
+    model_name="unitary/toxic-bert",
+    threshold=0.7,
+    device="cuda"  # Force specific device
+)
+```
+
+## Implementing Custom Safety Checkers
+
+You can create your own safety checkers by inheriting from `SafetyChecker`:
+
+```python
+from transformers.generation.safety import SafetyChecker, SafetyResult, SafetyViolation
+
+class MyCustomChecker(SafetyChecker):
+    def __init__(self, threshold=0.7):
+        self.threshold = threshold
+        # Your initialization here
+
+    @property
+    def supported_categories(self) -> list[str]:
+        """Declare what categories this checker detects."""
+        return ["violence", "hate_speech", "custom_category"]
+
+    def check_safety(self, text, **kwargs):
+        """
+        Check text for safety violations.
+
+        Args:
+            text: Single string or list of strings to check
+
+        Returns:
+            SafetyResult or list of SafetyResult
+        """
+        if isinstance(text, str):
+            return self._check_single(text)
+        return [self._check_single(t) for t in text]
+
+    def _check_single(self, text: str) -> SafetyResult:
+        # Your safety checking logic here
+        is_safe = True  # Your determination
+        violations = []  # List of SafetyViolation objects
+
+        if not is_safe:
+            violations.append(SafetyViolation(
+                category="your_category",
+                confidence=0.9,
+                severity="high",
+                description="Why this is unsafe"
+            ))
+
+        return SafetyResult(
+            is_safe=is_safe,
+            confidence=0.95,
+            violations=violations,
+            metadata={"checker": "MyCustomChecker"}
+        )
+```
+
+### LLM-Based Safety Checker Example
+
+```python
+import openai
+import json
+from transformers.generation.safety import SafetyChecker, SafetyResult, SafetyViolation
+
+class LLMSafetyChecker(SafetyChecker):
+    """Safety checker using an LLM API for multi-category detection."""
+
+    def __init__(self, api_key: str, categories: list[str] = None):
+        self.client = openai.OpenAI(api_key=api_key)
+        self.categories = categories or ["violence", "self_harm", "hate_speech", "sexual"]
+
+    @property
+    def supported_categories(self) -> list[str]:
+        return self.categories
+
+    def check_safety(self, text, **kwargs):
+        if isinstance(text, str):
+            return self._check_single(text)
+        return [self._check_single(t) for t in text]
+
+    def _check_single(self, text: str) -> SafetyResult:
+        prompt = f"""Evaluate if this text contains any of: {', '.join(self.categories)}
+
+Text: {text}
+
+Respond with JSON: {{"is_safe": true/false, "category": "...", "confidence": 0.0-1.0}}"""
+
+        try:
+            response = self.client.chat.completions.create(
+                model="gpt-4",
+                messages=[{"role": "user", "content": prompt}],
+                response_format={"type": "json_object"}
+            )
+            result = json.loads(response.choices[0].message.content)
+
+            violations = []
+            if not result["is_safe"]:
+                violations.append(SafetyViolation(
+                    category=result.get("category", "unknown"),
+                    confidence=result["confidence"],
+                    severity="high" if result["confidence"] > 0.8 else "medium",
+                    description=f"Detected {result['category']} content"
+                ))
+
+            return SafetyResult(
+                is_safe=result["is_safe"],
+                confidence=result["confidence"],
+                violations=violations,
+                metadata={"model": "gpt-4", "categories_checked": self.categories}
+            )
+        except Exception as e:
+            # Fail-safe: assume unsafe on error
+            return SafetyResult(
+                is_safe=False,
+                confidence=0.0,
+                violations=[SafetyViolation("error", 0.0, "high", str(e))],
+                metadata={"error": str(e)}
+            )
+
+# Usage
+llm_checker = LLMSafetyChecker(api_key="your-api-key")
+config = SafetyConfig.from_checker(llm_checker)
+```
+
+## Performance Optimization
+
+For high-latency checkers (like LLM APIs), use SafetyConfig.from_checker() with custom performance settings:
+
+```python
+from transformers.generation.safety import SafetyConfig
+
+# For high-latency checkers, optimize with larger caches and sliding windows
+config = SafetyConfig.from_checker(
+    your_checker,            # Your checker instance
+    cache_size=500,          # Large cache for API responses
+    unsafe_hash_limit=5000,  # Track more unsafe patterns
+    sliding_window_size=512, # Limit tokens sent to API
+    incremental_checking=True, # Avoid re-processing same content
+    return_violations=False, # Disable for better performance
+    return_metadata=False    # Disable for better performance
+)
+```
+
+## Files in This Directory
+
+- `checkers.py`: Reference implementation of `BasicToxicityChecker`
+- `__init__.py`: Exports for easy importing
+- `README.md`: This file - usage guide and examples
+
+## Further Reading
+
+- [Safe Generation Design Document](../../docs/0.safe_generation_design.md)
+- [Extensibility and Checker Strategy](../../docs/6.extensibility_and_checker_strategy.md)
+- [Core Safety Infrastructure](../../docs/1.core_safety_infrastructure.md)
diff --git a/examples/safe_generation/__init__.py b/examples/safe_generation/__init__.py
new file mode 100644
index 000000000000..ecadd611fb13
--- /dev/null
+++ b/examples/safe_generation/__init__.py
@@ -0,0 +1,42 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Safe Generation Example Implementations
+
+This module provides reference implementations of safety checkers for the transformers
+safe generation feature. These are example implementations that users can use directly
+or adapt for their specific needs.
+
+The core transformers library provides only the infrastructure (SafetyChecker abstract base,
+processors, configuration). Concrete implementations like BasicToxicityChecker are provided
+here as examples to demonstrate how to implement custom safety checkers.
+
+Example usage:
+    from examples.safe_generation import BasicToxicityChecker
+    from transformers import pipeline
+    from transformers.generation.safety import SafetyConfig
+
+    # Create a safety checker
+    checker = BasicToxicityChecker(threshold=0.7)
+
+    # Use with pipeline
+    config = SafetyConfig.from_checker(checker)
+    pipe = pipeline("text-generation", model="gpt2", safety_config=config)
+"""
+
+from .checkers import BasicToxicityChecker
+
+
+__all__ = ["BasicToxicityChecker"]
diff --git a/examples/safe_generation/checkers.py b/examples/safe_generation/checkers.py
new file mode 100644
index 000000000000..6141aca7bb76
--- /dev/null
+++ b/examples/safe_generation/checkers.py
@@ -0,0 +1,230 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from transformers.generation.safety import SafetyChecker, SafetyResult, SafetyViolation
+from transformers.utils import is_torch_available, logging
+
+
+if not is_torch_available():
+    raise ImportError("PyTorch is required to use safety checkers. Please install PyTorch: pip install torch")
+
+
+logger = logging.get_logger(__name__)
+
+
+class BasicToxicityChecker(SafetyChecker):
+    """
+    Toxicity checker using the s-nlp/roberta_toxicity_classifier model.
+
+    This checker uses a pre-trained RoBERTa model to detect toxic content in text. It supports both
+    single text and batch processing, with configurable thresholds and automatic device selection.
+
+    This is a reference implementation provided in the examples directory to demonstrate how to
+    implement custom safety checkers. The core transformers library provides only the infrastructure
+    (SafetyChecker abstract base class, processors, configuration).
+
+    Args:
+        model_name (`str`, *optional*, defaults to `"s-nlp/roberta_toxicity_classifier"`):
+            The name of the pre-trained model to use for toxicity detection.
+        threshold (`float`, *optional*, defaults to `0.7`):
+            The toxicity score threshold above which content is considered unsafe.
+        device (`str`, *optional*):
+            The device to run the model on. If None, automatically selects CUDA if available, else CPU.
+
+    Examples:
+    ```python
+    >>> from examples.safe_generation import BasicToxicityChecker
+    >>> from transformers.generation.safety import SafetyConfig
+    >>> from transformers import pipeline
+
+    >>> # Create checker
+    >>> checker = BasicToxicityChecker(threshold=0.7)
+
+    >>> # Use with SafetyConfig
+    >>> config = SafetyConfig.from_checker(checker)
+    >>> pipe = pipeline("text-generation", model="gpt2", safety_config=config)
+    ```
+    """
+
+    def __init__(
+        self,
+        model_name: str = "s-nlp/roberta_toxicity_classifier",
+        threshold: float = 0.7,
+        device: str | None = None,
+    ):
+        self.model_name = model_name
+        self.threshold = threshold
+        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
+
+        # Load model and tokenizer with error handling
+        try:
+            logger.info(f"Loading toxicity model: {model_name}")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
+            self.model.to(self.device)
+            self.model.eval()
+            logger.info(f"Successfully loaded toxicity model on {self.device}")
+        except Exception as e:
+            raise RuntimeError(
+                f"Failed to load toxicity model '{model_name}'. "
+                f"Please ensure the model exists and you have internet connectivity. "
+                f"Original error: {e}"
+            )
+
+    @property
+    def supported_categories(self) -> list[str]:
+        """Return list of safety categories this checker supports."""
+        return ["toxicity"]
+
+    def check_safety(self, text: str | list[str], **kwargs) -> SafetyResult | list[SafetyResult]:
+        """
+        Check text(s) for toxicity violations.
+
+        Args:
+            text (`Union[str, List[str]]`):
+                Single text string or list of texts to check for toxicity.
+            **kwargs:
+                Additional parameters (currently unused).
+
+        Returns:
+            `Union[SafetyResult, List[SafetyResult]]`:
+                SafetyResult for single text input, List[SafetyResult] for multiple texts.
+        """
+        if isinstance(text, str):
+            return self._check_single_text(text, **kwargs)
+        elif isinstance(text, list):
+            return [self._check_single_text(t, **kwargs) for t in text]
+        else:
+            raise TypeError(f"Expected string or list of strings, got {type(text)}")
+
+    def _check_single_text(self, text: str, **kwargs) -> SafetyResult:
+        """
+        Check single text for toxicity.
+
+        Args:
+            text (`str`): Text to check for toxicity.
+            **kwargs: Additional parameters (currently unused).
+
+        Returns:
+            `SafetyResult`: Result of the safety check.
+        """
+        # Input validation
+        if not isinstance(text, str):
+            raise TypeError(f"Expected string input, got {type(text)}")
+
+        # Handle empty text
+        if not text.strip():
+            return SafetyResult(
+                is_safe=True,
+                confidence=1.0,
+                violations=[],
+                metadata={"reason": "empty_text", "model_name": self.model_name},
+            )
+
+        # Handle very long text
+        original_length = len(text)
+        max_length = 10000  # Reasonable limit
+        if len(text) > max_length:
+            text = text[:max_length]
+            logger.warning(f"Text truncated from {original_length} to {max_length} characters")
+
+        # Tokenize and run inference
+        try:
+            inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True).to(
+                self.device
+            )
+
+            with torch.no_grad():
+                outputs = self.model(**inputs)
+                probabilities = F.softmax(outputs.logits, dim=-1)
+
+            # Extract toxicity probability (assuming binary classification: [non-toxic, toxic])
+            toxicity_score = probabilities[0][1].item()  # Toxic class probability
+
+        except Exception as e:
+            logger.error(f"Error during toxicity inference: {e}")
+            raise RuntimeError(f"Toxicity detection failed: {e}")
+
+        # Determine safety
+        is_safe = toxicity_score < self.threshold
+        violations = []
+
+        if not is_safe:
+            violations.append(
+                SafetyViolation(
+                    category="toxicity",
+                    confidence=toxicity_score,
+                    severity=self._get_severity(toxicity_score),
+                    description=f"Detected toxic content with {toxicity_score:.2%} confidence",
+                )
+            )
+
+        # Prepare metadata
+        metadata = {
+            "model_name": self.model_name,
+            "toxicity_score": toxicity_score,
+            "threshold": self.threshold,
+            "device": self.device,
+        }
+
+        if original_length > max_length:
+            metadata["truncated"] = True
+            metadata["original_length"] = original_length
+            metadata["processed_length"] = max_length
+
+        return SafetyResult(
+            is_safe=is_safe,
+            confidence=max(toxicity_score, 1.0 - toxicity_score),
+            violations=violations,
+            metadata=metadata,
+        )
+
+    def _get_severity(self, score: float) -> str:
+        """
+        Determine severity based on toxicity score.
+
+        Args:
+            score (`float`): Toxicity score from 0.0 to 1.0.
+
+        Returns:
+            `str`: Severity level ("low", "medium", "high", "critical").
+        """
+        if score >= 0.95:
+            return "critical"
+        elif score >= 0.85:
+            return "high"
+        elif score >= 0.75:
+            return "medium"
+        else:
+            return "low"
+
+    def get_config(self) -> dict[str, Any]:
+        """
+        Return checker configuration for serialization.
+
+        Returns:
+            `Dict[str, Any]`: Dictionary containing the checker's configuration.
+        """
+        return {
+            "checker_type": "BasicToxicityChecker",
+            "model_name": self.model_name,
+            "threshold": self.threshold,
+            "device": self.device,
+        }
diff --git a/examples/safety_generation_example.py b/examples/safety_generation_example.py
new file mode 100644
index 000000000000..885543fdc4bc
--- /dev/null
+++ b/examples/safety_generation_example.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""
+Example: Safe vs Regular Text Generation with Transformers Safety
+
+This example demonstrates how to compare regular generation to generation with
+real-time safety filtering (toxicity) using Transformers' safety utilities.
+
+"""
+
+import os
+import platform
+import sys
+from pathlib import Path
+
+import torch
+
+
+# Add safe_generation to path to import BasicToxicityChecker
+sys.path.insert(0, str(Path(__file__).parent / "safe_generation"))
+
+from safe_generation import BasicToxicityChecker
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers.generation.safety import SafetyConfig, SafetyLogitsProcessor, SafetyStoppingCriteria
+
+
+def get_device():
+    if torch.backends.mps.is_available() and platform.system() == "Darwin":
+        return torch.device("mps")
+    return torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def main():
+    """Compare regular generation vs safety-filtered generation."""
+
+    # Load a pre-trained model and tokenizer
+    model_name = os.environ.get("SAFETY_EXAMPLE_MODEL", "Qwen/Qwen2.5-3B")
+    print(f"Loading model: {model_name}")
+    print("(Using Qwen for consistent text generation)")
+
+    device = get_device()
+    model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
+    model.eval()
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # Build safety components with strict threshold
+    toxicity_threshold = 0.7  # Strict threshold for demo
+    safety_checker = BasicToxicityChecker(
+        model_name="s-nlp/roberta_toxicity_classifier", threshold=toxicity_threshold, device="cpu"
+    )
+
+    # Create safety config from checker
+    safety_config = SafetyConfig.from_checker(safety_checker)
+
+    from transformers.generation.logits_process import LogitsProcessorList
+    from transformers.generation.stopping_criteria import StoppingCriteriaList
+
+    safety_processor = SafetyLogitsProcessor(
+        safety_checker=safety_checker,
+        tokenizer=tokenizer,
+        safety_config=safety_config,
+        check_interval=1,
+    )
+    safety_stopper = SafetyStoppingCriteria(
+        safety_checker=safety_checker,
+        tokenizer=tokenizer,
+        safety_config=safety_config,
+        check_final_only=False,
+    )
+
+    safety_processors = LogitsProcessorList([safety_processor])
+    safety_criteria = StoppingCriteriaList([safety_stopper])
+
+    # Base generation configuration - deterministic for comparison
+    base_gen = GenerationConfig(
+        max_new_tokens=30,
+        do_sample=False,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+    prompts = [
+        'He went on a long tirade spouting insults and obscenities: "',
+    ]
+
+    print("\n" + "=" * 60)
+    print("SAFE TEXT GENERATION EXAMPLES (Regular vs Safe)")
+    print("=" * 60)
+    print("(Errors will raise; no soft fallbacks)")
+
+    for i, prompt in enumerate(prompts, 1):
+        print(f"\nExample {i}: '{prompt}'")
+        print("-" * 40)
+
+        # Tokenize input and move to model device
+        inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+        # 1) Regular generation (no safety)
+        with torch.no_grad():
+            outputs_regular = model.generate(
+                **inputs,
+                generation_config=base_gen,
+            )
+        text_regular = tokenizer.decode(outputs_regular[0], skip_special_tokens=True)
+        cont_regular = text_regular[len(prompt) :].strip()
+        print(f"Regular: {cont_regular}")
+
+        # 2) Safe generation (with safety filtering)
+        with torch.no_grad():
+            outputs_safe = model.generate(
+                **inputs,
+                generation_config=base_gen,
+                logits_processor=safety_processors,
+                stopping_criteria=safety_criteria,
+            )
+        text_safe = tokenizer.decode(outputs_safe[0], skip_special_tokens=True)
+        cont_safe = text_safe[len(prompt) :].strip()
+        print(f"Safe:    {cont_safe}")
+
+        if cont_regular != cont_safe:
+            print("Safety filtering applied - outputs differ")
+        else:
+            print("No safety filtering needed - outputs identical")
+
+            # Verify safety checker would detect issues in the output
+            regular_safety_result = safety_checker.check_safety(text_regular)
+            if not regular_safety_result.is_safe:
+                print("   WARNING: Safety checker detected violations in output but filtering didn't occur!")
+                print(f"   Violations: {[v.category for v in regular_safety_result.violations]}")
+                print(f"   Confidence: {regular_safety_result.confidence:.3f}")
+
+    print("\n" + "=" * 60)
+    print("HOW IT WORKS:")
+    print("=" * 60)
+    print(
+        """
+1. SafetyLogitsProcessor blocks ALL tokens when unsafe content is detected
+2. SafetyStoppingCriteria can halt generation if unsafe content is detected
+3. Both work during generation, stopping output when safety violations occur
+4. Deterministic generation allows direct comparison of safe vs regular outputs
+        """
+    )
+
+    print("\nDifferent Safety Levels:")
+    print("- strict:   threshold=0.5 (more restrictive)")
+    print("- moderate: threshold=0.7 (balanced)")
+    print("- lenient:  threshold=0.9 (less restrictive)")
+    print("\nCurrent demo uses: threshold=0.7 for reliable blocking")
+    print("\nTo use predefined presets:")
+    print("from transformers.generation.safety import STRICT_PRESET")
+    print("config = SafetyConfig.from_checker(checker, **STRICT_PRESET)")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/te_sentiment/README.md b/examples/te_sentiment/README.md
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/examples/te_sentiment/run_te_sentiment.py b/examples/te_sentiment/run_te_sentiment.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/main.py b/main.py
new file mode 100644
index 000000000000..87fd7f9ef3e5
--- /dev/null
+++ b/main.py
@@ -0,0 +1,48 @@
+from transformers import AutoModel, AutoProcessor
+
+
+model_path = "nvidia/audio-visual-flamingo-hf"
+model_kwargs = {
+    "load_audio_in_video": True,
+}
+processor_kwargs = {
+    "load_audio_in_video": True,
+    "num_video_frames": 128,
+    "audio_chunk_length": "max_3600",
+}
+
+model = AutoModel.from_pretrained(
+    model_path,
+    device_map="auto",
+    **model_kwargs,
+).eval()
+processor = AutoProcessor.from_pretrained(model_path, padding_side="left", use_fast=False, **processor_kwargs)
+
+conversation = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "video": "nvidia.mp4"},
+            {
+                "type": "text",
+                "text": "Assess the video, followed by a detailed description of it's video and audio contents.",
+            },
+        ],
+    }
+]
+
+inputs = processor.apply_chat_template(
+    conversation,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_dict=True,
+).to(model.device)
+
+output_ids = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    do_sample=False,
+)
+
+generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
+print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])
diff --git a/nvidia.mp4 b/nvidia.mp4
new file mode 100644
index 000000000000..8a716ccbc942
Binary files /dev/null and b/nvidia.mp4 differ
diff --git a/pr-classifications.jsonl b/pr-classifications.jsonl
new file mode 100644
index 000000000000..87f11301bd71
--- /dev/null
+++ b/pr-classifications.jsonl
@@ -0,0 +1,772 @@
+{"pr":45692,"decision":"defect","reason":"fixes Phi4 image processor auto-map override with trust_remote_code","title":"[Fix Phi4 test] Add option to override image_processor_auto_map with local code when trust_remote_code is True"}
+{"pr":45691,"decision":"defect","reason":"handles continuous batching worker thread errors with clearer restart failure path","title":"[serve] cb error "}
+{"pr":45690,"decision":"feature","reason":"adds reasoning content support to transformers serve chat/completion responses","title":"[serve] Support for reasoning "}
+{"pr":45687,"decision":"defect","reason":"fixes MoE histogram dtype/backend issue on MPS and non-CUDA hardware","title":"fix: Made histc_input robust for broader hardware"}
+{"pr":45686,"decision":"defect","reason":"fixes read-only permissions inherited when copying custom module files","title":"Fix custom-module copies inheriting read-only permissions"}
+{"pr":45683,"decision":"defect","reason":"prevents uint8 audio modules from 4-bit conversion crashes in multimodal quantization","title":"Exclude audio modules from conversion process"}
+{"pr":45682,"decision":"defect","reason":"restores LoRA hotswapping padding after PEFT integration changes","title":"FIX Restore LoRA hotswapping functionality"}
+{"pr":45681,"decision":"defect","reason":"restores DeepSeek tokenizer backend override to avoid incorrect tokenizer dispatch","title":"Restore TokenizersBackend override for DeepSeek V3/R1 tokenizer dispatch"}
+{"pr":45679,"decision":"other","reason":"test/CI marker change enabling PEFT tests, not a user-facing feature or defect fix","title":"TST Run fast PEFT tests in normal CI"}
+{"pr":45678,"decision":"defect","reason":"fixes Phi-4 multimodal test isolation by avoiding shared mutable sub-config mutation","title":"Fix shared config mutation issue in flash_attn_from_config"}
+{"pr":45671,"decision":"defect","reason":"updates Phi-4 multimodal test revision references to fix test execution","title":"Update latest revision for Phi-4-multimodal test"}
+{"pr":45670,"decision":"defect","reason":"corrects GLM-ASR auto-model mapping for multimodal LM dispatch","title":"[nit] glmasr should be in AutoModelForMultimodalLM"}
+{"pr":45668,"decision":"feature","reason":"adds GGUF loading support for Qwen3.5 MoE architecture","title":"[GGUF] Add support for Qwen3.5 MoE (qwen35moe arch)"}
+{"pr":45667,"decision":"other","reason":"internal typing-checker configuration chore, not a feature or defect fix","title":"chore(typing): add ty type checking for 3 pipeline files"}
+{"pr":45666,"decision":"feature","reason":"extends hub kernel fusion API with KernelConfig support","title":"Extended n-to-1 kernel fusion via  "}
+{"pr":45664,"decision":"documentation","reason":"README translation/documentation update","title":"Doc translate to Persian(farsi) "}
+{"pr":45662,"decision":"defect","reason":"fixes expert module overwrites when combining expert parallelism with FSDP2 rank-0 broadcast","title":"Fix EP + FSDP2: experts silently overwritten by rank-0 broadcast"}
+{"pr":45661,"decision":"feature","reason":"expands weight-converter mapping scope and fine-grained transform registration","title":"[Weight Converter] More fine-grained mappings on classes, scoping for every transforms (including weight converter)"}
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+{"pr":45653,"decision":"feature","reason":"improves continuous batching overall script and decode bucketing behavior","title":"[CB] Better overall script and decode bucketting"}
+{"pr":45651,"decision":"feature","reason":"optimizes LengthGroupedSampler dataset length computation","title":"[Trainer] Optimize LengthGroupedSampler computation with select_columns and tqdm"}
+{"pr":45649,"decision":"defect","reason":"fixes FSDP2 cpu_ram_efficient_loading OOM regression","title":"Fix OOM regression for FSDP2 + cpu_ram_efficient_loading on large models"}
+{"pr":45645,"decision":"defect","reason":"fixes xdist collisions for captured_info CI artifacts and debug logs","title":"Fix xdist collisions for captured_info artifacts and preserve CI debug logs"}
+{"pr":45643,"decision":"feature","reason":"adds DeepSeek V4 model support","title":"Add DeepSeek V4"}
+{"pr":45642,"decision":"defect","reason":"fixes trust_remote_code local cache collisions for local models","title":"Fix trust_remote_code local cache collisions for local models (#45632)"}
+{"pr":45640,"decision":"feature","reason":"defaults Trainer FSDP setup to FSDP2 and simplifies fsdp config API","title":"🚨🚨🚨 [Trainer] Default to FSDP2, simplify API around fsdp + fsdp_config"}
+{"pr":45639,"decision":"defect","reason":"makes patched testing debug logs xdist-safe","title":"Make patched testing debug logs xdist-safe"}
+{"pr":45638,"decision":"feature","reason":"adds multi-token prediction support for Qwen3.5","title":"Add Multi-Token Prediction (MTP) support for Qwen3.5"}
+{"pr":45635,"decision":"defect","reason":"speeds up dtype regex weight loading hotspot and reduces dtype test cost","title":"qa: speed up dtype regex weight load + reduce dtype tests to 3 random"}
+{"pr":45634,"decision":"feature","reason":"adds DeepGEMM BF16 integration isolation and refactor","title":"DeepGEMM BF16, isolation, refactor"}
+{"pr":45630,"decision":"feature","reason":"adds new Kimi2-6 model implementation, docs, auto mappings, and tests","title":"Add new model: Kimi2-6"}
+{"pr":45627,"decision":"defect","reason":"fixes AutoProcessor.from_pretrained to honor pre-built sub-processor kwargs","title":"Processing Utils: honor pre-built sub-processor kwargs in from_pretrained"}
+{"pr":45626,"decision":"feature","reason":"adds PP-FormulaNet model support, docs, mappings, and tests","title":"[Model] Add PP-FormulaNet Model Support"}
+{"pr":45621,"decision":"feature","reason":"improves grouped GEMM and expert parallel integration support","title":"Better Grouped GEMM + EP"}
+{"pr":45618,"decision":"feature","reason":"adds MTP speculative decoding candidate generator support","title":"Add MTP speculative decoding via MTPCandidateGenerator"}
+{"pr":45615,"decision":"defect","reason":"fixes qianfan_ocr model tests by adding XPU expectations","title":"fix(qianfan_ocr): add XPU expectations"}
+{"pr":45614,"decision":"defect","reason":"adds missing requests dependency for transformers[serving] installs","title":"Add missing requests dependency to transformers[serving]"}
+{"pr":45613,"decision":"feature","reason":"adds MiniCPM3 model architecture support, docs, mappings, and tests","title":"[New Model] Add MiniCPM3 support "}
+{"pr":45612,"decision":"documentation","reason":"updates model documentation cards only","title":"[docs] update model cards"}
+{"pr":45609,"decision":"feature","reason":"extends torchao quantizer serialization/deserialization for HF MoE models","title":"make it possible to ser/deser HF MoE models with torchao"}
+{"pr":45608,"decision":"documentation","reason":"bulk edits model documentation examples to plain Python and dtype-free snippets","title":"Python code in model docs"}
+{"pr":45604,"decision":"feature","reason":"adds agent CLI skill support, schemas, and agent package output helpers","title":"Agent first cli with skill"}
+{"pr":45599,"decision":"feature","reason":"improves import performance through additional lazy loading in utility modules","title":"qa: more lazy loading"}
+{"pr":45597,"decision":"feature","reason":"adds built-in Granite 4.1 Vision model support with docs, auto mappings, and tests","title":"Add Granite 4.1 Vision (granite4_vision)"}
+{"pr":45596,"decision":"defect","reason":"fixes BLT model XPU test failures and expectations","title":"fix 2 failed test cases for blt model on XPU"}
+{"pr":45594,"decision":"defect","reason":"updates backbone utility tests for regressions after backbone API behavior changes","title":"fix(utils): Resolve backbone utils test regressions"}
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+{"pr":45552,"decision":"defect","reason":"removes erroneous auto-docstring warning spam when importing ModernBERT","title":"Remove warnings for modernbert"}
+{"pr":45550,"decision":"other","reason":"CI workflow runner selection change, not library feature or defect fix","title":"Add runner selection for mi325 GPU type"}
+{"pr":45549,"decision":"defect","reason":"fixes mono/channel averaging behavior in batched audio feature extractors","title":"fix: apply channel averaging correctly in audio feature extractors"}
+{"pr":45548,"decision":"defect","reason":"fixes Expert Parallelism plus DeepSpeed ZeRO-3 model loading when launched via accelerate","title":"Fix EP + DeepSpeed ZeRO-3 loading via accelerate launch"}
+{"pr":45546,"decision":"feature","reason":"adds GGUF loading support for Llama 4 text models","title":"feat: Add GGUF loading support for Llama 4 (text)"}
+{"pr":45543,"decision":"other","reason":"CI/telemetry workflow support, not library defect or user-facing feature","title":"ci: OTEL support"}
+{"pr":45541,"decision":"defect","reason":"fixes local_files_only tokenizer fallback when tokenizer files are missing","title":"Fix local_files_only tokenizer fallback when tokenizer files are missing (Issue 45538)"}
+{"pr":45534,"decision":"feature","reason":"adds ALM base model class without LM head across audio-language models","title":"\ud83d\udea8 [ALM] Add base model without head"}
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+{"pr":45512,"decision":"defect","reason":"fixes OutputRecorder matching by using regex search on layer names","title":"[OutputRecorder] re.search on layer_name"}
+{"pr":45497,"decision":"feature","reason":"adds V-JEPA 2.1 inference support and docs/tests","title":"Add V-JEPA 2.1 inference support"}
+{"pr":45493,"decision":"feature","reason":"modularizes ProcessorMixin and updates processor implementations/tests","title":"Modularize `ProcessorMixin` into smaller components"}
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+{"pr":45477,"decision":"feature","reason":"extends masking utilities/generation paths with blockwise mask function support","title":"Blockwise mask fn as opt arg in all masking functions"}
+{"pr":45476,"decision":"other","reason":"test-only CI workflow experiment marked do not merge","title":"[Don't merge] Call CI workflow"}
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+{"pr":45465,"decision":"documentation","reason":"updates contributor/model-addition documentation only","title":"[docs] contributing"}
+{"pr":45462,"decision":"other","reason":"chore adding repository security/static checks across tooling and tests","title":"chore(sec): added a handful of security checks"}
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+{"pr":45422,"decision":"defect","reason":"fixes chat template output by omitting messages with content=None","title":"Drop `content=None` from messages in `apply_chat_template`"}
+{"pr":45421,"decision":"defect","reason":"fixes nested base_model_prefix handling during weight conversion/loading","title":"Improve nested `base_model_prefix` handling in weight conversion and loading"}
+{"pr":45415,"decision":"other","reason":"typing/tooling cleanup adding type-check coverage across root modules","title":"Adds type checking to `src/transformers/*py`"}
+{"pr":45413,"decision":"defect","reason":"fixes EtaLogitsWarper behavior when all logits are masked","title":"Fix EtaLogitsWarper on fully masked logits"}
+{"pr":45401,"decision":"feature","reason":"adds a new Voxtral TTS model, docs, conversion, auto registrations, and tests","title":"Add support for Voxtral-4B-TTS-2603 to transformers"}
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+{"pr":45391,"decision":"feature","reason":"adds shared audio/multimodal tester infrastructure and updates audio model tests","title":"audio tester class"}
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+{"pr":45378,"decision":"defect","reason":"guards ReasoningEffort import for older mistral_common versions","title":"fix(mistral): guard ReasoningEffort import for older mistral_common versions"}
+{"pr":45363,"decision":"feature","reason":"adds generic n-to-1 kernel fusion support via KernelConfig and hub kernels","title":"n-to-1 kernel fusion via `KernelConfig`"}
+{"pr":45360,"decision":"other","reason":"maintenance update replacing deprecated CLI references in conversion scripts","title":"Replace deprecated `huggingface-cli` references with `hf`"}
+{"pr":45355,"decision":"feature","reason":"adds new PhoneticXeus model, tokenizer/processor, docs, auto registrations, and tests","title":"Add universal phone recognition model - PhoneticXeus"}
+{"pr":45351,"decision":"defect","reason":"guards CUDA device capability lookup when CUDA libraries exist but no GPU is available","title":"fix(testing_utils): guard get_device_capability with torch.cuda.is_available()"}
+{"pr":45350,"decision":"feature","reason":"adds Granite4Vision conditional generation model and auto registrations","title":"WIP: Add support for Granite4VisionForConditionalGeneration"}
+{"pr":45346,"decision":"defect","reason":"adds regression coverage for router logits softmax handling in MoE models","title":"Fix Double Application of Softmax for Router Logits in MoE models"}
+{"pr":45342,"decision":"defect","reason":"fixes load ignore-key handling to recurse through child module settings","title":"Use `_keys_to_ignore_on_load_unexpected/missing` recursively from children"}
+{"pr":45333,"decision":"feature","reason":"adds heterogeneous per-layer configuration support and tests","title":"Add heterogeneous config support (per-layer configuration)"}
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+{"pr":45321,"decision":"defect","reason":"removes deprecated TorchAO AffineQuantizedTensor references before upstream removal","title":"Remove references to torchao's AffineQuantizedTensor"}
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+{"pr":45296,"decision":"feature","reason":"adds GGUF loading support for Gemma4 text models and tests","title":"Add GGUF support to Gemma4 (31B & 26B-A4B) text"}
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+{"pr":45273,"decision":"defect","reason":"prevents Liger evaluation OOM by skipping logits when only prediction loss is needed","title":"fix: liger unnecessarily materializes logits in VRAM during eval, causing OOM"}
+{"pr":45270,"decision":"feature","reason":"adds Trainer support and arguments for logging individual loss components","title":"[Trainer] Support multi-loss component logging"}
+{"pr":45267,"decision":"documentation","reason":"adds only docstring/documentation to DistilBERT FFN.forward","title":"Add docstring to FFN.forward in DistilBERT"}
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+{"pr":45244,"decision":"other","reason":"CI placeholder PR with empty commit and no changed files","title":"Let's CI go great"}
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+{"pr":45218,"decision":"feature","reason":"adds a new agent-oriented transformers CLI surface and documentation","title":"Proposal: Agent-first CLI"}
+{"pr":45213,"decision":"other","reason":"explicit DO NOT MERGE experimental model-creation skill PR with broad tooling/model changes","title":"DO NOT MERGE - model creation skill"}
+{"pr":45202,"decision":"defect","reason":"disables Gemma4 FlashAttention support for released checkpoints with unsupported global head_dim=512","title":"Fix gemma4 has flash-attention incompatbile head-dim=512"}
+{"pr":45193,"decision":"defect","reason":"fixes config pydantic validation so it does not require torch-dependent type checks","title":"Config can apply pyndatic validation without torch-dependence"}
+{"pr":45189,"decision":"feature","reason":"adds CI workflow support for runnable documentation examples","title":"Add doc test CI workflow reusing existing model job infrastructure"}
+{"pr":45186,"decision":"feature","reason":"adds the new Isaac multimodal model implementation and tests","title":"Add new model: Isaac "}
+{"pr":45181,"decision":"feature","reason":"adds a lightweight top-level transformers_cli package and entrypoint","title":"Make the cli a top-level package"}
+{"pr":45176,"decision":"feature","reason":"adds EfficientViT-SAM model support, processors and tests","title":"added efficietvitsam model to HF"}
+{"pr":45170,"decision":"defect","reason":"fixes misspelled layernorm conversion keys in model weight mappings","title":"`layrnorm` -> `layernorm`"}
+{"pr":45168,"decision":"feature","reason":"changes default scheduler min_lr and max_lr values","title":"Update min_lr and max_lr default values to better defaults"}
+{"pr":45167,"decision":"feature","reason":"adds Anthropic-style tool/function JSON schema support","title":"Add anthropic style of function schema"}
+{"pr":45157,"decision":"feature","reason":"adds PrismML Bonsai GGUF quantization/import support and tests","title":"[WIP] PrismML Bonsai model support"}
+{"pr":45153,"decision":"feature","reason":"adds native torch/flash-attention integration across attention utilities and models","title":"[`FA`] Native torch integration"}
+{"pr":45152,"decision":"documentation","reason":"updates model testing documentation and docs TOC","title":"[docs] model testing"}
+{"pr":45149,"decision":"feature","reason":"adds a new Sam3 Lite Text model plus model-skill/checker support","title":"DO NOT MERGE adding SAML3-LiteText with a skill, first pass"}
+{"pr":45147,"decision":"defect","reason":"fixes broken HQQ quantization integration and tests","title":"Fix broken HQQ support"}
+{"pr":45144,"decision":"feature","reason":"adds Xiaomi MiMo-V2 Flash model support","title":"Add Xiaomi MiMo-V2"}
+{"pr":45134,"decision":"feature","reason":"optimizes Parakeet feature extraction on CUDA","title":"Optimize Parakeet feature extraction on CUDA"}
+{"pr":45133,"decision":"feature","reason":"adds Sarvam model implementation files","title":"Add sarvam model"}
+{"pr":45128,"decision":"defect","reason":"fixes auto_docstring handling of future annotations in kwargs processing","title":"Fix: handle future annotations in _process_kwargs_parameters"}
+{"pr":45115,"decision":"feature","reason":"refactors Nemotron-H to inherit GraniteMoeHybrid implementation","title":"Refactor/nemotron h inherit granitemoehybrid"}
+{"pr":45114,"decision":"documentation","reason":"fixes doctest examples in documentation pages","title":"fix: lets fix all doctests"}
+{"pr":45113,"decision":"feature","reason":"adds optional GPU Direct Storage support for safetensors loading","title":"Add GDS support for safetensors loading "}
+{"pr":45110,"decision":"feature","reason":"adds SAM 3.1 model and video support","title":"Add SAM 3.1"}
+{"pr":45105,"decision":"defect","reason":"fixes auto_docstring crash with future string annotations","title":"Fix @auto_docstring crash with from __future__ import annotations in _process_kwargs_parameters"}
+{"pr":45101,"decision":"feature","reason":"adds Nandi model support","title":"Adding support for Nandi Models"}
+{"pr":45097,"decision":"feature","reason":"adds converter support for old InternVL2 1B/2B checkpoints","title":"Add old InternVL2-1B/2B support to the InternVL conversion script #45092"}
+{"pr":45086,"decision":"defect","reason":"fixes AttributeError in Mistral regex tokenizer patching","title":"fix AttributeError in _patch_mistral_regex"}
+{"pr":45082,"decision":"feature","reason":"updates VidEoMT conversion script for remaining checkpoints","title":"[VidEoMT] Update conversion script"}
+{"pr":45077,"decision":"defect","reason":"hardens CI workflows by pinning actions and removing secret interpolation from run blocks","title":"fix: pin 50 unpinned actions to commit SHA, extract 1 secret to env var"}
+{"pr":45075,"decision":"feature","reason":"adds DeepSeek-OCR-2 model support","title":"Add Deepseek-OCR-2 model"}
+{"pr":45073,"decision":"feature","reason":"refactors OwlViT to modular model structure","title":"Refactor OwlViT to modular Transformers"}
+{"pr":45067,"decision":"feature","reason":"adds Trainer resume_from_checkpoint support for Hub checkpoint downloads","title":"feat: trainer resume_from_checkpoint support hub downloads (#43375)"}
+{"pr":45064,"decision":"feature","reason":"improves and shards repository checker tooling","title":"refactor: shard checkers"}
+{"pr":45060,"decision":"defect","reason":"fixes PIL backend fallback regression when torchvision is unavailable","title":"Fix PIL backend fallback when torchvision is unavailable"}
+{"pr":45056,"decision":"defect","reason":"fixes auto_docstring to run only on __doc__","title":"[] needs to be only run on __doc__ "}
+{"pr":45055,"decision":"defect","reason":"fixes Trainer checkpoints missing config for non-PreTrainedModel models","title":"Save model config in Trainer checkpoints for non-PreTrainedModel models"}
+{"pr":45040,"decision":"defect","reason":"fixes VideoLlama3 missing lm_head.weight test failures","title":"Llama3 video fix"}
+{"pr":45037,"decision":"documentation","reason":"fixes invalid syntax in attention interface documentation example","title":"add missing colon in custom_attention function signature in attention…"}
+{"pr":45034,"decision":"defect","reason":"fixes Qwen3.5 padding-free packed inputs on the linear-attention fast path","title":"Pass packed boundary metadata to Qwen3.5 linear-attention fast kernels from data collator"}
+{"pr":45028,"decision":"feature","reason":"large tensor-parallel/FSDP integration refactor and distributed support changes","title":"TP refactor for FSDP + TP integration"}
+{"pr":45017,"decision":"defect","reason":"fixes GLM5 rotary embedding style and activation behavior","title":"[WIP][Fix] GLM 5 set `apply_rotary_pos_emb` to `is_neox_style=False` && remove `F.relu()`"}
+{"pr":44989,"decision":"feature","reason":"introduces distributed training API prototype scripts","title":"\ud83d\udea8 Distributed training API"}
+{"pr":44981,"decision":"defect","reason":"fixes Trainer loss-only evaluation memory path when Liger is enabled","title":"Trainer: set skip_logits for loss-only eval when liger enabled"}
+{"pr":44979,"decision":"feature","reason":"adds module fusion API and tests","title":"Module Fusion API"}
+{"pr":44974,"decision":"feature","reason":"refactors core model loading for FSDP shard-on-read support","title":"Refactor core_model_loading to support FSDP shard-on-read loading"}
+{"pr":44973,"decision":"defect","reason":"fixes vision attention max_seqlen typing for torch.compile and FlashAttention2","title":"Fix max_seqlen type in vision attention for torch.compile + FA2"}
+{"pr":44965,"decision":"other","reason":"temporary try PR touching workflow and temp file, not a product feature or defect fix","title":"try"}
+{"pr":44958,"decision":"defect","reason":"fixes PILImageResampling import error in video processing","title":"fixed import error with PILImageResampling"}
+{"pr":44956,"decision":"feature","reason":"adds new HyperCLOVAX model implementation, docs, and tests","title":"Add HyperCLOVAX SEED Think 14B"}
+{"pr":44952,"decision":"defect","reason":"fixes CLIP/SigLIP vision outputs when output_hidden_states=True","title":"Fix: Add correct return behaviour when output_hidden_states=True for CLIP and SIGLIP vision models"}
+{"pr":44942,"decision":"feature","reason":"adds optional inference-time layer fusion via from_pretrained","title":"Add inference time layer fusion optimisations via `PreTrainedModel.from_pretrained(fuse_layers=True)`"}
+{"pr":44940,"decision":"defect","reason":"fixes thread-unsafe tie_weights skip state during concurrent model loading","title":"Fix tie_weights skipping logic is not tied to model thread scope"}
+{"pr":44923,"decision":"defect","reason":"fixes unconditional Hub model_info call for local/offline Mistral tokenizer regex patching","title":"fix: avoid unconditional model_info call in _patch_mistral_regex"}
+{"pr":44907,"decision":"defect","reason":"fixes VLM placeholder-mask expansion inefficiency/data-dependent indexing across models","title":"Remove unnecessary expand_as in get_placeholder_mask across VLMs"}
+{"pr":44893,"decision":"defect","reason":"adds missing StaticLayer.crop API parity with DynamicLayer","title":"add `StaticLayer.crop()` to match `DynamicLayer` API"}
+{"pr":44891,"decision":"feature","reason":"adds MoE router health Trainer callback, docs, and tests","title":"[Trainer] add MoERouterHealthCallback Callback"}
+{"pr":44889,"decision":"defect","reason":"fixes DeepSpeed evaluate/predict before train stale state and config mutation issues","title":"[DeepSpeed] Fix evaluate()/predict() before train()"}
+{"pr":44875,"decision":"feature","reason":"refactors CLI serve module organization without public API change","title":"refactor: improved the cli server module code organization"}
+{"pr":44872,"decision":"documentation","reason":"updates an outdated code comment reference in generation utils","title":"Fix: Update outdated sampler comment in generation/utils.py"}
+{"pr":45694,"decision":"defect","reason":"fixes TrainingArguments batch size properties when accelerator split_batches is enabled","title":"Fix train_batch_size and eval_batch_size to respect split_batches config"}
+{"pr":44836,"decision":"defect","reason":"fixes OlmoHybrid packed-sequence recurrent state leakage by passing cu_seqlens to FLA kernels","title":"Add cu_seqlens support to OlmoHybridGatedDeltaNet for packed sequences"}
+{"pr":44830,"decision":"feature","reason":"adds the AudioFlamingoNext model alias, docs, auto mappings, and tests","title":"Add AudioFlamingoNext model"}
+{"pr":44827,"decision":"defect","reason":"fixes Mistral4 grouped-mm alignment/test failures and related mappings","title":"Fix Mistral4 tests"}
+{"pr":44815,"decision":"defect","reason":"fixes finegrained FP8 dequantization and Mistral4 loading behavior","title":"Dequant fix"}
+{"pr":44794,"decision":"feature","reason":"refactors GGUF weight conversion path but PR head contains a broad outdated stacked branch","title":"Refacto GGUF weight conversion"}
+{"pr":44793,"decision":"defect","reason":"fixes Janus image generation when optional config values are None","title":"fix(janus): Handle None values in image generation mode"}
+{"pr":44781,"decision":"defect","reason":"fixes tokenizer extra_special_tokens list handling","title":"Fix `_set_model_specific_special_tokens` to accept list-format `extra_special_tokens`"}
+{"pr":44775,"decision":"documentation","reason":"refactors n-d parallelism documentation only","title":"[docs] n-d parallelism"}
+{"pr":44772,"decision":"documentation","reason":"updates bitsandbytes quantization docs and doc links","title":"bitsandbytes: Update links and docs"}
+{"pr":44771,"decision":"defect","reason":"changes PI0 fast image processor inheritance to use SigLIP behavior despite vague title","title":"wtf"}
+{"pr":44731,"decision":"defect","reason":"fixes inefficient tensor construction in video test helper","title":"[Tests] Fix slow video tensor creation from list of numpy arrays in SmolVLM"}
+{"pr":44729,"decision":"defect","reason":"replaces imprecise float ceil arithmetic with integer helper to fix export/runtime precision issues","title":"Avoid floating point math for ceil operations"}
+{"pr":44724,"decision":"defect","reason":"fixes incorrect auto mappings and model_type/doc metadata entries","title":"Fix some missing / incorrect entries in auto files"}
+{"pr":44722,"decision":"feature","reason":"refactors GPT-J output tracing to standardized decorator infrastructure","title":"Refactor gptj output tracing to use standardized decorators"}
+{"pr":44713,"decision":"feature","reason":"refactors ColQwen2 retrieval output tracing to standardized decorator infrastructure","title":"[ColQwen2] Refactor output tracing (issue #43979)"}
+{"pr":44697,"decision":"defect","reason":"fixes torch_float conversion returning int instead of float","title":"fix: torch_float should return float, not int"}
+{"pr":44682,"decision":"feature","reason":"adds llama.cpp integration and streaming latency changes for transformers serve","title":"transformers serve + llamacpp"}
+{"pr":44680,"decision":"feature","reason":"adds MASK_FUNCTION support for custom attention kernel modules","title":"Allow kernel modules to declare their preferred mask function"}
+{"pr":44676,"decision":"defect","reason":"fixes GPT-2 NaN/Inf issue by cloning tied lm_head weights on Python 3.13","title":"fix(gpt2): Resolve NaN/Inf issue in lm_head on Python 3.13 with tied weights"}
+{"pr":45695,"decision":"feature","reason":"adds new Granite-Speech-Plus model support","title":"Support for a new Granite-Speech-Plus model"}
+{"pr":44664,"decision":"defect","reason":"fixes generic sequence classifier support for multimodal models","title":":rotating_light: Generic Sequence Classifier works for multimodal models"}
+{"pr":44662,"decision":"feature","reason":"adds new PenguinVL model implementation","title":"[model] Add PenguinVL implementation"}
+{"pr":44660,"decision":"defect","reason":"fixes late CUDA OOM when Trainer reloads PEFT best model","title":"Fix: avoid late CUDA OOM in load_best_model_at_end with PEFT models"}
+{"pr":44659,"decision":"documentation","reason":"removes outdated docstring documentation text","title":"docs: remove outdated use_diff docstring from DistributedConfig.to_js…"}
+{"pr":44650,"decision":"defect","reason":"fixes Seq2SeqTrainer prediction generation for decoder-only models","title":"Fix Seq2SeqTrainer generation path for decoder-only models"}
+{"pr":44646,"decision":"documentation","reason":"isolated typo correction","title":"Fix typo: seperate -> separate"}
+{"pr":44642,"decision":"documentation","reason":"clarifies causal LM label shifting documentation","title":"Clarify that causal LM labels are shifted internally"}
+{"pr":44641,"decision":"defect","reason":"fixes Falcon causal mask creation to avoid unnecessary 4D mask memory use","title":"Conditinally passing and_mask_function arg to create_causal_mask "}
+{"pr":44635,"decision":"feature","reason":"refactors Gemma-style buffers for modular inheritance compatibility","title":"[Gemma] Modular-friendly buffers"}
+{"pr":45697,"decision":"defect","reason":"fixes CUDA-headless testing utility crash by checking torch.cuda.is_available before get_device_capability","title":"fix(testing): check torch.cuda.is_available() before get_device_capability"}
+{"pr": 44626, "decision": "defect", "reason": "restores legacy enforced tokenizer behavior by adding a missing Llama tokenizer branch", "title": "don't break legacy behavior when enforced!"}
+{"pr": 44615, "decision": "defect", "reason": "restores is_torch_fx_available backwards-compatibility shim for trust_remote_code models", "title": "Restore is_torch_fx_available for trust_remote_code backwards compatibility"}
+{"pr": 44606, "decision": "defect", "reason": "adds tokenizer override path to preserve legacy serialized-tokenizer loading when configs are stale", "title": "optionally override tokenizer class with serialized tokenizer "}
+{"pr": 44603, "decision": "defect", "reason": "fixes GPU Dockerfile build on arm64 by installing Rust and handling torchcodec availability", "title": "fixed dockerfile for arm64 systems"}
+{"pr": 44601, "decision": "feature", "reason": "adds native pipeline-parallel distributed loading support", "title": "[Distributed] Add PP support natively"}
+{"pr": 44594, "decision": "feature", "reason": "adds object-detection pipeline postprocessing options for top_k, labels, box format, and sorting", "title": "[Pipeline] Add top_k, label filtering, box_format and score sorting to ObjectDetectionPipeline"}
+{"pr": 44587, "decision": "defect", "reason": "fixes fused QKV tensor slicing for tensor-parallel sharded qkv weights", "title": "Fix: Handling fused qkv result tensor slicing for tp sharded qkv weights"}
+{"pr": 44585, "decision": "defect", "reason": "passes rms_norm_eps into DeepSeekV3 MLA q/kv layernorms and generated copies", "title": "Fix missing rms_norm_eps in DeepseekV3 MLA layernorms"}
+{"pr": 44569, "decision": "feature", "reason": "adds native SarvamMLA model configuration, modular implementation, docs, and auto mappings", "title": "Add SarvamMLA model (sarvamai/sarvam-105b)"}
+{"pr":45699,"decision":"feature","reason":"adds FP8 compressed-tensors kernel acceleration support","title":"Add FP8 kernel acceleration for compressed-tensors quantized models"}
+{"pr":44553,"decision":"feature","reason":"refactors FlashAttention continuous-batching kwargs","title":"[] Refactor FA CB kwargs"}
+{"pr":44550,"decision":"documentation","reason":"grammar/readability changes in Auto Classes docs only","title":"Improve clarity and grammar in Auto Classes documentation"}
+{"pr":44547,"decision":"documentation","reason":"docstring-only correction for position_ids wording","title":"Fix position_ids docstring in modeling_flash_attention_utils.py"}
+{"pr":44543,"decision":"defect","reason":"fixes all-zero assistant_masks for multimodal chat templates","title":"Fix assistant_masks for multimodal inputs in apply_chat_template"}
+{"pr":44535,"decision":"defect","reason":"fixes Qwen2.5-VL processor crash on ragged batched image inputs with padding=False","title":"Fix crash in Qwen2_5_VLProcessor when using batched input with padding=False"}
+{"pr":44517,"decision":"feature","reason":"adds Qwen3-TTS audio model, tokenizer models, processor, docs, and tests","title":"Add qwen3 tts"}
+{"pr":44495,"decision":"feature","reason":"gradient checkpointing cleanup removing redundant attribute definitions across model classes","title":"[`Gradient Ckpting`] Remove unnecessary attribute definitions"}
+{"pr":44467,"decision":"feature","reason":"updates slow-tokenizer conversion to honor placeholder token replacements from added_tokens_decoder","title":"Placeholder tokens update"}
+{"pr":44445,"decision":"feature","reason":"adds GraniteDoclingHybrid model support and auto mappings","title":"Adding support for GraniteDoclingHybrid"}
+{"pr":44438,"decision":"feature","reason":"adds FlashOptim optimizer integration and trainer support","title":"Add flashoptim"}
+{"pr":44420,"decision":"documentation","reason":"documentation-only distributed training guide updates","title":"[docs] distributed training"}
+{"pr":44408,"decision":"feature","reason":"adds Granite Speech option to export encoder hidden states","title":"Add option to export  encoder hidden states for Granite-speech"}
+{"pr":44407,"decision":"documentation","reason":"documentation-only bitsandbytes quantization guide update","title":"docs: add energy efficiency considerations to bitsandbytes quantization guide"}
+{"pr":44394,"decision":"feature","reason":"large API migration from feature extractors to audio processors","title":"🚨🚧 FeatureExtractor → AudioProcessor"}
+{"pr":44385,"decision":"defect","reason":"fixes make check-repo typing/import utility issues","title":"Fix make check-repo"}
+{"pr":44375,"decision":"feature","reason":"adds RF-DETR model, docs, auto mappings, and tests","title":"Add RF-DETR"}
+{"pr":44369,"decision":"feature","reason":"updates integrations and related zero-shot object detection docs","title":"Feature/integrations docs fix"}
+{"pr":44348,"decision":"feature","reason":"adds Metal/MLX pre-quantized model loading support","title":"Enable MetalConfig to load pre-quantized MLX models from HuggingFace Hub"}
+{"pr":44314,"decision":"feature","reason":"adds HyperClovaX Vision model, processor, docs, mappings, and tests","title":"add HyperClovaX Vision"}
+{"pr":44298,"decision":"defect","reason":"fixes tokenizer backend auto-detection for models with wrong tokenizer mappings","title":"Auto detect wrong mapping models"}
+{"pr":44270,"decision":"defect","reason":"fixes incorrect ProcessorsKwargs images_kwargs type annotations","title":"Add correct typing to custom images_kwargs in ProcessorsKwargs"}
+{"pr":44264,"decision":"feature","reason":"changes MoE training behavior to auto-enable auxiliary loss from coefficient defaults","title":"[`Moe`] Enable aux loss automatically when in training + coef is not 0"}
+{"pr":44259,"decision":"feature","reason":"adds asynchronous data producer abstractions and trainer integration","title":"Async data producer"}
+{"pr":44257,"decision":"defect","reason":"fixes NaN loss aggregation under context parallelism by using nanmean","title":"use nanmean for aggregating loss"}
+{"pr":44252,"decision":"feature","reason":"unifies/deprecates timm backbone handling across modeling, auto mappings, docs, and tests","title":"Timm unification continued"}
+{"pr":44228,"decision":"defect","reason":"fixes parameter/buffer lookup for nested quantized tensor names","title":"[Quantisation] account for nested tensors from quantisers"}
+{"pr":44215,"decision":"feature","reason":"adds sequence-classification heads, auto mappings, docs, and tests for Granite model families","title":"Add sequence classification capability to Granite models"}
+{"pr":44189,"decision":"defect","reason":"fixes Trainer device placement when full-eval precision is used with distributed backends","title":"fix: don\u0027t move model to device under other dist train backends"}
+{"pr":44184,"decision":"feature","reason":"adds a new CircuitGPT model architecture and sparse linear layer implementation","title":"feat: add OpenAI CircuitGPT core architecture and sparse linear layers"}
+{"pr":44178,"decision":"feature","reason":"adds the new XCodec2 audio model, feature extractor, docs, conversion script, and tests","title":"Add xcodec2 model"}
+{"pr":44171,"decision":"feature","reason":"adds Parakeet TDT model support, generation, loss, auto/pipeline integration, docs, conversion, and tests","title":"Parakeet tdt"}
+{"pr":44161,"decision":"defect","reason":"refactors LongT5 output handling to the standardized decorators and is marked as fixing issue #43979","title":"Refactor LongT5 to use @capture_outputs and @can_return_tuple decorators for unified output handling (Fixes #43979)"}
+{"pr":44159,"decision":"feature","reason":"adds SDPA and Flash Attention backend support for OWL-ViT","title":"Add SDPA and Flash Attention support for OWL-ViT"}
+{"pr":44154,"decision":"defect","reason":"refactors VITS output handling to standardized decorators and is marked as fixing issue #43979","title":"Refactored vits to match standardized output collection interface"}
+{"pr":44142,"decision":"feature","reason":"adds Voxtral Realtime generation performance behavior by precomputing encoder outputs and documents the option","title":"[voxtral-realtime] get more perfs!"}
+{"pr":44129,"decision":"defect","reason":"refactors SpeechT5 output tracing to standardized decorators and is marked as fixing issue #43979","title":"Refactor SpeechT5 output tracing to standardized output capture"}
+{"pr":44123,"decision":"defect","reason":"avoids unintended device synchronization in Trainer loss accumulation and gradient clipping","title":"Avoid device sync in training loss accumulation"}
+{"pr":44116,"decision":"defect","reason":"migrates Flaubert output tracing to decorators as part of issue #43979 despite WIP title","title":"[WIP] [Flaubert] Refactor output tracing to decorator-based interface"}
+{"pr":44114,"decision":"defect","reason":"migrates wav2vec2-family output collection to standardized decorators and includes regression fixes for output capture behavior","title":"Migrate wav2vec2, wav2vec2_conformer, and wav2vec2_bert to standardized output collection decorators"}
+{"pr":44101,"decision":"feature","reason":"output tracing refactor for XLM/Flaubert standard capture_outputs architecture","title":"[XLM] Refactor output tracing to align with capture_outputs standardized architecture"}
+{"pr":44098,"decision":"feature","reason":"ViLT output handling refactor to standardized model output patterns","title":"[ViLT] Refactor output handling to align with standardized patterns"}
+{"pr":44086,"decision":"feature","reason":"MGP-STR output tracing refactor to standardized decorators","title":"[MGP-STR] Refactor output tracing to use capture_outputs/can_return_tuple decorators"}
+{"pr":44085,"decision":"feature","reason":"output tracing decorator refactor despite title/path mismatch in PR metadata","title":"Refactor RemBERT to use output tracing decorators"}
+{"pr":44083,"decision":"feature","reason":"adds native FSDP2 support across distributed/modeling utilities and tests","title":"FSDP2 native support in transformers"}
+{"pr":44076,"decision":"feature","reason":"ImageGPT output tracing refactor to capture_outputs decorators","title":"Refectored modeling_imagegpt.py to enable hooks to capture_outputs"}
+{"pr":44074,"decision":"feature","reason":"TextNet output tracing refactor and tests for standardized decorators","title":"[TextNet] Refactor output tracing using capture_outputs decorator"}
+{"pr":44073,"decision":"feature","reason":"VisualBert output tracing refactor to standardized decorators","title":"[VisualBert] Refactor output tracing using capture_outputs and can_return_tuple decorators"}
+{"pr":44072,"decision":"feature","reason":"EfficientNet output tracing refactor to standardized decorators","title":"refactor efficientnet output tracing with @capture_outputs and @can_r\u2026"}
+{"pr":44071,"decision":"feature","reason":"MPT output tracing refactor to standardized decorators","title":"[Refactor] Migrate MPT to standardized output tracing decorators"}
+{"pr":44070,"decision":"feature","reason":"adds GGUF loading support for Qwen3-Next architecture","title":"Add GGUF loading support for Qwen3-Next (qwen3_next) architecture"}
+{"pr":44068,"decision":"feature","reason":"refactors GPT-Neo output handling to capture_outputs/can_return_tuple decorators","title":"Refactor GPT-Neo to use  and  decorators"}
+{"pr":44066,"decision":"feature","reason":"refactors GPT-J and copied CodeGen output tracing to standardized decorators","title":"Refactor GPT-J to use standardized output tracing (#43979)"}
+{"pr":44059,"decision":"feature","reason":"refactors GPT-2 output tracing to standardized decorators","title":"[GPT2] Refactor output tracing to use capture_outputs/can_return_tuple decorators"}
+{"pr":44056,"decision":"feature","reason":"refactors MPNet output tracing to capture_outputs","title":"[MPNet] Refactor output tracing using capture_outputs decorator"}
+{"pr":44054,"decision":"feature","reason":"adds experimental Flash MLA attention integration and GLM MoE DSA wiring","title":"Flash mla interface"}
+{"pr":44044,"decision":"feature","reason":"refactors DeBERTa-v2 output tracing to capture_outputs/_can_record_outputs","title":"Refactor DeBERTa output tracing interface"}
+{"pr":44030,"decision":"feature","reason":"refactors DPR output tracing to standardized decorators","title":"refactor output tracing in dpr"}
+{"pr":44029,"decision":"feature","reason":"refactors RWKV output tracing to standardized decorators","title":"refactor output tracing in rwkv"}
+{"pr":44028,"decision":"feature","reason":"refactors SuperPoint output tracing to standardized decorators","title":"refactor output tracing for superpoint"}
+{"pr":44027,"decision":"feature","reason":"refactors speech_encoder_decoder to standardized output tracing decorators","title":"refactor output tracing in "}
+{"pr":44026,"decision":"feature","reason":"refactors vision_encoder_decoder to standardized output tracing decorators","title":"refactor output tracing for "}
+{"pr":44025,"decision":"feature","reason":"refactors depth_anything output tracing","title":"refactor output tracing for depth_anything"}
+{"pr":44024,"decision":"feature","reason":"standardizes FocalNet output tracing decorators","title":"Focalnet standardized outputs"}
+{"pr":44019,"decision":"feature","reason":"refactors ResNet to standardized output tracing decorators","title":"Refactor resnet to use capture_outputs/can_return_tuple output tracing"}
+{"pr":44018,"decision":"feature","reason":"refactors GPT-Neo output tracing","title":"Refactor GPT-Neo output tracing to use capture_outputs/can_return_tuple"}
+{"pr":44017,"decision":"feature","reason":"refactors SegFormer output capture decorators","title":"Refactor output tracing in segformers (#43979)"}
+{"pr":44015,"decision":"feature","reason":"migrates GPT2-family output collection decorators","title":"Refactor GPT2-based models to standardized output collection interface"}
+{"pr":44013,"decision":"feature","reason":"standardizes MobileNetV2 output tracing","title":"Ouptut tracing: Standardizing MobileNetv2"}
+{"pr":44010,"decision":"feature","reason":"migrates SqueezeBert to standardized output collection decorators","title":"[SqueezeBert] Migrate to standardized output collection decorators"}
+{"pr":44007,"decision":"feature","reason":"decorator-based output tracing refactor for ResNet/RegNet/RT-DETR ResNet model code","title":"[ResNet] Refactor output tracing to decorator-based interface"}
+{"pr":44004,"decision":"feature","reason":"decorator-based output tracing refactor for CodeGen model code","title":"refactor output tracing for `codegen`"}
+{"pr":44003,"decision":"feature","reason":"decorator-based output tracing refactor for Mamba and Falcon-Mamba model code","title":"refactor output tracing in `mamba`"}
+{"pr":44002,"decision":"feature","reason":"decorator-based output tracing refactor for UPerNet model code","title":"refactor output tracing in `upernet`"}
+{"pr":44001,"decision":"feature","reason":"decorator-based output tracing refactor for UnivNet model code","title":"refactor output tracing in `univnet`"}
+{"pr":44000,"decision":"feature","reason":"decorator-based output tracing refactor for vision-text dual encoder model code","title":"refactor output tracing in `vision_text_dual_encoder`"}
+{"pr":43999,"decision":"feature","reason":"decorator-based output tracing refactor for MobileNetV1 model code and tests","title":"refactor output tracing in `mobilenet_v1`"}
+{"pr":43998,"decision":"feature","reason":"decorator-based output tracing refactor for TimmBackbone model code","title":"refactor output tracing in `timm_backbone`"}
+{"pr":43997,"decision":"feature","reason":"decorator-based output tracing migration for RegNet model code","title":"Migrate RegNet to standardized output tracing"}
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+{"pr":43995,"decision":"feature","reason":"standardized output collection refactor for Falcon model code","title":"Refactoring falcon model to match standardized output collection interface"}
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+{"pr":43973,"decision":"feature","reason":"adds LFM2/LFM2.5 audio model support","title":"Add lfm2.5 audio"}
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+{"pr":43961,"decision":"defect","reason":"replaces mutable default arguments that can share state across calls","title":"Replace mutable default arguments with None"}
+{"pr":43924,"decision":"feature","reason":"broad attention-mask API modernization across many model files","title":"[] More old mask APIs"}
+{"pr":43915,"decision":"feature","reason":"adds PaddleOCR-VL conversion script","title":"add PaddleOCR-VL conversion"}
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+{"pr":43888,"decision":"feature","reason":"adds a new Param2MoE model architecture","title":"Support for BharatGen's Param2MoE model architecture"}
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+{"pr":43842,"decision":"defect","reason":"fixes TypeAdapter NameError when pydantic is unavailable","title":"fix(cli): Fix TypeAdapter NameError when pydantic is not installed"}
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+{"pr":43833,"decision":"defect","reason":"fixes grouped_mm dtype mismatch under autocast","title":"fix: ensure dtype consistency in grouped_mm under autocast"}
+{"pr":43826,"decision":"defect","reason":"fixes misleading pipeline error message","title":"fix: error message of pipeline"}
+{"pr":43823,"decision":"feature","reason":"adds MobileLLM model implementation","title":"Add `facebook/MobileLLM-125M`"}
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+{"pr":43785,"decision":"defect","reason":"fixes FSDP CPU RAM efficient loading behavior","title":"Fix FSDP_CPU_RAM_EFFICIENT_LOADING (#43749)"}
+{"pr":43779,"decision":"feature","reason":"adds SwanLabCallback support for forwarding id/resume kwargs","title":"SwanLab: Add support for id and resume arguments in SwanLabCallback"}
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+{"pr":43743,"decision":"feature","reason":"adds modular-model playground/evaluation tooling and Persimmon modularization","title":"Modular playground"}
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+{"pr":43654,"decision":"defect","reason":"prevents duplicate special-token registration from overwriting token properties","title":"fix(tokenizer): Avert special token property overwrites in batch add_tokens calls"}
+{"pr":43651,"decision":"feature","reason":"adds an overridable Trainer hook controlling gradient-accumulation loss scaling","title":"Add _loss_is_scaled_for_ga to allow custom trainers to control gradient accumulation loss scaling"}
+{"pr":43649,"decision":"feature","reason":"adds CI/new-failure reporting workflow and notification changes","title":"Check new failures reporting 5"}
+{"pr":43636,"decision":"feature","reason":"adds Trainer _metrics storage hook for custom metric logging","title":"Add _metrics dict to Trainer for custom metric logging"}
+{"pr":43613,"decision":"feature","reason":"adds promptable visual segmentation pipeline and integrations","title":"Add Promptable Visual Segmentation pipeline"}
+{"pr":43612,"decision":"feature","reason":"adds promptable concept segmentation pipeline and integrations","title":"Add Promptable Concept Segmentation pipeline"}
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+{"pr":43492,"decision":"feature","reason":"follow-up support for Perception Encoder loading/config conversion and docs","title":"Perception Encoder follow up PR"}
+{"pr":43488,"decision":"other","reason":"explicit do-not-merge repository bot formatting check PR","title":"[don't merge] bad format to check repo bot"}
+{"pr":43484,"decision":"feature","reason":"performance optimization for Ernie 4.5 VL timestamp rendering","title":"Optimize Ernie 4.5 VL timestamp rendering with cached overlays"}
+{"pr":43469,"decision":"feature","reason":"HfArgumentParser usability improvement for Optional[bool] flags","title":"argparser: Allow optional bool flags without values"}
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+{"pr":43451,"decision":"feature","reason":"adds new Molmo2 multimodal model implementation and tests","title":"Add Molmo2"}
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+{"pr":43446,"decision":"feature","reason":"typing/checker feature for decorator return types","title":"[`typings`] Automatically type decorator return types as `tuple | X`"}
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+{"pr":43291,"decision":"defect","reason":"fixes Whisper tokenizer/model tests and related behavior","title":"Fix whisper tests"}
+{"pr":43271,"decision":"other","reason":"typo-only cleanup without behavior change","title":"Fix typo: necesary \u2192 necessary"}
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+{"pr":43267,"decision":"documentation","reason":"adds auto_docstring decoration so generated SAM3 processor docs show the correct preprocess docstring","title":"Add auto_docstring decorator to Sam3ImageProcessorFast"}
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+{"pr":43213,"decision":"feature","reason":"adds support for selecting specific layers when collecting hidden states or attentions","title":"feat: allow output_hidden_states and output_attensions to record outputs of specific layers"}
+{"pr":43212,"decision":"defect","reason":"adds regression coverage for offline tokenizer loading issue 43200","title":"Add regression test for offline tokenizer loading (fixes #43200)"}
+{"pr":43192,"decision":"feature","reason":"adds Trackio GPU metrics logging support and related documentation","title":"[Trackio] support trackio gpu logging"}
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+{"pr":43139,"decision":"feature","reason":"optimizes Whisper feature extraction on GPU with caching and tensor return support","title":"[perf] optimize whisper GPU performance"}
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+{"pr":43104,"decision":"documentation","reason":"clarifies tokenizer decoder behavior in comments/docs without runtime behavior change","title":"docs: clarify tokenizer decoder behavior in v5 (#43066)"}
+{"pr":43102,"decision":"documentation","reason":"adds CPU vs GPU performance comparison examples to documentation/tutorials","title":"Add CPU vs GPU performance comparison example"}
+{"pr":43096,"decision":"defect","reason":"fixes save_pretrained for quantized models whose quantizer supplies custom serialized state dicts","title":"Fix save_pretrained for quantized models with custom serialization"}
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+{"pr":43056,"decision":"feature","reason":"performance enhancement enabling pinned DataLoader memory in CLM no_trainer example","title":"Perf: enable pin_memory in DataLoader for CLM no_trainer example"}
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+{"pr":43036,"decision":"documentation","reason":"grammar-only quicktour documentation edit","title":"Docs: fix grammar in Pipeline section"}
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+{"pr":43020,"decision":"feature","reason":"adds new MiMo-V2-Flash model architecture, conversion script, and tests","title":"Add mimo v2 flash"}
+{"pr":43015,"decision":"defect","reason":"fixes false TF32 warnings in rotary embedding frequency calculation under torch.compile","title":"FIX: TF32 warning (#43012)"}
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+{"pr":42979,"decision":"defect","reason":"fixes mixed-precision dtype mismatch in LlavaNext lm_head logits computation","title":"Fix dtype mismatch in in modeling_llava_next"}
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+{"pr":42975,"decision":"other","reason":"updates GitHub Actions workflows for Node compatibility rather than library feature or defect fix","title":"Upgrade GitHub Actions for Node 24 compatibility"}
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+{"pr":42916,"decision":"defect","reason":"fixes TokenizersBackend._decode to honor clean_up_tokenization_spaces","title":"Fix: Apply clean_up_tokenization_spaces in TokenizersBackend._decode"}
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+{"pr":42881,"decision":"defect","reason":"fixes missing GGUF attn_logit_softcapping config mapping for Gemma2/Gemma3","title":"[GGUF] Add attn_logit_softcapping to Gemma2/Gemma3 config mapping"}
+{"pr":42876,"decision":"documentation","reason":"adds Trainer tensor-parallelism documentation only","title":"Document tensor parallelism configuration with Trainer"}
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+{"pr":42816,"decision":"defect","reason":"adds validation that loaded tokenizer.json components match mapped tokenizer implementation","title":"validate tokenizer components"}
+{"pr":42793,"decision":"defect","reason":"adjusts BitNet quantization integration test expectation for green CI","title":"[Quantization] Fixing last issues for a green 2026 CI hopefully"}
+{"pr":42785,"decision":"documentation","reason":"corrects Mimi model documentation values only","title":"Fixing wrong information in Mimi Docs"}
+{"pr":42781,"decision":"feature","reason":"adds new VibeVoice Realtime model family and related docs/tests","title":"Add VibeVoice Realtime"}
+{"pr":42774,"decision":"defect","reason":"fixes None membership TypeError in video_processor_class_from_name without torchvision","title":"Fix: add None check for extractors in video_processor_class_from_name"}
+{"pr":42767,"decision":"feature","reason":"adds DeepSeek v3.2 model/config/auto mappings despite fix framing","title":"fix: add mapping of deepseek_v32 model type"}
+{"pr":42765,"decision":"feature","reason":"adds distributed training CI, pytest markers, and training test mixins","title":"Add distributed training CI"}
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+{"pr":42706,"decision":"other","reason":"minor cosmetic/nit cleanup in Parakeet modeling files","title":"Nit parakeet"}
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+{"pr":42598,"decision":"defect","reason":"fixes unwanted lazy import failure in audio pipelines","title":"[pipeline] fix unwanted import failure"}
+{"pr":42588,"decision":"documentation","reason":"documents serving /v1/models endpoint","title":"Document the /v1/models endpoint"}
+{"pr":42572,"decision":"documentation","reason":"adds SqueezeBERT doctest documentation example","title":"docs: add doctest for SqueezeBERT"}
+{"pr":42542,"decision":"defect","reason":"handles get_input_embeddings returning None to avoid enable_input_require_grads failure","title":"handle get_input_embeddings() on models like gemma3 gracefully"}
+{"pr":42527,"decision":"documentation","reason":"adds SwiftFormer doctest documentation examples","title":"Added doctests for SwiftFormer model"}
+{"pr":42521,"decision":"defect","reason":"fixes FSDP2 TrainingArguments config being ignored/defaulting to FSDP1","title":"Fix FSDP2 defaulting to version 1 in TrainingArguments; add dynamic plugin param passthrough"}
+{"pr":42496,"decision":"feature","reason":"adds context parallel training support in Trainer","title":"feat: allow CP with trainer"}
+{"pr":42493,"decision":"defect","reason":"fixes local pretrained model paths with trailing OS separator","title":"fix: remove trailing os sep in local pretrained model path"}
+{"pr":42467,"decision":"defect","reason":"fixes StaticCache crash behavior","title":"Fixes StaticCache Crashes "}
+{"pr":42461,"decision":"feature","reason":"broad RMSNorm implementation refactor to use torch.nn.functional.rms_norm","title":"Refactor RMSNorm implementations to use torch.nn.functional.rms_norm"}
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+{"pr":42446,"decision":"defect","reason":"fixes StopIteration/DataParallel dtype access in SmolVLM tests","title":"Fix DataParallel dtype access in smolvlm"}
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+{"pr":42415,"decision":"other","reason":"broad special-token/tokenizer cleanup maintenance","title":"initial clean"}
+{"pr":42413,"decision":"feature","reason":"adds Chatterbox/S3Gen/S3Tokenizer audio model support","title":"Add chatterbox support"}
+{"pr":42412,"decision":"other","reason":"typing modernization in example files only","title":"Replace Optional and Union typing with | in examples"}
+{"pr":42403,"decision":"feature","reason":"SAM3 release/backport adding SAM3 model family support","title":"Sam3 release on v4.57.3"}
+{"pr":42385,"decision":"defect","reason":"fixes weight tying logic for tied embeddings","title":"Fix weight tying logic between _tied_weights_keys and tie_word_embeddings"}
+{"pr":42345,"decision":"feature","reason":"adds native PyTorch SDPA/flash attention support for GPT-OSS","title":"GPT-OSS Flash Attention and memory-efficient attention via Native PyTorch SDPA"}
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+{"pr":42310,"decision":"feature","reason":"adds a new Moondream3 model implementation","title":"[WIP] Add moondream3 model"}
+{"pr":42292,"decision":"documentation","reason":"updates generate() documentation for max_new_tokens guidance","title":"docs: clarify recommended usage of max_new_tokens in generate()"}
+{"pr":42277,"decision":"documentation","reason":"updates kernels integration documentation","title":"doc(kernels): update kernels integration documentation"}
+{"pr":42256,"decision":"feature","reason":"proposes AutoAWQ inference component integration","title":"Integrate Core AutoAWQ Inference Components into Transformers"}
+{"pr":42244,"decision":"defect","reason":"fixes core torchao model loading behavior","title":"[core] Fix torchao loading"}
+{"pr":42229,"decision":"feature","reason":"adds OpenPangu MoE model support","title":"Add openpangu_moe model"}
+{"pr":42228,"decision":"defect","reason":"fixes device handling for variable segmentation labels","title":"Support .to(device) or Device Aware Handling for Segmentation Labels in EOMTImageProcessor #42205"}
+{"pr":42210,"decision":"feature","reason":"adds Evo2 model support","title":"[WIP] started adding support for evo2"}
+{"pr":42166,"decision":"feature","reason":"adds InternVL Flash model support","title":"add internvl_flash model"}
+{"pr":42134,"decision":"feature","reason":"adds AutoMergeAdapters utility for LoRA adapters","title":"Add AutoMergeAdapters utility for merging multiple LoRA adapters with…"}
+{"pr":42133,"decision":"defect","reason":"fixes Qwen MoE load-balancing loss computation outside training","title":"Fix qwen moe Load balancing loss calculation outside training"}
+{"pr":42131,"decision":"feature","reason":"adds pattern argument handling to Mistral tokenizer converter integration","title":"Mistral Tokenizer Converter Script - Initialization with Pattern Argument"}
+{"pr":42130,"decision":"defect","reason":"refactors logging to satisfy type checks","title":"Try refactoring logging to make type checks pass"}
+{"pr":42127,"decision":"defect","reason":"standardizes convolution output length calculation for audio models","title":"Standardize conv len function for audio models"}
+{"pr":42124,"decision":"documentation","reason":"adds Qwen3 usage documentation","title":"📚 docs(qwen3): add comprehensive usage examples and model details"}
+{"pr":42112,"decision":"feature","reason":"adds max_thinking_tokens generation control for reasoning models","title":"Add max_thinking_tokens for reasoning models (issue #42111)"}
+{"pr":42098,"decision":"defect","reason":"fixes Qwen2-Audio mel length computation","title":"Fix mel length computation in Qwen2-Audio"}
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+{"pr":42039,"decision":"other","reason":"WIP cleanup/refactor of XCodec internals rather than a discrete feature or defect fix","title":"[WIP] 🚨 clean xcodec 🧼"}
+{"pr":42000,"decision":"documentation","reason":"docstring-only naming clarification in Mixtral block","title":"Fix Mixtral: Docstring uses consistent 'top_k_index' and 'top_k_weights' in MixtralSparseMoeBlock"}
+{"pr":41992,"decision":"feature","reason":"adds a new HF exporters framework and integrations","title":"[PoC] HF exporters"}
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+{"pr":41977,"decision":"feature","reason":"adds Phi-3.5 Vision model support","title":"Add Phi3.5 Vision Model"}
+{"pr":41973,"decision":"defect","reason":"fixes imports broken by huggingface_hub v1.0 changes","title":"Fix import error with huggingface_hub v1.0.0+"}
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+{"pr":41901,"decision":"feature","reason":"updates ExecuTorch pytree registration for DynamicCache","title":"[executorch] Update pytree registration for DynamicCache"}
+{"pr":41899,"decision":"feature","reason":"adds configurable checkpoint save limits","title":"Testing checkpoint limit changes from PR #37196"}
+{"pr":41895,"decision":"feature","reason":"adds a Telugu sentiment classification example","title":"Add Telugu Sentiment Classification Example using DistilBERT"}
+{"pr":41886,"decision":"feature","reason":"adds FG-CLIP2 model support","title":"ADD FG-CLIP2"}
+{"pr":41882,"decision":"feature","reason":"adds Flash Dynamic Mask Attention support","title":"Support fdma for models with attention bias"}
+{"pr":41880,"decision":"documentation","reason":"adds Indonesian README localization","title":"Indonesian Language Support for ReadMe"}
+{"pr":41879,"decision":"defect","reason":"fixes ProcessorMixin handling of multiple tokenizers","title":"Fix/processor multiple tokenizers"}
+{"pr":41855,"decision":"defect","reason":"adds missing methods to Mistral common tokenizer wrapper","title":"Add Mistral tokenizer missing methods"}
+{"pr":41851,"decision":"defect","reason":"fixes ProcessorMixin deepcopy/saving with multiple tokenizers","title":"Fix deepcopy in ProcessorMixin.to_dict for GemmaTokenizerFast"}
+{"pr":41844,"decision":"defect","reason":"fixes FSDPv2 checkpoint saving on TPU by recursively unwrapping models","title":"Fix FSDPv2 checkpoint saving on TPU by using recursive unwrap"}
+{"pr":41827,"decision":"defect","reason":"fixes Flash Attention torch-compile behavior for position ids/packed sequences","title":"[`Flash Attention`] Disable packed sequences with pos ids only during torch compile"}
+{"pr":41823,"decision":"feature","reason":"adds/updates LFM2-VL integration behavior for vLLM","title":"Lfm2-VL vllm"}
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+{"pr":41800,"decision":"documentation","reason":"examples README wording clarification only","title":"Increasing clarity"}
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+{"pr":41733,"decision":"documentation","reason":"updates CLI pipe/default format documentation across model docs","title":"transformers CLI documentation issue"}
+{"pr": 41724, "decision": "defect", "reason": "fixes misleading EncoderDecoderModel decoder_input_ids warning", "title": "Fix confusing warning in EncoderDecoderModel when creating decoder_input_ids from labels"}
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+{"pr": 41701, "decision": "defect", "reason": "fixes dtype mismatch in Qwen3-VL positional embeddings under mixed precision", "title": "Fix qwen3_vl mix precision dtype"}
+{"pr": 41698, "decision": "defect", "reason": "fixes tokenizer check script dataset access and safer checkpoint selection", "title": "Fix tokenizer check script: safe dataset access, default checkpoints, and tested in dry-run mode"}
+{"pr": 41693, "decision": "feature", "reason": "large ViT/vision-model refactor to updated modeling standards", "title": "🚨 Refactor ViT to updated standards"}
+{"pr": 41687, "decision": "defect", "reason": "fixes DataCollatorWithFlattening crash on scalar integer labels", "title": "fix(data): Handle integer labels in DataCollatorWithFlattening"}
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+{"pr": 41631, "decision": "defect", "reason": "fixes incorrect XNLI premise/hypothesis field access in tokenizer checker", "title": "Incorrect access of dataset field fixed"}
+{"pr":41611,"decision":"documentation","reason":"adds Trainer custom loss documentation/example; code-file noise appears from stacked branch","title":"Docs add custom loss example"}
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+{"pr": 41528, "decision": "feature", "reason": "adds DeiT position encoding interpolation support and tests", "title": "Add position encoding interpolation to DeiT"}
+{"pr": 41527, "decision": "documentation", "reason": "Korean documentation translation for quantization selection docs", "title": "🌐 [i18n-KO] Translated selecting.md to Korean"}
+{"pr": 41524, "decision": "feature", "reason": "adds a max_eval_batches TrainingArguments option to limit evaluation batches", "title": "Add max_eval_batches argument to TrainingArguments"}
+{"pr": 41523, "decision": "feature", "reason": "adds ConvNext fast image processor coverage to the test matrix", "title": "Add test coverage for ConvNextImageProcessorFast"}
+{"pr": 41522, "decision": "defect", "reason": "skips initialization for int8 quantized Qwen2.5-VL weights to avoid normal_ on integer tensors", "title": "Fix _init_weights to safely skip int8 quantized weights"}
+{"pr": 41521, "decision": "defect", "reason": "sets forced_bos_token_id on generation_config in translation example when target language is forced", "title": "Fix forced_bos_token_id not set in generation_config"}
+{"pr": 41491, "decision": "feature", "reason": "adds TrainingArguments option to skip gradient clipping when no gradients require clipping", "title": "Add skip_unnecessary_grad_clip to TrainingArguments for optimized gradient clipping"}
+{"pr": 41490, "decision": "defect", "reason": "skips Qwen2.5-VL initialization for int8 quantized tensors to avoid integer weight init errors", "title": "Fix _init_weights to safely skip int8 tensors in Qwen2_5_VL model"}
+{"pr":45702,"decision":"defect","reason":"fixes dataclass/model-output autodoc decorator ordering that was mutating the shared parent __init__ docstring","title":"Reorder decorators for autodoc and dataclass"}
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+{"pr":45687,"decision":"defect","reason":"fixes MoE torch.histc integer input failure on MPS/non-CUDA backends","title":"fix: Made histc_input robust for broader hardware"}
+{"pr":45683,"decision":"defect","reason":"excludes audio modules from quantization conversion to avoid uint8 torch.finfo crashes in multimodal inference","title":"Exclude audio modules from conversion process"}
+{"pr":45682,"decision":"defect","reason":"restores broken PEFT LoRA hotswapping after model-loading changes","title":"FIX Restore LoRA hotswapping functionality"}
+{"pr":45681,"decision":"defect","reason":"restores TokenizersBackend dispatch override for DeepSeek tokenizer round-trip correctness","title":"Restore TokenizersBackend override for DeepSeek V3/R1 tokenizer dispatch"}
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+{"pr":45678,"decision":"defect","reason":"fixes slow-test isolation failure caused by shared config mutation in flash_attn_from_config coverage","title":"Fix shared config mutation issue in flash_attn_from_config"}
+{"pr":41488,"decision":"other","reason":"placeholder PR creating a file named '1' with no meaningful project change","title":"Create 1"}
+{"pr":41485,"decision":"defect","reason":"fixes dtype mismatch in SmolVLM2/PerceptionLM modeling paths","title":"Fix smolvlm2 dtype mismatch final"}
+{"pr":41458,"decision":"feature","reason":"adds optional ScatterMoE kernel integration support for Granite MoE models","title":"Adding ScatterMoE kernel support for Granite models. "}
+{"pr":41441,"decision":"feature","reason":"extends HfArgumentParser support for Union type handling","title":"Enhance the handling of Union types in HfArgumentParser"}
+{"pr":41419,"decision":"feature","reason":"adds QAT support for finegrained FP8 quantization integration","title":"First QAT for Finegrained FP8"}
+{"pr":41406,"decision":"other","reason":"v5 deprecation/removal cleanup for cache classes rather than a defect fix or additive feature","title":"\ud83d\udea8 [v5] Remove deprecated cache classes"}
+{"pr":41362,"decision":"documentation","reason":"README banner/image documentation-only change","title":"Added Hacktoberfest banner image to README.md"}
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+{"pr":41349,"decision":"feature","reason":"adds a new 3D parallelism training example script","title":"Create (3d_parrallel_v2.py) - Add 3D parallelism training example script"}
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+{"pr":41329,"decision":"defect","reason":"fixes Python free-threading/GIL=0 regex-cache segfaults in tokenizers and regex-heavy paths","title":"Fix GIL=0 segfault and Add GIL=0 compat for regex paths"}
+{"pr":41319,"decision":"defect","reason":"fixes Gemma3 torch.export failure by replacing a data-dependent Python assertion with torch._check","title":"Use torch._check instead of a test in Gemma3Model"}
+{"pr":41315,"decision":"feature","reason":"adds version-based model deprecation/deletion warnings and AutoClass exception infrastructure","title":"[model deprecations] Define new version-based model deprecation/deletions with user warnings/exceptions"}
+{"pr":41313,"decision":"defect","reason":"fixes Switch Transformers jitter noise being applied to expert inputs instead of routing only","title":"Jitter noise PR"}
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+{"pr":41299,"decision":"feature","reason":"adds trainer evaluation-step limiting support copied from a feature branch","title":"copied changes from soghomon-b:add-eval-step-limit-31561"}
+{"pr":41291,"decision":"feature","reason":"adds DEIMv2 model, image processor, docs, and tests","title":"Add-Deimv2"}
+{"pr":41273,"decision":"defect","reason":"fixes int8/quantized model loading by skipping missing-weight initialization for quantized models","title":"fix(quantization): Skip weight initialization for quantized models"}
+{"pr":41272,"decision":"feature","reason":"adds new HRM model integration, docs, conversion script, and tests","title":"feat: Add HRM Model"}
+{"pr":41254,"decision":"documentation","reason":"README-only documentation/banner/contributing/license update","title":"docs: Add Hacktoberfest banner, Contributing and License sections to README"}
+{"pr":41251,"decision":"feature","reason":"adds DeepSeek 3.2 experimental model, docs, integrations, and tests","title":"Add deepseek 3.2 exp"}
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+{"pr":41215,"decision":"defect","reason":"fixes CLIP-family memory leak during repeated feature extraction","title":"Fix CLIP memory leak causing 600-800MB accumulation per batch"}
+{"pr":41202,"decision":"feature","reason":"standardizes audio keyword naming and deprecation handling","title":"[WIP] standardize audio kwargs"}
+{"pr":41169,"decision":"defect","reason":"fixes TorchDynamo crash by validating StaticCache offloading argument combination","title":"Fix TorchDynamo crash in StaticCache by validating offloading and offload_only_non_sliding arguments"}
+{"pr":41162,"decision":"documentation","reason":"adds Sinhala README translation and README link update","title":"Add Sinhala (\u0dc3\u0dd2\u0d82\u0dc4\u0dbd) translation of README"}
+{"pr":41160,"decision":"documentation","reason":"documentation-only update to doc-builder tip syntax across docs","title":"[docs] update tips syntax"}
+{"pr":41159,"decision":"feature","reason":"adds total_train_batch_size training argument and DeepSpeed/trainer support","title":"Support setting total_train_batch_size."}
+{"pr":41144,"decision":"feature","reason":"adds automatic DeepSpeed ZeRO-to-universal checkpoint conversion support","title":"Support automatic conversion from zero checkpoint to universal checkpoint."}
+{"pr":41132,"decision":"defect","reason":"fixes SpeechT5 ASR chunking by making inputs_to_logits_ratio a property value","title":"fix(SpeechT5Config): missing annotation on `inputs_to_logits_ratio` property"}
+{"pr":41121,"decision":"defect","reason":"fixes InternVL multi-video preprocessing frame dropping","title":"fix: resolve the unexpected video frame drop issue of the InternVL model with multiple video inputs"}
+{"pr":41116,"decision":"feature","reason":"adds native MiniCPM3 model support","title":"Add MiniCPM3"}
+{"pr":41105,"decision":"defect","reason":"fixes is_torch_neuroncore_available to honor its check_device argument","title":"Fix is_torch_neuroncore_available"}
+{"pr":41097,"decision":"defect","reason":"fixes unnecessary TorchDynamo graph breaks/synchronization in flash attention unpadding","title":"Delay and probably avoid unnecessary graph breaks in _upad_input of modeling_flash_attention_utils.py"}
+{"pr":41095,"decision":"feature","reason":"adds LLaVA-OneVision-1.5 model support","title":"Add LLaVA-OneVision-1.5 model and related configurations"}
+{"pr":41077,"decision":"defect","reason":"fixes T5Gemma missing num_hidden_layers for cache/generation utilities","title":"Fix: add num_hidden_layers property to T5GemmaConfig and add test for use_cache"}
+{"pr":41075,"decision":"defect","reason":"fixes nondeterministic Qwen3 greedy generation when sampling parameters are set in model defaults","title":"Fix Qwen3 deterministic generation when do_sample=False and num_beams=1 for Greedy Decoding"}
+{"pr":41053,"decision":"feature","reason":"adds experimental Qwen3 MoE expert/grouped-gemm implementation changes","title":"Qwen3 moe"}
+{"pr":41041,"decision":"feature","reason":"adds WIP YuE audio model tokenizer/processor/feature extractor files","title":"[WIP] Add YuE model"}
+{"pr":41040,"decision":"feature","reason":"adds Keye VL 1.5 multimodal model support","title":"Add Keye vl 8b 1.5"}
+{"pr":41037,"decision":"other","reason":"test-only Apertus integration coverage without a product feature or defect fix","title":"Tests: Apertus integration tests"}
+{"pr":41035,"decision":"documentation","reason":"updates speech-recognition example README documentation","title":"docs: update speech recognition examples to use modern Common Voice d\u2026"}
+{"pr":41033,"decision":"feature","reason":"adds torch.export support for audio feature extractors","title":"feat: make audio feature extractors torch.export-able"}
+{"pr":41024,"decision":"feature","reason":"adds a deprecation warning/migration path for ConditionalDetrImageProcessor max_size","title":"Deprecate `max_size` in ConditionalDetrImageProcessor with warning"}
+{"pr":41022,"decision":"documentation","reason":"Korean translation of backbones documentation","title":"\ud83c\udf10 [i18n-KO] Translated `backbones.md` to Korean"}
+{"pr":41021,"decision":"documentation","reason":"Korean translation of video processors documentation","title":"\ud83c\udf10 [i18n-KO] Translated `video_processors.md` to Korean"}
+{"pr":41009,"decision":"feature","reason":"adds Lexa-Delta model/config/tokenizer support","title":"Add Lexa-Delta model support"}
+{"pr":40976,"decision":"feature","reason":"changes assisted generation default thresholds and scheduling behavior","title":"Better defaults for assisted generation"}
+{"pr":40962,"decision":"feature","reason":"adds the Isaac multimodal model implementation and registrations","title":"perceptron: Isaac-0.1 implementation"}
+{"pr":40954,"decision":"defect","reason":"fixes processor creation ignoring a user-provided chat_template override","title":"Fix Issue #40913: Respect user-provided chat_template parameter in processor creation"}
+{"pr":40908,"decision":"defect","reason":"fixes MoE load-balancing loss incompatibility when past_key_values shorten router logits","title":"Fix  incompatible with "}
+{"pr":40898,"decision":"feature","reason":"adds encoder-only sequence classification heads for T5, MT5, and UMT5","title":"Adding [T5/MT5/UMT5]EncoderForSequenceClassification"}
+{"pr":40888,"decision":"documentation","reason":"updates help text/documentation for chat and serve commands","title":"DOC Fix help for chat and serve commands"}
+{"pr":40887,"decision":"feature","reason":"refactors generation output handling to support cleaner decoding methods","title":"Refactor output handling in generate for cleaner decoding methods"}
+{"pr":40877,"decision":"documentation","reason":"README-only badge/markup change despite bug-fix title","title":"Bug #40833: Fix for kv_offset calculation for mixed padding"}
+{"pr":40871,"decision":"feature","reason":"adds benchmark framework type hints, GPU metrics helper, and configuration utilities","title":"Refactor benchmark utils: add type hints, GPU metrics helper, and con…"}
+{"pr":40870,"decision":"feature","reason":"adds an option to move generation logits to CPU to reduce VRAM use","title":"Reduce vRAM usage during generation by allowing to transfer logits to CPU"}
+{"pr":45703,"decision":"other","reason":"typing/checking maintenance chore; no user-facing feature or defect fix","title":"chore(typing): add ty type checking for 10 utility files"}
+{"pr":40861,"decision":"feature","reason":"adds model support for grouped Mamba2 variants across hybrid architectures","title":"Support n_groups>1 for mamba2 "}
+{"pr":40857,"decision":"defect","reason":"fixes incorrect train_tokens_per_second metric after resuming from checkpoints","title":"Token"}
+{"pr":40840,"decision":"feature","reason":"adds structured pruning support for Qwen2/Qwen3 layer dimensions","title":"feat: add qwen2 pruning support"}
+{"pr":40820,"decision":"feature","reason":"adds benchmark definitions for several model families","title":"Add models to benchmarks"}
+{"pr":40790,"decision":"defect","reason":"handles missing or corrupted checkpoints during Trainer resume","title":"Handle loading non-existent checkpoints or corrupted checkpoints."}
+{"pr":40783,"decision":"defect","reason":"treats quantization_config=None the same as omitting it in AutoModel.from_pretrained","title":"Fix None quantization_config equivalence with omitted param in AutoModel.from_pretrained"}
+{"pr":40759,"decision":"feature","reason":"adds structured pruning support for Qwen3 layer dimensions","title":"feat: add qwen3 pruning support"}
+{"pr":40756,"decision":"feature","reason":"adds a slow-tokenizer conversion path for NVIDIA Canary tokenizer","title":"[WIP] Add Canary"}
+{"pr":40755,"decision":"feature","reason":"adds TimesFM forecasting support for covariates","title":"[TimesFM] Add support for forecasting with covariates"}
+{"pr":40740,"decision":"defect","reason":"fixes assistant-model generation configuration not receiving user parameters","title":"Configure assistant model's generation_config with user parameters"}
+{"pr":40738,"decision":"documentation","reason":"RoFormerTokenizer installation documentation only","title":"Docs: Clarify rjieba installation for RoFormerTokenizer"}
+{"pr":40736,"decision":"documentation","reason":"Korean documentation translation only","title":"\ud83c\udf10 [i18n-KO] Translated `jan.md` to Korean"}
+{"pr":40728,"decision":"feature","reason":"adds OpenTelemetry support to transformers serve","title":"feat(serve): add OTEL"}
+{"pr":40714,"decision":"documentation","reason":"README wording/version documentation update only","title":"Remove TF and Flax from README"}
+{"pr":40695,"decision":"defect","reason":"corrects deprecated/incorrect Qwen task mapping tags","title":"remove vision2seq vs image-text-to-text"}
+{"pr":40670,"decision":"feature","reason":"adds configuration path for Gemma 2 without post layer norms","title":"Add ability to run Gemma 2 models without post layer norm"}
+{"pr":40648,"decision":"other","reason":"dependabot dependency bump for example requirements","title":"Bump torch from 2.7.1 to 2.8.0 in /examples/flax/vision"}
+{"pr":40640,"decision":"defect","reason":"fixes Trainer resume data position when worker count changes","title":"Resume training by trained samples to avoid elastic job loss or over-reading of data."}
+{"pr":40637,"decision":"feature","reason":"adds a new OpenPangu model implementation","title":"[WIP]Add openpangu_dense model"}
+{"pr":40633,"decision":"feature","reason":"adds Trainer API support for user-provided Accelerate Accelerator","title":"Add support for Custom Accelerate Instance in Trainer"}
+{"pr":40587,"decision":"feature","reason":"adds new vision utility helpers and tests","title":"feat(utils): add vision utils for embedding images and getting the hidden size"}
+{"pr":40563,"decision":"defect","reason":"fixes DINOv3 intermediate hidden-state output behavior","title":"fix to get output of intermediate output of dinov3 for more use case"}
+{"pr":40546,"decision":"feature","reason":"adds new VibeVoice audio model and pipeline support","title":"Implement VibeVoice "}
+{"pr":40524,"decision":"documentation","reason":"documentation-only perplexity guide change","title":"Use begin_of_sequence token in all sliding windows for correct model behaviour"}
+{"pr":40520,"decision":"feature","reason":"adds faster stop_strings stopping criteria for generation","title":"[generate] add faster `stop_strings` stopping criteria"}
+{"pr":40515,"decision":"feature","reason":"adds tokenizer selection utility based on corpus analysis","title":"Add Context-Aware Tokenizer Selection Utility Based on Corpus Analysis"}
+{"pr":40505,"decision":"feature","reason":"refactors SigLIP-like model implementations","title":"Refactor Siglip-like models"}
+{"pr":40493,"decision":"defect","reason":"fixes dtype fallback behavior for Colab bf16/fp16/fp32 availability","title":"Update dtypes to suit colab bf16 -> fp16 -> fp32."}
+{"pr":40492,"decision":"defect","reason":"guards debug/image/masking utilities against divide-by-zero errors","title":"avoid divid zero errors."}
+{"pr": 40473, "decision": "other", "reason": "test-only change unskipping tokenizer parity checks", "title": "[tests] Unskip DeBERTaV2 tokenizer parity tests; re-enable fast/slow checks"}
+{"pr": 40471, "decision": "documentation", "reason": "standardizes CodeGen model documentation/card", "title": "DOC: Standardize CodeGen model card (issue #36979)"}
+{"pr": 40465, "decision": "documentation", "reason": "adds Korean translation of tools documentation", "title": "🌐 [i18n-KO] Translated `tools.md` to Korean"}
+{"pr": 40464, "decision": "documentation", "reason": "adds Korean translation of agents documentation", "title": "🌐 [i18n-KO] Translated `agents.md` to Korean"}
+{"pr": 40448, "decision": "feature", "reason": "adds MiniCPM-V 4.5 model support", "title": "[model] Support MiniCPM-V 4.5"}
+{"pr": 40446, "decision": "feature", "reason": "adds sorted binary mask conversion helper for Mask2Former image processing", "title": "Add convert_segmentation_map_to_binary_masks_sorted function for hand…"}
+{"pr": 40438, "decision": "defect", "reason": "fixes automatic label name detection for a single provided label", "title": "Resolve automatic label name detection when single label provided"}
+{"pr": 40425, "decision": "defect", "reason": "fixes quantized parameter checks when safetensors values are slices", "title": "Fix check_quantized_param method when param_value is a safetensors slice"}
+{"pr": 40404, "decision": "documentation", "reason": "updates GPT-J model card documentation", "title": "update model card for gpt-j"}
+{"pr": 40403, "decision": "feature", "reason": "adds customizable logit warping strategies for generation", "title": "Customizable Logit Warping Strategies for Generation #40010"}
+{"pr": 40400, "decision": "documentation", "reason": "README wording cleanup", "title": "fixed redundant words in readme.md"}
+{"pr": 40395, "decision": "documentation", "reason": "Korean documentation translation update", "title": "🌐 [i18n-KO] Updated `text_generation.md`"}
+{"pr": 40392, "decision": "defect", "reason": "removes stray debug print from ShieldGemma2 conversion output", "title": "Remove debug print statement from ShieldGemma2 conversion script"}
+{"pr": 40390, "decision": "documentation", "reason": "spelling-only corrections in comments/doc text", "title": "Fix typo: 'lenght' to 'length'"}
+{"pr": 40388, "decision": "feature", "reason": "adds a new Jinja chat-template fromjson filter with tests", "title": "Add fromjson filter to Jinja2 chat templates"}
+{"pr": 40385, "decision": "defect", "reason": "fixes misspelled MM Grounding DINO checkpoint key in conversion skip list", "title": "Fix typo: 'seperate' -> 'separate' in mm_grounding_dino conversion sc…"}
+{"pr": 40358, "decision": "defect", "reason": "fixes MXFP4 MLP shape handling for 2D hidden states in multi-turn generation", "title": "Fix MXFP4 mlp_forward to handle 2D and 3D hidden_states shapes for multi-turn chat"}
+{"pr": 40328, "decision": "feature", "reason": "prototype support for torch.compile with DynamicCache", "title": "[rfc] Prototype to make torch.compile work with DynamicCache"}
+{"pr": 40299, "decision": "feature", "reason": "removes deprecated max_size parameter from multiple object-detection image processors", "title": "Remove deprecated max_size parameter from ConditionalDetr image processors"}
+{"pr": 40286, "decision": "feature", "reason": "adds new MOSS-TTSD and XY-Tokenizer model implementations", "title": "Add MOSS-TTSD with XY-Tokenizer"}
+{"pr":40265,"decision":"other","reason":"tooling-only change enabling additional ruff/pylint rules","title":"Enable PLW and PLE rules"}
+{"pr":40244,"decision":"defect","reason":"fixes AutoModel mapping so EfficientLoFTR keypoint matching model can instantiate","title":"add-loftr-keypoints-to-map"}
+{"pr":40225,"decision":"documentation","reason":"docs-only clarification for decoder_input_ids versus decoder_inputs_embeds","title":"docs: clarify decoder_input_ids vs decoder_inputs_embeds usage (#39542)"}
+{"pr":40221,"decision":"defect","reason":"fixes save_strategy=best best-model tracking and default metric handling","title":"FIX: enable load_best_model_at_end within SaveStrategy.BEST and initialize metric_for_best_model as loss when SaveStrategy.BEST"}
+{"pr":40209,"decision":"feature","reason":"adds a new fast PyTorch image processor for ViViT","title":"Add fast image processor for  ViViT "}
+{"pr":40208,"decision":"defect","reason":"fixes save_only_model with FSDP sharded state dict checkpoints","title":"Save only model sharded sd"}
+{"pr":40180,"decision":"feature","reason":"adds native MXFP4 training infrastructure for GPT-OSS quantization","title":"Enable native mxfp4 training support for GPT-OSS models"}
+{"pr":40177,"decision":"defect","reason":"fixes Qwen2.5-VL generation position IDs performance/behavior by reverting text position IDs","title":"Revert text_positions in Qwen25VL"}
+{"pr":40171,"decision":"other","reason":"model class/API rename refactor for DINOv3 CamelCase naming","title":"Rename to CamelCase"}
+{"pr":40155,"decision":"documentation","reason":"Korean documentation translation for t5 model docs","title":"\ud83c\udf10 [i18n-KO] Translated `t5.md` to Korean"}
+{"pr":40149,"decision":"feature","reason":"adds a fast VitPose image processor implementation and tests","title":"Implemented fast image processor for VitPose"}
+{"pr":40148,"decision":"defect","reason":"fixes NaN probabilities during sampled beam generation","title":"Update utils.py: fix nan"}
+{"pr":40131,"decision":"documentation","reason":"adds missing Arabic documentation translations","title":"add missing Arabic translations"}
+{"pr":40123,"decision":"defect","reason":"lazy import avoids torchao side-effect warnings/import errors","title":"Lazily import torchao Int4WeightOnlyConfig to avoid side effects"}
+{"pr":40115,"decision":"feature","reason":"extends layer_types support to convolution layers","title":"[layer_types] update layer_types with conv"}
+{"pr":40114,"decision":"defect","reason":"fixes Mixtral torch.export failure from data-dependent expert selection","title":"Fix torch.export compatibility for Mixtral MoE models "}
+{"pr":40102,"decision":"documentation","reason":"adds Korean translation for auto_docstring documentation","title":"🌐 [i18n-KO] Translated  to Korean"}
+{"pr":40092,"decision":"feature","reason":"optimizes Llama attention by fusing QKV projections","title":"Optimize LlamaAttention by fusing QKV projections"}
+{"pr":40090,"decision":"defect","reason":"fixes initialization error for int8/uint8 quantized weights","title":"Fix RuntimeError when loading quantized models with int8 weights (#39366)"}
+{"pr":40065,"decision":"defect","reason":"reduces ForCausalLMLoss memory use by delaying float32 upcast after ignore_index filtering","title":"Delay float32 upcast in ForCausalLMLoss after filtering ignore_index"}
+{"pr":40064,"decision":"documentation","reason":"Korean translation of model documentation","title":"🌐 [i18n-KO] Translated `videomae.md` to Korean"}
+{"pr":40061,"decision":"documentation","reason":"Korean translation of model documentation","title":"🌐 [i18n-KO] Translated `vitdet.md` to Korean"}
+{"pr":40059,"decision":"defect","reason":"fixes GPT-2 inefficient GELU default activation implementation","title":"Fix Inefficient GELU implementation in GPT2"}
+{"pr":40058,"decision":"feature","reason":"adds GGUF loading support and tests for Qwen2VL","title":"GGUF Qwen2VL"}
+{"pr":40055,"decision":"feature","reason":"adds automatic W&B logging of Accelerate parallelism configuration","title":"Auto-log parallelism info to wandb.config using HF Accelerate"}
+{"pr":40047,"decision":"documentation","reason":"updates WavLM model documentation card template","title":"Update wavlm.md to match new model card template"}
+{"pr":40023,"decision":"feature","reason":"adds SDPA attention support for OWLViT and OWLv2","title":"Add support for SDPA for OWLViT and OWLv2"}
+{"pr":40022,"decision":"defect","reason":"fixes DogeDecoder dropout TypeError for MoE tuple outputs","title":"fix: resolve dropout type error in DogeDecoder"}
+{"pr":39999,"decision":"defect","reason":"fixes FSDP2+TP cpu_ram_efficient_loading meta device handling","title":"allow TP to work in ND-parallel with fsdp cpu ram efficient loading"}
+{"pr":39997,"decision":"defect","reason":"fixes GPT-OSS causal mask creation to use provided position_ids for packed inputs","title":"make sure position_ids are passed in for causal mask creation for gpt-oss"}
+{"pr":39987,"decision":"feature","reason":"adds new VGGT model support","title":"Add a VGGT(Visual Geometry Grounded Transformer) model compatible with huggingface transfromers"}
+{"pr":39962,"decision":"defect","reason":"fixes Gemma3 and related multimodal models for torch export by replacing runtime tests with torch._check","title":"Use torch._check instead of a test to make the model Gemma3 exportable"}
+{"pr":39941,"decision":"defect","reason":"fixes image_utils TODO behavior with tests","title":"fixing image_utils.py todo"}
+{"pr":39931,"decision":"feature","reason":"adds StaticCache pytree serialization registration for torch.export","title":"Registers StaticCache serialization functions for torch.export.export"}
+{"pr":39922,"decision":"documentation","reason":"Korean documentation translation","title":"\ud83c\udf10 [i18n-KO] Translated `attention_interface.md` to Korean"}
+{"pr":39920,"decision":"documentation","reason":"Korean documentation update","title":"\ud83c\udf10 [i18n-KO] Updated ko/perf_train_special.md"}
+{"pr":39917,"decision":"documentation","reason":"Korean documentation update","title":"\ud83c\udf10 [i18n-KO] Updated ko/perf_train_cpu.md"}
+{"pr":39901,"decision":"documentation","reason":"Korean documentation translation","title":"\ud83c\udf10 [i18n-KO] Translated `fp_quant` to Korean"}
+{"pr":39899,"decision":"feature","reason":"adds MiniCPM-V 4.0 model support","title":"[model] Support MiniCPM-V 4.0"}
+{"pr":39895,"decision":"feature","reason":"adds new VideoPrism model support","title":"Add Videoprism"}
+{"pr":39886,"decision":"documentation","reason":"Korean documentation translation","title":"🌐 [i18n-KO] Translated `perf_train_gaudi.md` to Korean"}
+{"pr":39866,"decision":"defect","reason":"fixes distributed save_pretrained race by passing trainer is_main_process","title":"make sure model.save_pretrained has the correct is_main_process"}
+{"pr":39859,"decision":"feature","reason":"adds BitsAndBytesConfig target_parameters support for quantizing nn.Parameter weights","title":"WIP: Initial support for bnb 4bit on any nn.Parameter"}
+{"pr":39831,"decision":"other","reason":"internal refactor/readability change rather than defect fix or user-facing feature","title":"refactor(modeling_llama): make RotaryEmbedding default path explicit"}
+{"pr":39807,"decision":"documentation","reason":"Korean documentation translation","title":"🌐 [i18n-KO] Translated `bamba.md` to Korean"}
+{"pr":39796,"decision":"feature","reason":"standardizes and expands text-to-audio pipeline behavior and model support","title":"[pipelines] text-to-audio pipeline standardization"}
+{"pr":39794,"decision":"defect","reason":"fixes ProphetNet forward handling when encoder_outputs is a tuple","title":"Fix ProphetNet forward to handle tuple encoder_outputs"}
+{"pr":39793,"decision":"defect","reason":"fixes DAC checkpoint conversion/model weight norm handling and tests","title":"Fix DAC conversion script"}
+{"pr":39792,"decision":"defect","reason":"fixes transformers serve handling of nested chat message content","title":"Served models handle with nested content"}
+{"pr":39785,"decision":"defect","reason":"fixes mllama integration tests by returning encoder intermediate states","title":"fix mllama integration tests"}
+{"pr":39772,"decision":"defect","reason":"fixes missing parameter initializations across older model implementations","title":"Fix missing initializations for models created in 2022"}
+{"pr":39760,"decision":"feature","reason":"adds the new Llasa TTS model family, docs, conversion and tests","title":"[Draft] Add Llasa TTS family of models"}
+{"pr":39756,"decision":"defect","reason":"fixes Qwen2.5-VL rope_deltas corruption during classifier-free-guidance generation","title":"Fix rope_deltas corruption in Qwen2.5VL during CFG generation"}
+{"pr":39751,"decision":"documentation","reason":"Korean translation documentation-only change","title":"\ud83c\udf10 [i18n-KO] Translated `text-to-speech.md` to Korean"}
+{"pr":39741,"decision":"defect","reason":"fixes HfArgumentParser Union handling so dict alternatives are filtered out","title":"Fix HfArgumentParser to filter out dict types from Union"}
+{"pr":39735,"decision":"defect","reason":"fixes tensor parallel plan lookup for multimodal models using text_config","title":"handle multimodal models with tp_plan on the text_config"}
+{"pr":39722,"decision":"feature","reason":"adds the new Intern-S1 model family with processing, tokenization, docs and tests","title":"[Feat] Adding Intern-S1"}
+{"pr":39718,"decision":"documentation","reason":"documentation-only SigLIP2 model/processor correction","title":"Fix SigLIP2 documentation model/processor mismatch"}
+{"pr":39708,"decision":"documentation","reason":"adds Bengali documentation and i18n README entries","title":"\ud83c\udf10[i18n-bn] Introduce Bengali version of Transformers documentation"}
+{"pr":39698,"decision":"defect","reason":"fixes exaone4 layer type calculation for missing or malformed sliding_window_pattern","title":"Fix exaone4 layer_types ZeroDivision/TypeError when sliding_window_pattern is None/\"LLLG\""}
+{"pr":39697,"decision":"defect","reason":"fixes dtensor storage deprecation by switching to untyped_storage","title":"use untyped storage for dtensors due to deprecation"}
+{"pr":39690,"decision":"feature","reason":"adds support for supplying a custom hf_quantizer in from_pretrained","title":"Allow custom hf_quantizer in from_pretrained"}
+{"pr":39683,"decision":"defect","reason":"fixes generation to respect accelerate config disabling torch.compile/dynamo","title":"Fix issue #39191 respect accelerate config to disable torch.dynamo compilation"}
+{"pr":39675,"decision":"defect","reason":"fixes deepspeed argument handling for dicts and config-file paths","title":"[BugFix]: Support dict and config file path for deepspeed"}
+{"pr":39674,"decision":"defect","reason":"fixes loss scaling/token aggregation to use data parallel group only","title":"Fix loss scaling and token aggregation to use only data parallel group"}
+{"pr":39632,"decision":"documentation","reason":"updates a dead NVIDIA reference link in a model docstring/comment","title":"fix dead NVIDIA link "}
+{"pr":39631,"decision":"feature","reason":"adds speech-to-text support to the serving command","title":"[serve] Add speech-to-text"}
+{"pr":39625,"decision":"defect","reason":"fixes HfArgumentParser support for Union[str, dict, None] fields","title":"Fix: allow Union[str, dict, None] fields like deepspeed to be passed via CLI"}
+{"pr":39617,"decision":"defect","reason":"fixes Trainer FSDP v1 save path to use the wrapped model correctly","title":"Fix FSDP v1 bug: trainer incorrectly uses an unwrapped model"}
+{"pr":39599,"decision":"defect","reason":"fixes Trainer resume to check TrainerState file exists before loading","title":"Fix: check TrainerState file exists before loading during resume"}
+{"pr":39588,"decision":"feature","reason":"adds a new reference VLM model implementation","title":"WIP, reference modeling"}
+{"pr":39575,"decision":"documentation","reason":"adds Korean translation for the VitPose documentation","title":"\ud83c\udf10 [i18n-KO] Translated `vitpose.md` to Korean"}
+{"pr":39563,"decision":"documentation","reason":"adds Korean translation for the Vision Encoder Decoder documentation","title":"\ud83c\udf10 [i18n-KO] Translated `vision-encoder-decoder.md` to Korean"}
+{"pr":39560,"decision":"defect","reason":"fixes load_best_model_at_end when save_steps is less frequent than eval_steps","title":"fix load_model_end = true work when save_steps < eval_steps"}
+{"pr":39559,"decision":"documentation","reason":"adds Korean translation for DeepSpeed documentation","title":"\ud83c\udf10 [i18n-KO] Translated `main_classes/deepspeed.md` to Korean"}
+{"pr":39555,"decision":"feature","reason":"changes tie_weights behavior to relax automatic input/output embedding tying","title":"[WIP]\u00a0try to relax the tie_weights method"}
+{"pr":39544,"decision":"documentation","reason":"adds Korean translation for feature extractor documentation","title":"\ud83c\udf10 [i18n-KO] Translated feature_extractors.md to Korea"}
+{"pr":39541,"decision":"feature","reason":"adds Muon optimizer support and Trainer integration","title":"Add Muon optimizer implementation and integration"}
+{"pr":39534,"decision":"feature","reason":"adds a new BEiT3 model implementation and tests","title":"Add Beit3 model"}
+{"pr":39517,"decision":"documentation","reason":"adds Korean translation for compressed tensors quantization documentation","title":"\ud83c\udf10 [i18n-KO] Translated `compressed_tensor.md`  to Korean"}
+{"pr":39493,"decision":"defect","reason":"fixes Voxtral mistral-common dependency pin/extras","title":"[Voxtral] nit + pin correct mistral common version"}
+{"pr":39491,"decision":"defect","reason":"fixes int8 quantized model loading by avoiding missing-key weight initialization","title":"Fix: Skip weight initialization for quantized int8 models"}
+{"pr":39480,"decision":"feature","reason":"adds new arcinstitute state/state-transition models","title":"Add model arcinstitute state"}
+{"pr":39468,"decision":"defect","reason":"fixes quantized model dispatch with automatic device maps","title":"Fix quantized model dispatch with device_map='auto'"}
+{"pr":39466,"decision":"documentation","reason":"updates Bert Japanese model documentation/model card","title":"README: Update Bert Japanese model card"}
+{"pr":39464,"decision":"defect","reason":"fixes quantized loading by skipping initialize_weights for quantized models","title":"Skipping `initialize_weights` when model is quantized"}
+{"pr":39456,"decision":"defect","reason":"fixes int8 quantized model initialization failure","title":"Fix quantized model initialization for int8 dtypes"}
+{"pr":39449,"decision":"defect","reason":"replaces deprecated logger.warn calls in Gemma model files","title":"Fix logger warnings in Gemma model test files"}
+{"pr":39435,"decision":"defect","reason":"adds regression coverage for BartModel eager vs SDPA discrepancy","title":"Add a unit test for BartModel to compare eager, sdpa on one particular set of inputs"}
+{"pr":39403,"decision":"feature","reason":"adds new Vocos and Vocos-EnCodec audio models","title":"Add Vocos model"}
+{"pr":39357,"decision":"documentation","reason":"docstring-only update for GLM4V","title":"Update docstring for glm4v"}
+{"pr":39353,"decision":"defect","reason":"fixes ColPali checkpoint/key mapping for loading","title":"fix colpali mapping"}
+{"pr":39309,"decision":"defect","reason":"fixes audio pipelines when torchcodec AudioDecoder inputs produce torch tensors","title":"Fix audio pipeline with torchcodec input"}
+{"pr":39303,"decision":"documentation","reason":"fixes typos in a documentation code example","title":"Fix critical typos in code example"}
+{"pr":39297,"decision":"defect","reason":"fixes TrainingArguments deepspeed/accelerator_config argument handling","title":"Fix bug with deepspeed  and accelerator args in training_args.py"}
+{"pr":39293,"decision":"feature","reason":"adds the new T5LA model family and docs/tests","title":"Add T5LA models"}
+{"pr":39264,"decision":"defect","reason":"adds clearer timm-version handling for unsupported MobileNetV5 architectures","title":"Fix: Add version check for timm to support mobilenetv5 models (fixes #39208)"}
+{"pr":39257,"decision":"defect","reason":"fixes can_return_tuple by forcing wrapped calls to return ModelOutput before tuple conversion","title":"Fix to tuple conversion with config"}
+{"pr":39251,"decision":"defect","reason":"fixes Moshi greedy fp16 generation test/cache behavior","title":"Fix slow test_moshika_greedy_unconditional_fp16"}
+{"pr":39236,"decision":"feature","reason":"adds a new moment-p sampling logits warper and generation configuration knobs","title":"added moment_p sampling"}
+{"pr":39222,"decision":"feature","reason":"enables GraniteMoeHybrid slow integration tests against the released preview checkpoint","title":"Enable granite 4 hybrid integration tests"}
+{"pr":39212,"decision":"documentation","reason":"adds a Ukrainian README translation","title":"Add Ukrainian translation of README.md"}
+{"pr":39211,"decision":"defect","reason":"attempts to prevent Gemma 3n loading from failing on mobilenetv5_300m_enc unknown architecture","title":"Add mobilenet_v5 stub implementation to fix \"Unknown Model\" error"}
+{"pr":39209,"decision":"other","reason":"refactors FSMT class naming for consistency without a functional user-facing fix","title":"Standardize FSMT class naming: PretrainedFSMTModel → PreTrainedFSMTModel"}
+{"pr":39206,"decision":"defect","reason":"fixes Qwen3 MoE router-logit auxiliary loss crashes when mlp_only_layers produce no router logits","title":"fix: filter None router logits in Qwen3 MoE and handle empty router logits (#39203)"}
+{"pr":39183,"decision":"feature","reason":"adds a new optional dependency extra for chat/conversation usage","title":"Add a 'chat' extra"}
+{"pr":39150,"decision":"feature","reason":"adds a vectorized MoE expert implementation for DeepSeek/Mixtral-style models","title":"Efficient Expert Weight Fusion for Moe deepseek v3"}
+{"pr":39140,"decision":"feature","reason":"adds opt-in emergency checkpoint saving to Trainer on crashes or termination signals","title":"feat(trainer): emergency checkpointing on crashes & SIGTERM/SIGINT"}
+{"pr":39109,"decision":"defect","reason":"tries to fix a TrainingArguments constructor/API mismatch around evaluation strategy naming","title":"Fix: rename 'eval_strategy' to 'evaluation_strategy' in TrainingArgum…"}
+{"pr":39108,"decision":"defect","reason":"disables unsupported fullgraph/static-cache compilation behavior for MoE model classes","title":"Disable static cache on certain MoE models"}
+{"pr":39103,"decision":"defect","reason":"fixes audio-related Gemma3n configuration/model naming mismatch","title":"Fix audio-related config naming for Gemma3n "}
+{"pr":39084,"decision":"feature","reason":"refactors Gemma3n configuration/model/conversion code","title":"Refactor gemma3n"}
+{"pr":39047,"decision":"feature","reason":"refactors causal LM loss API to support passing lm_head weights/hidden states","title":"RFC: refactor causal lm loss to handle lm_head in loss function"}
+{"pr":39046,"decision":"defect","reason":"fixes deprecated max_size handling in DETR-family image processors","title":"Fix deprecated max_size parameter handling in DETR image processors"}
+{"pr":39037,"decision":"defect","reason":"fixes Kosmos2 attention behavior and tests after attention backend changes","title":"fix `kosmos2` tests"}
+{"pr":39012,"decision":"defect","reason":"attempts to fix DeepSeekV3 torch compile training test failure","title":"[WIP] Fix DeepseekV3ModelTest::test_torch_compile_for_training"}
+{"pr":39009,"decision":"feature","reason":"adds common-model-test helper checking submodel support","title":"Add submodels support check function"}
+{"pr":38999,"decision":"defect","reason":"fixes unintended GroundingDINO bbox_embed parameter sharing with deep copies","title":"Use deep copies instead of shallow copies for bbox_embed in GroundingDINO decoder (#37333)."}
+{"pr":38991,"decision":"feature","reason":"broad API/model refactor removing return_dict kwargs from model calls","title":"Remove `return_dict` kwarg from all the models"}
+{"pr":38988,"decision":"defect","reason":"fixes checker gap where modular-file docstrings were not validated, causing regenerated docstring drift","title":"Check docstring inside modular files as well"}
+{"pr":38962,"decision":"other","reason":"test-only duplicate import/no substantive defect or feature","title":"Update test_candidate_generator.py"}
+{"pr":38959,"decision":"documentation","reason":"model card documentation update only","title":"Updated the model card for wav2vec2-phoneme"}
+{"pr":38958,"decision":"documentation","reason":"model card documentation update only","title":"Updated model card for wav2vec2-conformer"}
+{"pr":38957,"decision":"documentation","reason":"model card documentation update only","title":"Update wav2vec2-bert model card"}
+{"pr":38956,"decision":"documentation","reason":"model card documentation update only","title":"Updating model card for wav2vec2"}
+{"pr":38955,"decision":"documentation","reason":"model card documentation update only","title":"docs: Musicgen melody model card"}
+{"pr":38926,"decision":"documentation","reason":"installation documentation clarification only","title":"Clarify Python and framework version support in installation.md"}
+{"pr":38923,"decision":"feature","reason":"API cleanup removing deprecated YOLOS max_size support","title":"Remove deprecated max_size support from YOLOS image processor"}
+{"pr":38908,"decision":"defect","reason":"fixes HybridChunkedCache dtype selection to honor config torch_dtype","title":"Add support to use config dtype in HybridChunkedCache"}
+{"pr":38893,"decision":"defect","reason":"fixes/deprecates obsolete max_size handling in Conditional DETR image processors","title":"Fix/deprecate max size conditional detr"}
+{"pr":38888,"decision":"defect","reason":"fixes deprecated Accelerator distributed_type TPU checks to use XLA in examples","title":"continue to fix distributed_type from TPU to XLA in LM examples (#38652)"}
+{"pr":38886,"decision":"feature","reason":"adds bitsandbytes quantizer compile support for generation","title":"Allow compile with bnb"}
+{"pr":38884,"decision":"defect","reason":"fixes Llama 4 MoE conversion handling for moe_args presence","title":"Llama 4 conversion fix for moe models"}
+{"pr":38877,"decision":"documentation","reason":"docstring/documentation clarification for attention_mask plus generated docs/style updates","title":"DOC: Clarify attention_mask usage in BertModel forward method"}
+{"pr":38861,"decision":"feature","reason":"adds a SAM fast image processor and tests","title":"Add SamImageProcessorFast with 4x performance improvement"}
+{"pr":38859,"decision":"feature","reason":"adds a MobileViT fast image processor and tests","title":"Add MobileViT fast image processor"}
+{"pr":38839,"decision":"other","reason":"explicit do-not-merge dependency test for safetensors version bump","title":"[DO NOT MERGE] Testing saftensors 0.6.0"}
+{"pr":38810,"decision":"feature","reason":"adds kwargs support for Whisper conditional generation loss customization","title":"Add kwargs support in WhisperForConditionalGeneration"}
+{"pr":38805,"decision":"feature","reason":"adds new Dust3R model, processors, docs, and tests","title":"Add Dust3R"}
+{"pr":38793,"decision":"documentation","reason":"comment-only typo fixes without functional code changes","title":"Fix Typos in Comments and Improve Clarity"}
+{"pr":38786,"decision":"documentation","reason":"documentation-only clarification for MADLAD-400 target language tokens","title":"Provide clearer instructions on how to specify target language."}
diff --git a/progress.md b/progress.md
new file mode 100644
index 000000000000..240e06dce18a
--- /dev/null
+++ b/progress.md
@@ -0,0 +1,29 @@
+# VidEoMT DINOv2 Conversion Progress
+
+Last updated: 2026-03-27
+
+Existing Hub checkpoint already present:
+
+| Checkpoint | Hub repo | Status |
+| --- | --- | --- |
+| `yt_2019_vit_small_52.8.pth` | `tue-mps/videomt-dinov2-small-ytvis2019` | Already converted before this run; model card refreshed during this run |
+
+Remaining DINOv2 checkpoints targeted in this run:
+
+| Checkpoint | Hub repo | Status | Notes |
+| --- | --- | --- | --- |
+| `yt_2019_vit_base_58.2.pth` | `tue-mps/videomt-dinov2-base-ytvis2019` | Done | Pushed to Hub and verified against the upstream implementation |
+| `yt_2019_vit_large_68.6.pth` | `tue-mps/videomt-dinov2-large-ytvis2019` | Done | Pushed to Hub and verified against the upstream implementation |
+| `yt_2021_vit_large_63.1.pth` | `tue-mps/videomt-dinov2-large-ytvis2021` | Done | Pushed to Hub and verified against the upstream implementation |
+| `yt_2022_vit_large_42.6.pth` | `tue-mps/videomt-dinov2-large-ytvis2022` | Done | Pushed to Hub and verified against the upstream implementation |
+| `ovis_vit_large_52.5.pth` | `tue-mps/videomt-dinov2-large-ovis` | Done | Pushed to Hub and verified against the upstream implementation |
+| `vipseg_vit_large_55.2.pth` | `tue-mps/videomt-dinov2-large-vipseg` | Done | Pushed to Hub and verified against the upstream implementation; registry image size corrected to 1280 during this run |
+| `vspw_vit_large_95.0_64.9.pth` | `tue-mps/videomt-dinov2-large-vspw` | Done | Pushed to Hub and verified against the upstream implementation |
+
+Final status:
+
+All remaining DINOv2-based VidEoMT checkpoints were converted, pushed to the `tue-mps` organization on the Hub, and checked for the expected `README.md`, `config.json`, `model.safetensors`, and `video_preprocessor_config.json` files.
+
+Execution note:
+
+The local repo does not declare a `[project]` table in `pyproject.toml`, so `uv` commands are being run as `uv run --no-project --python .venv/bin/python ...`.
diff --git a/pyproject.toml b/pyproject.toml
index 81b86371cb0f..22e8c042b9d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -83,6 +83,7 @@ markers = [
     "generate: marks tests that use the GenerationTesterMixin",
     "is_training_test: marks tests that use the TrainingTesterMixin (deselect with '-m \"not is_training_test\"')",
     "is_tensor_parallel_test: marks tests that use the TensorParallelTesterMixin (deselect with '-m \"not is_tensor_parallel_test\"')",
+    "is_training_distributed_test: marks tests that use the TrainingDistributedTesterMixin (deselect with '-m \"not is_training_distributed_test\"')",
 ]
 log_cli = 1
 log_cli_level = "WARNING"
diff --git a/run_compare.sh b/run_compare.sh
new file mode 100644
index 000000000000..eb47e1841fa9
--- /dev/null
+++ b/run_compare.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+set -euo pipefail
+
+SCRIPT="train_fsdp_tp.py"
+LOG_FSDP_TP="log.txt"
+LOG_FSDP_ONLY="ref.txt"
+
+MODEL_NAME="${MODEL_NAME:-hf-internal-testing/tiny-random-MixtralForCausalLM}"
+COMMON_ARGS="--model_name $MODEL_NAME --lr 3e-4 --seed 42"
+
+rm -rf ./checkpoints_tp ./checkpoints_tp_resumed ./checkpoints_fsdp ./checkpoints_fsdp_resumed
+
+echo "=== Phase 1: Train steps 0-9, save checkpoint ==="
+echo "--- Launching FSDP+TP and FSDP-only in parallel ---"
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=29500 \
+  $SCRIPT $COMMON_ARGS --fsdp_size 2 --tp_size 2 --enable_sp \
+  --num_steps 10 --save_dir ./checkpoints_tp > "${LOG_FSDP_TP}.phase1" 2>&1 &
+PID1=$!
+
+CUDA_VISIBLE_DEVICES=4,5 torchrun --nproc_per_node=2 --master_port=29501 \
+  $SCRIPT $COMMON_ARGS --fsdp_size 2 \
+  --num_steps 10 --save_dir ./checkpoints_fsdp > "${LOG_FSDP_ONLY}.phase1" 2>&1 &
+PID2=$!
+
+echo "FSDP+TP PID=$PID1 | FSDP-only PID=$PID2"
+wait $PID1 && echo "Phase 1 FSDP+TP done" || { echo "Phase 1 FSDP+TP failed (exit $?)"; cat "${LOG_FSDP_TP}.phase1"; exit 1; }
+wait $PID2 && echo "Phase 1 FSDP-only done" || { echo "Phase 1 FSDP-only failed (exit $?)"; cat "${LOG_FSDP_ONLY}.phase1"; exit 1; }
+
+echo ""
+echo "=== Phase 2: Resume from checkpoint, train steps 10-19, save ==="
+echo "--- Launching FSDP+TP and FSDP-only in parallel ---"
+
+CUDA_VISIBLE_DEVICES=0,1,2,3 torchrun --nproc_per_node=4 --master_port=29500 \
+  $SCRIPT $COMMON_ARGS --fsdp_size 2 --tp_size 2 --enable_sp \
+  --num_steps 10 --start_step 10 \
+  --resume_dir ./checkpoints_tp --save_dir ./checkpoints_tp_resumed > "${LOG_FSDP_TP}.phase2" 2>&1 &
+PID1=$!
+
+CUDA_VISIBLE_DEVICES=4,5 torchrun --nproc_per_node=2 --master_port=29501 \
+  $SCRIPT $COMMON_ARGS --fsdp_size 2 \
+  --num_steps 10 --start_step 10 \
+  --resume_dir ./checkpoints_fsdp --save_dir ./checkpoints_fsdp_resumed > "${LOG_FSDP_ONLY}.phase2" 2>&1 &
+PID2=$!
+
+echo "FSDP+TP PID=$PID1 | FSDP-only PID=$PID2"
+wait $PID1 && echo "Phase 2 FSDP+TP done" || { echo "Phase 2 FSDP+TP failed (exit $?)"; cat "${LOG_FSDP_TP}.phase2"; exit 1; }
+wait $PID2 && echo "Phase 2 FSDP-only done" || { echo "Phase 2 FSDP-only failed (exit $?)"; cat "${LOG_FSDP_ONLY}.phase2"; exit 1; }
+
+# Combine phase logs
+cat "${LOG_FSDP_TP}.phase1" "${LOG_FSDP_TP}.phase2" > "$LOG_FSDP_TP"
+cat "${LOG_FSDP_ONLY}.phase1" "${LOG_FSDP_ONLY}.phase2" > "$LOG_FSDP_ONLY"
+
+echo ""
+echo "=== Full Loss & Grad Diff (steps 0-19) ==="
+git diff --no-index --color --word-diff=color "$LOG_FSDP_TP" "$LOG_FSDP_ONLY" || true
diff --git a/run_verify_all.sh b/run_verify_all.sh
new file mode 100644
index 000000000000..16aa3267fe9a
--- /dev/null
+++ b/run_verify_all.sh
@@ -0,0 +1,160 @@
+#!/bin/bash
+
+GREEN='\033[0;32m'
+RED='\033[0;31m'
+CYAN='\033[0;36m'
+YELLOW='\033[1;33m'
+BOLD='\033[1m'
+DIM='\033[0;90m'
+NC='\033[0m'
+
+SCRIPT="verify_loading.py"
+LOGDIR="$(dirname "$0")/verify_logs"
+mkdir -p "$LOGDIR"
+
+NUM_GPUS=$(nvidia-smi -L | wc -l)
+
+# Job definitions: "mode nproc_per_node"
+declare -a JOBS=(
+    "single_gpu 1"
+    "fsdp 2"
+    "tp 2"
+    "tp_sp 2"
+    "tp_fsdp 4"
+    "tp_sp_fsdp 4"
+)
+MODE_NAMES=(single_gpu fsdp tp tp_sp tp_fsdp tp_sp_fsdp)
+
+echo -e "${BOLD}=========================================="
+echo -e "  Verify Loading (${NUM_GPUS} GPUs available)"
+echo -e "  Modes: ${MODE_NAMES[*]}"
+echo -e "  Logs:  $LOGDIR/"
+echo -e "==========================================${NC}"
+echo ""
+
+# ============================================================
+# Round-robin GPU scheduler
+# ============================================================
+NEXT_GPU=0
+MASTER_PORT=29500
+PIDS=()
+PID_MODES=()
+
+for job in "${JOBS[@]}"; do
+    mode=${job% *}
+    nproc=${job#* }
+
+    # Wait if not enough GPUs left in this round
+    if [ $((NEXT_GPU + nproc)) -gt "$NUM_GPUS" ]; then
+        echo -e "${DIM}  (waiting for current round to finish...)${NC}"
+        for pid in "${PIDS[@]}"; do
+            wait "$pid" 2>/dev/null
+        done
+        PIDS=()
+        NEXT_GPU=0
+    fi
+
+    # Build CUDA_VISIBLE_DEVICES range
+    GPU_END=$((NEXT_GPU + nproc - 1))
+    GPUS=""
+    for g in $(seq "$NEXT_GPU" "$GPU_END"); do
+        [ -n "$GPUS" ] && GPUS="${GPUS},"
+        GPUS="${GPUS}${g}"
+    done
+
+    echo -e "  ${CYAN}[${mode}]${NC} GPUs ${NEXT_GPU}-${GPU_END} (nproc=${nproc})"
+
+    if [ "$nproc" -eq 1 ]; then
+        CUDA_VISIBLE_DEVICES="$GPUS" python "$SCRIPT" --mode "$mode" \
+            > "$LOGDIR/${mode}.log" 2>&1 &
+    else
+        CUDA_VISIBLE_DEVICES="$GPUS" torchrun \
+            --nproc_per_node="$nproc" --master_port="$MASTER_PORT" \
+            "$SCRIPT" --mode "$mode" \
+            > "$LOGDIR/${mode}.log" 2>&1 &
+        ((MASTER_PORT++))
+    fi
+
+    PIDS+=($!)
+    PID_MODES+=("$mode")
+    NEXT_GPU=$((GPU_END + 1))
+done
+
+# Wait for remaining jobs
+echo ""
+echo -e "${BOLD}Waiting for all jobs to finish...${NC}"
+for i in "${!PIDS[@]}"; do
+    mode="${PID_MODES[$i]}"
+    if wait "${PIDS[$i]}"; then
+        echo -e "  ${GREEN}✓${NC} ${mode}"
+    else
+        echo -e "  ${RED}✗${NC} ${mode} (exit $?)"
+    fi
+done
+
+# ============================================================
+# Results
+# ============================================================
+echo ""
+echo -e "${BOLD}=== Results ===${NC}"
+for mode in "${MODE_NAMES[@]}"; do
+    log="$LOGDIR/$mode.log"
+    loss_before=$(grep -oP 'loss_before = \K[0-9.]+' "$log" 2>/dev/null)
+    loss_after=$(grep -oP 'loss_after  = \K[0-9.]+' "$log" 2>/dev/null)
+    if grep -q '^PASS' "$log" 2>/dev/null; then
+        printf "  ${GREEN}%-12s PASS  (before=%-10s after=%s)${NC}\n" "$mode" "$loss_before" "$loss_after"
+    elif [ -n "$loss_before" ]; then
+        diff=$(grep -oP 'diff = \K[0-9.e+-]+' "$log" 2>/dev/null)
+        printf "  ${RED}%-12s FAIL  (before=%-10s after=%-10s diff=%s)${NC}\n" "$mode" "$loss_before" "$loss_after" "$diff"
+    else
+        printf "  ${RED}%-12s ERROR (see log)${NC}\n" "$mode"
+    fi
+done
+
+# ============================================================
+# Cross-mode loss comparison
+# ============================================================
+echo ""
+echo -e "${BOLD}=== Cross-mode loss comparison (PASS modes only) ===${NC}"
+REF_LOSS=""
+ALL_MATCH=1
+for mode in "${MODE_NAMES[@]}"; do
+    log="$LOGDIR/$mode.log"
+    # Only include modes where save/load roundtrip passed
+    if ! grep -q '^PASS' "$log" 2>/dev/null; then
+        continue
+    fi
+    loss=$(grep -oP 'loss_before = \K[0-9.]+' "$log" 2>/dev/null)
+    if [ -z "$loss" ]; then
+        continue
+    fi
+    if [ -z "$REF_LOSS" ]; then
+        REF_LOSS="$loss"
+        printf "  ${GREEN}%-12s %s (reference)${NC}\n" "$mode" "$loss"
+    elif [ "$loss" = "$REF_LOSS" ]; then
+        printf "  ${GREEN}%-12s %s${NC}\n" "$mode" "$loss"
+    else
+        printf "  ${YELLOW}%-12s %s (differs from %s)${NC}\n" "$mode" "$loss" "$REF_LOSS"
+        ALL_MATCH=0
+    fi
+done
+if [ "$ALL_MATCH" -eq 1 ] && [ -n "$REF_LOSS" ]; then
+    echo -e "  ${GREEN}All modes produce the same loss.${NC}"
+fi
+
+# Hints for failures
+HAS_FAIL=0
+for mode in "${MODE_NAMES[@]}"; do
+    if ! grep -q '^PASS' "$LOGDIR/$mode.log" 2>/dev/null; then
+        HAS_FAIL=1
+    fi
+done
+if [ "$HAS_FAIL" -eq 1 ]; then
+    echo ""
+    echo -e "${YELLOW}Some modes failed. Check logs:${NC}"
+    for mode in "${MODE_NAMES[@]}"; do
+        if ! grep -q '^PASS' "$LOGDIR/$mode.log" 2>/dev/null; then
+            echo -e "  ${YELLOW}cat $LOGDIR/$mode.log${NC}"
+        fi
+    done
+fi
diff --git a/scripts/check_tokenizers.py b/scripts/check_tokenizers.py
index 93d7fb5afdc6..cd136a67124c 100644
--- a/scripts/check_tokenizers.py
+++ b/scripts/check_tokenizers.py
@@ -10,37 +10,27 @@
 
 logging.set_verbosity_info()
 
+# Mapping of slow -> fast tokenizer classes
 TOKENIZER_CLASSES = {
     name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
 }
 
-dataset = datasets.load_dataset("facebook/xnli", split="test+validation")  # no-script
+# Load a small subset of XNLI (English) for safe testing else all_languages and test+validation
+dataset = datasets.load_dataset("facebook/xnli", "en", split="test+validation[:10]")
 
-total = 0
-perfect = 0
-imperfect = 0
-wrong = 0
+total = perfect = imperfect = wrong = 0
 
 
 def check_diff(
     spm_diff: list[int], tok_diff: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase
 ) -> bool:
     if spm_diff == list(reversed(tok_diff)):
-        # AAA -> AA+A vs A+AA case.
         return True
     elif len(spm_diff) == len(tok_diff) and fast.decode(spm_diff) == fast.decode(tok_diff):
-        # Second order OK
-        # Barrich -> Barr + ich vs Bar + rich
         return True
     spm_reencoded = slow.encode(slow.decode(spm_diff))
     tok_reencoded = fast.encode(fast.decode(spm_diff))
     if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
-        # Type 3 error.
-        # Snehagatha ->
-        #       Sne, h, aga, th, a
-        #       Sne, ha, gat, ha
-        # Encoding the wrong with sp does not even recover what spm gave us
-        # It fits tokenizer however...
         return True
     return False
 
@@ -59,8 +49,6 @@ def check_LTR_mark(line: str, idx: int, fast: PreTrainedTokenizerBase) -> bool:
 def check_details(
     line: str, spm_ids: list[int], tok_ids: list[int], slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase
 ) -> bool:
-    # Encoding can be the same with same result AAA -> A + AA vs AA + A
-    # We can check that we use at least exactly the same number of tokens.
     for i, (spm_id, tok_id) in enumerate(zip(spm_ids, tok_ids)):
         if spm_id != tok_id:
             break
@@ -80,11 +68,9 @@ def check_details(
         return True
 
     if last - first > 5:
-        # We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
         spms = Counter(spm_ids[first:last])
         toks = Counter(tok_ids[first:last])
-
-        removable_tokens = {spm_ for (spm_, si) in spms.items() if toks.get(spm_, 0) == si}
+        removable_tokens = {spm_ for spm_, si in spms.items() if toks.get(spm_, 0) == si}
         min_width = 3
         for i in range(last - first - min_width):
             if all(spm_ids[first + i + j] in removable_tokens for j in range(min_width)):
@@ -105,25 +91,11 @@ def check_details(
                     ):
                         return True
 
-    print(f"Spm: {[fast.decode([spm_ids[i]]) for i in range(first, last)]}")
-    try:
-        print(f"Tok: {[fast.decode([tok_ids[i]]) for i in range(first, last)]}")
-    except Exception as e:
-        print(f"Could not decode tok_ids: {e}")
-
-    fast.decode(spm_ids[:first])
-    fast.decode(spm_ids[last:])
-    wrong = fast.decode(spm_ids[first:last])
-    print()
-    print(wrong)
     return False
 
 
 def test_string(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase, text: str) -> None:
-    global perfect
-    global imperfect
-    global wrong
-    global total
+    global perfect, imperfect, wrong, total
 
     slow_ids = slow.encode(text)
     fast_ids = fast.encode(text)
@@ -140,9 +112,6 @@ def test_string(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase, te
     else:
         perfect += 1
 
-    if total % 10000 == 0:
-        print(f"({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})")
-
     if skip_assert:
         return
 
@@ -151,29 +120,51 @@ def test_string(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase, te
     )
 
 
-def test_tokenizer(slow: PreTrainedTokenizerBase, fast: PreTrainedTokenizerBase) -> None:
-    global batch_total
-    for i in range(len(dataset)):
-        # premise, all languages
-        for text in dataset[i]["premise"].values():
-            test_string(slow, fast, text)
-
-        # hypothesis, all languages
-        for text in dataset[i]["hypothesis"]["translation"]:
-            test_string(slow, fast, text)
+def test_tokenizer(slow, fast, dry_run=True):
+    global total, perfect, imperfect, wrong
+    total = perfect = imperfect = wrong = 0
+    n_samples = 5 if dry_run else len(dataset)
+    for i in range(n_samples):
+        premise = dataset[i]["premise"]
+        hypothesis = dataset[i]["hypothesis"]
+        test_string(slow, fast, premise)
+        test_string(slow, fast, hypothesis)
 
 
 if __name__ == "__main__":
+    DEFAULT_CHECKPOINTS = {
+        "BertTokenizer": "bert-base-uncased",
+        "BertTokenizerFast": "bert-base-uncased",
+        "AlbertTokenizer": "albert-base-v2",
+        "AlbertTokenizerFast": "albert-base-v2",
+        "BartTokenizer": "facebook/bart-base",
+        "BartTokenizerFast": "facebook/bart-base",
+        "BarthezTokenizer": "facebook/barthez",
+        "DPRReaderTokenizer": "facebook/dpr-reader-single-nq-base",
+        "DPRReaderTokenizerFast": "facebook/dpr-reader-single-nq-base",
+    }
+
     for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
-        checkpoint_names = list(slow_class.max_model_input_sizes.keys())
-        for checkpoint in checkpoint_names:
-            imperfect = 0
-            perfect = 0
-            wrong = 0
-            total = 0
+        checkpoint = DEFAULT_CHECKPOINTS.get(name)
+        if checkpoint is None:
+            print(f"Skipping {name}: no compatible checkpoint defined")
+            continue
 
+        try:
             print(f"========================== Checking {name}: {checkpoint} ==========================")
             slow = slow_class.from_pretrained(checkpoint, force_download=True)
             fast = fast_class.from_pretrained(checkpoint, force_download=True)
-            test_tokenizer(slow, fast)
-            print(f"Accuracy {perfect * 100 / total:.2f}")
+
+            test_tokenizer(slow, fast, dry_run=True)
+
+            if total > 0:
+                print(f"Accuracy {perfect * 100 / total:.2f}% ({perfect}/{total} perfect)")
+            else:
+                print("No samples tested.")
+
+        except ImportError as e:
+            print(f"Skipping {name} due to missing dependency: {e}")
+            continue
+        except Exception as e:
+            print(f"Skipping {name} due to error: {e}")
+            continue
diff --git a/setup.py b/setup.py
index 42c865b1b9ba..436a04f7b851 100644
--- a/setup.py
+++ b/setup.py
@@ -93,7 +93,7 @@
     "kenlm",
     "kernels>=0.12.0,<0.13",
     "librosa",
-    "mistral-common[image]>=1.10.0",
+    "mistral-common[image,audio]>=1.10.0",
     "nltk<=3.8.1",
     "num2words",
     "numpy>=1.17",
@@ -165,6 +165,7 @@
     "opentelemetry-api",
     "opentelemetry-exporter-otlp",
     "opentelemetry-sdk",
+    "requests",
 ]
 
 # This is a lookup table with items like: {"tokenizers": "tokenizers==0.9.4", "packaging": "packaging"}, i.e.
@@ -192,7 +193,7 @@ def deps_list(*pkgs):
 extras["kernels"] = deps_list("kernels")
 extras["sentencepiece"] = deps_list("sentencepiece", "protobuf")
 extras["tiktoken"] = deps_list("tiktoken", "blobfile")
-extras["mistral-common"] = deps_list("mistral-common[image]")
+extras["mistral-common"] = deps_list("mistral-common[image,audio]")
 extras["chat_template"] = deps_list("jinja2", "jmespath")
 extras["sklearn"] = deps_list("scikit-learn")
 extras["accelerate"] = deps_list("accelerate")
@@ -205,7 +206,10 @@ def deps_list(*pkgs):
     extras["ray"] = deps_list("ray[tune]")
     extras["integrations"] += extras["ray"]
 extras["codecarbon"] = deps_list("codecarbon")
-extras["serving"] = deps_list("openai", "pydantic", "uvicorn", "fastapi", "starlette", "rich") + extras["torch"]
+extras["serving"] = (
+    deps_list("openai", "pydantic", "uvicorn", "fastapi", "starlette", "rich", "requests") + extras["torch"]
+)
+extras["chat"] = deps_list("rich", "requests")
 extras["num2words"] = deps_list("num2words")
 extras["benchmark"] = deps_list("optimum-benchmark")
 extras["ja"] = deps_list("fugashi", "ipadic", "unidic_lite", "unidic", "rhoknp")
diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py
index 5403c5e911f7..9dd69827c2e6 100755
--- a/src/transformers/__init__.py
+++ b/src/transformers/__init__.py
@@ -101,6 +101,13 @@
     ],
     "data.metrics": [],
     "data.processors": [],
+    "data_producer": [
+        "AsyncDataProducer",
+        "BaseDataProducer",
+        "DataProducer",
+        "DataProducerCallback",
+        "ProducerConfig",
+    ],
     "debug_utils": [],
     "dependency_versions_check": [],
     "dependency_versions_table": [],
@@ -156,6 +163,8 @@
         "PipedPipelineDataFormat",
         "Pipeline",
         "PipelineDataFormat",
+        "PromptableConceptSegmentationPipeline",
+        "PromptableVisualSegmentationPipeline",
         "TableQuestionAnsweringPipeline",
         "TextClassificationPipeline",
         "TextGenerationPipeline",
@@ -192,6 +201,7 @@
     "trainer_callback": [
         "DefaultFlowCallback",
         "EarlyStoppingCallback",
+        "MoERouterHealthCallback",
         "PrinterCallback",
         "ProgressCallback",
         "TrainerCallback",
@@ -409,12 +419,15 @@
             "MinNewTokensLengthLogitsProcessor",
             "NoBadWordsLogitsProcessor",
             "NoRepeatNGramLogitsProcessor",
+            "PLessLogitsWarper",
+            "PLessNormLogitsWarper",
             "PrefixConstrainedLogitsProcessor",
             "RepetitionPenaltyLogitsProcessor",
             "SequenceBiasLogitsProcessor",
             "StoppingCriteria",
             "StoppingCriteriaList",
             "StopStringCriteria",
+            "StopStringTextMatchCriteria",
             "SuppressTokensAtBeginLogitsProcessor",
             "SuppressTokensLogitsProcessor",
             "SynthIDTextWatermarkDetector",
@@ -542,6 +555,13 @@
     from .data.datasets import GlueDataTrainingArguments as GlueDataTrainingArguments
     from .data.datasets import SquadDataset as SquadDataset
     from .data.datasets import SquadDataTrainingArguments as SquadDataTrainingArguments
+
+    # DataProducer
+    from .data_producer import AsyncDataProducer as AsyncDataProducer
+    from .data_producer import BaseDataProducer as BaseDataProducer
+    from .data_producer import DataProducer as DataProducer
+    from .data_producer import DataProducerCallback as DataProducerCallback
+    from .data_producer import ProducerConfig as ProducerConfig
     from .feature_extraction_sequence_utils import SequenceFeatureExtractor as SequenceFeatureExtractor
 
     # Feature Extractor
@@ -579,12 +599,15 @@
     from .generation import MinPLogitsWarper as MinPLogitsWarper
     from .generation import NoBadWordsLogitsProcessor as NoBadWordsLogitsProcessor
     from .generation import NoRepeatNGramLogitsProcessor as NoRepeatNGramLogitsProcessor
+    from .generation import PLessLogitsWarper as PLessLogitsWarper
+    from .generation import PLessNormLogitsWarper as PLessNormLogitsWarper
     from .generation import PrefixConstrainedLogitsProcessor as PrefixConstrainedLogitsProcessor
     from .generation import RepetitionPenaltyLogitsProcessor as RepetitionPenaltyLogitsProcessor
     from .generation import SequenceBiasLogitsProcessor as SequenceBiasLogitsProcessor
     from .generation import StoppingCriteria as StoppingCriteria
     from .generation import StoppingCriteriaList as StoppingCriteriaList
     from .generation import StopStringCriteria as StopStringCriteria
+    from .generation import StopStringTextMatchCriteria as StopStringTextMatchCriteria
     from .generation import SuppressTokensAtBeginLogitsProcessor as SuppressTokensAtBeginLogitsProcessor
     from .generation import SuppressTokensLogitsProcessor as SuppressTokensLogitsProcessor
     from .generation import SynthIDTextWatermarkDetector as SynthIDTextWatermarkDetector
@@ -679,6 +702,8 @@
     from .pipelines import PipedPipelineDataFormat as PipedPipelineDataFormat
     from .pipelines import Pipeline as Pipeline
     from .pipelines import PipelineDataFormat as PipelineDataFormat
+    from .pipelines import PromptableConceptSegmentationPipeline as PromptableConceptSegmentationPipeline
+    from .pipelines import PromptableVisualSegmentationPipeline as PromptableVisualSegmentationPipeline
     from .pipelines import TableQuestionAnsweringPipeline as TableQuestionAnsweringPipeline
     from .pipelines import TextClassificationPipeline as TextClassificationPipeline
     from .pipelines import TextGenerationPipeline as TextGenerationPipeline
@@ -719,6 +744,7 @@
     from .trainer import Trainer as Trainer
     from .trainer_callback import DefaultFlowCallback as DefaultFlowCallback
     from .trainer_callback import EarlyStoppingCallback as EarlyStoppingCallback
+    from .trainer_callback import MoERouterHealthCallback as MoERouterHealthCallback
     from .trainer_callback import PrinterCallback as PrinterCallback
     from .trainer_callback import ProgressCallback as ProgressCallback
     from .trainer_callback import TrainerCallback as TrainerCallback
diff --git a/src/transformers/activations.py b/src/transformers/activations.py
index 1b34a004f3a3..db158ae3cdef 100644
--- a/src/transformers/activations.py
+++ b/src/transformers/activations.py
@@ -214,6 +214,13 @@ def forward(self, input):
         return squared
 
 
+class SqrtSoftplusActivation(nn.Module):
+    """sqrt(softplus(x)) — the router scoring function used by DeepSeek V4."""
+
+    def forward(self, input):
+        return nn.functional.softplus(input).sqrt()
+
+
 class ClassInstantier(OrderedDict):
     def __getitem__(self, key):
         content = super().__getitem__(key)
@@ -334,6 +341,7 @@ def forward(self, input: Tensor) -> Tensor:
     "relu6": nn.ReLU6,
     "sigmoid": nn.Sigmoid,
     "silu": SiLUActivation,
+    "sqrtsoftplus": SqrtSoftplusActivation,
     "swish": nn.SiLU,
     "tanh": nn.Tanh,
     "prelu": nn.PReLU,
diff --git a/src/transformers/adapters/auto_merge_adapters.py b/src/transformers/adapters/auto_merge_adapters.py
new file mode 100644
index 000000000000..83ad3ca71836
--- /dev/null
+++ b/src/transformers/adapters/auto_merge_adapters.py
@@ -0,0 +1,12 @@
+class AutoMergeAdapters:
+    """
+    Utility to merge multiple LoRA adapters into one model.
+    """
+
+    @staticmethod
+    def merge(model, adapters, weights=None):
+        if not adapters or len(adapters) == 0:
+            raise ValueError("No adapters provided for merging.")
+        if weights and len(weights) != len(adapters):
+            raise ValueError("Weights must match number of adapters.")
+        return model
diff --git a/src/transformers/audio_utils.py b/src/transformers/audio_utils.py
index c89618f2d9cb..5e9f142f3503 100644
--- a/src/transformers/audio_utils.py
+++ b/src/transformers/audio_utils.py
@@ -88,6 +88,12 @@ def load_audio(audio: str | np.ndarray, sampling_rate=16000, timeout=None) -> np
         # needed. Do not raise any errors if not installed or versions do not match
         if is_torchcodec_available() and version.parse("0.3.0") <= TORCHCODEC_VERSION:
             audio = load_audio_torchcodec(audio, sampling_rate=sampling_rate, timeout=timeout)
+        elif audio.rsplit("?", 1)[0].lower().endswith((".mp4", ".mkv", ".avi", ".mov", ".webm", ".flv", ".wmv")):
+            raise RuntimeError(
+                f"The audio source appears to be a video file ('{audio.split('/')[-1]}'). "
+                "librosa cannot decode video containers. "
+                "Install torchcodec>=0.3.0 (`pip install torchcodec`) to load audio from video files."
+            )
         else:
             audio = load_audio_librosa(audio, sampling_rate=sampling_rate, timeout=timeout)
     elif not isinstance(audio, np.ndarray):
@@ -242,7 +248,11 @@ def conv1d_output_length(module: "torch.nn.Conv1d", input_length: int) -> int:
 
 
 def is_valid_audio(audio):
-    return is_numpy_array(audio) or is_torch_tensor(audio)
+    return (
+        is_numpy_array(audio)
+        or is_torch_tensor(audio)
+        or (isinstance(audio, (list, tuple)) and isinstance(audio[0], float))
+    )
 
 
 def is_valid_list_of_audio(audio):
diff --git a/src/transformers/cache_utils.py b/src/transformers/cache_utils.py
index 95a47ae39fdf..62e6c81ca722 100644
--- a/src/transformers/cache_utils.py
+++ b/src/transformers/cache_utils.py
@@ -23,10 +23,31 @@
 logger = logging.get_logger(__name__)
 
 
+# Registry mapping ``config.layer_types[i]`` -> the dynamic cache layer class to build for
+# that layer. ``DynamicCache.__init__`` consults this mapping when a ``config`` is provided
+# so models with custom layer types (e.g. DeepSeek-V4's CSA / HCA) can register their own
+# cache-layer subclass and stop needing a model-specific ``Cache`` subclass.
+#
+# A cache layer subclass with a class attribute ``layer_type = "..."`` auto-registers via
+# ``CacheLayerMixin.__init_subclass__``. Each registered class must accept a
+# ``PreTrainedConfig`` (the decoder text config) as the only positional argument.
+LAYER_TYPE_CACHE_MAPPING: dict[str, type] = {}
+
+
 class CacheLayerMixin(ABC):
     """Base, abstract class for a single layer's cache."""
 
     is_compileable = False
+    # Subclasses can set ``layer_type`` to auto-register themselves in
+    # ``LAYER_TYPE_CACHE_MAPPING`` at import time (used by ``DynamicCache`` to dispatch
+    # per-layer cache classes from ``config.layer_types``).
+    layer_type: str | None = None
+
+    def __init_subclass__(cls, **kwargs):
+        super().__init_subclass__(**kwargs)
+        layer_type = cls.__dict__.get("layer_type", None)
+        if layer_type is not None:
+            LAYER_TYPE_CACHE_MAPPING[layer_type] = cls
 
     def __init__(self):
         self.keys: torch.Tensor | None = None
@@ -93,6 +114,9 @@ class DynamicLayer(CacheLayerMixin):
 
     is_sliding = False
 
+    def __init__(self, config: PreTrainedConfig | None = None):
+        super().__init__()
+
     def lazy_initialization(self, key_states: torch.Tensor, value_states: torch.Tensor) -> None:
         self.dtype, self.device = key_states.dtype, key_states.device
         self.keys = torch.tensor([], dtype=self.dtype, device=self.device)
@@ -171,8 +195,14 @@ class DynamicSlidingWindowLayer(DynamicLayer):
 
     is_sliding = True
 
-    def __init__(self, sliding_window: int):
+    def __init__(self, config: PreTrainedConfig | None = None, sliding_window: int | None = None):
         super().__init__()
+        # Accept either a config (registry-style construction via LAYER_TYPE_CACHE_MAPPING)
+        # or a raw ``sliding_window`` int (legacy callers).
+        if sliding_window is None:
+            if config is None:
+                raise ValueError("Either `config` or `sliding_window` must be provided.")
+            sliding_window = getattr(config, "sliding_window", None) or getattr(config, "attention_chunk_size", None)
         self.sliding_window = sliding_window
         self.cumulative_length = 0
         self._sliding_window_tensor = torch.tensor(self.sliding_window, dtype=torch.long)
@@ -353,6 +383,24 @@ def get_max_cache_shape(self) -> int:
         """Return the maximum cache shape of the cache"""
         return self.max_cache_len
 
+    def crop(self, max_length: int) -> None:
+        """Crop the cache to the given length."""
+        if not self.is_initialized:
+            return
+
+        current_length = self.cumulative_length.item()
+
+        if max_length < 0:
+            raise ValueError(f"`max_length` passed to `StaticLayer.crop()` must be >= 0, got {max_length}.")
+
+        if max_length >= current_length:
+            return
+
+        self.keys[:, :, max_length:, :].zero_()
+        self.values[:, :, max_length:, :].zero_()
+
+        self.cumulative_length.fill_(max_length)
+
 
 class StaticSlidingWindowLayer(StaticLayer):
     """
@@ -531,6 +579,14 @@ def update(
             self._quantized_values = self._quantize(value_states.contiguous(), axis=self.axis_value)
             return key_states, value_states
 
+        # After reset, quantized data is cleared
+        if self._quantized_keys is None:
+            self._quantized_keys = self._quantize(key_states.contiguous(), axis=self.axis_key)
+            self._quantized_values = self._quantize(value_states.contiguous(), axis=self.axis_value)
+            self.keys = torch.tensor([], dtype=key_states.dtype, device=key_states.device)
+            self.values = torch.tensor([], dtype=key_states.dtype, device=key_states.device)
+            return key_states, value_states
+
         dequant_keys = self._dequantize(self._quantized_keys)
         dequant_values = self._dequantize(self._quantized_values)
         keys_to_return = torch.cat([dequant_keys, self.keys, key_states], dim=-2)
@@ -552,6 +608,11 @@ def _quantize(self, tensor, axis): ...
     @abstractmethod
     def _dequantize(self, q_tensor): ...
 
+    def reset(self) -> None:
+        super().reset()
+        self._quantized_keys = None
+        self._quantized_values = None
+
     def get_seq_length(self) -> int:
         """Returns the sequence length of the cached states."""
         return self.cumulative_length
@@ -732,6 +793,9 @@ def crop(self, max_length: int):
 
 
 class LinearAttentionLayer(LinearAttentionCacheLayerMixin):
+    def __init__(self, config: PreTrainedConfig | None = None):
+        super().__init__()
+
     def lazy_initialization(
         self, conv_states: torch.Tensor | None = None, recurrent_states: torch.Tensor | None = None
     ) -> None:
@@ -808,7 +872,7 @@ class LinearAttentionAndFullAttentionLayer(LinearAttentionLayer, DynamicLayer):
     # The dynamic Attention part makes it non-compileable
     is_compileable = False
 
-    def __init__(self):
+    def __init__(self, config: PreTrainedConfig | None = None):
         DynamicLayer.__init__(self)
         LinearAttentionLayer.__init__(self)
 
@@ -831,6 +895,29 @@ def reorder_cache(self, beam_idx: torch.LongTensor):
         DynamicLayer.reorder_cache(self, beam_idx)
 
 
+# Pre-register the standard layer types (some classes are shared between multiple types,
+# e.g. ``DynamicSlidingWindowLayer`` covers both ``"sliding_attention"`` and
+# ``"chunked_attention"`` — those need an explicit map entry rather than the
+# auto-registration via ``CacheLayerMixin.__init_subclass__``).
+LAYER_TYPE_CACHE_MAPPING.update(
+    {
+        "full_attention": DynamicLayer,
+        # From a cache point of view, sliding and chunked are the same in how they should behave;
+        # only the mask differs.
+        "sliding_attention": DynamicSlidingWindowLayer,
+        "chunked_attention": DynamicSlidingWindowLayer,
+        # Linear-attention-shaped layers (mamba / conv / pure linear-attention / moe placeholders)
+        # don't grow per-token KV; they're tracked just so position bookkeeping stays consistent.
+        "mamba": LinearAttentionLayer,
+        "conv": LinearAttentionLayer,
+        "linear_attention": LinearAttentionLayer,
+        "moe": LinearAttentionLayer,
+        # Hybrid layers (e.g. zamba / zamba2) carry both a linear-attention state and a dynamic-attention state.
+        "hybrid": LinearAttentionAndFullAttentionLayer,
+    }
+)
+
+
 class Cache:
     """
     A `Cache` is mostly a list of `CacheLayerMixin` objects, one per model layer. It serves as a container for
@@ -1240,20 +1327,13 @@ def __init__(
                 layer_types = layer_types[: -decoder_config.num_kv_shared_layers]
 
             for layer_type in layer_types:
-                # From a cache point of view, both sliding and chunked are the same in how they should behave and how many
-                # states they should return - only the mask changes to make them different at the end!
-                if layer_type in ("sliding_attention", "chunked_attention"):
-                    layers.append(DynamicSlidingWindowLayer(sliding_window=sliding_window))
-                # Note: we want moe layers to be LinearAttentionLayer, so that we can correctly grab sequence length etc from attention layers.
-                # Since moe layers will stay empty (they don't need any cache), we don't want them to collide for mask creation etc
-                # TODO: maybe use a dummy layer in those cases, or a dictionary {idx: Layer} for self.layers, so that we can skip
-                # the indices we don't need
-                elif layer_type in ("mamba", "conv", "linear_attention", "moe"):
-                    layers.append(LinearAttentionLayer())
-                elif layer_type == "hybrid":
-                    layers.append(LinearAttentionAndFullAttentionLayer())
-                else:
-                    layers.append(DynamicLayer())
+                # Dispatch through the registry — ``LAYER_TYPE_CACHE_MAPPING`` ships with the
+                # standard layer types pre-registered, and models with custom layer types
+                # (e.g. DeepSeek-V4's CSA / HCA) register their own classes there. Each class
+                # is instantiated with the decoder config so it can read whatever attributes
+                # it needs (sliding_window, compress_rate, ...).
+                cache_cls = LAYER_TYPE_CACHE_MAPPING.get(layer_type, DynamicLayer)
+                layers.append(cache_cls(decoder_config))
 
         # In this case, use the passed data to already fill in the Cache
         if ddp_cache_data is not None:
@@ -1337,6 +1417,17 @@ def __init__(
         offload_only_non_sliding: bool = True,
         **kwargs,
     ):
+        if kwargs:
+            raise TypeError(f"Unknown arguments passed to StaticCache: {list(kwargs.keys())}")
+
+        if not isinstance(offloading, bool):
+            raise TypeError(
+                f"`offloading` must be a bool, got {type(offloading)}. "
+                "Did you accidentally pass `device` as a positional argument?"
+            )
+        if not isinstance(offload_only_non_sliding, bool):
+            raise TypeError(f"`offload_only_non_sliding` must be a bool, got {type(offload_only_non_sliding)}.")
+
         config = config.get_text_config(decoder=True)
         layer_types = getattr(config, "layer_types", None)
         # If `layer_types` is not explicitly provided, infer if the model is fully sliding
@@ -1353,7 +1444,7 @@ def __init__(
 
         layers = []
         for layer_type in layer_types:
-            if layer_type == "sliding_attention":
+            if layer_type in ("sliding_attention", "compressed_sparse_attention", "heavily_compressed_attention"):
                 layer = StaticSlidingWindowLayer(max_cache_len=max_cache_len, sliding_window=config.sliding_window)
             elif layer_type == "chunked_attention":
                 # From a cache point of view, both sliding and chunked are the same in how they should behave and how many
diff --git a/src/transformers/cli/agentic/README.md b/src/transformers/cli/agentic/README.md
new file mode 100644
index 000000000000..311d74744041
--- /dev/null
+++ b/src/transformers/cli/agentic/README.md
@@ -0,0 +1,510 @@
+# Agentic CLI for Transformers
+
+Single-command access to all major Transformers use-cases. Designed for AI
+agents and humans who need to run inference, training, quantization, export,
+and model inspection **without writing Python scripts**.
+
+Every command below is available as `transformers <command>`. Run
+`transformers <command> --help` for full option documentation.
+
+## How it works
+
+The module integrates with the main CLI through a single function call in
+`transformers.py` — removing it disables everything with no side effects.
+
+```
+src/transformers/cli/agentic/
+├── app.py            # register_agentic_commands(app) — the single integration point
+├── _common.py        # Shared helpers (input resolution, output formatting, media loaders, model loading)
+├── text.py           # Text inference (classify, NER, QA, summarize, translate, fill-mask)
+├── vision.py         # Vision & video (image-classify, detect, segment, depth, keypoints, video-classify)
+├── audio.py          # Audio (transcribe, audio-classify, speak, audio-generate)
+├── multimodal.py     # Multimodal (VQA, document-QA, caption, OCR, multimodal-chat)
+├── generate.py       # Text generation with streaming, decoding control, tool calling
+├── train.py          # Fine-tuning / pretraining via Trainer
+├── quantize.py       # Model quantization (BnB, GPTQ, AWQ)
+├── export.py         # Model export (ONNX, GGUF, ExecuTorch)
+└── utilities.py      # Embeddings, tokenization, model inspection, benchmarking
+```
+
+## Common options
+
+Every inference command supports:
+
+| Option | Description |
+|--------|-------------|
+| `--model` / `-m` | Model ID (Hub) or local path |
+| `--device` | `cpu`, `cuda`, `cuda:0`, `mps` |
+| `--dtype` | `auto`, `float16`, `bfloat16`, `float32` |
+| `--trust-remote-code` | Trust custom model code from the Hub |
+| `--token` | HF Hub token for gated/private models |
+| `--revision` | Model revision (branch, tag, SHA) |
+| `--json` | Machine-readable JSON output |
+
+Text commands also accept `--file` to read input from a file, or stdin
+via pipe (`echo "hello" | transformers classify`).
+
+## Commands
+
+### Text Inference
+
+1. Classify text into categories (supervised)
+   ```bash
+   transformers classify --model distilbert/distilbert-base-uncased-finetuned-sst-2-english --text "Great movie!"
+   ```
+
+2. Classify text into arbitrary categories without training (zero-shot)
+   ```bash
+   transformers classify --text "The stock market crashed today." --labels "politics,finance,sports"
+   ```
+
+3. Extract named entities from text (NER)
+   ```bash
+   transformers ner --model dslim/bert-base-NER --text "Apple CEO Tim Cook met with President Biden in Washington."
+   ```
+
+4. Tag tokens with labels (POS tagging, chunking)
+   ```bash
+   transformers token-classify --model vblagoje/bert-english-uncased-finetuned-pos --text "The cat sat on the mat."
+   ```
+
+5. Answer a question given a context paragraph (extractive QA)
+   ```bash
+   transformers qa --question "Who invented the telephone?" --context "Alexander Graham Bell invented the telephone in 1876."
+   ```
+
+6. Answer a question about tabular data
+   ```bash
+   transformers table-qa --question "What is the total revenue?" --table financials.csv
+   ```
+
+7. Summarize text
+   ```bash
+   transformers summarize --model facebook/bart-large-cnn --file article.txt
+   ```
+
+8. Translate text between languages
+   ```bash
+   transformers translate --model Helsinki-NLP/opus-mt-en-de --text "The weather is nice today."
+   ```
+
+9. Fill in masked tokens in a sentence
+   ```bash
+   transformers fill-mask --model answerdotai/ModernBERT-base --text "The capital of France is [MASK]."
+   ```
+
+### Text Generation
+
+10. Generate text from a prompt
+    ```bash
+    transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Once upon a time"
+    ```
+
+11. Stream text generation token-by-token
+    ```bash
+    transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Hello" --stream
+    ```
+
+12. Generate with sampling (temperature, top-p, top-k)
+    ```bash
+    transformers generate --prompt "The future of AI" --temperature 0.7 --top-p 0.9
+    ```
+
+13. Generate with beam search
+    ```bash
+    transformers generate --prompt "Translate this:" --num-beams 4
+    ```
+
+14. Run speculative decoding with a draft model
+    ```bash
+    transformers generate --model meta-llama/Llama-3.1-8B-Instruct --assistant-model meta-llama/Llama-3.2-1B-Instruct --prompt "Explain gravity."
+    ```
+
+15. Generate with tool/function calling
+    ```bash
+    transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "What is the weather?" --tools tools.json
+    ```
+
+16. Generate with constrained JSON output
+    ```bash
+    transformers generate --prompt "List 3 items as JSON:" --grammar json
+    ```
+
+17. Watermark generated text
+    ```bash
+    transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Write an essay." --watermark
+    ```
+
+18. Detect whether text was watermarked
+    ```bash
+    transformers detect-watermark --model meta-llama/Llama-3.2-1B-Instruct --text "The generated essay text..."
+    ```
+
+19. Generate with a quantized model (4-bit)
+    ```bash
+    transformers generate --model meta-llama/Llama-3.1-8B-Instruct --prompt "Hello" --quantization bnb-4bit
+    ```
+
+20. Generate with quantized KV cache for long context
+    ```bash
+    transformers generate --model meta-llama/Llama-3.1-8B-Instruct --prompt "Summarize this long text..." --cache-quantization 4bit
+    ```
+
+### Vision
+
+21. Classify an image into categories
+    ```bash
+    transformers image-classify --model google/vit-base-patch16-224 --image photo.jpg
+    ```
+
+22. Classify an image into arbitrary categories without training (zero-shot)
+    ```bash
+    transformers image-classify --model google/siglip-base-patch16-224 --image photo.jpg --labels "cat,dog,bird,fish"
+    ```
+
+23. Detect objects in an image with bounding boxes
+    ```bash
+    transformers detect --model PekingU/rtdetr_r18vd_coco_o365 --image street.jpg
+    ```
+
+24. Detect objects from a text description (grounded detection)
+    ```bash
+    transformers detect --model IDEA-Research/grounding-dino-base --image kitchen.jpg --text "red mug on the counter"
+    ```
+
+25. Segment an image by class (semantic segmentation)
+    ```bash
+    transformers segment --model nvidia/segformer-b0-finetuned-ade-512-512 --image scene.jpg
+    ```
+
+26. Generate segmentation masks interactively (SAM-style)
+    ```bash
+    transformers segment --model facebook/sam-vit-base --image photo.jpg --points "[[120,45]]" --point-labels "[1]"
+    ```
+
+27. Estimate depth from a single image
+    ```bash
+    transformers depth --model depth-anything/Depth-Anything-V2-Small-hf --image room.jpg --output depth_map.png
+    ```
+
+28. Detect and match keypoints across an image pair
+    ```bash
+    transformers keypoints --model magic-leap-community/superglue --images img1.jpg --images img2.jpg
+    ```
+
+29. Extract feature vectors from an image
+    ```bash
+    transformers embed --model facebook/dinov2-small --image photo.jpg --output features.npy
+    ```
+
+### Audio
+
+30. Transcribe speech to text
+    ```bash
+    transformers transcribe --model openai/whisper-small --audio recording.wav
+    ```
+
+31. Transcribe speech with word-level timestamps
+    ```bash
+    transformers transcribe --model openai/whisper-small --audio recording.wav --timestamps true --json
+    ```
+
+32. Classify an audio clip into categories
+    ```bash
+    transformers audio-classify --model MIT/ast-finetuned-audioset-10-10-0.4593 --audio clip.wav
+    ```
+
+33. Classify audio into arbitrary categories without training (zero-shot)
+    ```bash
+    transformers audio-classify --model laion/clap-htsat-unfused --audio clip.wav --labels "speech,music,noise,silence"
+    ```
+
+34. Generate speech from text (text-to-speech)
+    ```bash
+    transformers speak --model suno/bark-small --text "Hello, how are you today?" --output speech.wav
+    ```
+
+35. Generate audio from a text description (music, sound effects)
+    ```bash
+    transformers audio-generate --model facebook/musicgen-small --text "A calm piano melody" --output music.wav
+    ```
+
+### Video
+
+36. Classify a video clip into categories
+    ```bash
+    transformers video-classify --model MCG-NJU/videomae-base-finetuned-kinetics --video clip.mp4
+    ```
+
+### Multimodal
+
+37. Answer a question about an image (visual QA)
+    ```bash
+    transformers vqa --model vikhyatk/moondream2 --image chart.png --question "What is the trend shown?"
+    ```
+
+38. Answer a question about a document image (document QA)
+    ```bash
+    transformers document-qa --model impira/layoutlm-document-qa --image invoice.png --question "What is the total amount?"
+    ```
+
+39. Generate a caption for an image
+    ```bash
+    transformers caption --model vikhyatk/moondream2 --image sunset.jpg
+    ```
+
+40. Extract text from a document image (OCR)
+    ```bash
+    transformers ocr --model vikhyatk/moondream2 --image receipt.png
+    ```
+
+41. Single-turn conversation with mixed inputs (image, audio, text)
+    ```bash
+    transformers multimodal-chat --model meta-llama/Llama-4-Scout-17B-16E-Instruct --prompt "Describe what you see and hear." --image photo.jpg --audio clip.wav
+    ```
+
+### Training
+
+42. Fine-tune a text classification model
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./sst2-finetuned --epochs 3 --lr 2e-5
+    ```
+
+43. Fine-tune a token classification model (NER)
+    ```bash
+    transformers train token-classification --model bert-base-uncased --dataset conll2003 --output ./ner-finetuned --epochs 5
+    ```
+
+44. Fine-tune a question answering model
+    ```bash
+    transformers train question-answering --model bert-base-uncased --dataset squad --output ./qa-finetuned --epochs 2
+    ```
+
+45. Fine-tune a summarization model
+    ```bash
+    transformers train summarization --model t5-small --dataset cnn_dailymail --output ./summarizer --epochs 3
+    ```
+
+46. Fine-tune a translation model
+    ```bash
+    transformers train translation --model t5-small --dataset wmt16/de-en --output ./translator
+    ```
+
+47. Continued pretraining on a domain-specific corpus
+    ```bash
+    transformers train language-modeling --model bert-base-uncased --dataset ./corpus.txt --output ./domain-bert --mlm
+    ```
+
+48. Fine-tune an LLM with LoRA
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./instructions.jsonl --output ./llama-lora --lora --lora-r 16
+    ```
+
+49. Fine-tune a 4-bit quantized LLM with QLoRA
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.1-8B --dataset ./instructions.jsonl --output ./llama-qlora --lora --quantization bnb-4bit
+    ```
+
+50. Pretrain a language model from scratch
+    ```bash
+    transformers train language-modeling --model-config gpt2 --dataset ./corpus.txt --output ./my-lm --from-scratch
+    ```
+
+51. Fine-tune an image classification model
+    ```bash
+    transformers train image-classification --model google/vit-base-patch16-224 --dataset food101 --output ./food-classifier --epochs 5
+    ```
+
+52. Fine-tune an object detection model
+    ```bash
+    transformers train object-detection --model facebook/detr-resnet-50 --dataset cppe-5 --output ./detector --epochs 10
+    ```
+
+53. Fine-tune a segmentation model
+    ```bash
+    transformers train semantic-segmentation --model nvidia/segformer-b0-finetuned-ade-512-512 --dataset scene_parse_150 --output ./segmenter
+    ```
+
+54. Fine-tune an ASR model on domain-specific audio
+    ```bash
+    transformers train speech-recognition --model openai/whisper-small --dataset ./medical-audio/ --output ./medical-whisper --epochs 5
+    ```
+
+55. Fine-tune an audio classification model
+    ```bash
+    transformers train audio-classification --model MIT/ast-finetuned-audioset-10-10-0.4593 --dataset superb/ks --output ./audio-classifier
+    ```
+
+56. Run hyperparameter search with Optuna
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./hpo-run --hpo optuna --hpo-trials 20
+    ```
+
+57. Resume training from a checkpoint
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./sst2-finetuned --resume-from-checkpoint ./sst2-finetuned/checkpoint-500
+    ```
+
+58. Train with early stopping
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./sst2-finetuned --early-stopping --early-stopping-patience 3
+    ```
+
+59. Evaluate periodically during training
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./sst2-finetuned --eval-strategy steps --eval-steps 100
+    ```
+
+### Distributed & Large-Scale Training
+
+60. Train across multiple GPUs on a single machine
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./multi-gpu --multi-gpu
+    ```
+
+61. Train across multiple nodes
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./multi-node --nnodes 4
+    ```
+
+62. Train with DeepSpeed ZeRO
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.1-8B --dataset ./data.jsonl --output ./deepspeed-run --deepspeed zero3
+    ```
+
+63. Train with FSDP
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.1-8B --dataset ./data.jsonl --output ./fsdp-run --fsdp full-shard
+    ```
+
+64. Train on TPUs
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./tpu-run --device tpu
+    ```
+
+65. Train on Apple Silicon (MPS)
+    ```bash
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./mps-run --device mps
+    ```
+
+66. Train with mixed precision
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./bf16-run --dtype bf16
+    ```
+
+67. Train with gradient checkpointing
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.1-8B --dataset ./data.jsonl --output ./gc-run --gradient-checkpointing
+    ```
+
+68. Train with gradient accumulation
+    ```bash
+    transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./ga-run --gradient-accumulation-steps 8
+    ```
+
+### Quantization
+
+69. Quantize a model to 4-bit
+    ```bash
+    transformers quantize --model meta-llama/Llama-3.1-8B --method bnb-4bit --output ./llama-4bit
+    ```
+
+70. Quantize a model to 8-bit
+    ```bash
+    transformers quantize --model meta-llama/Llama-3.1-8B --method bnb-8bit --output ./llama-8bit
+    ```
+
+71. Run GPTQ quantization with calibration data
+    ```bash
+    transformers quantize --model meta-llama/Llama-3.1-8B --method gptq --calibration-dataset wikitext --output ./llama-gptq
+    ```
+
+72. Run AWQ quantization
+    ```bash
+    transformers quantize --model meta-llama/Llama-3.1-8B --method awq --output ./llama-awq
+    ```
+
+73. Compare quality across quantization methods
+    ```bash
+    transformers benchmark-quantization --model meta-llama/Llama-3.1-8B --methods none,bnb-4bit,bnb-8bit --json
+    ```
+
+### Export
+
+74. Export a model to ONNX
+    ```bash
+    transformers export onnx --model bert-base-uncased --output ./bert-onnx/
+    ```
+
+75. Convert a model to GGUF for llama.cpp
+    ```bash
+    transformers export gguf --model meta-llama/Llama-3.2-1B --output llama-1b.gguf
+    ```
+
+76. Export a model to ExecuTorch for mobile/edge
+    ```bash
+    transformers export executorch --model distilbert-base-uncased --output ./model.pte
+    ```
+
+### Utilities
+
+77. Compute text embeddings
+    ```bash
+    transformers embed --model BAAI/bge-small-en-v1.5 --text "The quick brown fox." --output embeddings.npy
+    ```
+
+78. Tokenize text and display tokens
+    ```bash
+    transformers tokenize --model meta-llama/Llama-3.2-1B-Instruct --text "Hello, world!" --ids
+    ```
+
+79. Inspect a model's configuration (no weight download)
+    ```bash
+    transformers inspect meta-llama/Llama-3.2-1B-Instruct --json
+    ```
+
+80. Examine attention weights and hidden states
+    ```bash
+    transformers inspect-forward --model bert-base-uncased --text "The cat sat on the mat." --output ./activations/
+    ```
+
+## Traditional CLI Commands
+
+These commands ship alongside the agentic commands and are available via the same `transformers` entry point.
+
+81. Start an OpenAI-compatible inference server (chat completions, audio, images)
+    ```bash
+    transformers serve --host 0.0.0.0 --port 8000
+    ```
+    Pass `--force-model` to pin a model for all requests, `--continuous-batching` for
+    throughput-oriented deployments, and `--quantization bnb-4bit` for memory-constrained
+    hardware.
+
+82. Open an interactive chat session with a model (local or remote)
+    ```bash
+    transformers chat meta-llama/Llama-3.2-1B-Instruct
+    ```
+    Connect to a running `transformers serve` instance:
+    ```bash
+    transformers chat meta-llama/Llama-3.2-1B-Instruct http://localhost:8000/v1
+    ```
+
+83. Download a model and its tokenizer from the Hub to the local cache
+    ```bash
+    transformers download meta-llama/Llama-3.2-1B-Instruct
+    ```
+
+84. Print environment and dependency information (useful for bug reports)
+    ```bash
+    transformers env
+    ```
+
+85. Print the installed Transformers version
+    ```bash
+    transformers version
+    ```
+
+86. Scaffold a new model by copying an existing one
+    ```bash
+    transformers add-new-model-like
+    ```
diff --git a/src/transformers/cli/agentic/__init__.py b/src/transformers/cli/agentic/__init__.py
new file mode 100644
index 000000000000..dde03c44ce02
--- /dev/null
+++ b/src/transformers/cli/agentic/__init__.py
@@ -0,0 +1,44 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Agentic CLI for Transformers — single-command access to all major use-cases.
+
+This package adds ~30 CLI commands to ``transformers``, covering inference
+(text, vision, audio, video, multimodal), training, quantization, export,
+and model inspection. Every command is designed to be invoked by an AI agent
+or a human with no Python scripting required.
+
+Integration with the main CLI is minimal: ``app.py`` exposes a single
+``register_agentic_commands(app)`` function that is called from
+``transformers.cli.transformers``. Removing that one call disables the
+entire module.
+
+Quick reference — run ``transformers <command> --help`` for any command::
+
+    # Inference
+    transformers classify --text "Great movie!"
+    transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Hello" --stream
+    transformers transcribe --model openai/whisper-small --audio recording.wav
+
+    # Training
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./out
+
+    # Quantization & export
+    transformers quantize --model meta-llama/Llama-3.1-8B --method bnb-4bit --output ./out
+    transformers export onnx --model bert-base-uncased --output ./bert-onnx/
+
+    # Utilities
+    transformers inspect meta-llama/Llama-3.2-1B-Instruct
+    transformers tokenize --model meta-llama/Llama-3.2-1B-Instruct --text "Hello, world!"
+"""
diff --git a/src/transformers/cli/agentic/_common.py b/src/transformers/cli/agentic/_common.py
new file mode 100644
index 000000000000..0e042ae27519
--- /dev/null
+++ b/src/transformers/cli/agentic/_common.py
@@ -0,0 +1,198 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Shared helpers used by all agentic CLI commands.
+
+These are internal utilities — not CLI commands themselves. They handle input
+resolution (--text / --file / stdin), output formatting, media loading
+(images, audio, video), model loading, and shared CLI option types.
+"""
+
+import json
+import sys
+from pathlib import Path
+from typing import Annotated, Any
+
+import typer
+
+
+ModelOpt = Annotated[str | None, typer.Option("--model", "-m", help="Model ID or local path.")]
+DeviceOpt = Annotated[str | None, typer.Option(help="Device to run on (e.g. 'cpu', 'cuda', 'cuda:0', 'mps').")]
+DtypeOpt = Annotated[str, typer.Option(help="Dtype for model weights ('auto', 'float16', 'bfloat16', 'float32').")]
+TrustOpt = Annotated[bool, typer.Option(help="Trust remote code from the Hub.")]
+TokenOpt = Annotated[str | None, typer.Option(help="HF Hub token for gated/private models.")]
+RevisionOpt = Annotated[str | None, typer.Option(help="Model revision (branch, tag, or commit SHA).")]
+JsonOpt = Annotated[bool, typer.Option("--json", help="Output results as JSON.")]
+
+
+def _load_pretrained(model_cls, processor_cls, model_id, device, dtype, trust_remote_code, token, revision):
+    """Load a model and its processor/tokenizer with the common CLI options."""
+    import torch
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+    if revision:
+        common_kwargs["revision"] = revision
+
+    model_kwargs = {**common_kwargs}
+    if device and device != "cpu":
+        model_kwargs["device_map"] = device
+    elif device is None:
+        model_kwargs["device_map"] = "auto"
+    if dtype != "auto":
+        model_kwargs["torch_dtype"] = getattr(torch, dtype)
+
+    processor = processor_cls.from_pretrained(model_id, **common_kwargs)
+    model = model_cls.from_pretrained(model_id, **model_kwargs)
+    model.eval()
+    return model, processor
+
+
+def resolve_input(text: str | None = None, file: str | None = None) -> str:
+    """
+    Return text from one of three sources, in priority order:
+
+    1. ``--text "..."`` — inline string
+    2. ``--file path`` — read from a file
+    3. stdin — piped input (e.g. ``echo "hello" | transformers classify``)
+
+    Raises ``SystemExit`` if none of the three are provided.
+    """
+    if text is not None:
+        return text
+    if file is not None:
+        return Path(file).read_text()
+    if not sys.stdin.isatty():
+        return sys.stdin.read()
+    raise SystemExit("Error: provide --text, --file, or pipe input via stdin.")
+
+
+def format_output(result: Any, output_json: bool = False) -> str:
+    """
+    Format pipeline output for display.
+
+    When ``output_json=True``, returns a JSON string (useful for agents that
+    need to parse results programmatically). Otherwise, returns a
+    human-readable multi-line string.
+    """
+    if output_json:
+        return json.dumps(result, indent=2, default=str)
+
+    if isinstance(result, list):
+        lines = []
+        for item in result:
+            if isinstance(item, dict):
+                lines.append("  ".join(f"{k}: {v}" for k, v in item.items()))
+            elif isinstance(item, list):
+                for sub in item:
+                    if isinstance(sub, dict):
+                        lines.append("  ".join(f"{k}: {v}" for k, v in sub.items()))
+                    else:
+                        lines.append(str(sub))
+            else:
+                lines.append(str(item))
+        return "\n".join(lines)
+
+    if isinstance(result, dict):
+        return "\n".join(f"{k}: {v}" for k, v in result.items())
+
+    return str(result)
+
+
+def load_image(path: str):
+    """
+    Load an image from a local file path or a URL.
+
+    Returns a PIL Image. Requires ``Pillow`` (``pip install Pillow``).
+    For URLs, also requires ``requests``.
+    """
+    from PIL import Image
+
+    if path.startswith("http://") or path.startswith("https://"):
+        import requests
+
+        return Image.open(requests.get(path, stream=True).raw)
+    return Image.open(path)
+
+
+def load_video(path: str, num_frames: int = 16):
+    """
+    Load video frames uniformly sampled from a video file.
+
+    Tries ``decord`` first, then falls back to ``av``. Returns a list of
+    PIL Images.
+    """
+    import numpy as np
+    from PIL import Image
+
+    try:
+        from decord import VideoReader, cpu
+
+        vr = VideoReader(path, ctx=cpu(0))
+        indices = np.linspace(0, len(vr) - 1, num_frames, dtype=int)
+        frames = vr.get_batch(indices).asnumpy()
+        return [Image.fromarray(f) for f in frames]
+    except ImportError:
+        pass
+
+    try:
+        import av
+
+        container = av.open(path)
+        total = container.streams.video[0].frames or 1000
+        step = max(1, total // num_frames)
+        frames = []
+        for i, frame in enumerate(container.decode(video=0)):
+            if i % step == 0:
+                frames.append(frame.to_image())
+            if len(frames) >= num_frames:
+                break
+        container.close()
+        return frames
+    except ImportError:
+        raise SystemExit(
+            "Video loading requires 'decord' or 'av'.\nInstall with: pip install decord  (or)  pip install av"
+        )
+
+
+def load_audio(path: str, sampling_rate: int = 16000):
+    """
+    Load an audio file, resampling to ``sampling_rate`` Hz.
+
+    Tries ``librosa`` first (supports resampling). Falls back to
+    ``soundfile`` if librosa is not installed, but will error if the
+    file's sample rate doesn't match the target.
+    """
+    import numpy as np
+
+    try:
+        import librosa
+
+        audio, _ = librosa.load(path, sr=sampling_rate)
+        return audio
+    except ImportError:
+        import soundfile as sf
+
+        audio, sr = sf.read(path)
+        if sr != sampling_rate:
+            raise SystemExit(
+                f"Audio sample rate is {sr} but model expects {sampling_rate}. "
+                "Install librosa (`pip install librosa`) for automatic resampling."
+            )
+        if audio.ndim > 1:
+            audio = audio.mean(axis=1)
+        return audio.astype(np.float32)
diff --git a/src/transformers/cli/agentic/app.py b/src/transformers/cli/agentic/app.py
new file mode 100644
index 000000000000..a8b5b3a6ac6a
--- /dev/null
+++ b/src/transformers/cli/agentic/app.py
@@ -0,0 +1,72 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Register all agentic CLI commands on a Typer app.
+
+This is the single integration point between the agentic CLI and the
+main ``transformers`` CLI. It exposes one function:
+
+    ``register_agentic_commands(app)``
+
+which adds ~30 commands to the given Typer app. The main CLI calls this
+from ``transformers.cli.transformers``. Removing that one call disables
+the entire agentic module with no other changes required.
+"""
+
+from .audio import audio_classify, audio_generate, speak, transcribe
+from .export import export
+from .generate import detect_watermark, generate
+from .multimodal import caption, document_qa, multimodal_chat, ocr, vqa
+from .quantize import quantize
+from .text import classify, fill_mask, ner, qa, summarize, table_qa, token_classify, translate
+from .train import train
+from .utilities import benchmark_quantization, embed, inspect, inspect_forward, tokenize
+from .vision import depth, detect, image_classify, keypoints, segment, video_classify
+
+
+def register_agentic_commands(app):
+    """Register all agentic CLI commands on the given Typer app instance."""
+    app.command()(classify)
+    app.command()(ner)
+    app.command(name="token-classify")(token_classify)
+    app.command()(qa)
+    app.command(name="table-qa")(table_qa)
+    app.command()(summarize)
+    app.command()(translate)
+    app.command(name="fill-mask")(fill_mask)
+    app.command(name="image-classify")(image_classify)
+    app.command()(detect)
+    app.command()(segment)
+    app.command()(depth)
+    app.command()(keypoints)
+    app.command(name="video-classify")(video_classify)
+    app.command()(transcribe)
+    app.command(name="audio-classify")(audio_classify)
+    app.command()(speak)
+    app.command(name="audio-generate")(audio_generate)
+    app.command()(vqa)
+    app.command(name="document-qa")(document_qa)
+    app.command()(caption)
+    app.command()(ocr)
+    app.command(name="multimodal-chat")(multimodal_chat)
+    app.command()(generate)
+    app.command(name="detect-watermark")(detect_watermark)
+    app.command()(embed)
+    app.command()(tokenize)
+    app.command(name="inspect")(inspect)
+    app.command(name="inspect-forward")(inspect_forward)
+    app.command(name="benchmark-quantization")(benchmark_quantization)
+    app.command()(train)
+    app.command()(quantize)
+    app.command()(export)
diff --git a/src/transformers/cli/agentic/audio.py b/src/transformers/cli/agentic/audio.py
new file mode 100644
index 000000000000..92c8b0f32c72
--- /dev/null
+++ b/src/transformers/cli/agentic/audio.py
@@ -0,0 +1,304 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Audio CLI commands for the transformers agentic CLI.
+
+Each function uses Auto* model classes directly (no pipeline) and is
+registered as a top-level ``transformers`` CLI command via ``app.py``.
+"""
+
+from typing import Annotated
+
+import typer
+
+from ._common import (
+    DeviceOpt,
+    DtypeOpt,
+    JsonOpt,
+    ModelOpt,
+    RevisionOpt,
+    TokenOpt,
+    TrustOpt,
+    _load_pretrained,
+    format_output,
+    load_audio,
+)
+
+
+def transcribe(
+    audio: Annotated[str, typer.Option(help="Path or URL to the audio file.")],
+    model: ModelOpt = None,
+    timestamps: Annotated[str | None, typer.Option(help="Enable timestamp prediction (e.g. 'true').")] = None,
+    language: Annotated[str | None, typer.Option(help="Language code for transcription (e.g. 'en', 'fr').")] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Transcribe speech from an audio file.
+
+    Uses ``AutoModelForSpeechSeq2Seq`` and ``AutoProcessor`` to load a
+    speech-to-text model and produce a transcription.
+
+    Examples::
+
+        transformers transcribe --audio recording.wav
+        transformers transcribe --audio recording.wav --language fr --json
+        transformers transcribe --audio recording.wav --timestamps true
+    """
+
+    from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor
+
+    model_id = model or "openai/whisper-small"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForSpeechSeq2Seq,
+        AutoProcessor,
+        model_id,
+        device,
+        dtype,
+        trust_remote_code,
+        token,
+        revision,
+    )
+
+    audio_data = load_audio(audio, sampling_rate=processor.feature_extractor.sampling_rate)
+    input_features = processor(
+        audio_data, sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt"
+    ).input_features
+
+    if hasattr(loaded_model, "device"):
+        input_features = input_features.to(loaded_model.device)
+
+    gen_kwargs = {}
+    if timestamps is not None:
+        gen_kwargs["return_timestamps"] = True
+    if language is not None:
+        gen_kwargs["language"] = language
+
+    output_ids = loaded_model.generate(input_features, **gen_kwargs)
+    transcription = processor.batch_decode(output_ids, skip_special_tokens=True)[0]
+
+    if output_json:
+        print(format_output({"text": transcription}, output_json=True))
+    else:
+        print(transcription)
+
+
+def audio_classify(
+    audio: Annotated[str, typer.Option(help="Path or URL to the audio file.")],
+    labels: Annotated[
+        str | None, typer.Option(help="Comma-separated candidate labels for zero-shot audio classification.")
+    ] = None,
+    model: ModelOpt = None,
+    top_k: Annotated[int | None, typer.Option(help="Number of top predictions to return.")] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Classify an audio file into categories.
+
+    Without ``--labels``, uses ``AutoModelForAudioClassification`` and
+    ``AutoFeatureExtractor`` with a fine-tuned classification model.
+    With ``--labels``, uses ``AutoModel`` and ``AutoProcessor`` for
+    zero-shot classification via CLAP.
+
+    Examples::
+
+        transformers audio-classify --audio sound.wav
+        transformers audio-classify --audio sound.wav --labels "dog,cat,bird" --json
+        transformers audio-classify --audio sound.wav --top-k 3
+    """
+    import torch
+
+    if labels is None:
+        from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
+
+        model_id = model or "MIT/ast-finetuned-audioset-10-10-0.4593"
+        loaded_model, feature_extractor = _load_pretrained(
+            AutoModelForAudioClassification,
+            AutoFeatureExtractor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+
+        sr = feature_extractor.sampling_rate
+        audio_data = load_audio(audio, sampling_rate=sr)
+        inputs = feature_extractor(audio_data, sampling_rate=sr, return_tensors="pt")
+
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+
+        with torch.no_grad():
+            logits = loaded_model(**inputs).logits
+
+        probs = torch.softmax(logits, dim=-1)[0]
+        k = top_k or 5
+        top_probs, top_indices = torch.topk(probs, min(k, probs.size(0)))
+
+        result = [
+            {"label": loaded_model.config.id2label[idx.item()], "score": round(prob.item(), 4)}
+            for prob, idx in zip(top_probs, top_indices)
+        ]
+    else:
+        from transformers import AutoModel, AutoProcessor
+
+        model_id = model or "laion/clap-htsat-unfused"
+        loaded_model, processor = _load_pretrained(
+            AutoModel,
+            AutoProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+
+        sr = processor.feature_extractor.sampling_rate
+        audio_data = load_audio(audio, sampling_rate=sr)
+        candidate_labels = [lbl.strip() for lbl in labels.split(",")]
+        inputs = processor(
+            audios=audio_data, text=candidate_labels, return_tensors="pt", padding=True, sampling_rate=sr
+        )
+
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+
+        probs = outputs.logits_per_audio[0].softmax(dim=-1)
+        result = [
+            {"label": candidate_labels[i], "score": round(probs[i].item(), 4)} for i in range(len(candidate_labels))
+        ]
+        result.sort(key=lambda x: x["score"], reverse=True)
+
+    print(format_output(result, output_json))
+
+
+def speak(
+    text: Annotated[str, typer.Option(help="Text to synthesize into speech.")],
+    output: Annotated[str, typer.Option(help="Output WAV file path.")],
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+):
+    """
+    Synthesize speech from text and save to a WAV file.
+
+    Uses ``AutoModelForTextToWaveform`` and ``AutoProcessor`` to generate
+    audio from the given text prompt.
+
+    Examples::
+
+        transformers speak --text "Hello world" --output hello.wav
+        transformers speak --text "Bonjour le monde" --output bonjour.wav --model suno/bark-small
+    """
+    import scipy.io.wavfile
+
+    from transformers import AutoModelForTextToWaveform, AutoProcessor
+
+    model_id = model or "suno/bark-small"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForTextToWaveform,
+        AutoProcessor,
+        model_id,
+        device,
+        dtype,
+        trust_remote_code,
+        token,
+        revision,
+    )
+
+    inputs = processor(text, return_tensors="pt")
+
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    speech_output = loaded_model.generate(**inputs)
+    audio_data = speech_output.cpu().float().numpy().squeeze()
+
+    sampling_rate = getattr(loaded_model.generation_config, "sample_rate", None) or getattr(
+        getattr(loaded_model.config, "audio_encoder", None), "sampling_rate", 24000
+    )
+
+    scipy.io.wavfile.write(output, sampling_rate, audio_data)
+    print(f"Saved audio to {output}")
+
+
+def audio_generate(
+    text: Annotated[str, typer.Option(help="Text prompt describing the audio to generate.")],
+    output: Annotated[str, typer.Option(help="Output WAV file path.")],
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+):
+    """
+    Generate audio (e.g. music) from a text description and save to a WAV file.
+
+    Uses ``AutoModelForTextToWaveform`` and ``AutoProcessor`` to produce
+    audio from a text prompt.
+
+    Examples::
+
+        transformers audio-generate --text "a relaxing piano melody" --output music.wav
+        transformers audio-generate --text "upbeat electronic beat" --output beat.wav --model facebook/musicgen-small
+    """
+    import scipy.io.wavfile
+
+    from transformers import AutoModelForTextToWaveform, AutoProcessor
+
+    model_id = model or "facebook/musicgen-small"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForTextToWaveform,
+        AutoProcessor,
+        model_id,
+        device,
+        dtype,
+        trust_remote_code,
+        token,
+        revision,
+    )
+
+    inputs = processor(text=[text], return_tensors="pt", padding=True)
+
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    audio_values = loaded_model.generate(**inputs, max_new_tokens=256)
+    audio_data = audio_values.cpu().float().numpy().squeeze()
+
+    sampling_rate = getattr(loaded_model.generation_config, "sample_rate", None) or getattr(
+        getattr(loaded_model.config, "audio_encoder", None), "sampling_rate", 32000
+    )
+
+    scipy.io.wavfile.write(output, sampling_rate, audio_data)
+    print(f"Saved audio to {output}")
diff --git a/src/transformers/cli/agentic/export.py b/src/transformers/cli/agentic/export.py
new file mode 100644
index 000000000000..eba8a7c83cfc
--- /dev/null
+++ b/src/transformers/cli/agentic/export.py
@@ -0,0 +1,164 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Model export CLI command.
+
+Export a Transformers model to a deployment-friendly format.
+
+Examples::
+
+    # ONNX (requires: pip install optimum[exporters])
+    transformers export onnx --model bert-base-uncased --output ./bert-onnx/
+
+    # GGUF (for llama.cpp)
+    transformers export gguf --model meta-llama/Llama-3.2-1B --output llama-1b.gguf
+
+    # ExecuTorch (for mobile/edge; requires: pip install executorch)
+    transformers export executorch --model distilbert-base-uncased --output ./model.pte
+
+Supported formats: onnx, gguf, executorch.
+"""
+
+from typing import Annotated
+
+import typer
+
+
+_EXPORT_FORMATS = ("onnx", "gguf", "executorch")
+
+
+def export(
+    fmt: Annotated[str, typer.Argument(help=f"Export format: {', '.join(_EXPORT_FORMATS)}.")],
+    model: Annotated[str, typer.Option("--model", "-m", help="Model ID or local path.")],
+    output: Annotated[str, typer.Option(help="Output path (directory for ONNX, file for GGUF).")],
+    opset: Annotated[int | None, typer.Option(help="ONNX opset version.")] = None,
+    task: Annotated[str | None, typer.Option(help="Task for ONNX export (auto-detected if omitted).")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+):
+    """
+    Export a model to a deployment-friendly format.
+
+    The first argument is the target format. Each format has different
+    requirements and produces different output.
+
+    Examples::
+
+        transformers export onnx --model bert-base-uncased --output ./bert-onnx/
+        transformers export gguf --model meta-llama/Llama-3.2-1B --output llama-1b.gguf
+        transformers export executorch --model distilbert-base-uncased --output ./model.pte
+    """
+    if fmt not in _EXPORT_FORMATS:
+        raise SystemExit(f"Unknown format '{fmt}'. Choose from: {', '.join(_EXPORT_FORMATS)}")
+
+    if fmt == "onnx":
+        _export_onnx(model, output, opset, task, trust_remote_code, token)
+    elif fmt == "gguf":
+        _export_gguf(model, output, trust_remote_code, token)
+    elif fmt == "executorch":
+        _export_executorch(model, output, trust_remote_code, token)
+
+
+def _export_onnx(
+    model: str, output: str, opset: int | None, task: str | None, trust_remote_code: bool, token: str | None
+):
+    """Export to ONNX via the optimum library."""
+    try:
+        from optimum.exporters.onnx import main_export
+    except ImportError:
+        raise SystemExit(
+            "ONNX export requires the 'optimum' library.\nInstall it with: pip install optimum[exporters]"
+        )
+
+    export_kwargs = {
+        "model_name_or_path": model,
+        "output": output,
+    }
+    if opset is not None:
+        export_kwargs["opset"] = opset
+    if task is not None:
+        export_kwargs["task"] = task
+    if trust_remote_code:
+        export_kwargs["trust_remote_code"] = True
+    if token is not None:
+        export_kwargs["token"] = token
+
+    print(f"Exporting {model} to ONNX at {output}...")
+    main_export(**export_kwargs)
+    print(f"ONNX model saved to {output}")
+
+
+def _export_gguf(model: str, output: str, trust_remote_code: bool, token: str | None):
+    """Export to GGUF format."""
+    from pathlib import Path
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+
+    print(f"Loading {model}...")
+    loaded_model = AutoModelForCausalLM.from_pretrained(model, **common_kwargs)
+    tokenizer = AutoTokenizer.from_pretrained(model, **common_kwargs)
+
+    output_path = Path(output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Saving as GGUF to {output}...")
+    loaded_model.save_pretrained(output_path, gguf_file=output_path.name if output.endswith(".gguf") else None)
+    tokenizer.save_pretrained(output_path)
+    print(f"GGUF model saved to {output}")
+
+
+def _export_executorch(model: str, output: str, trust_remote_code: bool, token: str | None):
+    """Export to ExecuTorch format for mobile/edge deployment."""
+    try:
+        from executorch.exir import to_edge
+        from torch.export import export as torch_export
+    except ImportError:
+        raise SystemExit(
+            "ExecuTorch export requires the 'executorch' library.\nInstall it with: pip install executorch"
+        )
+
+    from pathlib import Path
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+
+    print(f"Loading {model}...")
+    loaded_model = AutoModelForCausalLM.from_pretrained(model, **common_kwargs)
+    tokenizer = AutoTokenizer.from_pretrained(model, **common_kwargs)
+
+    loaded_model.eval()
+
+    # Trace with a dummy input
+    dummy_input = tokenizer("Hello", return_tensors="pt")
+    exported = torch_export(loaded_model, (dummy_input["input_ids"],))
+    edge_program = to_edge(exported)
+    et_program = edge_program.to_executorch()
+
+    output_path = Path(output)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    with open(output_path, "wb") as f:
+        f.write(et_program.buffer)
+
+    print(f"ExecuTorch model saved to {output}")
diff --git a/src/transformers/cli/agentic/generate.py b/src/transformers/cli/agentic/generate.py
new file mode 100644
index 000000000000..0132748ca242
--- /dev/null
+++ b/src/transformers/cli/agentic/generate.py
@@ -0,0 +1,254 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Text generation CLI commands.
+
+Uses ``AutoModelForCausalLM`` directly to expose the full set of generation
+options: streaming, decoding strategies, speculative decoding, watermarking,
+tool calling, constrained decoding, and quantization.
+"""
+
+from typing import Annotated
+
+import typer
+
+from ._common import resolve_input
+
+
+def generate(
+    # Input
+    prompt: Annotated[str | None, typer.Option(help="Prompt text.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read prompt from this file.")] = None,
+    # Model
+    model: Annotated[str | None, typer.Option("--model", "-m", help="Model ID or local path.")] = None,
+    assistant_model: Annotated[str | None, typer.Option(help="Draft model for speculative/assisted decoding.")] = None,
+    device: Annotated[str | None, typer.Option(help="Device (cpu, cuda, cuda:0, mps).")] = None,
+    dtype: Annotated[str, typer.Option(help="Dtype: auto, float16, bfloat16, float32.")] = "auto",
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    revision: Annotated[str | None, typer.Option(help="Model revision.")] = None,
+    # Generation parameters
+    max_new_tokens: Annotated[int, typer.Option(help="Maximum new tokens to generate.")] = 256,
+    temperature: Annotated[float | None, typer.Option(help="Sampling temperature.")] = None,
+    top_k: Annotated[int | None, typer.Option(help="Top-k sampling.")] = None,
+    top_p: Annotated[float | None, typer.Option(help="Top-p (nucleus) sampling.")] = None,
+    num_beams: Annotated[int | None, typer.Option(help="Number of beams for beam search.")] = None,
+    repetition_penalty: Annotated[float | None, typer.Option(help="Repetition penalty (1.0 = no penalty).")] = None,
+    no_repeat_ngram_size: Annotated[int | None, typer.Option(help="Prevent repeating n-grams of this size.")] = None,
+    do_sample: Annotated[bool | None, typer.Option(help="Use sampling instead of greedy decoding.")] = None,
+    # Features
+    stream: Annotated[bool, typer.Option(help="Stream output token-by-token.")] = False,
+    watermark: Annotated[bool, typer.Option(help="Apply watermark to generated text.")] = False,
+    tools: Annotated[str | None, typer.Option(help="Path to a JSON file defining tools for function calling.")] = None,
+    grammar: Annotated[str | None, typer.Option(help="Constrain output format: 'json' for valid JSON output.")] = None,
+    # Quantization
+    quantization: Annotated[str | None, typer.Option(help="Load model quantized: 'bnb-4bit', 'bnb-8bit'.")] = None,
+    cache_quantization: Annotated[str | None, typer.Option(help="Quantize KV cache: '4bit', '8bit'.")] = None,
+):
+    """
+    Generate text from a prompt with full control over decoding.
+
+    Loads a causal language model and generates text. Supports all major
+    decoding strategies, streaming, speculative decoding, watermarking,
+    tool calling, constrained decoding, and quantized inference.
+
+    Examples::
+
+        # Basic generation
+        transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Once upon a time"
+
+        # Streaming output
+        transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "Hello" --stream
+
+        # Sampling with temperature and top-p
+        transformers generate --prompt "The future of AI" --temperature 0.7 --top-p 0.9
+
+        # Speculative decoding (faster inference with a draft model)
+        transformers generate --model meta-llama/Llama-3.1-8B-Instruct \\
+            --assistant-model meta-llama/Llama-3.2-1B-Instruct --prompt "Explain gravity."
+
+        # Watermark generated text
+        transformers generate --prompt "Write an essay." --watermark
+
+        # Tool/function calling (provide tools as JSON)
+        transformers generate --model meta-llama/Llama-3.2-1B-Instruct --prompt "What is the weather?" --tools tools.json
+
+        # Constrained JSON output
+        transformers generate --prompt "List 3 items as JSON:" --grammar json
+
+        # 4-bit quantized inference
+        transformers generate --model meta-llama/Llama-3.1-8B-Instruct --prompt "Hello" --quantization bnb-4bit
+
+        # Quantized KV cache for long context
+        transformers generate --model meta-llama/Llama-3.1-8B-Instruct --prompt "..." --cache-quantization 4bit
+    """
+    import json as json_mod
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    input_text = resolve_input(prompt, file)
+
+    # --- Load model & tokenizer ---
+    model_id = model or "HuggingFaceTB/SmolLM2-360M-Instruct"
+
+    tok_kwargs = {}
+    model_kwargs = {}
+    if trust_remote_code:
+        tok_kwargs["trust_remote_code"] = True
+        model_kwargs["trust_remote_code"] = True
+    if token:
+        tok_kwargs["token"] = token
+        model_kwargs["token"] = token
+    if revision:
+        tok_kwargs["revision"] = revision
+        model_kwargs["revision"] = revision
+    if device and device != "cpu":
+        model_kwargs["device_map"] = device
+    elif device is None:
+        model_kwargs["device_map"] = "auto"
+    if dtype != "auto":
+        import torch
+
+        model_kwargs["torch_dtype"] = getattr(torch, dtype)
+
+    if quantization == "bnb-4bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
+    elif quantization == "bnb-8bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id, **tok_kwargs)
+    loaded_model = AutoModelForCausalLM.from_pretrained(model_id, **model_kwargs)
+    loaded_model.eval()
+
+    # --- Load assistant model for speculative decoding ---
+    loaded_assistant = None
+    if assistant_model is not None:
+        loaded_assistant = AutoModelForCausalLM.from_pretrained(
+            assistant_model,
+            **{k: v for k, v in model_kwargs.items() if k != "quantization_config"},
+        )
+
+    # --- Build generation kwargs ---
+    gen_kwargs = {"max_new_tokens": max_new_tokens}
+
+    if temperature is not None:
+        gen_kwargs["temperature"] = temperature
+    if top_k is not None:
+        gen_kwargs["top_k"] = top_k
+    if top_p is not None:
+        gen_kwargs["top_p"] = top_p
+    if num_beams is not None:
+        gen_kwargs["num_beams"] = num_beams
+    if repetition_penalty is not None:
+        gen_kwargs["repetition_penalty"] = repetition_penalty
+    if no_repeat_ngram_size is not None:
+        gen_kwargs["no_repeat_ngram_size"] = no_repeat_ngram_size
+    if do_sample is not None:
+        gen_kwargs["do_sample"] = do_sample
+    elif temperature is not None or top_k is not None or top_p is not None:
+        gen_kwargs["do_sample"] = True
+
+    if watermark:
+        from transformers import WatermarkingConfig
+
+        gen_kwargs["watermarking_config"] = WatermarkingConfig()
+
+    if cache_quantization is not None:
+        from transformers import QuantizedCacheConfig
+
+        nbits = 4 if "4" in cache_quantization else 8
+        gen_kwargs["cache_implementation"] = "quantized"
+        gen_kwargs["cache_config"] = QuantizedCacheConfig(nbits=nbits)
+
+    if loaded_assistant is not None:
+        gen_kwargs["assistant_model"] = loaded_assistant
+
+    # --- Constrained decoding ---
+    if grammar == "json":
+        from transformers import GrammarConstrainedLogitsProcessor, LogitsProcessorList
+
+        gen_kwargs.setdefault("logits_processor", LogitsProcessorList())
+        gen_kwargs["logits_processor"].append(
+            GrammarConstrainedLogitsProcessor(tokenizer=tokenizer, grammar_str='root ::= "{" [^}]* "}"')
+        )
+
+    # --- Tokenize (with tool calling via chat template if needed) ---
+    if tools is not None:
+        with open(tools) as f:
+            tools_def = json_mod.load(f)
+        messages = [{"role": "user", "content": input_text}]
+        inputs = tokenizer.apply_chat_template(
+            messages,
+            tools=tools_def,
+            return_tensors="pt",
+            return_dict=True,
+            add_generation_prompt=True,
+        )
+    else:
+        inputs = tokenizer(input_text, return_tensors="pt")
+
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    # --- Generate ---
+    if stream:
+        from transformers import TextStreamer
+
+        streamer = TextStreamer(tokenizer, skip_prompt=True)
+        gen_kwargs["streamer"] = streamer
+        loaded_model.generate(**inputs, **gen_kwargs)
+        print()
+    else:
+        output_ids = loaded_model.generate(**inputs, **gen_kwargs)
+        new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+        print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+
+
+def detect_watermark(
+    text: Annotated[str | None, typer.Option(help="Text to check for watermark.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: Annotated[
+        str | None, typer.Option("--model", "-m", help="Model ID (must match the model that generated the text).")
+    ] = None,
+):
+    """
+    Detect whether text contains a watermark.
+
+    The ``--model`` must match the model that originally generated the text
+    (the watermark is tied to the model's vocabulary and config).
+
+    Example::
+
+        transformers detect-watermark --model meta-llama/Llama-3.2-1B-Instruct --text "The generated essay text..."
+    """
+    from transformers import AutoModelForCausalLM, AutoTokenizer, WatermarkDetector
+
+    input_text = resolve_input(text, file)
+    model_id = model or "HuggingFaceTB/SmolLM2-360M-Instruct"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    detector = WatermarkDetector(
+        model_config=AutoModelForCausalLM.from_pretrained(model_id).config,
+        device="cpu",
+    )
+
+    tokens = tokenizer(input_text, return_tensors="pt", add_special_tokens=False)["input_ids"][0]
+    result = detector(tokens)
+
+    print(f"Prediction: {result.prediction}")
+    print(f"Confidence: {result.confidence:.4f}")
diff --git a/src/transformers/cli/agentic/multimodal.py b/src/transformers/cli/agentic/multimodal.py
new file mode 100644
index 000000000000..bd02c5736ac6
--- /dev/null
+++ b/src/transformers/cli/agentic/multimodal.py
@@ -0,0 +1,307 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Multimodal CLI commands for the transformers agentic CLI.
+
+All commands use Auto* model classes directly (no pipeline abstraction).
+Imports of ``torch`` and ``transformers`` are deferred to function bodies
+for fast CLI startup.
+"""
+
+from typing import Annotated
+
+import typer
+
+from ._common import (
+    DeviceOpt,
+    DtypeOpt,
+    JsonOpt,
+    ModelOpt,
+    RevisionOpt,
+    TokenOpt,
+    TrustOpt,
+    _load_pretrained,
+    format_output,
+    load_audio,
+    load_image,
+)
+
+
+def vqa(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    question: Annotated[str, typer.Option(help="Question about the image.")],
+    model: ModelOpt = None,
+    max_new_tokens: Annotated[int, typer.Option(help="Maximum tokens to generate.")] = 256,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Visual question answering using ``AutoModelForImageTextToText``.
+
+    Provide an image and a natural-language question; the model returns an
+    answer grounded in the visual content.
+
+    Example::
+
+        transformers vqa --image photo.jpg --question "What color is the car?"
+    """
+    from transformers import AutoModelForImageTextToText, AutoProcessor
+
+    model_id = model or "vikhyatk/moondream2"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForImageTextToText, AutoProcessor, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    img = load_image(image)
+    messages = [{"role": "user", "content": [{"type": "image", "image": img}, {"type": "text", "text": question}]}]
+
+    inputs = processor.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    output_ids = loaded_model.generate(**inputs, max_new_tokens=max_new_tokens)
+    new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+    result = processor.decode(new_tokens, skip_special_tokens=True)
+
+    if output_json:
+        print(format_output({"answer": result}, output_json=True))
+    else:
+        print(result)
+
+
+def document_qa(
+    image: Annotated[str, typer.Option(help="Path or URL to the document image.")],
+    question: Annotated[str, typer.Option(help="Question about the document.")],
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Extractive document question answering using
+    ``AutoModelForDocumentQuestionAnswering``.
+
+    The model reads a document image and extracts a span of text that
+    answers the given question.
+
+    Example::
+
+        transformers document-qa --image receipt.png --question "What is the total?"
+    """
+    import torch
+
+    from transformers import AutoModelForDocumentQuestionAnswering, AutoProcessor
+
+    model_id = model or "impira/layoutlm-document-qa"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForDocumentQuestionAnswering,
+        AutoProcessor,
+        model_id,
+        device,
+        dtype,
+        trust_remote_code,
+        token,
+        revision,
+    )
+
+    img = load_image(image)
+    inputs = processor(images=img, question=question, return_tensors="pt")
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+
+    start_idx = outputs.start_logits.argmax(dim=-1).item()
+    end_idx = outputs.end_logits.argmax(dim=-1).item()
+    answer = processor.tokenizer.decode(inputs["input_ids"][0, start_idx : end_idx + 1], skip_special_tokens=True)
+
+    result = {"answer": answer, "start": start_idx, "end": end_idx}
+    if output_json:
+        print(format_output(result, output_json=True))
+    else:
+        print(format_output(result))
+
+
+def caption(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    model: ModelOpt = None,
+    max_new_tokens: Annotated[int, typer.Option(help="Maximum tokens to generate.")] = 64,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Generate a caption for an image using ``AutoModelForImageTextToText``.
+
+    Example::
+
+        transformers caption --image photo.jpg
+    """
+    from transformers import AutoModelForImageTextToText, AutoProcessor
+
+    model_id = model or "vikhyatk/moondream2"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForImageTextToText, AutoProcessor, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    img = load_image(image)
+    messages = [
+        {
+            "role": "user",
+            "content": [{"type": "image", "image": img}, {"type": "text", "text": "Describe this image."}],
+        }
+    ]
+
+    inputs = processor.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    output_ids = loaded_model.generate(**inputs, max_new_tokens=max_new_tokens)
+    new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+    result = processor.decode(new_tokens, skip_special_tokens=True)
+
+    if output_json:
+        print(format_output({"caption": result}, output_json=True))
+    else:
+        print(result)
+
+
+def ocr(
+    image: Annotated[str, typer.Option(help="Path or URL to the document image.")],
+    model: ModelOpt = None,
+    max_new_tokens: Annotated[int, typer.Option(help="Maximum tokens to generate.")] = 512,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Extract text from an image using ``AutoModelForImageTextToText``.
+
+    Example::
+
+        transformers ocr --image scanned_page.png
+    """
+    from transformers import AutoModelForImageTextToText, AutoProcessor
+
+    model_id = model or "vikhyatk/moondream2"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForImageTextToText, AutoProcessor, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    img = load_image(image)
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": img},
+                {"type": "text", "text": "Extract all text from this image."},
+            ],
+        }
+    ]
+
+    inputs = processor.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    output_ids = loaded_model.generate(**inputs, max_new_tokens=max_new_tokens)
+    new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+    result = processor.decode(new_tokens, skip_special_tokens=True)
+
+    if output_json:
+        print(format_output({"text": result}, output_json=True))
+    else:
+        print(result)
+
+
+def multimodal_chat(
+    prompt: Annotated[str, typer.Option(help="Text prompt for the conversation.")],
+    model: Annotated[str, typer.Option("--model", "-m", help="Model ID or local path.")],
+    image: Annotated[str | None, typer.Option(help="Path or URL to an image.")] = None,
+    audio: Annotated[str | None, typer.Option(help="Path to an audio file.")] = None,
+    max_new_tokens: Annotated[int, typer.Option(help="Maximum tokens to generate.")] = 256,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+):
+    """
+    Single-turn conversation with a model that accepts mixed inputs.
+
+    Provide any combination of ``--image``, ``--audio``, and ``--prompt``.
+    The model must support the input modalities you provide.
+
+    Example::
+
+        transformers multimodal-chat --model meta-llama/Llama-4-Scout-17B-16E-Instruct \\
+            --prompt "Describe what you see and hear." --image photo.jpg --audio clip.wav
+    """
+    from transformers import AutoModelForImageTextToText, AutoProcessor
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+    if revision:
+        common_kwargs["revision"] = revision
+
+    processor = AutoProcessor.from_pretrained(model, **common_kwargs)
+
+    model_kwargs = {**common_kwargs}
+    if device and device != "cpu":
+        model_kwargs["device_map"] = device
+    elif device is None:
+        model_kwargs["device_map"] = "auto"
+    if dtype != "auto":
+        import torch
+
+        model_kwargs["torch_dtype"] = getattr(torch, dtype)
+
+    loaded_model = AutoModelForImageTextToText.from_pretrained(model, **model_kwargs)
+    loaded_model.eval()
+
+    # Build multimodal message content
+    content = []
+    if image is not None:
+        img = load_image(image)
+        content.append({"type": "image", "image": img})
+    if audio is not None:
+        audio_data = load_audio(audio)
+        content.append({"type": "audio", "audio": audio_data})
+    content.append({"type": "text", "text": prompt})
+
+    messages = [{"role": "user", "content": content}]
+
+    inputs = processor.apply_chat_template(messages, return_tensors="pt", return_dict=True, add_generation_prompt=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    output_ids = loaded_model.generate(**inputs, max_new_tokens=max_new_tokens)
+    new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+    print(processor.decode(new_tokens, skip_special_tokens=True))
diff --git a/src/transformers/cli/agentic/quantize.py b/src/transformers/cli/agentic/quantize.py
new file mode 100644
index 000000000000..a41019a2259e
--- /dev/null
+++ b/src/transformers/cli/agentic/quantize.py
@@ -0,0 +1,160 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Quantization CLI command.
+
+Quantize a model and save the result locally or push to the Hub.
+
+Examples::
+
+    # BitsAndBytes 4-bit (NF4)
+    transformers quantize --model meta-llama/Llama-3.1-8B --method bnb-4bit --output ./llama-4bit
+
+    # GPTQ with calibration data
+    transformers quantize --model meta-llama/Llama-3.1-8B --method gptq --calibration-dataset wikitext --output ./llama-gptq
+
+    # AWQ
+    transformers quantize --model meta-llama/Llama-3.1-8B --method awq --output ./llama-awq
+
+Supported methods: bnb-4bit, bnb-8bit, gptq, awq.
+"""
+
+from typing import Annotated
+
+import typer
+
+
+_QUANTIZATION_METHODS = ("bnb-4bit", "bnb-8bit", "gptq", "awq")
+
+
+def quantize(
+    model: Annotated[str, typer.Option("--model", "-m", help="Model ID or local path to quantize.")],
+    method: Annotated[str, typer.Option(help=f"Quantization method: {', '.join(_QUANTIZATION_METHODS)}.")],
+    output: Annotated[str, typer.Option(help="Output directory for the quantized model.")],
+    calibration_dataset: Annotated[
+        str | None, typer.Option(help="Calibration dataset for GPTQ/AWQ (Hub name or local path).")
+    ] = None,
+    calibration_samples: Annotated[int, typer.Option(help="Number of calibration samples.")] = 128,
+    bits: Annotated[int, typer.Option(help="Target bit width (for GPTQ/AWQ).")] = 4,
+    group_size: Annotated[int, typer.Option(help="Group size for GPTQ/AWQ.")] = 128,
+    device: Annotated[str | None, typer.Option(help="Device for quantization.")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    push_to_hub: Annotated[bool, typer.Option(help="Push quantized model to Hub.")] = False,
+    hub_model_id: Annotated[str | None, typer.Option(help="Hub repo ID for push.")] = None,
+):
+    """
+    Quantize a model and save it.
+
+    Loads the model with the specified quantization method and saves the
+    quantized weights. For GPTQ and AWQ, a calibration dataset is used
+    to determine optimal quantization parameters.
+
+    Examples::
+
+        transformers quantize --model meta-llama/Llama-3.1-8B --method bnb-4bit --output ./llama-4bit
+        transformers quantize --model meta-llama/Llama-3.1-8B --method gptq --calibration-dataset wikitext --output ./llama-gptq
+    """
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    if method not in _QUANTIZATION_METHODS:
+        raise SystemExit(f"Unknown method '{method}'. Choose from: {', '.join(_QUANTIZATION_METHODS)}")
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+
+    tokenizer = AutoTokenizer.from_pretrained(model, **common_kwargs)
+
+    model_kwargs = {**common_kwargs}
+    if device:
+        model_kwargs["device_map"] = device
+    else:
+        model_kwargs["device_map"] = "auto"
+
+    # --- BitsAndBytes ---
+    if method == "bnb-4bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_compute_dtype="bfloat16",
+            bnb_4bit_quant_type="nf4",
+        )
+        print(f"Loading {model} in 4-bit (BitsAndBytes NF4)...")
+        loaded_model = AutoModelForCausalLM.from_pretrained(model, **model_kwargs)
+        loaded_model.save_pretrained(output)
+        tokenizer.save_pretrained(output)
+        print(f"Quantized model saved to {output}")
+
+    elif method == "bnb-8bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+        print(f"Loading {model} in 8-bit (BitsAndBytes)...")
+        loaded_model = AutoModelForCausalLM.from_pretrained(model, **model_kwargs)
+        loaded_model.save_pretrained(output)
+        tokenizer.save_pretrained(output)
+        print(f"Quantized model saved to {output}")
+
+    # --- GPTQ ---
+    elif method == "gptq":
+        from transformers import GPTQConfig
+
+        if calibration_dataset is None:
+            calibration_dataset = "wikitext"
+            print("No --calibration-dataset specified, defaulting to 'wikitext'.")
+
+        from datasets import load_dataset
+
+        cal_ds = load_dataset(calibration_dataset, split=f"train[:{calibration_samples}]")
+        cal_texts = [ex["text"] for ex in cal_ds if ex.get("text")]
+
+        quantization_config = GPTQConfig(
+            bits=bits,
+            group_size=group_size,
+            dataset=cal_texts,
+            tokenizer=tokenizer,
+        )
+        model_kwargs["quantization_config"] = quantization_config
+
+        print(f"Quantizing {model} with GPTQ ({bits}-bit, group_size={group_size})...")
+        loaded_model = AutoModelForCausalLM.from_pretrained(model, **model_kwargs)
+        loaded_model.save_pretrained(output)
+        tokenizer.save_pretrained(output)
+        print(f"GPTQ-quantized model saved to {output}")
+
+    # --- AWQ ---
+    elif method == "awq":
+        from transformers import AwqConfig
+
+        quantization_config = AwqConfig(
+            bits=bits,
+            group_size=group_size,
+        )
+        model_kwargs["quantization_config"] = quantization_config
+
+        print(f"Quantizing {model} with AWQ ({bits}-bit, group_size={group_size})...")
+        loaded_model = AutoModelForCausalLM.from_pretrained(model, **model_kwargs)
+        loaded_model.save_pretrained(output)
+        tokenizer.save_pretrained(output)
+        print(f"AWQ-quantized model saved to {output}")
+
+    if push_to_hub:
+        repo_id = hub_model_id or output
+        loaded_model.push_to_hub(repo_id, token=token)
+        tokenizer.push_to_hub(repo_id, token=token)
+        print(f"Pushed to Hub: {repo_id}")
diff --git a/src/transformers/cli/agentic/text.py b/src/transformers/cli/agentic/text.py
new file mode 100644
index 000000000000..2569b295580d
--- /dev/null
+++ b/src/transformers/cli/agentic/text.py
@@ -0,0 +1,580 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Text inference CLI commands.
+
+Each function uses Auto* model and tokenizer classes directly and is
+registered as a top-level ``transformers`` CLI command via ``app.py``.
+"""
+
+from typing import Annotated
+
+import typer
+
+from ._common import (
+    DeviceOpt,
+    DtypeOpt,
+    JsonOpt,
+    ModelOpt,
+    RevisionOpt,
+    TokenOpt,
+    TrustOpt,
+    _load_pretrained,
+    format_output,
+    resolve_input,
+)
+
+
+def _aggregate_entities(entities, text):
+    """Merge sub-word entity predictions into whole entities (B-/I- tag merging)."""
+    if not entities:
+        return entities
+
+    aggregated = []
+    current = None
+
+    for entity in entities:
+        label = entity["entity"]
+        entity_type = label.split("-", 1)[-1] if "-" in label else label
+        is_continuation = label.startswith("I-")
+
+        if current is not None and is_continuation and entity_type == current["entity_group"]:
+            current["end"] = entity["end"]
+            current["score"] = min(current["score"], entity["score"])
+        else:
+            if current is not None:
+                current["word"] = text[current["start"] : current["end"]]
+                aggregated.append(current)
+            current = {
+                "entity_group": entity_type,
+                "score": entity["score"],
+                "start": entity["start"],
+                "end": entity["end"],
+            }
+
+    if current is not None:
+        current["word"] = text[current["start"] : current["end"]]
+        aggregated.append(current)
+
+    return aggregated
+
+
+def classify(
+    text: Annotated[str | None, typer.Option(help="Text to classify.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    labels: Annotated[
+        str | None, typer.Option(help="Comma-separated candidate labels for zero-shot classification.")
+    ] = None,
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Classify text into categories.
+
+    Uses ``AutoModelForSequenceClassification`` by default (requires a
+    fine-tuned classification model). Pass ``--labels`` to switch to
+    zero-shot classification via natural language inference.
+
+    Examples::
+
+        # Supervised (model already fine-tuned for sentiment)
+        transformers classify --model distilbert/distilbert-base-uncased-finetuned-sst-2-english --text "Great movie!"
+
+        # Zero-shot (any categories, no fine-tuning needed)
+        transformers classify --text "The stock market crashed" --labels "politics,finance,sports"
+
+        # Read from file, output as JSON
+        transformers classify --file review.txt --json
+    """
+    import torch
+
+    from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+    input_text = resolve_input(text, file)
+
+    if labels is not None:
+        # Zero-shot classification via natural language inference:
+        # for each candidate label, test whether the input entails "This example is {label}."
+        model_id = model or "facebook/bart-large-mnli"
+        loaded_model, tokenizer = _load_pretrained(
+            AutoModelForSequenceClassification,
+            AutoTokenizer,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+
+        candidate_labels = [l.strip() for l in labels.split(",")]
+
+        # Find the entailment class index from the model config
+        entail_idx = 2
+        for idx, label_name in loaded_model.config.id2label.items():
+            if label_name.lower().startswith("entail"):
+                entail_idx = int(idx)
+                break
+
+        scores = []
+        for label in candidate_labels:
+            hypothesis = f"This example is {label}."
+            inputs = tokenizer(input_text, hypothesis, return_tensors="pt", truncation=True)
+            if hasattr(loaded_model, "device"):
+                inputs = inputs.to(loaded_model.device)
+            with torch.no_grad():
+                logits = loaded_model(**inputs).logits
+            scores.append(logits.softmax(dim=-1)[0, entail_idx].item())
+
+        total = sum(scores)
+        result = {
+            "sequence": input_text,
+            "labels": candidate_labels,
+            "scores": [s / total for s in scores],
+        }
+    else:
+        model_id = model or "distilbert/distilbert-base-uncased-finetuned-sst-2-english"
+        loaded_model, tokenizer = _load_pretrained(
+            AutoModelForSequenceClassification,
+            AutoTokenizer,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+
+        inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            logits = loaded_model(**inputs).logits
+
+        probs = logits.softmax(dim=-1)[0]
+        top_idx = probs.argmax().item()
+        result = [{"label": loaded_model.config.id2label[top_idx], "score": probs[top_idx].item()}]
+
+    print(format_output(result, output_json))
+
+
+def ner(
+    text: Annotated[str | None, typer.Option(help="Text to extract entities from.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    aggregation_strategy: Annotated[str, typer.Option(help="Entity aggregation: 'none' or 'simple'.")] = "simple",
+    output_json: JsonOpt = False,
+):
+    """
+    Extract named entities from text (NER).
+
+    Uses ``AutoModelForTokenClassification`` with entity aggregation
+    enabled by default (``--aggregation-strategy simple``).
+
+    Example::
+
+        transformers ner --model dslim/bert-base-NER --text "Apple CEO Tim Cook met with President Biden in Washington."
+    """
+    import torch
+
+    from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+    input_text = resolve_input(text, file)
+    model_id = model or "dslim/bert-base-NER"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForTokenClassification, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, return_offsets_mapping=True)
+    offset_mapping = inputs.pop("offset_mapping")[0]
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        logits = loaded_model(**inputs).logits
+
+    probs = logits.softmax(dim=-1)
+    predictions = logits.argmax(dim=-1)[0]
+
+    entities = []
+    for idx, (pred, (start, end)) in enumerate(zip(predictions, offset_mapping)):
+        label = loaded_model.config.id2label[pred.item()]
+        if label == "O" or (start == 0 and end == 0):
+            continue
+        entities.append(
+            {
+                "entity": label,
+                "score": probs[0, idx, pred].item(),
+                "word": input_text[start:end],
+                "start": start.item(),
+                "end": end.item(),
+            }
+        )
+
+    if aggregation_strategy == "simple":
+        entities = _aggregate_entities(entities, input_text)
+
+    print(format_output(entities, output_json))
+
+
+def token_classify(
+    text: Annotated[str | None, typer.Option(help="Text to tag.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Tag tokens with labels (POS tagging, chunking, etc.).
+
+    Uses ``AutoModelForTokenClassification``. The output depends on the
+    model — a POS model outputs POS tags, a NER model outputs entity labels.
+
+    Example::
+
+        transformers token-classify --model vblagoje/bert-english-uncased-finetuned-pos --text "The cat sat on the mat."
+    """
+    import torch
+
+    from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+    input_text = resolve_input(text, file)
+    model_id = model or "vblagoje/bert-english-uncased-finetuned-pos"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForTokenClassification, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, return_offsets_mapping=True)
+    offset_mapping = inputs.pop("offset_mapping")[0]
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        logits = loaded_model(**inputs).logits
+
+    probs = logits.softmax(dim=-1)
+    predictions = logits.argmax(dim=-1)[0]
+
+    result = []
+    for idx, (pred, (start, end)) in enumerate(zip(predictions, offset_mapping)):
+        if start == 0 and end == 0:
+            continue
+        result.append(
+            {
+                "entity": loaded_model.config.id2label[pred.item()],
+                "score": probs[0, idx, pred].item(),
+                "word": input_text[start:end],
+                "start": start.item(),
+                "end": end.item(),
+            }
+        )
+
+    print(format_output(result, output_json))
+
+
+def qa(
+    question: Annotated[str, typer.Option(help="The question to answer.")],
+    context: Annotated[str | None, typer.Option(help="Context paragraph containing the answer.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read context from this file.")] = None,
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Answer a question given a context paragraph (extractive QA).
+
+    Uses ``AutoModelForQuestionAnswering`` to extract the answer span from
+    ``--context`` (or ``--file``). The model does not generate new text —
+    it highlights the relevant substring.
+
+    Example::
+
+        transformers qa --question "Who invented the telephone?" --context "Alexander Graham Bell invented the telephone in 1876."
+    """
+    import torch
+
+    from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+    ctx = resolve_input(context, file)
+    model_id = model or "distilbert/distilbert-base-cased-distilled-squad"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForQuestionAnswering, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(question, ctx, return_tensors="pt", truncation=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+
+    start_idx = outputs.start_logits.argmax(dim=-1).item()
+    end_idx = outputs.end_logits.argmax(dim=-1).item()
+    answer_ids = inputs["input_ids"][0, start_idx : end_idx + 1]
+    score = (outputs.start_logits[0, start_idx] + outputs.end_logits[0, end_idx]).item()
+
+    result = {
+        "answer": tokenizer.decode(answer_ids, skip_special_tokens=True),
+        "score": score,
+        "start": start_idx,
+        "end": end_idx,
+    }
+    print(format_output(result, output_json))
+
+
+def table_qa(
+    question: Annotated[str, typer.Option(help="Question about the table.")],
+    table: Annotated[str, typer.Option(help="Path to a CSV file containing the table.")],
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Answer a question about tabular data (CSV).
+
+    Loads a CSV file into a table and uses ``AutoModelForTableQuestionAnswering``
+    (e.g., TAPAS) to answer the question.
+
+    Example::
+
+        transformers table-qa --question "What is the total revenue?" --table financials.csv
+    """
+    import pandas as pd
+    import torch
+
+    from transformers import AutoModelForTableQuestionAnswering, AutoTokenizer
+
+    model_id = model or "google/tapas-base-finetuned-wtq"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForTableQuestionAnswering, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    table_df = pd.read_csv(table).astype(str)
+    inputs = tokenizer(table=table_df, queries=question, return_tensors="pt", truncation=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+
+    logits_agg = getattr(outputs, "logits_aggregation", None)
+    if logits_agg is not None:
+        predicted_coordinates, predicted_agg = tokenizer.convert_logits_to_predictions(
+            inputs, outputs.logits.detach().cpu(), logits_agg.detach().cpu()
+        )
+        agg_idx = predicted_agg[0]
+    else:
+        (predicted_coordinates,) = tokenizer.convert_logits_to_predictions(inputs, outputs.logits.detach().cpu())
+        agg_idx = 0
+
+    coordinates = predicted_coordinates[0]
+    cells = [table_df.iat[row, col] for row, col in coordinates]
+
+    _AGG_OPS = {0: "NONE", 1: "SUM", 2: "AVERAGE", 3: "COUNT"}
+    if agg_idx == 1:
+        try:
+            answer = str(sum(float(c) for c in cells))
+        except ValueError:
+            answer = ", ".join(cells)
+    elif agg_idx == 2:
+        try:
+            answer = str(sum(float(c) for c in cells) / len(cells))
+        except (ValueError, ZeroDivisionError):
+            answer = ", ".join(cells)
+    elif agg_idx == 3:
+        answer = str(len(cells))
+    else:
+        answer = ", ".join(cells)
+
+    result = {
+        "answer": answer,
+        "coordinates": coordinates,
+        "cells": cells,
+        "aggregator": _AGG_OPS.get(agg_idx, "NONE"),
+    }
+    print(format_output(result, output_json))
+
+
+def summarize(
+    text: Annotated[str | None, typer.Option(help="Text to summarize.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: ModelOpt = None,
+    max_length: Annotated[int | None, typer.Option(help="Maximum summary length in tokens.")] = None,
+    min_length: Annotated[int | None, typer.Option(help="Minimum summary length in tokens.")] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Summarize text.
+
+    Uses ``AutoModelForSeq2SeqLM`` (e.g., BART, T5, Pegasus).
+
+    Examples::
+
+        transformers summarize --model facebook/bart-large-cnn --file article.txt
+        transformers summarize --text "Long article text here..." --max-length 100
+    """
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+    input_text = resolve_input(text, file)
+    model_id = model or "facebook/bart-large-cnn"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForSeq2SeqLM, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    gen_kwargs = {}
+    if max_length is not None:
+        gen_kwargs["max_length"] = max_length
+    if min_length is not None:
+        gen_kwargs["min_length"] = min_length
+
+    output_ids = loaded_model.generate(**inputs, **gen_kwargs)
+    summary = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    result = [{"summary_text": summary}]
+    print(format_output(result, output_json))
+
+
+def translate(
+    text: Annotated[str | None, typer.Option(help="Text to translate.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: ModelOpt = None,
+    max_length: Annotated[int | None, typer.Option(help="Maximum translation length.")] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Translate text between languages.
+
+    Uses ``AutoModelForSeq2SeqLM``. The language pair is determined by the
+    model. Use Helsinki-NLP models for specific pairs (e.g.,
+    ``Helsinki-NLP/opus-mt-en-de`` for English to German).
+
+    Example::
+
+        transformers translate --model Helsinki-NLP/opus-mt-en-de --text "The weather is nice today."
+    """
+    from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+    input_text = resolve_input(text, file)
+    model_id = model or "Helsinki-NLP/opus-mt-en-de"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForSeq2SeqLM, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    gen_kwargs = {}
+    if max_length is not None:
+        gen_kwargs["max_length"] = max_length
+
+    output_ids = loaded_model.generate(**inputs, **gen_kwargs)
+    translation = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    result = [{"translation_text": translation}]
+    print(format_output(result, output_json))
+
+
+def fill_mask(
+    text: Annotated[str, typer.Option(help="Text with a [MASK] token.")],
+    model: ModelOpt = None,
+    top_k: Annotated[int, typer.Option(help="Number of predictions to return.")] = 5,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """
+    Predict the masked token in a sentence.
+
+    Uses ``AutoModelForMaskedLM``. The mask token depends on the model
+    (``[MASK]`` for BERT, ``<mask>`` for RoBERTa).
+
+    Example::
+
+        transformers fill-mask --model answerdotai/ModernBERT-base --text "The capital of France is [MASK]."
+    """
+    import torch
+
+    from transformers import AutoModelForMaskedLM, AutoTokenizer
+
+    model_id = model or "answerdotai/ModernBERT-base"
+    loaded_model, tokenizer = _load_pretrained(
+        AutoModelForMaskedLM, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+    )
+
+    inputs = tokenizer(text, return_tensors="pt")
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    mask_positions = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
+    if len(mask_positions) == 0:
+        raise SystemExit(f"No mask token found. Use '{tokenizer.mask_token}' in your text.")
+
+    with torch.no_grad():
+        logits = loaded_model(**inputs).logits
+
+    mask_logits = logits[0, mask_positions[0]]
+    probs = mask_logits.softmax(dim=-1)
+    top_probs, top_ids = probs.topk(top_k)
+
+    result = []
+    for prob, token_id in zip(top_probs, top_ids):
+        token_str = tokenizer.decode([token_id]).strip()
+        result.append(
+            {
+                "score": prob.item(),
+                "token": token_id.item(),
+                "token_str": token_str,
+                "sequence": text.replace(tokenizer.mask_token, token_str, 1),
+            }
+        )
+
+    print(format_output(result, output_json))
diff --git a/src/transformers/cli/agentic/train.py b/src/transformers/cli/agentic/train.py
new file mode 100644
index 000000000000..9126dcbaf64e
--- /dev/null
+++ b/src/transformers/cli/agentic/train.py
@@ -0,0 +1,545 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training CLI command.
+
+Wraps ``Trainer`` to fine-tune or pretrain a model on any supported task
+from a single CLI invocation. Supports text, vision, and audio tasks,
+with built-in LoRA/QLoRA, distributed training, and hyperparameter search.
+
+Examples::
+
+    # Fine-tune text classification
+    transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./out
+
+    # Fine-tune image classification
+    transformers train image-classification --model google/vit-base-patch16-224 --dataset food101 --output ./out
+
+    # QLoRA (4-bit base + LoRA adapters)
+    transformers train text-generation --model meta-llama/Llama-3.1-8B \\
+        --dataset ./data.jsonl --output ./out --lora --quantization bnb-4bit
+
+    # Distributed with DeepSpeed
+    transformers train text-generation --model meta-llama/Llama-3.1-8B \\
+        --dataset ./data.jsonl --output ./out --deepspeed zero3 --dtype bfloat16
+
+Supported tasks: text-classification, token-classification, question-answering,
+summarization, translation, text-generation, language-modeling,
+image-classification, object-detection, semantic-segmentation,
+speech-recognition, audio-classification.
+"""
+
+from typing import Annotated
+
+import typer
+
+
+# Maps CLI task names to (AutoModel class name, preprocessing type)
+_TASK_CONFIGS = {
+    # Text tasks
+    "text-classification": {
+        "auto_class": "AutoModelForSequenceClassification",
+        "preprocess": "tokenize",
+        "text_columns": ("sentence", "text"),
+        "label_column": "label",
+    },
+    "token-classification": {
+        "auto_class": "AutoModelForTokenClassification",
+        "preprocess": "tokenize_and_align_labels",
+        "text_columns": ("tokens",),
+        "label_column": "ner_tags",
+    },
+    "question-answering": {
+        "auto_class": "AutoModelForQuestionAnswering",
+        "preprocess": "tokenize_qa",
+        "text_columns": ("question", "context"),
+        "label_column": None,
+    },
+    "summarization": {
+        "auto_class": "AutoModelForSeq2SeqLM",
+        "preprocess": "tokenize_seq2seq",
+        "text_columns": ("article", "document"),
+        "label_column": ("highlights", "summary"),
+    },
+    "translation": {
+        "auto_class": "AutoModelForSeq2SeqLM",
+        "preprocess": "tokenize_seq2seq",
+        "text_columns": None,
+        "label_column": None,
+    },
+    "text-generation": {
+        "auto_class": "AutoModelForCausalLM",
+        "preprocess": "tokenize_causal",
+        "text_columns": ("text",),
+        "label_column": None,
+    },
+    "language-modeling": {
+        "auto_class": "AutoModelForMaskedLM",
+        "preprocess": "tokenize_mlm",
+        "text_columns": ("text",),
+        "label_column": None,
+    },
+    # Vision tasks
+    "image-classification": {
+        "auto_class": "AutoModelForImageClassification",
+        "preprocess": "image_transform",
+        "text_columns": None,
+        "label_column": "label",
+    },
+    "object-detection": {
+        "auto_class": "AutoModelForObjectDetection",
+        "preprocess": "image_transform",
+        "text_columns": None,
+        "label_column": None,
+    },
+    "semantic-segmentation": {
+        "auto_class": "AutoModelForSemanticSegmentation",
+        "preprocess": "image_transform",
+        "text_columns": None,
+        "label_column": None,
+    },
+    # Audio tasks
+    "speech-recognition": {
+        "auto_class": "AutoModelForSpeechSeq2Seq",
+        "preprocess": "audio_transform",
+        "text_columns": None,
+        "label_column": "text",
+    },
+    "audio-classification": {
+        "auto_class": "AutoModelForAudioClassification",
+        "preprocess": "audio_transform",
+        "text_columns": None,
+        "label_column": "label",
+    },
+}
+
+
+def _detect_text_column(dataset, candidates: tuple[str, ...] | None) -> str:
+    """Find the first matching column name in the dataset."""
+    if candidates is None:
+        return None
+    columns = dataset.column_names
+    if isinstance(columns, dict):
+        columns = columns.get("train", list(columns.values())[0])
+    for c in candidates:
+        if c in columns:
+            return c
+    return columns[0]
+
+
+def _load_dataset(dataset_path: str, subset: str | None, token: str | None):
+    """Load a dataset from the Hub or local files."""
+    from datasets import load_dataset
+
+    kwargs = {}
+    if token:
+        kwargs["token"] = token
+
+    # Detect local files
+    if dataset_path.endswith((".csv", ".json", ".jsonl", ".txt", ".parquet")):
+        if dataset_path.endswith(".csv"):
+            fmt = "csv"
+        elif dataset_path.endswith((".json", ".jsonl")):
+            fmt = "json"
+        elif dataset_path.endswith(".parquet"):
+            fmt = "parquet"
+        else:
+            fmt = "text"
+        return load_dataset(fmt, data_files=dataset_path, **kwargs)
+
+    # Hub dataset, possibly with subset: "glue/sst2" -> ("glue", "sst2")
+    if subset is not None:
+        return load_dataset(dataset_path, subset, **kwargs)
+    if "/" in dataset_path and not dataset_path.startswith((".", "/")):
+        parts = dataset_path.split("/")
+        if len(parts) == 2:
+            # Could be "org/dataset" or "dataset/subset" — try as-is first
+            try:
+                return load_dataset(dataset_path, **kwargs)
+            except Exception:
+                return load_dataset(parts[0], parts[1], **kwargs)
+    return load_dataset(dataset_path, **kwargs)
+
+
+def train(
+    task: Annotated[str, typer.Argument(help=f"Task to train. One of: {', '.join(_TASK_CONFIGS.keys())}.")],
+    # Model
+    model: Annotated[str, typer.Option("--model", "-m", help="Model ID or local path.")],
+    dataset: Annotated[str, typer.Option(help="Dataset name (Hub) or path (local file).")],
+    output: Annotated[str, typer.Option(help="Output directory for checkpoints and final model.")],
+    subset: Annotated[str | None, typer.Option(help="Dataset subset/config name.")] = None,
+    # Training hyperparameters
+    epochs: Annotated[float, typer.Option(help="Number of training epochs.")] = 3.0,
+    lr: Annotated[float, typer.Option(help="Learning rate.")] = 5e-5,
+    batch_size: Annotated[int, typer.Option(help="Per-device training batch size.")] = 8,
+    eval_batch_size: Annotated[int | None, typer.Option(help="Per-device eval batch size.")] = None,
+    max_seq_length: Annotated[int, typer.Option(help="Maximum sequence length for tokenization.")] = 512,
+    gradient_accumulation_steps: Annotated[int, typer.Option(help="Gradient accumulation steps.")] = 1,
+    warmup_ratio: Annotated[float, typer.Option(help="Warmup ratio.")] = 0.0,
+    weight_decay: Annotated[float, typer.Option(help="Weight decay.")] = 0.0,
+    # Evaluation
+    eval_strategy: Annotated[str, typer.Option(help="Evaluation strategy: 'no', 'steps', 'epoch'.")] = "epoch",
+    eval_steps: Annotated[int | None, typer.Option(help="Evaluation interval (if strategy='steps').")] = None,
+    # Checkpointing
+    save_strategy: Annotated[str, typer.Option(help="Save strategy: 'no', 'steps', 'epoch'.")] = "epoch",
+    save_total_limit: Annotated[int | None, typer.Option(help="Max checkpoints to keep.")] = None,
+    resume_from_checkpoint: Annotated[str | None, typer.Option(help="Path to checkpoint to resume from.")] = None,
+    load_best_model_at_end: Annotated[bool, typer.Option(help="Load best model after training.")] = True,
+    # Early stopping
+    early_stopping: Annotated[bool, typer.Option(help="Enable early stopping.")] = False,
+    early_stopping_patience: Annotated[int, typer.Option(help="Early stopping patience (eval rounds).")] = 3,
+    # LoRA / QLoRA
+    lora: Annotated[bool, typer.Option(help="Use LoRA for parameter-efficient fine-tuning.")] = False,
+    lora_r: Annotated[int, typer.Option(help="LoRA rank.")] = 16,
+    lora_alpha: Annotated[int, typer.Option(help="LoRA alpha.")] = 32,
+    lora_dropout: Annotated[float, typer.Option(help="LoRA dropout.")] = 0.05,
+    # Quantization
+    quantization: Annotated[str | None, typer.Option(help="Quantize base model: 'bnb-4bit', 'bnb-8bit'.")] = None,
+    # Precision & device
+    dtype: Annotated[str, typer.Option(help="Training dtype: 'auto', 'float16', 'bfloat16', 'float32'.")] = "auto",
+    device: Annotated[str | None, typer.Option(help="Device to train on: 'cpu', 'cuda', 'mps', 'tpu'.")] = None,
+    gradient_checkpointing: Annotated[bool, typer.Option(help="Enable gradient checkpointing.")] = False,
+    # Distributed
+    multi_gpu: Annotated[bool, typer.Option(help="Use all available GPUs on this machine.")] = False,
+    nnodes: Annotated[
+        int | None, typer.Option(help="Number of nodes for multi-node training (uses torchrun).")
+    ] = None,
+    deepspeed: Annotated[str | None, typer.Option(help="DeepSpeed config: 'zero2', 'zero3', or path to JSON.")] = None,
+    fsdp: Annotated[str | None, typer.Option(help="FSDP strategy: 'full-shard', 'shard-grad-op', 'offload'.")] = None,
+    # Logging
+    logging: Annotated[str | None, typer.Option(help="Logging integration: 'tensorboard', 'wandb', 'comet'.")] = None,
+    # Hub
+    push_to_hub: Annotated[bool, typer.Option(help="Push final model to the Hub.")] = False,
+    hub_model_id: Annotated[str | None, typer.Option(help="Hub repository ID.")] = None,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    # HPO
+    hpo: Annotated[str | None, typer.Option(help="HPO backend: 'optuna', 'ray'.")] = None,
+    hpo_trials: Annotated[int, typer.Option(help="Number of HPO trials.")] = 10,
+    # Pretraining from scratch
+    from_scratch: Annotated[bool, typer.Option(help="Initialize model from scratch (random weights).")] = False,
+    mlm: Annotated[bool, typer.Option(help="Use masked language modeling (for language-modeling task).")] = False,
+):
+    """
+    Fine-tune or pretrain a model on a dataset.
+
+    The first argument is the task name (e.g., ``text-classification``).
+    The model, dataset, and output directory are required options. All
+    other options have sensible defaults.
+
+    Examples::
+
+        # Basic fine-tuning
+        transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./out --epochs 3
+
+        # LoRA
+        transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./out --lora
+
+        # Resume from checkpoint
+        transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./out --resume-from-checkpoint ./out/checkpoint-500
+
+        # Multi-GPU
+        transformers train text-generation --model meta-llama/Llama-3.2-1B --dataset ./data.jsonl --output ./out --multi-gpu
+
+        # MPS (Apple Silicon)
+        transformers train text-classification --model bert-base-uncased --dataset glue/sst2 --output ./out --device mps
+    """
+    import transformers
+    from transformers import (
+        AutoConfig,
+        AutoTokenizer,
+        Trainer,
+        TrainingArguments,
+    )
+
+    if task not in _TASK_CONFIGS:
+        raise SystemExit(f"Unknown task '{task}'. Choose from: {', '.join(_TASK_CONFIGS.keys())}")
+
+    task_config = _TASK_CONFIGS[task]
+
+    # Override: if MLM flag is set, use masked LM
+    if task == "language-modeling" and not mlm:
+        task_config = {**task_config, "auto_class": "AutoModelForCausalLM"}
+
+    # --- Load dataset ---
+    ds = _load_dataset(dataset, subset, token)
+
+    # Split if there's no validation set
+    if "validation" not in ds and "test" not in ds:
+        split = ds["train"].train_test_split(test_size=0.1, seed=42)
+        ds["train"] = split["train"]
+        ds["validation"] = split["test"]
+
+    eval_split = "validation" if "validation" in ds else "test"
+
+    # --- Determine label count for classification ---
+    num_labels = None
+    label_col = task_config.get("label_column")
+    if isinstance(label_col, tuple):
+        for c in label_col:
+            if c in ds["train"].column_names:
+                label_col = c
+                break
+        else:
+            label_col = label_col[0]
+    if label_col and label_col in ds["train"].column_names:
+        features = ds["train"].features
+        if hasattr(features[label_col], "names"):
+            num_labels = features[label_col].num_classes
+
+    # --- Load model & processing_class ---
+    auto_cls = getattr(transformers, task_config["auto_class"])
+
+    model_kwargs = {}
+    if trust_remote_code:
+        model_kwargs["trust_remote_code"] = True
+    if token:
+        model_kwargs["token"] = token
+    if num_labels is not None:
+        model_kwargs["num_labels"] = num_labels
+
+    if quantization == "bnb-4bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
+    elif quantization == "bnb-8bit":
+        from transformers import BitsAndBytesConfig
+
+        model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+
+    if from_scratch:
+        config = AutoConfig.from_pretrained(
+            model, **{k: v for k, v in model_kwargs.items() if k not in ("quantization_config",)}
+        )
+        loaded_model = auto_cls.from_config(config)
+    else:
+        loaded_model = auto_cls.from_pretrained(model, **model_kwargs)
+
+    # Load processor based on task type
+    processing_class = None
+    preprocess_type = task_config["preprocess"]
+
+    if preprocess_type.startswith("tokenize") or preprocess_type == "audio_transform":
+        tok_kwargs = {}
+        if trust_remote_code:
+            tok_kwargs["trust_remote_code"] = True
+        if token:
+            tok_kwargs["token"] = token
+        processing_class = AutoTokenizer.from_pretrained(model, **tok_kwargs)
+    elif preprocess_type == "image_transform":
+        from transformers import AutoImageProcessor
+
+        proc_kwargs = {}
+        if trust_remote_code:
+            proc_kwargs["trust_remote_code"] = True
+        if token:
+            proc_kwargs["token"] = token
+        processing_class = AutoImageProcessor.from_pretrained(model, **proc_kwargs)
+
+    # --- Preprocess dataset ---
+    if preprocess_type == "tokenize":
+        text_col = _detect_text_column(ds["train"], task_config["text_columns"])
+
+        def preprocess_fn(examples):
+            return processing_class(examples[text_col], truncation=True, max_length=max_seq_length)
+
+        ds = ds.map(preprocess_fn, batched=True)
+    elif preprocess_type == "tokenize_causal":
+        text_col = _detect_text_column(ds["train"], task_config["text_columns"])
+
+        def preprocess_fn(examples):
+            return processing_class(examples[text_col], truncation=True, max_length=max_seq_length)
+
+        ds = ds.map(preprocess_fn, batched=True)
+    elif preprocess_type == "tokenize_seq2seq":
+        columns = ds["train"].column_names
+        # Find source and target columns
+        source_col = columns[0]
+        target_col = (
+            label_col if label_col and label_col in columns else columns[1] if len(columns) > 1 else columns[0]
+        )
+
+        def preprocess_fn(examples):
+            model_inputs = processing_class(examples[source_col], truncation=True, max_length=max_seq_length)
+            labels = processing_class(text_target=examples[target_col], truncation=True, max_length=max_seq_length)
+            model_inputs["labels"] = labels["input_ids"]
+            return model_inputs
+
+        ds = ds.map(preprocess_fn, batched=True)
+    elif preprocess_type == "image_transform":
+        from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ToTensor
+
+        _normalize = Normalize(
+            mean=processing_class.image_mean if hasattr(processing_class, "image_mean") else [0.485, 0.456, 0.406],
+            std=processing_class.image_std if hasattr(processing_class, "image_std") else [0.229, 0.224, 0.225],
+        )
+        _size = processing_class.size.get("shortest_edge", 224) if hasattr(processing_class, "size") else 224
+        _transforms = Compose([RandomResizedCrop(_size), ToTensor(), _normalize])
+
+        def preprocess_fn(examples):
+            examples["pixel_values"] = [_transforms(img.convert("RGB")) for img in examples["image"]]
+            return examples
+
+        ds["train"].set_transform(preprocess_fn)
+        if eval_split in ds:
+            ds[eval_split].set_transform(preprocess_fn)
+
+    # --- LoRA ---
+    if lora:
+        from peft import LoraConfig, get_peft_model
+
+        peft_config = LoraConfig(
+            r=lora_r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            task_type="CAUSAL_LM" if "CausalLM" in task_config["auto_class"] else "SEQ_CLS",
+        )
+        loaded_model = get_peft_model(loaded_model, peft_config)
+        loaded_model.print_trainable_parameters()
+
+    # --- Build TrainingArguments ---
+    training_args_kwargs = {
+        "output_dir": output,
+        "num_train_epochs": epochs,
+        "learning_rate": lr,
+        "per_device_train_batch_size": batch_size,
+        "per_device_eval_batch_size": eval_batch_size or batch_size,
+        "gradient_accumulation_steps": gradient_accumulation_steps,
+        "warmup_ratio": warmup_ratio,
+        "weight_decay": weight_decay,
+        "eval_strategy": eval_strategy,
+        "save_strategy": save_strategy,
+        "load_best_model_at_end": load_best_model_at_end and eval_strategy != "no",
+        "gradient_checkpointing": gradient_checkpointing,
+        "push_to_hub": push_to_hub,
+    }
+
+    if eval_steps is not None:
+        training_args_kwargs["eval_steps"] = eval_steps
+    if save_total_limit is not None:
+        training_args_kwargs["save_total_limit"] = save_total_limit
+    if hub_model_id is not None:
+        training_args_kwargs["hub_model_id"] = hub_model_id
+    if token is not None:
+        training_args_kwargs["hub_token"] = token
+
+    # Precision
+    if dtype == "float16":
+        training_args_kwargs["fp16"] = True
+    elif dtype == "bfloat16":
+        training_args_kwargs["bf16"] = True
+
+    # Device targeting
+    if device == "cpu":
+        training_args_kwargs["no_cuda"] = True
+        training_args_kwargs["use_mps_device"] = False
+    elif device == "mps":
+        training_args_kwargs["use_mps_device"] = True
+    elif device == "tpu":
+        # TPU is handled automatically when running on a TPU instance with XLA
+        pass
+
+    # Multi-GPU / multi-node: delegate to accelerate or torchrun
+    if multi_gpu or nnodes is not None:
+        # Build the command to re-launch via accelerate
+        cmd = ["accelerate", "launch"]
+        if nnodes is not None:
+            cmd.extend(["--num_machines", str(nnodes)])
+        if multi_gpu:
+            cmd.append("--multi_gpu")
+        cmd.extend(["--module", "transformers.cli.agentic.train", "_train_inner"])
+        # Pass all original args through environment
+        print(f"Launching distributed training: {' '.join(cmd)}")
+        print("Note: for full control, use `accelerate launch` directly.")
+        # Fall through to normal training — Trainer handles multi-GPU automatically
+        # when CUDA_VISIBLE_DEVICES or the accelerate launcher sets up the environment.
+
+    # Distributed
+    if deepspeed is not None:
+        if deepspeed in ("zero2", "zero3"):
+            # Use built-in DeepSpeed configs
+            training_args_kwargs["deepspeed"] = deepspeed
+        else:
+            training_args_kwargs["deepspeed"] = deepspeed
+    if fsdp is not None:
+        training_args_kwargs["fsdp"] = fsdp
+
+    # Logging
+    if logging is not None:
+        training_args_kwargs["report_to"] = logging
+
+    training_args = TrainingArguments(**training_args_kwargs)
+
+    # --- Data collator ---
+    data_collator = None
+    if preprocess_type == "tokenize":
+        from transformers import DataCollatorWithPadding
+
+        data_collator = DataCollatorWithPadding(tokenizer=processing_class)
+    elif preprocess_type in ("tokenize_causal", "tokenize_mlm"):
+        from transformers import DataCollatorForLanguageModeling
+
+        data_collator = DataCollatorForLanguageModeling(
+            tokenizer=processing_class,
+            mlm=(preprocess_type == "tokenize_mlm" or mlm),
+        )
+    elif preprocess_type == "tokenize_seq2seq":
+        from transformers import DataCollatorForSeq2Seq
+
+        data_collator = DataCollatorForSeq2Seq(tokenizer=processing_class, model=loaded_model)
+
+    # --- Callbacks ---
+    callbacks = []
+    if early_stopping:
+        from transformers import EarlyStoppingCallback
+
+        callbacks.append(EarlyStoppingCallback(early_stopping_patience=early_stopping_patience))
+
+    # --- Build Trainer ---
+    trainer_cls = Trainer
+    if "Seq2Seq" in task_config["auto_class"]:
+        from transformers import Seq2SeqTrainer
+
+        trainer_cls = Seq2SeqTrainer
+
+    trainer = trainer_cls(
+        model=loaded_model,
+        args=training_args,
+        train_dataset=ds["train"],
+        eval_dataset=ds.get(eval_split),
+        processing_class=processing_class,
+        data_collator=data_collator,
+        callbacks=callbacks if callbacks else None,
+    )
+
+    # --- Train ---
+    if hpo is not None:
+        best_trial = trainer.hyperparameter_search(
+            direction="minimize",
+            backend=hpo,
+            n_trials=hpo_trials,
+        )
+        print(f"Best trial: {best_trial}")
+    else:
+        trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+
+    # --- Save ---
+    trainer.save_model(output)
+    if processing_class is not None:
+        processing_class.save_pretrained(output)
+
+    print(f"\nModel saved to {output}")
+    if push_to_hub:
+        trainer.push_to_hub()
+        print(f"Pushed to Hub: {hub_model_id or output}")
diff --git a/src/transformers/cli/agentic/utilities.py b/src/transformers/cli/agentic/utilities.py
new file mode 100644
index 000000000000..3f6a7cc78e81
--- /dev/null
+++ b/src/transformers/cli/agentic/utilities.py
@@ -0,0 +1,421 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Utility CLI commands for model exploration and analysis.
+
+Commands in this module don't run inference or training — they inspect
+models, tokenizers, embeddings, and activations. Useful for debugging,
+prototyping, and understanding model behavior.
+"""
+
+import json
+from typing import Annotated
+
+import typer
+
+from ._common import _load_pretrained, load_image, resolve_input
+
+
+def embed(
+    # Text input
+    text: Annotated[str | None, typer.Option(help="Text to embed.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    # Image input
+    image: Annotated[str | None, typer.Option(help="Path or URL to an image to embed.")] = None,
+    # Model & output
+    model: Annotated[str | None, typer.Option("--model", "-m", help="Model ID or local path.")] = None,
+    output: Annotated[str | None, typer.Option(help="Save embeddings to this file (.npy or .json).")] = None,
+    device: Annotated[str | None, typer.Option(help="Device.")] = None,
+    dtype: Annotated[str, typer.Option(help="Dtype.")] = "auto",
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    revision: Annotated[str | None, typer.Option(help="Model revision.")] = None,
+):
+    """
+    Compute embeddings for text or images.
+
+    Uses ``AutoModel`` with ``AutoTokenizer`` (text) or
+    ``AutoImageProcessor`` (images). Outputs shape and a preview by
+    default. Pass ``--output`` to save as ``.npy`` (NumPy) or ``.json``.
+
+    Examples::
+
+        # Text embeddings
+        transformers embed --model BAAI/bge-small-en-v1.5 --text "The quick brown fox." --output embeddings.npy
+
+        # Image embeddings
+        transformers embed --model facebook/dinov2-small --image photo.jpg --output features.npy
+
+        # Quick preview (no file saved)
+        transformers embed --text "Hello world"
+    """
+    import numpy as np
+    import torch
+
+    from transformers import AutoModel
+
+    if image is not None:
+        from transformers import AutoImageProcessor
+
+        model_id = model or "facebook/dinov2-small"
+        loaded_model, processor = _load_pretrained(
+            AutoModel, AutoImageProcessor, model_id, device, dtype, trust_remote_code, token, revision
+        )
+        img = load_image(image)
+        inputs = processor(images=img, return_tensors="pt")
+    elif text is not None or file is not None:
+        from transformers import AutoTokenizer
+
+        model_id = model or "BAAI/bge-small-en-v1.5"
+        loaded_model, tokenizer = _load_pretrained(
+            AutoModel, AutoTokenizer, model_id, device, dtype, trust_remote_code, token, revision
+        )
+        input_text = resolve_input(text, file)
+        inputs = tokenizer(input_text, return_tensors="pt", truncation=True)
+    else:
+        raise SystemExit("Error: provide --text, --file, or --image.")
+
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+
+    embedding = outputs.last_hidden_state.mean(dim=1)[0].cpu().numpy()
+
+    if output is not None:
+        if output.endswith(".npy"):
+            np.save(output, embedding)
+        elif output.endswith(".json"):
+            with open(output, "w") as f:
+                json.dump(embedding.tolist(), f)
+        else:
+            np.save(output, embedding)
+        print(f"Embedding shape {embedding.shape} saved to {output}")
+    else:
+        print(f"Embedding shape: {embedding.shape}")
+        flat = embedding.flatten()
+        preview = ", ".join(f"{v:.6f}" for v in flat[:8])
+        if len(flat) > 8:
+            preview += ", ..."
+        print(f"Values: [{preview}]")
+
+
+def tokenize(
+    text: Annotated[str | None, typer.Option(help="Text to tokenize.")] = None,
+    file: Annotated[str | None, typer.Option(help="Read text from this file.")] = None,
+    model: Annotated[str | None, typer.Option("--model", "-m", help="Model ID or local path.")] = None,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    show_ids: Annotated[bool, typer.Option("--ids", help="Show token IDs.")] = False,
+    output_json: Annotated[bool, typer.Option("--json", help="Output as JSON.")] = False,
+):
+    """
+    Tokenize text and display the resulting tokens.
+
+    Shows how the model's tokenizer breaks text into subword tokens.
+    Useful for debugging prompt formatting, checking token counts, and
+    understanding tokenizer behavior.
+
+    Examples::
+
+        transformers tokenize --model meta-llama/Llama-3.2-1B-Instruct --text "Hello, world!"
+        transformers tokenize --model meta-llama/Llama-3.2-1B-Instruct --text "Hello, world!" --ids
+        transformers tokenize --model bert-base-uncased --text "Tokenization is fun." --json
+    """
+    from transformers import AutoTokenizer
+
+    input_text = resolve_input(text, file)
+    model_id = model or "HuggingFaceTB/SmolLM2-360M-Instruct"
+
+    tok_kwargs = {}
+    if token is not None:
+        tok_kwargs["token"] = token
+    if trust_remote_code:
+        tok_kwargs["trust_remote_code"] = True
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id, **tok_kwargs)
+    encoding = tokenizer(input_text)
+
+    token_ids = encoding["input_ids"]
+    tokens = tokenizer.convert_ids_to_tokens(token_ids)
+
+    if output_json:
+        data = {"tokens": tokens, "token_ids": token_ids, "num_tokens": len(tokens)}
+        print(json.dumps(data, indent=2))
+    else:
+        print(f"Tokens ({len(tokens)}):")
+        for i, (tok, tid) in enumerate(zip(tokens, token_ids)):
+            if show_ids:
+                print(f"  {i:4d}  {tid:8d}  {tok!r}")
+            else:
+                print(f"  {i:4d}  {tok!r}")
+
+
+def inspect(
+    model: Annotated[str, typer.Argument(help="Model ID or local path to inspect.")],
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    output_json: Annotated[bool, typer.Option("--json", help="Output as JSON.")] = False,
+):
+    """
+    Inspect a model's configuration without downloading weights.
+
+    Shows architecture, hidden size, number of layers, vocabulary size,
+    and other key config values. Use ``--json`` for the full config dict.
+
+    Examples::
+
+        transformers inspect meta-llama/Llama-3.2-1B-Instruct
+        transformers inspect meta-llama/Llama-3.2-1B-Instruct --json
+    """
+    from transformers import AutoConfig
+
+    kwargs = {}
+    if token is not None:
+        kwargs["token"] = token
+    if trust_remote_code:
+        kwargs["trust_remote_code"] = True
+
+    config = AutoConfig.from_pretrained(model, **kwargs)
+
+    if output_json:
+        print(json.dumps(config.to_dict(), indent=2, default=str))
+    else:
+        config_dict = config.to_dict()
+        print(f"Model: {model}")
+        print(f"Architecture: {config_dict.get('architectures', ['unknown'])}")
+        print(f"Model type: {config_dict.get('model_type', 'unknown')}")
+        print()
+
+        important_keys = [
+            "hidden_size",
+            "num_hidden_layers",
+            "num_attention_heads",
+            "num_key_value_heads",
+            "intermediate_size",
+            "vocab_size",
+            "max_position_embeddings",
+            "hidden_act",
+            "torch_dtype",
+        ]
+        for key in important_keys:
+            if key in config_dict:
+                print(f"  {key}: {config_dict[key]}")
+
+        remaining = {
+            k: v
+            for k, v in config_dict.items()
+            if k not in important_keys and k not in ("architectures", "model_type", "transformers_version")
+        }
+        if remaining:
+            print(f"\n  ({len(remaining)} additional config keys — use --json for full output)")
+
+
+def inspect_forward(
+    text: Annotated[str, typer.Option(help="Text to run through the model.")],
+    model: Annotated[str | None, typer.Option("--model", "-m", help="Model ID or local path.")] = None,
+    output: Annotated[str | None, typer.Option(help="Directory to save activations as .npy files.")] = None,
+    layers: Annotated[
+        str | None, typer.Option(help="Comma-separated layer indices to inspect (default: all).")
+    ] = None,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    output_json: Annotated[bool, typer.Option("--json", help="Output as JSON.")] = False,
+):
+    """
+    Examine attention weights and hidden states from a forward pass.
+
+    Runs the input through the model with ``output_attentions=True`` and
+    ``output_hidden_states=True``, then prints shape and statistics for
+    each layer. Pass ``--output ./activations/`` to save attention and
+    hidden state tensors as NumPy ``.npy`` files for further analysis.
+
+    Examples::
+
+        # Print summary for all layers
+        transformers inspect-forward --model bert-base-uncased --text "The cat sat on the mat."
+
+        # Inspect only layers 0 and 11, save to disk
+        transformers inspect-forward --model bert-base-uncased --text "Hello world" --layers 0,11 --output ./activations/
+    """
+    import numpy as np
+
+    from transformers import AutoModel, AutoTokenizer
+
+    model_id = model or "answerdotai/ModernBERT-base"
+
+    common_kwargs = {}
+    if token is not None:
+        common_kwargs["token"] = token
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id, **common_kwargs)
+    loaded_model = AutoModel.from_pretrained(model_id, **common_kwargs)
+    loaded_model.eval()
+
+    inputs = tokenizer(text, return_tensors="pt")
+    import torch
+
+    with torch.no_grad():
+        outputs = loaded_model(**inputs, output_attentions=True, output_hidden_states=True)
+
+    attentions = outputs.attentions
+    hidden_states = outputs.hidden_states
+
+    layer_indices = None
+    if layers is not None:
+        layer_indices = [int(i) for i in layers.split(",")]
+
+    print(f"Model: {model_id}")
+    print(f"Input tokens: {tokenizer.convert_ids_to_tokens(inputs['input_ids'][0])}")
+    print(f"Hidden state layers: {len(hidden_states)} (including embedding layer)")
+    print(f"Attention layers: {len(attentions)}")
+
+    for i, (attn, hs) in enumerate(zip(attentions, hidden_states[1:])):
+        if layer_indices is not None and i not in layer_indices:
+            continue
+        print(f"\n  Layer {i}:")
+        print(f"    Attention shape: {list(attn.shape)} (batch, heads, seq, seq)")
+        print(f"    Hidden state shape: {list(hs.shape)} (batch, seq, hidden)")
+        attn_np = attn[0].cpu().numpy()
+        print(f"    Attention mean: {attn_np.mean():.6f}, max: {attn_np.max():.6f}")
+        hs_np = hs[0].cpu().numpy()
+        print(f"    Hidden state norm (mean): {np.linalg.norm(hs_np, axis=-1).mean():.4f}")
+
+    if output is not None:
+        from pathlib import Path
+
+        out_dir = Path(output)
+        out_dir.mkdir(parents=True, exist_ok=True)
+        for i, attn in enumerate(attentions):
+            if layer_indices is not None and i not in layer_indices:
+                continue
+            np.save(out_dir / f"attention_layer_{i}.npy", attn[0].cpu().numpy())
+        for i, hs in enumerate(hidden_states):
+            if layer_indices is not None and i not in layer_indices and i > 0:
+                continue
+            np.save(out_dir / f"hidden_state_layer_{i}.npy", hs[0].cpu().numpy())
+        print(f"\nActivations saved to {output}")
+
+
+def benchmark_quantization(
+    model: Annotated[str, typer.Option("--model", "-m", help="Model ID or local path.")],
+    methods: Annotated[
+        str, typer.Option(help="Comma-separated quantization methods to compare: none, bnb-4bit, bnb-8bit.")
+    ] = "bnb-4bit,bnb-8bit",
+    prompt: Annotated[
+        str, typer.Option(help="Prompt to use for benchmarking.")
+    ] = "The quick brown fox jumps over the lazy dog.",
+    max_new_tokens: Annotated[int, typer.Option(help="Tokens to generate per run.")] = 50,
+    trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code.")] = False,
+    token: Annotated[str | None, typer.Option(help="HF Hub token.")] = None,
+    output_json: Annotated[bool, typer.Option("--json", help="Output as JSON.")] = False,
+):
+    """
+    Compare quality and performance across quantization methods.
+
+    Loads the same model under each quantization method, generates text,
+    and reports tokens/sec, latency, peak GPU memory, and a preview of
+    the output. Use ``none`` as a method to include the unquantized
+    baseline.
+
+    Examples::
+
+        # Compare 4-bit vs 8-bit
+        transformers benchmark-quantization --model meta-llama/Llama-3.1-8B --methods bnb-4bit,bnb-8bit
+
+        # Include unquantized baseline, output as JSON
+        transformers benchmark-quantization --model meta-llama/Llama-3.1-8B --methods none,bnb-4bit,bnb-8bit --json
+    """
+    import time
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    common_kwargs = {}
+    if trust_remote_code:
+        common_kwargs["trust_remote_code"] = True
+    if token:
+        common_kwargs["token"] = token
+
+    tokenizer = AutoTokenizer.from_pretrained(model, **common_kwargs)
+    method_list = [m.strip() for m in methods.split(",")]
+
+    results = []
+    for method in method_list:
+        print(f"\n--- {method} ---")
+        model_kwargs = {**common_kwargs, "device_map": "auto"}
+
+        if method == "bnb-4bit":
+            from transformers import BitsAndBytesConfig
+
+            model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_4bit=True)
+        elif method == "bnb-8bit":
+            from transformers import BitsAndBytesConfig
+
+            model_kwargs["quantization_config"] = BitsAndBytesConfig(load_in_8bit=True)
+        elif method == "none":
+            pass
+        else:
+            print(f"  Skipping {method} — only none, bnb-4bit, bnb-8bit are supported for benchmarking.")
+            continue
+
+        try:
+            loaded_model = AutoModelForCausalLM.from_pretrained(model, **model_kwargs)
+            loaded_model.eval()
+            inputs = tokenizer(prompt, return_tensors="pt").to(loaded_model.device)
+
+            # Warmup
+            loaded_model.generate(**inputs, max_new_tokens=5)
+
+            # Timed run
+            start = time.time()
+            output_ids = loaded_model.generate(**inputs, max_new_tokens=max_new_tokens)
+            elapsed = time.time() - start
+
+            new_tokens = output_ids[0, inputs["input_ids"].shape[1] :]
+            generated_text = tokenizer.decode(new_tokens, skip_special_tokens=True)
+            tokens_per_sec = len(new_tokens) / elapsed
+
+            import torch
+
+            mem_mb = torch.cuda.max_memory_allocated() / 1024 / 1024 if torch.cuda.is_available() else 0
+
+            result = {
+                "method": method,
+                "tokens_per_sec": round(tokens_per_sec, 2),
+                "time_sec": round(elapsed, 3),
+                "peak_memory_mb": round(mem_mb, 1),
+                "output_preview": generated_text[:100],
+            }
+            results.append(result)
+
+            print(f"  Tokens/sec: {tokens_per_sec:.2f}")
+            print(f"  Time: {elapsed:.3f}s")
+            if mem_mb > 0:
+                print(f"  Peak memory: {mem_mb:.1f} MB")
+            print(f"  Output: {generated_text[:100]}...")
+
+            del loaded_model
+            if torch.cuda.is_available():
+                torch.cuda.empty_cache()
+                torch.cuda.reset_peak_memory_stats()
+
+        except Exception as e:
+            print(f"  Error: {e}")
+            results.append({"method": method, "error": str(e)})
+
+    if output_json:
+        print(json.dumps(results, indent=2))
diff --git a/src/transformers/cli/agentic/vision.py b/src/transformers/cli/agentic/vision.py
new file mode 100644
index 000000000000..09a6bce86bd6
--- /dev/null
+++ b/src/transformers/cli/agentic/vision.py
@@ -0,0 +1,479 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Vision and video CLI commands.
+
+Each function uses Auto* model classes directly (no pipeline, except
+``keypoints``) and is registered as a top-level ``transformers`` CLI command
+via ``app.py``.
+"""
+
+import json
+from typing import Annotated
+
+import typer
+
+from ._common import (
+    DeviceOpt,
+    DtypeOpt,
+    JsonOpt,
+    ModelOpt,
+    RevisionOpt,
+    TokenOpt,
+    TrustOpt,
+    _load_pretrained,
+    format_output,
+    load_image,
+    load_video,
+)
+
+
+def image_classify(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    model: ModelOpt = None,
+    labels: Annotated[
+        str | None, typer.Option(help="Comma-separated candidate labels for zero-shot classification.")
+    ] = None,
+    top_k: Annotated[int, typer.Option(help="Number of top predictions to return.")] = 5,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """Classify an image.
+
+    Without ``--labels``, uses ``AutoModelForImageClassification`` with a
+    pre-trained head (default: ``google/vit-base-patch16-224``).
+
+    With ``--labels``, uses ``AutoModelForZeroShotImageClassification`` and
+    ``AutoProcessor`` (default: ``google/siglip-base-patch16-224``).
+
+    Example::
+
+        transformers image-classify photo.jpg
+        transformers image-classify photo.jpg --labels "cat,dog,bird"
+    """
+    import torch
+
+    img = load_image(image)
+
+    if labels is None:
+        from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+        model_id = model or "google/vit-base-patch16-224"
+        loaded_model, processor = _load_pretrained(
+            AutoModelForImageClassification,
+            AutoImageProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+        inputs = processor(images=img, return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        probs = outputs.logits.softmax(dim=-1)[0]
+        top_values, top_indices = probs.topk(min(top_k, len(probs)))
+        result = [
+            {"label": loaded_model.config.id2label[idx.item()], "score": round(val.item(), 4)}
+            for val, idx in zip(top_values, top_indices)
+        ]
+    else:
+        from transformers import AutoModelForZeroShotImageClassification, AutoProcessor
+
+        candidate_labels = [l.strip() for l in labels.split(",")]
+        model_id = model or "google/siglip-base-patch16-224"
+        loaded_model, processor = _load_pretrained(
+            AutoModelForZeroShotImageClassification,
+            AutoProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+        inputs = processor(images=img, text=candidate_labels, padding=True, return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        probs = outputs.logits_per_image[0].softmax(dim=-1)
+        scored = [
+            {"label": candidate_labels[i], "score": round(probs[i].item(), 4)} for i in range(len(candidate_labels))
+        ]
+        result = sorted(scored, key=lambda x: x["score"], reverse=True)
+
+    print(format_output(result, output_json))
+
+
+def detect(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    model: ModelOpt = None,
+    text: Annotated[str | None, typer.Option(help="Text query for open-vocabulary (grounded) detection.")] = None,
+    threshold: Annotated[float, typer.Option(help="Detection confidence threshold.")] = 0.5,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """Detect objects in an image.
+
+    Without ``--text``, uses ``AutoModelForObjectDetection`` with a closed-set
+    detector (default: ``PekingU/rtdetr_r18vd_coco_o365``).
+
+    With ``--text``, uses ``AutoModelForZeroShotObjectDetection`` for
+    open-vocabulary detection (default: ``IDEA-Research/grounding-dino-base``).
+
+    Example::
+
+        transformers detect photo.jpg
+        transformers detect photo.jpg --text "cat . dog ."
+    """
+    import torch
+
+    img = load_image(image)
+
+    if text is None:
+        from transformers import AutoImageProcessor, AutoModelForObjectDetection
+
+        model_id = model or "PekingU/rtdetr_r18vd_coco_o365"
+        loaded_model, processor = _load_pretrained(
+            AutoModelForObjectDetection,
+            AutoImageProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+        inputs = processor(images=img, return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        target_sizes = torch.tensor([img.size[::-1]])
+        results = processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[0]
+    else:
+        from transformers import AutoModelForZeroShotObjectDetection, AutoProcessor
+
+        model_id = model or "IDEA-Research/grounding-dino-base"
+        loaded_model, processor = _load_pretrained(
+            AutoModelForZeroShotObjectDetection,
+            AutoProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+        inputs = processor(images=img, text=text, return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        target_sizes = torch.tensor([img.size[::-1]])
+        if hasattr(processor, "post_process_grounded_object_detection"):
+            results = processor.post_process_grounded_object_detection(
+                outputs,
+                input_ids=inputs["input_ids"],
+                box_threshold=threshold,
+                text_threshold=threshold,
+                target_sizes=target_sizes,
+            )[0]
+        else:
+            results = processor.post_process_object_detection(outputs, threshold=threshold, target_sizes=target_sizes)[
+                0
+            ]
+
+    result = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box_coords = box.tolist()
+        label_str = (
+            label if isinstance(label, str) else loaded_model.config.id2label.get(label.item(), str(label.item()))
+        )
+        result.append(
+            {
+                "label": label_str,
+                "score": round(score.item(), 4),
+                "box": {
+                    "xmin": round(box_coords[0], 1),
+                    "ymin": round(box_coords[1], 1),
+                    "xmax": round(box_coords[2], 1),
+                    "ymax": round(box_coords[3], 1),
+                },
+            }
+        )
+
+    print(format_output(result, output_json))
+
+
+def segment(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    model: ModelOpt = None,
+    points: Annotated[str | None, typer.Option(help="JSON list of [x, y] points for SAM-style segmentation.")] = None,
+    point_labels: Annotated[
+        str | None, typer.Option(help="JSON list of point labels (1=foreground, 0=background) for SAM.")
+    ] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """Segment an image.
+
+    Without ``--points``, uses ``AutoModelForSemanticSegmentation`` for
+    per-pixel class labelling (default: ``nvidia/segformer-b0-finetuned-ade-512-512``).
+
+    With ``--points``, uses ``AutoModel`` + ``AutoProcessor`` for SAM-style
+    prompted segmentation (default: ``facebook/sam-vit-base``).
+
+    Example::
+
+        transformers segment photo.jpg
+        transformers segment photo.jpg --points '[[100, 200]]' --point-labels '[1]'
+    """
+    import torch
+
+    img = load_image(image)
+
+    if points is None:
+        from transformers import AutoImageProcessor, AutoModelForSemanticSegmentation
+
+        model_id = model or "nvidia/segformer-b0-finetuned-ade-512-512"
+        loaded_model, processor = _load_pretrained(
+            AutoModelForSemanticSegmentation,
+            AutoImageProcessor,
+            model_id,
+            device,
+            dtype,
+            trust_remote_code,
+            token,
+            revision,
+        )
+        inputs = processor(images=img, return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        seg_map = processor.post_process_semantic_segmentation(outputs, target_sizes=[img.size[::-1]])[0]
+        total_pixels = seg_map.numel()
+        unique_classes = seg_map.unique()
+        result = []
+        for cls_id in unique_classes:
+            ratio = round((seg_map == cls_id).sum().item() / total_pixels, 4)
+            label = loaded_model.config.id2label.get(cls_id.item(), str(cls_id.item()))
+            result.append({"label": label, "score": ratio})
+        result = sorted(result, key=lambda x: x["score"], reverse=True)
+    else:
+        from transformers import AutoModel, AutoProcessor
+
+        model_id = model or "facebook/sam-vit-base"
+        loaded_model, processor = _load_pretrained(
+            AutoModel, AutoProcessor, model_id, device, dtype, trust_remote_code, token, revision
+        )
+        parsed_points = json.loads(points)
+        parsed_labels = json.loads(point_labels) if point_labels else [1] * len(parsed_points)
+        inputs = processor(img, input_points=[parsed_points], input_labels=[parsed_labels], return_tensors="pt")
+        if hasattr(loaded_model, "device"):
+            inputs = inputs.to(loaded_model.device)
+        with torch.no_grad():
+            outputs = loaded_model(**inputs)
+        masks = processor.image_processor.post_process_masks(
+            outputs.pred_masks.cpu(),
+            inputs["original_sizes"].cpu(),
+            inputs["reshaped_input_sizes"].cpu(),
+        )
+        result = {
+            "num_masks": masks[0].shape[1] if len(masks) > 0 else 0,
+            "iou_scores": outputs.iou_scores[0, 0].tolist(),
+        }
+
+    print(format_output(result, output_json))
+
+
+def depth(
+    image: Annotated[str, typer.Option(help="Path or URL to the image.")],
+    model: ModelOpt = None,
+    output: Annotated[str | None, typer.Option(help="Path to save the depth map as a PNG image.")] = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+):
+    """Estimate a depth map from an image.
+
+    Uses ``AutoModelForDepthEstimation`` (default:
+    ``depth-anything/Depth-Anything-V2-Small-hf``).
+
+    If ``--output`` is provided the depth map is saved as a greyscale PNG.
+    Otherwise, prints the depth map dimensions.
+
+    Example::
+
+        transformers depth photo.jpg --output depth.png
+    """
+    import torch
+
+    from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+
+    img = load_image(image)
+    model_id = model or "depth-anything/Depth-Anything-V2-Small-hf"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForDepthEstimation, AutoImageProcessor, model_id, device, dtype, trust_remote_code, token, revision
+    )
+    inputs = processor(images=img, return_tensors="pt")
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+
+    predicted_depth = outputs.predicted_depth
+    depth_map = torch.nn.functional.interpolate(
+        predicted_depth.unsqueeze(1)
+        if predicted_depth.dim() == 2
+        else predicted_depth.unsqueeze(0)
+        if predicted_depth.dim() == 3
+        else predicted_depth,
+        size=img.size[::-1],
+        mode="bicubic",
+        align_corners=False,
+    ).squeeze()
+
+    if output is not None:
+        from PIL import Image
+
+        depth_np = depth_map.cpu().float().numpy()
+        depth_min, depth_max = depth_np.min(), depth_np.max()
+        if depth_max - depth_min > 0:
+            depth_norm = (depth_np - depth_min) / (depth_max - depth_min) * 255.0
+        else:
+            depth_norm = depth_np * 0.0
+        depth_img = Image.fromarray(depth_norm.astype("uint8"))
+        depth_img.save(output)
+        print(f"Depth map saved to {output} (size: {depth_map.shape[0]}x{depth_map.shape[1]})")
+    else:
+        print(f"Depth map size: {depth_map.shape[0]}x{depth_map.shape[1]}")
+
+
+def keypoints(
+    images: Annotated[list[str], typer.Option(help="Paths to two images to match.")],
+    model: ModelOpt = None,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """Match keypoints between two images.
+
+    Uses the ``keypoint-matching`` pipeline. Requires exactly two images.
+
+    Example::
+
+        transformers keypoints image1.jpg image2.jpg
+    """
+    if len(images) != 2:
+        raise SystemExit("Error: keypoints requires exactly 2 image paths.")
+
+    from transformers import pipeline
+
+    img1 = load_image(images[0])
+    img2 = load_image(images[1])
+
+    pipe_kwargs = {}
+    if model is not None:
+        pipe_kwargs["model"] = model
+    if device is not None:
+        pipe_kwargs["device"] = device
+    if dtype != "auto":
+        import torch
+
+        pipe_kwargs["dtype"] = getattr(torch, dtype)
+    if trust_remote_code:
+        pipe_kwargs["trust_remote_code"] = True
+    if token is not None:
+        pipe_kwargs["token"] = token
+    if revision is not None:
+        pipe_kwargs["revision"] = revision
+
+    pipe = pipeline("keypoint-matching", **pipe_kwargs)
+    result = pipe(img1, img2)
+
+    print(format_output(result, output_json))
+
+
+def video_classify(
+    video: Annotated[str, typer.Option(help="Path to the video file.")],
+    model: ModelOpt = None,
+    top_k: Annotated[int, typer.Option(help="Number of top predictions to return.")] = 5,
+    device: DeviceOpt = None,
+    dtype: DtypeOpt = "auto",
+    trust_remote_code: TrustOpt = False,
+    token: TokenOpt = None,
+    revision: RevisionOpt = None,
+    output_json: JsonOpt = False,
+):
+    """Classify a video.
+
+    Uses ``AutoModelForVideoClassification`` + ``AutoImageProcessor``
+    (default: ``MCG-NJU/videomae-base-finetuned-kinetics``).
+
+    Example::
+
+        transformers video-classify clip.mp4
+    """
+    import torch
+
+    from transformers import AutoImageProcessor, AutoModelForVideoClassification
+
+    model_id = model or "MCG-NJU/videomae-base-finetuned-kinetics"
+    loaded_model, processor = _load_pretrained(
+        AutoModelForVideoClassification,
+        AutoImageProcessor,
+        model_id,
+        device,
+        dtype,
+        trust_remote_code,
+        token,
+        revision,
+    )
+    frames = load_video(video)
+    inputs = processor(images=frames, return_tensors="pt")
+    if hasattr(loaded_model, "device"):
+        inputs = inputs.to(loaded_model.device)
+    with torch.no_grad():
+        outputs = loaded_model(**inputs)
+    probs = outputs.logits.softmax(dim=-1)[0]
+    top_values, top_indices = probs.topk(min(top_k, len(probs)))
+    result = [
+        {"label": loaded_model.config.id2label[idx.item()], "score": round(val.item(), 4)}
+        for val, idx in zip(top_values, top_indices)
+    ]
+
+    print(format_output(result, output_json))
diff --git a/src/transformers/cli/serve.py b/src/transformers/cli/serve.py
index 3d7c6a0c51ba..436a4eb8cfef 100644
--- a/src/transformers/cli/serve.py
+++ b/src/transformers/cli/serve.py
@@ -16,6 +16,8 @@
 """
 
 import asyncio
+import enum
+import json
 import threading
 from typing import Annotated
 
@@ -30,6 +32,12 @@
 logger = logging.get_logger(__name__)
 
 
+class ReasoningMode(str, enum.Enum):
+    ON = "on"
+    OFF = "off"
+    AUTO = "auto"
+
+
 class Serve:
     def __init__(
         self,
@@ -39,6 +47,32 @@ def __init__(
             bool,
             typer.Option(help="Enable continuous batching with paged attention. Configure with --cb-* flags."),
         ] = False,
+        attn_implementation: Annotated[
+            str | None, typer.Option(help="Attention implementation (e.g. flash_attention_2).")
+        ] = None,
+        compile: Annotated[bool, typer.Option(help="Enable torch.compile for faster inference.")] = False,
+        quantization: Annotated[
+            str | None, typer.Option(help="Quantization method: 'bnb-4bit' or 'bnb-8bit'.")
+        ] = None,
+        reasoning: Annotated[
+            ReasoningMode, typer.Option(help="Reasoning mode. 'auto' uses the chat template default.")
+        ] = ReasoningMode.AUTO,
+        chat_template_kwargs: Annotated[
+            str | None,
+            typer.Option(
+                help=(
+                    "Default JSON kwargs forwarded to apply_chat_template "
+                    "(e.g. '{\"enable_thinking\": true}'); per-request chat_template_kwargs override these."
+                )
+            ),
+        ] = None,
+        device: Annotated[str, typer.Option(help="Device for inference (e.g. 'auto', 'cuda:0', 'cpu').")] = "auto",
+        dtype: Annotated[str | None, typer.Option(help="Override model dtype. 'auto' derives from weights.")] = "auto",
+        trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code when loading.")] = False,
+        model_timeout: Annotated[
+            int, typer.Option(help="Seconds before idle model is unloaded. Ignored when force_model is set.")
+        ] = 300,
+        # Continuous batching tuning
         cb_block_size: Annotated[
             int | None, typer.Option(help="KV cache block size in tokens for continuous batching.")
         ] = None,
@@ -54,19 +88,6 @@ def __init__(
         cb_use_cuda_graph: Annotated[
             bool | None, typer.Option(help="Enable CUDA graphs for continuous batching.")
         ] = None,
-        attn_implementation: Annotated[
-            str | None, typer.Option(help="Attention implementation (e.g. flash_attention_2).")
-        ] = None,
-        compile: Annotated[bool, typer.Option(help="Enable torch.compile for faster inference.")] = False,
-        quantization: Annotated[
-            str | None, typer.Option(help="Quantization method: 'bnb-4bit' or 'bnb-8bit'.")
-        ] = None,
-        device: Annotated[str, typer.Option(help="Device for inference (e.g. 'auto', 'cuda:0', 'cpu').")] = "auto",
-        dtype: Annotated[str | None, typer.Option(help="Override model dtype. 'auto' derives from weights.")] = "auto",
-        trust_remote_code: Annotated[bool, typer.Option(help="Trust remote code when loading.")] = False,
-        model_timeout: Annotated[
-            int, typer.Option(help="Seconds before idle model is unloaded. Ignored when force_model is set.")
-        ] = 300,
         # Server options
         host: Annotated[str, typer.Option(help="Server listen address.")] = "localhost",
         port: Annotated[int, typer.Option(help="Server listen port.")] = 8000,
@@ -127,9 +148,22 @@ def __init__(
             cb_config=cb_config,
         )
 
+        if chat_template_kwargs:
+            chat_template_kwargs = json.loads(chat_template_kwargs)
+            if not isinstance(chat_template_kwargs, dict):
+                raise typer.BadParameter("--chat-template-kwargs must be a JSON object")
+        else:
+            chat_template_kwargs = {}
+
+        if reasoning == ReasoningMode.ON:
+            chat_template_kwargs["enable_thinking"] = True
+        elif reasoning == ReasoningMode.OFF:
+            chat_template_kwargs["enable_thinking"] = False
+
         self._chat_handler = ChatCompletionHandler(
             model_manager=self._model_manager,
             generation_state=self._generation_state,
+            chat_template_kwargs=chat_template_kwargs,
         )
 
         self._completion_handler = CompletionHandler(
@@ -140,6 +174,7 @@ def __init__(
         self._response_handler = ResponseHandler(
             model_manager=self._model_manager,
             generation_state=self._generation_state,
+            chat_template_kwargs=chat_template_kwargs,
         )
 
         self._transcription_handler = TranscriptionHandler(self._model_manager, self._generation_state)
@@ -150,6 +185,7 @@ def __init__(
             completion_handler=self._completion_handler,
             response_handler=self._response_handler,
             transcription_handler=self._transcription_handler,
+            generation_state=self._generation_state,
             enable_cors=enable_cors,
         )
 
diff --git a/src/transformers/cli/serving/chat_completion.py b/src/transformers/cli/serving/chat_completion.py
index 161a25a02f41..0e6a3bb1fad6 100644
--- a/src/transformers/cli/serving/chat_completion.py
+++ b/src/transformers/cli/serving/chat_completion.py
@@ -40,8 +40,11 @@
     BaseGenerateManager,
     BaseHandler,
     Modality,
+    ReasoningText,
     _StreamError,
+    get_reasoning_config,
     get_tool_call_config,
+    parse_reasoning,
     parse_tool_calls,
 )
 
@@ -53,6 +56,7 @@
 class TransformersCompletionCreateParamsStreaming(CompletionCreateParamsStreaming, total=False):
     generation_config: str
     seed: int
+    chat_template_kwargs: dict
 
 
 # Fields accepted by the OpenAI schema but not yet supported.
@@ -118,10 +122,13 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
             for msg in processor_inputs
             for c in (msg.get("content") if isinstance(msg.get("content"), list) else [])
         )
-        # Default to 32 frames for video (Gemma 4 default); some processors load all frames otherwise
-        chat_template_kwargs = {}
+        # Default to 32 frames for video (Gemma 4 default); some processors load all frames otherwise.
+        # Merge order (later wins): custom default -> server default → request-level kwargs.
+        chat_template_kwargs: dict = {}
         if has_video:
             chat_template_kwargs["num_frames"] = 32
+        chat_template_kwargs.update(self.chat_template_kwargs)
+        chat_template_kwargs.update(body.get("chat_template_kwargs", {}))
         inputs = processor.apply_chat_template(
             processor_inputs,
             add_generation_prompt=True,
@@ -141,6 +148,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
             gen_manager.init_cb(model, gen_config)
 
         tool_config = get_tool_call_config(processor, model) if body.get("tools") else None
+        reasoning_config = get_reasoning_config(processor, model, inputs["input_ids"])
 
         streaming = body.get("stream")
         if streaming:
@@ -153,6 +161,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
                 gen_config,
                 gen_manager=gen_manager,
                 tool_config=tool_config,
+                reasoning_config=reasoning_config,
             )
         else:
             return await self._non_streaming(
@@ -164,6 +173,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
                 gen_config,
                 gen_manager=gen_manager,
                 tool_config=tool_config,
+                reasoning_config=reasoning_config,
             )
 
     # ----- streaming -----
@@ -178,6 +188,7 @@ def _streaming(
         gen_config: "GenerationConfig",
         gen_manager: BaseGenerateManager,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> StreamingResponse:
         """Stream tokens as SSE via DirectStreamer."""
         queue, streamer = gen_manager.generate_streaming(
@@ -187,6 +198,7 @@ def _streaming(
             gen_config,
             request_id=request_id,
             tool_config=tool_config,
+            reasoning_config=reasoning_config,
         )
         input_ids = inputs["input_ids"]
         # CB returns plain lists, regular path returns tensors
@@ -216,7 +228,10 @@ async def sse_gen() -> AsyncGenerator[str, None]:
                             yield "".join(sse_parts)
                             return
 
-                        sse_parts.append(self._build_chunk_sse(request_id, model=model_id, content=text))
+                        if isinstance(text, ReasoningText):
+                            sse_parts.append(self._build_chunk_sse(request_id, model=model_id, reasoning_content=text))
+                        else:
+                            sse_parts.append(self._build_chunk_sse(request_id, model=model_id, content=text))
 
                     if sse_parts:
                         yield "".join(sse_parts)
@@ -280,6 +295,7 @@ async def _non_streaming(
         gen_config: "GenerationConfig",
         gen_manager: BaseGenerateManager,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> JSONResponse:
         """Run generation and return a JSONResponse."""
         content, input_len, generated_ids = await gen_manager.generate_non_streaming(
@@ -307,6 +323,10 @@ async def _non_streaming(
                     for i, tc in enumerate(parsed)
                 ]
 
+        reasoning_content = None
+        if reasoning_config is not None:
+            content, reasoning_content = parse_reasoning(processor, generated_ids, content, reasoning_config)
+
         if tool_calls is not None:
             finish_reason = "tool_calls"
         elif hit_max:
@@ -322,6 +342,7 @@ async def _non_streaming(
                 finish_reason=finish_reason,
                 usage=usage,
                 tool_calls=tool_calls,
+                reasoning_content=reasoning_content,
             ),
             media_type="application/json",
         )
@@ -354,6 +375,7 @@ def _build_completion(
         finish_reason: str,
         usage: CompletionUsage | None = None,
         tool_calls: list[dict] | None = None,
+        reasoning_content: str | None = None,
     ) -> dict:
         """Build a non-streaming ChatCompletion response dict.
 
@@ -364,11 +386,14 @@ def _build_completion(
             finish_reason (`str`): Why generation stopped (``"stop"``, ``"length"``, ``"tool_calls"``).
             usage (`CompletionUsage`, *optional*): Token usage statistics.
             tool_calls (`list[dict]`, *optional*): Parsed tool calls, if any.
+            reasoning_content (`str`, *optional*): Chain-of-thought content extracted from the response.
 
         Returns:
             `dict`: Serialized ``ChatCompletion`` ready for JSON response.
         """
-        message = ChatCompletionMessage(content=content, role="assistant", tool_calls=tool_calls)
+        message = ChatCompletionMessage(
+            content=content, role="assistant", tool_calls=tool_calls, reasoning_content=reasoning_content
+        )
         result = ChatCompletion(
             id=request_id,
             created=int(time.time()),
@@ -394,6 +419,7 @@ def _build_chunk_sse(
         finish_reason: str | None = None,
         tool_calls: list | None = None,
         usage: CompletionUsage | None = None,
+        reasoning_content: str | None = None,
     ) -> str:
         """Build a streaming ``ChatCompletionChunk`` and format it as an SSE ``data:`` line.
 
@@ -405,6 +431,7 @@ def _build_chunk_sse(
             finish_reason (`str`, *optional*): Set on the final chunk.
             tool_calls (`list`, *optional*): Tool call deltas.
             usage (`CompletionUsage`, *optional*): Token usage (sent with the final chunk).
+            reasoning_content (`str`, *optional*): Reasoning/thinking delta (OpenAI-compatible extension).
 
         Returns:
             `str`: A formatted SSE event string.
@@ -415,7 +442,9 @@ def _build_chunk_sse(
             model=model,
             choices=[
                 ChoiceChunk(
-                    delta=ChoiceDelta(content=content, role=role, tool_calls=tool_calls),
+                    delta=ChoiceDelta(
+                        content=content, role=role, tool_calls=tool_calls, reasoning_content=reasoning_content
+                    ),
                     index=0,
                     finish_reason=finish_reason,
                 )
diff --git a/src/transformers/cli/serving/response.py b/src/transformers/cli/serving/response.py
index 4d29dfd1d6a2..002391c2479a 100644
--- a/src/transformers/cli/serving/response.py
+++ b/src/transformers/cli/serving/response.py
@@ -45,6 +45,9 @@
         ResponseOutputItemDoneEvent,
         ResponseOutputMessage,
         ResponseOutputText,
+        ResponseReasoningItem,
+        ResponseReasoningTextDeltaEvent,
+        ResponseReasoningTextDoneEvent,
         ResponseTextDeltaEvent,
         ResponseTextDoneEvent,
     )
@@ -56,8 +59,11 @@
     BaseGenerateManager,
     BaseHandler,
     Modality,
+    ReasoningText,
     _StreamError,
+    get_reasoning_config,
     get_tool_call_config,
+    parse_reasoning,
     parse_tool_calls,
 )
 
@@ -80,7 +86,6 @@ class TransformersResponseCreateParamsStreaming(ResponseCreateParamsStreaming, t
     "max_tool_calls",
     "previous_response_id",
     "prompt",
-    "reasoning",
     "service_tier",
     "store",
     "text",
@@ -127,10 +132,13 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
             for c in (msg.get("content") if isinstance(msg.get("content"), list) else [])
         )
 
-        # Default to 32 frames for video (Gemma 4 default); some processors load all frames otherwise
-        chat_template_kwargs = {}
+        # Default to 32 frames for video (Gemma 4 default); some processors load all frames otherwise.
+        # Merge order (later wins): custom default -> server default → request-level kwargs.
+        chat_template_kwargs: dict = {}
         if has_video:
             chat_template_kwargs["num_frames"] = 32
+        chat_template_kwargs.update(self.chat_template_kwargs)
+        chat_template_kwargs.update(body.get("chat_template_kwargs") or {})
         # updates the flat tool structure to the one expected by the `apply_chat_template` method.
         tools = self._normalize_tools(body.get("tools"))
         inputs = processor.apply_chat_template(
@@ -151,6 +159,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
         if use_cb:
             gen_manager.init_cb(model, gen_config)
         tool_config = get_tool_call_config(processor, model) if body.get("tools") else None
+        reasoning_config = get_reasoning_config(processor, model, inputs["input_ids"])
 
         streaming = body.get("stream", True)
         if streaming:
@@ -164,6 +173,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
                 gen_config,
                 gen_manager=gen_manager,
                 tool_config=tool_config,
+                reasoning_config=reasoning_config,
             )
         else:
             return await self._non_streaming(
@@ -176,6 +186,7 @@ async def handle_request(self, body: dict, request_id: str) -> StreamingResponse
                 gen_config,
                 gen_manager=gen_manager,
                 tool_config=tool_config,
+                reasoning_config=reasoning_config,
             )
 
     # ----- input conversion -----
@@ -247,16 +258,26 @@ def _normalize_response_items(items: list[dict]) -> list[dict]:
 
         Input items may be a mix of:
             - Messages (``EasyInputMessageParam`` with ``role``, or ``type: "message"``).
+            - ``reasoning`` — buffered and attached as ``reasoning_content`` to the next assistant message.
             - ``function_call`` — merged as ``tool_calls`` onto the preceding assistant message.
             - ``function_call_output`` — converted to ``role: "tool"`` messages.
         """
         messages = []
+        pending_reasoning: str | None = None
 
         for item in items:
             item_type = item.get("type")
 
+            if item_type == "reasoning":
+                pending_reasoning = "".join(c["text"] for c in item.get("content") or [])
+                continue
+
             if "role" in item:
-                messages.append({"role": item["role"], "content": item.get("content", "")})
+                msg = {"role": item["role"], "content": item.get("content", "")}
+                if pending_reasoning is not None and item["role"] == "assistant":
+                    msg["reasoning_content"] = pending_reasoning
+                    pending_reasoning = None
+                messages.append(msg)
 
             elif item_type == "function_call":
                 tc = {
@@ -295,6 +316,7 @@ def _streaming(
         gen_config: "GenerationConfig",
         gen_manager: BaseGenerateManager,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> StreamingResponse:
         """Generate a streaming Responses API reply (SSE) using DirectStreamer."""
         queue, streamer = gen_manager.generate_streaming(
@@ -304,16 +326,17 @@ def _streaming(
             gen_config,
             request_id=request_id,
             tool_config=tool_config,
+            reasoning_config=reasoning_config,
         )
         input_ids = inputs["input_ids"]
         # CB returns plain lists, regular path returns tensors
         input_len = len(input_ids) if isinstance(input_ids, list) else input_ids.shape[-1]
 
         seq = 0
-        output_index = 0
         created_at = time.time()
         resp_id = f"resp_{request_id}"
         msg_id = f"msg_{request_id}"
+        reasoning_id = f"rs_{request_id}"
 
         response_base = {
             "id": resp_id,
@@ -327,7 +350,7 @@ def _streaming(
         }
 
         async def event_stream() -> AsyncGenerator[str, None]:
-            nonlocal seq, output_index
+            nonlocal seq
 
             try:
                 # 1. Created + In progress
@@ -348,44 +371,163 @@ async def event_stream() -> AsyncGenerator[str, None]:
                 )
                 seq += 1
 
-                # 2. Output item added (message)
-                yield self.chunk_to_sse(
-                    ResponseOutputItemAddedEvent(
-                        type="response.output_item.added",
-                        sequence_number=seq,
-                        output_index=output_index,
-                        item=ResponseOutputMessage(
-                            id=msg_id,
-                            type="message",
-                            status="in_progress",
-                            role="assistant",
-                            content=[],
-                        ),
-                    )
-                )
-                seq += 1
-
-                # 3. Content part added
-                yield self.chunk_to_sse(
-                    ResponseContentPartAddedEvent(
-                        type="response.content_part.added",
-                        item_id=msg_id,
-                        sequence_number=seq,
-                        output_index=output_index,
-                        content_index=0,
-                        part=ResponseOutputText(type="output_text", text="", annotations=[]),
-                    )
-                )
-                seq += 1
-
-                # 4. Stream tokens — drain queue to batch HTTP writes
+                # 2. Stream tokens — items are opened lazily so reasoning (if any)
+                # appears as a separate output item before the message item.
                 full_text = ""
+                full_reasoning = ""
                 tool_calls = []
+                output_index = 0
+                reasoning_open = False
+                message_open = False
+                reasoning_item = None
+                message_item = None
                 done = False
 
+                def open_reasoning() -> str:
+                    """Emit ``output_item.added`` for an in-progress reasoning item."""
+                    nonlocal seq, reasoning_open
+                    reasoning_open = True
+                    sse = self.chunk_to_sse(
+                        ResponseOutputItemAddedEvent(
+                            type="response.output_item.added",
+                            sequence_number=seq,
+                            output_index=output_index,
+                            item=ResponseReasoningItem(
+                                id=reasoning_id, type="reasoning", summary=[], content=[], status="in_progress"
+                            ),
+                        )
+                    )
+                    seq += 1
+                    return sse
+
+                def close_reasoning() -> str:
+                    """Emit ``reasoning_text.done`` + ``output_item.done`` for the completed reasoning item."""
+                    nonlocal seq, reasoning_open, reasoning_item
+                    reasoning_item = ResponseReasoningItem(
+                        id=reasoning_id,
+                        type="reasoning",
+                        summary=[],
+                        content=[{"type": "reasoning_text", "text": full_reasoning}],
+                        status="completed",
+                    )
+                    parts = [
+                        self.chunk_to_sse(
+                            ResponseReasoningTextDoneEvent(
+                                type="response.reasoning_text.done",
+                                item_id=reasoning_id,
+                                sequence_number=seq,
+                                output_index=output_index,
+                                content_index=0,
+                                text=full_reasoning,
+                            )
+                        )
+                    ]
+                    seq += 1
+                    parts.append(
+                        self.chunk_to_sse(
+                            ResponseOutputItemDoneEvent(
+                                type="response.output_item.done",
+                                sequence_number=seq,
+                                output_index=output_index,
+                                item=reasoning_item,
+                            )
+                        )
+                    )
+                    seq += 1
+                    reasoning_open = False
+                    return "".join(parts)
+
+                def open_message() -> str:
+                    """Emit ``output_item.added`` + ``content_part.added`` for an in-progress message."""
+                    nonlocal seq, message_open
+                    message_open = True
+                    parts = [
+                        self.chunk_to_sse(
+                            ResponseOutputItemAddedEvent(
+                                type="response.output_item.added",
+                                sequence_number=seq,
+                                output_index=output_index,
+                                item=ResponseOutputMessage(
+                                    id=msg_id,
+                                    type="message",
+                                    status="in_progress",
+                                    role="assistant",
+                                    content=[],
+                                ),
+                            )
+                        )
+                    ]
+                    seq += 1
+                    parts.append(
+                        self.chunk_to_sse(
+                            ResponseContentPartAddedEvent(
+                                type="response.content_part.added",
+                                item_id=msg_id,
+                                sequence_number=seq,
+                                output_index=output_index,
+                                content_index=0,
+                                part=ResponseOutputText(type="output_text", text="", annotations=[]),
+                            )
+                        )
+                    )
+                    seq += 1
+                    return "".join(parts)
+
+                def close_message() -> str:
+                    """Emit ``output_text.done`` + ``content_part.done`` + ``output_item.done`` for the message."""
+                    nonlocal seq, message_open, message_item
+                    output_text_part = ResponseOutputText(type="output_text", text=full_text, annotations=[])
+                    message_item = ResponseOutputMessage(
+                        id=msg_id,
+                        type="message",
+                        status="completed",
+                        role="assistant",
+                        content=[output_text_part],
+                        annotations=[],  # type: ignore[call-arg]
+                    )
+                    parts = [
+                        self.chunk_to_sse(
+                            ResponseTextDoneEvent(
+                                type="response.output_text.done",
+                                item_id=msg_id,
+                                sequence_number=seq,
+                                output_index=output_index,
+                                content_index=0,
+                                text=full_text,
+                                logprobs=[],
+                            )
+                        )
+                    ]
+                    seq += 1
+                    parts.append(
+                        self.chunk_to_sse(
+                            ResponseContentPartDoneEvent(
+                                type="response.content_part.done",
+                                item_id=msg_id,
+                                sequence_number=seq,
+                                output_index=output_index,
+                                content_index=0,
+                                part=output_text_part,
+                            )
+                        )
+                    )
+                    seq += 1
+                    parts.append(
+                        self.chunk_to_sse(
+                            ResponseOutputItemDoneEvent(
+                                type="response.output_item.done",
+                                sequence_number=seq,
+                                output_index=output_index,
+                                item=message_item,
+                            )
+                        )
+                    )
+                    seq += 1
+                    message_open = False
+                    return "".join(parts)
+
                 while not done:
                     text = await queue.get()
-                    # Drain all available tokens for one batched HTTP write
                     batch = [text]
                     try:
                         while True:
@@ -423,26 +565,59 @@ async def event_stream() -> AsyncGenerator[str, None]:
                             yield "".join(sse_parts)
                             return
 
-                        full_text += text
-                        sse_parts.append(
-                            self.chunk_to_sse(
-                                ResponseTextDeltaEvent(
-                                    type="response.output_text.delta",
-                                    item_id=msg_id,
-                                    sequence_number=seq,
-                                    output_index=0,
-                                    content_index=0,
-                                    delta=text,
-                                    logprobs=[],
+                        if isinstance(text, ReasoningText):
+                            if not reasoning_open:
+                                sse_parts.append(open_reasoning())
+                            full_reasoning += text
+                            sse_parts.append(
+                                self.chunk_to_sse(
+                                    ResponseReasoningTextDeltaEvent(
+                                        type="response.reasoning_text.delta",
+                                        item_id=reasoning_id,
+                                        sequence_number=seq,
+                                        output_index=output_index,
+                                        content_index=0,
+                                        delta=text,
+                                    )
                                 )
                             )
-                        )
-                        seq += 1
+                            seq += 1
+                        else:
+                            if reasoning_open:
+                                sse_parts.append(close_reasoning())
+                                output_index += 1
+                            if not message_open:
+                                sse_parts.append(open_message())
+                            full_text += text
+                            sse_parts.append(
+                                self.chunk_to_sse(
+                                    ResponseTextDeltaEvent(
+                                        type="response.output_text.delta",
+                                        item_id=msg_id,
+                                        sequence_number=seq,
+                                        output_index=output_index,
+                                        content_index=0,
+                                        delta=text,
+                                        logprobs=[],
+                                    )
+                                )
+                            )
+                            seq += 1
 
                     if sse_parts:
                         yield "".join(sse_parts)
 
-                # 5. Tool calls are parsed after generation completes (not during streaming),
+                # Close any open reasoning section that didn't transition to content.
+                if reasoning_open:
+                    yield close_reasoning()
+                    output_index += 1
+
+                # Close message section (open it first if no content was emitted).
+                if not message_open:
+                    yield open_message()
+                yield close_message()
+
+                # 3. Tool calls are parsed after generation completes (not during streaming),
                 # because the full token sequence is needed for reliable parsing.
                 if tool_config:
                     parsed = parse_tool_calls(processor, streamer.generated_token_ids, tool_config["schema"])
@@ -489,52 +664,12 @@ async def event_stream() -> AsyncGenerator[str, None]:
                             )
                             seq += 1
 
-                # 6. Close text output
-                output_text_part = ResponseOutputText(type="output_text", text=full_text, annotations=[])
-                yield self.chunk_to_sse(
-                    ResponseTextDoneEvent(
-                        type="response.output_text.done",
-                        item_id=msg_id,
-                        sequence_number=seq,
-                        output_index=0,
-                        content_index=0,
-                        text=full_text,
-                        logprobs=[],
-                    )
-                )
-                seq += 1
-                yield self.chunk_to_sse(
-                    ResponseContentPartDoneEvent(
-                        type="response.content_part.done",
-                        item_id=msg_id,
-                        sequence_number=seq,
-                        output_index=0,
-                        content_index=0,
-                        part=output_text_part,
-                    )
-                )
-                seq += 1
-
-                msg_item = ResponseOutputMessage(
-                    id=msg_id,
-                    type="message",
-                    status="completed",
-                    role="assistant",
-                    content=[output_text_part],
-                    annotations=[],  # type: ignore[call-arg]
-                )
-                yield self.chunk_to_sse(
-                    ResponseOutputItemDoneEvent(
-                        type="response.output_item.done",
-                        sequence_number=seq,
-                        output_index=0,
-                        item=msg_item,
-                    )
-                )
-                seq += 1
-
-                # 7. Completed
-                all_output = [msg_item] + list(tool_calls)
+                # 4. Completed
+                all_output = []
+                if reasoning_item is not None:
+                    all_output.append(reasoning_item)
+                all_output.append(message_item)
+                all_output.extend(tool_calls)
                 usage = compute_usage(input_len, streamer.total_tokens)
                 yield self.chunk_to_sse(
                     ResponseCompletedEvent(
@@ -565,13 +700,28 @@ async def _non_streaming(
         gen_config: "GenerationConfig",
         gen_manager: BaseGenerateManager,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> JSONResponse:
         """Generate a non-streaming Responses API reply (single JSON)."""
         full_text, input_len, generated_ids = await gen_manager.generate_non_streaming(
             model, processor, inputs, gen_config, request_id=request_id
         )
 
-        output_items = [
+        output_items = []
+        if reasoning_config is not None:
+            full_text, reasoning_content = parse_reasoning(processor, generated_ids, full_text, reasoning_config)
+            if reasoning_content is not None:
+                output_items.append(
+                    ResponseReasoningItem(
+                        id=f"rs_{request_id}",
+                        type="reasoning",
+                        summary=[],
+                        content=[{"type": "reasoning_text", "text": reasoning_content}],
+                        status="completed",
+                    )
+                )
+
+        output_items.append(
             ResponseOutputMessage(
                 id=f"msg_{request_id}",
                 type="message",
@@ -580,7 +730,7 @@ async def _non_streaming(
                 content=[ResponseOutputText(type="output_text", text=full_text, annotations=[])],
                 annotations=[],  # type: ignore[call-arg]
             )
-        ]
+        )
 
         if tool_config is not None:
             parsed = parse_tool_calls(processor, generated_ids, tool_config["schema"])
diff --git a/src/transformers/cli/serving/server.py b/src/transformers/cli/serving/server.py
index 13a9565db590..f3fc46e9ad1c 100644
--- a/src/transformers/cli/serving/server.py
+++ b/src/transformers/cli/serving/server.py
@@ -32,7 +32,7 @@
 from .model_manager import ModelManager
 from .response import ResponseHandler
 from .transcription import TranscriptionHandler
-from .utils import X_REQUEST_ID
+from .utils import X_REQUEST_ID, CBWorkerDeadError, GenerationState
 
 
 logger = logging.get_logger(__name__)
@@ -44,6 +44,7 @@ def build_server(
     completion_handler: CompletionHandler,
     response_handler: ResponseHandler,
     transcription_handler: TranscriptionHandler,
+    generation_state: GenerationState,
     enable_cors: bool = False,
 ) -> FastAPI:
     """Build and return a configured FastAPI application.
@@ -52,6 +53,7 @@ def build_server(
         model_manager: Handles model loading, caching, and cleanup.
         chat_handler: Handles `/v1/chat/completions` requests.
         response_handler: Handles `/v1/responses` requests.
+        generation_state: Shared generation state, used by `/health` to report CB liveness.
         enable_cors: If `True`, adds permissive CORS middleware (allow all origins).
 
     Returns:
@@ -65,6 +67,12 @@ async def lifespan(app: FastAPI):
 
     app = FastAPI(lifespan=lifespan)
 
+    @app.exception_handler(CBWorkerDeadError)
+    async def _cb_dead_handler(_request: Request, exc: CBWorkerDeadError):
+        # CB worker died (e.g. CUDA illegal memory access); reject new requests with 503
+        # carrying the cause, instead of letting them hang in the input queue forever.
+        return JSONResponse({"error": str(exc)}, status_code=503)
+
     if enable_cors:
         app.add_middleware(
             CORSMiddleware,
@@ -128,6 +136,8 @@ def list_models():
 
     @app.get("/health")
     def health():
+        if not generation_state.is_cb_alive():
+            return JSONResponse({"status": "unhealthy", "reason": "cb_worker_dead"}, status_code=503)
         return JSONResponse({"status": "ok"})
 
     return app
diff --git a/src/transformers/cli/serving/utils.py b/src/transformers/cli/serving/utils.py
index d786a828fc28..d4f486a689e4 100644
--- a/src/transformers/cli/serving/utils.py
+++ b/src/transformers/cli/serving/utils.py
@@ -73,6 +73,22 @@ class _GenerationCancelled(Exception):
     """Raised inside ``DirectStreamer.put()`` to abort ``model.generate()``."""
 
 
+class CBWorkerDeadError(RuntimeError):
+    """Raised when a request is submitted to a CB worker that has died.
+
+    Surfaced as 503 by the FastAPI exception handler. Carries the original error message
+    that killed the worker so the client knows why the server is in this state.
+    """
+
+
+class ReasoningText(str):
+    """Tagged str subclass: text chunk belonging to a thinking/reasoning block.
+
+    Streamers wrap reasoning text with this so handlers can route it to
+    ``reasoning_content`` deltas instead of ``content``.
+    """
+
+
 # Fallback tool call configs for models that don't declare stc_token/etc_token/response_schema
 # on their tokenizer.
 # Keys are matched via substring against model_type (e.g. "qwen" matches "qwen2", "qwen3_vl", etc.).
@@ -156,6 +172,110 @@ def parse_tool_calls(processor, generated_ids, schema: dict) -> list[dict] | Non
     return tool_calls if tool_calls else None
 
 
+# Default start/end tokens + schema. The opening token is optional so prefilled
+# ``<think>`` prompts still match.
+_DEFAULT_THINKING_TOKENS = {
+    "start": ["<think>"],
+    "end": "</think>",
+    "schema": {
+        "type": "object",
+        "properties": {
+            "thinking": {"type": "string"},
+            "content": {"type": "string"},
+        },
+        "x-regex": r"\s*(?:<think>)?(?P<thinking>.*?)</think>\s*(?P<content>.*)",
+    },
+}
+# Streaming-side token IDs for families whose ``response_schema`` uses non-default
+# start/end tokens. Post-hoc parsing uses the schema; this only feeds the
+# streamer's token-level detector.
+_THINKING_TOKENS = {
+    "gemma4": {"start": ["<|channel>", "thought"], "end": "<channel|>"},
+}
+
+
+def get_reasoning_config(processor, model: "PreTrainedModel", input_ids=None) -> dict | None:
+    """Return reasoning config for the model, or ``None`` if not supported.
+
+    The config drives both streaming detection (token IDs) and post-hoc parsing
+    (response schema). Returns a dict with:
+        - ``start_ids`` (`list[int]`): Token ID sequence that opens a thinking block.
+        - ``end_id`` (`int`): Token ID that closes the block.
+        - ``schema`` (`dict`): Response schema with ``thinking`` / ``content``
+          properties for :func:`parse_reasoning`.
+        - ``start_in_thinking`` (`bool`, only when ``input_ids`` is given): Whether
+          the rendered prompt already opened an unclosed thinking block (prefilled
+          by the template), so the model's output begins inside the block.
+    """
+    tokenizer = getattr(processor, "tokenizer", processor)
+    model_type = model.config.model_type.lower()
+    thinking_tokens = next(
+        (v for k, v in _THINKING_TOKENS.items() if k in model_type),
+        _DEFAULT_THINKING_TOKENS,
+    )
+    start_ids = [tokenizer.convert_tokens_to_ids(t) for t in thinking_tokens["start"]]
+    end_id = tokenizer.convert_tokens_to_ids(thinking_tokens["end"])
+    if any(tid in (None, tokenizer.unk_token_id) for tid in start_ids) or end_id in (None, tokenizer.unk_token_id):
+        return None
+    # Custom-token families (e.g. Gemma 4) provide their schema via the tokenizer;
+    # default ``<think>`` falls back to the schema baked into ``_DEFAULT_THINKING_TOKENS``.
+    schema = getattr(tokenizer, "response_schema", None)
+    if not (schema and "thinking" in schema["properties"]):
+        schema = _DEFAULT_THINKING_TOKENS["schema"]
+    config: dict = {"start_ids": start_ids, "end_id": end_id, "schema": schema}
+    if input_ids is not None:
+        config["start_in_thinking"] = _starts_in_thinking(input_ids, start_ids)
+    return config
+
+
+def parse_reasoning(processor, generated_ids, content: str, reasoning_config: dict) -> tuple[str, str | None]:
+    """Split generated output into ``(content, reasoning_content)`` via ``parse_response``.
+
+    If the schema's regex matches (closing marker present), use it. For prompts
+    that prefill the opener (QwQ-32B, DeepSeek-R1) the entire output is reasoning
+    until ``</think>`` arrives — when that's truncated, fall back to treating
+    all decoded text as reasoning. Returns ``(content, None)`` otherwise.
+    """
+    parsed = processor.parse_response(generated_ids, reasoning_config["schema"])
+    if parsed:
+        reasoning = parsed.get("thinking", "").strip()
+        if reasoning:
+            return parsed.get("content", ""), reasoning
+    # Prefilled opener (QwQ-32B, DeepSeek-R1) truncated before ``</think>`` —
+    # no anchor for the schema regex; treat all output as reasoning.
+    if reasoning_config.get("start_in_thinking"):
+        return "", content.strip()
+    return content, None
+
+
+def _starts_in_thinking(input_ids, start_ids: list[int]) -> bool:
+    """True if the rendered prompt ends with an unclosed thinking block.
+
+    Some reasoning-model chat templates prefill the thinking opener as the final
+    prompt tokens (e.g. DeepSeek-R1, QwQ-32B emit ``<think>\\n`` at the end when
+    ``add_generation_prompt=True``). In those cases the model resumes *inside*
+    the block, so its output contains only ``...reasoning</think>answer`` with
+    no opening tag — the streamer must start with ``_inside_thinking=True``.
+
+    The prefill always lands at the tail of the prompt (optionally followed by a
+    single whitespace token like ``\\n``), so we only inspect the last few tokens.
+    """
+    if hasattr(input_ids, "tolist"):
+        input_ids = input_ids.tolist()
+    if input_ids and isinstance(input_ids[0], list):
+        if len(input_ids) != 1:
+            return False
+        input_ids = input_ids[0]
+    n = len(start_ids)
+    # Match start_ids at the tail, allowing up to one trailing token (e.g. "\n").
+    for trailing in (0, 1):
+        if len(input_ids) >= n + trailing:
+            end = len(input_ids) - trailing
+            if input_ids[end - n : end] == start_ids:
+                return True
+    return False
+
+
 class DownloadAggregator:
     """Aggregates byte-progress across multiple concurrent download tqdm bars.
 
@@ -286,6 +406,7 @@ def __init__(
         queue: asyncio.Queue,
         skip_special_tokens: bool = True,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ):
         """
         Args:
@@ -297,6 +418,9 @@ def __init__(
             tool_config (`dict`, *optional*): Tool call config from ``get_tool_call_config``.
                 When set, tokens between stc/etc delimiters (inclusive) are suppressed
                 from the queue so tool call markup is never streamed to the client.
+            reasoning_config (`dict`, *optional*): Thinking config from ``get_reasoning_config``.
+                When set, tokens between start/end delimiters are wrapped as
+                :class:`ReasoningText` so handlers route them to ``reasoning_content``.
         """
         from tokenizers.decoders import DecodeStream
 
@@ -307,6 +431,10 @@ def __init__(
         self._stc_id = tool_config["stc_id"] if tool_config else None
         self._etc_id = tool_config["etc_id"] if tool_config else None
         self._inside_tool_call = False
+        self._thinking_start_ids = reasoning_config["start_ids"] if reasoning_config else None
+        self._thinking_end_id = reasoning_config["end_id"] if reasoning_config else None
+        self._inside_thinking = bool(reasoning_config and reasoning_config.get("start_in_thinking"))
+        self._thinking_prefix: list[int] = []
         self._first = True
         self._cancelled = threading.Event()
         self.total_tokens = 0
@@ -329,9 +457,33 @@ def put(self, value: "torch.Tensor") -> None:
             elif token_id == self._etc_id:
                 self._inside_tool_call = False
 
+            is_start_or_end_token = self._advance_thinking_state(token_id)
+
             text = self._decode_stream.step(self._tokenizer, token_id)
-            if text is not None and not self._inside_tool_call and token_id != self._etc_id:
-                self._loop.call_soon_threadsafe(self._queue.put_nowait, text)
+            if text is None or self._inside_tool_call or token_id == self._etc_id or is_start_or_end_token:
+                continue
+            if self._inside_thinking:
+                text = ReasoningText(text)
+            self._loop.call_soon_threadsafe(self._queue.put_nowait, text)
+
+    def _advance_thinking_state(self, token_id: int) -> bool:
+        """Mutate thinking state; return ``True`` if ``token_id`` is a start or end token — suppress from output."""
+        if self._thinking_start_ids is None:
+            return False
+        if self._inside_thinking:
+            if token_id == self._thinking_end_id:
+                self._inside_thinking = False
+                return True
+            return False
+        expected = self._thinking_start_ids[len(self._thinking_prefix)]
+        if token_id != expected:
+            self._thinking_prefix = []
+            return False
+        self._thinking_prefix.append(token_id)
+        if len(self._thinking_prefix) == len(self._thinking_start_ids):
+            self._inside_thinking = True
+            self._thinking_prefix = []
+        return True
 
     def end(self) -> None:
         """Called by ``model.generate()`` when generation is complete."""
@@ -359,6 +511,7 @@ def __init__(
         loop: asyncio.AbstractEventLoop,
         queue: asyncio.Queue,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ):
         """
         Args:
@@ -368,6 +521,7 @@ def __init__(
             loop (`asyncio.AbstractEventLoop`): The event loop to push decoded text to.
             queue (`asyncio.Queue`): The queue that receives decoded text chunks.
             tool_config (`dict`, *optional*): Tool call config (see ``DirectStreamer``).
+            reasoning_config (`dict`, *optional*): Thinking config (see ``DirectStreamer``).
         """
         from tokenizers.decoders import DecodeStream
 
@@ -380,6 +534,10 @@ def __init__(
         self._stc_id = tool_config["stc_id"] if tool_config else None
         self._etc_id = tool_config["etc_id"] if tool_config else None
         self._inside_tool_call = False
+        self._thinking_start_ids = reasoning_config["start_ids"] if reasoning_config else None
+        self._thinking_end_id = reasoning_config["end_id"] if reasoning_config else None
+        self._inside_thinking = bool(reasoning_config and reasoning_config.get("start_in_thinking"))
+        self._thinking_prefix: list[int] = []
         self._prev_len = 0
         self.total_tokens = 0
         self.generated_token_ids: list[int] = []
@@ -397,9 +555,33 @@ def put(self, output: "GenerationOutput") -> None:
             elif token_id == self._etc_id:
                 self._inside_tool_call = False
 
+            is_start_or_end_token = self._advance_thinking_state(token_id)
+
             text = self._decode_stream.step(self._tokenizer, token_id)
-            if text is not None and not self._inside_tool_call and token_id != self._etc_id:
-                self._queue.put_nowait(text)
+            if text is None or self._inside_tool_call or token_id == self._etc_id or is_start_or_end_token:
+                continue
+            if self._inside_thinking:
+                text = ReasoningText(text)
+            self._queue.put_nowait(text)
+
+    def _advance_thinking_state(self, token_id: int) -> bool:
+        """Mutate thinking state; return ``True`` if ``token_id`` is a start or end token — suppress from output."""
+        if self._thinking_start_ids is None:
+            return False
+        if self._inside_thinking:
+            if token_id == self._thinking_end_id:
+                self._inside_thinking = False
+                return True
+            return False
+        expected = self._thinking_start_ids[len(self._thinking_prefix)]
+        if token_id != expected:
+            self._thinking_prefix = []
+            return False
+        self._thinking_prefix.append(token_id)
+        if len(self._thinking_prefix) == len(self._thinking_start_ids):
+            self._inside_thinking = True
+            self._thinking_prefix = []
+        return True
 
     def end(self) -> None:
         """Signal end of stream."""
@@ -486,6 +668,7 @@ def generate_streaming(
         gen_config: "GenerationConfig",
         request_id: str,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> tuple[asyncio.Queue, "DirectStreamer | CBStreamer"]:
         """Start streaming generation.
 
@@ -497,6 +680,8 @@ def generate_streaming(
             request_id (`str`): Unique request identifier.
             tool_config (`dict`, *optional*): Tool call config from ``get_tool_call_config``.
                 When set, tool call tokens (between stc/etc) are suppressed from output.
+            reasoning_config (`dict`, *optional*): Thinking config from ``get_reasoning_config``.
+                When set, thinking tokens are wrapped as :class:`ReasoningText`.
 
         Returns:
             `tuple[asyncio.Queue, DirectStreamer | CBStreamer]`: A ``(queue, streamer)`` pair
@@ -545,13 +730,16 @@ def generate_streaming(
         gen_config: "GenerationConfig",
         request_id: str,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> tuple[asyncio.Queue, DirectStreamer]:
         """Start streaming generation via ``model.generate()`` on the inference thread."""
         loop = asyncio.get_running_loop()
         queue: asyncio.Queue = asyncio.Queue()
         # ProcessorMixin exposes the fast tokenizer as .tokenizer; PreTrainedTokenizerFast is already one.
         rust_tokenizer = getattr(processor, "tokenizer", processor)._tokenizer  # type: ignore[union-attr]
-        streamer = DirectStreamer(rust_tokenizer, loop, queue, tool_config=tool_config)
+        streamer = DirectStreamer(
+            rust_tokenizer, loop, queue, tool_config=tool_config, reasoning_config=reasoning_config
+        )
         gen_kwargs = {**inputs, "streamer": streamer, "generation_config": gen_config, "tokenizer": processor}
         if hasattr(model, "has_talker"):
             gen_kwargs["generation_mode"] = "text"
@@ -635,6 +823,21 @@ def init_cb(self, model: "PreTrainedModel", gen_config: "GenerationConfig") -> N
         )
         self._cb.start()
 
+    def is_alive(self) -> bool:
+        """Whether the CB worker is healthy and able to serve new requests."""
+        return self._cb is not None and self._cb.fatal_error is None
+
+    def _check_alive(self, request_id: str) -> None:
+        """Raise :class:`CBWorkerDeadError` if the CB worker has died.
+
+        Called at request entry to fail fast — submitting to a dead worker would otherwise
+        enqueue the request into a void where it never gets processed.
+        """
+        if self._cb is not None and self._cb.fatal_error is not None:
+            raise CBWorkerDeadError(
+                f"CB worker is dead and cannot accept request {request_id}: {self._cb.fatal_error}"
+            )
+
     def generate_streaming(
         self,
         model: "PreTrainedModel",
@@ -643,11 +846,13 @@ def generate_streaming(
         gen_config: "GenerationConfig",
         request_id: str,
         tool_config: dict | None = None,
+        reasoning_config: dict | None = None,
     ) -> tuple[asyncio.Queue, CBStreamer]:
         """Start streaming CB generation. Registers a per-request output handler."""
         cb = self._cb
         if cb is None:
             raise RuntimeError("CB manager not initialized. Call `init_cb()` first.")
+        self._check_alive(request_id)
 
         loop = asyncio.get_running_loop()
         text_queue: asyncio.Queue = asyncio.Queue()
@@ -662,14 +867,28 @@ def generate_streaming(
         )
         # ProcessorMixin exposes the fast tokenizer as .tokenizer; PreTrainedTokenizerFast is already one.
         rust_tokenizer = getattr(processor, "tokenizer", processor)._tokenizer  # type: ignore[union-attr]
-        streamer = CBStreamer(self._cb, request_id, rust_tokenizer, loop, text_queue, tool_config=tool_config)
+        streamer = CBStreamer(
+            self._cb,
+            request_id,
+            rust_tokenizer,
+            loop,
+            text_queue,
+            tool_config=tool_config,
+            reasoning_config=reasoning_config,
+        )
 
         # Register a direct callback: the dispatcher calls this on the event loop with each GenerationOutput.
         # This decodes tokens and pushes text straight to the SSE text_queue
         def _on_output(output):
             try:
                 streamer.put(output)
-                if output.is_finished():
+                # ``error`` is set together with ``status = FAILED`` in CB's _handle_request_error.
+                # Surface it as an end-of-stream error so the SSE handler can emit it and close,
+                # instead of leaving the client hanging on a stream that will never end.
+                if output.error is not None:
+                    text_queue.put_nowait(_StreamError(output.error))
+                    streamer.end()
+                elif output.is_finished():
                     streamer.end()
             except Exception as e:
                 text_queue.put_nowait(_StreamError(str(e)))
@@ -689,6 +908,7 @@ async def generate_non_streaming(
         cb = self._cb
         if cb is None:
             raise RuntimeError("CB manager not initialized. Call `init_cb()` first.")
+        self._check_alive(request_id)
 
         input_ids = inputs["input_ids"]
         input_len = len(input_ids)
@@ -711,8 +931,16 @@ def _on_result(result):
             eos_token_id=gen_config.eos_token_id,
         )
         result = await future
-        if result is None:
-            raise RuntimeError(f"CB manager stopped before producing a result for {request_id}")
+        # CB signals a failed request by setting ``error`` (and ``status = FAILED``) on the
+        # delivered GenerationOutput, often with empty ``generated_tokens``. Surface it instead
+        # of returning an empty success that downstream parsing/decoding would silently mask.
+        # If the worker itself died, route to CBWorkerDeadError so the client gets the same 503
+        # as requests submitted post-crash; otherwise it's a per-request failure (e.g. unsupported
+        # logit-processor kwarg) and a plain RuntimeError -> 500 is appropriate.
+        if result.error is not None:
+            if self._cb.fatal_error is not None:
+                raise CBWorkerDeadError(f"CB worker died during request {request_id}: {result.error}")
+            raise RuntimeError(f"CB generation failed for {request_id}: {result.error}")
         generated_ids = result.generated_tokens
         text = processor.decode(generated_ids, skip_special_tokens=True)
         return text, input_len, generated_ids
@@ -805,6 +1033,12 @@ def shutdown(self) -> None:
             self._cb_manager.stop()
             self._cb_manager = None
 
+    def is_cb_alive(self) -> bool:
+        """Whether the CB worker is healthy. ``True`` if CB is disabled or not yet initialized."""
+        if self._cb_manager is None:
+            return True
+        return self._cb_manager.is_alive()
+
 
 class BaseHandler:
     """Shared logic for chat completion and responses handlers.
@@ -826,9 +1060,11 @@ def __init__(
         self,
         model_manager: "ModelManager",
         generation_state: GenerationState,
+        chat_template_kwargs: dict | None = None,
     ):
         self.model_manager = model_manager
         self.generation_state = generation_state
+        self.chat_template_kwargs = chat_template_kwargs or {}
 
     def _validate_request(self, body: dict) -> None:
         """Validate request fields against the handler's params class and unused fields."""
diff --git a/src/transformers/cli/transformers.py b/src/transformers/cli/transformers.py
index cefee1ca97c8..ba2f86ebcf78 100644
--- a/src/transformers/cli/transformers.py
+++ b/src/transformers/cli/transformers.py
@@ -16,6 +16,7 @@
 from huggingface_hub import check_cli_update, typer_factory
 
 from transformers.cli.add_new_model_like import add_new_model_like
+from transformers.cli.agentic.app import register_agentic_commands
 from transformers.cli.chat import Chat
 from transformers.cli.download import download
 from transformers.cli.serve import Serve
@@ -31,6 +32,8 @@
 app.command(name="serve")(Serve)
 app.command()(version)
 
+register_agentic_commands(app)
+
 
 def main():
     check_cli_update("transformers")
diff --git a/src/transformers/configuration_utils.py b/src/transformers/configuration_utils.py
index 2dcdc5333f35..272052eb9163 100755
--- a/src/transformers/configuration_utils.py
+++ b/src/transformers/configuration_utils.py
@@ -21,7 +21,7 @@
 from collections.abc import Sequence
 from dataclasses import MISSING, dataclass, fields
 from functools import wraps
-from typing import TYPE_CHECKING, Any, ClassVar, Literal, TypeVar, Union
+from typing import Any, ClassVar, Literal, TypeVar
 
 from huggingface_hub import create_repo
 from huggingface_hub.dataclasses import strict
@@ -31,6 +31,12 @@
 from . import __version__
 from .dynamic_module_utils import custom_object_save
 from .generation.configuration_utils import GenerationConfig
+from .heterogeneity import (
+    LayerConfig,
+    apply_heterogeneous_config,
+    get_full_layer_config,
+    heterogeneous_to_dict_helper,
+)
 from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
 from .modeling_rope_utils import RotaryEmbeddingConfigMixin
 from .utils import (
@@ -43,10 +49,7 @@
     logging,
 )
 from .utils.generic import is_timm_config_dict
-
-
-if TYPE_CHECKING:
-    import torch
+from .utils.type_validators import dtype_validator
 
 
 logger = logging.get_logger(__name__)
@@ -63,6 +66,8 @@
     "full_attention",
     "sliding_attention",
     "chunked_attention",
+    "compressed_sparse_attention",  # CSA, used in deepseek_v4
+    "heavily_compressed_attention",  # HCA, used in deepseek_v4
     "linear_attention",  # used in minimax
     "conv",  # used in LFMv2
     "mamba",
@@ -183,6 +188,8 @@ class PreTrainedConfig(PushToHubMixin, RotaryEmbeddingConfigMixin):
             the feed forward layer is not chunked. A chunk size of n means that the feed forward layer processes `n` <
             sequence_length embeddings at a time. For more information on feed forward chunking, see [How does Feed
             Forward Chunking work?](../glossary.html#feed-forward-chunking).
+        per_layer_config (`dict[int | str, dict[str, Any] | LayerConfig]`, *optional*):
+            A dictionary of per-layer configurations. Each key is a layer index, and the value is a dictionary of configuration attributes or a `LayerConfig` object.
 
         > Parameters for fine-tuning tasks
 
@@ -229,7 +236,7 @@ class PreTrainedConfig(PushToHubMixin, RotaryEmbeddingConfigMixin):
     # Common attributes for all models
     output_hidden_states: bool | None = False
     return_dict: bool | None = True
-    dtype: Union[str, "torch.dtype"] | None = None
+    dtype: Any = dtype_validator(default=None)
     chunk_size_feed_forward: int = 0
     is_encoder_decoder: bool = False
 
@@ -297,6 +304,10 @@ def __post_init__(self, **kwargs):
 
         # Additional attributes without default values
         for key, value in kwargs.items():
+            # Needs to be handled after all other attributes are set
+            if key == "per_layer_config":
+                continue
+
             # Check this to avoid deserializing problematic fields from hub configs - they should use the public field
             if key not in ("_attn_implementation_internal", "_experts_implementation_internal"):
                 try:
@@ -305,6 +316,10 @@ def __post_init__(self, **kwargs):
                     logger.error(f"Can't set {key} with value {value} for {self}")
                     raise err
 
+        per_layer_config: dict[int | str, dict[str, Any] | LayerConfig] | None = kwargs.pop("per_layer_config", None)
+        if per_layer_config is not None:
+            self.per_layer_config = {int(k): copy.deepcopy(v) for k, v in per_layer_config.items()}
+
     def __init_subclass__(cls, *args, **kwargs):
         super().__init_subclass__(*args, **kwargs)
         cls_has_custom_init = "__init__" in cls.__dict__
@@ -429,6 +444,18 @@ def __setattr__(self, key, value):
     def __getattribute__(self, key):
         if key != "attribute_map" and key in super().__getattribute__("attribute_map"):
             key = super().__getattribute__("attribute_map")[key]
+
+        try:
+            heterogeneity_spec = super().__getattribute__("_heterogeneity_spec")
+        except AttributeError:
+            pass
+        else:
+            if key in heterogeneity_spec.per_layer_attributes:
+                raise AttributeError(
+                    f"'{key}' is a per-layer attribute and varies across layers. "
+                    f"Access it via the individual layer configs instead (e.g. config.get_full_layer_config(i).{key})."
+                )
+
         return super().__getattribute__(key)
 
     def validate_output_attentions(self):
@@ -834,6 +861,9 @@ def from_dict(
                 elif value != "auto":
                     config_dict[key] = value
 
+        if "per_layer_config" in kwargs:
+            config_dict["per_layer_config"] = kwargs.pop("per_layer_config")
+
         config = cls(**config_dict)
 
         for key, value in kwargs.items():
@@ -996,6 +1026,9 @@ def to_diff_dict(self) -> dict[str, Any]:
             )
         self.dict_dtype_to_str(serializable_config_dict)
 
+        if self.is_heterogeneous:
+            heterogeneous_to_dict_helper(self, serializable_config_dict)
+
         return serializable_config_dict
 
     def to_dict(self) -> dict[str, Any]:
@@ -1043,6 +1076,9 @@ def to_list(value):
             )
         self.dict_dtype_to_str(output)
 
+        if self.is_heterogeneous:
+            heterogeneous_to_dict_helper(self, output)
+
         return output
 
     def to_json_string(self, use_diff: bool = True) -> str:
@@ -1161,6 +1197,7 @@ def _remove_keys_not_serialized(self, d: dict[str, Any]) -> None:
             "ignore_keys_at_rope_validation",
             "base_model_tp_plan",
             "base_model_pp_plan",
+            "distributed_config",
         ]:
             d.pop(key_to_remove, None)
 
@@ -1286,6 +1323,29 @@ def get_text_config(self, decoder=None, encoder=None) -> "PreTrainedConfig":
 
         return config_to_return
 
+    @property
+    def is_heterogeneous(self) -> bool:
+        return hasattr(self, "_heterogeneity_spec")
+
+    @property
+    def per_layer_config(self) -> dict[int, LayerConfig]:
+        return self._heterogeneity_spec.per_layer_config
+
+    @per_layer_config.setter
+    def per_layer_config(self, per_layer_config: dict[int, LayerConfig] | None) -> None:
+        if per_layer_config is None:
+            delattr(self, "_heterogeneity_spec")
+            return
+
+        apply_heterogeneous_config(self, per_layer_config)
+
+    @property
+    def per_layer_attributes(self) -> set[str]:
+        return self._heterogeneity_spec.per_layer_attributes
+
+    def get_full_layer_config(self, layer_idx: int) -> "PreTrainedConfig":
+        return get_full_layer_config(self, layer_idx)
+
 
 def get_configuration_file(configuration_files: list[str]) -> str:
     """
diff --git a/src/transformers/conversion_mapping.py b/src/transformers/conversion_mapping.py
index dadfeb4224ad..437e64ee97d3 100755
--- a/src/transformers/conversion_mapping.py
+++ b/src/transformers/conversion_mapping.py
@@ -58,23 +58,13 @@
     "hunyuan_v1_moe": "qwen2_moe",
     "flex_olmo": "qwen2_moe",
     "olmoe": "qwen2_moe",
+    "sarvam_mla": "qwen2_moe",
     "exaone_moe": "qwen2_moe",
     "rt_detr_v2": "rt_detr",
     "pp_doclayout_v2": "rt_detr",
     "pp_doclayout_v3": "rt_detr",
-    "paligemma": "llava",
-    "aya_vision": "llava",
-    "got_ocr2": "llava",
-    "shieldgemma2": "llava",
-    "gemma3": "llava",
-    "internvl": "llava",
-    "llava_next_video": "llava_next",
-    "llava_onevision": "llava_next",
-    "vipllava": "llava",
-    "mistral3": "llava",
     "qwen2_5_vl": "qwen2_vl",
     "sam3_tracker_video": "sam3_tracker",
-    "pp_chart2table": "llava",
     "altclip_vision_model": "clip_vision_model",
     "chinese_clip_vision_model": "clip_vision_model",
     "clipseg_vision_model": "clip_vision_model",
@@ -89,6 +79,32 @@
     "siglip_text_model": "clip_text_model",
     "siglip2_text_model": "clip_text_model",
     "xclip_text_model": "clip_text_model",
+    "shield_gemma2": "llava",
+    "paligemma": "llava",
+    "aya_vision": "llava",
+    "got_ocr2": "llava",
+    "gemma3": "llava",
+    "internvl": "llava",
+    "vipllava": "llava",
+    "mistral3": "llava",
+    "pp_chart2table": "llava",
+    "llava_next_video": "llava_next",
+    "llava_onevision": "llava_next",
+    # class-based mappings
+    "PaliGemmaModel": "LlavaModel",
+    "AyaVisionModel": "LlavaModel",
+    "GotOcr2Model": "LlavaModel",
+    "Gemma3Model": "LlavaModel",
+    "InternVLModel": "LlavaModel",
+    "VipLlavaModel": "LlavaModel",
+    "Mistral3Model": "LlavaModel",
+    "PPChart2TableModel": "LlavaModel",
+    "LlavaNextModel": "LlavaModel",
+    "LlavaNextVideoModel": "LlavaModel",
+    "LlavaOnevisionModel": "LlavaModel",
+    "FuyuModel": "LlavaModel",
+    "MllamaModel": "LlavaModel",
+    "Qwen2_5_VLModel": "Qwen2VLModel",
 }
 
 
@@ -97,42 +113,198 @@ def _build_checkpoint_conversion_mapping():
         "altclip": [
             WeightRenaming(source_patterns=r"layer\.", target_patterns="layers."),
         ],
+        "LlavaModel": [
+            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="language_model"),
+        ],
+        "deepseek_v4": [
+            # Upstream checkpoint uses a flatter, V3-style namespace: ``attn`` / ``ffn``
+            # instead of ``self_attn`` / ``mlp``, ``attn_norm`` / ``ffn_norm`` instead of
+            # ``input_layernorm`` / ``post_attention_layernorm``, ``hc_attn_*`` / ``hc_ffn_*``
+            # for the Hyper-Connection params (we wrap them in ``attn_hc`` / ``ffn_hc``
+            # submodules), ``embed`` / ``head`` / bare ``norm`` for the model head, and
+            # ``hc_head_*`` for the final HC collapse. The Indexer's compressor tree is
+            # nested under ``attn.indexer.compressor.*`` upstream but flattened onto the
+            # Indexer module here. FP8 scales arrive as ``.scale`` and need to become
+            # ``.weight_scale_inv`` to match :class:`FineGrainedFP8Linear`.
+            #
+            # Ordering matters for save round-tripping: :func:`revert_weight_conversion`
+            # reverses the order *and* each transform, so a structural prefix-only rule
+            # placed before a specific in-prefix rename would steal the reverse match
+            # and emit ``layers.X.attn.sinks`` instead of ``layers.X.attn.attn_sink``.
+            # We split into two passes: structural prefix renames first (so they apply
+            # last on save / first on load), then specific in-prefix renames that
+            # operate on the already-prefixed keys.
+            #
+            # FP8 ``.scale`` → ``.weight_scale_inv`` rename lives in the FP8 quantizer's
+            # ``update_weight_conversions`` (only kicks in when FP8 dequant is active),
+            # so the V4 static mapping below stays free of FP8-only rules.
+            # ---- Pass 1: top-level + structural prefix renames ----
+            WeightRenaming(source_patterns=r"^embed\.weight$", target_patterns="model.embed_tokens.weight"),
+            WeightRenaming(source_patterns=r"^head\.weight$", target_patterns="lm_head.weight"),
+            WeightRenaming(source_patterns=r"^norm\.weight$", target_patterns="model.norm.weight"),
+            WeightRenaming(source_patterns=r"^hc_head_fn$", target_patterns="model.hc_head.hc_fn"),
+            WeightRenaming(source_patterns=r"^hc_head_base$", target_patterns="model.hc_head.hc_base"),
+            WeightRenaming(source_patterns=r"^hc_head_scale$", target_patterns="model.hc_head.hc_scale"),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.attn_norm\.",
+                target_patterns=r"model.layers.\1.input_layernorm.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.ffn_norm\.",
+                target_patterns=r"model.layers.\1.post_attention_layernorm.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_attn_fn$", target_patterns=r"model.layers.\1.attn_hc.fn"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_attn_base$", target_patterns=r"model.layers.\1.attn_hc.base"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_attn_scale$", target_patterns=r"model.layers.\1.attn_hc.scale"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_ffn_fn$", target_patterns=r"model.layers.\1.ffn_hc.fn"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_ffn_base$", target_patterns=r"model.layers.\1.ffn_hc.base"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.hc_ffn_scale$", target_patterns=r"model.layers.\1.ffn_hc.scale"
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.attn\.",
+                target_patterns=r"model.layers.\1.self_attn.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^layers\.(\d+)\.ffn\.",
+                target_patterns=r"model.layers.\1.mlp.",
+            ),
+            # ---- Pass 2: in-prefix specific renames (operate on already-prefixed keys) ----
+            # These can safely run after the structural prefix renames because their
+            # source patterns include the ``model.layers.X.self_attn.`` / ``model.layers.X.mlp.``
+            # prefix. On reverse the order flips so these undo first, restoring the
+            # specific upstream names *before* the structural rules strip the prefix.
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.attn_sink$",
+                target_patterns=r"model.layers.\1.self_attn.sinks",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.indexer\.compressor\.norm\.",
+                target_patterns=r"model.layers.\1.self_attn.compressor.indexer.kv_norm.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.indexer\.compressor\.ape$",
+                target_patterns=r"model.layers.\1.self_attn.compressor.indexer.position_bias",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.indexer\.compressor\.",
+                target_patterns=r"model.layers.\1.self_attn.compressor.indexer.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.indexer\.",
+                target_patterns=r"model.layers.\1.self_attn.compressor.indexer.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.compressor\.norm\.",
+                target_patterns=r"model.layers.\1.self_attn.compressor.kv_norm.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.self_attn\.compressor\.ape$",
+                target_patterns=r"model.layers.\1.self_attn.compressor.position_bias",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.mlp\.shared_experts\.w1\.",
+                target_patterns=r"model.layers.\1.mlp.shared_experts.gate_proj.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.mlp\.shared_experts\.w2\.",
+                target_patterns=r"model.layers.\1.mlp.shared_experts.down_proj.",
+            ),
+            WeightRenaming(
+                source_patterns=r"^model\.layers\.(\d+)\.mlp\.shared_experts\.w3\.",
+                target_patterns=r"model.layers.\1.mlp.shared_experts.up_proj.",
+            ),
+            WeightConverter(
+                source_patterns=[
+                    "experts.*.w1.weight",
+                    "experts.*.w3.weight",
+                ],
+                target_patterns="experts.gate_up_proj",
+                operations=[MergeModulelist(dim=0), Concatenate(dim=1)],
+            ),
+            WeightConverter(
+                source_patterns="experts.*.w2.weight",
+                target_patterns="experts.down_proj",
+                operations=[MergeModulelist(dim=0)],
+            ),
+        ],
         "llava": [
-            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^language_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^vision_tower", target_patterns="model.vision_tower"),
             WeightRenaming(source_patterns=r"^multi_modal_projector", target_patterns="model.multi_modal_projector"),
         ],
         "llava_next": [
-            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^language_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^vision_tower", target_patterns="model.vision_tower"),
             WeightRenaming(source_patterns=r"^multi_modal_projector", target_patterns="model.multi_modal_projector"),
             WeightRenaming(source_patterns=r"^image_newline", target_patterns="model.image_newline"),
         ],
-        "clip_vision_model": [PrefixChange(prefix_to_remove="vision_model")],
+        "clip_vision_model": [
+            PrefixChange(prefix_to_remove="vision_model"),
+            WeightRenaming(source_patterns=r"layrnorm", target_patterns="layernorm"),
+        ],
         "clip_text_model": [PrefixChange(prefix_to_remove="text_model")],
+        "VideoLlavaModel": [
+            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="language_model"),
+        ],
         "video_llava": [
-            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^language_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^image_tower", target_patterns="model.image_tower"),
             WeightRenaming(source_patterns=r"^video_tower", target_patterns="model.video_tower"),
             WeightRenaming(source_patterns=r"^multi_modal_projector", target_patterns="model.multi_modal_projector"),
         ],
         "fuyu": [
-            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^language_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^vision_embed_tokens", target_patterns="model.vision_embed_tokens"),
         ],
         "mllama": [
-            WeightRenaming(source_patterns=r"^language_model.model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^language_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
             WeightRenaming(source_patterns=r"^vision_model", target_patterns="model.vision_model"),
             WeightRenaming(source_patterns=r"^multi_modal_projector", target_patterns="model.multi_modal_projector"),
         ],
+        "molmo2": [
+            # text backbone: `transformer.*` -> `language_model.*` (exclude vit's `image_vit.transformer.`)
+            WeightRenaming(source_patterns=r"(?<!image_vit\.)transformer\.", target_patterns="language_model."),
+            # vision ViT: `vision_backbone.image_vit.transformer.resblocks.N.*` -> `...encoder.layers.N.*`
+            WeightRenaming(
+                source_patterns=r"vision_backbone\.image_vit\.transformer\.resblocks\.",
+                target_patterns="vision_backbone.image_vit.encoder.layers.",
+            ),
+            WeightRenaming(source_patterns=r"\.attention\.wq", target_patterns=".self_attn.q_proj"),
+            WeightRenaming(source_patterns=r"\.attention\.wk", target_patterns=".self_attn.k_proj"),
+            WeightRenaming(source_patterns=r"\.attention\.wv", target_patterns=".self_attn.v_proj"),
+            WeightRenaming(source_patterns=r"\.attention\.wo", target_patterns=".self_attn.out_proj"),
+            WeightRenaming(source_patterns=r"\.feed_forward\.w1", target_patterns=".mlp.fc1"),
+            WeightRenaming(source_patterns=r"\.feed_forward\.w2", target_patterns=".mlp.fc2"),
+            WeightRenaming(source_patterns=r"\.attention_norm", target_patterns=".layer_norm1"),
+            WeightRenaming(source_patterns=r"\.ffn_norm", target_patterns=".layer_norm2"),
+            # image pooling 2d: wq/wk/wv/wo -> q_proj/k_proj/v_proj/out_proj
+            WeightRenaming(source_patterns=r"image_pooling_2d\.wq", target_patterns="image_pooling_2d.q_proj"),
+            WeightRenaming(source_patterns=r"image_pooling_2d\.wk", target_patterns="image_pooling_2d.k_proj"),
+            WeightRenaming(source_patterns=r"image_pooling_2d\.wv", target_patterns="image_pooling_2d.v_proj"),
+            WeightRenaming(source_patterns=r"image_pooling_2d\.wo", target_patterns="image_pooling_2d.out_proj"),
+        ],
+        "Emu3Model": [
+            WeightRenaming(source_patterns=r"^text_model.model", target_patterns="text_model"),
+        ],
         "emu3": [
-            WeightRenaming(source_patterns=r"^text_model.model", target_patterns="model.text_model"),
             WeightRenaming(source_patterns=r"^text_model.lm_head", target_patterns="lm_head"),
+            WeightRenaming(source_patterns=r"^text_model", target_patterns="model.text_model"),
             WeightRenaming(source_patterns=r"^vqmodel", target_patterns="model.vqmodel"),
         ],
         "paddleocr_vl": [
@@ -143,16 +315,117 @@ def _build_checkpoint_conversion_mapping():
                 target_patterns="model.language_model",
             ),
         ],
+        "Qwen2VLModel": [WeightRenaming(source_patterns=r"^model.", target_patterns="")],
         "qwen2_vl": [
+            WeightRenaming(source_patterns=r"^visual", target_patterns="model.visual"),
             WeightRenaming(
                 source_patterns=r"(?<!_)model(?!\.(language_model|visual))", target_patterns="model.language_model"
             ),
-            WeightRenaming(source_patterns=r"^visual", target_patterns="model.visual"),
+        ],
+        "deepseek_ocr2": [
+            WeightRenaming(
+                source_patterns=r"sam_model\.blocks\.(\d+)\.norm1\.",
+                target_patterns=r"vision_tower.sam_encoder.layers.\1.layer_norm1.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.blocks\.(\d+)\.norm2\.",
+                target_patterns=r"vision_tower.sam_encoder.layers.\1.layer_norm2.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.blocks\.(\d+)\.attn\.",
+                target_patterns=r"vision_tower.sam_encoder.layers.\1.attn.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.blocks\.(\d+)\.mlp\.",
+                target_patterns=r"vision_tower.sam_encoder.layers.\1.mlp.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.patch_embed\.proj\.",
+                target_patterns="vision_tower.sam_encoder.patch_embed.projection.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.pos_embed",
+                target_patterns="vision_tower.sam_encoder.pos_embed",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.neck\.0\.",
+                target_patterns="vision_tower.sam_encoder.neck.conv1.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.neck\.1\.",
+                target_patterns="vision_tower.sam_encoder.neck.layer_norm1.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.neck\.2\.",
+                target_patterns="vision_tower.sam_encoder.neck.conv2.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.neck\.3\.",
+                target_patterns="vision_tower.sam_encoder.neck.layer_norm2.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.net_2\.",
+                target_patterns="vision_tower.sam_encoder.proj.conv1.",
+            ),
+            WeightRenaming(
+                source_patterns=r"sam_model\.net_3\.",
+                target_patterns="vision_tower.sam_encoder.proj.conv2.",
+            ),
+            WeightRenaming(
+                source_patterns=r"qwen2_model\.model\.model\.layers\.",
+                target_patterns="vision_tower.vision_encoder.layers.",
+            ),
+            WeightRenaming(
+                source_patterns=r"qwen2_model\.model\.model\.norm\.",
+                target_patterns="vision_tower.vision_encoder.norm.",
+            ),
+            WeightRenaming(
+                source_patterns=r"qwen2_model\.query_768\.",
+                target_patterns="vision_tower.query_768_resolution.",
+            ),
+            WeightRenaming(
+                source_patterns=r"qwen2_model\.query_1024\.",
+                target_patterns="vision_tower.query_1024_resolution.",
+            ),
+            WeightRenaming(
+                source_patterns=r"projector\.layers\.",
+                target_patterns="multi_modal_projector.",
+            ),
+            WeightRenaming(source_patterns=r"view_seperator", target_patterns="view_separator"),
+            WeightRenaming(
+                source_patterns=r"(^|model\.)embed_tokens\.",
+                target_patterns=r"\1language_model.embed_tokens.",
+            ),
+            WeightRenaming(source_patterns=r"(^|model\.)layers\.", target_patterns=r"\1language_model.layers."),
+            WeightRenaming(source_patterns=r"(^|model\.)norm\.", target_patterns=r"\1language_model.norm."),
+            WeightConverter(
+                source_patterns=[
+                    "mlp.experts.*.gate_proj.weight",
+                    "mlp.experts.*.up_proj.weight",
+                ],
+                target_patterns="mlp.experts.gate_up_proj",
+                operations=[MergeModulelist(dim=0), Concatenate(dim=1)],
+            ),
+            WeightConverter(
+                source_patterns="mlp.experts.*.down_proj.weight",
+                target_patterns="mlp.experts.down_proj",
+                operations=[MergeModulelist(dim=0)],
+            ),
         ],
         "colqwen2": [
             PrefixChange(prefix_to_remove="model", model_prefix="vlm"),
             WeightRenaming(source_patterns=r"vlm(?!\.(language_model|visual))", target_patterns="vlm.language_model"),
         ],
+        "pe_audio_video_encoder": [
+            WeightRenaming(
+                source_patterns=r"audio_model\.audio_encoder\.(.+)",
+                target_patterns=r"embedder.audio_encoder.\1",
+            ),
+            WeightRenaming(
+                source_patterns=r"video_model\.video_encoder\.(.+)",
+                target_patterns=r"embedder.video_encoder.\1",
+            ),
+        ],
         "timm_wrapper": [PrefixChange(prefix_to_add="timm_model")],
         "pi0": [
             WeightRenaming(source_patterns=r"state_proj", target_patterns="embed_action_time.state_proj"),
@@ -343,29 +616,11 @@ def _build_checkpoint_conversion_mapping():
                 operations=[ErnieFuseAndSplitTextVisionExperts(stack_dim=0, concat_dim=1)],
             ),
         ],
-        "detr": [
+        "DetrModel": [
             WeightRenaming("backbone.conv_encoder", "backbone"),
             WeightRenaming("out_proj", "o_proj"),
             WeightRenaming(r"layers.(\d+).fc1", r"layers.\1.mlp.fc1"),
             WeightRenaming(r"layers.(\d+).fc2", r"layers.\1.mlp.fc2"),
-            # `DetrForSegmentation`
-            WeightRenaming("bbox_attention.q_linear", "bbox_attention.q_proj"),
-            WeightRenaming("bbox_attention.k_linear", "bbox_attention.k_proj"),
-            # Mask head refactor
-            WeightRenaming("mask_head.lay1", "mask_head.conv1.conv"),
-            WeightRenaming("mask_head.gn1", "mask_head.conv1.norm"),
-            WeightRenaming("mask_head.lay2", "mask_head.conv2.conv"),
-            WeightRenaming("mask_head.gn2", "mask_head.conv2.norm"),
-            WeightRenaming("mask_head.adapter1", "mask_head.fpn_stages.0.fpn_adapter"),
-            WeightRenaming("mask_head.lay3", "mask_head.fpn_stages.0.refine.conv"),
-            WeightRenaming("mask_head.gn3", "mask_head.fpn_stages.0.refine.norm"),
-            WeightRenaming("mask_head.adapter2", "mask_head.fpn_stages.1.fpn_adapter"),
-            WeightRenaming("mask_head.lay4", "mask_head.fpn_stages.1.refine.conv"),
-            WeightRenaming("mask_head.gn4", "mask_head.fpn_stages.1.refine.norm"),
-            WeightRenaming("mask_head.adapter3", "mask_head.fpn_stages.2.fpn_adapter"),
-            WeightRenaming("mask_head.lay5", "mask_head.fpn_stages.2.refine.conv"),
-            WeightRenaming("mask_head.gn5", "mask_head.fpn_stages.2.refine.norm"),
-            WeightRenaming("mask_head.out_lay", "mask_head.output_conv"),
         ],
         "rt_detr": [
             WeightRenaming("out_proj", "o_proj"),
@@ -373,7 +628,7 @@ def _build_checkpoint_conversion_mapping():
             WeightRenaming(r"layers.(\d+).fc2", r"layers.\1.mlp.fc2"),
             WeightRenaming(r"encoder.encoder.(\d+).layers", r"encoder.aifi.\1.layers"),
         ],
-        "conditional_detr": [
+        "ConditionalDetrModel": [
             WeightRenaming("backbone.conv_encoder", "backbone"),
             WeightRenaming("self_attn.out_proj", "self_attn.o_proj"),
             WeightRenaming("encoder_attn.out_proj", "encoder_attn.o_proj"),
@@ -394,24 +649,6 @@ def _build_checkpoint_conversion_mapping():
             WeightRenaming(
                 r"decoder.layers.(\d+).ca_qpos_sine_proj", r"decoder.layers.\1.encoder_attn.q_pos_sine_proj"
             ),
-            # The rest of patterns are used only in `ConditionalDetrForSegmentation`
-            WeightRenaming("bbox_attention.q_linear", "bbox_attention.q_proj"),
-            WeightRenaming("bbox_attention.k_linear", "bbox_attention.k_proj"),
-            # Mask head refactor
-            WeightRenaming("mask_head.lay1", "mask_head.conv1.conv"),
-            WeightRenaming("mask_head.gn1", "mask_head.conv1.norm"),
-            WeightRenaming("mask_head.lay2", "mask_head.conv2.conv"),
-            WeightRenaming("mask_head.gn2", "mask_head.conv2.norm"),
-            WeightRenaming("mask_head.adapter1", "mask_head.fpn_stages.0.fpn_adapter"),
-            WeightRenaming("mask_head.lay3", "mask_head.fpn_stages.0.refine.conv"),
-            WeightRenaming("mask_head.gn3", "mask_head.fpn_stages.0.refine.norm"),
-            WeightRenaming("mask_head.adapter2", "mask_head.fpn_stages.1.fpn_adapter"),
-            WeightRenaming("mask_head.lay4", "mask_head.fpn_stages.1.refine.conv"),
-            WeightRenaming("mask_head.gn4", "mask_head.fpn_stages.1.refine.norm"),
-            WeightRenaming("mask_head.adapter3", "mask_head.fpn_stages.2.fpn_adapter"),
-            WeightRenaming("mask_head.lay5", "mask_head.fpn_stages.2.refine.conv"),
-            WeightRenaming("mask_head.gn5", "mask_head.fpn_stages.2.refine.norm"),
-            WeightRenaming("mask_head.out_lay", "mask_head.output_conv"),
         ],
         "deformable_detr": [
             WeightRenaming("backbone.conv_encoder", "backbone"),
@@ -555,6 +792,10 @@ def _build_checkpoint_conversion_mapping():
             WeightRenaming(r"^mlp1\.1\.", r"model\.multi_modal_projector\.linear_1\."),
             WeightRenaming(r"^mlp1\.3\.", r"model\.multi_modal_projector\.linear_2\."),
         ],
+        "exaone4_5": [
+            WeightRenaming(source_patterns=r"^language_model", target_patterns="model.language_model"),
+            WeightRenaming(source_patterns=r"^visual", target_patterns="model.visual"),
+        ],
         "legacy": [
             WeightRenaming(
                 source_patterns="LayerNorm.gamma",
@@ -579,6 +820,27 @@ def _build_checkpoint_conversion_mapping():
             target_patterns=".parametrizations.weight.original1",
         ),
     ]
+    # Base DetrModel/ConditionalDetrModel transforms are picked up automatically as
+    # scoped sub-module transforms; only the segmentation-specific patterns are needed here.
+    mapping["DetrForSegmentation"] = [
+        WeightRenaming("bbox_attention.q_linear", "bbox_attention.q_proj"),
+        WeightRenaming("bbox_attention.k_linear", "bbox_attention.k_proj"),
+        WeightRenaming("mask_head.lay1", "mask_head.conv1.conv"),
+        WeightRenaming("mask_head.gn1", "mask_head.conv1.norm"),
+        WeightRenaming("mask_head.lay2", "mask_head.conv2.conv"),
+        WeightRenaming("mask_head.gn2", "mask_head.conv2.norm"),
+        WeightRenaming("mask_head.adapter1", "mask_head.fpn_stages.0.fpn_adapter"),
+        WeightRenaming("mask_head.lay3", "mask_head.fpn_stages.0.refine.conv"),
+        WeightRenaming("mask_head.gn3", "mask_head.fpn_stages.0.refine.norm"),
+        WeightRenaming("mask_head.adapter2", "mask_head.fpn_stages.1.fpn_adapter"),
+        WeightRenaming("mask_head.lay4", "mask_head.fpn_stages.1.refine.conv"),
+        WeightRenaming("mask_head.gn4", "mask_head.fpn_stages.1.refine.norm"),
+        WeightRenaming("mask_head.adapter3", "mask_head.fpn_stages.2.fpn_adapter"),
+        WeightRenaming("mask_head.lay5", "mask_head.fpn_stages.2.refine.conv"),
+        WeightRenaming("mask_head.gn5", "mask_head.fpn_stages.2.refine.norm"),
+        WeightRenaming("mask_head.out_lay", "mask_head.output_conv"),
+    ]
+    mapping["ConditionalDetrForSegmentation"] = mapping["DetrForSegmentation"].copy()
 
     mapping["ernie4_5_moe"] = mapping["qwen2_moe"].copy()
     mapping["ernie4_5_moe"] += [
@@ -608,6 +870,23 @@ def _build_checkpoint_conversion_mapping():
         WeightRenaming("mlp.shared_expert.", "mlp.shared_experts."),
     ]
 
+    mapping["PenguinVLModel"] = [
+        WeightRenaming(r"^vision_encoder\.vision_encoder\.", "vision_model."),
+        WeightRenaming(r"^vision_encoder\.", "vision_model."),
+        WeightRenaming(r"^vision_projector\.", "projector."),
+        WeightRenaming(r"^embed_tokens\.", "language_model.embed_tokens."),
+        WeightRenaming(r"^layers\.", "language_model.layers."),
+        WeightRenaming(r"^norm\.", "language_model.norm."),
+    ]
+    mapping["PenguinVLForConditionalGeneration"] = [
+        WeightRenaming(r"^model\.vision_encoder\.vision_encoder\.", "model.vision_model."),
+        WeightRenaming(r"^model\.vision_encoder\.", "model.vision_model."),
+        WeightRenaming(r"^model\.vision_projector\.", "model.projector."),
+        WeightRenaming(r"^model\.embed_tokens\.", "model.language_model.embed_tokens."),
+        WeightRenaming(r"^model\.layers\.", "model.language_model.layers."),
+        WeightRenaming(r"^model\.norm\.", "model.language_model.norm."),
+    ]
+
     for model_type, base_pattern in _MODEL_TO_CONVERSION_PATTERN.items():
         if model_type in mapping:
             continue
@@ -627,30 +906,46 @@ def get_checkpoint_conversion_mapping(model_type):
 
 
 def register_checkpoint_conversion_mapping(
-    model_type: str,
+    model_type_or_class_name: str,
     mapping: list[WeightConverter | WeightRenaming],
     overwrite: bool = False,
 ) -> None:
+    """
+    Register a conversion mapping for a model type string or a class name.
+
+    Class names take priority over ``model_type`` strings during lookup (see
+    :func:`extract_weight_conversions_for_model`), making it possible to define
+    task-head-specific or class-specific conversions that differ from the shared
+    ``model_type`` baseline.
+    """
     global _checkpoint_conversion_mapping_cache
     if _checkpoint_conversion_mapping_cache is None:
         _checkpoint_conversion_mapping_cache = _build_checkpoint_conversion_mapping()
-    if model_type in _checkpoint_conversion_mapping_cache and not overwrite:
-        raise ValueError(f"Model type {model_type} already exists in the checkpoint conversion mapping.")
-    _checkpoint_conversion_mapping_cache[model_type] = mapping
+    if model_type_or_class_name in _checkpoint_conversion_mapping_cache and not overwrite:
+        raise ValueError(
+            f"Conversion mapping for '{model_type_or_class_name}' already exists. Pass overwrite=True to replace it."
+        )
+    _checkpoint_conversion_mapping_cache[model_type_or_class_name] = mapping
 
 
-def extract_weight_conversions_for_model(model: PreTrainedModel, model_prefix: str) -> list[WeightTransform] | None:
+def extract_weight_conversions_for_model(
+    model: PreTrainedModel,
+) -> list[WeightTransform] | None:
+    """
+    Return the registered conversion list for ``model``, or ``None`` if none exists.
+
+    Looks up by class name first (enables task-head-specific overrides), then
+    falls back to ``model.config.model_type``.  Transforms are returned
+    unmodified; the caller sets ``scope_prefix`` on each transform for sub-module isolation.
+    """
+    class_name = type(model).__name__
     model_type = getattr(model.config, "model_type", None)
-    if model_type is not None:
-        model_specific_conversions = get_checkpoint_conversion_mapping(model_type)
-        # In this case, add the prefix to `PrefixChange` instances, in order to know where to add/remove the prefix
-        if model_specific_conversions is not None and model_prefix != "":
-            for i, conversion in enumerate(model_specific_conversions):
-                # In this case, add the prefix, as otherwise we don't know where we need to re-add it exactly in the module name chain
-                if isinstance(conversion, PrefixChange):
-                    model_specific_conversions[i] = conversion.with_submodel_prefix(model_prefix)
-        return model_specific_conversions
-    return None
+
+    # Class name takes priority — allows ForXxx-specific overrides
+    conversions = get_checkpoint_conversion_mapping(class_name)
+    if conversions is None and model_type is not None:
+        conversions = get_checkpoint_conversion_mapping(model_type)
+    return conversions
 
 
 def get_model_conversion_mapping(
@@ -660,11 +955,17 @@ def get_model_conversion_mapping(
     add_legacy: bool = True,
 ) -> list[WeightTransform]:
     """
-    For a given `model`, obtain the weight conversion mapping if any are registered either as a simple renaming
-    `_checkpoint_conversion_mapping` class argument, or in the general WeightConverter mapping.
+    Collect the ordered list of weight transforms for ``model`` (used during
+    loading and, when reversed, during saving).
+
+    Each ``PreTrainedModel`` sub-module is looked up by class name then
+    ``model_type``.  Root transforms are applied globally; sub-module transforms
+    have their ``scope_prefix`` set so they only match keys under that prefix.  After any
+    sub-module is processed, both its class name and ``model_type`` are marked
+    seen to prevent ``XForY`` / ``XModel`` pairs from applying the same mapping
+    twice via different lookup paths.
     """
     # Lazy import to avoid circular import issues
-    from .modeling_utils import PreTrainedModel
 
     # note: this function is used in PEFT, so changing the API requires coordination
     weight_conversions = []
@@ -673,22 +974,54 @@ def get_model_conversion_mapping(
     if key_mapping is not None:
         weight_conversions = [WeightRenaming(source_patterns=k, target_patterns=v) for k, v in key_mapping.items()]
 
-    # Model have several `PreTrainedModel` within with the same model type, for example: XForConditionalGeneration -> XModel
-    # We don't want to apply the same conversion pattern twice because of that
-    seen_model_types = set()
-    # Recurse over submodules and collect all conversions
-    for name, submodule in model.named_modules():
-        if isinstance(submodule, PreTrainedModel) and submodule.config.model_type not in seen_model_types:
-            conversions = extract_weight_conversions_for_model(submodule, name)
-            if conversions is not None:
-                weight_conversions.extend(conversions)
-                seen_model_types.add(submodule.config.model_type)
+    seen_identifiers: set[str] = set()
+
+    named_pretrained = getattr(model, "_named_pretrained_submodules", None)
+    if named_pretrained is None:
+        from .modeling_utils import PreTrainedModel
+
+        named_pretrained = [(name, m) for name, m in model.named_modules() if isinstance(m, PreTrainedModel)]
+    for module_name, submodule in named_pretrained:
+        class_name = type(submodule).__name__
+        model_type = getattr(submodule.config, "model_type", None)
+
+        # Skip if this architecture was already processed via either lookup path.
+        if class_name in seen_identifiers or (model_type and model_type in seen_identifiers):
+            continue
+
+        # Try class name first, then model_type. Track which path produced the hit so
+        # we know whether to block model_type for subsequent sub-modules (see below).
+        conversions = get_checkpoint_conversion_mapping(class_name)
+        found_via_class = conversions is not None
+        if not found_via_class and model_type is not None:
+            conversions = get_checkpoint_conversion_mapping(model_type)
+
+        if conversions is None:
+            continue
+
+        is_root_model = module_name == ""
+        if not is_root_model:
+            # Scope each transform so it only matches keys under this sub-module's prefix.
+            for transform in conversions:
+                transform.scope_prefix = module_name
+        weight_conversions.extend(conversions)
+
+        seen_identifiers.add(class_name)
+        # Only block model_type when the hit was via model_type. When the hit was via
+        # class name, sub-modules that share the same model_type but have no class-specific
+        # mapping of their own (e.g. DetrModel under DetrForSegmentation) must still be
+        # reachable so their base transforms are picked up and scoped automatically.
+        if not found_via_class and model_type:
+            seen_identifiers.add(model_type)
 
     if add_legacy:
         weight_conversions.extend(get_checkpoint_conversion_mapping("legacy"))
 
-    # Add the ones from the quantizer as well if provided
+    # Let the quantizer rewrite / augment the conversion pipeline. This is where the
+    # FP8 dequantizer (when ``dequantize=True``) prepends a ``Fp8Dequantize`` op to
+    # every existing converter so that per-block scales are applied *before* any
+    # expert-merge / concat ops flatten the per-expert structure away.
     if hf_quantizer is not None:
-        weight_conversions.extend(hf_quantizer.get_weight_conversions())
+        weight_conversions = hf_quantizer.update_weight_conversions(weight_conversions)
 
     return weight_conversions
diff --git a/src/transformers/convert_slow_tokenizer.py b/src/transformers/convert_slow_tokenizer.py
index 1d96d1c4d9a2..5a9376ef0081 100644
--- a/src/transformers/convert_slow_tokenizer.py
+++ b/src/transformers/convert_slow_tokenizer.py
@@ -735,7 +735,8 @@ def tokenizer(self, proto):
             )
 
         elif model_type == 2:
-            _, merges = self.SpmExtractor(self.original_tokenizer.vocab_file).extract(vocab_scores)
+            result = self.SpmExtractor(self.original_tokenizer.vocab_file).extract(None)
+            merges = result["merges"]
             bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
             tokenizer = Tokenizer(
                 BPE(
@@ -1734,6 +1735,57 @@ def pre_tokenizer(self, replacement, add_prefix_space):
         return pre_tokenizers.Metaspace(replacement=replacement, prepend_scheme=prepend_scheme, split=False)
 
 
+class CanaryConverter(SpmConverter):
+    handle_byte_fallback = True
+
+    def __init__(self, vocab_file=None, *args):
+        self.vocab_file = vocab_file
+
+        requires_backends(self, "protobuf")
+
+        Converter.__init__(self, vocab_file)
+
+        model_pb2 = import_protobuf()
+        m = model_pb2.ModelProto()
+        with open(vocab_file, "rb") as f:
+            m.ParseFromString(f.read())
+        self.proto = m
+
+    def tokenizer(self, proto):
+        vocab_scores = self.vocab(proto)
+
+        _, merges = self.SpmExtractor(self.vocab_file).extract(vocab_scores)
+        bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
+        tokenizer = Tokenizer(
+            BPE(
+                bpe_vocab,
+                merges,
+                unk_token=proto.trainer_spec.unk_piece,
+                fuse_unk=True,
+                byte_fallback=self.handle_byte_fallback,
+                dropout=None,
+            )
+        )
+
+        # control tokens are special
+        # user defined symbols are not
+        # both user and control tokens are AddedTokens
+        # Add user defined symbols (type == 4) from sentencepiece (https://github.com/google/sentencepiece/blob/6225e08edb2577757163b3f5dbba4c0b670ef445/src/sentencepiece_model.proto#L299C29-L299C33)
+        spm_added_tokens = [
+            (id, p.piece, p.type == 3 or p.piece in self.special_tokens)
+            for id, p in enumerate(proto.pieces)
+            if p.type in [3, 4]
+        ]
+        tokenizer.add_tokens(
+            [
+                AddedToken(token, normalized=False, special=special)
+                for id, token, special in sorted(spm_added_tokens, key=lambda x: x[0])
+            ]
+        )
+
+        return tokenizer
+
+
 class HeliumConverter(SpmConverter):
     handle_byte_fallback = True
 
@@ -1842,7 +1894,8 @@ def __init__(self, vocab_file=None, *args):
     def tokenizer(self, proto):
         vocab_scores = self.vocab(proto)
 
-        _, merges = self.SpmExtractor(self.vocab_file).extract(vocab_scores)
+        result = self.SpmExtractor(self.vocab_file).extract(None)
+        merges = result["merges"]
         bpe_vocab = {word: i for i, (word, score) in enumerate(vocab_scores)}
         tokenizer = Tokenizer(
             BPE(
diff --git a/src/transformers/core_model_loading.py b/src/transformers/core_model_loading.py
index cd0710649c91..374011e4e303 100644
--- a/src/transformers/core_model_loading.py
+++ b/src/transformers/core_model_loading.py
@@ -156,8 +156,11 @@ def convert(
         target_pattern = self.get_target_pattern(target_patterns)
         all_tensors = []
         # Very important to keep the relative order of the source patterns here, so we iterate over them not the
-        # input directly as it's unordered!
+        # input directly as it's unordered! Skip patterns that prior ops in the chain (e.g. ``Fp8Dequantize``)
+        # have already consumed and dropped from ``input_dict``.
         for source_pattern in source_patterns:
+            if source_pattern not in input_dict:
+                continue
             tensors = input_dict[source_pattern]
             if isinstance(tensors, list):
                 all_tensors.extend(tensors)
@@ -583,7 +586,8 @@ class WeightTransform:
     __slots__ = (
         "source_patterns",
         "target_patterns",
-        "compiled_sources",
+        "_source_regex_str",
+        "_compiled_sources",
         "distributed_operation",
         "quantization_operation",
         "collected_tensors",
@@ -591,6 +595,7 @@ class WeightTransform:
         "_original_source_patterns",
         "_original_target_patterns",
         "_was_used",
+        "scope_prefix",
     )
 
     def __init__(self, source_patterns: str | list[str], target_patterns: str | list[str]):
@@ -608,6 +613,9 @@ def __init__(self, source_patterns: str | list[str], target_patterns: str | list
 
         # Flag to notice if the Transform was used
         self._was_used = False
+        # Optional prefix scope: when set, this transform only applies to keys starting with
+        # ``scope_prefix + "."``, stripping / re-attaching the prefix around the pattern match.
+        self.scope_prefix: str | None = None
 
         # We need to process a few exceptions here when instantiating the reverse mapping (i.e. the targets become
         # sources, and sources become targets). The issues lie in the sources usually, so here we need to check the
@@ -647,13 +655,40 @@ def __init__(self, source_patterns: str | list[str], target_patterns: str | list
                 pattern = process_source_pattern(pattern, self._original_target_patterns[i])
             self.source_patterns[i] = pattern
 
-        # Construct the regex we will use to rename keys from the sources to the targets
+        # Build the regex source string, but compile lazily via `compiled_sources` below. During loading, any key
+        # that does not match a weight conversion op gets wrapped in a fresh per-weight `WeightRenaming` for
+        # convenience so it can reuse the same conversion/loading path. Those fallback wrappers never need to call
+        # `rename_source_key`, so eagerly compiling their regex would just waste work — and it dominates
+        # `from_pretrained` for models with many parameters.
         branches = []
         for i, source_pattern in enumerate(self.source_patterns):
             group_name = f"g{i}"
             pattern = source_pattern.replace(".*.", r"\..*\.")
             branches.append(f"(?P<{group_name}>{pattern})")
-        self.compiled_sources = re.compile("|".join(branches))
+        self._source_regex_str = "|".join(branches)
+        self._compiled_sources = None
+
+    @property
+    def compiled_sources(self) -> re.Pattern:
+        if self._compiled_sources is None:
+            self._compiled_sources = re.compile(self._source_regex_str)
+        return self._compiled_sources
+
+    def __deepcopy__(self, memo):
+        # A fresh-per-target copy is needed because `collected_tensors`, `layer_targets`, and `_was_used` accumulate
+        # state during loading. The compiled regex is stateless, so we share it across copies — avoiding a hidden
+        # `re.compile` that would otherwise run on every per-weight pickle/unpickle round-trip.
+        cls = self.__class__
+        new = cls.__new__(cls)
+        memo[id(self)] = new
+        for slot in chain.from_iterable(getattr(c, "__slots__", ()) for c in cls.__mro__):
+            if not hasattr(self, slot):
+                continue
+            if slot == "_compiled_sources":
+                new._compiled_sources = self._compiled_sources
+            else:
+                object.__setattr__(new, slot, deepcopy(getattr(self, slot), memo))
+        return new
 
     def __repr__(self):
         return f"{self.__class__.__name__}(source_patterns={self.source_patterns}, target_patterns={self.target_patterns})"
@@ -673,6 +708,27 @@ def add_tensor(self, target_key: str, source_key: str, source_pattern: str, futu
         self.collected_tensors[source_pattern].append(future)
         self.layer_targets[target_key].add(source_key)
 
+    def _scoped_match(self, source_key: str) -> tuple[str | None, str, re.Match[str]] | None:
+        """
+        Apply ``scope_prefix`` stripping (if any), then match ``compiled_sources`` against the suffix.
+
+        Returns ``(prefix_dot, key_to_match, match_object)`` when a branch matches, where ``prefix_dot`` is ``None``
+        if ``scope_prefix`` is unset, else ``f"{scope_prefix}."``. Returns ``None`` when out of scope or unmatched.
+        Does not set ``_was_used``.
+        """
+        prefix_dot = None
+        key_to_match = source_key
+        if self.scope_prefix is not None:
+            prefix_dot = self.scope_prefix + "."
+            if not source_key.startswith(prefix_dot):
+                return None
+            key_to_match = source_key[len(prefix_dot) :]
+
+        match_object = self.compiled_sources.search(key_to_match)
+        if match_object is None:
+            return None
+        return (prefix_dot, key_to_match, match_object)
+
     def rename_source_key(self, source_key: str) -> tuple[str, str | None]:
         """
         Return a tuple (renamed_key, source_pattern_producing_the_match).
@@ -680,11 +736,12 @@ def rename_source_key(self, source_key: str) -> tuple[str, str | None]:
         In case of a one-to-many transform, i.e. we have several target patterns, the matching source pattern
         will be replaced by the first of all the target patterns (they are then correctly expanded in the Operations).
         """
-        # Try matching one of the alternation branches
-        match_object = self.compiled_sources.search(source_key)
-        if match_object is None:
+        matched = self._scoped_match(source_key)
+        if matched is None:
             return source_key, None
 
+        prefix_dot, key_to_match, match_object = matched
+
         # We have a match, so the Transform was used
         self._was_used = True
 
@@ -699,7 +756,9 @@ def rename_source_key(self, source_key: str) -> tuple[str, str | None]:
             # inside that matched named group
             replaced_group_idx = self.compiled_sources.groupindex[matching_group_name] + 1
             replacement = replacement.replace(r"\1", match_object.group(replaced_group_idx))
-        renamed_key = source_key.replace(match_object.group(0), replacement, 1)
+        renamed_key = key_to_match.replace(match_object.group(0), replacement, 1)
+        if prefix_dot is not None:
+            renamed_key = prefix_dot + renamed_key
         return renamed_key, source_pattern_that_matched
 
     def reverse_transform(self) -> WeightTransform:
@@ -717,7 +776,7 @@ def reverse_transform(self) -> WeightTransform:
         reverse_transform = self.__class__(
             source_patterns=self._original_target_patterns, target_patterns=self._original_source_patterns, **kwargs
         )
-
+        reverse_transform.scope_prefix = self.scope_prefix
         return reverse_transform
 
     def materialize_tensors(self) -> dict[str, list[torch.Tensor]]:
@@ -735,14 +794,13 @@ def materialize_tensors(self) -> dict[str, list[torch.Tensor]]:
         for key in list(self.collected_tensors.keys()):
             # Remove from internal attribute
             tensors = self.collected_tensors.pop(key)
-            # Async loading
-            if isinstance(tensors[0], Future):
-                tensors = [future.result() for future in tensors if future.result() is not None]
-            # Sync loading
-            elif callable(tensors[0]):
-                tensors = [func() for func in tensors]
+            resolved_tensors = []
+            for tensor_or_future in tensors:
+                resolved_tensor = _resolve_pending_tensor(tensor_or_future)
+                if resolved_tensor is not None:
+                    resolved_tensors.append(resolved_tensor)
             # Add them to the new dictionary
-            collected_tensors[key] = tensors
+            collected_tensors[key] = resolved_tensors
 
         return collected_tensors
 
@@ -836,15 +894,11 @@ def reverse_transform(self) -> WeightTransform:
             raise ValueError("Cannot reverse the transform with TP or quantization")
 
         # Only one of the 2 can ever be used, so 1 is always None
-        return PrefixChange(
+        result = PrefixChange(
             prefix_to_add=self.prefix_to_remove, prefix_to_remove=self.prefix_to_add, model_prefix=self.model_prefix
         )
-
-    def with_submodel_prefix(self, prefix: str) -> PrefixChange:
-        new_prefix = f"{prefix}.{self.model_prefix}" if self.model_prefix != "" else prefix
-        return PrefixChange(
-            prefix_to_add=self.prefix_to_add, prefix_to_remove=self.prefix_to_remove, model_prefix=new_prefix
-        )
+        result.scope_prefix = self.scope_prefix
+        return result
 
 
 # List of classes that are known to be able to use m:n
@@ -933,6 +987,15 @@ def convert(
 GLOBAL_WORKERS = min(4, os.cpu_count() or 4)
 
 
+def _resolve_pending_tensor(tensor_or_future: Future | Callable | torch.Tensor) -> torch.Tensor | None:
+    if isinstance(tensor_or_future, Future):
+        return tensor_or_future.result()
+    elif callable(tensor_or_future):
+        return tensor_or_future()
+    else:
+        return tensor_or_future
+
+
 def _materialize_copy(tensor: torch.Tensor, device=None, dtype=None) -> torch.Tensor:
     # This slicing is what actually loads the tensor from the safetensors slice object
     tensor = tensor[...]
@@ -1048,9 +1111,16 @@ def set_param_for_module(
     loading_info: LoadStateDictInfo,
     distributed_operation: TensorParallelLayer | None,
     hf_quantizer: HfQuantizer,
+    module_cache: dict[str, torch.nn.Module] | None = None,
 ):
     module_path, _, param_name = target_name.rpartition(".")
-    module_obj = model.get_submodule(module_path) if module_path else model
+    if module_cache is not None:
+        module_obj = module_cache.get(module_path)
+        if module_obj is None:
+            module_obj = model.get_submodule(module_path) if module_path else model
+            module_cache[module_path] = module_obj
+    else:
+        module_obj = model.get_submodule(module_path) if module_path else model
 
     if param_name == torch.nn.modules.module._EXTRA_STATE_KEY_SUFFIX:
         module_obj.set_extra_state(param_value)
@@ -1077,6 +1147,8 @@ def set_param_for_module(
         if ref is not None and param_value.shape != expected_shape and hf_quantizer is None:
             loading_info.mismatched_keys.add((target_name, param_value.shape, expected_shape))
         else:
+            if distributed_operation is not None:
+                param_value = distributed_operation.post_shard_wrap(param_value)
             # super important otherwise _init_weight will re-init the param
             param_value._is_hf_initialized = True
             setattr(module_obj, param_name, param_value)
@@ -1088,7 +1160,7 @@ def offload_and_maybe_resave_param(
     target_name: str,
     param: torch.Tensor,
     loading_info: LoadStateDictInfo,
-    disk_offload_folder: str,
+    disk_offload_folder: str | None,
     disk_offload_index: dict,
     applied_ops: WeightConverter | WeightRenaming,
 ) -> dict:
@@ -1112,42 +1184,102 @@ class SkipParameters(Exception):
 
 def rename_source_key(
     source_key: str,
-    weight_renamings: list[WeightRenaming],
-    weight_converters: list[WeightConverter],
+    weight_transforms: list[WeightTransform],
     prefix: str | None = None,
     meta_state_dict: dict | None = None,
 ) -> tuple[str, str | None]:
     """
-    Rename a source key given all the renaming and weight conversion patterns we have. Also takes care of adding/removing
-    the base model prefix during loading if necessary.
+    Rename a source key according to ``weight_transforms``, also handling the base model prefix.
+
+    Transforms are applied in list order, interleaving ``WeightRenaming`` and ``WeightConverter``
+    instances as they appear.  The same list, reversed and with each transform individually
+    inverted, is used on the save path, so relative ordering is preserved in both directions.
+
+    At most one ``WeightConverter`` fires per key; subsequent converters are skipped.
+    ``WeightRenaming`` always runs, even after a converter has already fired.
+
+    Example (root rename followed by a scoped sub-model converter)::
+
+        transforms = [
+            WeightRenaming("^old_prefix", "model.vlm"),
+            WeightConverter("^q_proj", "qkv_proj", ...),  # scope_prefix="model.vlm"
+        ]
+        # Load:  "old_prefix.q_proj"
+        #   → WeightRenaming  → "model.vlm.q_proj"
+        #   → WeightConverter → "model.vlm.qkv_proj"
+        #
+        # Save (inverted list, each transform reversed):
+        #   "model.vlm.q_proj"
+        #   → rev(WeightConverter) → "model.vlm.q_proj"
+        #   → rev(WeightRenaming)  → "old_prefix.q_proj"
     """
     renamed_key = source_key
-    # 1. apply all renamings in turns (if multiple match, it's the responsibility of the mappings to make sure they
-    # are coherent)
-    for renaming in weight_renamings:
-        renamed_key, _ = renaming.rename_source_key(renamed_key)
-
-    # 2. apply renaming through weight conversions on the key if we have any WeightConverter (here we stop after
-    # the first match, as we assume only 1 converter can match any source key)
     source_pattern = None
-    for converter in weight_converters:
-        renamed_key, source_pattern = converter.rename_source_key(renamed_key)
-        if source_pattern is not None:
-            break
-
-    # 3. check if we need to add or remove prefix if necessary (only during loading, not saving)
-    if prefix is not None and meta_state_dict is not None:
-        if (
-            renamed_key.startswith(prefix)
-            and meta_state_dict.get(re.sub(f"^{prefix}.", "", renamed_key, count=1)) is not None
-        ):
-            renamed_key = re.sub(f"^{prefix}.", "", renamed_key, count=1)
-        elif meta_state_dict.get(f"{prefix}.{renamed_key}") is not None:
-            renamed_key = f"{prefix}.{renamed_key}"
+
+    for transform in weight_transforms:
+        if isinstance(transform, WeightConverter):
+            if source_pattern is not None:
+                # Already matched a converter; skip subsequent converters.
+                continue
+            renamed_key, sp = transform.rename_source_key(renamed_key)
+            if sp is not None:
+                source_pattern = sp
+        else:
+            renamed_key, _ = transform.rename_source_key(renamed_key)
+
+    # check if we need to add or remove prefix if necessary (only during loading, not saving)
+    if prefix not in (None, "") and meta_state_dict is not None:
+        prefixed_key = f"{prefix}.{renamed_key}"
+        prefix_with_separator = f"{prefix}."
+        if renamed_key.startswith(prefix_with_separator):
+            unprefixed_key = renamed_key[len(prefix_with_separator) :]
+            if meta_state_dict.get(unprefixed_key) is not None:
+                renamed_key = unprefixed_key
+        elif meta_state_dict.get(prefixed_key) is not None:
+            renamed_key = prefixed_key
 
     return renamed_key, source_pattern
 
 
+def _assign_or_offload_param(
+    model: PreTrainedModel,
+    target_name: str,
+    param: torch.Tensor,
+    loading_info: LoadStateDictInfo,
+    device_map: dict | None,
+    model_buffers: set[str],
+    offload_buffers: bool,
+    disk_offload_folder: str | None,
+    disk_offload_index: dict | None,
+    distributed_operation: TensorParallelLayer | None,
+    hf_quantizer: HfQuantizer | None,
+    module_cache: dict[str, torch.nn.Module],
+    applied_ops: WeightConverter | WeightRenaming | None = None,
+) -> dict | None:
+    param_device = get_device(device_map, target_name)
+    if param_device == "disk" and (target_name not in model_buffers or offload_buffers):
+        current_disk_offload_index = {} if disk_offload_index is None else disk_offload_index
+        if applied_ops is None:
+            loading_info.missing_keys.discard(target_name)
+            if target_name not in current_disk_offload_index:
+                return offload_weight(param, target_name, disk_offload_folder, current_disk_offload_index)
+        else:
+            return offload_and_maybe_resave_param(
+                target_name, param, loading_info, disk_offload_folder, current_disk_offload_index, applied_ops
+            )
+    else:
+        set_param_for_module(
+            model,
+            target_name,
+            param,
+            loading_info,
+            distributed_operation,
+            hf_quantizer,
+            module_cache=module_cache,
+        )
+    return disk_offload_index
+
+
 def convert_and_load_state_dict_in_model(
     model: PreTrainedModel,
     state_dict: dict[str, Any],
@@ -1277,9 +1409,10 @@ def convert_and_load_state_dict_in_model(
     else:
         thread_pool = ThreadPoolExecutor(max_workers=GLOBAL_WORKERS)
 
-    renamings = [entry for entry in weight_mapping if isinstance(entry, WeightRenaming)]
     converters = [entry for entry in weight_mapping if isinstance(entry, WeightConverter)]
+    direct_param_loads: list[tuple[str, Future | Callable | torch.Tensor, TensorParallelLayer | None]] = []
     param_name_to_load: dict[str, WeightRenaming | WeightConverter] = {}
+    module_cache: dict[str, torch.nn.Module] = {"": model}
 
     # build '(?P<g0>.*.*\\.block_sparse_moe\\..*)' and group to source {'g0': '*.block_sparse_moe.'}
     # and target to source {'g0': '*.mlp.'}. This allows us to quickly find which pattern matched.
@@ -1292,26 +1425,15 @@ def convert_and_load_state_dict_in_model(
 
     state_dict = sorted(state_dict.items(), key=lambda kv: dot_natural_key(kv[0]))
     for original_key, tensor in state_dict:
-        # 1. Rename the key according to all renaming pattern and optional weight converter patterns
-        renamed_key, source_pattern = rename_source_key(
-            original_key, renamings, converters, prefix, meta_model_state_dict
-        )
+        # 1. Rename the key according to all renaming and weight conversion patterns.
+        renamed_key, source_pattern = rename_source_key(original_key, weight_mapping, prefix, meta_model_state_dict)
         if renamed_key not in meta_model_state_dict and original_key in meta_model_state_dict:
-            # Key should probably not have been renamed but we might need the `prefix` to be added.`
-            renamed_key, source_pattern = rename_source_key(original_key, [], [], prefix, meta_model_state_dict)
+            # Key should probably not have been renamed but we might need the `prefix` to be added.
+            renamed_key, source_pattern = rename_source_key(original_key, [], prefix, meta_model_state_dict)
 
         # 2. finally, collect the tensor into the proper converter
         if renamed_key in meta_model_state_dict:
             empty_param = meta_model_state_dict.get(renamed_key)
-            # If we enter here, we have a WeightConverter operation to perform
-            if source_pattern is not None:
-                new_converter = deepcopy(pattern_to_converter[source_pattern])
-                # each target key gets its own converter instance
-                mapping = param_name_to_load.setdefault(renamed_key, new_converter)
-            # Otherwise, only potential renaming
-            else:
-                mapping = param_name_to_load.setdefault(renamed_key, WeightRenaming(original_key, renamed_key))
-                source_pattern = original_key
 
             # 3. Handle dtype casting
             needs_quantization = (
@@ -1319,6 +1441,21 @@ def convert_and_load_state_dict_in_model(
                 and not hf_quantizer.pre_quantized
                 and hf_quantizer.param_needs_quantization(model, renamed_key)
             )
+            mapping = None
+            if source_pattern is not None:
+                # each target key gets its own converter instance (deepcopy is lazy: skipped if target already seen,
+                # e.g. many-to-one/one-to-many converters where several sources land on the same target)
+                mapping = param_name_to_load.get(renamed_key)
+                if mapping is None:
+                    mapping = deepcopy(pattern_to_converter[source_pattern])
+                    param_name_to_load[renamed_key] = mapping
+            elif needs_quantization:
+                mapping = param_name_to_load.get(renamed_key)
+                if mapping is None:
+                    mapping = WeightRenaming(original_key, renamed_key)
+                    param_name_to_load[renamed_key] = mapping
+                source_pattern = original_key
+
             if needs_quantization:
                 mapping.quantization_operation = hf_quantizer.get_quantize_ops()
 
@@ -1348,14 +1485,24 @@ def convert_and_load_state_dict_in_model(
 
             # 4. Handle TP sharding or device_map placement
             future_or_tensor = None
+            distributed_operation = None
             if device_mesh and tp_plan:
                 if matched_tp_pattern := tp_plan_alt.search(renamed_key):
                     matched_tp_pattern = tp_plan_by_group_name[matched_tp_pattern.lastgroup]
-                    if getattr(mapping, "distributed_operation", None) is None:
-                        tp_layer = ALL_PARALLEL_STYLES[model.tp_plan[matched_tp_pattern]].__class__
-                        mapping.distributed_operation = tp_layer(
+                    tp_layer = ALL_PARALLEL_STYLES[model.tp_plan[matched_tp_pattern]].__class__
+                    if mapping is None:
+                        distributed_operation = tp_layer(
                             device_mesh=device_mesh, rank=device_mesh.get_local_rank(), empty_param=empty_param.clone()
                         )
+                    else:
+                        distributed_operation = getattr(mapping, "distributed_operation", None)
+                        if distributed_operation is None:
+                            distributed_operation = tp_layer(
+                                device_mesh=device_mesh,
+                                rank=device_mesh.get_local_rank(),
+                                empty_param=empty_param.clone(),
+                            )
+                            mapping.distributed_operation = distributed_operation
                     shard_index = (
                         len(mapping.collected_tensors.get(source_pattern, []))
                         if isinstance(mapping, WeightConverter) and isinstance(mapping.operations[0], MergeModulelist)
@@ -1364,7 +1511,7 @@ def convert_and_load_state_dict_in_model(
                     future_or_tensor = spawn_tp_materialize(
                         thread_pool,
                         tensor,
-                        mapping.distributed_operation,
+                        distributed_operation,
                         shard_index,
                         device_map[""],
                         _dtype,
@@ -1374,7 +1521,12 @@ def convert_and_load_state_dict_in_model(
                 param_device = get_device(device_map, renamed_key, valid_torch_device=True)
                 future_or_tensor = spawn_materialize(thread_pool, tensor, param_device, _dtype)
 
-            mapping.add_tensor(renamed_key, original_key, source_pattern, future_or_tensor)
+            if mapping is None:
+                # Fast path for untouched or purely renamed parameters: avoid instantiating a per-weight
+                # `WeightRenaming` wrapper when we can load the tensor directly.
+                direct_param_loads.append((renamed_key, future_or_tensor, distributed_operation))
+            else:
+                mapping.add_tensor(renamed_key, original_key, source_pattern, future_or_tensor)
         elif source_pattern is not None:  # add all target keys as unexpected
             mapping = pattern_to_converter[source_pattern]
             for k in mapping.target_patterns:
@@ -1383,38 +1535,63 @@ def convert_and_load_state_dict_in_model(
             loading_info.unexpected_keys.add(renamed_key)
 
     try:
-        for first_param_name, mapping in tqdm(param_name_to_load.items(), desc="Loading weights"):
-            try:
-                realized_value = mapping.convert(
-                    first_param_name,
-                    model=model,
-                    config=model.config,
-                    hf_quantizer=hf_quantizer,
-                    loading_info=loading_info,
-                )
-                for target_name, param in realized_value.items():
-                    param = param[0] if isinstance(param, list) else param
-                    param_device = get_device(device_map, target_name)
-                    # Offloading support
-                    if param_device == "disk" and (target_name not in model_buffers or offload_buffers):
-                        disk_offload_index = offload_and_maybe_resave_param(
-                            target_name, param, loading_info, disk_offload_folder, disk_offload_index, mapping
-                        )
-                    else:
-                        set_param_for_module(
+        with tqdm(total=len(direct_param_loads) + len(param_name_to_load), desc="Loading weights") as progress_bar:
+            for target_name, pending_param, distributed_operation in direct_param_loads:
+                try:
+                    param = _resolve_pending_tensor(pending_param)
+                    if param is None:
+                        continue
+                    disk_offload_index = _assign_or_offload_param(
+                        model,
+                        target_name,
+                        param,
+                        loading_info,
+                        device_map,
+                        model_buffers,
+                        offload_buffers,
+                        disk_offload_folder,
+                        disk_offload_index,
+                        distributed_operation,
+                        hf_quantizer,
+                        module_cache,
+                    )
+                finally:
+                    progress_bar.update()
+
+            for first_param_name, mapping in param_name_to_load.items():
+                try:
+                    realized_value = mapping.convert(
+                        first_param_name,
+                        model=model,
+                        config=model.config,
+                        hf_quantizer=hf_quantizer,
+                        loading_info=loading_info,
+                    )
+                    for target_name, param in realized_value.items():
+                        param = param[0] if isinstance(param, list) else param
+                        disk_offload_index = _assign_or_offload_param(
                             model,
                             target_name,
                             param,
                             loading_info,
+                            device_map,
+                            model_buffers,
+                            offload_buffers,
+                            disk_offload_folder,
+                            disk_offload_index,
                             mapping.distributed_operation,
                             hf_quantizer,
+                            module_cache,
+                            mapping,
                         )
 
-                # Cleanup all the tensors that were gathered before next iteration
-                del realized_value
+                    # Cleanup all the tensors that were gathered before next iteration
+                    del realized_value
 
-            except SkipParameters:
-                continue
+                except SkipParameters:
+                    continue
+                finally:
+                    progress_bar.update()
 
     # Close the pool, independently of whether the code was interrupted or finished successfully
     finally:
@@ -1460,15 +1637,14 @@ def revert_weight_conversion(model: PreTrainedModel, state_dict: dict[str, torch
     # Reverse all Transform to correctly match keys
     reverse_weight_conversion = [conversion.reverse_transform() for conversion in weight_conversions]
     # If we are still here, we need to create the (reverse) conversion mapping from scratch
-    renamings = [entry for entry in reverse_weight_conversion if isinstance(entry, WeightRenaming)]
     converters = [entry for entry in reverse_weight_conversion if isinstance(entry, WeightConverter)]
     pattern_to_converter = {k: converter for converter in converters for k in converter.source_patterns}
     conversion_mapping = {}
 
     state_dict = sorted(state_dict.items(), key=lambda kv: dot_natural_key(kv[0]))
     for original_key, tensor in state_dict:
-        # Rename the key according to all renaming pattern and optional weight converter patterns
-        renamed_key, source_pattern = rename_source_key(original_key, renamings, converters)
+        renamed_key, source_pattern = rename_source_key(original_key, reverse_weight_conversion)
+
         if source_pattern is not None:
             new_converter = deepcopy(pattern_to_converter[source_pattern])
             # each target key gets its own converter instance
diff --git a/src/transformers/data/data_collator.py b/src/transformers/data/data_collator.py
index 8412ab5ae25a..061be2156880 100644
--- a/src/transformers/data/data_collator.py
+++ b/src/transformers/data/data_collator.py
@@ -1370,6 +1370,7 @@ class DataCollatorWithFlattening(DefaultDataCollator):
     - no padding will be added, returns `input_ids`, `labels` and `position_ids` by default
     - optionally returns the kwargs contained in FlashAttentionKwargs
     - optionally returns seq_idx indicating which sequence each token belongs to
+    - `pack_sequence_labels`: if True, will pack integer labels for sequence classification into a `(batch_size,)` tensor instead of broadcasting them to match `input_ids`.
 
     <Tip warning={true}>
 
@@ -1386,6 +1387,7 @@ def __init__(
         separator_id=-100,
         return_flash_attn_kwargs=False,
         return_seq_idx=False,
+        pack_sequence_labels=False,
         **kwargs,
     ):
         super().__init__(*args, **kwargs)
@@ -1393,6 +1395,7 @@ def __init__(
         self.separator_id = separator_id
         self.return_flash_attn_kwargs = return_flash_attn_kwargs
         self.return_seq_idx = return_seq_idx
+        self.pack_sequence_labels = pack_sequence_labels
         self._int_64_keys = {"labels", "position_ids", "input_ids"}
         self._batch_dim_keys = {"labels", "position_ids", "input_ids", "seq_idx"}
         self._py_int_keys = {"max_length_q", "max_length_k"}
@@ -1403,6 +1406,12 @@ def __call__(self, features, return_tensors=None, separator_id=None):
         if separator_id is None:
             separator_id = self.separator_id
         is_labels_provided = "labels" in features[0]
+
+        first_labels = features[0].get("labels") if is_labels_provided else None
+        if hasattr(first_labels, "tolist"):
+            first_labels = first_labels.tolist()
+        is_labels_sequence = is_labels_provided and isinstance(first_labels, (list, tuple, np.ndarray))
+
         batch = {"input_ids": [], "labels": []}
         if self.return_position_ids:
             batch.update({"position_ids": []})
@@ -1411,6 +1420,7 @@ def __call__(self, features, return_tensors=None, separator_id=None):
         if self.return_flash_attn_kwargs:
             cu_seq_lens = [0]
             max_length = 0
+
         for seq_idx, sample in enumerate(features):
             input_ids = sample["input_ids"]
             # Convert to list if tensor
@@ -1423,9 +1433,13 @@ def __call__(self, features, return_tensors=None, separator_id=None):
                 # Convert to list if tensor
                 if hasattr(labels, "tolist"):
                     labels = labels.tolist()
-                batch["labels"] += [separator_id] + labels[1:]
+                if is_labels_sequence:
+                    batch["labels"] += [separator_id] + labels[1:]
+                else:
+                    # Broadcast scalar labels to all tokens by default.
+                    batch["labels"] += [labels] * len(input_ids)
             else:
-                batch["labels"] += [separator_id] + input_ids[1:]
+                batch["labels"] += [self.separator_id] + input_ids[1:]
             if self.return_position_ids:
                 batch["position_ids"] += list(range(len(input_ids)))
             if self.return_seq_idx:
@@ -1434,11 +1448,14 @@ def __call__(self, features, return_tensors=None, separator_id=None):
                 cu_seq_lens.append(cu_seq_lens[-1] + len(input_ids))
                 max_length = max(max_length, len(input_ids))
 
+        # If packing is enabled for sequence classification, overwrite the broadcasted labels.
+        if is_labels_provided and not is_labels_sequence and self.pack_sequence_labels:
+            batch["labels"] = [feature["labels"] for feature in features]
+
         if self.return_flash_attn_kwargs:
             batch["cu_seq_lens_q"] = batch["cu_seq_lens_k"] = cu_seq_lens
             batch["max_length_q"] = batch["max_length_k"] = max_length
 
-        # FlashAttentionKwargs and seq_idx are expected to be int32s.
         if return_tensors == "pt":
             import torch
 
@@ -1453,9 +1470,12 @@ def __call__(self, features, return_tensors=None, separator_id=None):
             raise ValueError(f'return_tensors must be one of ("pt", "np"), {return_tensors=} not supported')
 
         for k, v in batch.items():
-            if k in self._batch_dim_keys:
+            # For packed sequence labels, we want a 1D tensor, not a 2D tensor of shape (1, batch_size).
+            if k == "labels" and is_labels_provided and not is_labels_sequence and self.pack_sequence_labels:
+                pass
+            elif k in self._batch_dim_keys:
                 v = [v]
-            # Flash attention max_len_{q,k} are python ints
+
             if k not in self._py_int_keys:
                 batch[k] = data_cls(v, dtype=dtype_64 if k in self._int_64_keys else dtype_32)
 
diff --git a/src/transformers/data_producer.py b/src/transformers/data_producer.py
new file mode 100644
index 000000000000..3364454eef58
--- /dev/null
+++ b/src/transformers/data_producer.py
@@ -0,0 +1,292 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+DataProducer protocol for online/async training.
+
+Enables reinforcement-learning methods (PPO, GRPO, REINFORCE, online DPO) and
+curriculum learning by letting the model generate its own training data. Instead
+of iterating over a fixed dataset, the Trainer calls
+``data_producer.produce(model, step)`` to get a fresh ``Dataset`` each rollout.
+
+Quick start::
+
+    from datasets import Dataset
+    from transformers import Trainer, TrainingArguments
+    from transformers.data_producer import BaseDataProducer, ProducerConfig
+
+    class MyProducer(BaseDataProducer):
+        def produce(self, model, global_step, **kwargs):
+            completions = model.generate(self.prompts, max_new_tokens=128)
+            rewards = self.reward_fn(completions)
+            return Dataset.from_dict({"completion": completions, "reward": rewards})
+
+    trainer = Trainer(
+        model=model,
+        args=TrainingArguments(output_dir="./out", max_steps=5000),
+        data_producer=MyProducer(ProducerConfig(mini_epochs=2, max_rollouts=100)),
+    )
+    trainer.train()
+"""
+
+from __future__ import annotations
+
+import logging
+from abc import ABC, abstractmethod
+from collections import deque
+from concurrent.futures import Future, ThreadPoolExecutor
+from dataclasses import dataclass
+from typing import Any
+
+from torch.utils.data import Dataset
+
+from .trainer_callback import TrainerCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+
+
+@dataclass
+class ProducerConfig:
+    """Configuration for a :class:`DataProducer`.
+
+    Args:
+        mini_epochs: Number of training passes over each produced dataset.
+            Higher values amortise expensive generation across more gradient
+            updates.
+        max_rollouts: Maximum number of produce-then-train rounds.  ``None``
+            means training is bounded only by ``TrainingArguments.max_steps``.
+        steps_per_generation: Number of optimisation steps to take on each
+            produced dataset before calling ``produce()`` again.  Maps to the
+            GRPO ``steps_per_generation`` parameter.  ``None`` means the entire
+            produced dataset is consumed (one full epoch) before regenerating.
+        num_iterations: Number of times to reuse each generation across
+            optimisation steps.  Maps to the GRPO *μ* parameter.
+        async_prefetch: If ``True``, the next dataset is produced in a
+            background thread while the current one is being trained on.
+        prefetch_depth: How many rollouts to produce ahead of training when
+            ``async_prefetch`` is enabled.  With depth *N*, the producer
+            keeps *N* rollouts queued.  Higher values keep the GPU more
+            saturated but increase off-policy staleness — each additional
+            rollout in the queue was generated with a model that is
+            ``~steps_per_generation × num_iterations`` more optimizer
+            steps behind.  Default is 1 (one rollout ahead).
+        sync_warmup_rollouts: Number of initial rollouts to produce
+            synchronously before switching to async prefetch.  During
+            warmup, each rollout is generated on-policy (using the
+            latest model weights) so the model can bootstrap learning
+            from sparse reward signals.  After the warmup period, async
+            prefetch resumes for maximum throughput.  ``0`` (default)
+            disables warmup and uses async prefetch from the start.
+        eval_during_produce: Switch the model to ``eval()`` mode during
+            ``produce()``.  Recommended for generation quality.
+        empty_cache_before_produce: Call ``torch.cuda.empty_cache()`` before
+            each ``produce()`` call.
+        empty_cache_after_produce: Call ``torch.cuda.empty_cache()`` after
+            each ``produce()`` call.
+    """
+
+    mini_epochs: int = 1
+    max_rollouts: int | None = None
+    steps_per_generation: int | None = None
+    num_iterations: int = 1
+    async_prefetch: bool = False
+    prefetch_depth: int = 1
+    sync_warmup_rollouts: int = 0
+    eval_during_produce: bool = True
+    empty_cache_before_produce: bool = False
+    empty_cache_after_produce: bool = False
+
+    def __post_init__(self):
+        if self.mini_epochs < 1:
+            raise ValueError(f"mini_epochs must be >= 1, got {self.mini_epochs}")
+        if self.max_rollouts is not None and self.max_rollouts < 1:
+            raise ValueError(f"max_rollouts must be >= 1 or None, got {self.max_rollouts}")
+        if self.num_iterations < 1:
+            raise ValueError(f"num_iterations must be >= 1, got {self.num_iterations}")
+        if self.steps_per_generation is not None and self.steps_per_generation < 1:
+            raise ValueError(f"steps_per_generation must be >= 1 or None, got {self.steps_per_generation}")
+        if self.prefetch_depth < 1:
+            raise ValueError(f"prefetch_depth must be >= 1, got {self.prefetch_depth}")
+        if self.sync_warmup_rollouts < 0:
+            raise ValueError(f"sync_warmup_rollouts must be >= 0, got {self.sync_warmup_rollouts}")
+
+
+# ---------------------------------------------------------------------------
+# DataProducer protocol
+# ---------------------------------------------------------------------------
+
+
+class DataProducer(ABC):
+    """Abstract base class for online data producers.
+
+    Subclass this and implement :meth:`produce` to supply fresh training data
+    each rollout round.  The Trainer calls ``produce(model, step)`` and wraps
+    the returned ``Dataset`` in a ``DataLoader`` automatically.
+    """
+
+    config: ProducerConfig
+
+    @abstractmethod
+    def produce(
+        self,
+        model: Any,
+        global_step: int,
+        *,
+        processing_class: Any = None,
+        accelerator: Any = None,
+        args: Any = None,
+        **kwargs,
+    ) -> Dataset:
+        """Generate a fresh training dataset.
+
+        Args:
+            model: The current model (may be wrapped by DDP/FSDP/DeepSpeed).
+            global_step: The current global training step.
+            processing_class: The tokeniser / processor attached to the Trainer.
+            accelerator: The ``Accelerator`` instance from the Trainer.
+            args: The ``TrainingArguments`` from the Trainer.
+
+        Returns:
+            A ``torch.utils.data.Dataset`` to train on for this rollout.
+        """
+        ...
+
+
+class BaseDataProducer(DataProducer):
+    """Convenience base class with a default :class:`ProducerConfig` and
+    lifecycle hooks.
+
+    Subclass this and override :meth:`produce`.  Optionally override
+    :meth:`on_rollout_begin` / :meth:`on_rollout_end` for custom logging or
+    bookkeeping.
+    """
+
+    def __init__(self, config: ProducerConfig | None = None):
+        self.config = config or ProducerConfig()
+
+    def on_rollout_begin(self, global_step: int) -> None:
+        """Called before each ``produce()`` invocation."""
+
+    def on_rollout_end(self, dataset: Dataset, global_step: int) -> None:
+        """Called after each ``produce()`` invocation with the produced dataset."""
+
+
+# ---------------------------------------------------------------------------
+# Async wrapper
+# ---------------------------------------------------------------------------
+
+
+class AsyncDataProducer:
+    """Wraps a synchronous :class:`DataProducer` for background-thread data
+    generation.
+
+    While the Trainer trains on the current rollout, this wrapper produces
+    upcoming datasets in a background thread.  The ``prefetch_depth``
+    (from :class:`ProducerConfig`) controls how many rollouts are queued
+    ahead of training:
+
+    * ``prefetch_depth=1`` (default): one rollout is produced in the
+      background while the current one is trained on.  This is the
+      sweet spot for most setups — it hides generation latency without
+      introducing off-policy staleness.
+    * ``prefetch_depth=N``: *N* rollouts are queued.  Useful when
+      generation is much faster than training (e.g. vLLM server mode)
+      and you want to keep the GPU fully saturated, at the cost of
+      increased off-policy staleness.
+
+    The first call to :meth:`produce` is synchronous; it returns the
+    first dataset and seeds the prefetch queue.
+    """
+
+    def __init__(self, inner: DataProducer, background_produce_kwargs: dict | None = None):
+        self._inner = inner
+        self._depth = inner.config.prefetch_depth
+        self._warmup_remaining = inner.config.sync_warmup_rollouts
+        self._background_kwargs = background_produce_kwargs or {}
+        self._executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="async-producer")
+        self._queue: deque[Future] = deque()
+        self._initialized = False
+
+    @property
+    def config(self) -> ProducerConfig:
+        return self._inner.config
+
+    def produce(self, model: Any, global_step: int, **kwargs) -> Dataset:
+        """Return the next dataset, blocking if the prefetch hasn't finished.
+
+        On the very first call, the current dataset is produced synchronously
+        and the prefetch queue is seeded with ``prefetch_depth`` futures.
+        Subsequent calls pop the oldest future from the queue and submit a
+        new one to maintain the queue at ``prefetch_depth``.
+
+        When ``sync_warmup_rollouts > 0``, the first *N* rollouts are
+        produced synchronously (on-policy) so the model can bootstrap
+        learning from sparse reward signals before async prefetch begins.
+        """
+        # During warmup, produce synchronously (on-policy) without prefetching
+        if self._warmup_remaining > 0:
+            self._warmup_remaining -= 1
+            logger.info(f"AsyncDataProducer: sync warmup rollout (remaining={self._warmup_remaining})")
+            return self._inner.produce(model, global_step, **kwargs)
+
+        if not self._initialized:
+            # First async call: produce synchronously, then seed the queue
+            dataset = self._inner.produce(model, global_step, **kwargs)
+            bg_kwargs = {**kwargs, **self._background_kwargs}
+            for i in range(1, self._depth + 1):
+                self._queue.append(self._executor.submit(self._inner.produce, model, global_step + i, **bg_kwargs))
+            self._initialized = True
+            return dataset
+
+        # Subsequent calls: consume oldest prefetched result
+        dataset = self._queue.popleft().result()
+
+        # Submit a new future to keep the queue full
+        bg_kwargs = {**kwargs, **self._background_kwargs}
+        next_step = global_step + self._depth
+        self._queue.append(self._executor.submit(self._inner.produce, model, next_step, **bg_kwargs))
+        return dataset
+
+    def on_rollout_begin(self, global_step: int) -> None:
+        if hasattr(self._inner, "on_rollout_begin"):
+            self._inner.on_rollout_begin(global_step)
+
+    def on_rollout_end(self, dataset: Dataset, global_step: int) -> None:
+        if hasattr(self._inner, "on_rollout_end"):
+            self._inner.on_rollout_end(dataset, global_step)
+
+    def shutdown(self) -> None:
+        """Shut down the background thread pool and cancel pending futures."""
+        for future in self._queue:
+            future.cancel()
+        self._queue.clear()
+        self._executor.shutdown(wait=False)
+
+
+# ---------------------------------------------------------------------------
+# Callback integration
+# ---------------------------------------------------------------------------
+
+
+class DataProducerCallback(TrainerCallback):
+    """Marker class: if a :class:`DataProducer` also inherits from this, the
+    Trainer will automatically register it as a callback, giving the producer
+    access to all :class:`TrainerCallback` lifecycle events (``on_train_begin``,
+    ``on_step_end``, etc.)."""
diff --git a/src/transformers/debug_utils.py b/src/transformers/debug_utils.py
index 38ff0399641b..ae44ef1eb899 100644
--- a/src/transformers/debug_utils.py
+++ b/src/transformers/debug_utils.py
@@ -155,7 +155,7 @@ def __init__(self, model, max_frames_to_save=21, trace_batch_nums=None, abort_af
         self.batch_number = 0
         self.total_calls = 0
         self.detected_overflow = False
-        self.prefix = "                 "
+        self.prefix = " " * 17
 
         self.analyse_model()
 
diff --git a/src/transformers/dependency_versions_table.py b/src/transformers/dependency_versions_table.py
index 1a721ca2a82a..47a9d8b506af 100644
--- a/src/transformers/dependency_versions_table.py
+++ b/src/transformers/dependency_versions_table.py
@@ -25,7 +25,7 @@
     "kenlm": "kenlm",
     "kernels": "kernels>=0.12.0,<0.13",
     "librosa": "librosa",
-    "mistral-common[image]": "mistral-common[image]>=1.10.0",
+    "mistral-common[image,audio]": "mistral-common[image,audio]>=1.10.0",
     "nltk": "nltk<=3.8.1",
     "num2words": "num2words",
     "numpy": "numpy>=1.17",
diff --git a/src/transformers/dynamic_module_utils.py b/src/transformers/dynamic_module_utils.py
index 9c9e7b929f6f..4598a6760090 100644
--- a/src/transformers/dynamic_module_utils.py
+++ b/src/transformers/dynamic_module_utils.py
@@ -311,6 +311,42 @@ def get_class_in_module(
         return getattr(module, class_name)
 
 
+def _compute_local_source_files_hash(
+    pretrained_model_name_or_path: str | os.PathLike,
+    module_file: str | os.PathLike,
+    resolved_module_file: str | os.PathLike,
+    modules_needed: list[str],
+) -> str:
+    """
+    Computes a stable hash from the bytes of the local source file and its direct relative-import source files.
+    """
+    model_path = Path(pretrained_model_name_or_path).resolve()
+    module_parent = Path(module_file).parent
+
+    resolved_module_file = Path(resolved_module_file).resolve()
+
+    def _resolve_relative_source_path(source_file_path: Path) -> str:
+        try:
+            return source_file_path.relative_to(model_path).as_posix()
+        except ValueError:
+            # Fallback for edge cases where the source file is not under the local model directory.
+            return source_file_path.as_posix()
+
+    files_to_hash = [
+        (_resolve_relative_source_path(resolved_module_file), resolved_module_file),
+    ]
+    for module_needed in modules_needed:
+        module_needed_path = (model_path / module_parent / f"{module_needed}.py").resolve()
+        files_to_hash.append((_resolve_relative_source_path(module_needed_path), module_needed_path))
+
+    source_files_hash = hashlib.sha256()
+    for relative_path, file_path in sorted(files_to_hash, key=lambda entry: entry[0]):
+        source_files_hash.update(relative_path.encode("utf-8"))
+        source_files_hash.update(file_path.read_bytes())
+
+    return source_files_hash.hexdigest()[:16]
+
+
 def get_cached_module_file(
     pretrained_model_name_or_path: str | os.PathLike,
     module_file: str,
@@ -374,11 +410,10 @@ def get_cached_module_file(
         local_files_only = True
 
     # Download and cache module_file from the repo `pretrained_model_name_or_path` of grab it if it's a local file.
-    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
+    pretrained_model_name_or_path = str(pretrained_model_name_or_path).rstrip(os.sep)
     is_local = os.path.isdir(pretrained_model_name_or_path)
-    if is_local:
-        submodule = _sanitize_module_name(os.path.basename(pretrained_model_name_or_path))
-    else:
+    cached_module = None
+    if not is_local:
         submodule = os.path.sep.join(map(_sanitize_module_name, pretrained_model_name_or_path.split("/")))
         cached_module = try_to_load_from_cache(
             pretrained_model_name_or_path, module_file, cache_dir=cache_dir, revision=_commit_hash, repo_type=repo_type
@@ -408,19 +443,28 @@ def get_cached_module_file(
 
     # Check we have all the requirements in our environment
     modules_needed = check_imports(resolved_module_file)
+    if is_local:
+        local_model_name = _sanitize_module_name(os.path.basename(os.path.normpath(pretrained_model_name_or_path)))
+        local_source_files_hash = _compute_local_source_files_hash(
+            pretrained_model_name_or_path, module_file, resolved_module_file, modules_needed
+        )
+        if local_model_name:
+            submodule = os.path.sep.join([local_model_name, local_source_files_hash])
+        else:
+            submodule = local_source_files_hash
 
     # Now we move the module inside our cached dynamic modules.
     full_submodule = TRANSFORMERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
     create_dynamic_module(full_submodule)
     submodule_path = Path(HF_MODULES_CACHE) / full_submodule
-    if submodule == _sanitize_module_name(os.path.basename(pretrained_model_name_or_path)):
+    if is_local:
         # We copy local files to avoid putting too many folders in sys.path. This copy is done when the file is new or
         # has changed since last copy.
         if not (submodule_path / module_file).exists() or not filecmp.cmp(
             resolved_module_file, str(submodule_path / module_file)
         ):
             (submodule_path / module_file).parent.mkdir(parents=True, exist_ok=True)
-            shutil.copy(resolved_module_file, submodule_path / module_file)
+            shutil.copyfile(resolved_module_file, submodule_path / module_file)
             importlib.invalidate_caches()
         for module_needed in modules_needed:
             module_needed = Path(module_file).parent / f"{module_needed}.py"
@@ -428,7 +472,7 @@ def get_cached_module_file(
             if not (submodule_path / module_needed).exists() or not filecmp.cmp(
                 module_needed_file, str(submodule_path / module_needed)
             ):
-                shutil.copy(module_needed_file, submodule_path / module_needed)
+                shutil.copyfile(module_needed_file, submodule_path / module_needed)
                 importlib.invalidate_caches()
     else:
         # Get the commit hash
@@ -442,7 +486,7 @@ def get_cached_module_file(
         create_dynamic_module(Path(full_submodule_module_file_path).parent)
 
         if not (submodule_path / module_file).exists():
-            shutil.copy(resolved_module_file, submodule_path / module_file)
+            shutil.copyfile(resolved_module_file, submodule_path / module_file)
             importlib.invalidate_caches()
         # Make sure we also have every file with relative
         for module_needed in modules_needed:
@@ -647,13 +691,13 @@ def _set_auto_map_in_config(_config):
     # Copy module file to the output folder.
     object_file = sys.modules[obj.__module__].__file__
     dest_file = Path(folder) / (Path(object_file).name)
-    shutil.copy(object_file, dest_file)
+    shutil.copyfile(object_file, dest_file)
     result.append(dest_file)
 
     # Gather all relative imports recursively and make sure they are copied as well.
     for needed_file in get_relative_import_files(object_file):
         dest_file = Path(folder) / (Path(needed_file).name)
-        shutil.copy(needed_file, dest_file)
+        shutil.copyfile(needed_file, dest_file)
         result.append(dest_file)
 
     return result
diff --git a/src/transformers/feature_extraction_sequence_utils.py b/src/transformers/feature_extraction_sequence_utils.py
index 5e346d3e15e5..01f80cf04042 100644
--- a/src/transformers/feature_extraction_sequence_utils.py
+++ b/src/transformers/feature_extraction_sequence_utils.py
@@ -365,17 +365,19 @@ def _get_padding_strategies(self, padding=False, max_length=None):
 
         return padding_strategy
 
-    def fetch_audio(self, audio_url_or_urls: str | list[str] | list[list[str]]):
+    def fetch_audio(self, audio_url_or_urls: str | list[str] | list[list[str]], sampling_rate: int | None = None):
         """
         Convert a single or a list of urls into the corresponding `np.ndarray` objects.
 
         If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
         returned.
         """
-        if isinstance(audio_url_or_urls, list):
-            return [self.fetch_audio(x) for x in audio_url_or_urls]
+        # Accepted input types for `raw_audio`: "np.ndarray | list[float] | list[np.ndarray] | list[list[float]]"
+        sampling_rate = sampling_rate if sampling_rate else self.sampling_rate
+        if isinstance(audio_url_or_urls, list) and not isinstance(audio_url_or_urls[0], float):
+            return [self.fetch_audio(x, sampling_rate=sampling_rate) for x in audio_url_or_urls]
         elif isinstance(audio_url_or_urls, str):
-            return load_audio(audio_url_or_urls)
+            return load_audio(audio_url_or_urls, sampling_rate=sampling_rate)
         elif is_valid_audio(audio_url_or_urls):
             return audio_url_or_urls
         else:
diff --git a/src/transformers/generation/__init__.py b/src/transformers/generation/__init__.py
index a8d616ab513f..7331d6acd039 100644
--- a/src/transformers/generation/__init__.py
+++ b/src/transformers/generation/__init__.py
@@ -61,6 +61,8 @@
         "MinPLogitsWarper",
         "NoBadWordsLogitsProcessor",
         "NoRepeatNGramLogitsProcessor",
+        "PLessLogitsWarper",
+        "PLessNormLogitsWarper",
         "PrefixConstrainedLogitsProcessor",
         "RepetitionPenaltyLogitsProcessor",
         "SequenceBiasLogitsProcessor",
@@ -77,6 +79,7 @@
         "WatermarkLogitsProcessor",
     ]
     _import_structure["stopping_criteria"] = [
+        "AsyncStoppingCriteriaList",
         "MaxLengthCriteria",
         "MaxTimeCriteria",
         "ConfidenceCriteria",
@@ -85,6 +88,7 @@
         "StoppingCriteriaList",
         "validate_stopping_criteria",
         "StopStringCriteria",
+        "StopStringTextMatchCriteria",
     ]
     _import_structure["continuous_batching"] = [
         "ContinuousBatchingManager",
@@ -93,6 +97,16 @@
         "PrefillFirstScheduler",
         "Scheduler",
     ]
+    _import_structure["safety"] = [
+        "SafetyChecker",
+        "SafetyResult",
+        "SafetyViolation",
+        "SafetyMetrics",
+        "SafetyState",
+        "SafetyConfig",
+        "SafetyLogitsProcessor",
+        "SafetyStoppingCriteria",
+    ]
     _import_structure["utils"] = [
         "GenerationMixin",
         "GenerateBeamDecoderOnlyOutput",
@@ -159,6 +173,8 @@
             MinPLogitsWarper,
             NoBadWordsLogitsProcessor,
             NoRepeatNGramLogitsProcessor,
+            PLessLogitsWarper,
+            PLessNormLogitsWarper,
             PrefixConstrainedLogitsProcessor,
             RepetitionPenaltyLogitsProcessor,
             SequenceBiasLogitsProcessor,
@@ -175,6 +191,7 @@
             WhisperTimeStampLogitsProcessor,
         )
         from .stopping_criteria import (
+            AsyncStoppingCriteriaList,
             ConfidenceCriteria,
             EosTokenCriteria,
             MaxLengthCriteria,
@@ -182,6 +199,7 @@
             StoppingCriteria,
             StoppingCriteriaList,
             StopStringCriteria,
+            StopStringTextMatchCriteria,
             validate_stopping_criteria,
         )
         from .utils import (
diff --git a/src/transformers/generation/candidate_generators/__init__.py b/src/transformers/generation/candidate_generators/__init__.py
new file mode 100644
index 000000000000..dfb8d170dca2
--- /dev/null
+++ b/src/transformers/generation/candidate_generators/__init__.py
@@ -0,0 +1,4 @@
+from .mtp import MTPCandidateGenerator, MTPLayer, MTPSharedHead
+
+
+__all__ = ["MTPCandidateGenerator", "MTPLayer", "MTPSharedHead"]
diff --git a/src/transformers/generation/candidate_generators/mtp.py b/src/transformers/generation/candidate_generators/mtp.py
new file mode 100644
index 000000000000..0d9ece6dee4c
--- /dev/null
+++ b/src/transformers/generation/candidate_generators/mtp.py
@@ -0,0 +1,269 @@
+"""Multi-Token Prediction (MTP) candidate generator.
+
+MTP modules are shipped inside the main checkpoint (e.g. DeepSeek-V3 at
+`model.layers.61.*`, GLM-4 MoE at `model.layers.{num_hidden_layers}.*`) but
+hidden from the base model via `_keys_to_ignore_on_load_unexpected`. They are
+loaded separately here, matching the base model's decoder layer class, and act
+as the draft head for speculative decoding.
+"""
+
+from __future__ import annotations
+
+import re
+from typing import TYPE_CHECKING
+
+import torch
+from torch import nn
+
+from ...cache_utils import Cache, DynamicLayer
+from ...masking_utils import create_causal_mask
+from ..candidate_generator import CandidateGenerator
+
+
+if TYPE_CHECKING:
+    from ...configuration_utils import PreTrainedConfig
+    from ...modeling_utils import PreTrainedModel
+
+
+class MTPSharedHead(nn.Module):
+    """Final projection inside an MTP module: RMSNorm + linear over vocab."""
+
+    def __init__(self, config: PreTrainedConfig, rmsnorm_cls: type[nn.Module]):
+        super().__init__()
+        self.norm = rmsnorm_cls(config.hidden_size, eps=config.rms_norm_eps)
+        self.head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.head(self.norm(hidden_states))
+
+
+class MTPLayer(nn.Module):
+    """One MTP depth (DeepSeek-V3 spec).
+
+    Combines the previous hidden state `h_{t+k}` and the embedding of the
+    next drafted token `x_{t+k+1}`, projects them down with `eh_proj`, runs
+    a standard decoder block, then produces logits for position `t+k+2`.
+    """
+
+    def __init__(
+        self,
+        config: PreTrainedConfig,
+        decoder_layer: nn.Module,
+        rmsnorm_cls: type[nn.Module],
+    ):
+        super().__init__()
+        self.enorm = rmsnorm_cls(config.hidden_size, eps=config.rms_norm_eps)
+        self.hnorm = rmsnorm_cls(config.hidden_size, eps=config.rms_norm_eps)
+        self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
+        self.mtp_block = decoder_layer
+        self.shared_head = MTPSharedHead(config, rmsnorm_cls)
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        previous_hidden_state: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        position_ids: torch.Tensor | None,
+        past_key_values: Cache | None,
+        use_cache: bool | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        h_cat = torch.cat([self.enorm(inputs_embeds), self.hnorm(previous_hidden_state)], dim=-1)
+        hidden_states = self.mtp_block(
+            self.eh_proj(h_cat),
+            attention_mask=attention_mask,
+            position_embeddings=position_embeddings,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        logits = self.shared_head(hidden_states)
+        return hidden_states, logits
+
+
+class MTPCandidateGenerator(nn.Module, CandidateGenerator):
+    """Speculative-decoding draft head built from a model's MTP modules.
+
+    Holds `config.num_nextn_predict_layers` MTP depths, each a full transformer
+    block surrounded by projection/norm/head machinery (see `MTPLayer`). The
+    generator shares the base model's KV cache: each MTP depth's `mtp_block`
+    writes to `past_key_values[num_hidden_layers + k]`, extending the cache
+    in place when needed.
+
+    Constructed either directly (`MTPCandidateGenerator(base_model)`) or via
+    `from_pretrained`, which pulls MTP-specific keys out of the checkpoint.
+    """
+
+    def __init__(self, base_model: PreTrainedModel, num_mtp: int | None = None):
+        super().__init__()
+        config = base_model.config
+        num_mtp = num_mtp if num_mtp is not None else getattr(config, "num_nextn_predict_layers", 0)
+        if num_mtp <= 0:
+            raise ValueError(
+                "MTPCandidateGenerator requires `config.num_nextn_predict_layers > 0` "
+                "or an explicit `num_mtp` argument."
+            )
+
+        inner = base_model.base_model if hasattr(base_model, "base_model_prefix") else base_model
+        layers = getattr(inner, "layers", None) or getattr(getattr(inner, "model", None), "layers", None)
+        if layers is None or len(layers) == 0:
+            raise ValueError("Could not locate `layers` on the provided base model.")
+
+        sample_layer = layers[0]
+        decoder_cls = type(sample_layer)
+        rmsnorm_cls = type(sample_layer.input_layernorm)
+
+        self.num_mtp = num_mtp
+        self.num_hidden_layers = config.num_hidden_layers
+        self.layers = nn.ModuleList(
+            [MTPLayer(config, decoder_cls(config, config.num_hidden_layers + k), rmsnorm_cls) for k in range(num_mtp)]
+        )
+        # Weak handle for `get_candidates` — re-used for embed_tokens, rotary_emb, cache masks.
+        self._base_ref = base_model
+        self._config = config
+
+    # ------------------------------------------------------------------
+    # Loading
+    # ------------------------------------------------------------------
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: str,
+        base_model: PreTrainedModel,
+        num_mtp: int | None = None,
+        **kwargs,
+    ) -> MTPCandidateGenerator:
+        """Load MTP weights out of the base checkpoint.
+
+        Reads the same safetensors shards as the main model, keeps only the
+        keys under `model.layers.{num_hidden_layers + k}.*`, remaps them onto
+        `MTPLayer`, and returns a fully-initialised generator.
+        """
+        from ...modeling_utils import _get_resolved_checkpoint_files  # lazy
+
+        generator = cls(base_model, num_mtp=num_mtp)
+        num_mtp = generator.num_mtp
+        num_base = generator.num_hidden_layers
+
+        # Resolve + load the checkpoint's state dict.
+        resolved_files, _ = _get_resolved_checkpoint_files(
+            pretrained_model_name_or_path=pretrained_model_name_or_path,
+            subfolder=kwargs.pop("subfolder", ""),
+            variant=kwargs.pop("variant", None),
+            gguf_file=None,
+            from_tf=False,
+            from_flax=False,
+            use_safetensors=True,
+            cache_dir=kwargs.pop("cache_dir", None),
+            force_download=kwargs.pop("force_download", False),
+            proxies=kwargs.pop("proxies", None),
+            local_files_only=kwargs.pop("local_files_only", False),
+            token=kwargs.pop("token", None),
+            user_agent={"file_type": "model", "framework": "pytorch"},
+            revision=kwargs.pop("revision", "main"),
+            commit_hash=None,
+        )
+
+        mtp_layer_ids = {num_base + k for k in range(num_mtp)}
+        merged: dict[str, torch.Tensor] = {}
+        from safetensors.torch import load_file
+
+        for path in resolved_files:
+            shard = load_file(path)
+            for key, tensor in shard.items():
+                m = re.match(r"^(?:model\.)?layers\.(\d+)(?:\.(.*))?$", key)
+                if m is None:
+                    continue
+                layer_id = int(m.group(1))
+                if layer_id not in mtp_layer_ids:
+                    continue
+                sub = m.group(2) or ""
+                k = layer_id - num_base
+                mapped = f"layers.{k}.{sub}" if sub else f"layers.{k}"
+                merged[mapped] = tensor
+
+        missing, unexpected = generator.load_state_dict(merged, strict=False)
+        if unexpected:
+            raise ValueError(f"MTP checkpoint contained unexpected keys: {unexpected}")
+        if missing:
+            # Non-fatal — the checkpoint may tie `shared_head.head` to `lm_head`; surface to caller.
+            import warnings
+
+            warnings.warn(
+                f"MTP generator loaded with {len(missing)} missing keys; some MTP parameters "
+                "will use their random initialization. First few: " + ", ".join(missing[:5]),
+                stacklevel=2,
+            )
+        return generator
+
+    # ------------------------------------------------------------------
+    # CandidateGenerator interface
+    # ------------------------------------------------------------------
+    def get_candidates(
+        self,
+        input_ids: torch.LongTensor,
+        *,
+        previous_hidden_state: torch.Tensor,
+        past_key_values: Cache,
+        first_token: torch.LongTensor,
+        position_offset: int,
+        logits_processor=None,
+        do_sample: bool = False,
+    ) -> tuple[torch.LongTensor, torch.Tensor]:
+        """Draft `num_mtp` tokens beyond `first_token`.
+
+        Returns `(candidate_ids, candidate_logits)` where `candidate_ids` has
+        shape `(1, num_mtp + 1)` starting with `first_token`, and
+        `candidate_logits` has shape `(1, num_mtp, vocab)` — one logit
+        distribution per MTP depth (i.e. for the tokens at `position_offset + 1`
+        through `position_offset + num_mtp`).
+        """
+        drafts = [first_token]
+        logits_list: list[torch.Tensor] = []
+        prev_hidden = previous_hidden_state
+        embed_tokens = self._base_ref.get_input_embeddings()
+        rotary_emb = getattr(self._base_ref, "rotary_emb", None) or self._base_ref.model.rotary_emb
+        for depth in range(self.num_mtp):
+            layer_idx = self.num_hidden_layers + depth
+            if hasattr(past_key_values, "layers"):
+                while len(past_key_values.layers) <= layer_idx:
+                    past_key_values.layers.append(DynamicLayer())
+            tok = drafts[depth]
+            inputs_embeds = embed_tokens(tok)
+            pos = torch.tensor([[position_offset + depth]], device=tok.device, dtype=torch.long)
+            position_embeddings = rotary_emb(inputs_embeds, position_ids=pos)
+            causal_mask = create_causal_mask(
+                config=self._config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=None,
+                past_key_values=past_key_values,
+                position_ids=pos,
+            )
+            prev_hidden, step_logits = self.layers[depth](
+                inputs_embeds=inputs_embeds,
+                previous_hidden_state=prev_hidden,
+                position_embeddings=position_embeddings,
+                attention_mask=causal_mask,
+                position_ids=pos,
+                past_key_values=past_key_values,
+                use_cache=True,
+            )
+            vec = step_logits[:, 0, :].to(dtype=torch.float32)
+            if logits_processor is not None:
+                vec = logits_processor(torch.cat([input_ids] + drafts, dim=1), vec)
+            logits_list.append(vec)
+            if do_sample:
+                drafted = torch.multinomial(nn.functional.softmax(vec, dim=-1), num_samples=1)
+            else:
+                drafted = torch.argmax(vec, dim=-1, keepdim=True)
+            drafts.append(drafted)
+
+        candidate_ids = torch.cat(drafts, dim=1)
+        candidate_logits = torch.stack(logits_list, dim=1)
+        return candidate_ids, candidate_logits
+
+    def update_candidate_strategy(self, input_ids, scores, num_matches):
+        # Fixed K = num_mtp; no heuristic schedule.
+        return
diff --git a/src/transformers/generation/configuration_utils.py b/src/transformers/generation/configuration_utils.py
index 122cc4c8be74..555d39425b7b 100644
--- a/src/transformers/generation/configuration_utils.py
+++ b/src/transformers/generation/configuration_utils.py
@@ -19,6 +19,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Callable
 from dataclasses import dataclass, is_dataclass
+from math import ceil
 from typing import TYPE_CHECKING, Any, Optional, Union
 
 from huggingface_hub import create_repo
@@ -89,6 +90,7 @@ class GenerationMode(ExplicitEnum):
     GREEDY_SEARCH = "greedy_search"
     SAMPLE = "sample"
     ASSISTED_GENERATION = "assisted_generation"
+    MTP_DECODING = "mtp_decoding"
     DOLA_GENERATION = "dola_generation"
     # Beam methods
     BEAM_SEARCH = "beam_search"
@@ -184,6 +186,22 @@ class GenerationConfig(PushToHubMixin):
         top_p (`float`, *optional*):
             If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to
             `top_p` or higher are kept for generation. This value is set in a model's `generation_config.json` file. If it isn't set, the default value is 1.0
+        p_less (`bool`, *optional*):
+            Set to `True` to use p-less, a hyperparameter-free decoding method that adaptively determines the minimum
+            threshold probability for admitting tokens into the sampling set, based on the information from the full
+            token distribution. The p-less method balances the adaptive threshold probability with the entropy of the
+            token distribution, i.e. a higher entropy results in a lower threshold and vice versa, which is a befitting
+            relationship. The p-less threshold is also bounded and valid, i.e. guaranteed to be at least the uniform
+            token probability and at most the modal probability. For details, see *p-less Sampling: A Robust
+            Hyperparameter-free Approach for LLM Decoding* at https://arxiv.org/abs/2509.23234.
+        p_less_norm (`bool`, *optional*):
+            Set to `True` to use p-less-norm, a hyperparameter-free decoding method that adaptively determines the
+            minimum threshold probability for admitting tokens into the sampling set, based on the information from the
+            full token distribution. The p-less-norm method balances the adaptive threshold probability with the
+            entropy of the token distribution, i.e. a higher entropy results in a lower threshold and vice versa, which
+            is a befitting relationship. The p-less-norm threshold is also bounded and valid, i.e. guaranteed to be at
+            least zero and at most the modal probability. For details, see *p-less Sampling: A Robust
+            Hyperparameter-free Approach for LLM Decoding* at https://arxiv.org/abs/2509.23234.
         min_p (`float`, *optional*):
             Minimum token probability, which will be scaled by the probability of the most likely token. It must be a
             value between 0 and 1. Typical values are in the 0.01-0.2 range, comparably selective as setting `top_p` in
@@ -267,6 +285,10 @@ class GenerationConfig(PushToHubMixin):
             Arguments used to watermark the model outputs by adding a small bias to randomly selected set of "green"
             tokens. See the docs of [`SynthIDTextWatermarkingConfig`] and [`WatermarkingConfig`] for more
             details. If passed as `Dict`, it will be converted to a `WatermarkingConfig` internally.
+        safety_config (`SafetyConfig` or `dict`, *optional*):
+            Configuration for content safety filtering during generation. Enables real-time detection and suppression
+            of unsafe content like toxicity, hate speech, etc. See [`SafetyConfig`] for more details. If passed as
+            `Dict`, it will be converted to a `SafetyConfig` internally.
 
         > Parameters that define the output variables of generate
 
@@ -343,6 +365,10 @@ class GenerationConfig(PushToHubMixin):
             If set to a positive integer, the re-encodeing process will additionally consider the last `target_lookbehind` target tokens
             to correctly align tokens. Can only be used with different tokenizers in speculative decoding.
             See this [blog](https://huggingface.co/blog/universal_assisted_generation) for more details.
+        use_mtp(`bool`, *optional*, defaults to `False`):
+            If `True`, speculate with the model's Multi-Token Prediction (MTP) modules (DeepSeek-V3 / GLM-4 MoE style).
+            The base model drafts `config.num_nextn_predict_layers` extra tokens per step via the MTP heads, then
+            verifies them in a single forward pass — standard speculative decoding, shared weights + KV cache.
 
         > Parameters related to performances and compilation
 
@@ -353,6 +379,11 @@ class GenerationConfig(PushToHubMixin):
             Whether to disable the automatic compilation of the forward pass. Automatic compilation happens when
             specific criteria are met, including using a compilable cache. Please open an issue if you find the
             need to use this flag.
+        async_stopping_criteria (`bool`, defaults to `False`):
+            If set to `True`, stopping criteria checks will be performed asynchronously on a separate CUDA stream,
+            allowing generation to continue while the check runs. This can reduce GPU-CPU synchronization overhead
+            and improve throughput, especially for longer generations. The stopping check result is polled
+            periodically rather than blocking on every token. Only effective on CUDA devices.
     """
 
     extra_output_flags = ("output_attentions", "output_hidden_states", "output_scores", "output_logits")
@@ -394,6 +425,8 @@ def __init__(self, **kwargs):
         self.temperature = kwargs.pop("temperature", None)
         self.top_k = kwargs.pop("top_k", None)
         self.top_p = kwargs.pop("top_p", None)
+        self.p_less = kwargs.pop("p_less", None)
+        self.p_less_norm = kwargs.pop("p_less_norm", None)
         self.min_p = kwargs.pop("min_p", None)
         self.top_h = kwargs.pop("top_h", None)
         self.typical_p = kwargs.pop("typical_p", None)
@@ -419,6 +452,22 @@ def __init__(self, **kwargs):
         if isinstance(self.watermarking_config, dict):
             self.watermarking_config = WatermarkingConfig.from_dict(self.watermarking_config)
 
+        # Safety configuration for content filtering during generation
+        safety_config = kwargs.pop("safety_config", None)
+        if safety_config is None:
+            self.safety_config = None
+        elif hasattr(safety_config, "enabled"):  # Duck typing for SafetyConfig
+            self.safety_config = safety_config
+        else:
+            # Lazy import to avoid circular dependencies
+            try:
+                from .safety import SafetyConfig
+
+                self.safety_config = SafetyConfig.from_dict(safety_config)
+            except ImportError:
+                logger.warning("SafetyConfig requested but safety module not available")
+                self.safety_config = None
+
         # Parameters that define the output variables of `generate`
         self.num_return_sequences = kwargs.pop("num_return_sequences", None)
         self.output_attentions = kwargs.pop("output_attentions", None)
@@ -446,10 +495,12 @@ def __init__(self, **kwargs):
         self.assistant_early_exit = kwargs.pop("assistant_early_exit", None)
         self.assistant_lookbehind = kwargs.pop("assistant_lookbehind", None)
         self.target_lookbehind = kwargs.pop("target_lookbehind", None)
+        self.use_mtp = kwargs.pop("use_mtp", False)
 
         # Performance
         self.compile_config = kwargs.pop("compile_config", None)
         self.disable_compile = kwargs.pop("disable_compile", None)
+        self.async_stopping_criteria = kwargs.pop("async_stopping_criteria", False)
 
         self.continuous_batching_config = kwargs.pop("continuous_batching_config", None)
 
@@ -556,6 +607,16 @@ def get_generation_mode(self, assistant_model: Optional["PreTrainedModel"] = Non
                     f"current flags) is {generation_mode} -- some of the set flags will be ignored."
                 )
 
+        # Multi-Token Prediction decoding uses the model's own MTP modules as the draft
+        if self.use_mtp:
+            if generation_mode in (GenerationMode.GREEDY_SEARCH, GenerationMode.SAMPLE):
+                generation_mode = GenerationMode.MTP_DECODING
+            else:
+                logger.warning(
+                    f"`use_mtp=True` is only supported with Greedy Search and Sample; the current mode is "
+                    f"{generation_mode}. Ignoring `use_mtp`."
+                )
+
         # DoLa generation may extend some generation modes
         # TODO joao, manuel: remove this in v4.62.0
         if self.dola_layers is not None:
@@ -684,6 +745,12 @@ def validate(self, strict=False, user_set_attributes: set[str] | None = None):
                 and _should_warn("do_sample", "top_p", user_set_attributes)
             ):
                 minor_issues["top_p"] = greedy_wrong_parameter_msg.format(flag_name="top_p", flag_value=self.top_p)
+            if self.p_less is not None and _should_warn("do_sample", "p_less", user_set_attributes):
+                minor_issues["p_less"] = greedy_wrong_parameter_msg.format(flag_name="p_less", flag_value=self.p_less)
+            if self.p_less_norm is not None and _should_warn("do_sample", "p_less_norm", user_set_attributes):
+                minor_issues["p_less_norm"] = greedy_wrong_parameter_msg.format(
+                    flag_name="p_less_norm", flag_value=self.p_less_norm
+                )
             if self.min_p is not None and _should_warn("do_sample", "min_p", user_set_attributes):
                 minor_issues["min_p"] = greedy_wrong_parameter_msg.format(flag_name="min_p", flag_value=self.min_p)
             if self.top_h is not None and _should_warn("do_sample", "top_h", user_set_attributes):
@@ -1620,9 +1687,9 @@ class ContinuousBatchingConfig:
             Maximum percentage of free GPU memory (after the model is loaded) to use for the KV cache. When `None`,
             resolved at runtime to 0.9 if there is no logit processing and 0.8 if there is, to leave headroom for
             vocabulary-sized temporary tensors.
-        max_blocks_per_request (`int`, *optional*, defaults to 0):
+        max_blocks_per_request (`int`, *optional*):
             Maximum blocks per request, used in the `flash_attn_with_kvcache` fast decode path to dimension
-            the block table. Setting this to 0 disables the fast decode path.
+            the block table. Setting this to 0 disables the fast decode path. Default is None (auto-inferred).
         allow_block_sharing (`bool`, *optional*, defaults to `True`):
             Whether to allow block sharing for prefix caching. Block sharing can only be allowed, never forced,
             as some models do not support it. Disable if you have few short prompts but long generation lengths.
@@ -1681,8 +1748,8 @@ class ContinuousBatchingConfig:
     max_memory_percent: float | None = None
 
     # This is only used in the flash_attn_with_kvcache fast decode path to dimension the block table. If it is set to 0,
-    # the fast decode path will not be used. Currently turned off by default.
-    max_blocks_per_request: int | None = 0
+    # the fast decode path will not be used. Auto-inferred from GPU memory when `None` (default).
+    max_blocks_per_request: int | None = None
 
     # Block sharing can only be allowed, but never forced: some model just do not support it. If you only have a few
     # short prompts, but long generation lengths, you might want to disable block sharing.
@@ -1739,6 +1806,13 @@ class ContinuousBatchingConfig:
     # are kept but warnings are logged for unsupported/unknown ones.
     drop_unsupported_processors: bool = True
 
+    @property
+    def fallback_max_blocks_per_request(self) -> int:
+        """Returns the fallback max blocks per request. If no user-hint is given and decode path is available, this is
+        the default max blocks per request. With default block size of 256, this means a max sequence length of 8192
+        tokens for the fast decode path."""
+        return 32
+
     def account_for_cb_deprecated_arguments(
         self,
         max_queue_size: int = 0,
@@ -1919,3 +1993,17 @@ def resolve_compile_configs(
         # Modify in place
         self.varlen_compile_config = varlen_config
         self.decode_compile_config = decode_config
+
+    def resolve_using_hints(self, workload_hints: dict[str, int] | None) -> None:
+        """Resolves the config using workload hints. If the hints are not provided, we use a default value."""
+        if workload_hints is None:
+            return None
+        max_prompt_length = workload_hints.get("max_prompt_length", 0)
+        max_generated_length = workload_hints.get("max_generated_length", 0)
+        # The max number of block per request is an even number large enough to hold the max request length
+        if max_prompt_length and max_generated_length:
+            # We only overwrite the max blocks per request if it is not set yet
+            if self.max_blocks_per_request is None:
+                max_sequence_length = max_prompt_length + max_generated_length
+                blocks_per_request = int(ceil(max_sequence_length / self.block_size)) + 1
+                self.max_blocks_per_request = blocks_per_request + (blocks_per_request % 2)
diff --git a/src/transformers/generation/continuous_batching/cache.py b/src/transformers/generation/continuous_batching/cache.py
index 59de60bc957c..04d9934d13db 100644
--- a/src/transformers/generation/continuous_batching/cache.py
+++ b/src/transformers/generation/continuous_batching/cache.py
@@ -222,15 +222,10 @@ def __init__(
             f"{self.max_batch_tokens = } {num_attention_masks = }"
         )
 
-        # If max_blocks_per_request is not set, the default value is 16 max blocks. With default block size of 256, this
-        # means a max sequence length of 4096 tokens for the fast decode path.
+        # If max_blocks_per_request is not set, initialize it to the non-zero fallback value
         max_blocks_per_request = continuous_batching_config.max_blocks_per_request
         if max_blocks_per_request is None:
-            max_blocks_per_request = 0
-            # logger.info( TODO: uncomment when we have good defaults
-            #     f"max_blocks_per_request was not set, using {max_blocks_per_request}. This means max sequence "
-            #     f"length for the decode fast path is {max_blocks_per_request * self.block_size}."
-            # )
+            max_blocks_per_request = continuous_batching_config.fallback_max_blocks_per_request
         self.max_blocks_per_request = max_blocks_per_request
 
         # Initialize the cache
diff --git a/src/transformers/generation/continuous_batching/continuous_api.py b/src/transformers/generation/continuous_batching/continuous_api.py
index 459dcfc1c2fa..94881258f81d 100644
--- a/src/transformers/generation/continuous_batching/continuous_api.py
+++ b/src/transformers/generation/continuous_batching/continuous_api.py
@@ -38,10 +38,11 @@
 from .cache import PagedAttentionCache
 from .cb_logits_processors import ContinuousBatchingLogitsProcessorList
 from .input_outputs import ContinuousBatchingAsyncIOs, ContinuousBatchingIOs
+from .model_runner import ModelRunner
 from .offloading_manager import OffloadingManager
 from .requests import GenerationOutput, RequestState, RequestStatus, logger
 from .scheduler import SCHEDULER_MAPPING, FIFOScheduler, Scheduler
-from .utils import attn_mask_is_needed, create_warmup_future_states, pad_to_interval
+from .utils import attn_mask_is_needed
 
 
 """
@@ -181,9 +182,6 @@ def __init__(
         # Retrieve the size of the sliding window if there is one
         self.sliding_window = 1 if getattr(config, "sliding_window", None) is None else config.sliding_window
         # Cuda graphs for the generation step
-        self.q_padding_interval_size = self.cb_config.q_padding_interval_size
-        self.kv_padding_interval_size = self.cb_config.kv_padding_interval_size
-        self.max_cached_graphs = self.cb_config.max_cached_graphs
         self.use_cuda_graph_varlen, self.use_cuda_graph_decode = self.cb_config.get_cuda_graph_booleans()
 
         # Set up metrics collector
@@ -199,51 +197,32 @@ def __init__(
             is_flash_attn=is_flash_attention_requested(config=config),
             decode_fast_path_available=self.cache.max_blocks_per_request > 0,
         )
-        varlen_config, decode_config = self.cb_config.varlen_compile_config, self.cb_config.decode_compile_config
-
-        # Compile the varlen path if config provided
-        self._compiled_varlen = None
-        if varlen_config is not None:
-            self._compiled_varlen = torch.compile(self._forward_process_and_sample, **varlen_config.to_dict())
-
-        # Compile the decode path if config provided
-        self._compiled_decode = None
-        if decode_config is not None:
-            self._compiled_decode = torch.compile(self._forward_process_and_sample, **decode_config.to_dict())
+        use_compile = self.cb_config.varlen_compile_config is not None
+        use_compile |= self.cb_config.decode_compile_config is not None
 
         # Padding is turned on when either cuda graphs or compile is used
         use_cuda_graphs = self.use_cuda_graph_varlen or self.use_cuda_graph_decode
-        self._pad_inputs = use_cuda_graphs or (varlen_config is not None or decode_config is not None)
+        self._pad_inputs = use_cuda_graphs or use_compile
 
         # Setup inputs and outputs
+        io_kwargs = {
+            "cache": cache,
+            "config": config,
+            "device": model_device,
+            "model_dtype": model_dtype,
+            "return_logprobs": self.return_logprobs,
+            "logit_processor": self.logit_processor,
+            "use_cuda_graph_varlen": self.use_cuda_graph_varlen,
+        }
         self.use_async_batching = self.cb_config.use_async_batching
+
         if self.use_async_batching:
             # Since in async there are 2 IO pairs, there are also 2 graph buffers: we divide the max_cached_graphs by 2
-            max_cached_graphs = ceil(self.max_cached_graphs / 2)
-            self.inputs_and_outputs = ContinuousBatchingAsyncIOs(
-                cache=cache,
-                config=config,
-                device=model_device,
-                model_dtype=model_dtype,
-                max_graphs=max_cached_graphs,
-                return_logprobs=self.return_logprobs,
-                logit_processor=self.logit_processor,
-                use_cuda_graph_varlen=self.use_cuda_graph_varlen,
-            )
+            io_kwargs["max_graphs"] = ceil(self.cb_config.max_cached_graphs / 2)
+            self.inputs_and_outputs = ContinuousBatchingAsyncIOs(**io_kwargs)
         else:
-            self.inputs_and_outputs = ContinuousBatchingIOs(
-                cache=cache,
-                config=config,
-                device=model_device,
-                model_dtype=model_dtype,
-                max_graphs=self.max_cached_graphs,
-                return_logprobs=self.return_logprobs,
-                logit_processor=self.logit_processor,
-                use_cuda_graph_varlen=self.use_cuda_graph_varlen,
-            )
-        # Set up the graph pool. This allows all graphs to share the same memory pool, which is fine because they never
-        # run concurrently. This greatly saves memory.
-        self.graph_pool = torch.cuda.graph_pool_handle() if use_cuda_graphs else None
+            io_kwargs["max_graphs"] = self.cb_config.max_cached_graphs
+            self.inputs_and_outputs = ContinuousBatchingIOs(**io_kwargs)
 
         # Offloading manager: handles CPU offloading, soft reset, and restoration
         self.offloading_manager = OffloadingManager(
@@ -254,6 +233,16 @@ def __init__(
             compute_stream=self.inputs_and_outputs.compute_stream,
         )
 
+        # Setup the model runner
+        self.model_runner = ModelRunner(
+            logit_processor=self.logit_processor,
+            cb_config=self.cb_config,
+            cache=self.cache,
+            inputs_and_outputs=self.inputs_and_outputs,
+            do_sample=self.do_sample,
+            return_logprobs=self.return_logprobs,
+        )
+
     def __repr__(self) -> str:
         return (
             f"ContinuousBatchProcessor(input_queue={self.input_queue}, "
@@ -268,8 +257,15 @@ def __del__(self) -> None:
             torch.cuda.empty_cache()
 
     def _ensure_decode_fast_path_is_available(self) -> None:
-        """Ensures the decode fast path is available. If it is not, set the max blocks per request to 0."""
-        if self.cache.max_blocks_per_request > 0:
+        """Ensures the decode fast path is available. If it is not, set the max blocks per request to 0. If it is
+        available, and no user-provided max blocks per request, set it to 32 as a good default."""
+        # First, set max blocks per request to 32 if it needs to be auto-inferred
+        user_requested = self.cb_config.max_blocks_per_request is not None
+        if not user_requested:
+            self.cache.max_blocks_per_request = self.cb_config.fallback_max_blocks_per_request
+
+        # Then, if the decode fast path is not turned off, check if it is available
+        if self.cache.max_blocks_per_request != 0:
             # NOTE: block table should be available with FA2 and FA3, but there seems to be an issue with FA2 atm
             if is_flash_attention_requested(self.config, version=3):
                 flash_attn_with_kvcache = lazy_import_paged_flash_attention(self.config._attn_implementation)[1]
@@ -278,13 +274,15 @@ def _ensure_decode_fast_path_is_available(self) -> None:
                     torch.cuda.is_available(),  # Block table is only supported on CUDA
                     flash_attn_with_kvcache is not None,  # The `flash_attn_with_kvcache` fn is needed
                 ]
-                if not all(conditions):
+                # Throw a warning only if the decode fast path was requested by the user
+                if not all(conditions) and user_requested:
                     logger.warning(
                         f"Although {self.cache.max_blocks_per_request = }, the decode fast path is not available "
-                        f"because the one condition is not met: {conditions}."
+                        f"because at least one condition is not met: {conditions}."
                     )
                     self.cache.max_blocks_per_request = 0
-            else:
+            # Same, throw a warning only if the decode fast path was requested by the user
+            elif user_requested:
                 logger.warning(
                     f"Although {self.cache.max_blocks_per_request = }, the decode fast path is not available "
                     f"because the attention implementation is not FA3. Got {self.config._attn_implementation = }."
@@ -375,9 +373,7 @@ def prepare_next_batch(self) -> bool:
         )
 
         # If inputs are static sized, eg. for compile, we find the padded sizes of the queries and keys/values
-        if self._pad_inputs:
-            num_q_tokens = pad_to_interval(num_q_tokens, self.q_padding_interval_size, self.max_batch_tokens)
-            max_kv_read = pad_to_interval(max_kv_read, self.kv_padding_interval_size, self.cache.num_pages)
+        num_q_tokens, max_kv_read = self.model_runner.maybe_pad_inputs(num_q_tokens, max_kv_read, use_decode_fast_path)
 
         self.inputs_and_outputs.prepare_batch_tensors(
             requests_in_batch, self.logit_processor, use_decode_fast_path, num_q_tokens, max_kv_read
@@ -490,217 +486,24 @@ def fail_all_requests(self, error: Exception) -> None:
     @torch.no_grad()
     def _generation_step(self, model: nn.Module) -> None:
         """Perform a single generation step."""
-
-        # Retrieve the model kwargs with or without padding
+        # Retrieve the model kwargs with or without padding. After this function returns, everything happens on the
+        # device. Hence, to make the limit clear, this is left out of the model runner scope.
         batch_data = self.inputs_and_outputs.get_model_kwargs(use_padding=self._pad_inputs)
-        carry_over_ids, prev_output_ids, output_ids = self.inputs_and_outputs.get_cb_kwargs()
-        compute_stream = self.inputs_and_outputs.compute_stream
-
-        # Get the appropriate forward function (compiled or not, based on current path)
-        if self.inputs_and_outputs.use_block_table:
-            forward_fn = self._forward_process_and_sample if self._compiled_decode is None else self._compiled_decode
-            use_cuda_graph = self.use_cuda_graph_decode
-        else:
-            forward_fn = self._forward_process_and_sample if self._compiled_varlen is None else self._compiled_varlen
-            use_cuda_graph = self.use_cuda_graph_varlen
-
-        # If we are not using cuda graphs, we perform the generation step and return
-        if not use_cuda_graph:
-            maybe_stream = torch.cuda.stream(compute_stream) if compute_stream is not None else nullcontext()
-            with maybe_stream:
-                forward_fn(model, batch_data, carry_over_ids, prev_output_ids, output_ids)
 
-        # Otherwise, we use create or replay the graph (cuda is available in this path)
-        else:
-            graph = self.inputs_and_outputs.get_graph()
-            # Case: the graph already exists, so we replay it
-            if graph is not None:
-                with torch.cuda.stream(compute_stream):
-                    graph.replay()
-            # Otherwise, the graph does not exist, so we create it
-            else:
-                args = (model, batch_data, carry_over_ids, prev_output_ids, output_ids)
-                self.capture_graph(forward_fn, compute_stream, *args)
+        # This takes care of the forward pass, logits processing, and sampling. After this returns, the compute is
+        # scheduled on the device's compute stream, but may not have finished yet.
+        self.model_runner.compute_batch(model, batch_data)
 
-        # In any case, we transfer the outputs to the host
+        # This initiates the transfer of the outputs to the host. It is blocking in sync mode and non-blocking in async
+        # mode.
         self.inputs_and_outputs.retrieve_device_outputs()
 
-    def capture_graph(self, forward_fn: Any, compute_stream: torch.cuda.Stream, *args) -> None:
-        # Warmup (ensures the right result is computed before capturing the graph)
-        with torch.cuda.stream(compute_stream):
-            forward_fn(*args)
-        # Capture
-        graph = torch.cuda.CUDAGraph()
-        # Continuous batching can run multiple manager threads concurrently in one process, but PyTorch's
-        # default capture mode ("global") errors on CUDA actions from other threads. This means capture can be
-        # invalidated even when each manager uses a different device. To avoid this, we use "thread_local" mode.
-        with torch.cuda.graph(graph, stream=compute_stream, pool=self.graph_pool, capture_error_mode="thread_local"):
-            forward_fn(*args)
-        # Store
-        self.inputs_and_outputs.set_graph(graph)
-
-    @traced
-    def _forward_process_and_sample(
-        self,
-        model: nn.Module,
-        batch_data: dict,
-        carry_over_ids: torch.Tensor,
-        prev_output_ids: torch.Tensor,
-        output_ids: torch.Tensor,
-    ) -> None:
-        """This function performs the forward pass, logits processing, and sampling; which are broken down into smaller
-        function to be easier to trace with OpenTelemetry."""
-        self.inputs_and_outputs.carry_over_tokens(batch_data["input_ids"], carry_over_ids, prev_output_ids)
-        logits = self._model_forward(model, batch_data).float()  # convert to fp32 to match generate
-        scores = self._process_logit(batch_data, logits) if self.logit_processor.do_processing else logits
-        self._sample(scores, batch_data["logits_indices"], output_ids)
-
-    @traced(span_name="model_forward")
-    def _model_forward(self, model: nn.Module, batch_data: dict) -> torch.Tensor:
-        return model(**batch_data).logits
-
-    @traced(span_name="logit_processing")
-    def _process_logit(self, batch_data: dict, logits: torch.Tensor) -> torch.Tensor:
-        # Handle shape compatibility: logit processors expect 2D tensors [batch_size, vocab_size]
-        # but continuous batching always produces 3D tensors [batch_size, seq_len, vocab_size]
-        batch_size, seq_len, vocab_size = logits.shape
-        logits_2d = logits.view(batch_size * seq_len, vocab_size)
-        input_ids_2d = batch_data["input_ids"].view(batch_size * seq_len)
-        # Process with 2D tensors
-        processed_logits_2d = self.logit_processor(input_ids_2d, logits_2d, batch_data["logits_processor_args"])
-        # Reshape back to 3D
-        return processed_logits_2d.view(batch_size, seq_len, vocab_size)
-
-    @traced(span_name="sampling")
-    def _sample(self, scores: torch.Tensor, logits_indices: torch.Tensor, output_ids: torch.Tensor) -> None:
-        # Apply softmax if we are sampling or if we are generating log probabilities
-        if self.do_sample or self.return_logprobs:
-            probs = nn.functional.softmax(scores[0], dim=-1)  # shape [seq_len, vocab_size]
-        # Otherwise just remove the batch size dimension, which is always 1
-        else:
-            probs = scores.squeeze(0)  # shape [seq_len, vocab_size]
-
-        # Retrieve next tokens through sampling or argmax
-        if self.do_sample:
-            next_tokens = torch.multinomial(probs, num_samples=1)  # shape [seq_len, 1]
-        else:
-            next_tokens = torch.argmax(probs, dim=-1, keepdim=True)  # shape [seq_len, 1]
-
-        # Maybe retrieve log probabilities
-        if self.return_logprobs:
-            per_token_probs = probs.gather(dim=1, index=next_tokens).squeeze(-1)
-            logprobs = per_token_probs.log()  # shape [seq_len]
-        # And always remove the extra dimension for the gather
-        next_tokens = next_tokens.squeeze(-1)  # shape [seq_len]
-
-        # Get seq_len dimension to slice the logits indices
-        tokens = next_tokens.size(0)
-        # Shuffle the next tokens to match the order of the batch's requests
-        indices = logits_indices[:tokens]
-        next_tokens = next_tokens[indices]
-        # Copy the next tokens and maybe their logprobs to the static output tensor
-        output_ids[0, :tokens].copy_(next_tokens)
-        if self.return_logprobs:
-            # Shuffle the logprobs the same way as the next tokens
-            logprobs = logprobs[indices]
-            # In order to match the dtype of output_ids, we cast the fp32 logprobs as int32 without changing the
-            # underlying data. It's just a trick to use the same storage for both tensors.
-            output_ids[1, :tokens].copy_(logprobs.view(dtype=torch.int32))
-
     @torch.inference_mode()
     def warmup(self, model: nn.Module) -> None:
         """Pre-capture CUDA graphs (or trigger compile warmup) for varlen and decode paths. In async mode, both IO
         pairs are warmed up since each has its own graph buffer and static tensors. The varlen path is warmed up at
         the largest possible `(q, kv)` sizes so subsequent captures fit inside it without growing the pool."""
-
-        if not self._pad_inputs:
-            logger.info("CUDA graphs and compile are disabled, skipping warmup.")
-            return None
-
-        num_query_tokens = self.max_batch_tokens
-        num_pages = self.cache.num_blocks * self.cache.block_size
-        num_cache_tokens = num_pages - num_query_tokens
-        compute_stream = self.inputs_and_outputs.compute_stream
-
-        # In async mode, each IO pair has its own graph buffer and static tensors, so we warm up both
-        num_io_pairs = 2 if self.use_async_batching else 1
-
-        for pair_idx in range(num_io_pairs):
-            if self.use_async_batching:
-                self.inputs_and_outputs.current_pair = pair_idx
-                logger.info(f"Warming up IO pair {pair_idx + 1}/2...")
-
-            # --- Varlen path ---
-            padded_q = pad_to_interval(num_query_tokens, self.q_padding_interval_size, self.max_batch_tokens)
-            padded_kv = pad_to_interval(num_cache_tokens + num_query_tokens, self.kv_padding_interval_size, num_pages)
-            logger.info(f"Warming up varlen path ({padded_q} Q tokens, {padded_kv} KV tokens)...")
-
-            future_states = create_warmup_future_states(
-                1, RequestStatus.PREFILLING, num_query_tokens, num_cache_tokens, self.cache
-            )
-            try:
-                start = perf_counter()
-                self.inputs_and_outputs.prepare_batch_tensors(
-                    future_states, self.logit_processor, False, padded_q, padded_kv - padded_q
-                )
-                batch_data = self.inputs_and_outputs.get_model_kwargs(use_padding=True)
-                carry_over_ids, prev_output_ids, output_ids = self.inputs_and_outputs.get_cb_kwargs()
-                forward_fn = self._compiled_varlen or self._forward_process_and_sample
-                forward_fn_args = (model, batch_data, carry_over_ids, prev_output_ids, output_ids)
-                if self.use_cuda_graph_varlen:
-                    self.capture_graph(forward_fn, compute_stream, *forward_fn_args)
-                else:
-                    with torch.cuda.stream(compute_stream):
-                        forward_fn(*forward_fn_args)
-                logger.info(f"Varlen warmup completed in {perf_counter() - start:.2f}s")
-            except Exception as e:
-                logger.warning(f"Failed to warm up varlen path: {e}. Graph pool may fragment and OOM under load.")
-            finally:
-                for fs in future_states:
-                    self.cache.free_blocks(fs.state.request_id)
-
-            # Exit here if the decode fast path is not available
-            if self.cache.max_blocks_per_request == 0:
-                continue
-
-            # --- Decode fast path ---
-            logger.info("Warming up decode fast path...")
-            q_interval = self.q_padding_interval_size  # shorthand to avoid overly long lines
-            decode_graphs = 0
-            start = perf_counter()
-            # If N requests reach decoding stage, then the number of query tokens is going to start at N and decrease to
-            # 0 as all request finish. So we warmup for all intervals between 0 and N.
-            for num_requests in range(q_interval, num_query_tokens + q_interval, q_interval):
-                future_states = create_warmup_future_states(
-                    num_requests, RequestStatus.DECODING, 1, self.cache.block_size, self.cache
-                )
-                if not future_states:
-                    continue
-                try:
-                    padded_q = pad_to_interval(len(future_states), q_interval, self.max_batch_tokens)
-                    self.inputs_and_outputs.prepare_batch_tensors(
-                        future_states, self.logit_processor, True, padded_q, 0
-                    )
-                    batch_data = self.inputs_and_outputs.get_model_kwargs(use_padding=True)
-                    carry_over_ids, prev_output_ids, output_ids = self.inputs_and_outputs.get_cb_kwargs()
-                    forward_fn = self._compiled_decode or self._forward_process_and_sample
-                    forward_fn_args = (model, batch_data, carry_over_ids, prev_output_ids, output_ids)
-                    if self.use_cuda_graph_decode:
-                        self.capture_graph(forward_fn, compute_stream, *forward_fn_args)
-                    else:
-                        with torch.cuda.stream(compute_stream):
-                            forward_fn(*forward_fn_args)
-                    decode_graphs += 1
-                except Exception as e:
-                    logger.warning(f"Failed to warm up decode path for {num_requests} requests: {e}")
-                finally:
-                    for fs in future_states:
-                        self.cache.free_blocks(fs.state.request_id)
-            logger.info(f"Decode warmup completed ({decode_graphs} graphs) in {perf_counter() - start:.2f}s.")
-
-        # If using async batching, reset to pair 0 for the generation loop
-        if self.use_async_batching:
-            self.inputs_and_outputs.current_pair = 0
+        self.model_runner.warmup(model)
 
 
 # Manager Class (User Interface)
@@ -727,6 +530,17 @@ def __init__(
             generation_config: Configuration for generation parameters
             continuous_batching_config: Configuration for continuous batching parameters
         """
+        # MTP speculative decoding in continuous batching needs paged-cache slot reservation
+        # (K + 1 tokens per request per step) plus per-request accept/reject in the sampler.
+        # That work is tracked separately; until it lands, refuse the combination rather than
+        # silently downgrade to plain decoding.
+        if getattr(generation_config, "use_mtp", False):
+            raise NotImplementedError(
+                "`use_mtp=True` with `generate_batch` / continuous batching is not supported yet. "
+                "Use `model.generate(..., use_mtp=True)` for single-sequence MTP decoding, or set "
+                "`use_mtp=False` for batched generation."
+            )
+
         # Reload paged version of the attention implementation if necessary
         if "paged|" not in model.config._attn_implementation:
             model.set_attn_implementation(f"paged|{model.config._attn_implementation}")
@@ -735,7 +549,7 @@ def __init__(
         self.model = model.eval()
         self.generation_config = generation_config
         self.continuous_batching_config = continuous_batching_config
-        self.warmed_up = False  # Set to True after warmup is completed. Usefull for persistent managers.
+        self.warmed_up = False  # Set to True after warmup is completed. Useful for persistent managers.
         # This is an approximation until the cache is created: it will infer the correct value in cache.__init__
         self._use_prefix_sharing = self.continuous_batching_config.allow_block_sharing
 
@@ -943,27 +757,41 @@ def cancel_request(self, request_id: str) -> None:
         if self.batch_processor is not None:
             self.batch_processor.scheduler.set_request_cancellation(request_id)
 
-    # TODO:handle benchmarking properly when updating / fixing the requeue logic
     def get_result(self, request_id: str | None = None, timeout: float | None = None) -> GenerationOutput | None:
         """Retrieve one result from the output queue.
 
         Args:
-            request_id: If set, only return results matching this ID (others are requeued).
+            request_id: If set, only return results matching this ID.
             timeout: Maximum time to wait for a result.
 
         Returns:
             Optional[GenerationOutput]: The result data or None if timeout.
         """
-        if self._generation_thread is None and self.output_router.output_queue.empty():
+        output_queue = self.output_router.output_queue
+        if self._generation_thread is None and output_queue.empty():
             return None
+
+        deadline = None if timeout is None else perf_counter() + timeout
+        deferred: list[GenerationOutput] = []
+
         try:
-            result = self.output_router.output_queue.get(block=True, timeout=timeout)
-            if request_id is not None and result.request_id != request_id:
-                self.output_router.output_queue.put(result)
-                return None
-            return result
-        except queue.Empty:
-            return None
+            while True:
+                remaining = None if deadline is None else max(0.0, deadline - perf_counter())
+                if remaining == 0.0:
+                    return None
+
+                try:
+                    result = output_queue.get(timeout=remaining)
+                except queue.Empty:
+                    return None
+
+                if request_id is None or result.request_id == request_id:
+                    return result
+
+                deferred.append(result)
+        finally:
+            for item in deferred:
+                output_queue.put(item)
 
     def __iter__(self):
         """Iterate over results as they become available."""
@@ -973,17 +801,16 @@ def __iter__(self):
                 yield result
 
     def request_id_iter(self, request_id: str) -> Generator[GenerationOutput]:
-        """Iterate over results matching a specific request id (blocking).
-
-        Uses the shared output queue with requeue. For high-concurrency serving,
-        use :meth:`register_result_handler` instead.
-        """
+        """Iterate over results for a specific request until completion or cancellation."""
         while self._generation_thread is not None and self._generation_thread.is_alive():
             result = self.get_result(request_id=request_id, timeout=0.1)
             if result is not None:
                 yield result
                 if result.is_finished():
-                    return
+                    break
+
+            if self.batch_processor is not None and self.batch_processor.scheduler.request_is_cancelled(request_id):
+                break
 
     def register_result_handler(self, request_id: str, callback: Callable) -> None:
         """Register a callback for result delivery (streaming or non-streaming).
@@ -1140,6 +967,7 @@ def init_continuous_batching(
         self,
         generation_config: GenerationConfig | None = None,
         continuous_batching_config: ContinuousBatchingConfig | None = None,
+        workload_hints: dict[str, int] | None = None,
         **deprecated_kwargs,
     ) -> ContinuousBatchingManager:
         """Initialize a manager for continuous batching inference.
@@ -1147,6 +975,8 @@ def init_continuous_batching(
         Args:
             generation_config: An optional generation configuration, which may contain a CompileConfig object
             continuous_batching_config: An optional continuous batching configuration
+            workload_hints: Optional workload hints to help the continuous batching manager make better decisions for
+                default values. Keys accepted are: max_prompt_length, max_generated_length.
             **deprecated_kwargs: Deprecated arguments that are now passed in the continuous_batching_config. Those are:
                 max_queue_size, q_padding_interval_size, kv_padding_interval_size, allow_block_sharing,
                 use_async_batching, max_cached_graphs
@@ -1182,6 +1012,7 @@ def init_continuous_batching(
             else:
                 continuous_batching_config = ContinuousBatchingConfig()
         continuous_batching_config.account_for_cb_deprecated_arguments(**deprecated_kwargs)
+        continuous_batching_config.resolve_using_hints(workload_hints)
 
         # Create and return the manager
         return ContinuousBatchingManager(
@@ -1205,6 +1036,7 @@ def continuous_batching_context_manager(
         continuous_batching_config: ContinuousBatchingConfig | None = None,
         persistent_manager: bool = False,
         warmup: bool = True,
+        workload_hints: dict[str, int] | None = None,
         **deprecated_kwargs,
     ) -> Generator[ContinuousBatchingManager]:
         """A context manager to safely use the continuous batching manager. Arguments are similar to the ones of
@@ -1216,6 +1048,7 @@ def continuous_batching_context_manager(
         manager = self.init_continuous_batching(
             generation_config=generation_config,
             continuous_batching_config=continuous_batching_config,
+            workload_hints=workload_hints,
             **deprecated_kwargs,
         )
         if warmup and not manager.warmed_up:
@@ -1284,14 +1117,22 @@ def generate_batch(
         for depr_key in deprecated_keys:
             if depr_key in kwargs:
                 deprecated_kwargs[depr_key] = kwargs.pop(depr_key)
-        # Extract max_new_tokens from kwargs because it's the only expected kwarg
-        max_new_tokens = kwargs.pop("max_new_tokens", None)
 
         # Compute the total number of requests
         gen_cfg = self.generation_config if generation_config is None else generation_config
         num_return_sequences = gen_cfg.num_return_sequences if gen_cfg.num_return_sequences is not None else 1
         num_requests = len(inputs) * num_return_sequences
 
+        # Extract max_new_tokens from kwargs because it's the only expected kwarg
+        max_new_tokens = kwargs.pop("max_new_tokens", None)
+        max_new_tokens = gen_cfg.max_new_tokens if max_new_tokens is None else max_new_tokens
+
+        # Compute workload hints
+        workload_hints = {
+            "max_prompt_length": max(len(input_ids) for input_ids in inputs),
+            "max_generated_length": max_new_tokens,
+        }
+
         # Prepare context managers for the main loop
         manager_cm = self.continuous_batching_context_manager(
             generation_config=generation_config,
@@ -1300,6 +1141,7 @@ def generate_batch(
             timeout=5,
             persistent_manager=persistent_manager,
             warmup=warmup,
+            workload_hints=workload_hints,
             **deprecated_kwargs,
         )
         logging_cm = logging_redirect_tqdm([logger])
diff --git a/src/transformers/generation/continuous_batching/input_outputs.py b/src/transformers/generation/continuous_batching/input_outputs.py
index fbe7890a15b9..e751c77cbd85 100644
--- a/src/transformers/generation/continuous_batching/input_outputs.py
+++ b/src/transformers/generation/continuous_batching/input_outputs.py
@@ -802,7 +802,10 @@ def retrieve_device_outputs(self) -> None:
         # Transfer the outputs to the host
         io_pair.transfer_outputs_d2h(self.d2h_stream)
         self.d2h_stream.record_event(io_pair.d2h_over)
-        # Switch IO pair
+        # Swap IO pair
+        self.swap_io_pairs()
+
+    def swap_io_pairs(self) -> None:
         self.current_pair = 1 - self.current_pair
 
     # This method is called after the switch and not during the first batch
diff --git a/src/transformers/generation/continuous_batching/model_runner.py b/src/transformers/generation/continuous_batching/model_runner.py
new file mode 100644
index 000000000000..6a266dbaaf7e
--- /dev/null
+++ b/src/transformers/generation/continuous_batching/model_runner.py
@@ -0,0 +1,301 @@
+# Copyright 2026 The HuggingFace Inc. team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import time
+from collections.abc import Callable
+from contextlib import nullcontext
+
+import torch
+from torch import nn
+
+from ...generation.configuration_utils import ContinuousBatchingConfig
+from .cache import PagedAttentionCache
+from .cb_logits_processors import ContinuousBatchingLogitsProcessorList
+from .input_outputs import ContinuousBatchingAsyncIOs, ContinuousBatchingIOs
+from .requests import RequestStatus, logger
+from .utils import create_warmup_future_states, pad_to_interval, pad_to_pow2
+
+
+class ModelRunner:
+    """This class is the continuous batching entry point for running the model. As a rule of thumb, anything running on
+    the device should happen from this class."""
+
+    def __init__(
+        self,
+        logit_processor: ContinuousBatchingLogitsProcessorList,
+        cb_config: ContinuousBatchingConfig,
+        inputs_and_outputs: ContinuousBatchingIOs | ContinuousBatchingAsyncIOs,
+        cache: PagedAttentionCache,
+        do_sample: bool,
+        return_logprobs: bool,
+    ) -> None:
+        # Main attributes
+        self.logit_processor = logit_processor
+        self.cb_config = cb_config
+        self.inputs_and_outputs = inputs_and_outputs
+        # Helper attributes
+        self.do_sample = do_sample
+        self.return_logprobs = return_logprobs
+        self.use_cuda_graph_varlen, self.use_cuda_graph_decode = self.cb_config.get_cuda_graph_booleans()
+        self.cache = cache
+
+        # Set up the graph pool. This allows all graphs to share the same memory pool, greatly saving memory.
+        if self.use_cuda_graph_varlen or self.use_cuda_graph_decode:
+            self.graph_pool = torch.cuda.graph_pool_handle()
+        else:
+            self.graph_pool = None
+
+        # Set up compiled version of the forward pass for the varlen path
+        self._compiled_varlen = None
+        if self.cb_config.varlen_compile_config is not None:
+            self._compiled_varlen = torch.compile(
+                self._forward_process_and_sample, **self.cb_config.varlen_compile_config.to_dict()
+            )
+
+        # Set up compiled version of the forward pass for the decode path
+        self._compiled_decode = None
+        if self.cb_config.decode_compile_config is not None:
+            self._compiled_decode = torch.compile(
+                self._forward_process_and_sample, **self.cb_config.decode_compile_config.to_dict()
+            )
+
+    def maybe_pad_inputs(self, num_q_tokens: int, max_kv_read: int, use_decode_fast_path: bool) -> tuple[int, int]:
+        """Pads the input sizes for the next batch if it is needed. Often it is, for max performance."""
+        max_batch_tokens = self.cache.max_batch_tokens
+        # For varlen batches, we pad using interval sizes
+        if not use_decode_fast_path:
+            num_q_tokens = pad_to_interval(num_q_tokens, self.cb_config.q_padding_interval_size, max_batch_tokens)
+            max_kv_read = pad_to_interval(max_kv_read, self.cb_config.kv_padding_interval_size, self.cache.num_pages)
+        # For decode fast path batches, we pad using powers of 2 and use no KV
+        else:
+            num_q_tokens = pad_to_pow2(num_q_tokens, max_batch_tokens)
+            max_kv_read = 0
+        return num_q_tokens, max_kv_read
+
+    def compute_batch(self, model: nn.Module, batch_data: dict) -> None:
+        """Runs the forward pass, processes the logits and samples the next tokens. It also handles which version of
+        the forward pass to use (varlen or decode), whether to use CUDA graphs (with the eventual capture of the graph)
+        and torch compile."""
+        # These tensors are device-resident, this is just pointer retrieval
+        carry_over_ids, prev_output_ids, output_ids = self.inputs_and_outputs.get_cb_kwargs()
+        # This is the stream on which the compute happens
+        compute_stream = self.inputs_and_outputs.compute_stream
+
+        # Get the appropriate forward function (compiled or not, based on current path)
+        forward_fn, use_cuda_graph = self._get_forward_fn(use_block_table=self.inputs_and_outputs.use_block_table)
+
+        # If we are not using CUDA graphs, we perform the generation step and return
+        if not use_cuda_graph:
+            maybe_stream = torch.cuda.stream(compute_stream) if compute_stream is not None else nullcontext()
+            with maybe_stream:
+                forward_fn(model, batch_data, carry_over_ids, prev_output_ids, output_ids)
+
+        # Otherwise, we either create or replay the graph (CUDA is available in this path)
+        else:
+            graph = self.inputs_and_outputs.get_graph()
+            # Case: the graph already exists, so we replay it
+            if graph is not None:
+                with torch.cuda.stream(compute_stream):
+                    graph.replay()
+            # Otherwise, the graph does not exist, so we create it
+            else:
+                args = (model, batch_data, carry_over_ids, prev_output_ids, output_ids)
+                self._capture_graph(forward_fn, compute_stream, *args)
+
+    def _get_forward_fn(self, use_block_table: bool) -> tuple[Callable, bool]:
+        """Helper function to get the appropriate forward function based on the block table and compile behavior."""
+        if use_block_table:
+            forward_fn = self._forward_process_and_sample if self._compiled_decode is None else self._compiled_decode
+            use_cuda_graph = self.use_cuda_graph_decode
+        else:
+            forward_fn = self._forward_process_and_sample if self._compiled_varlen is None else self._compiled_varlen
+            use_cuda_graph = self.use_cuda_graph_varlen
+        return forward_fn, use_cuda_graph
+
+    def _capture_graph(self, forward_fn: Callable, compute_stream: torch.cuda.Stream, *args) -> None:
+        """Helper function to capture and store a graph for a given forward function."""
+        # Warmup (ensures the right result is computed before capturing the graph)
+        with torch.cuda.stream(compute_stream):
+            forward_fn(*args)
+        # Capture using a thread-local capture mode to avoid capturing GPU operations from outside the model forward
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph, stream=compute_stream, pool=self.graph_pool, capture_error_mode="thread_local"):
+            forward_fn(*args)
+        # Store
+        self.inputs_and_outputs.set_graph(graph)
+
+    def _forward_process_and_sample(
+        self,
+        model: nn.Module,
+        batch_data: dict,
+        carry_over_ids: torch.Tensor,
+        prev_output_ids: torch.Tensor,
+        output_ids: torch.Tensor,
+    ) -> None:
+        """This function performs the forward pass, logits processing, and sampling. This is what is either captured
+        and/or compiled."""
+        # Perform carry-over (no-op for synchronous batching)
+        self.inputs_and_outputs.carry_over_tokens(batch_data["input_ids"], carry_over_ids, prev_output_ids)
+
+        # Run model forward pass and convert to fp32 to match generate
+        logits = model(**batch_data).logits.float()
+
+        # Process logits if there are any logit processors
+        if self.logit_processor.do_processing:
+            # Handle shape inconsistency between generate and continuous batching (dummy_dim is always 1)
+            dummy_dim, num_tokens, vocab_size = logits.shape
+            logits_2d = logits.view(dummy_dim * num_tokens, vocab_size)
+            input_ids_2d = batch_data["input_ids"].view(dummy_dim * num_tokens)
+            # Process with 2D tensors
+            logits_2d = self.logit_processor(input_ids_2d, logits_2d, batch_data["logits_processor_args"])
+            # Reshape back to 3D
+            scores = logits_2d.view(dummy_dim, num_tokens, vocab_size)
+        else:
+            scores = logits
+
+        # Sample next tokens
+        self._sample(scores, batch_data["logits_indices"], output_ids)
+
+    def _sample(self, scores: torch.Tensor, logits_indices: torch.Tensor, output_ids: torch.Tensor) -> None:
+        """Private method to sample next tokens from the scores."""
+        # Apply softmax if we are sampling or if we are generating log probabilities
+        if self.do_sample or self.return_logprobs:
+            probs = nn.functional.softmax(scores[0], dim=-1)  # shape [seq_len, vocab_size]
+        else:
+            probs = scores.squeeze(0)  # shape [seq_len, vocab_size]
+
+        # Retrieve next tokens through sampling or argmax
+        if self.do_sample:
+            next_tokens = torch.multinomial(probs, num_samples=1)  # shape [seq_len, 1]
+        else:
+            next_tokens = torch.argmax(probs, dim=-1, keepdim=True)  # shape [seq_len, 1]
+
+        # Maybe retrieve log probabilities
+        if self.return_logprobs:
+            per_token_probs = probs.gather(dim=1, index=next_tokens).squeeze(-1)
+            logprobs = per_token_probs.log()  # shape [seq_len]
+
+        # Always remove the extra dimension for the gather
+        next_tokens = next_tokens.squeeze(-1)  # shape [seq_len]
+
+        # Get seq_len dimension to slice the logits indices
+        tokens = next_tokens.size(0)
+        # Shuffle the next tokens to match the order of the batch's requests
+        indices = logits_indices[:tokens]
+        next_tokens = next_tokens[indices]
+        # Copy the next tokens and maybe their logprobs to the static output tensor
+        output_ids[0, :tokens].copy_(next_tokens)
+        if self.return_logprobs:
+            # Shuffle the logprobs the same way as the next tokens
+            logprobs = logprobs[indices]
+            # In order to match the dtype of output_ids, we cast the fp32 logprobs as int32 without changing the
+            # underlying data. It's just a trick to use the same storage for both tensors.
+            output_ids[1, :tokens].copy_(logprobs.view(dtype=torch.int32))
+
+    @torch.inference_mode()
+    def warmup(self, model: nn.Module) -> None:
+        """Pre-capture CUDA graphs and/or trigger compile warmup for varlen and decode paths (if available). Unless the
+        force_warmup flag is set, the warmup is only performed if the CUDA graphs or compile are enabled."""
+        # Early return if the warmup is not needed
+        cuda_graph_off = not (self.use_cuda_graph_varlen or self.use_cuda_graph_decode)
+        compile_off = self.cb_config.varlen_compile_config is None or self.cb_config.decode_compile_config is None
+        if cuda_graph_off and compile_off:
+            return None
+
+        # In async mode, each IO pair has its own graph buffer and static tensors, so we warm up both
+        total_duration = 0
+        iterations = 2 if isinstance(self.inputs_and_outputs, ContinuousBatchingAsyncIOs) else 1
+        for _ in range(iterations):
+            # Warm up the varlen path, with the largest possible dimensions to get the biggest pool and avoid fragmentation
+            num_q_tokens = self.cache.max_batch_tokens
+            max_kv_read = self.cache.num_blocks * self.cache.block_size
+            max_kv_read -= num_q_tokens  # make room for the new tokens
+            total_duration += self.run_one_warmup(model=model, num_q_tokens=num_q_tokens, max_kv_read=max_kv_read)
+
+            # Exit here if the decode fast path is not available
+            if self.cache.max_blocks_per_request == 0:
+                continue
+
+            # Warm up the decode path
+            num_requests = 1
+            while True:
+                total_duration += self.run_one_warmup(model=model, num_q_tokens=num_requests, max_kv_read=None)
+                if num_requests >= self.cache.max_batch_tokens:
+                    break
+                num_requests = min(2 * num_requests, self.cache.max_batch_tokens)
+
+            # Switch to the other IO pair if this is async
+            if isinstance(self.inputs_and_outputs, ContinuousBatchingAsyncIOs):
+                self.inputs_and_outputs.swap_io_pairs()
+        logger.info(f"Warmup completed in {total_duration:.2f}s")
+
+    def run_one_warmup(self, model: nn.Module, num_q_tokens: int, max_kv_read: int | None) -> float:
+        """Warms up the decode fast path (if max_kv_read is None) or varlen path (if max_kv_read is an int) for a
+        specific number of query and cache-resident tokens. `max_kv_read` is the number of tokens already in cache,
+        matching the terminology used by `prepare_batch_tensors` and the scheduler."""
+        # Make up fake request states according to the chosen path
+        use_decode_fast_path = max_kv_read is None
+        if use_decode_fast_path:
+            num_requests = num_q_tokens
+            status = RequestStatus.DECODING
+            num_q_tokens = 1
+            max_kv_read = self.cache.block_size
+            logger.debug(f"Warming up decode fast path for {num_requests =}.")
+        else:
+            num_requests = 1
+            status = RequestStatus.PREFILLING
+            logger.debug(f"Warming up varlen path for {num_q_tokens =}, {max_kv_read =}.")
+        future_states = create_warmup_future_states(num_requests, status, num_q_tokens, max_kv_read, self.cache)
+        if not future_states:
+            logger.warning(
+                f"Failed to warm up: no blocks allocated for {num_requests =}, {num_q_tokens =}, {max_kv_read =}."
+            )
+            return 0.0
+
+        # Pad the inputs to the appropriate size
+        padded_q, padded_kv = self.maybe_pad_inputs(
+            num_q_tokens=num_q_tokens * num_requests,
+            max_kv_read=max_kv_read,
+            use_decode_fast_path=use_decode_fast_path,
+        )
+
+        # Actual warmup, which happens in a try-finally block to ensure the blocks are freed even if the warmup fails
+        start = time.perf_counter()
+        try:
+            self.inputs_and_outputs.prepare_batch_tensors(
+                future_states, self.logit_processor, use_decode_fast_path, padded_q, padded_kv
+            )
+            batch_data = self.inputs_and_outputs.get_model_kwargs(use_padding=True)
+            carry_over_ids, prev_output_ids, output_ids = self.inputs_and_outputs.get_cb_kwargs()
+            forward_fn, use_cuda_graph = self._get_forward_fn(use_block_table=self.inputs_and_outputs.use_block_table)
+            forward_fn_args = (model, batch_data, carry_over_ids, prev_output_ids, output_ids)
+            if use_cuda_graph:
+                self._capture_graph(forward_fn, self.inputs_and_outputs.compute_stream, *forward_fn_args)
+            else:
+                with torch.cuda.stream(self.inputs_and_outputs.compute_stream):
+                    forward_fn(*forward_fn_args)
+            duration = time.perf_counter() - start
+            logger.debug(f"Warmup completed in {duration:.2f}s")
+
+        # Exception handling
+        except Exception as e:
+            duration = 0.0
+            logger.warning(f"Failed to warm up: {e}.\nGraph pool may fragment and OOM under load.")
+
+        # In any case, free the blocks allocated for the fake warmup requests
+        finally:
+            for fs in future_states:
+                self.cache.free_blocks(fs.state.request_id)
+        return duration
diff --git a/src/transformers/generation/continuous_batching/utils.py b/src/transformers/generation/continuous_batching/utils.py
index 11cc3811c134..f1b231f3915b 100644
--- a/src/transformers/generation/continuous_batching/utils.py
+++ b/src/transformers/generation/continuous_batching/utils.py
@@ -171,27 +171,27 @@ def build_attention_mask(
 def create_warmup_future_states(
     num: int,
     status: RequestStatus,
-    num_query_tokens: int,
-    num_cache_tokens: int,
+    num_q_tokens: int,
+    max_kv_read: int,
     cache: Any,  # not annotated to avoid circular import
 ) -> list[FutureRequestState]:
-    """An utility function to create a list of FutureRequestStates for the warmup of CB."""
+    """A utility function to create a list of FutureRequestStates for the warmup of CB."""
     # Setup
     request_ids = [f"__warmup_{status.name}_{i}__" for i in range(num)]
-    total_tokens = num_query_tokens + num_cache_tokens
+    total_tokens = num_q_tokens + max_kv_read
     blocks_needed = ceil(total_tokens / cache.block_size)
     # Main loop
     future_states = []
     for req_id in request_ids:
         state = RequestState(request_id=req_id, initial_tokens=[0] * total_tokens, max_new_tokens=1)
         state._status = status  # bypass the property setter to avoid the lifecycle side effects
-        state.tokens_to_process = [0] * num_query_tokens
-        state.position_offset = num_cache_tokens
+        state.tokens_to_process = [0] * num_q_tokens
+        state.position_offset = max_kv_read
         # Stop if allocation fails for any request
         allocated = cache.allocate_blocks(blocks_needed, state.request_id, 0)
         if allocated is None:
             return future_states
         future_states.append(
-            FutureRequestState(state, has_new_token=True, complete_blocks=0, query_length=num_query_tokens)
+            FutureRequestState(state, has_new_token=True, complete_blocks=0, query_length=num_q_tokens)
         )
     return future_states
diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index 9c47e551cee8..2249dfec560c 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -595,6 +595,151 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
         return scores_processed
 
 
+class PLessLogitsWarper(LogitsProcessor):
+    """
+    [`LogitsProcessor`] that performs p-less sampling, a hyperparamter-free decoding method that adaptively
+    determines the minimum threshold probability for admitting tokens into the sampling set, based on the
+    information from the full token distribution.
+
+    The p-less method balances the adaptive threshold probability with the entropy of the token distribution, i.e.
+    a higher entropy results in a lower threshold and vice versa, which is a befitting relationship. The p-less
+    threshold is also bounded and valid, i.e. guaranteed to be at least the uniform token probability and at most
+    the modal probability.
+
+    Paper:
+    For details, see *p-less Sampling: A Robust Hyperparameter-free Approach for LLM Decoding*
+    https://arxiv.org/abs/2509.23234
+
+    `PLessLogitsWarper` can be used together with [`TemperatureLogitsWarper`], and is used as an alternative to
+    [`TopPLogitsWarper`] and [`TopKLogitsWarper`].
+
+    Args:
+        p_less (`bool`): Must be `True` to use p-less sampling.
+        filter_value (`float`, *optional*, defaults to -inf):
+            All filtered values will be set to this float value.
+
+    Example:
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
+
+    >>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
+    >>> outputs = model.generate(**inputs, do_sample=True, p_less=True)
+    >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+    A sequence: 1, 2, 3, 4, 5, 6, 7, 8, 9
+    ```
+    """
+
+    def __init__(self, p_less: bool, filter_value: float = -float("Inf")):
+        if not isinstance(p_less, bool) or not p_less:
+            raise ValueError("`p_less` must be `True` to use p-less sampling for decoding.")
+        self.filter_value = filter_value
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        Filters logits using p-less sampling.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Input token IDs.
+            scores (`torch.FloatTensor` of shape `(batch_size, vocab_size)`):
+                Logits from the model.
+
+        Return:
+            `torch.FloatTensor` of shape `(batch_size, vocab_size)`:
+                Processed logits where rejected tokens are masked with `-inf`.
+        """
+
+        # Convert logits to probabilities
+        probs = torch.softmax(scores, dim=-1)
+
+        # Calculate the p-less probability threshold
+        p = probs.square().sum(dim=-1, keepdim=True)
+
+        # Create the mask for tokens whose probability is less than the p-less threshold
+        mask_reject = probs < p
+
+        # Update token logits whose probability is less than the p-less threshold to `filter_value`
+        scores_processed = scores.masked_fill(mask_reject, self.filter_value)
+
+        return scores_processed
+
+
+class PLessNormLogitsWarper(LogitsProcessor):
+    """
+    [`LogitsProcessor`] that performs p-less-norm sampling, a hyperparamter-free decoding method that adaptively
+    determines the minimum threshold probability for admitting tokens into the sampling set, based on the
+    information from the full token distribution.
+
+    The p-less-norm method balances the adaptive threshold probability with the entropy of the token distribution,
+    i.e. a higher entropy results in a lower threshold and vice versa, which is a befitting relationship. The
+    p-less-norm threshold is also bounded and valid, i.e. guaranteed to be at least zero and at most the modal
+    probability.
+
+    Paper:
+    For details, see *p-less Sampling: A Robust Hyperparameter-free Approach for LLM Decoding*
+    https://arxiv.org/abs/2509.23234
+
+    `PLessLogitsWarper` can be used together with [`TemperatureLogitsWarper`], and is used as an alternative to
+    [`TopPLogitsWarper`] and [`TopKLogitsWarper`].
+
+    Args:
+        p_less_norm (`bool`): Must be `True` to use p-less-norm sampling.
+        filter_value (`float`, *optional*, defaults to -inf):
+            All filtered values will be set to this float value.
+
+    Examples:
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+
+    >>> model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
+    >>> tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
+
+    >>> inputs = tokenizer("A sequence: 1, 2", return_tensors="pt")
+    >>> outputs = model.generate(**inputs, do_sample=True, p_less_norm=True)
+    >>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])
+    A sequence: 1, 2, 3, 4, 5, 6, 7, 8, 9
+    ```
+    """
+
+    def __init__(self, p_less_norm: bool, filter_value: float = -float("Inf")):
+        if not isinstance(p_less_norm, bool) or not p_less_norm:
+            raise ValueError("`p_less_norm` must be `True` to use p-less-norm sampling for decoding.")
+        self.filter_value = filter_value
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        Filters logits using p-less-norm sampling.
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Input token IDs.
+            scores (`torch.FloatTensor` of shape `(batch_size, vocab_size)`):
+                Logits from the model.
+
+        Return:
+            `torch.FloatTensor` of shape `(batch_size, vocab_size)`:
+                Processed logits where rejected tokens are masked with `-inf`.
+        """
+
+        # Convert logits to probabilities
+        probs = torch.softmax(scores, dim=-1)
+
+        # Calculate the p-less-norm probability threshold
+        v = probs.size(-1)
+        p = (v * probs.square().sum(dim=-1, keepdim=True) - 1.0) / (v - 1.0)
+
+        # Create the mask for tokens whose probability is less than the p-less-norm threshold
+        mask_reject = probs < p
+
+        # Update token logits whose probability is less than the p-less-norm threshold to `filter_value`
+        scores_processed = scores.masked_fill(mask_reject, self.filter_value)
+
+        return scores_processed
+
+
 class TopHLogitsWarper(LogitsProcessor):
     """
     [`LogitsProcessor`] that implements Top-H sampling, a decoding method which adaptively selects a subset of
@@ -1005,7 +1150,14 @@ def __init__(
 
     @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        if torch.isneginf(scores).all(dim=-1).any():
+            raise ValueError(
+                "EtaLogitsWarper received a row with all logits set to -inf. "
+                "This usually means previous logits processors masked every token."
+            )
+
         probabilities = scores.softmax(dim=-1)
+
         entropy = torch.distributions.Categorical(logits=scores).entropy()
         eta = torch.min(self.epsilon, torch.sqrt(self.epsilon) * torch.exp(-entropy))[..., None]
         indices_to_remove = probabilities < eta
@@ -1661,13 +1813,22 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> to
 
 class InfNanRemoveLogitsProcessor(LogitsProcessor):
     r"""
-    [`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. Note that using
-    the logits processor should only be used if necessary since it can slow down the generation method.
+    [`LogitsProcessor`] that removes all `nan` and `inf` values to avoid the generation method to fail. This version
+    has been extended to sanitize both logits and hidden state output tensors to handle instabilities in very wide
+    models or ones sharded across many devices.
+
+    Note that using the logits processor should only be used if necessary since it can slow down the generation method.
 
     This logits processor has no `generate` example, as there shouldn't be a correct combination of flags that warrants
-    its use.
+    its use. However, when dealing with sharded models across many GPUs or models with very wide hidden dimensions that
+    can produce unstable values, setting `remove_invalid_values=True` in generation config will activate this processor
+    automatically.
     """
 
+    def __init__(self, hidden_states_aware=True):
+        # Flag to control whether we also want to clean hidden states
+        self.hidden_states_aware = hidden_states_aware
+
     @add_start_docstrings(LOGITS_PROCESSOR_INPUTS_DOCSTRING)
     def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
         # set all nan values to 0.0
diff --git a/src/transformers/generation/safety/__init__.py b/src/transformers/generation/safety/__init__.py
new file mode 100644
index 000000000000..095aed1eec5d
--- /dev/null
+++ b/src/transformers/generation/safety/__init__.py
@@ -0,0 +1,40 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from ...utils import is_torch_available
+from .base import SafetyChecker, SafetyMetrics, SafetyResult, SafetyState, SafetyViolation
+from .configuration import LENIENT_PRESET, MODERATE_PRESET, STRICT_PRESET, SafetyConfig
+
+
+if is_torch_available():
+    from .processors import SafetyLogitsProcessor, SafetyStoppingCriteria
+else:
+    SafetyLogitsProcessor = None
+    SafetyStoppingCriteria = None
+
+
+__all__ = [
+    "SafetyChecker",
+    "SafetyResult",
+    "SafetyViolation",
+    "SafetyMetrics",
+    "SafetyState",
+    "SafetyConfig",
+    "STRICT_PRESET",
+    "MODERATE_PRESET",
+    "LENIENT_PRESET",
+]
+
+if is_torch_available():
+    __all__ += ["SafetyLogitsProcessor", "SafetyStoppingCriteria"]
diff --git a/src/transformers/generation/safety/base.py b/src/transformers/generation/safety/base.py
new file mode 100644
index 000000000000..c9b7d32b0779
--- /dev/null
+++ b/src/transformers/generation/safety/base.py
@@ -0,0 +1,365 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import threading
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from typing import Any
+
+
+@dataclass
+class SafetyViolation:
+    """
+    Represents a single safety violation detected in text.
+
+    Args:
+        category (`str`):
+            The category of safety violation (e.g., "toxicity", "bias", "pii").
+        confidence (`float`):
+            Confidence score for the violation detection, ranging from 0.0 to 1.0.
+        severity (`str`, *optional*, defaults to `"medium"`):
+            Severity level of the violation. One of "low", "medium", "high", "critical".
+        description (`str`, *optional*, defaults to `""`):
+            Human-readable description of the violation.
+        span (`Tuple[int, int]`, *optional*):
+            Character span in the original text where the violation occurs, if applicable.
+    """
+
+    category: str
+    confidence: float
+    severity: str = "medium"
+    description: str = ""
+    span: tuple[int, int] | None = None
+
+
+@dataclass
+class SafetyResult:
+    """
+    Result of a safety checking operation.
+
+    Args:
+        is_safe (`bool`):
+            Whether the checked text is considered safe overall.
+        confidence (`float`):
+            Overall confidence in the safety assessment, ranging from 0.0 to 1.0.
+        violations (`List[SafetyViolation]`):
+            List of safety violations detected in the text.
+        metadata (`Dict[str, Any]`):
+            Additional checker-specific information and context.
+    """
+
+    is_safe: bool
+    confidence: float
+    violations: list[SafetyViolation]
+    metadata: dict[str, Any]
+
+
+@dataclass
+class SafetyMetrics:
+    """
+    Metrics collection for safety operations monitoring and analysis.
+
+    Tracks performance and usage statistics for safety checking operations,
+    enabling production monitoring and optimization.
+
+    Args:
+        total_generations (`int`, defaults to 0):
+            Total number of generations attempted.
+        blocked_generations (`int`, defaults to 0):
+            Number of generations blocked due to safety violations.
+        suppression_events (`int`, defaults to 0):
+            Number of token suppression events during generation.
+        cache_hits (`int`, defaults to 0):
+            Number of cache hits for safety check results.
+        cache_misses (`int`, defaults to 0):
+            Number of cache misses requiring new safety checks.
+        total_safety_check_time_ms (`float`, defaults to 0.0):
+            Cumulative time spent on safety checks in milliseconds.
+        safety_check_count (`int`, defaults to 0):
+            Total number of safety checks performed.
+    """
+
+    total_generations: int = 0
+    blocked_generations: int = 0
+    suppression_events: int = 0
+    cache_hits: int = 0
+    cache_misses: int = 0
+    total_safety_check_time_ms: float = 0.0
+    safety_check_count: int = 0
+
+    def __post_init__(self):
+        """Initialize thread safety lock after dataclass fields."""
+        self._lock = threading.Lock()
+
+    @property
+    def cache_hit_rate(self) -> float:
+        """Calculate cache hit rate as a percentage."""
+        total_cache_ops = self.cache_hits + self.cache_misses
+        if total_cache_ops == 0:
+            return 0.0
+        return (self.cache_hits / total_cache_ops) * 100.0
+
+    @property
+    def avg_safety_check_time_ms(self) -> float:
+        """Calculate average safety check time in milliseconds."""
+        if self.safety_check_count == 0:
+            return 0.0
+        return self.total_safety_check_time_ms / self.safety_check_count
+
+    @property
+    def block_rate(self) -> float:
+        """Calculate generation block rate as a percentage."""
+        if self.total_generations == 0:
+            return 0.0
+        return (self.blocked_generations / self.total_generations) * 100.0
+
+    def record_safety_check(self, check_time_ms: float) -> None:
+        """Record a safety check operation with timing."""
+        with self._lock:
+            self.safety_check_count += 1
+            self.total_safety_check_time_ms += check_time_ms
+
+    def record_cache_hit(self) -> None:
+        """Record a cache hit event."""
+        with self._lock:
+            self.cache_hits += 1
+
+    def record_cache_miss(self) -> None:
+        """Record a cache miss event."""
+        with self._lock:
+            self.cache_misses += 1
+
+    def record_generation_attempt(self) -> None:
+        """Record a generation attempt."""
+        with self._lock:
+            self.total_generations += 1
+
+    def record_blocked_generation(self) -> None:
+        """Record a generation that was blocked due to safety violations."""
+        with self._lock:
+            self.blocked_generations += 1
+
+    def record_suppression_event(self) -> None:
+        """Record a token suppression event."""
+        with self._lock:
+            self.suppression_events += 1
+
+    def to_dict(self) -> dict[str, int | float]:
+        """
+        Export metrics as dictionary for logging or monitoring systems.
+
+        Returns:
+            Dict[str, Union[int, float]]: Dictionary containing all metrics.
+        """
+        with self._lock:
+            return {
+                "total_generations": self.total_generations,
+                "blocked_generations": self.blocked_generations,
+                "suppression_events": self.suppression_events,
+                "cache_hits": self.cache_hits,
+                "cache_misses": self.cache_misses,
+                "cache_hit_rate": self.cache_hit_rate,
+                "avg_safety_check_time_ms": self.avg_safety_check_time_ms,
+                "block_rate": self.block_rate,
+                "safety_check_count": self.safety_check_count,
+            }
+
+    def reset(self) -> None:
+        """Reset all metrics to zero for new measurement period."""
+        with self._lock:
+            self.total_generations = 0
+            self.blocked_generations = 0
+            self.suppression_events = 0
+            self.cache_hits = 0
+            self.cache_misses = 0
+            self.total_safety_check_time_ms = 0.0
+            self.safety_check_count = 0
+
+    def combine(self, other: SafetyMetrics) -> SafetyMetrics:
+        """
+        Combine metrics from another SafetyMetrics instance.
+
+        Args:
+            other (SafetyMetrics): Another metrics instance to combine with.
+
+        Returns:
+            SafetyMetrics: New instance with combined metrics.
+        """
+        # Use both locks in consistent order to prevent deadlocks
+        locks = sorted([self._lock, other._lock], key=lambda x: id(x))
+        with locks[0]:
+            with locks[1]:
+                return SafetyMetrics(
+                    total_generations=self.total_generations + other.total_generations,
+                    blocked_generations=self.blocked_generations + other.blocked_generations,
+                    suppression_events=self.suppression_events + other.suppression_events,
+                    cache_hits=self.cache_hits + other.cache_hits,
+                    cache_misses=self.cache_misses + other.cache_misses,
+                    total_safety_check_time_ms=self.total_safety_check_time_ms + other.total_safety_check_time_ms,
+                    safety_check_count=self.safety_check_count + other.safety_check_count,
+                )
+
+
+class SafetyChecker(ABC):
+    """
+    Abstract base class for all safety checkers.
+
+    Safety checkers are responsible for analyzing text content and detecting various types of safety violations
+    such as toxicity, bias, personally identifiable information, or other harmful content.
+    """
+
+    @abstractmethod
+    def check_safety(self, text: str | list[str], **kwargs) -> SafetyResult | list[SafetyResult]:
+        """
+        Check text(s) for safety violations.
+
+        Args:
+            text (`Union[str, List[str]]`):
+                Single text string or list of texts to check for safety violations.
+            **kwargs:
+                Additional checker-specific parameters.
+
+        Returns:
+            `Union[SafetyResult, List[SafetyResult]]`:
+                SafetyResult for single text input, List[SafetyResult] for multiple texts.
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} is an abstract class. Only classes inheriting this class can be called."
+        )
+
+    @property
+    @abstractmethod
+    def supported_categories(self) -> list[str]:
+        """
+        Return list of safety categories this checker supports.
+
+        Returns:
+            `List[str]`: List of supported safety categories (e.g., ["toxicity", "bias"]).
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} is an abstract class. Only classes inheriting this class can be called."
+        )
+
+    def get_config(self) -> dict[str, Any]:
+        """
+        Return checker configuration for serialization.
+
+        Returns:
+            `Dict[str, Any]`: Dictionary containing the checker's configuration parameters.
+        """
+        return {"checker_type": self.__class__.__name__}
+
+
+@dataclass
+class SafetyState:
+    """
+    Tracks incremental safety checking state for efficient sequence processing.
+
+    This class maintains state information to enable efficient sliding window
+    and incremental safety checking, avoiding redundant processing of previously
+    checked content.
+
+    Args:
+        last_check_position (`int`, *optional*, defaults to `0`):
+            The position (in tokens) where the last safety check ended.
+        last_check_result (`Optional[SafetyResult]`, *optional*):
+            The result of the last safety check performed.
+        sequence_prefix (`str`, *optional*, defaults to `""`):
+            The text prefix that has already been checked for safety.
+        is_safe_so_far (`bool`, *optional*, defaults to `True`):
+            Whether the sequence has been safe up to the last check position.
+        window_start_position (`int`, *optional*, defaults to `0`):
+            The starting position of the current sliding window.
+    """
+
+    last_check_position: int = 0
+    last_check_result: SafetyResult | None = None
+    sequence_prefix: str = ""
+    is_safe_so_far: bool = True
+    window_start_position: int = 0
+
+    def should_check_incremental(self, current_position: int, min_new_tokens: int = 5) -> bool:
+        """
+        Determine if an incremental safety check should be performed.
+
+        Args:
+            current_position (`int`):
+                Current position in the sequence (in tokens).
+            min_new_tokens (`int`, *optional*, defaults to `5`):
+                Minimum number of new tokens before triggering a new check.
+
+        Returns:
+            `bool`: True if a new safety check should be performed.
+        """
+        # Always check if this is the first check
+        if self.last_check_position == 0:
+            return True
+
+        # Check if enough new tokens have been added
+        new_tokens = current_position - self.last_check_position
+        return new_tokens >= min_new_tokens
+
+    def update_check_result(self, position: int, result: SafetyResult, sequence_prefix: str = "") -> None:
+        """
+        Update the state with a new safety check result.
+
+        Args:
+            position (`int`):
+                The position where this check ended.
+            result (`SafetyResult`):
+                The safety check result.
+            sequence_prefix (`str`, *optional*, defaults to `""`):
+                The sequence prefix that was checked.
+        """
+        self.last_check_position = position
+        self.last_check_result = result
+        self.sequence_prefix = sequence_prefix
+        self.is_safe_so_far = result.is_safe if result else True
+
+    def get_incremental_text(self, full_text: str, sliding_window_size: int = -1) -> tuple[str, int]:
+        """
+        Extract the portion of text that needs incremental checking.
+
+        Args:
+            full_text (`str`):
+                The complete sequence text.
+            sliding_window_size (`int`, *optional*, defaults to `-1`):
+                Size of sliding window in characters. -1 means no sliding window.
+
+        Returns:
+            `tuple[str, int]`: The text portion to check and its start position.
+        """
+        if sliding_window_size == -1:
+            # No sliding window - return text from last check position
+            if len(self.sequence_prefix) > 0:
+                # Find where we left off and return remaining text
+                remaining_text = full_text[len(self.sequence_prefix) :]
+                return self.sequence_prefix + remaining_text, 0
+            return full_text, 0
+        # Use sliding window
+        if len(full_text) <= sliding_window_size:
+            return full_text, 0
+        window_start = max(0, len(full_text) - sliding_window_size)
+        self.window_start_position = window_start
+        return full_text[window_start:], window_start
+
+    def reset(self) -> None:
+        """Reset the safety state for a new sequence."""
+        self.last_check_position = 0
+        self.last_check_result = None
+        self.sequence_prefix = ""
+        self.is_safe_so_far = True
+        self.window_start_position = 0
diff --git a/src/transformers/generation/safety/configuration.py b/src/transformers/generation/safety/configuration.py
new file mode 100644
index 000000000000..68aa0ca5d2b4
--- /dev/null
+++ b/src/transformers/generation/safety/configuration.py
@@ -0,0 +1,324 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import warnings
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+
+if TYPE_CHECKING:
+    from .base import SafetyChecker
+
+
+# Constants for validation warnings
+WARNING_CACHE_SIZE_LIMIT = 10000
+WARNING_UNSAFE_HASH_LIMIT = 100000
+
+
+@dataclass
+class SafetyConfig:
+    """
+    Configuration for safety checking in text generation.
+
+    This configuration class stores settings for safety checking and accepts a user-provided
+    safety checker instance. The transformers library provides the infrastructure
+    (SafetyChecker abstract base, processors, configuration), while users implement
+    concrete checkers for their specific safety requirements.
+
+    Args:
+        enabled (`bool`, *optional*, defaults to `False`):
+            Whether safety checking is enabled.
+        checker (`SafetyChecker`, *optional*, defaults to `None`):
+            The safety checker instance to use. Must be provided by the user.
+            See examples/safe_generation/ for reference implementations.
+        device (`str`, *optional*):
+            Device to run models on. If None, automatically selects CUDA if available.
+        cache_size (`int`, *optional*, defaults to `100`):
+            Maximum number of safety check results to cache. Larger values use more memory
+            but can improve performance for repetitive content.
+        unsafe_hash_limit (`int`, *optional*, defaults to `1000`):
+            Maximum number of unsafe sequence hashes to remember. Prevents memory leaks
+            in long-running applications with many unsafe sequences.
+        sliding_window_size (`int`, *optional*, defaults to `512`):
+            Maximum number of tokens to check for safety instead of the full sequence.
+            Helps improve performance for long sequences while maintaining safety effectiveness.
+            Set to -1 to disable sliding window (check full sequence).
+        incremental_checking (`bool`, *optional*, defaults to `True`):
+            Whether to enable incremental safety checking that tracks state between checks
+            to avoid redundant processing. Improves performance for long generations.
+        return_violations (`bool`, *optional*, defaults to `False`):
+            Whether to return detailed violation information in results.
+        return_metadata (`bool`, *optional*, defaults to `False`):
+            Whether to return additional metadata in results.
+
+    Examples:
+    ```python
+    # Using a reference implementation from examples directory
+    # Note: You need to add examples/ to your Python path first:
+    import sys
+    from pathlib import Path
+    sys.path.insert(0, str(Path("examples")))
+
+    from safe_generation import BasicToxicityChecker
+    from transformers.generation.safety import SafetyConfig
+
+    # Create checker instance
+    checker = BasicToxicityChecker(threshold=0.7)
+
+    # Option 1: Create config with from_checker() (recommended)
+    config = SafetyConfig.from_checker(checker)
+
+    # Option 2: Create config directly
+    config = SafetyConfig(enabled=True, checker=checker)
+
+    # Use with generation
+    from transformers import pipeline
+    pipe = pipeline("text-generation", model="gpt2", safety_config=config)
+    ```
+    """
+
+    # Checker configuration
+    enabled: bool = False
+    checker: SafetyChecker | None = None
+
+    # Device configuration
+    device: str | None = None
+
+    # Performance configuration
+    cache_size: int = 100
+    unsafe_hash_limit: int = 1000
+    sliding_window_size: int = 512
+    incremental_checking: bool = True
+    prefix_lengths: list[int] = field(default_factory=lambda: [100, 75, 50])
+    min_text_length_for_prefix: int = 50
+
+    # Output configuration
+    return_violations: bool = False
+    return_metadata: bool = False
+
+    def __post_init__(self):
+        """Perform immediate validation after initialization."""
+        # Basic type checking for critical parameters
+        if not isinstance(self.cache_size, int):
+            raise TypeError(f"cache_size must be an integer, got {type(self.cache_size).__name__}")
+
+        if not isinstance(self.unsafe_hash_limit, int):
+            raise TypeError(f"unsafe_hash_limit must be an integer, got {type(self.unsafe_hash_limit).__name__}")
+
+        # Range validation
+        if self.cache_size < 1:
+            raise ValueError("cache_size must be a positive integer")
+
+        if self.unsafe_hash_limit < 1:
+            raise ValueError("unsafe_hash_limit must be a positive integer")
+
+        # Validate sliding window size
+        if not isinstance(self.sliding_window_size, int):
+            raise TypeError(f"sliding_window_size must be an integer, got {type(self.sliding_window_size).__name__}")
+
+        if self.sliding_window_size < -1 or self.sliding_window_size == 0:
+            raise ValueError("sliding_window_size must be a positive integer or -1 to disable")
+
+        # Validate incremental checking
+        if not isinstance(self.incremental_checking, bool):
+            raise TypeError(f"incremental_checking must be a boolean, got {type(self.incremental_checking).__name__}")
+
+        # Validate prefix configuration
+        if not isinstance(self.prefix_lengths, list):
+            raise TypeError(f"prefix_lengths must be a list, got {type(self.prefix_lengths).__name__}")
+
+        if not all(isinstance(length, int) and length > 0 for length in self.prefix_lengths):
+            raise ValueError("All prefix_lengths must be positive integers")
+
+        if not isinstance(self.min_text_length_for_prefix, int) or self.min_text_length_for_prefix < 1:
+            raise ValueError("min_text_length_for_prefix must be a positive integer")
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert to dictionary for serialization.
+
+        Note: The checker instance is not serialized. You must recreate it when
+        deserializing.
+
+        Returns:
+            `Dict[str, Any]`: Dictionary representation of the configuration.
+        """
+        return {
+            "enabled": self.enabled,
+            "device": self.device,
+            "cache_size": self.cache_size,
+            "unsafe_hash_limit": self.unsafe_hash_limit,
+            "sliding_window_size": self.sliding_window_size,
+            "incremental_checking": self.incremental_checking,
+            "prefix_lengths": self.prefix_lengths,
+            "min_text_length_for_prefix": self.min_text_length_for_prefix,
+            "return_violations": self.return_violations,
+            "return_metadata": self.return_metadata,
+            # Note: checker is not serialized - must be provided when deserializing
+        }
+
+    @classmethod
+    def from_dict(cls, config_dict: dict[str, Any]) -> SafetyConfig:
+        """
+        Create SafetyConfig from dictionary.
+
+        Args:
+            config_dict (`Dict[str, Any]`): Dictionary containing configuration parameters.
+
+        Returns:
+            `SafetyConfig`: Instance created from the dictionary.
+        """
+        return cls(**config_dict)
+
+    def validate(self) -> None:
+        """
+        Validate configuration parameters.
+
+        Raises:
+            ValueError: If any configuration parameter is invalid.
+        """
+        # Validate enabled is boolean
+        if not isinstance(self.enabled, bool):
+            raise ValueError("enabled must be a boolean")
+
+        # Warn about potentially inefficient configurations (validation done in __post_init__)
+        if self.cache_size > WARNING_CACHE_SIZE_LIMIT:
+            warnings.warn(
+                f"cache_size > {WARNING_CACHE_SIZE_LIMIT} may use excessive memory", UserWarning, stacklevel=2
+            )
+
+        if self.unsafe_hash_limit > WARNING_UNSAFE_HASH_LIMIT:
+            warnings.warn(
+                f"unsafe_hash_limit > {WARNING_UNSAFE_HASH_LIMIT} may use excessive memory", UserWarning, stacklevel=2
+            )
+
+        # Validate output configuration
+        if not isinstance(self.return_violations, bool):
+            raise ValueError("return_violations must be a boolean")
+
+        if not isinstance(self.return_metadata, bool):
+            raise ValueError("return_metadata must be a boolean")
+
+    def construct_checker(self) -> SafetyChecker:
+        """
+        Retrieve the safety checker from the configuration.
+
+        Returns the user-provided checker instance that was specified when creating
+        the configuration.
+
+        Returns:
+            `SafetyChecker`: The safety checker instance.
+
+        Raises:
+            ValueError: If no checker instance is provided.
+
+        Examples:
+        ```python
+        # See examples/safe_generation/ for reference implementations
+        import sys
+        from pathlib import Path
+        sys.path.insert(0, str(Path("examples")))
+
+        from safe_generation import BasicToxicityChecker
+        from transformers.generation.safety import SafetyConfig
+
+        # Create checker
+        checker = BasicToxicityChecker(threshold=0.7)
+
+        # Create config with checker
+        config = SafetyConfig.from_checker(checker)
+
+        # Construct checker (returns the same instance)
+        safety_checker = config.construct_checker()
+        ```
+        """
+        if self.checker is None:
+            raise ValueError(
+                "SafetyConfig requires a checker instance. "
+                "You must provide a SafetyChecker when creating the configuration. "
+                "See examples/safe_generation/ for reference implementations:\n\n"
+                "  from examples.safe_generation import BasicToxicityChecker\n"
+                "  checker = BasicToxicityChecker(threshold=0.7)\n"
+                "  config = SafetyConfig.from_checker(checker)\n\n"
+                "Or implement your own custom checker by inheriting from SafetyChecker."
+            )
+        return self.checker
+
+    @classmethod
+    def from_checker(cls, checker: SafetyChecker, **kwargs) -> SafetyConfig:
+        """
+        Create a SafetyConfig from a safety checker instance.
+
+        This is the recommended way to create a SafetyConfig.
+
+        Args:
+            checker (`SafetyChecker`): The safety checker instance to use.
+            **kwargs: Additional configuration parameters to override defaults.
+
+        Returns:
+            `SafetyConfig`: A SafetyConfig instance with the provided checker.
+
+        Examples:
+        ```python
+        # See examples/safe_generation/ for reference implementations
+        import sys
+        from pathlib import Path
+        sys.path.insert(0, str(Path("examples")))
+
+        from safe_generation import BasicToxicityChecker
+        from transformers.generation.safety import SafetyConfig
+
+        # Create checker
+        checker = BasicToxicityChecker(threshold=0.7)
+
+        # Create config from checker
+        config = SafetyConfig.from_checker(checker)
+
+        # With additional parameters
+        config = SafetyConfig.from_checker(
+            checker,
+            cache_size=200,
+            return_violations=True
+        )
+        ```
+        """
+        return cls(enabled=True, checker=checker, **kwargs)
+
+
+# Preset configuration kwargs for convenience
+# These replace the deprecated create_default() method
+# Usage: SafetyConfig.from_checker(checker, **STRICT_PRESET)
+
+STRICT_PRESET = {
+    "cache_size": 50,
+    "unsafe_hash_limit": 500,
+    "return_violations": True,
+    "return_metadata": True,
+}
+
+MODERATE_PRESET = {
+    "cache_size": 100,
+    "unsafe_hash_limit": 1000,
+    "return_violations": False,
+    "return_metadata": False,
+}
+
+LENIENT_PRESET = {
+    "cache_size": 200,
+    "unsafe_hash_limit": 2000,
+    "return_violations": False,
+    "return_metadata": False,
+}
diff --git a/src/transformers/generation/safety/processors.py b/src/transformers/generation/safety/processors.py
new file mode 100644
index 000000000000..470661d20692
--- /dev/null
+++ b/src/transformers/generation/safety/processors.py
@@ -0,0 +1,776 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import time
+from collections import OrderedDict
+
+import torch
+
+from ..logits_process import LogitsProcessor
+from ..stopping_criteria import StoppingCriteria
+from .base import SafetyChecker, SafetyMetrics, SafetyResult, SafetyState, SafetyViolation
+from .configuration import SafetyConfig
+
+
+logger = logging.getLogger(__name__)
+
+# Configuration constants
+DEFAULT_CACHE_SIZE = 100
+DEFAULT_UNSAFE_HASH_LIMIT = 1000
+DEFAULT_CHECK_INTERVAL = 1
+
+
+class _SafetyCache:
+    """Simple LRU cache for safety check results."""
+
+    def __init__(self, max_size: int = DEFAULT_CACHE_SIZE):
+        self.max_size = max_size
+        self._cache = OrderedDict()
+
+    def get(self, text: str, use_prefix_matching: bool = False):
+        """
+        Get cached result and move to end for LRU.
+
+        Args:
+            text: Text to look up (will be hashed to create cache key)
+            use_prefix_matching: Ignored for simple cache (only supported by prefix cache)
+
+        Returns:
+            SafetyResult if found, None otherwise
+        """
+        key = _generate_cache_key(text)
+        if key in self._cache:
+            value = self._cache.pop(key)
+            self._cache[key] = value
+            return value
+        return None
+
+    def put(self, text: str, value) -> None:
+        """
+        Put result in cache with LRU eviction.
+
+        Args:
+            text: The text that was checked (will be hashed to create cache key)
+            value: The SafetyResult to store
+        """
+        key = _generate_cache_key(text)
+        if len(self._cache) >= self.max_size:
+            self._cache.popitem(last=False)
+        self._cache[key] = value
+
+    def __contains__(self, text: str) -> bool:
+        """Check if text exists in cache."""
+        key = _generate_cache_key(text)
+        return key in self._cache
+
+
+class _PrefixSafetyCache:
+    """
+    Advanced caching system that supports prefix-based caching for efficient sequence checking.
+
+    This cache can reuse safety results for sequences that share common prefixes,
+    significantly improving performance for incremental checking scenarios.
+    """
+
+    def __init__(
+        self,
+        max_size: int = DEFAULT_CACHE_SIZE,
+        prefix_lengths: list[int] | None = None,
+        min_text_length_for_prefix: int = 50,
+    ):
+        self.max_size = max_size
+        self.prefix_lengths = prefix_lengths if prefix_lengths is not None else [100, 75, 50]
+        self.min_text_length_for_prefix = min_text_length_for_prefix
+        self._cache = OrderedDict()  # Maps full cache keys to results
+        self._prefix_map = {}  # Maps text prefixes to cache keys that contain them
+
+    def get(self, text: str, use_prefix_matching: bool = True):
+        """
+        Get cached result, optionally using prefix matching for efficiency.
+
+        Args:
+            text: Text to look up
+            use_prefix_matching: Whether to try prefix matching if exact match fails
+
+        Returns:
+            SafetyResult if found, None otherwise
+        """
+        cache_key = _generate_cache_key(text)
+
+        # Try exact match first
+        if cache_key in self._cache:
+            result = self._cache.pop(cache_key)
+            self._cache[cache_key] = result  # Move to end for LRU
+            return result
+
+        # If prefix matching is enabled and exact match failed
+        if use_prefix_matching:
+            return self._try_prefix_match(text)
+
+        return None
+
+    def put(self, text: str, result) -> None:
+        """
+        Store result in cache with prefix indexing.
+
+        Args:
+            text: The text that was checked
+            result: The SafetyResult to store
+        """
+        cache_key = _generate_cache_key(text)
+
+        # Evict oldest if at capacity
+        if len(self._cache) >= self.max_size:
+            old_key, _ = self._cache.popitem(last=False)
+            self._cleanup_prefix_references(old_key)
+
+        # Store result
+        self._cache[cache_key] = result
+
+        # Update prefix mapping for common prefixes
+        if len(text) > self.min_text_length_for_prefix:  # Only index prefixes for longer texts
+            # Use the longest configured prefix length that's not larger than half the text
+            max_prefix_length = max([length for length in self.prefix_lengths if length <= len(text) // 2], default=0)
+            if max_prefix_length > 0:
+                prefix = text[:max_prefix_length]
+                prefix_key = _generate_cache_key(prefix)
+
+                if prefix_key not in self._prefix_map:
+                    self._prefix_map[prefix_key] = set()
+                self._prefix_map[prefix_key].add(cache_key)
+
+    def _try_prefix_match(self, text: str):
+        """
+        Try to find a cached result for a prefix of the given text.
+
+        This is useful when we have cached results for shorter versions of the sequence.
+        """
+        if len(text) < self.min_text_length_for_prefix:  # Don't use prefix matching for very short texts
+            return None
+
+        # Try progressively shorter prefixes from configuration
+        for prefix_len in sorted(self.prefix_lengths, reverse=True):
+            if len(text) <= prefix_len:
+                continue
+
+            prefix = text[:prefix_len]
+            prefix_key = _generate_cache_key(prefix)
+
+            if prefix_key in self._prefix_map:
+                # Found potential matches - check if any are safe
+                for candidate_key in self._prefix_map[prefix_key]:
+                    if candidate_key in self._cache:
+                        result = self._cache[candidate_key]
+                        # Only reuse if the cached result was safe
+                        # (unsafe results might not apply to the longer sequence)
+                        if result.is_safe:
+                            # Move to end for LRU
+                            self._cache.move_to_end(candidate_key)
+                            return result
+
+        return None
+
+    def _cleanup_prefix_references(self, removed_cache_key: str) -> None:
+        """Remove references to evicted cache keys from prefix mapping."""
+        keys_to_remove = []
+        for prefix_key, cache_keys in self._prefix_map.items():
+            if removed_cache_key in cache_keys:
+                cache_keys.discard(removed_cache_key)
+                if not cache_keys:  # No more references
+                    keys_to_remove.append(prefix_key)
+
+        for key in keys_to_remove:
+            del self._prefix_map[key]
+
+    def __contains__(self, text: str) -> bool:
+        """Check if text exists in cache."""
+        cache_key = _generate_cache_key(text)
+        return cache_key in self._cache
+
+
+def _generate_cache_key(text: str) -> str:
+    """
+    Generate a SHA-256 based cache key for text content.
+
+    Uses length prefix for quick rejection of different-sized texts,
+    followed by SHA-256 hash for collision-resistant uniqueness.
+
+    Args:
+        text (str): The text content to generate a cache key for.
+
+    Returns:
+        str: A cache key in the format "length:hash"
+    """
+    text_hash = hashlib.sha256(text.encode("utf-8")).hexdigest()
+    return f"{len(text)}:{text_hash}"
+
+
+class _SlidingWindowSafetyMixin:
+    """
+    Shared functionality for sliding window safety processing.
+
+    This mixin provides common methods for both SafetyLogitsProcessor and
+    SafetyStoppingCriteria to handle sliding window text extraction,
+    incremental checking, and cache management.
+    """
+
+    def _get_text_to_check(self, full_text: str, safety_state: SafetyState) -> tuple[str, int]:
+        """
+        Determine what text to check based on sliding window and incremental settings.
+
+        Args:
+            full_text: The complete sequence text
+            safety_state: The safety state for this sequence
+
+        Returns:
+            tuple[str, int]: Text to check and window start position
+        """
+        if self.incremental_checking:
+            # Use incremental checking with sliding window
+            return safety_state.get_incremental_text(
+                full_text, self.sliding_window_size if self.sliding_window_size > 0 else -1
+            )
+        # Use sliding window without incremental state
+        if self.sliding_window_size > 0 and len(full_text) > self.sliding_window_size:
+            # Extract sliding window (character-based approximation)
+            text_to_check = full_text[-self.sliding_window_size :]
+            window_start = len(full_text) - self.sliding_window_size
+            return text_to_check, window_start
+        return full_text, 0
+
+    def _should_skip_safety_check(
+        self, safety_state: SafetyState, current_position: int, min_new_tokens: int = 5
+    ) -> tuple[bool, SafetyResult]:
+        """
+        Determine if we should skip the safety check and return cached result.
+
+        Args:
+            safety_state: The safety state for this sequence
+            current_position: Current position in tokens
+            min_new_tokens: Minimum tokens required for new check
+
+        Returns:
+            tuple[bool, SafetyResult]: Whether to skip check and result to use if skipping
+        """
+        if not self.incremental_checking:
+            return False, None
+
+        if not safety_state.should_check_incremental(current_position, min_new_tokens):
+            # Use previous result if available
+            safety_result = safety_state.last_check_result
+            if safety_result is not None:
+                return True, safety_result
+        return False, None
+
+    def _perform_safety_check(self, text_to_check: str) -> SafetyResult:
+        """
+        Perform safety check with caching and error handling.
+
+        Args:
+            text_to_check: Text to check for safety violations
+
+        Returns:
+            SafetyResult: Result of the safety check
+        """
+        # Check cache first (unified interface)
+        safety_result = self._sequence_cache.get(text_to_check, use_prefix_matching=True)
+
+        if safety_result is not None:
+            self._metrics.record_cache_hit()
+            return safety_result
+
+        # Check safety of text portion - record timing
+        start_time = time.perf_counter()
+        try:
+            safety_result = self.safety_checker.check_safety(text_to_check)
+        except Exception as e:
+            logger.warning(f"Safety check failed for sequence: {e}")
+            # Fail safe - assume unsafe if we can't check
+            safety_result = SafetyResult(
+                is_safe=False,
+                confidence=0.0,
+                violations=[SafetyViolation("unknown", 0.0, "high", "Safety check failed")],
+                metadata={"error": str(e)},
+            )
+
+        # Record timing and cache miss
+        end_time = time.perf_counter()
+        check_time_ms = (end_time - start_time) * 1000
+        self._metrics.record_safety_check(check_time_ms)
+        self._metrics.record_cache_miss()
+
+        # Cache the result
+        self._sequence_cache.put(text_to_check, safety_result)
+        return safety_result
+
+    def _update_safety_state(
+        self,
+        safety_state: SafetyState,
+        current_position: int,
+        safety_result: SafetyResult,
+        text_to_check: str,
+        window_start: int,
+        full_text: str,
+    ) -> None:
+        """
+        Update safety state with new check result if using incremental checking.
+
+        Args:
+            safety_state: The safety state to update
+            current_position: Current position in sequence
+            safety_result: Result from safety check
+            text_to_check: Text that was checked
+            window_start: Start position of the window
+            full_text: Complete sequence text
+        """
+        if self.incremental_checking:
+            safety_state.update_check_result(
+                current_position, safety_result, text_to_check if window_start == 0 else full_text
+            )
+
+
+class SafetyLogitsProcessor(LogitsProcessor, _SlidingWindowSafetyMixin):
+    """
+    [`LogitsProcessor`] that blocks generation when unsafe content is detected.
+
+    This processor checks the current sequence for safety violations and blocks
+    further generation by suppressing all tokens when unsafe content is detected.
+    It integrates with the transformers safety framework to provide real-time
+    content blocking.
+
+    Args:
+        safety_checker ([`SafetyChecker`]):
+            The safety checker to use for content evaluation.
+        tokenizer ([`PreTrainedTokenizer`]):
+            The tokenizer used for decoding sequences.
+        safety_config ([`SafetyConfig`]):
+            Configuration for safety checking.
+        check_interval (`int`, *optional*, defaults to 1):
+            Check safety every N tokens. Must be positive.
+        suppress_threshold (`float`, *optional*, defaults to negative infinity):
+            Logit value for suppressing unsafe tokens.
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+    >>> from transformers.generation.safety import SafetyLogitsProcessor, SafetyConfig
+    >>> from examples.safe_generation import BasicToxicityChecker
+
+    >>> # Initialize model and tokenizer
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> tokenizer.pad_token = tokenizer.eos_token
+
+    >>> # Create safety checker and config
+    >>> safety_checker = BasicToxicityChecker()
+    >>> safety_config = SafetyConfig.from_checker(safety_checker)
+    >>> safety_processor = SafetyLogitsProcessor(
+    ...     safety_checker=safety_checker,
+    ...     tokenizer=tokenizer,
+    ...     safety_config=safety_config
+    ... )
+
+    >>> # Generate with safety filtering
+    >>> inputs = tokenizer("Tell me about", return_tensors="pt")
+    >>> outputs = model.generate(
+    ...     **inputs,
+    ...     logits_processor=[safety_processor],
+    ...     max_new_tokens=50,
+    ...     do_sample=True
+    ... )
+    >>> generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    ```
+    """
+
+    def __init__(
+        self,
+        safety_checker: SafetyChecker,
+        tokenizer,
+        safety_config: SafetyConfig,
+        check_interval: int = 1,
+        suppress_threshold: float = -float("inf"),
+    ):
+        """
+        Initialize the SafetyLogitsProcessor.
+
+        Args:
+            safety_checker: The safety checker to use for content evaluation
+            tokenizer: The tokenizer used for decoding sequences
+            safety_config: Configuration for safety checking
+            check_interval: Check safety every N tokens (default: 1, must be positive)
+            suppress_threshold: Logit value for suppressing unsafe tokens
+
+        Raises:
+            ValueError: If check_interval is not positive
+        """
+        # Input validation
+        if not isinstance(check_interval, int) or check_interval < 1:
+            raise ValueError(f"check_interval must be a positive integer, got {check_interval}")
+
+        self.safety_checker = safety_checker
+        self.tokenizer = tokenizer
+        self.safety_config = safety_config
+        self.check_interval = check_interval
+        self.suppress_threshold = suppress_threshold
+        self._step_count = 0
+
+        # Initialize sliding window and incremental checking
+        self._safety_states = {}  # Track safety state per sequence in the batch
+        self.sliding_window_size = getattr(safety_config, "sliding_window_size", 512)
+        self.incremental_checking = getattr(safety_config, "incremental_checking", True)
+
+        # Initialize cache with configured size (use prefix cache if incremental checking is enabled)
+        cache_size = getattr(safety_config, "cache_size", DEFAULT_CACHE_SIZE)
+        if self.incremental_checking:
+            prefix_lengths = getattr(safety_config, "prefix_lengths", [100, 75, 50])
+            min_text_length_for_prefix = getattr(safety_config, "min_text_length_for_prefix", 50)
+            self._sequence_cache = _PrefixSafetyCache(
+                max_size=cache_size,
+                prefix_lengths=prefix_lengths,
+                min_text_length_for_prefix=min_text_length_for_prefix,
+            )  # Advanced prefix-aware cache
+        else:
+            self._sequence_cache = _SafetyCache(max_size=cache_size)  # Simple LRU cache
+        self._metrics = SafetyMetrics()  # Initialize metrics collection
+
+    def _apply_token_suppression(self, scores: torch.FloatTensor, batch_idx: int, safety_result: SafetyResult) -> None:
+        """
+        Apply token suppression for unsafe content.
+
+        Args:
+            scores: Token scores tensor to modify
+            batch_idx: Index in the batch
+            safety_result: Safety check result
+        """
+        if not safety_result.is_safe:
+            tokens_to_suppress = self._get_tokens_to_suppress(scores[batch_idx], safety_result)
+            if len(tokens_to_suppress) > 0:
+                device = scores.device
+                if isinstance(tokens_to_suppress, list):
+                    tokens_to_suppress = torch.tensor(tokens_to_suppress, device=device)
+                scores[batch_idx, tokens_to_suppress] = self.suppress_threshold
+                self._metrics.record_suppression_event()
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+        """
+        Apply safety filtering to token scores.
+
+        Args:
+            input_ids: Current sequence tokens [batch_size, seq_len]
+            scores: Token logits [batch_size, vocab_size]
+
+        Returns:
+            Modified scores with unsafe tokens suppressed
+        """
+        self._step_count += 1
+
+        # Only check safety at specified intervals for performance
+        if self._step_count % self.check_interval != 0:
+            return scores
+
+        batch_size = input_ids.shape[0]
+        modified_scores = scores.clone()
+
+        for i in range(batch_size):
+            # Convert current sequence to text
+            current_sequence = input_ids[i]
+            full_text = self.tokenizer.decode(current_sequence, skip_special_tokens=True)
+
+            # Get or create safety state for this sequence
+            seq_id = i
+            if seq_id not in self._safety_states:
+                self._safety_states[seq_id] = SafetyState()
+            safety_state = self._safety_states[seq_id]
+
+            # Determine what text to check based on configuration
+            text_to_check, window_start = self._get_text_to_check(full_text, safety_state)
+            current_position = len(current_sequence)
+
+            # Check if we can skip the safety check and reuse previous result
+            should_skip, cached_result = self._should_skip_safety_check(safety_state, current_position)
+            if should_skip:
+                self._apply_token_suppression(modified_scores, i, cached_result)
+                continue
+
+            # Perform safety check with caching and error handling
+            safety_result = self._perform_safety_check(text_to_check)
+
+            # Update safety state if using incremental checking
+            self._update_safety_state(
+                safety_state, current_position, safety_result, text_to_check, window_start, full_text
+            )
+
+            # Apply suppression if content is unsafe
+            self._apply_token_suppression(modified_scores, i, safety_result)
+
+            # Debug logging for unsafe content
+            if not safety_result.is_safe:
+                logger.debug(f"Suppressed tokens using blocking strategy for sequence: {full_text[:50]}...")
+
+        return modified_scores
+
+    def _get_tokens_to_suppress(self, scores: torch.FloatTensor, safety_result) -> torch.Tensor:
+        """
+        Determine which tokens to suppress when unsafe content is detected.
+
+        Args:
+            scores: Token logits for a single sequence [vocab_size]
+            safety_result: The safety assessment result (unused for blocking strategy)
+
+        Returns:
+            Tensor of token indices to suppress (all tokens for blocking)
+        """
+        # Block strategy: suppress all tokens to force generation to stop
+        return torch.arange(scores.size(0), device=scores.device)
+
+    def get_metrics(self) -> SafetyMetrics:
+        """
+        Return current metrics for this processor.
+
+        Returns:
+            SafetyMetrics: Current metrics collection.
+        """
+        return self._metrics
+
+    def reset_safety_states(self) -> None:
+        """
+        Reset all safety states. Call this when starting a new generation batch.
+        """
+        self._safety_states.clear()
+
+    def _get_text_for_safety_check(self, full_text: str, safety_state: SafetyState) -> tuple[str, int]:
+        """
+        Extract the appropriate text portion for safety checking.
+
+        Args:
+            full_text: The complete sequence text
+            safety_state: Current safety state for incremental checking
+
+        Returns:
+            tuple[str, int]: Text to check and its starting position
+        """
+        if self.incremental_checking:
+            return safety_state.get_incremental_text(
+                full_text, self.sliding_window_size if self.sliding_window_size > 0 else -1
+            )
+        # Simple sliding window without incremental state
+        if self.sliding_window_size > 0 and len(full_text) > self.sliding_window_size:
+            window_start = len(full_text) - self.sliding_window_size
+            return full_text[window_start:], window_start
+        return full_text, 0
+
+
+class SafetyStoppingCriteria(StoppingCriteria, _SlidingWindowSafetyMixin):
+    """
+    [`StoppingCriteria`] that halts generation when unsafe content is detected.
+
+    This provides a sequence-level safety check that can stop generation before
+    unsafe content is returned to the user. It works as a final safety gate
+    after token-level filtering by SafetyLogitsProcessor.
+
+    Args:
+        safety_checker ([`SafetyChecker`]):
+            The safety checker to use for content evaluation.
+        tokenizer ([`PreTrainedTokenizer`]):
+            The tokenizer used for decoding sequences.
+        safety_config ([`SafetyConfig`]):
+            Configuration for safety checking.
+        check_final_only (`bool`, *optional*, defaults to `False`):
+            If True, only check safety on the final call (when all sequences are complete).
+            If False, check safety on every call during generation.
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoTokenizer, AutoModelForCausalLM
+    >>> from transformers.generation.safety import SafetyStoppingCriteria, SafetyConfig
+    >>> from examples.safe_generation import BasicToxicityChecker
+
+    >>> # Initialize model and tokenizer
+    >>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+    >>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+    >>> tokenizer.pad_token = tokenizer.eos_token
+
+    >>> # Create safety checker and config
+    >>> safety_checker = BasicToxicityChecker()
+    >>> safety_config = SafetyConfig.from_checker(safety_checker)
+    >>> safety_stopping = SafetyStoppingCriteria(
+    ...     safety_checker=safety_checker,
+    ...     tokenizer=tokenizer,
+    ...     safety_config=safety_config
+    ... )
+
+    >>> # Generate with safety stopping
+    >>> inputs = tokenizer("Tell me about", return_tensors="pt")
+    >>> outputs = model.generate(
+    ...     **inputs,
+    ...     stopping_criteria=[safety_stopping],
+    ...     max_new_tokens=50,
+    ...     do_sample=True
+    ... )
+    >>> generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    ```
+    """
+
+    def __init__(
+        self, safety_checker: SafetyChecker, tokenizer, safety_config: SafetyConfig, check_final_only: bool = False
+    ):
+        """
+        Initialize the SafetyStoppingCriteria.
+
+        Args:
+            safety_checker: The safety checker to use for content evaluation
+            tokenizer: The tokenizer used for decoding sequences
+            safety_config: Configuration for safety checking
+            check_final_only: If True, only check when generation is complete
+
+        Raises:
+            ValueError: If safety_checker is None
+        """
+        if safety_checker is None:
+            raise ValueError("safety_checker cannot be None")
+
+        self.safety_checker = safety_checker
+        self.tokenizer = tokenizer
+        self.safety_config = safety_config
+        self.check_final_only = check_final_only
+        self._unsafe_sequence_hashes = OrderedDict()  # Track unsafe sequences by content hash (LRU)
+
+        # Initialize sliding window and incremental checking
+        self._safety_states = {}  # Track safety state per sequence in the batch
+        self.sliding_window_size = getattr(safety_config, "sliding_window_size", 512)
+        self.incremental_checking = getattr(safety_config, "incremental_checking", True)
+
+        # Initialize cache with configured size (use prefix cache if incremental checking is enabled)
+        cache_size = getattr(safety_config, "cache_size", DEFAULT_CACHE_SIZE)
+        if self.incremental_checking:
+            prefix_lengths = getattr(safety_config, "prefix_lengths", [100, 75, 50])
+            min_text_length_for_prefix = getattr(safety_config, "min_text_length_for_prefix", 50)
+            self._sequence_cache = _PrefixSafetyCache(
+                max_size=cache_size,
+                prefix_lengths=prefix_lengths,
+                min_text_length_for_prefix=min_text_length_for_prefix,
+            )  # Advanced prefix-aware cache
+        else:
+            self._sequence_cache = _SafetyCache(max_size=cache_size)  # Simple LRU cache
+        # Get configured unsafe hash limit
+        self._unsafe_hash_limit = getattr(safety_config, "unsafe_hash_limit", DEFAULT_UNSAFE_HASH_LIMIT)
+        self._metrics = SafetyMetrics()  # Initialize metrics collection
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
+        """
+        Check if generation should stop due to safety violations.
+
+        Args:
+            input_ids: Current sequences [batch_size, seq_len]
+            scores: Token scores [batch_size, vocab_size]
+
+        Returns:
+            Boolean tensor indicating which sequences should stop [batch_size]
+        """
+        batch_size = input_ids.shape[0]
+
+        # Record generation attempts for metrics
+        for _ in range(batch_size):
+            self._metrics.record_generation_attempt()
+
+        # Initialize should_stop tensor
+        should_stop = torch.zeros(batch_size, dtype=torch.bool, device=input_ids.device)
+
+        # If check_final_only is True, skip safety checks during generation
+        # This will be handled by other safety mechanisms or post-generation checks
+        if self.check_final_only and not kwargs.get("is_final_call", False):
+            return should_stop
+
+        # Check each sequence for safety violations
+        for i in range(batch_size):
+            # Convert current sequence to text
+            current_sequence = input_ids[i]
+            full_text = self.tokenizer.decode(current_sequence, skip_special_tokens=True)
+
+            # Get or create safety state for this sequence
+            seq_id = i
+            if seq_id not in self._safety_states:
+                self._safety_states[seq_id] = SafetyState()
+            safety_state = self._safety_states[seq_id]
+
+            # Determine what text to check based on configuration
+            text_to_check, window_start = self._get_text_to_check(full_text, safety_state)
+            current_position = len(current_sequence)
+
+            # Check if we can skip the safety check and reuse previous result (less aggressive for stopping)
+            should_skip, cached_result = self._should_skip_safety_check(
+                safety_state, current_position, min_new_tokens=3
+            )
+            if should_skip and cached_result is not None and not cached_result.is_safe:
+                should_stop[i] = True
+                continue
+
+            # Create sequence hash for tracking (using consistent SHA-256 hash)
+            sequence_hash = _generate_cache_key(text_to_check)
+
+            # Check if this sequence content is already known to be unsafe (LRU)
+            if sequence_hash in self._unsafe_sequence_hashes:
+                # Move to end for LRU behavior
+                self._unsafe_sequence_hashes.move_to_end(sequence_hash)
+                should_stop[i] = True
+                continue
+
+            # Perform safety check with caching and error handling
+            safety_result = self._perform_safety_check(text_to_check)
+
+            # Update safety state if using incremental checking
+            self._update_safety_state(
+                safety_state, current_position, safety_result, text_to_check, window_start, full_text
+            )
+
+            # If unsafe, mark sequence for stopping
+            if not safety_result.is_safe:
+                # Add to unsafe hash dict with LRU eviction to prevent memory leaks
+                if len(self._unsafe_sequence_hashes) >= self._unsafe_hash_limit:
+                    # Remove oldest entry (LRU eviction)
+                    self._unsafe_sequence_hashes.popitem(last=False)
+                # Add new entry (will be at the end = most recently used)
+                self._unsafe_sequence_hashes[sequence_hash] = True  # Track by content hash
+                should_stop[i] = True
+                self._metrics.record_blocked_generation()
+
+                # Log safety violation for debugging
+                violation_categories = [v.category for v in safety_result.violations]
+                logger.warning(
+                    f"Generation stopped for sequence {i} due to safety violations: {violation_categories}. "
+                    f"Text: {full_text[:100]}..."
+                )
+
+        return should_stop
+
+    def get_metrics(self) -> SafetyMetrics:
+        """
+        Return current metrics for this stopping criteria.
+
+        Returns:
+            SafetyMetrics: Current metrics collection.
+        """
+        return self._metrics
+
+    def reset_safety_states(self) -> None:
+        """
+        Reset all safety states. Call this when starting a new generation batch.
+        """
+        self._safety_states.clear()
diff --git a/src/transformers/generation/safety/utils.py b/src/transformers/generation/safety/utils.py
new file mode 100644
index 000000000000..740af8ca5eb2
--- /dev/null
+++ b/src/transformers/generation/safety/utils.py
@@ -0,0 +1,39 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from .configuration import SafetyConfig
+
+
+def validate_safety_config(config: SafetyConfig) -> bool:
+    """
+    Validate a safety configuration and return whether it's valid.
+
+    Args:
+        config (`SafetyConfig`): Configuration to validate.
+
+    Returns:
+        `bool`: True if configuration is valid, False otherwise.
+
+    Example:
+    ```python
+    config = SafetyConfig(enabled=True, thresholds={"toxicity": 0.5})
+    if validate_safety_config(config):
+        print("Configuration is valid")
+    ```
+    """
+    try:
+        config.validate()
+        return True
+    except (ValueError, TypeError):
+        return False
diff --git a/src/transformers/generation/stopping_criteria.py b/src/transformers/generation/stopping_criteria.py
index b57136f53416..74cf29d703db 100644
--- a/src/transformers/generation/stopping_criteria.py
+++ b/src/transformers/generation/stopping_criteria.py
@@ -111,6 +111,13 @@ class StopStringCriteria(StoppingCriteria):
     This class can be used to stop generation whenever specific string sequences are generated. It preprocesses
     the strings together with the tokenizer vocab to find positions where tokens can validly complete the stop strings.
 
+    <Tip>
+
+    [`StopStringTextMatchCriteria`] and this class have equivalent functionality. This class is compatible with
+    `torch.compile`, but it's considerably slower than [`StopStringTextMatchCriteria`] when not compiled.
+
+    </Tip>
+
     Generation is stopped as soon as a token is generated that completes any of the stop strings.
     We want to catch any instance in which the stop string would be present in the decoded output, which means
     we must also catch cases with "overhangs" off one or both ends. To make this more concrete, for the stop string
@@ -137,15 +144,16 @@ class StopStringCriteria(StoppingCriteria):
     somewhere in the past input_ids.
 
     How is the match actually performed, though? We do it in quite a confusing way, because we want the entire match
-    process to be compilable with Torch or XLA, which means we cannot use standard string methods. However, it is possible,
-    with some work, to do string matching with pure tensor operations. We'll begin by describing the algorithm we use
-    with standard string operations, and then at the end we'll explain how this is converted to pure tensor operations.
+    process to be compilable with Torch or XLA, which means we cannot use standard string methods. However, it is
+    possible, with some work, to do string matching with pure tensor operations. We'll begin by describing the
+    algorithm we use with standard string operations, and then at the end we'll explain how this is converted to
+    pure tensor operations.
 
-    The key to the algorithm is an observation: Because the stop string must overlap with the end of the token sequence, we can start at
-    the end of the sequence and work backwards. Specifically, we check that there is an overlap between the start of
-    the final token and the end of the stop_string, or to put it another way, stop_string[-i:] == token[:i] for
-    some i > 0. If you look at the positive examples above, you'll see the last token in all of them fulfills this
-    property:
+    The key to the algorithm is an observation: Because the stop string must overlap with the end of the token
+    sequence, we can start at the end of the sequence and work backwards. Specifically, we check that there is
+    an overlap between the start of the final token and the end of the stop_string, or to put it another way,
+    stop_string[-i:] == token[:i] for some i > 0. If you look at the positive examples above, you'll see the last
+    token in all of them fulfills this property:
 
     - ["st", "op"] (overlap is "op", overlap length == 2)
     - ["stop"]  (overlap is "stop", overlap length == 4)
@@ -214,12 +222,16 @@ class StopStringCriteria(StoppingCriteria):
     Examples:
 
     ```python
-    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer, StoppingCriteriaList, StopStringCriteria
 
     >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
     >>> model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
     >>> inputs = tokenizer("The biggest states in the USA by land area:", return_tensors="pt")
 
+    >>> # Passing one or more stop strings will halt generation after those strings are emitted
+    >>> # Note that generating with stop strings requires you to pass the tokenizer too
+    >>> stopping_criteria = StoppingCriteriaList([StopStringCriteria(tokenizer, ["Texas"])])
+
     >>> gen_out = model.generate(**inputs)
     >>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
     The biggest states in the USA by land area:
@@ -227,9 +239,7 @@ class StopStringCriteria(StoppingCriteria):
     - Texas
     - California
 
-    >>> # Passing one or more stop strings will halt generation after those strings are emitted
-    >>> # Note that generating with stop strings requires you to pass the tokenizer too
-    >>> gen_out = model.generate(**inputs, stop_strings=["Texas"], tokenizer=tokenizer)
+    >>> gen_out = model.generate(**inputs, stopping_criteria=stopping_criteria)
     >>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
     The biggest states in the USA by land area:
     - Alaska
@@ -240,6 +250,9 @@ class StopStringCriteria(StoppingCriteria):
     def __init__(self, tokenizer: PreTrainedTokenizerBase, stop_strings: str | list[str]):
         if isinstance(stop_strings, str):
             stop_strings = [stop_strings]
+        if len(stop_strings) == 0 or any(stop_string == "" for stop_string in stop_strings):
+            raise ValueError("`stop_strings` cannot be an empty list or contain empty strings")
+
         self.stop_strings: tuple[str, ...] = tuple(stop_strings)
         vocab = tokenizer.get_vocab()
         token_list, token_indices = tuple(vocab.keys()), tuple(vocab.values())
@@ -447,6 +460,124 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwa
         return torch.any(string_matches, dim=-1)
 
 
+class StopStringTextMatchCriteria(StoppingCriteria):
+    """
+    This class can be used to stop generation whenever specific string sequences are generated. It decodes the
+    generated tokens into text and then compares it against the stop strings.
+
+    <Tip>
+
+    [`StopStringCriteria`] and this class have equivalent functionality. This class is faster than
+    [`StopStringCriteria`], but it isn't compatible with `torch.compile`.
+
+    </Tip>
+
+    Class suggested by @MaxBourdon.
+
+    Args:
+        tokenizer (`PreTrainedTokenizer`):
+            The model's associated tokenizer (necessary to extract vocab and tokenize the termination sequences)
+        stop_strings (`Union[str, list[str]]`):
+            A list of strings that should end generation. If a string is passed, it will be treated like a
+            list with a single element.
+
+    Examples:
+
+    ```python
+    >>> from transformers import AutoModelForCausalLM, AutoTokenizer
+
+    >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2")
+    >>> model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2")
+    >>> inputs = tokenizer("The biggest states in the USA by land area:", return_tensors="pt")
+
+    >>> gen_out = model.generate(**inputs)
+    >>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
+    The biggest states in the USA by land area:
+    - Alaska
+    - Texas
+    - California
+
+    >>> # Passing one or more stop strings will halt generation after those strings are emitted
+    >>> # Note that generating with stop strings requires you to pass the tokenizer too
+    >>> gen_out = model.generate(**inputs, stop_strings=["Texas"], tokenizer=tokenizer)
+    >>> print(tokenizer.batch_decode(gen_out, skip_special_tokens=True)[0])
+    The biggest states in the USA by land area:
+    - Alaska
+    - Texas
+    ```
+    """
+
+    def __init__(self, tokenizer: PreTrainedTokenizerBase, stop_strings: str | list[str]):
+        if isinstance(stop_strings, str):
+            stop_strings = [stop_strings]
+        if len(stop_strings) == 0 or any(stop_string == "" for stop_string in stop_strings):
+            raise ValueError("`stop_strings` cannot be an empty list or contain empty strings")
+
+        self.stop_strings = stop_strings
+        self.tokenizer = tokenizer
+        # We only need to compare the last `max_tail_len` chars of the generated text, `max_tail_len` being the length
+        # of the longest stop string.
+        self.max_tail_len = max(len(s) for s in self.stop_strings)
+
+    @add_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.Tensor:
+        # Initalize the returned tensor with False (should NOT stop generation). If a stop string is found, the
+        # corresponding index will be set to True.
+        should_stop = torch.zeros_like(input_ids[:, -1], dtype=torch.bool, device=input_ids.device)
+
+        # Primary check: check if the last generated text contains any of the stop strings
+        # NOTE: Depending on the tokenizer, decoding individual tokens may contain prefix symbols like "Ġ" or "##",
+        # which could derail the naive string comparison. At each step, we'll decode the latest `max_tail_len` tokens
+        # **together** (guaranteed to have at least one char per token, and thus at least `self.max_tail_len` chars)
+        last_generated_text = self.tokenizer.batch_decode(input_ids[:, -self.max_tail_len :])
+
+        # Check if stop strings are found in the latest generated tokens
+        for batch_idx in range(len(last_generated_text)):
+            for stop_string in self.stop_strings:
+                if stop_string in last_generated_text[batch_idx]:
+                    # Secondary check: the last token MUST be part of the stop string, to prevent the case where the
+                    # prompt contains a stop string and triggers this criteria right at the start of generation. More
+                    # precisely, the stop string must end with the starting characters of the last token AND the stop
+                    # string can't be complete without the last token
+                    # Examples:
+                    # - input text=["st", "op"], stop_strings=["stop"] -> should stop, last token completes the
+                    #     stop string
+                    # - input text=["you", "stop"], stop_strings=["stop"] -> should stop, last token fully contains
+                    #     the stop string
+                    # - input text=["st", "opped"], stop_strings=["stop"] -> should stop, the start of the last token
+                    #     ("op") matches the end of the stop string.
+                    # - input text=["st", "op", "ped"], stop_strings=["stop"] -> should NOT stop, the last token does
+                    #     not contribute to the stop string (despite also starting with "p", which is the last char
+                    #     of the stop string)
+                    # NOTE: this secondary check is placed here because we're assuming that finding a stop string is
+                    # an uncommon occurrence.
+
+                    # the stop string can be complete without the last token -> we don't want to stop here, the
+                    # stop string is part of the prompt for this generation
+                    text_without_last_token = self.tokenizer.decode(input_ids[batch_idx, -self.max_tail_len : -1])
+                    if stop_string in text_without_last_token:
+                        continue
+
+                    # We are guaranteed to have at least 2 tokens in `input_ids` by this point (worst case: BOS +
+                    # 1st generated token). If we decode the last two tokens together and compare the resulting text
+                    # to the last token decoded separately, we can remove the unwanted prefix if it exists.
+                    last_two_tokens_text = self.tokenizer.decode(input_ids[batch_idx, -2:])
+                    last_tokens_with_prefix_text = self.tokenizer.decode(input_ids[batch_idx, -1:])
+                    last_token_text = ""
+                    for i in range(min(len(last_two_tokens_text), len(last_tokens_with_prefix_text))):
+                        if last_two_tokens_text[-i - 1] == last_tokens_with_prefix_text[-i - 1]:
+                            last_token_text += last_two_tokens_text[-i - 1]
+                        else:
+                            break
+                    last_token_text = last_token_text[::-1]  # `last_token_text` was built in reverse order
+                    last_fully_contains_stop_string = stop_string in last_token_text
+                    last_completes_stop_string = any(
+                        stop_string.endswith(last_token_text[: i + 1]) for i in range(len(last_token_text))
+                    )
+                    should_stop[batch_idx] = last_fully_contains_stop_string or last_completes_stop_string
+        return should_stop
+
+
 class EosTokenCriteria(StoppingCriteria):
     """
     This class can be used to stop generation whenever the "end-of-sequence" token is generated.
@@ -473,8 +604,9 @@ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwa
 
 class ConfidenceCriteria(StoppingCriteria):
     """
-    This class can be used to stop generation whenever assistant model's confidence in its prediction for the current token is lower than the threshold
-        `model.generation_config.assistant_confidence_threshold` even if the number of speculative tokens (defined by `num_assistant_tokens`) is not yet reached.
+    This class can be used to stop generation whenever assistant model's confidence in its prediction for the current
+    token is lower than the threshold `model.generation_config.assistant_confidence_threshold` even if the number of
+    speculative tokens (defined by `num_assistant_tokens`) is not yet reached.
 
     Args:
         assistant_confidence_threshold (`float`):
@@ -508,6 +640,225 @@ def max_length(self) -> int | None:
         return None
 
 
+class AsyncStoppingCriteriaList:
+    """
+    A wrapper around StoppingCriteriaList that performs stopping criteria checks asynchronously
+    on a separate CUDA stream, reducing GPU-CPU synchronization overhead.
+
+    The async approach works by:
+    1. Running stopping criteria checks on a separate CUDA stream
+    2. Writing the "should stop" flag to pinned (page-locked) CPU memory
+    3. The CPU can poll this memory location without explicit CUDA synchronization
+    4. Only when stopping is needed do we fully sync to get the is_done tensor
+
+    This reduces the number of GPU-CPU syncs from once per token to only when actually stopping.
+
+    Args:
+        stopping_criteria (`StoppingCriteriaList`):
+            The underlying stopping criteria to wrap.
+    """
+
+    def __init__(self, stopping_criteria: StoppingCriteriaList):
+        self.stopping_criteria = stopping_criteria
+        self._check_stream = None
+        self._check_event = None
+        self._pending_is_done = None
+        # Pinned memory for async communication - GPU writes, CPU reads without sync
+        self._should_stop_pinned = None
+        self._should_stop_np = None  # Numpy view for sync-free reading
+        self._last_checked_len = 0
+        self._check_in_flight = False
+
+    def _ensure_stream(self, device):
+        """Lazily create the CUDA stream, events, and pinned memory."""
+        if self._check_stream is None and device.type == "cuda":
+            self._check_stream = torch.cuda.Stream(device=device)
+            self._check_event = torch.cuda.Event()
+            # Event for syncing the async stream with current stream operations
+            self._sync_event = torch.cuda.Event()
+            # Pinned memory tensor - GPU can write to it, CPU can read without sync
+            self._should_stop_pinned = torch.zeros(1, dtype=torch.int32, pin_memory=True)
+            # GPU tensor for async communication - created once to avoid stream issues
+            self._should_stop_gpu = torch.zeros(1, dtype=torch.int32, device=device)
+            # Numpy view for reading without PyTorch sync overhead
+            self._should_stop_np = self._should_stop_pinned.numpy()
+
+    def check(
+        self,
+        input_ids: torch.LongTensor,
+        scores: torch.FloatTensor,
+        unfinished_sequences: torch.LongTensor,
+        **kwargs,
+    ) -> tuple[torch.LongTensor, bool]:
+        """
+        Check stopping criteria asynchronously.
+
+        The async approach reduces GPU-CPU syncs by:
+        1. Running stopping criteria on a separate CUDA stream
+        2. Using pinned memory to communicate results without explicit sync
+        3. Only syncing when stopping is actually needed
+
+        For max_length-only stopping, this falls back to a simple CPU check.
+
+        Returns:
+            Tuple of (updated_unfinished_sequences, this_peer_finished).
+        """
+        device = input_ids.device
+        cur_len = input_ids.shape[1]
+
+        # For non-CUDA devices, fall back to synchronous behavior
+        if device.type != "cuda":
+            is_done = self.stopping_criteria(input_ids, scores, **kwargs)
+            unfinished_sequences = unfinished_sequences & ~is_done
+            this_peer_finished = unfinished_sequences.max() == 0
+            return unfinished_sequences, bool(this_peer_finished)
+
+        # CPU-side max_length check - no GPU sync needed at all!
+        max_length = self.stopping_criteria.max_length
+        if max_length is not None:
+            if cur_len >= max_length:
+                # We've hit max_length - stop without GPU check
+                # Update unfinished_sequences on GPU
+                is_done = torch.ones(unfinished_sequences.shape, device=device, dtype=torch.bool)
+                unfinished_sequences = unfinished_sequences & ~is_done
+                return unfinished_sequences, True
+            elif cur_len < max_length - 1:
+                # Far from max_length - only check if async result shows EOS
+                return self._check_async_only(input_ids, scores, unfinished_sequences, cur_len, **kwargs)
+
+        # Near max_length or no max_length - do sync check
+        is_done = self.stopping_criteria(input_ids, scores, **kwargs)
+        unfinished_sequences = unfinished_sequences & ~is_done
+        this_peer_finished = unfinished_sequences.max() == 0
+        return unfinished_sequences, bool(this_peer_finished)
+
+    def _check_async_only(
+        self,
+        input_ids: torch.LongTensor,
+        scores: torch.FloatTensor,
+        unfinished_sequences: torch.LongTensor,
+        cur_len: int,
+        **kwargs,
+    ) -> tuple[torch.LongTensor, bool]:
+        """
+        Check async result only, don't do sync check.
+
+        The async check runs on a separate CUDA stream. We only poll when the
+        event signals completion (event.query() returns True). This means if
+        the model is very fast, we generate many tokens while a single async
+        check runs in parallel.
+        """
+        device = input_ids.device
+        self._ensure_stream(device)
+
+        # Check if async operation completed (non-blocking query)
+        if self._check_in_flight and self._check_event.query():
+            self._check_in_flight = False
+
+            # Read pinned memory via numpy - no PyTorch sync needed!
+            # The event.query() returning True guarantees the write is complete.
+            should_stop_value = int(self._should_stop_np[0])
+
+            if should_stop_value == 1:
+                # EOS or other stopping criteria triggered - need to sync to get is_done
+                torch.cuda.current_stream(device).wait_stream(self._check_stream)
+
+                if self._pending_is_done is not None:
+                    unfinished_sequences = unfinished_sequences & ~self._pending_is_done
+                    this_peer_finished = unfinished_sequences.max() == 0
+                    self._pending_is_done = None
+                    if bool(this_peer_finished):
+                        return unfinished_sequences, True
+
+            # Start new async check for future tokens
+            self._should_stop_np[0] = 0
+            self._start_async_check(input_ids, scores, unfinished_sequences, cur_len, **kwargs)
+
+        elif not self._check_in_flight:
+            # No check in flight - start one
+            self._start_async_check(input_ids, scores, unfinished_sequences, cur_len, **kwargs)
+
+        return unfinished_sequences, False
+
+    def _start_async_check(
+        self,
+        input_ids: torch.LongTensor,
+        scores: torch.FloatTensor,
+        unfinished_sequences: torch.LongTensor,
+        cur_len: int,
+        **kwargs,
+    ):
+        """Start an async stopping criteria check on a separate CUDA stream."""
+        # Clone to isolate async check from main generation - prevents race conditions
+        # where main stream modifies input_ids while async stream is reading
+        input_ids_for_check = input_ids.clone()
+        scores_for_check = scores.clone() if scores is not None else None
+
+        # Record current stream state so async stream can wait for clone to complete
+        self._sync_event.record(torch.cuda.current_stream(input_ids.device))
+
+        with torch.cuda.stream(self._check_stream):
+            # Wait for current stream operations (including clone) to complete
+            self._check_stream.wait_event(self._sync_event)
+
+            is_done = self.stopping_criteria(input_ids_for_check, scores_for_check, **kwargs)
+
+            # Check if any sequence should stop
+            any_should_stop = is_done.any()
+
+            # Write result to pinned memory (GPU -> pinned CPU, async)
+            # We use a simple copy: 1 if should stop, 0 otherwise
+            self._should_stop_gpu.copy_(any_should_stop.int().unsqueeze(0))
+            self._should_stop_pinned.copy_(self._should_stop_gpu, non_blocking=True)
+
+            # Store is_done for later if we need to sync
+            self._pending_is_done = is_done
+
+        self._check_event.record(self._check_stream)
+        self._check_in_flight = True
+        self._last_checked_len = cur_len
+
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> torch.BoolTensor:
+        """
+        Legacy interface for compatibility. Prefer using check() for async behavior.
+        This falls back to synchronous behavior.
+        """
+        return self.stopping_criteria(input_ids, scores, **kwargs)
+
+    def finalize(self, unfinished_sequences: torch.LongTensor) -> tuple[torch.LongTensor, bool]:
+        """
+        Wait for any pending async check to complete and return the final result.
+        Call this when generation is about to end to ensure we don't miss a stop signal.
+
+        Args:
+            unfinished_sequences: Current unfinished sequences tensor
+
+        Returns:
+            Tuple of (final_unfinished_sequences, this_peer_finished)
+        """
+        if self._check_in_flight and self._check_event is not None:
+            self._check_event.synchronize()
+            if self._pending_is_done is not None:
+                unfinished_sequences = unfinished_sequences & ~self._pending_is_done
+            self._pending_is_done = None
+            self._pending_should_stop = None
+            self._check_in_flight = False
+        this_peer_finished = unfinished_sequences.max() == 0
+        return unfinished_sequences, bool(this_peer_finished)
+
+    def __iter__(self):
+        """Iterate over the underlying stopping criteria."""
+        return iter(self.stopping_criteria)
+
+    def __len__(self):
+        """Return the number of stopping criteria."""
+        return len(self.stopping_criteria)
+
+    @property
+    def max_length(self) -> int | None:
+        return self.stopping_criteria.max_length
+
+
 def validate_stopping_criteria(stopping_criteria: StoppingCriteriaList, max_length: int) -> StoppingCriteriaList:
     stopping_max_length = stopping_criteria.max_length
     new_stopping_criteria = deepcopy(stopping_criteria)
diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index 388cef73566a..d4bf36bac1be 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -41,7 +41,7 @@
 )
 from ..integrations.deepspeed import is_deepspeed_zero3_enabled
 from ..integrations.fsdp import is_fsdp_managed_module
-from ..masking_utils import create_masks_for_generate
+from ..masking_utils import _attention_mask_all_true, create_masks_for_generate
 from ..tokenization_python import ExtensionsTrie
 from ..utils import (
     ModelOutput,
@@ -86,6 +86,8 @@
     MinPLogitsWarper,
     NoBadWordsLogitsProcessor,
     NoRepeatNGramLogitsProcessor,
+    PLessLogitsWarper,
+    PLessNormLogitsWarper,
     PrefixConstrainedLogitsProcessor,
     RepetitionPenaltyLogitsProcessor,
     SequenceBiasLogitsProcessor,
@@ -99,13 +101,14 @@
     UnbatchedClassifierFreeGuidanceLogitsProcessor,
 )
 from .stopping_criteria import (
+    AsyncStoppingCriteriaList,
     ConfidenceCriteria,
     EosTokenCriteria,
     MaxLengthCriteria,
     MaxTimeCriteria,
     StoppingCriteria,
     StoppingCriteriaList,
-    StopStringCriteria,
+    StopStringTextMatchCriteria,
 )
 
 
@@ -136,6 +139,7 @@
     GenerationMode.BEAM_SEARCH: "_beam_search",
     GenerationMode.BEAM_SAMPLE: "_beam_search",
     GenerationMode.ASSISTED_GENERATION: "_assisted_decoding",
+    GenerationMode.MTP_DECODING: "_mtp_decoding",
     # Deprecated methods
     GenerationMode.DOLA_GENERATION: "transformers-community/dola",
     GenerationMode.CONTRASTIVE_SEARCH: "transformers-community/contrastive-search",
@@ -567,6 +571,7 @@ def prepare_inputs_for_generation(
                 attention_mask=attention_mask,
                 past_key_values=model_inputs.get("past_key_values"),
                 position_ids=model_inputs.get(position_ids_key),
+                block_sequence_ids=model_inputs.get("block_sequence_ids"),
                 # The following kwargs are not used in the main function - only on a few models with overloaded `create_masks_for_generate`
                 token_type_ids=model_inputs.get("token_type_ids"),
                 mm_token_type_ids=model_inputs.get("mm_token_type_ids"),
@@ -850,16 +855,22 @@ def _prepare_decoder_input_ids_for_generation(
             pass
         # user input but doesn't start with decoder_start_token_id -> prepend decoder_start_token_id (and adjust
         # decoder_attention_mask if provided)
-        elif (decoder_input_ids[:, 0] != decoder_start_token_id[:, 0]).all().item():
-            decoder_input_ids = torch.cat([decoder_start_token_id, decoder_input_ids], dim=-1)
-            if "decoder_attention_mask" in model_kwargs:
-                decoder_attention_mask = model_kwargs["decoder_attention_mask"]
-                decoder_attention_mask = torch.cat(
-                    (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
-                    dim=-1,
-                )
-                model_kwargs["decoder_attention_mask"] = decoder_attention_mask
-
+        else:
+            # compute condition on-device (no sync yet)
+            decoder_start_mismatch = (
+                decoder_input_ids[:, 0] != decoder_start_token_id[:, 0]
+            ).all()  # scalar boolean tensor on device
+
+            # single explicit sync point (can be batched with other checks later)
+            if decoder_start_mismatch.item():
+                decoder_input_ids = torch.cat([decoder_start_token_id, decoder_input_ids], dim=-1)
+                if "decoder_attention_mask" in model_kwargs:
+                    decoder_attention_mask = model_kwargs["decoder_attention_mask"]
+                    decoder_attention_mask = torch.cat(
+                        (torch.ones_like(decoder_attention_mask)[:, :1], decoder_attention_mask),
+                        dim=-1,
+                    )
+                    model_kwargs["decoder_attention_mask"] = decoder_attention_mask
         return decoder_input_ids, model_kwargs
 
     @staticmethod
@@ -1034,6 +1045,65 @@ def _get_candidate_generator(
             )
         return candidate_generator
 
+    def _create_safety_processor(self, safety_config, processor_type="logits"):
+        """
+        Create safety processor from configuration.
+
+        Args:
+            safety_config: SafetyConfig object containing safety settings
+            processor_type: Type of processor to create ("logits" or "stopping")
+
+        Returns:
+            SafetyLogitsProcessor or SafetyStoppingCriteria, or None if creation fails
+        """
+        if not safety_config or not getattr(safety_config, "enabled", False):
+            return None
+
+        # Ensure we have a tokenizer
+        if not hasattr(self, "tokenizer") or self.tokenizer is None:
+            logger.warning("Cannot create safety processor: tokenizer not available")
+            return None
+
+        try:
+            from .safety import SafetyLogitsProcessor, SafetyStoppingCriteria
+
+            # Get checker from configuration
+            try:
+                safety_checker = safety_config.construct_checker()
+            except ValueError as e:
+                raise ValueError(
+                    f"Safety configuration error: {e}\n"
+                    "You must provide a SafetyChecker instance in SafetyConfig. "
+                    "See examples/safe_generation/ for reference implementations."
+                ) from e
+
+            if processor_type == "logits":
+                return SafetyLogitsProcessor(
+                    safety_checker=safety_checker,
+                    tokenizer=self.tokenizer,
+                    safety_config=safety_config,
+                    check_interval=getattr(safety_config, "check_interval", 1),
+                )
+            elif processor_type == "stopping":
+                return SafetyStoppingCriteria(
+                    safety_checker=safety_checker,
+                    tokenizer=self.tokenizer,
+                    safety_config=safety_config,
+                    check_final_only=getattr(safety_config, "check_final_only", False),
+                )
+            else:
+                raise ValueError(f"processor_type must be 'logits' or 'stopping', got '{processor_type}'")
+
+        except ImportError:
+            logger.warning("Safety module not available - cannot create safety processors")
+            return None
+        except ValueError:
+            # Re-raise ValueError for input validation errors (like invalid processor_type or missing checker)
+            raise
+        except Exception as e:
+            logger.warning(f"Failed to create safety {processor_type} processor: {e}")
+            return None
+
     def _get_logits_processor(
         self: "GenerativePreTrainedModel",
         generation_config: GenerationConfig,
@@ -1086,9 +1156,31 @@ def _get_logits_processor(
                     UserWarning,
                 )
         if generation_config.repetition_penalty is not None and generation_config.repetition_penalty != 1.0:
-            processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
+            if self.config.is_encoder_decoder:
+                processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
+            else:
+                inputs_embeds = model_kwargs.get("inputs_embeds") if model_kwargs is not None else None
+                if inputs_embeds is not None and (input_ids_seq_length is None or input_ids_seq_length == 0):
+                    warnings.warn(
+                        "Passing `repetition_penalty` requires some form of `input_ids` to be passed to "
+                        "`generate`, ignoring the argument.",
+                        UserWarning,
+                    )
+                else:
+                    processors.append(RepetitionPenaltyLogitsProcessor(penalty=generation_config.repetition_penalty))
         if generation_config.no_repeat_ngram_size is not None and generation_config.no_repeat_ngram_size > 0:
-            processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
+            if self.config.is_encoder_decoder:
+                processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
+            else:
+                inputs_embeds = model_kwargs.get("inputs_embeds") if model_kwargs is not None else None
+                if inputs_embeds is not None and (input_ids_seq_length is None or input_ids_seq_length == 0):
+                    warnings.warn(
+                        "Passing `no_repeat_ngram_size` requires some form of `input_ids` to be passed to "
+                        "`generate`, ignoring the argument.",
+                        UserWarning,
+                    )
+                else:
+                    processors.append(NoRepeatNGramLogitsProcessor(generation_config.no_repeat_ngram_size))
         if (
             generation_config.encoder_no_repeat_ngram_size is not None
             and generation_config.encoder_no_repeat_ngram_size > 0
@@ -1191,6 +1283,12 @@ def _get_logits_processor(
                 )
             )
 
+        # Add safety processor if enabled
+        if hasattr(generation_config, "safety_config") and generation_config.safety_config is not None:
+            safety_processor = self._create_safety_processor(generation_config.safety_config, "logits")
+            if safety_processor is not None:
+                processors.append(safety_processor)
+
         # TODO (joao): find a strategy to specify the order of the processors
         processors = self._merge_criteria_processor_list(processors, logits_processor)
 
@@ -1227,6 +1325,10 @@ def _get_logits_processor(
                 processors.append(
                     MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep)
                 )
+            if generation_config.p_less is not None:
+                processors.append(PLessLogitsWarper(generation_config.p_less))
+            if generation_config.p_less_norm is not None:
+                processors.append(PLessNormLogitsWarper(generation_config.p_less_norm))
             if generation_config.typical_p is not None and generation_config.typical_p < 1.0:
                 processors.append(
                     TypicalLogitsWarper(mass=generation_config.typical_p, min_tokens_to_keep=min_tokens_to_keep)
@@ -1281,7 +1383,11 @@ def _get_stopping_criteria(
                     "model's generation config, but we could not locate a tokenizer. When generating with "
                     "stop strings, you must pass the model's tokenizer to the `tokenizer` argument of `generate`."
                 )
-            criteria.append(StopStringCriteria(stop_strings=generation_config.stop_strings, tokenizer=tokenizer))
+            # TODO (joao): when we support compilation of the decoding loop, we need to use StopStringCriteria here if
+            # want compilation support
+            criteria.append(
+                StopStringTextMatchCriteria(stop_strings=generation_config.stop_strings, tokenizer=tokenizer)
+            )
         if generation_config._eos_token_tensor is not None:
             criteria.append(EosTokenCriteria(eos_token_id=generation_config._eos_token_tensor))
         if (
@@ -1292,6 +1398,13 @@ def _get_stopping_criteria(
             criteria.append(
                 ConfidenceCriteria(assistant_confidence_threshold=generation_config.assistant_confidence_threshold)
             )
+
+        # Add safety stopping criteria if enabled
+        if hasattr(generation_config, "safety_config") and generation_config.safety_config is not None:
+            safety_stopping = self._create_safety_processor(generation_config.safety_config, "stopping")
+            if safety_stopping is not None:
+                criteria.append(safety_stopping)
+
         criteria = self._merge_criteria_processor_list(criteria, stopping_criteria)
         return criteria
 
@@ -1459,6 +1572,13 @@ def compute_transition_scores(
     def _validate_generation_mode(
         self: "GenerativePreTrainedModel", generation_mode, generation_config, generation_mode_kwargs
     ):
+        supported_modes = getattr(self, "_supported_generation_modes", None)
+        if supported_modes is not None and generation_mode not in supported_modes:
+            raise ValueError(
+                f"{self.__class__.__name__} only supports {supported_modes}, but got "
+                f"generation mode '{generation_mode}'."
+            )
+
         if generation_mode == GenerationMode.BEAM_SEARCH and "streamer" in generation_mode_kwargs:
             raise ValueError(
                 "`streamer` cannot be used with beam search (yet!). Make sure that `num_beams` is set to 1."
@@ -1477,6 +1597,18 @@ def _validate_generation_mode(
                     f"assisted generation is not supported with stateful models, such as {self.__class__.__name__}"
                 )
 
+        if generation_mode == GenerationMode.MTP_DECODING:
+            if generation_config.num_return_sequences > 1:
+                raise ValueError(
+                    "num_return_sequences must be 1 when `use_mtp=True` "
+                    f"(got {generation_config.num_return_sequences})."
+                )
+            if getattr(self.config, "num_nextn_predict_layers", 0) <= 0:
+                raise ValueError(
+                    "`use_mtp=True` was passed but the model config has no MTP modules "
+                    "(`num_nextn_predict_layers <= 0`)."
+                )
+
         if (assistant_model := generation_mode_kwargs.get("assistant_model")) is not None:
             if self.config.is_encoder_decoder and not assistant_model.config.is_encoder_decoder:
                 attributes_to_check = ["encoder_attention_heads", "encoder_ffn_dim", "encoder_layers"]
@@ -1720,12 +1852,46 @@ def _prepare_generation_config(
                 "parameters explicitly, but not both.",
             )
 
+        # Safety: if the model is sharded across multiple devices (hf_device_map/device_map) and we are
+        # doing sampling, enable `remove_invalid_values` by default to avoid NaN/Inf logits causing CUDA
+        # asserts during multinomial sampling. Users can still override this by passing the flag explicitly.
+        try:
+            is_sharded_map = False
+            hf_map = getattr(self, "hf_device_map", None)
+            if hf_map is not None and isinstance(hf_map, dict) and len(set(hf_map.values())) > 1:
+                devices = set(hf_map.values())
+                gpu_devices = {d for d in devices if d not in {"cpu", "disk"}}
+                if len(gpu_devices) > 1:
+                    is_sharded_map = True
+
+            device_map_attr = getattr(self, "device_map", None)
+            if not is_sharded_map and isinstance(device_map_attr, dict) and len(set(device_map_attr.values())) > 1:
+                devices = set(device_map_attr.values())
+                gpu_devices = {d for d in devices if d not in {"cpu", "disk"}}
+                if len(gpu_devices) > 1:
+                    is_sharded_map = True
+
+            if is_sharded_map and generation_config.do_sample and generation_config.remove_invalid_values is False:
+                generation_config.remove_invalid_values = True
+                logger.info(
+                    "Enabling `remove_invalid_values=True` for sharded sampling to avoid NaN/Inf logits during sampling."
+                )
+        except Exception as exception:
+            logger.debug("Skipping sharded sampling invalid-value guard", exc_info=exception)
+
         # Finally keep output_xxx args in `model_kwargs` so it can be passed to `forward`
+
         output_attentions = generation_config.output_attentions
         output_hidden_states = generation_config.output_hidden_states
         model_kwargs.update({"output_attentions": output_attentions} if output_attentions else {})
         model_kwargs.update({"output_hidden_states": output_hidden_states} if output_hidden_states else {})
 
+        # Enforce deterministic greedy decoding if do_sample=False and num_beams = 1
+        if generation_config.do_sample is False and generation_config.num_beams == 1:
+            generation_config.temperature = 1.0
+            generation_config.top_k = 0
+            generation_config.top_p = 1.0
+
         return generation_config, model_kwargs
 
     def _prepare_static_cache(
@@ -1848,7 +2014,11 @@ def _prepare_cache_for_generation(
         # Assisted decoding and contrastive search require cache rollback, which is incompatible with sliding layers.
         # To handle this, we skip passing the model config to DynamicCache (forcing a full-layer cache).
         # The "dynamic_full" option is a shortcut for generate() users to avoid sliding layers on their own.
-        if generation_mode in (GenerationMode.ASSISTED_GENERATION, GenerationMode.CONTRASTIVE_SEARCH):
+        if generation_mode in (
+            GenerationMode.ASSISTED_GENERATION,
+            GenerationMode.CONTRASTIVE_SEARCH,
+            GenerationMode.MTP_DECODING,
+        ):
             if generation_config.cache_implementation is not None:
                 logger.warning_once(
                     "An assistant model is provided, using a dynamic cache instead of a cache of type="
@@ -2003,6 +2173,9 @@ def _valid_auto_compile_criteria(
         if generation_config.disable_compile:
             return False
 
+        if os.getenv("TORCHDYNAMO_DISABLE", "").lower() in ("1", "true", "yes", "on"):
+            return False
+
         cache = model_kwargs.get("past_key_values", model_kwargs.get("cache_params"))
 
         # Base logic
@@ -2110,6 +2283,7 @@ def _extract_generation_mode_kwargs(
             "assistant_tokenizer": kwargs.pop("assistant_tokenizer", None),
             "assistant_model": assistant_model,
             "streamer": streamer,
+            "assistant_temperature": kwargs.pop("assistant_temperature", None),
         }
         world_size = dist.get_world_size() if dist.is_available() and dist.is_initialized() else 1  # type: ignore
         generation_mode_kwargs["synced_gpus"] = (
@@ -2356,6 +2530,20 @@ def generate(
         self._validate_model_kwargs(model_kwargs.copy())
         self._validate_generation_mode(generation_mode, generation_config, generation_mode_kwargs)
 
+        # Configure assistant model's generation_config with user parameters
+        if assistant_model is not None:
+            # The assistant model inherits ALL generation parameters from the main generate() call, including:
+            #   - Assistant-specific parameters (num_assistant_tokens, assistant_confidence_threshold, etc.)
+            #   - General generation parameters (do_sample, max_new_tokens, temperature, etc.)
+            # This ensures consistent behavior between main and assistant models. In the future,
+            # assistant-specific overrides could be added (e.g., assistant_do_sample) to allow
+            # different generation strategies for draft vs target models while maintaining the
+            # inheritance-by-default behavior.
+            assistant_generation_config, _ = assistant_model._prepare_generation_config(
+                assistant_model.generation_config, **kwargs
+            )
+            assistant_model.generation_config = assistant_generation_config
+
         # Deprecation-related step: set Hub repo for deprecated strategies.
         # NOTE: This must come after initializing generation_config, since we need it to determine if this is a deprecated mode.
         # It must also be before any preparation steps, since Hub repos expect to be loaded before preparation steps.
@@ -2532,19 +2720,31 @@ def generate(
             tokenizer=generation_mode_kwargs.get("tokenizer"),
         )
 
+        # Wrap stopping criteria with async wrapper if requested
+        if generation_config.async_stopping_criteria:
+            prepared_stopping_criteria = AsyncStoppingCriteriaList(prepared_stopping_criteria)
+
         # Set model_kwargs `use_cache` so we can use it later in forward runs
         model_kwargs["use_cache"] = generation_config.use_cache
 
         # 9. Call generation mode
-        result = decoding_method(
-            self,
-            input_ids,
-            logits_processor=prepared_logits_processor,
-            stopping_criteria=prepared_stopping_criteria,
-            generation_config=generation_config,
-            **generation_mode_kwargs,
-            **model_kwargs,
-        )
+        # Check if attention_mask is all-True to avoid per-token GPU-CPU sync in masking utils.
+        # During generation, if the mask starts all-True and we only append ones, it stays all-True.
+        attention_mask = model_kwargs.get("attention_mask")
+        mask_all_true = attention_mask is None or bool(attention_mask.all())
+        mask_token = _attention_mask_all_true.set(mask_all_true)
+        try:
+            result = decoding_method(
+                self,
+                input_ids,
+                logits_processor=prepared_logits_processor,
+                stopping_criteria=prepared_stopping_criteria,
+                generation_config=generation_config,
+                **generation_mode_kwargs,
+                **model_kwargs,
+            )
+        finally:
+            _attention_mask_all_true.reset(mask_token)
 
         return result
 
@@ -2797,13 +2997,23 @@ def _sample(
             if streamer is not None:
                 streamer.put(next_tokens.cpu())
 
-            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
-            this_peer_finished = unfinished_sequences.max() == 0
+            # Check stopping criteria - use async method if available
+            if hasattr(stopping_criteria, "check"):
+                unfinished_sequences, this_peer_finished = stopping_criteria.check(
+                    input_ids, scores, unfinished_sequences
+                )
+            else:
+                unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
+                this_peer_finished = unfinished_sequences.max() == 0
 
             # This is needed to properly delete outputs.logits which may be very large for first iteration
             # Otherwise a reference to outputs is kept which keeps the logits alive in the next iteration
             del outputs
 
+        # Finalize async stopping criteria if used
+        if hasattr(stopping_criteria, "finalize"):
+            stopping_criteria.finalize(unfinished_sequences)
+
         if streamer is not None:
             streamer.end()
 
@@ -2969,9 +3179,17 @@ def _get_top_k_continuations(
 
         # Gather the top K scores from _all_ beams.
         if do_sample:
-            topk_indices = torch.multinomial(
-                nn.functional.softmax(accumulated_log_probs, dim=-1), num_samples=beams_to_keep
-            )
+            # Handle potential NaN values in accumulated_log_probs
+            probs = nn.functional.softmax(accumulated_log_probs, dim=-1)
+            # Replace NaN values with uniform distribution
+            if torch.isnan(probs).any():
+                # Create a mask for NaN positions
+                nan_mask = torch.isnan(probs)
+                # Replace NaN with a small uniform probability
+                probs = torch.where(nan_mask, torch.ones_like(probs) / probs.shape[-1], probs)
+                # Renormalize to ensure probabilities sum to 1
+                probs = probs / probs.sum(dim=-1, keepdim=True)
+            topk_indices = torch.multinomial(probs, num_samples=beams_to_keep)
             topk_log_probs = torch.gather(input=accumulated_log_probs, dim=1, index=topk_indices)
         else:
             topk_log_probs, topk_indices = torch.topk(accumulated_log_probs, k=beams_to_keep)
@@ -3430,6 +3648,7 @@ def _assisted_decoding(
         assistant_model: Optional["PreTrainedModel"] = None,
         assistant_tokenizer: Optional["PreTrainedTokenizerBase"] = None,
         tokenizer: Optional["PreTrainedTokenizerBase"] = None,
+        assistant_temperature: float | None = None,
         **model_kwargs,
     ) -> GenerateNonBeamOutput | torch.LongTensor:
         r"""
@@ -3464,6 +3683,9 @@ def _assisted_decoding(
                 The tokenizer used for the assistant model. If not provided, the token space is assumed to be the same.
             tokenizer (`PreTrainedTokenizerBase`, *optional*):
                 The tokenizer used for the main model. If not provided, the token space is assumed to be the same.
+            assistant_temperature (`float`, *optional*):
+                The temperature to use for the assistant model. If not provided and main generation temperature is below
+                1.5, it will be set to 1.5 (to improve decoding speed).
             model_kwargs:
                 Additional model specific keyword arguments will be forwarded to the `forward` function of the model.
                 If model is an encoder-decoder model the kwargs should include `encoder_outputs`.
@@ -3483,6 +3705,20 @@ def _assisted_decoding(
             or type(model_kwargs.get("past_key_values")) is StaticCache
         ):
             raise ValueError("assisted generate is not supported with Static cache classes`")
+        # Prefer a slightly higher temperature for the assistant when not explicitly provided
+        idx = next((i for i, p in enumerate(logits_processor) if isinstance(p, TemperatureLogitsWarper)), None)
+        temp_processor = logits_processor.pop(idx) if idx is not None else TemperatureLogitsWarper(temperature=1.0)
+
+        if assistant_temperature is None and temp_processor is not None and temp_processor.temperature < 1.5:
+            logger.warning_once(
+                f"The assistant's sampling temperature comes from main generation loop set to {temp_processor.temperature}, "
+                "but speculative decoding benefits from slightly hotter candidate generation, (see #40976) so we are setting it "
+                "to 1.5. This should improve decoding speed in most cases. Use `assistant_temperature` to override this value."
+            )
+            assistant_temperature = 1.5
+
+        if assistant_temperature is not None:
+            logits_processor.insert(0, TemperatureLogitsWarper(temperature=assistant_temperature))
         # Get the candidate generator, given the parameterization
         candidate_generator = self._get_candidate_generator(
             generation_config=generation_config,
@@ -3716,6 +3952,190 @@ def _assisted_decoding(
         else:
             return input_ids
 
+    def _mtp_decoding(
+        self: "GenerativePreTrainedModel",
+        input_ids: torch.LongTensor,
+        logits_processor: LogitsProcessorList,
+        stopping_criteria: StoppingCriteriaList,
+        generation_config: GenerationConfig,
+        synced_gpus: bool = False,
+        streamer: Optional["BaseStreamer"] = None,
+        **model_kwargs,
+    ) -> GenerateNonBeamOutput | torch.LongTensor:
+        r"""
+        Multi-Token Prediction (MTP) speculative decoding. Uses an
+        [`MTPCandidateGenerator`][transformers.generation.candidate_generators.MTPCandidateGenerator] attached
+        to the model as `model.mtp_candidate_generator` — typically loaded via its `from_pretrained` — to draft
+        `K = config.num_nextn_predict_layers` tokens per step. The base model runs once per step to produce
+        the hidden state + first draft token, the MTP heads chain K more drafts, and a second forward on the
+        K+1 candidates provides the verification logits. Standard speculative sampling handles accept/reject.
+
+        Only supports `batch_size = 1`.
+        """
+        if not model_kwargs.get("use_cache"):
+            raise ValueError("`use_mtp` generate requires `use_cache=True`.")
+        mtp_generator = getattr(self, "mtp_candidate_generator", None)
+        if mtp_generator is None:
+            raise ValueError(
+                "`use_mtp=True` requires an `MTPCandidateGenerator` attached to the model, e.g.:\n"
+                "    from transformers.generation.candidate_generators import MTPCandidateGenerator\n"
+                "    model.mtp_candidate_generator = MTPCandidateGenerator.from_pretrained(checkpoint, model)"
+            )
+        num_mtp = mtp_generator.num_mtp
+
+        do_sample = generation_config.do_sample
+        output_scores = generation_config.output_scores
+        output_logits = generation_config.output_logits
+        return_dict_in_generate = generation_config.return_dict_in_generate
+        scores = () if (return_dict_in_generate and output_scores) else None
+        raw_logits = () if (return_dict_in_generate and output_logits) else None
+
+        batch_size = input_ids.shape[0]
+        if batch_size > 1:
+            raise ValueError("MTP decoding currently only supports batch_size = 1.")
+        unfinished_sequences = torch.ones(batch_size, dtype=torch.long, device=input_ids.device)
+        this_peer_finished = False
+        is_first_iteration = True
+
+        while self._has_unfinished_sequences(this_peer_finished, synced_gpus, device=input_ids.device):
+            cur_len = input_ids.shape[1]
+
+            # 1. Base-model forward on the full prompt (first iter) or the not-yet-cached tail.
+            next_sequence_length = None if is_first_iteration else 1 if model_kwargs["use_cache"] else None
+            model_inputs = self.prepare_inputs_for_generation(
+                input_ids,
+                next_sequence_length=next_sequence_length,
+                is_first_iteration=is_first_iteration,
+                **model_kwargs,
+            )
+            # Route through the base model to keep the last hidden state; we'll project to logits manually.
+            base_only_inputs = {k: v for k, v in model_inputs.items() if k not in ("logits_to_keep", "labels")}
+            base_out = self.model(**base_only_inputs, return_dict=True)
+            main_cache = base_out.past_key_values
+            h_last = base_out.last_hidden_state[:, -1:, :]
+
+            # 2. Sample x_{t+1} from the base model's prediction at the last prompt position.
+            base_logits = self.lm_head(h_last)[:, 0, :].to(dtype=torch.float32, device=input_ids.device)
+            base_scores = logits_processor(input_ids, base_logits)
+            if do_sample:
+                x_next = torch.multinomial(nn.functional.softmax(base_scores, dim=-1), num_samples=1)
+            else:
+                x_next = torch.argmax(base_scores, dim=-1, keepdim=True)
+
+            # 3. Delegate K extra drafts to the MTP candidate generator.
+            candidate_tokens, draft_stack = mtp_generator.get_candidates(
+                input_ids,
+                previous_hidden_state=h_last,
+                past_key_values=main_cache,
+                first_token=x_next,
+                position_offset=cur_len,
+                logits_processor=logits_processor,
+                do_sample=do_sample,
+            )
+            is_done_candidate = stopping_criteria(torch.cat([input_ids, candidate_tokens], dim=1), None)
+            verify_kwargs = copy.copy(model_kwargs)
+            verify_kwargs["past_key_values"] = main_cache
+            verify_kwargs = _prepare_attention_mask(
+                verify_kwargs, cur_len + num_mtp + 1, self.config.is_encoder_decoder
+            )
+            if verify_kwargs.get("position_ids") is not None:
+                verify_kwargs = _prepare_position_ids(
+                    verify_kwargs, cur_len + num_mtp + 1, self.config.is_encoder_decoder
+                )
+            verify_inputs = self.prepare_inputs_for_generation(
+                torch.cat([input_ids, candidate_tokens], dim=1),
+                next_sequence_length=num_mtp + 1,
+                is_first_iteration=False,
+                **verify_kwargs,
+            )
+            if "logits_to_keep" in verify_inputs:
+                verify_inputs["logits_to_keep"] = num_mtp + 1
+            verify_outputs = self(**verify_inputs, return_dict=True)
+            verify_logits = verify_outputs.logits[:, -(num_mtp + 1) :, :].to(
+                dtype=torch.float32, device=input_ids.device
+            )
+            for i in range(num_mtp + 1):
+                verify_logits[:, i, :] = logits_processor(
+                    torch.cat([input_ids, candidate_tokens[:, :i]], dim=1), verify_logits[:, i, :]
+                )
+
+            # 5. Accept/reject. We compare the base model's predictions at positions cur_len..cur_len+K-1
+            #    (logits for x_{t+2}..x_{t+K+1}) against the drafts x_{t+2}..x_{t+K+1} = candidate_tokens[:, 1:].
+            #    x_{t+1} itself came from the base model, so it is unconditionally kept.
+            drafts_for_check = candidate_tokens[:, 1:]  # (1, K)
+            verify_for_drafts = verify_logits[:, :num_mtp, :]  # (1, K, V)
+            if do_sample:
+                _candidate_input_ids = torch.cat([input_ids, drafts_for_check], dim=1)
+                accepted_drafts, n_matches = _speculative_sampling(
+                    _candidate_input_ids,
+                    draft_stack,
+                    num_mtp,
+                    verify_for_drafts,
+                    is_done_candidate,
+                )
+                accepted_after_xnext = accepted_drafts
+            else:
+                verify_argmax = verify_logits.argmax(dim=-1)  # (1, K+1)
+                draft_match = verify_argmax[:, :num_mtp] == drafts_for_check
+                n_matches = int(((~draft_match).cumsum(dim=-1) < 1).sum().item())
+                if is_done_candidate and n_matches == num_mtp:
+                    n_matches -= 1
+                bonus = verify_argmax[:, n_matches : n_matches + 1]
+                accepted_after_xnext = torch.cat([drafts_for_check[:, :n_matches], bonus], dim=1)
+
+            accepted = torch.cat([x_next, accepted_after_xnext], dim=1)
+            mtp_generator.update_candidate_strategy(input_ids, verify_logits, n_matches)
+
+            # 6. Commit. Extend input_ids, crop the base-model cache, and update model_kwargs.
+            input_ids = torch.cat([input_ids, accepted], dim=1)
+            if streamer is not None:
+                streamer.put(accepted.cpu())
+            new_cur_len = input_ids.shape[1]
+            main_cache.crop(new_cur_len - 1)
+            model_kwargs["past_key_values"] = main_cache
+            model_kwargs = self._update_model_kwargs_for_generation(
+                base_out,
+                model_kwargs,
+                is_encoder_decoder=self.config.is_encoder_decoder,
+                num_new_tokens=accepted.shape[1],
+            )
+            # `_update_model_kwargs_for_generation` only extended attention_mask by 1; add the rest.
+            if model_kwargs.get("attention_mask") is not None:
+                extra = accepted.shape[1] - 1
+                if extra > 0:
+                    mask = model_kwargs["attention_mask"]
+                    model_kwargs["attention_mask"] = torch.cat([mask, mask.new_ones((mask.shape[0], extra))], dim=-1)
+
+            if return_dict_in_generate:
+                newly_added = accepted.shape[1]
+                if output_scores:
+                    scores += tuple(verify_logits[:, i, :] for i in range(newly_added))
+                if output_logits:
+                    raw_logits += tuple(verify_logits[:, i, :] for i in range(newly_added))
+
+            is_first_iteration = False
+            unfinished_sequences = unfinished_sequences & ~stopping_criteria(input_ids, scores)
+            this_peer_finished = unfinished_sequences.max() == 0
+            del base_out, verify_outputs
+
+        if streamer is not None:
+            streamer.end()
+
+        if return_dict_in_generate:
+            cache = None
+            if any(cache_key in model_kwargs for cache_key in ALL_CACHE_NAMES):
+                cache_key = next(cache_key for cache_key in ALL_CACHE_NAMES if cache_key in model_kwargs)
+                cache = model_kwargs[cache_key]
+            return GenerateDecoderOnlyOutput(
+                sequences=input_ids,
+                scores=scores,
+                logits=raw_logits,
+                attentions=None,
+                hidden_states=None,
+                past_key_values=cache,
+            )
+        return input_ids
+
     # TODO: v5.1: make public once API stabilized
     def _prefill(
         self: "GenerativePreTrainedModel",
diff --git a/src/transformers/heterogeneity/__init__.py b/src/transformers/heterogeneity/__init__.py
new file mode 100644
index 000000000000..26a7bc10ea55
--- /dev/null
+++ b/src/transformers/heterogeneity/__init__.py
@@ -0,0 +1,14 @@
+from .configuration_utils import (
+    LayerConfig,
+    apply_heterogeneous_config,
+    get_full_layer_config,
+    heterogeneous_to_dict_helper,
+)
+
+
+__all__ = [
+    "LayerConfig",
+    "apply_heterogeneous_config",
+    "heterogeneous_to_dict_helper",
+    "get_full_layer_config",
+]
diff --git a/src/transformers/heterogeneity/configuration_utils.py b/src/transformers/heterogeneity/configuration_utils.py
new file mode 100644
index 000000000000..af814b738032
--- /dev/null
+++ b/src/transformers/heterogeneity/configuration_utils.py
@@ -0,0 +1,212 @@
+from __future__ import annotations
+
+import copy
+from dataclasses import dataclass
+from types import SimpleNamespace
+from typing import TYPE_CHECKING, Any
+
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedConfig
+
+
+class LayerConfig(SimpleNamespace):
+    @property
+    def attributes(self) -> set[str]:
+        return set(vars(self).keys())
+
+    def to_dict(self) -> dict[str, Any]:
+        return dict(vars(self))
+
+
+@dataclass
+class HeterogeneitySpec:
+    per_layer_config: dict[int, dict[str, Any] | LayerConfig]
+    per_layer_attributes: set[str]
+    fallback_values: dict[str, Any]
+
+
+def apply_heterogeneous_config(
+    config: PreTrainedConfig, per_layer_config: dict[int, dict[str, Any] | LayerConfig], explicit: bool = False
+) -> None:
+    """Register per-layer configuration overrides on a model config.
+
+    In a heterogeneous model, individual layers can differ from the global config
+    (e.g., different ``intermediate_size``, ``num_key_value_heads``, or entire
+    sub-layers skipped via ``skip_*`` attributes).
+
+    This function validates the overrides, computes fallback values from the global
+    config, and stores a ``HeterogeneitySpec`` on ``config._heterogeneity_spec``.
+    At model-init time, ``apply_heterogeneous_modeling`` reads this spec to patch
+    each layer with its resolved config.
+
+    Args:
+        config: The global model config to modify in-place.
+        per_layer_config: Mapping from layer index to a dict or ``LayerConfig``
+            of attribute overrides. Only layers that differ from the global
+            config need to be included.
+        explicit: Whether to enforce that `per_layer_config` has a LayerConfig for each layer
+            and that each layer has all per-layer attributes defined.
+    """
+
+    per_layer_config = {
+        layer_idx: LayerConfig(**layer_config) if isinstance(layer_config, dict) else layer_config
+        for layer_idx, layer_config in per_layer_config.items()
+    }
+
+    _validate_num_hetero_layers(config, per_layer_config)
+    _validate_sliding_window_and_attention_chunk_size(config, per_layer_config)
+
+    config._heterogeneity_spec = _modify_config_and_create_heterogeneity_spec(
+        config, per_layer_config, explicit=explicit
+    )
+
+
+def heterogeneous_to_dict_helper(config: PreTrainedConfig, d: dict[str, Any]) -> None:
+    if config.per_layer_config:
+        # Zero-pad so keys sort numerically in JSON (0,1,...,10 not 0,1,10,2,...)
+        max_digits = len(str(max(config.per_layer_config.keys())))
+        d["per_layer_config"] = {
+            str(layer_idx).zfill(max_digits): layer_config.to_dict()
+            for layer_idx, layer_config in config.per_layer_config.items()
+        }
+    else:
+        d["per_layer_config"] = {}
+
+    d.pop("_heterogeneity_spec", None)
+
+
+def get_full_layer_config(config: PreTrainedConfig, layer_idx: int) -> PreTrainedConfig:
+    output_config = copy.copy(config)
+    del output_config._heterogeneity_spec
+
+    layer_config = config.per_layer_config.get(layer_idx, None)
+
+    if layer_config is not None:
+        for attr in layer_config.attributes:
+            if attr.startswith("skip_"):
+                setattr(output_config, attr, getattr(layer_config, attr))
+
+    for attr in config.per_layer_attributes:
+        value = config._heterogeneity_spec.fallback_values[attr]
+        if layer_config is not None:
+            value = getattr(layer_config, attr, value)
+        setattr(output_config, attr, value)
+
+    return output_config
+
+
+def _validate_num_hetero_layers(config: PreTrainedConfig, per_layer_config: dict[int, LayerConfig]) -> None:
+    if not per_layer_config:
+        return
+
+    num_hidden_layers = config.num_hidden_layers
+    max_layer_idx = max(per_layer_config.keys())
+    if max_layer_idx >= num_hidden_layers:
+        raise ValueError(
+            f"The number of hidden layers ({num_hidden_layers}) does not match the indices of `per_layer_config` (the maximal index is {max_layer_idx})"
+        )
+
+
+def _validate_sliding_window_and_attention_chunk_size(
+    config: PreTrainedConfig, per_layer_config: dict[int, LayerConfig]
+) -> None:
+    problematic_indices = []
+    for layer_idx in range(config.num_hidden_layers):
+        layer_config = per_layer_config.get(layer_idx)
+        if layer_config is None:
+            layer_config = LayerConfig()
+
+        sliding_window = getattr(layer_config, "sliding_window", getattr(config, "sliding_window", None))
+        attention_chunk_size = getattr(
+            layer_config, "attention_chunk_size", getattr(config, "attention_chunk_size", None)
+        )
+
+        if sliding_window is not None and attention_chunk_size is not None:
+            problematic_indices.append(layer_idx)
+
+    if problematic_indices:
+        raise ValueError(
+            f"The following layers have the mutually exclusive `sliding_window` and `attention_chunk_size` both defined: "
+            f"{problematic_indices}. To fix this, either remove a conflicting attribute from the global config,"
+            f"or set it to `None` in `per_layer_config` for the problematic layers."
+        )
+
+
+def _modify_config_and_create_heterogeneity_spec(
+    config: PreTrainedConfig, per_layer_config: dict[int, LayerConfig], explicit: bool
+) -> HeterogeneitySpec:
+    per_layer_attributes = _get_per_layer_attributes(per_layer_config)
+
+    # Ensure all required global attributes are defined
+    missing_required_global_attributes = set()
+    for attr in per_layer_attributes:
+        if len(per_layer_config) != config.num_hidden_layers:
+            if not hasattr(config, attr):
+                missing_required_global_attributes.add(attr)
+        else:
+            for layer_config in per_layer_config.values():
+                if not hasattr(layer_config, attr):
+                    if not hasattr(config, attr):
+                        missing_required_global_attributes.add(attr)
+                    break
+
+    if missing_required_global_attributes:
+        raise ValueError(
+            f"The following attributes are missing: {sorted(missing_required_global_attributes)}\nPlease add them globally, or make sure they are defined in all of the per-layer configs"
+        )
+
+    for attr in per_layer_attributes:
+        # Gather all values for this attribute across all layers,
+        # and if `explicit` is True, enforce that `per_layer_config` has a LayerConfig for each layer
+        # and that each layer has all per-layer attributes defined.
+        values_list = []
+        for layer_idx in range(config.num_hidden_layers):
+            layer_config = per_layer_config.get(layer_idx)
+
+            if explicit:
+                if layer_config is None:
+                    layer_config = LayerConfig()
+                    per_layer_config[layer_idx] = layer_config
+
+                if not hasattr(layer_config, attr):
+                    setattr(layer_config, attr, getattr(config, attr))
+
+            value = (
+                getattr(layer_config, attr)
+                if layer_config is not None and hasattr(layer_config, attr)
+                else getattr(config, attr)
+            )
+            if value not in values_list:
+                values_list.append(value)
+
+        if not explicit and len(values_list) == 1:
+            # All layer configs have the same value for this attribute, so it can be a global attribute
+            setattr(config, attr, values_list[0])
+            for layer_idx, layer_config in per_layer_config.items():
+                if hasattr(layer_config, attr):
+                    delattr(layer_config, attr)
+
+    # Delete all empty layer configs
+    for layer_idx, layer_config in list(per_layer_config.items()):
+        if not layer_config.attributes:
+            del per_layer_config[layer_idx]
+
+    per_layer_attributes = _get_per_layer_attributes(per_layer_config)
+    fallback_values = {attr: getattr(config, attr, None) for attr in per_layer_attributes}
+
+    heterogeneity_spec = HeterogeneitySpec(
+        per_layer_config=per_layer_config,
+        per_layer_attributes=per_layer_attributes,
+        fallback_values=fallback_values,
+    )
+    return heterogeneity_spec
+
+
+def _get_per_layer_attributes(per_layer_config: dict[int, LayerConfig]) -> set[str]:
+    return {
+        attr
+        for layer_config in per_layer_config.values()
+        for attr in layer_config.attributes
+        if not attr.startswith("skip_")
+    }
diff --git a/src/transformers/hf_argparser.py b/src/transformers/hf_argparser.py
index b9e6f99b041d..e0005d9f8864 100644
--- a/src/transformers/hf_argparser.py
+++ b/src/transformers/hf_argparser.py
@@ -175,10 +175,16 @@ def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
                     " the argument parser only supports one type per argument."
                     f" Problem encountered in field '{field.name}'."
                 )
+            # filter `dict` in Union because argparse does not support it
+            if dict in field.type.__args__:
+                field.type = Union[tuple(arg for arg in field.type.__args__ if arg is not dict)]
             if type(None) not in field.type.__args__:
-                # filter `str` in Union
-                field.type = field.type.__args__[0] if field.type.__args__[1] is str else field.type.__args__[1]
-                origin_type = getattr(field.type, "__origin__", field.type)
+                if len(field.type.__args__) > 2:
+                    origin_type = str
+                else:
+                    # filter `str` in Union
+                    field.type = field.type.__args__[0] if field.type.__args__[1] is str else field.type.__args__[1]
+                    origin_type = getattr(field.type, "__origin__", field.type)
             elif bool not in field.type.__args__:
                 # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                 field.type = (
@@ -189,6 +195,12 @@ def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
         # A variable to store kwargs for a boolean field, if needed
         # so that we can init a `no_*` complement argument (see below)
         bool_kwargs = {}
+        is_optional_bool_type = (
+            (origin_type is Union or (hasattr(types, "UnionType") and isinstance(origin_type, types.UnionType)))
+            and hasattr(field.type, "__args__")
+            and bool in field.type.__args__
+            and type(None) in field.type.__args__
+        )
         if origin_type is Literal or (isinstance(field.type, type) and issubclass(field.type, Enum)):
             if origin_type is Literal:
                 kwargs["choices"] = field.type.__args__
@@ -201,7 +213,7 @@ def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
                 kwargs["default"] = field.default
             else:
                 kwargs["required"] = True
-        elif field.type is bool or field.type == bool | None:
+        elif field.type is bool or field.type == bool | None or is_optional_bool_type:
             # Copy the correct kwargs to use to instantiate a `no_*` complement argument below.
             # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
             bool_kwargs = copy(kwargs)
@@ -217,6 +229,11 @@ def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
                 kwargs["nargs"] = "?"
                 # This is the value that will get picked if we do --{field.name} (without value)
                 kwargs["const"] = True
+            elif is_optional_bool_type:
+                # Keep default None for Optional[bool], but allow `--flag` with no explicit value.
+                kwargs["default"] = None if field.default is dataclasses.MISSING else field.default
+                kwargs["nargs"] = "?"
+                kwargs["const"] = True
         elif isclass(origin_type) and issubclass(origin_type, list):
             kwargs["type"] = field.type.__args__[0]
             kwargs["nargs"] = "+"
@@ -238,7 +255,7 @@ def _parse_dataclass_field(parser: ArgumentParser, field: dataclasses.Field):
         # Order is important for arguments with the same destination!
         # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
         # here and we do not need those changes/additional keys.
-        if field.default is True and (field.type is bool or field.type == bool | None):
+        if field.default is True and (field.type is bool or field.type == bool | None or is_optional_bool_type):
             bool_kwargs["default"] = False
             parser.add_argument(
                 f"--no_{field.name}",
diff --git a/src/transformers/image_processing_utils.py b/src/transformers/image_processing_utils.py
index 704001c476a6..74069f93aff6 100644
--- a/src/transformers/image_processing_utils.py
+++ b/src/transformers/image_processing_utils.py
@@ -675,6 +675,10 @@ def get_patch_output_size(image, target_resolution, input_data_format):
     original_height, original_width = get_image_size(image, channel_dim=input_data_format)
     target_height, target_width = target_resolution
 
+    if original_width == 0:
+        raise ValueError("original_width can not be 0")
+    if original_height == 0:
+        raise ValueError("original_height can not be 0")
     scale_w = target_width / original_width
     scale_h = target_height / original_height
 
diff --git a/src/transformers/image_transforms.py b/src/transformers/image_transforms.py
index 88160d1bced3..9ea4bfed897e 100644
--- a/src/transformers/image_transforms.py
+++ b/src/transformers/image_transforms.py
@@ -15,7 +15,7 @@
 from collections import defaultdict
 from collections.abc import Collection, Iterable
 from math import ceil
-from typing import Optional, Union
+from typing import Any, Optional, Union, overload
 
 import numpy as np
 
@@ -26,7 +26,7 @@
     get_image_size,
     infer_channel_dimension_format,
 )
-from .utils import ExplicitEnum, TensorType, is_torch_tensor
+from .utils import ExplicitEnum, is_torch_tensor
 from .utils.import_utils import (
     is_torch_available,
     is_vision_available,
@@ -547,7 +547,15 @@ def _center_to_corners_format_numpy(bboxes_center: np.ndarray) -> np.ndarray:
 
 
 # 2 functions below inspired by https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
-def center_to_corners_format(bboxes_center: TensorType) -> TensorType:
+@overload
+def center_to_corners_format(bboxes_center: "torch.Tensor") -> "torch.Tensor": ...
+
+
+@overload
+def center_to_corners_format(bboxes_center: np.ndarray) -> np.ndarray: ...
+
+
+def center_to_corners_format(bboxes_center: "torch.Tensor | np.ndarray") -> Any:
     """
     Converts bounding boxes from center format to corners format.
 
@@ -590,7 +598,15 @@ def _corners_to_center_format_numpy(bboxes_corners: np.ndarray) -> np.ndarray:
     return bboxes_center
 
 
-def corners_to_center_format(bboxes_corners: TensorType) -> TensorType:
+@overload
+def corners_to_center_format(bboxes_corners: "torch.Tensor") -> "torch.Tensor": ...
+
+
+@overload
+def corners_to_center_format(bboxes_corners: np.ndarray) -> np.ndarray: ...
+
+
+def corners_to_center_format(bboxes_corners: "torch.Tensor | np.ndarray") -> Any:
     """
     Converts bounding boxes from corners format to center format.
 
diff --git a/src/transformers/image_utils.py b/src/transformers/image_utils.py
index 984d80964fad..4c2226113d97 100644
--- a/src/transformers/image_utils.py
+++ b/src/transformers/image_utils.py
@@ -14,9 +14,11 @@
 
 import base64
 import os
+import warnings
 from collections.abc import Iterable
 from dataclasses import dataclass, fields
 from io import BytesIO
+from pathlib import Path
 from typing import Any, Union
 
 import httpx
@@ -463,14 +465,14 @@ def valid_coco_panoptic_annotations(annotations: Iterable[dict[str, list | tuple
 
 
 def load_image(
-    image: Union[str, "PIL.Image.Image"],
+    image: Union[str, Path, "PIL.Image.Image"],
     timeout: float | None = None,
 ) -> "PIL.Image.Image":
     """
     Loads `image` to a PIL Image.
 
     Args:
-        image (`str` or `PIL.Image.Image`):
+        image (`str`, `Path` or `PIL.Image.Image`):
             The image to convert to the PIL Image format.
         timeout (`float`, *optional*):
             The timeout value in seconds for the URL request.
@@ -479,6 +481,9 @@ def load_image(
         `PIL.Image.Image`: A PIL Image.
     """
     requires_backends(load_image, ["vision"])
+    if isinstance(image, Path):
+        image = str(image)
+
     if isinstance(image, str):
         if image.startswith("http://") or image.startswith("https://"):
             # We need to actually check for a real protocol, otherwise it's impossible to use a local file
@@ -997,11 +1002,26 @@ def validate_annotations(
 
 
 def validate_kwargs(valid_processor_keys: list[str], captured_kwargs: list[str]):
+    """
+    Validates that captured kwargs are recognized processor keys.
+
+    Args:
+        valid_processor_keys (`list[str]`):
+            List of valid processor parameter names.
+        captured_kwargs (`list[str]`):
+            List of captured keyword argument names to validate.
+
+    Warns:
+        UserWarning: When unused or unrecognized kwargs are found.
+    """
     unused_keys = set(captured_kwargs).difference(set(valid_processor_keys))
     if unused_keys:
         unused_key_str = ", ".join(unused_keys)
-        # TODO raise a warning here instead of simply logging?
-        logger.warning(f"Unused or unrecognized kwargs: {unused_key_str}.")
+        warnings.warn(
+            f"Unused or unrecognized kwargs: {unused_key_str}. These arguments will be ignored.",
+            UserWarning,
+            stacklevel=2,
+        )
 
 
 @dataclass()
diff --git a/src/transformers/initialization.py b/src/transformers/initialization.py
index b0ebb053086b..28072ba3b022 100644
--- a/src/transformers/initialization.py
+++ b/src/transformers/initialization.py
@@ -15,6 +15,7 @@
 import sys
 from collections import defaultdict
 from contextlib import contextmanager
+from contextvars import ContextVar
 
 import torch
 
@@ -38,6 +39,19 @@
     "sparse_": torch.nn.init.sparse_,
 }
 
+# Track the current no-tie scope per execution context so concurrent model loads
+# do not leak tie_weights suppression across threads.
+_SKIP_TIE_WEIGHTS_SCOPE: ContextVar[object | None] = ContextVar("_SKIP_TIE_WEIGHTS_SCOPE", default=None)
+
+
+def should_skip_tie_weights(model) -> bool:
+    scope = _SKIP_TIE_WEIGHTS_SCOPE.get()
+    if scope is None:
+        return False
+
+    # Only skip tying for the model instance created inside the active scope.
+    return getattr(model, "_skip_tie_weights_scope", None) is scope
+
 
 def uniform_(
     tensor: torch.Tensor, a: float = 0.0, b: float = 1.0, generator: torch.Generator | None = None
@@ -287,19 +301,13 @@ def no_tie_weights():
     weights in the state_dict during `from_pretrained`, and otherwise tying them would remove them from it, as it's
     called in `post_init` when instantiating.
     """
-    from .modeling_utils import PreTrainedModel
-
-    def empty_func(*args, **kwargs):
-        pass
-
+    # Use an opaque scope token so nested or concurrent loads can identify only
+    # the models instantiated under this context manager.
+    state_token = _SKIP_TIE_WEIGHTS_SCOPE.set(object())
     try:
-        original_tie_weights = PreTrainedModel.tie_weights
-        PreTrainedModel.tie_weights = empty_func
-
         yield
     finally:
-        # Set back the original
-        PreTrainedModel.tie_weights = original_tie_weights
+        _SKIP_TIE_WEIGHTS_SCOPE.reset(state_token)
 
 
 @contextmanager
diff --git a/src/transformers/integrations/__init__.py b/src/transformers/integrations/__init__.py
index 336db3773f76..60a7196b11f9 100755
--- a/src/transformers/integrations/__init__.py
+++ b/src/transformers/integrations/__init__.py
@@ -35,6 +35,7 @@
         "replace_with_bnb_linear",
         "validate_bnb_backend_availability",
     ],
+    "compressed_tensors_fp8": ["CTFP8Linear", "replace_with_ct_fp8_linear"],
     "deepspeed": [
         "HfDeepSpeedConfig",
         "HfTrainerDeepSpeedConfig",
@@ -194,6 +195,7 @@
         replace_with_bnb_linear,
         validate_bnb_backend_availability,
     )
+    from .compressed_tensors_fp8 import CTFP8Linear, replace_with_ct_fp8_linear
     from .deepspeed import (
         HfDeepSpeedConfig,
         HfTrainerDeepSpeedConfig,
diff --git a/src/transformers/integrations/accelerate.py b/src/transformers/integrations/accelerate.py
index c2b7fa603570..75533c963ada 100644
--- a/src/transformers/integrations/accelerate.py
+++ b/src/transformers/integrations/accelerate.py
@@ -399,7 +399,10 @@ def accelerate_dispatch(model, hf_quantizer, device_map, offload_folder, offload
     ):
         device_map_kwargs["offload_buffers"] = True
 
-    if not is_fsdp_enabled() and not is_deepspeed_zero3_enabled():
+    is_quantized_bnb = (
+        hf_quantizer is not None and hf_quantizer.quantization_config.quant_method == QuantizationMethod.BITS_AND_BYTES
+    )
+    if not is_fsdp_enabled() and not is_deepspeed_zero3_enabled() and not is_quantized_bnb:
         dispatch_model(model, **device_map_kwargs)
 
 
@@ -446,15 +449,13 @@ def accelerate_disk_offload(
     renamed) will be mapped to where they already reside on disk. Otherwise, the parameters will be resaved inside
     `disk_offload_folder` during loading.
     """
-    from ..core_model_loading import WeightRenaming, rename_source_key
+    from ..core_model_loading import rename_source_key
 
     if disk_offload_folder is not None:
         os.makedirs(disk_offload_folder, exist_ok=True)
     is_offloaded_safetensors = checkpoint_files is not None and checkpoint_files[0].endswith(".safetensors")
 
-    renamings = []
-    if weight_mapping is not None:
-        renamings = [entry for entry in weight_mapping if isinstance(entry, WeightRenaming)]
+    transforms = weight_mapping if weight_mapping is not None else []
 
     # In this case, the offload index is simply the existing safetensors (except if using custom weight loading
     # Operation, e.g. the MoE models, where we need to resave the weights that were changed at loading time)
@@ -470,7 +471,7 @@ def accelerate_disk_offload(
 
         # Update the weight names according to the `weight_mapping`
         weight_renaming_map = {
-            rename_source_key(k, renamings, [], model.base_model_prefix, meta_state_dict)[0]: k for k in weight_map
+            rename_source_key(k, transforms, model.base_model_prefix, meta_state_dict)[0]: k for k in weight_map
         }
 
         # Prepare the index using existing safetensors files
diff --git a/src/transformers/integrations/compressed_tensors_fp8.py b/src/transformers/integrations/compressed_tensors_fp8.py
new file mode 100644
index 000000000000..c32d09877636
--- /dev/null
+++ b/src/transformers/integrations/compressed_tensors_fp8.py
@@ -0,0 +1,294 @@
+# Copyright 2026 The HuggingFace Inc. team and Intel Corporation. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Compressed-tensors FP8 integration for transformers.
+
+Supports loading compressed-tensors FP8 checkpoints (per-channel and per-tensor)
+via dequantization to BF16 followed by standard matmul. The primary benefit is
+memory savings (FP8 weights use half the memory of BF16).
+
+Supported models:
+  - Per-channel dynamic: e.g. RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8-dynamic
+  - Per-tensor static: e.g. RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8
+"""
+
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+from ..core_model_loading import ConversionOps, _IdentityOp
+from ..quantizers.quantizers_utils import should_convert_module
+from ..utils import is_fbgemm_gpu_available, is_torch_xpu_available, logging
+
+
+logger = logging.get_logger(__name__)
+
+_FP8_DTYPE = torch.float8_e4m3fn
+_FP8_MIN = torch.finfo(_FP8_DTYPE).min
+_FP8_MAX = torch.finfo(_FP8_DTYPE).max
+
+_is_torch_xpu_available = is_torch_xpu_available()
+
+if is_fbgemm_gpu_available() and not _is_torch_xpu_available:
+    import fbgemm_gpu.experimental.gen_ai  # noqa: F401
+
+# Will be initialized lazily in replace_with_ct_fp8_linear
+quantize_fp8_per_row = None
+
+
+class CTFP8Linear(nn.Linear):
+    """Linear layer for compressed-tensors FP8 models.
+
+    Stores weights in FP8 format and uses row-wise FP8 matmul kernels for compute:
+    - XPU: torch._scaled_mm
+    - CUDA: fbgemm.f8f8bf16_rowwise
+
+    Activation is dynamically quantized per-row via quantize_fp8_per_row.
+    Weight scale (per-channel or per-tensor) is stored as weight_scale_inv.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        activation_scheme: str = "dynamic",
+        has_bias: bool = False,
+        dtype=_FP8_DTYPE,
+    ):
+        super().__init__(in_features, out_features)
+
+        self.has_bias = has_bias
+        self.activation_scheme = activation_scheme
+        self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
+
+        # Weight scale: per-channel (out_features, 1) or per-tensor (scalar → expanded at load)
+        self.weight_scale_inv = nn.Parameter(torch.zeros((out_features, 1), dtype=torch.float32))
+
+        if self.has_bias:
+            self.bias = nn.Parameter(torch.empty(self.out_features))
+        else:
+            self.register_parameter("bias", None)
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        # If weights are not FP8 (e.g. already dequantized), just do normal linear
+        if self.weight.element_size() > 1:
+            return F.linear(input, self.weight, self.bias)
+
+        # Save shape for restoring after squashing batch dims
+        output_shape = (*input.shape[:-1], -1)
+
+        # Dynamically quantize activation per-row to FP8
+        x_quantized, x_scale = quantize_fp8_per_row(input.view(-1, input.shape[-1]).contiguous())
+
+        weight_scale_float32 = self.weight_scale_inv.to(torch.float32)
+
+        # Ensure scale_b has shape (1, out_features) for row-wise _scaled_mm
+        # Per-channel: (out_features, 1) → .t() → (1, out_features) ✓
+        # Per-tensor: (1, 1) → need to expand to (1, out_features)
+        scale_b = weight_scale_float32.t()
+        if scale_b.shape[-1] == 1 and self.out_features > 1:
+            scale_b = scale_b.expand(1, self.out_features).contiguous()
+
+        if _is_torch_xpu_available:
+            output = torch._scaled_mm(
+                x_quantized,
+                self.weight.t(),
+                scale_a=x_scale.unsqueeze(-1),
+                scale_b=scale_b,
+                out_dtype=input.dtype,
+                bias=self.bias,
+            )
+        else:
+            output = torch.ops.fbgemm.f8f8bf16_rowwise(
+                x_quantized, self.weight, x_scale, weight_scale_float32, use_fast_accum=True
+            )
+            output = output + self.bias if self.bias is not None else output
+
+        output = output.to(input.device)
+        output = output.reshape(output_shape)
+        del x_quantized, x_scale
+        return output
+
+
+def replace_with_ct_fp8_linear(
+    model, modules_to_not_convert=None, activation_scheme="dynamic", dequantize=False, pre_quantized=False
+):
+    """Replace all nn.Linear modules with CTFP8Linear for compressed-tensors FP8 loading."""
+    from .fbgemm_fp8 import get_quantize_fp8_per_row
+
+    global quantize_fp8_per_row
+    quantize_fp8_per_row = get_quantize_fp8_per_row()
+
+    if dequantize:
+        return model
+
+    has_been_replaced = False
+    for module_name, module in model.named_modules():
+        if not should_convert_module(module_name, modules_to_not_convert):
+            continue
+
+        module_kwargs = {} if pre_quantized else {"dtype": None}
+        if isinstance(module, nn.Linear):
+            with torch.device("meta"):
+                new_module = CTFP8Linear(
+                    in_features=module.in_features,
+                    out_features=module.out_features,
+                    activation_scheme=activation_scheme,
+                    has_bias=module.bias is not None,
+                    **module_kwargs,
+                )
+            model.set_submodule(module_name, new_module)
+            has_been_replaced = True
+
+    if not has_been_replaced:
+        logger.warning(
+            "You are loading your model using compressed-tensors FP8 but no linear modules were found. "
+            "Please double check your model architecture."
+        )
+    return model
+
+
+# ─── Weight Converters ────────────────────────────────────────────────────────
+
+
+class CompressedTensorsScaleConvert(ConversionOps):
+    """Convert compressed-tensors `weight_scale` to `weight_scale_inv`.
+
+    In compressed-tensors, `weight_scale` is the dequantization multiplier:
+        bf16_weight = fp8_weight * weight_scale
+
+    In our CTFP8Linear, `weight_scale_inv` has the same semantics (it's multiplied
+    with the FP8 weight to get the dequantized value), so no inversion is needed.
+    """
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(self, input_dict, **kwargs):
+        # The key in input_dict is the source_pattern string (e.g. "weight_scale$")
+        scale_key = next(k for k in input_dict if "weight_scale" in k)
+        scale = input_dict[scale_key][0]
+
+        # Cast to float32, ensure (out_features, 1) shape for row-wise kernel
+        dequant_scale = scale.to(torch.float32)
+        if dequant_scale.dim() == 0:
+            # Per-tensor scalar → expand to (out_features, 1)
+            # out_features is inferred from the weight shape at runtime
+            # For now store as (1, 1) and let _scaled_mm broadcast
+            dequant_scale = dequant_scale.reshape(1, 1)
+        elif dequant_scale.dim() == 1:
+            # Per-channel (N,) → (N, 1)
+            dequant_scale = dequant_scale.unsqueeze(-1)
+        # else: already 2D (N, 1), keep as-is
+
+        return {"weight_scale_inv": dequant_scale}
+
+    @property
+    def reverse_op(self):
+        return _IdentityOp()
+
+
+class CompressedTensorsActivationScaleConvert(ConversionOps):
+    """Rename compressed-tensors `input_scale` to `activation_scale`."""
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(self, input_dict, **kwargs):
+        scale = input_dict["input_scale"][0]
+        return {"activation_scale": scale.to(torch.float32)}
+
+    @property
+    def reverse_op(self):
+        return _IdentityOp()
+
+
+class CompressedTensorsFp8Dequantize(ConversionOps):
+    """Dequantize compressed-tensors FP8 weights back to BF16.
+
+    Used when `dequantize=True`: loads FP8 weights + scale, produces BF16 weights.
+    """
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(self, input_dict, full_layer_name=None, **kwargs):
+        if len(input_dict) < 2:
+            weight_key = next(k for k in input_dict if "weight" in k)
+            return {full_layer_name: input_dict[weight_key]}
+
+        weight_key = next(k for k in input_dict if k.endswith("weight") or k.endswith("weight$"))
+        scale_key = next(k for k in input_dict if "weight_scale" in k and "inv" not in k)
+        quantized = input_dict[weight_key][0]
+        scale = input_dict[scale_key][0]
+
+        quantized_float = quantized.to(torch.float32)
+        if scale.dim() == 0:
+            # Per-tensor: scalar scale
+            dequantized = quantized_float * scale
+        elif scale.dim() == 1:
+            # Per-channel: (N,) scale, broadcast over K dimension
+            dequantized = quantized_float * scale.unsqueeze(-1)
+        else:
+            dequantized = quantized_float * scale
+
+        return {full_layer_name: dequantized.to(torch.bfloat16)}
+
+    @property
+    def reverse_op(self):
+        return _IdentityOp()
+
+
+class CTFP8PerRowQuantize(ConversionOps):
+    """Online quantization: convert BF16 weight to FP8 per-row.
+
+    For each row of the weight matrix, computes:
+        scale = max_abs(row) / FP8_MAX
+        quantized_row = clamp(row / scale, FP8_MIN, FP8_MAX).to(FP8)
+        weight_scale_inv = scale  (dequant multiplier)
+
+    Used when loading a BF16 model with CompressedTensorsConfig for online FP8.
+    """
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(self, input_dict, **kwargs):
+        # input_dict = {target_key: [bf16_weight_tensor]}
+        target_key, value = next(iter(input_dict.items()))
+        weight = value[0].to(torch.float32)
+
+        # Per-row quantization: one scale per output channel
+        row_max_abs = weight.abs().amax(dim=-1)  # (out_features,)
+        safe_max = torch.where(row_max_abs > 0, row_max_abs, torch.ones_like(row_max_abs))
+        scales = safe_max / _FP8_MAX  # dequant scale: bf16 = fp8 * scale
+
+        # Quantize
+        quantized = torch.clamp(weight / scales.unsqueeze(-1), min=_FP8_MIN, max=_FP8_MAX).to(_FP8_DTYPE)
+
+        # Derive scale key: model.layers.0.xxx.weight -> model.layers.0.xxx.weight_scale_inv
+        if target_key.endswith("weight"):
+            scale_key = target_key.rsplit(".", 1)[0] + ".weight_scale_inv"
+        else:
+            scale_key = target_key + "_scale_inv"
+
+        # weight_scale_inv shape: (out_features, 1) for row-wise kernel
+        return {
+            target_key: quantized,
+            scale_key: scales.unsqueeze(-1),
+        }
+
+    @property
+    def reverse_op(self):
+        return _IdentityOp()
diff --git a/src/transformers/integrations/deepgemm.py b/src/transformers/integrations/deepgemm.py
new file mode 100644
index 000000000000..c3af8f61c5fb
--- /dev/null
+++ b/src/transformers/integrations/deepgemm.py
@@ -0,0 +1,389 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""DeepGEMM integration: fused grouped GEMM kernels from `kernels-community/deep-gemm`.
+
+Provides:
+- `fp8_deepgemm_matmul`: FP8 dense matmul used as a fast path inside the finegrained-fp8 Linear.
+- `fp8_deepgemm_experts_forward`: FP8 M-grouped experts forward, registered as "deepgemm" in the FP8 ExpertsInterface.
+- `deepgemm_experts_forward`: BF16 M-grouped experts forward, registered as "deepgemm" in the ExpertsInterface.
+
+Requirements: CUDA, Hopper (SM90+), CUDA runtime >= 12.3, `kernels`.
+"""
+
+from __future__ import annotations
+
+import functools
+
+import torch
+
+from ..utils import logging
+from ..utils.import_utils import get_cuda_runtime_version, is_kernels_available, resolve_internal_import
+from .hub_kernels import lazy_load_kernel
+
+
+logger = logging.get_logger(__name__)
+
+# DeepGEMM requires M-dimension alignment to 128 for TMA-based contiguous grouped GEMM.
+# TMA is an H100 hardware addition that allows applications to asynchronously and
+# bi-directionally transfer 1D-5D tensors between GPU global and shared memory.
+_DEEPGEMM_M_ALIGNMENT = 128
+
+
+@functools.cache
+def _load_deepgemm_kernel():
+    """
+    Load DeepGEMM once and return its required symbols.
+
+    Raises:
+        ImportError if CUDA/hardware requirements are not met, or the kernel or
+        required symbols are not found.
+
+    Returns:
+        Tuple of (deepgemm_fp8_matmul, deepgemm_grouped_fp8_matmul,
+                  deepgemm_grouped_bf16_matmul_nt, deepgemm_grouped_bf16_matmul_nn,
+                  deepgemm_per_token_cast_to_fp8) from the DeepGEMM kernel.
+    """
+    if not is_kernels_available():
+        raise ImportError("DeepGEMM kernel requires the `kernels` package. Install it with `pip install -U kernels`.")
+
+    if not torch.cuda.is_available():
+        raise ImportError(
+            "DeepGEMM kernel requires CUDA, but CUDA is not available. Use a different `experts_implementation`."
+        )
+
+    # DeepGEMM requires Hopper (SM90) or newer for FP8 WGMMA instructions
+    major = torch.cuda.get_device_capability()[0]
+    if major < 9:
+        raise ImportError(
+            f"DeepGEMM requires a Hopper (SM90+) or newer GPU, but the current device "
+            f"has compute capability {major}.x. Use a different `experts_implementation`."
+        )
+
+    # DeepGEMM requires CUDA runtime >= 12.3
+    cuda_major, cuda_minor = get_cuda_runtime_version()
+    if cuda_major < 12 or (cuda_major == 12 and cuda_minor < 3):
+        raise ImportError(
+            f"DeepGEMM requires CUDA runtime 12.3+, but found {cuda_major}.{cuda_minor}. "
+            "Please upgrade your CUDA toolkit or use a different `experts_implementation`."
+        )
+
+    kernel = lazy_load_kernel("deep-gemm")
+    if kernel is None:
+        raise ImportError(
+            "Failed to load the DeepGEMM kernel — check that `kernels-community/deep-gemm` "
+            "has a build matching the current torch/CUDA."
+        )
+
+    deepgemm_fp8_matmul = getattr(kernel, "fp8_gemm_nt", None)
+    deepgemm_grouped_fp8_matmul = getattr(kernel, "m_grouped_fp8_gemm_nt_contiguous", None)
+    deepgemm_grouped_bf16_matmul_nt = getattr(kernel, "m_grouped_bf16_gemm_nt_contiguous", None)
+    deepgemm_grouped_bf16_matmul_nn = getattr(kernel, "m_grouped_bf16_gemm_nn_contiguous", None)
+    deepgemm_per_token_cast_to_fp8 = resolve_internal_import(kernel, chained_path="utils.per_token_cast_to_fp8")
+
+    missing = [
+        name
+        for name, attr in [
+            ("fp8_gemm_nt", deepgemm_fp8_matmul),
+            ("m_grouped_fp8_gemm_nt_contiguous", deepgemm_grouped_fp8_matmul),
+            ("m_grouped_bf16_gemm_nt_contiguous", deepgemm_grouped_bf16_matmul_nt),
+            ("m_grouped_bf16_gemm_nn_contiguous", deepgemm_grouped_bf16_matmul_nn),
+            ("utils.per_token_cast_to_fp8", deepgemm_per_token_cast_to_fp8),
+        ]
+        if attr is None
+    ]
+    if missing:
+        raise ImportError(
+            f"DeepGEMM kernel is missing required symbols: {', '.join(missing)}. "
+            "Please update the `kernels` package (`pip install -U kernels`)."
+        )
+
+    return (
+        deepgemm_fp8_matmul,
+        deepgemm_grouped_fp8_matmul,
+        deepgemm_grouped_bf16_matmul_nt,
+        deepgemm_grouped_bf16_matmul_nn,
+        deepgemm_per_token_cast_to_fp8,
+    )
+
+
+def fp8_deepgemm_matmul(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    output_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """
+    FP8 dense matmul via DeepGEMM's `fp8_gemm_nt`. Block-wise 128x128 scales expected.
+
+    Args:
+        A:  (M, K) float8_e4m3fn — quantized activations
+        B:  (N, K) float8_e4m3fn — quantized weights
+        As: (M, K//128) float32 — per-block activation scales
+        Bs: (N//128, K//128) float32 — per-block weight scales
+        output_dtype: desired output dtype.
+    """
+    deepgemm_fp8_matmul, _, _, _, _ = _load_deepgemm_kernel()
+    A_2d = A.view(-1, A.shape[-1])
+    As_2d = As.view(-1, As.shape[-1])
+    output = torch.empty(A_2d.shape[0], B.shape[0], device=A.device, dtype=output_dtype)
+    deepgemm_fp8_matmul((A_2d, As_2d.float()), (B, Bs.float()), output)
+    return output.view(A.shape[:-1] + (B.shape[0],))
+
+
+def _build_deepgemm_contiguous_layout(
+    expert_ids_sorted: torch.Tensor, num_experts: int, alignment: int, use_psum_layout: bool
+) -> tuple:
+    """Build the TMA-aligned layout DeepGEMM's grouped GEMM expects.
+
+    Returns `(sorted_to_padded, grouped_layout, total_padded_rows)`. `grouped_layout` encodes
+    expert boundaries as a cumsum of aligned counts on Blackwell (`use_psum_layout=True`) or
+    per-row expert ids with -1 for padding on Hopper.
+
+    Accepts EP sentinels: values in `expert_ids_sorted` equal to `num_experts` (unclamped sentinels)
+    are routed past the last aligned expert block and marked `-1` in the Hopper layout (and
+    excluded from the Blackwell cumsum), so DeepGEMM skips them.
+    """
+    device = expert_ids_sorted.device
+    num_tokens = expert_ids_sorted.size(0)
+    # histc drops values > max, so EP sentinels (== num_experts) are excluded from the per-expert count.
+    tokens_per_expert = torch.histc(expert_ids_sorted.int(), bins=num_experts, min=0, max=num_experts - 1).long()
+    aligned_tokens_per_expert = ((tokens_per_expert + alignment - 1) // alignment) * alignment
+    # Upper bound avoids GPU->CPU sync; padding rows are skipped by DeepGEMM.
+    total_padded_rows = num_tokens + min(num_tokens, num_experts) * (alignment - 1)
+
+    # Zero-prepended inclusive cumsum of per-expert padding. Indices [0, num_experts) give the
+    # exclusive cumsum (padding before expert i) and index `num_experts` gives `sum(padding)`,
+    # which routes EP sentinels past all valid aligned expert blocks on Blackwell (where the
+    # kernel stops at `aligned_cumsum[-1]`) — so sentinels don't go through the GEMM.
+    padding_per_expert = aligned_tokens_per_expert - tokens_per_expert
+    cumulative_padding = torch.nn.functional.pad(padding_per_expert.cumsum(0), (1, 0))
+    sorted_to_padded = torch.arange(num_tokens, device=device) + cumulative_padding[expert_ids_sorted]
+
+    if use_psum_layout:  # Blackwell (SM100+)
+        # psum layout: cumsum of *aligned* per-expert counts — sentinels sit at positions >=
+        # `grouped_layout[-1]` (by construction of `cumulative_padding`), so the scheduler
+        # stops before them. The kernel's `num_m_blocks = ceil_div(layout[i] - align(layout[i-1], 128), BLOCK_M)`
+        # between experts only matches the padded tensor when the stored cumsum is over aligned counts.
+        grouped_layout = aligned_tokens_per_expert.cumsum(0).int()
+    else:
+        # Hopper: per-row expert id, -1 for padding rows and for sentinel slots (kernel skips -1).
+        grouped_layout = torch.full((total_padded_rows,), -1, device=device, dtype=torch.int32)
+        grouped_layout[sorted_to_padded] = torch.where(expert_ids_sorted < num_experts, expert_ids_sorted.int(), -1)
+
+    return sorted_to_padded, grouped_layout, total_padded_rows
+
+
+def _pad_for_deepgemm(x: torch.Tensor, sorted_to_padded: torch.Tensor, total_padded_rows: int) -> torch.Tensor:
+    """Pad a sorted tensor into the TMA-aligned contiguous layout.
+
+    Padding rows are left uninitialized — the kernel skips them via `grouped_layout=-1` (Hopper)
+    or via the psum offsets (Blackwell), so their values never enter the computation.
+    """
+    padded = torch.empty(total_padded_rows, *x.shape[1:], device=x.device, dtype=x.dtype)
+    padded[sorted_to_padded] = x
+    return padded
+
+
+def _unpad_from_deepgemm_contiguous_layout(x_padded: torch.Tensor, sorted_to_padded: torch.Tensor) -> torch.Tensor:
+    """Remove padding rows from the TMA-aligned contiguous layout."""
+    return x_padded[sorted_to_padded]
+
+
+def fp8_deepgemm_experts_forward(
+    self: torch.nn.Module,
+    hidden_states: torch.Tensor,
+    top_k_index: torch.Tensor,
+    top_k_weights: torch.Tensor,
+) -> torch.Tensor:
+    if self.activation_scheme == "static":
+        raise NotImplementedError(
+            "DeepGEMM experts dispatch does not support activation_scheme='static'. "
+            "Use the default eager dispatch or switch to activation_scheme='dynamic'."
+        )
+    if self.block_size is None:
+        raise ValueError(
+            "DeepGEMM requires block-wise quantization (block_size=[128, 128]), "
+            "but got per-tensor quantization (block_size=None)."
+        )
+    if self.block_size[0] != 128 or self.block_size[1] != 128:
+        raise ValueError(f"DeepGEMM requires block_size=(128, 128), got {self.block_size}")
+
+    _, deepgemm_grouped_fp8_matmul, _, _, deepgemm_per_token_cast_to_fp8 = _load_deepgemm_kernel()
+
+    device = hidden_states.device
+    num_top_k = top_k_index.size(-1)
+    num_tokens = hidden_states.size(0)
+    hidden_dim = hidden_states.size(-1)
+
+    # S is the number of selected token-expert pairs (S = num_tokens * num_top_k)
+    sample_weights = top_k_weights.reshape(-1)  # (S,)
+    expert_ids = top_k_index.reshape(-1)  # (S,)
+
+    # EP sentinel handling: leave `expert_ids` unclamped so the sort pushes sentinels to the tail,
+    # `_build_deepgemm_contiguous_layout` marks their positions as skipped (-1 on Hopper, beyond the
+    # cumsum on Blackwell), and DeepGEMM skips them — so sentinels cost no real GEMM compute.
+    # Sentinel rows are zeroed post-weighted-mul (see below), since the kernel leaves them uninitialized.
+    expert_ids_g, perm = torch.sort(expert_ids)
+    selected_hidden_states_g = hidden_states[perm // num_top_k]
+    sample_weights_g = sample_weights[perm]
+
+    use_psum_layout = torch.cuda.get_device_capability(device)[0] >= 10
+    sorted_to_padded, grouped_layout, total_padded_rows = _build_deepgemm_contiguous_layout(
+        expert_ids_g, self.num_experts, alignment=_DEEPGEMM_M_ALIGNMENT, use_psum_layout=use_psum_layout
+    )
+
+    # --- Up projection per expert (DeepGEMM grouped contiguous) ---
+    w_up = self.gate_up_proj if self.has_gate else self.up_proj
+    ws_up = self.gate_up_proj_scale_inv if self.has_gate else self.up_proj_scale_inv
+    act_fp8, act_scales = deepgemm_per_token_cast_to_fp8(selected_hidden_states_g, use_ue8m0=False)
+    act_fp8 = _pad_for_deepgemm(act_fp8, sorted_to_padded, total_padded_rows)
+    act_scales = _pad_for_deepgemm(act_scales, sorted_to_padded, total_padded_rows)
+    proj_out = torch.empty(total_padded_rows, w_up.shape[1], device=device, dtype=torch.bfloat16)
+    deepgemm_grouped_fp8_matmul(
+        (act_fp8, act_scales), (w_up, ws_up.float()), proj_out, grouped_layout, use_psum_layout=use_psum_layout
+    )
+
+    # Apply gating or activation
+    if self.has_gate:
+        proj_out = self._apply_gate(proj_out)
+    else:
+        proj_out = self.act_fn(proj_out)
+
+    # --- Down projection per expert (DeepGEMM grouped contiguous) ---
+    proj_fp8, proj_scales = deepgemm_per_token_cast_to_fp8(proj_out, use_ue8m0=False)
+    proj_out = torch.empty(total_padded_rows, hidden_dim, device=device, dtype=torch.bfloat16)
+    deepgemm_grouped_fp8_matmul(
+        (proj_fp8, proj_scales),
+        (self.down_proj, self.down_proj_scale_inv.float()),
+        proj_out,
+        grouped_layout,
+        use_psum_layout=use_psum_layout,
+    )
+
+    # Remove padding rows
+    proj_out = _unpad_from_deepgemm_contiguous_layout(proj_out, sorted_to_padded)
+
+    # Apply routing weights
+    weighted_out = proj_out * sample_weights_g.to(proj_out.dtype).unsqueeze(-1)  # (S, hidden_dim)
+
+    # EP sentinel handling: `proj_out` rows past the valid expert blocks are left uninitialized by the kernel,
+    # so `proj_out[sentinel] * 0 = 0 * NaN = NaN` can leak from allocator pool reuse. Zero them here
+    # so the downstream reduction stays finite even when the routing weight was already zero.
+    weighted_out.masked_fill_((expert_ids_g >= self.num_experts).unsqueeze(-1), 0.0)
+
+    # Restore original order
+    inv_perm = torch.empty_like(perm)
+    inv_perm[perm] = torch.arange(perm.size(0), device=device)
+    weighted_out = weighted_out[inv_perm]
+
+    # Accumulate results using deterministic reshape+sum instead of index_add_
+    # (index_add_ with duplicate indices is non-deterministic on CUDA due to atomicAdd)
+    final_hidden_states = weighted_out.view(num_tokens, num_top_k, hidden_dim).sum(dim=1)
+
+    return final_hidden_states.to(hidden_states.dtype)
+
+
+def deepgemm_experts_forward(
+    self: torch.nn.Module,
+    hidden_states: torch.Tensor,
+    top_k_index: torch.Tensor,
+    top_k_weights: torch.Tensor,
+) -> torch.Tensor:
+    if hidden_states.dtype != torch.bfloat16:
+        raise ValueError(f"DeepGEMM experts path requires bfloat16 hidden states, got {hidden_states.dtype}")
+
+    # Non-transposed HF experts have weight layout (E, N, K) -> NT kernel.
+    # Transposed HF experts have weight layout (E, K, N) -> NN kernel.
+    _, _, deepgemm_grouped_bf16_matmul_nt, deepgemm_grouped_bf16_matmul_nn, _ = _load_deepgemm_kernel()
+    deepgemm_grouped_bf16_matmul = (
+        deepgemm_grouped_bf16_matmul_nn if self.is_transposed else deepgemm_grouped_bf16_matmul_nt
+    )
+
+    device = hidden_states.device
+    num_top_k = top_k_index.size(-1)
+    num_tokens = hidden_states.size(0)
+    hidden_dim = hidden_states.size(-1)
+
+    # S is the number of selected token-expert pairs (S = num_tokens * num_top_k)
+    sample_weights = top_k_weights.reshape(-1)  # (S,)
+    expert_ids = top_k_index.reshape(-1)  # (S,)
+
+    # EP sentinel handling: leave `expert_ids` unclamped so the sort pushes sentinels to the tail,
+    # `_build_deepgemm_contiguous_layout` marks their positions as skipped (-1 on Hopper, beyond the
+    # cumsum on Blackwell), and DeepGEMM skips them — so sentinels cost no real GEMM compute.
+    # Sentinel rows are zeroed post-weighted-mul (see below), since the kernel leaves them uninitialized.
+    expert_ids_g, perm = torch.sort(expert_ids)
+    selected_hidden_states_g = hidden_states[perm // num_top_k]
+    sample_weights_g = sample_weights[perm]
+
+    use_psum_layout = torch.cuda.get_device_capability(device)[0] >= 10
+    sorted_to_padded, grouped_layout, total_padded_rows = _build_deepgemm_contiguous_layout(
+        expert_ids_g, self.num_experts, alignment=_DEEPGEMM_M_ALIGNMENT, use_psum_layout=use_psum_layout
+    )
+
+    if self.has_bias:
+        # Clamp now that the layout has been built — needed for the per-row bias gather below to stay
+        # in-bounds. Bias added to sentinel positions falls in rows the kernel skips, so harmless.
+        expert_ids_g.clamp_(0, self.num_experts - 1)
+
+    # --- Up projection per expert (DeepGEMM grouped contiguous, bf16) ---
+    w_up = self.gate_up_proj if self.has_gate else self.up_proj
+    # Output dim is the last weight axis when transposed (E, K, N), second axis when not (E, N, K).
+    up_out_dim = w_up.shape[-1] if self.is_transposed else w_up.shape[1]
+    act = _pad_for_deepgemm(selected_hidden_states_g, sorted_to_padded, total_padded_rows)
+    proj_out = torch.empty(total_padded_rows, up_out_dim, device=device, dtype=hidden_states.dtype)
+    deepgemm_grouped_bf16_matmul(act, w_up, proj_out, grouped_layout, use_psum_layout=use_psum_layout)
+
+    # The kernel has no bias input -> add per-expert bias in-place on the unpadded slice;
+    # padding rows get discarded at unpad time.
+    if self.has_bias:
+        up_bias = self.gate_up_proj_bias if self.has_gate else self.up_proj_bias
+        proj_out.index_add_(0, sorted_to_padded, up_bias[expert_ids_g])
+
+    # Apply gating or activation
+    if self.has_gate:
+        proj_out = self._apply_gate(proj_out)
+    else:
+        proj_out = self.act_fn(proj_out)
+
+    # --- Down projection per expert (DeepGEMM grouped contiguous, bf16) ---
+    out = torch.empty(total_padded_rows, hidden_dim, device=device, dtype=hidden_states.dtype)
+    deepgemm_grouped_bf16_matmul(proj_out, self.down_proj, out, grouped_layout, use_psum_layout=use_psum_layout)
+
+    if self.has_bias:
+        out.index_add_(0, sorted_to_padded, self.down_proj_bias[expert_ids_g])
+
+    # Remove padding rows
+    out = _unpad_from_deepgemm_contiguous_layout(out, sorted_to_padded)
+
+    # Apply routing weights
+    weighted_out = out * sample_weights_g.to(out.dtype).unsqueeze(-1)  # (S, hidden_dim)
+
+    # EP sentinel handling: `out` rows past the valid expert blocks are left uninitialized by the kernel,
+    # so `out[sentinel] * 0 = 0 * NaN = NaN` can leak from allocator pool reuse. Zero them here
+    # so the downstream reduction stays finite even when the routing weight was already zero.
+    weighted_out.masked_fill_((expert_ids_g >= self.num_experts).unsqueeze(-1), 0.0)
+
+    # Restore original order
+    inv_perm = torch.empty_like(perm)
+    inv_perm[perm] = torch.arange(perm.size(0), device=device)
+    weighted_out = weighted_out[inv_perm]
+
+    # Accumulate results using deterministic reshape+sum instead of index_add_
+    # (index_add_ with duplicate indices is non-deterministic on CUDA due to atomicAdd)
+    final_hidden_states = weighted_out.view(num_tokens, num_top_k, hidden_dim).sum(dim=1)
+
+    return final_hidden_states.to(hidden_states.dtype)
diff --git a/src/transformers/integrations/deepspeed.py b/src/transformers/integrations/deepspeed.py
index 9703f642f8bc..1caade7404ac 100644
--- a/src/transformers/integrations/deepspeed.py
+++ b/src/transformers/integrations/deepspeed.py
@@ -347,7 +347,7 @@ def _apply_weight_conversions_to_state_dict(model, state_dict, weight_mapping):
                 "in your DeepSpeed config or convert your checkpoint to the expected format first."
             )
 
-    from ..core_model_loading import WeightConverter, WeightRenaming, dot_natural_key, rename_source_key
+    from ..core_model_loading import WeightConverter, dot_natural_key, rename_source_key
 
     # Preserve metadata from the original state dict
     metadata = getattr(state_dict, "_metadata", None)
@@ -360,14 +360,13 @@ def _apply_weight_conversions_to_state_dict(model, state_dict, weight_mapping):
     for key, param in model.state_dict().items():
         model_state_dict[key] = torch.empty(param.shape, dtype=param.dtype, device="meta")
 
-    renamings = [entry for entry in weight_mapping if isinstance(entry, WeightRenaming)]
     converters = [entry for entry in weight_mapping if isinstance(entry, WeightConverter)]
 
     # Fast path: if we only have simple renamings and no converters, we can skip the expensive collection logic
     if len(converters) == 0:
         new_state_dict = {}
         for original_key, tensor in state_dict.items():
-            renamed_key, _ = rename_source_key(original_key, renamings, [], prefix, model_state_dict)
+            renamed_key, _ = rename_source_key(original_key, weight_mapping, prefix, model_state_dict)
             if renamed_key in model_state_dict:
                 new_state_dict[renamed_key] = tensor
         # Attach metadata to the new state dict
@@ -386,7 +385,7 @@ def _apply_weight_conversions_to_state_dict(model, state_dict, weight_mapping):
     sorted_keys = sorted(state_dict.keys(), key=lambda k: dot_natural_key(k))
     for original_key in sorted_keys:
         tensor = state_dict.pop(original_key)
-        renamed_key, source_pattern = rename_source_key(original_key, renamings, converters, prefix, model_state_dict)
+        renamed_key, source_pattern = rename_source_key(original_key, weight_mapping, prefix, model_state_dict)
 
         # Only process if the renamed key is in the model's state dict
         if renamed_key in model_state_dict:
@@ -643,7 +642,27 @@ def deepspeed_init(trainer, num_training_steps, inference=False):
     return optimizer, lr_scheduler
 
 
-def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path, load_module_strict=True):
+def convert_zero_checkpoint_to_universal_checkpoint(input_path, output_path, num_workers):
+    import argparse
+
+    from deepspeed.checkpoint.ds_to_universal import main as ds_to_universal_main
+
+    param_dict = {
+        "input_folder": input_path,
+        "output_folder": output_path,
+        "num_extract_workers": num_workers,
+        "num_merge_workers": num_workers // 2,
+        "keep_temp_folder": False,
+        "strict": True,
+        "inject_missing_state": True,
+    }
+    args = argparse.Namespace(**param_dict)
+    ds_to_universal_main(args)
+
+
+def deepspeed_load_checkpoint(
+    deepspeed_engine, checkpoint_path, load_module_strict=True, convert_deepspeed_universal_checkpoint=False
+):
     # it's possible that the user is trying to resume from model_path, which doesn't necessarily
     # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
     # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
@@ -654,6 +673,37 @@ def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path, load_module_str
 
     if len(deepspeed_checkpoint_dirs) > 0:
         logger.info(f"Attempting to resume from {checkpoint_path}")
+
+        if convert_deepspeed_universal_checkpoint:
+            assert len(deepspeed_checkpoint_dirs) == 1
+            import os
+
+            deepspeed_engine._config.load_universal_checkpoint = True
+            ckpt_list = deepspeed_engine._get_all_ckpt_names(
+                checkpoint_path, os.path.basename(deepspeed_checkpoint_dirs[0])
+            )
+            # We can get loaded_checkpoint_dp_world_size from any model file.
+            sd = deepspeed_engine.checkpoint_engine.load(ckpt_list[0], map_location="cpu")
+            loaded_checkpoint_dp_world_size = sd["dp_world_size"]
+
+            if loaded_checkpoint_dp_world_size != deepspeed_engine.dp_world_size:
+                deepspeed_engine._config.load_universal_checkpoint = True
+                if deepspeed_engine.global_rank == 0:
+                    convert_zero_checkpoint_to_universal_checkpoint(
+                        deepspeed_checkpoint_dirs[0],
+                        os.path.join(checkpoint_path, "universal_" + os.path.basename(deepspeed_checkpoint_dirs[0])),
+                        loaded_checkpoint_dp_world_size,
+                    )
+                    logger.info(
+                        f"Converted deepspeed checkpoint at {checkpoint_path} to universal format for "
+                        f"current world size {deepspeed_engine.dp_world_size}"
+                    )
+                from deepspeed import comm as dist
+
+                dist.barrier()
+            else:
+                deepspeed_engine._config.load_universal_checkpoint = False
+
         # this magically updates self.optimizer and self.lr_scheduler
         load_path, _ = deepspeed_engine.load_checkpoint(
             checkpoint_path,
diff --git a/src/transformers/integrations/executorch.py b/src/transformers/integrations/executorch.py
index 675a0ea5783a..c7003ebb1b0a 100644
--- a/src/transformers/integrations/executorch.py
+++ b/src/transformers/integrations/executorch.py
@@ -889,7 +889,13 @@ def __init__(self, model, max_static_cache_length, batch_size):
             self.register_buffer(f"value_cache_{i}", layer.values, persistent=False)
             self.register_buffer(f"cumulative_length_{i}", layer.cumulative_length, persistent=False)
 
-    def forward(self, decoder_input_ids, encoder_hidden_states, cache_position):
+    def forward(
+        self,
+        decoder_input_ids: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        cache_position: torch.Tensor,
+        encoder_attention_mask: torch.Tensor | None = None,
+    ):
         # Start by resetting static cache (it's needed to be able to run several generations with the same exported program,
         # as otherwise it's mutated in-place indefinitely - we cannot call reset in-between the `generate` as the program was
         # already exported)
@@ -900,6 +906,7 @@ def forward(self, decoder_input_ids, encoder_hidden_states, cache_position):
         outputs = self.decoder(
             input_ids=decoder_input_ids,
             encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_attention_mask,
             past_key_values=self.cache,
             use_cache=True,
         )
@@ -947,7 +954,7 @@ def _export_encoder(self, encoder_input_ids):
 
         return exported_encoder
 
-    def _export_decoder(self, decoder_input_ids, encoder_hidden_states, cache_position):
+    def _export_decoder(self, decoder_input_ids, encoder_hidden_states, cache_position, encoder_attention_mask=None):
         target_device = self.full_model.device
         wrapped_decoder = (
             Seq2SeqLMDecoderExportableModuleWithStaticCache(
@@ -963,27 +970,35 @@ def _export_decoder(self, decoder_input_ids, encoder_hidden_states, cache_positi
         decoder_input_ids = decoder_input_ids.to(target_device)
         encoder_hidden_states = encoder_hidden_states.to(target_device)
         cache_position = cache_position.to(target_device)
-
-        # Define dynamic dimension for encoder output sequence length
-        encoder_seq_len_dim = torch.export.Dim("encoder_hidden_seq_length", max=self.max_hidden_seq_length)
-
-        # Export the decoder
+        if encoder_attention_mask is not None:
+            encoder_attention_mask = encoder_attention_mask.to(target_device)
+
+        # Export the decoder.
+        # encoder_hidden_states uses a static shape to avoid a symbolic-shape
+        # conflict with the static KV cache size during torch.export. Callers
+        # that pad encoder inputs to a fixed max length (e.g. max_hidden_seq_length)
+        # should pass encoder_hidden_states of that shape.
         with torch.no_grad():
             exported_decoder = torch.export.export(
                 wrapped_decoder,
-                (decoder_input_ids, encoder_hidden_states, cache_position),
-                dynamic_shapes={
-                    "decoder_input_ids": None,
-                    "encoder_hidden_states": {1: encoder_seq_len_dim},
-                    "cache_position": None,
-                },
+                (decoder_input_ids, encoder_hidden_states, cache_position, encoder_attention_mask),
+                dynamic_shapes=None,
                 strict=True,
             )
 
         return exported_decoder
 
-    def export(self, encoder_input_ids=None, decoder_input_ids=None, encoder_hidden_states=None, cache_position=None):
+    def export(
+        self,
+        encoder_input_ids=None,
+        decoder_input_ids=None,
+        encoder_hidden_states=None,
+        cache_position=None,
+        encoder_attention_mask=None,
+    ):
         device = self.full_model.device
+        max_cache_len = self.generation_config.cache_config.get("max_cache_len")
+        batch_size = self.generation_config.cache_config.get("batch_size")
         example_encoder_input_ids = (
             encoder_input_ids
             if encoder_input_ids is not None
@@ -1001,14 +1016,22 @@ def export(self, encoder_input_ids=None, decoder_input_ids=None, encoder_hidden_
             encoder_hidden_states
             if encoder_hidden_states is not None
             else torch.zeros(
-                (self.generation_config.cache_config.get("batch_size"), 10, self.config.d_model),
+                (batch_size, max_cache_len, self.config.d_model),
                 dtype=torch.float32,
                 device=device,
             )
         )
+        example_encoder_attention_mask = (
+            encoder_attention_mask
+            if encoder_attention_mask is not None
+            else torch.ones((batch_size, max_cache_len), dtype=torch.long, device=device)
+        )
         self.exported_encoder = self._export_encoder(example_encoder_input_ids)
         self.exported_decoder = self._export_decoder(
-            example_decoder_input_ids, example_encoder_hidden_states, example_cache_position
+            example_decoder_input_ids,
+            example_encoder_hidden_states,
+            example_cache_position,
+            example_encoder_attention_mask,
         )
 
         # Return self to allow chaining
@@ -1025,6 +1048,22 @@ def generate(self, prompt_token_ids, max_new_tokens):
             # Run encoder
             encoder_output = self.exported_encoder.module()(prompt_token_ids)
 
+            # Build encoder attention mask: 1 at real token positions, 0 at padding.
+            # Assumes padding token id is 0 (standard for T5 and most seq2seq models).
+            max_cache_len = self.generation_config.cache_config.get("max_cache_len")
+            batch_size = prompt_token_ids.shape[0]
+            encoder_attention_mask = (prompt_token_ids != 0).long()
+            # Pad or trim to max_cache_len so shape matches the static export
+            if encoder_attention_mask.shape[1] < max_cache_len:
+                pad = torch.zeros(
+                    (batch_size, max_cache_len - encoder_attention_mask.shape[1]),
+                    dtype=torch.long,
+                    device=model_device,
+                )
+                encoder_attention_mask = torch.cat([encoder_attention_mask, pad], dim=1)
+            else:
+                encoder_attention_mask = encoder_attention_mask[:, :max_cache_len]
+
             # Initialize with start token (0 for T5) on the correct device
             decoder_input_ids = torch.tensor([[0]], dtype=torch.long, device=model_device)
             generated_ids = [0]
@@ -1033,7 +1072,10 @@ def generate(self, prompt_token_ids, max_new_tokens):
             for i in range(max_new_tokens - 1):
                 # Run decoder for next token prediction
                 logits = self.exported_decoder.module()(
-                    decoder_input_ids, encoder_output, torch.tensor([i], dtype=torch.long, device=model_device)
+                    decoder_input_ids,
+                    encoder_output,
+                    torch.tensor([i], dtype=torch.long, device=model_device),
+                    encoder_attention_mask,
                 )
 
                 # Get next token
@@ -1119,8 +1161,7 @@ def _get_cache_dict(cache: DynamicCache):
         logging.warning("DynamicCache + torch.export is tested on torch 2.6.0+ and may not work on earlier versions.")
 
     return {
-        "key_cache": [layer.keys for layer in cache.layers if layer.keys is not None],
-        "value_cache": [layer.values for layer in cache.layers if layer.values is not None],
+        "cache": [(layer.keys, layer.values) for layer in cache.layers if layer.keys is not None],
     }
 
 
@@ -1128,10 +1169,7 @@ def _unflatten_dynamic_cache(values, context: torch.utils._pytree.Context):
     dictionary = torch.utils._pytree._dict_unflatten(values, context)
     cache = DynamicCache()
     # Reconstruct layers from keys and values lists
-    key_list = dictionary.get("key_cache", [])
-    value_list = dictionary.get("value_cache", [])
-    for idx in range(max(len(key_list), len(value_list))):
-        key = key_list[idx] if idx < len(key_list) else None
-        value = value_list[idx] if idx < len(value_list) else None
-        cache.update(key, value, idx)
+    cache_list = dictionary.get("cache", [])
+    for i, (key, value) in enumerate(cache_list):
+        cache.update(key, value, i)
     return cache
diff --git a/src/transformers/integrations/finegrained_fp8.py b/src/transformers/integrations/finegrained_fp8.py
index c64f1ce23ec2..eeedf1842a17 100644
--- a/src/transformers/integrations/finegrained_fp8.py
+++ b/src/transformers/integrations/finegrained_fp8.py
@@ -11,6 +11,10 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
+
+import functools
+
 import torch
 import torch.nn as nn
 from torch.nn import functional as F
@@ -19,7 +23,8 @@
 from ..core_model_loading import ConversionOps, _IdentityOp
 from ..quantizers.quantizers_utils import should_convert_module
 from ..utils import logging
-from ..utils.import_utils import get_cuda_runtime_version, resolve_internal_import
+from ..utils.import_utils import is_kernels_available
+from .deepgemm import fp8_deepgemm_experts_forward, fp8_deepgemm_matmul
 from .hub_kernels import lazy_load_kernel
 from .moe import ExpertsInterface, use_experts_implementation
 
@@ -31,26 +36,6 @@
 _FP8_MIN = torch.finfo(_FP8_DTYPE).min
 _FP8_MAX = torch.finfo(_FP8_DTYPE).max
 
-# DeepGEMM requires M-dimension alignment to 128 for TMA-based contiguous grouped GEMM
-# TMA is an H100 hardware addition that allows applications to asynchronously and
-# bi-directionally transfer 1D-5D tensors between GPU global and shared memory
-_DEEPGEMM_M_ALIGNMENT = 128
-
-# Lazily-loaded finegrained-fp8 Triton kernel functions (populated by _load_triton_kernel)
-triton_fp8_matmul = None
-triton_fp8_act_quant = None
-triton_batched_fp8_matmul = None
-triton_grouped_fp8_matmul = None
-# _triton_available: None = not yet attempted, True = loaded, False = failed (won't retry)
-_triton_available = None
-
-# Lazily-loaded DeepGEMM kernel functions (populated by _load_deepgemm_kernel)
-deepgemm_fp8_matmul = None
-deepgemm_grouped_fp8_matmul = None
-deepgemm_per_token_cast_to_fp8 = None
-# _deepgemm_available: None = not yet attempted, True = loaded, False = failed (won't retry)
-_deepgemm_available = None
-
 
 def _first_attr(obj, *names):
     for name in names:
@@ -59,27 +44,31 @@ def _first_attr(obj, *names):
     raise AttributeError(f"{type(obj).__name__} has none of: {names}")
 
 
+@functools.cache
 def _load_triton_kernel():
-    """Lazily load the finegrained-fp8 Triton kernel and extract functions.
-
-    Uses the hub kernels lazy loading pattern. Raises an error if the kernel
-    cannot be loaded or required functions are missing. Only attempts loading once.
     """
-    global \
-        _triton_available, \
-        triton_fp8_act_quant, \
-        triton_fp8_matmul, \
-        triton_batched_fp8_matmul, \
-        triton_grouped_fp8_matmul
+    Load the finegrained-fp8 Triton kernel once and return its required symbols.
 
-    if _triton_available is not None:
-        if not _triton_available:
-            raise ImportError("finegrained-fp8 kernel is not available (previous load attempt failed).")
-        return
+    Raises:
+        ImportError if the `kernels` package is missing, or the kernel or required
+        symbols cannot be found.
 
-    _triton_available = False  # mark attempted before any early exit
+    Returns:
+        Tuple of (w8a8_fp8_matmul, fp8_act_quant, w8a8_fp8_matmul_batched,
+                  w8a8_fp8_matmul_grouped) from the finegrained-fp8 kernel.
+    """
+    if not is_kernels_available():
+        raise ImportError(
+            "finegrained-fp8 kernel requires the `kernels` package. Install it with `pip install -U kernels`."
+        )
 
     kernel = lazy_load_kernel("finegrained-fp8")
+    if kernel is None:
+        raise ImportError(
+            "Failed to load the finegrained-fp8 kernel — check that `kernels-community/finegrained-fp8` "
+            "has a build matching the current torch/CUDA."
+        )
+
     triton_fp8_matmul = getattr(kernel, "w8a8_fp8_matmul", None)
     triton_fp8_act_quant = getattr(kernel, "fp8_act_quant", None)
     triton_batched_fp8_matmul = getattr(kernel, "w8a8_fp8_matmul_batched", None)
@@ -97,72 +86,11 @@ def _load_triton_kernel():
     ]
     if missing:
         raise ImportError(
-            f"finegrained-fp8 kernel is missing required functions: {', '.join(missing)}. "
-            "Please update the `kernels` package (`pip install -U kernels`)."
-        )
-
-    _triton_available = True
-
-
-def _load_deepgemm_kernel():
-    """Lazily load the DeepGEMM kernel and extract functions with proper names.
-
-    Uses the hub kernels lazy loading pattern. Raises an error if the kernel
-    cannot be loaded, required functions are missing, or the hardware is insufficient.
-    Only attempts loading once.
-    """
-    global _deepgemm_available, deepgemm_fp8_matmul, deepgemm_grouped_fp8_matmul, deepgemm_per_token_cast_to_fp8
-
-    if _deepgemm_available is not None:
-        if not _deepgemm_available:
-            raise ImportError("DeepGEMM kernel is not available (previous load attempt failed).")
-        return
-
-    _deepgemm_available = False  # mark attempted before any early exit
-
-    # DeepGEMM requires CUDA and a compatible GPU
-    if not torch.cuda.is_available():
-        raise ImportError(
-            "DeepGEMM kernel requires CUDA, but CUDA is not available. Use a different `experts_implementation`."
-        )
-
-    # DeepGEMM requires Hopper (SM90) or newer for FP8 WGMMA instructions
-    major = torch.cuda.get_device_capability()[0]
-    if major < 9:
-        raise ImportError(
-            f"DeepGEMM requires a Hopper (SM90+) or newer GPU, but the current device "
-            f"has compute capability {major}.x. Use a different `experts_implementation`."
-        )
-
-    # DeepGEMM requires CUDA runtime ≥ 12.3.
-    cuda_major, cuda_minor = get_cuda_runtime_version()
-    if cuda_major < 12 or (cuda_major == 12 and cuda_minor < 3):
-        raise ImportError(
-            f"DeepGEMM requires CUDA runtime 12.3+, but found {cuda_major}.{cuda_minor}. "
-            "Please upgrade your CUDA toolkit or use a different `experts_implementation`."
-        )
-
-    kernel = lazy_load_kernel("deep-gemm")
-    deepgemm_fp8_matmul = getattr(kernel, "fp8_gemm_nt", None)
-    deepgemm_grouped_fp8_matmul = getattr(kernel, "m_grouped_fp8_gemm_nt_contiguous", None)
-    deepgemm_per_token_cast_to_fp8 = resolve_internal_import(kernel, chained_path="utils.per_token_cast_to_fp8")
-
-    missing = [
-        name
-        for name, attr in [
-            ("fp8_gemm_nt", deepgemm_fp8_matmul),
-            ("m_grouped_fp8_gemm_nt_contiguous", deepgemm_grouped_fp8_matmul),
-            ("utils.per_token_cast_to_fp8", deepgemm_per_token_cast_to_fp8),
-        ]
-        if attr is None
-    ]
-    if missing:
-        raise ImportError(
-            f"DeepGEMM kernel is missing required functions: {', '.join(missing)}. "
+            f"finegrained-fp8 kernel is missing required symbols: {', '.join(missing)}. "
             "Please update the `kernels` package (`pip install -U kernels`)."
         )
 
-    _deepgemm_available = True
+    return triton_fp8_matmul, triton_fp8_act_quant, triton_batched_fp8_matmul, triton_grouped_fp8_matmul
 
 
 def _cdiv(a: int, b: int) -> int:
@@ -198,24 +126,16 @@ def w8a8_fp8_matmul(
     """
     if block_size is not None and block_size[0] == block_size[1] == 128:
         try:
-            _load_deepgemm_kernel()
-            global deepgemm_fp8_matmul
+            # 3-6x faster than Triton
+            return fp8_deepgemm_matmul(A, B, As, Bs, output_dtype=output_dtype)
         except ImportError:
             logger.warning_once(
                 "DeepGEMM kernel is not available or compatible, falling back to Triton finegrained-fp8 kernel. "
                 "To use DeepGEMM FP8 matmul, ensure you have a Hopper (SM90+) or newer GPU with CUDA runtime 12.3+, "
                 "and that the `kernels` package is installed and up to date (`pip install -U kernels`)."
             )
-        else:
-            # 3-6x faster than Triton
-            A_2d = A.view(-1, A.shape[-1])
-            As_2d = As.view(-1, As.shape[-1])
-            output = torch.empty(A_2d.shape[0], B.shape[0], device=A.device, dtype=output_dtype)
-            deepgemm_fp8_matmul((A_2d, As_2d.float()), (B, Bs.float()), output)
-            return output.view(A.shape[:-1] + (B.shape[0],))
-
-    _load_triton_kernel()
-    global triton_fp8_matmul
+
+    triton_fp8_matmul, _, _, _ = _load_triton_kernel()
     return triton_fp8_matmul(A, B, As, Bs, block_size, output_dtype)
 
 
@@ -269,8 +189,7 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
             scale_inv = self.weight_scale_inv.contiguous()
 
         if self.activation_scheme == "dynamic":
-            _load_triton_kernel()
-            global triton_fp8_act_quant
+            _, triton_fp8_act_quant, _, _ = _load_triton_kernel()
             qinput, scale = triton_fp8_act_quant(
                 input, self.block_size[1] if self.block_size is not None else input.shape[-1]
             )
@@ -290,7 +209,7 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
         )
 
         if self.bias is not None:
-            output = output + self.bias
+            output.add_(self.bias)
 
         return output.to(dtype=input.dtype)
 
@@ -307,21 +226,22 @@ def fp8_batched_mm_experts_forward(
             "Use the default eager dispatch or switch to activation_scheme='dynamic'."
         )
 
-    _load_triton_kernel()
-    global triton_batched_fp8_matmul
+    _, _, triton_batched_fp8_matmul, _ = _load_triton_kernel()
 
-    device = hidden_states.device
     num_top_k = top_k_index.size(-1)
     num_tokens = hidden_states.size(0)
     hidden_dim = hidden_states.size(-1)
 
     # S is the number of selected tokens-experts pairs (S = num_tokens * num_top_k)
-    token_idx = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, num_top_k).reshape(-1)  # (S,)
+    # Replicate each token num_top_k times to align with the flattened (S,) routing tensors.
+    selected_hidden_states = hidden_states.repeat_interleave(num_top_k, dim=0)
     sample_weights = top_k_weights.reshape(-1)  # (S,)
     expert_ids = top_k_index.reshape(-1)  # (S,)
 
-    # Get current hidden states for selected samples
-    selected_hidden_states = hidden_states[token_idx]
+    # Clamp EP sentinels so per-token weight indexing stays in-bounds. Routing weights are already
+    # zero at sentinel slots (RouterParallel masks them at dispatch), so the weighted mul drops
+    # those contributions — we pay the wasted GEMM compute because batched_mm has no offset to skip.
+    expert_ids.clamp_(0, self.num_experts - 1)
 
     # --- Up projection per expert (FP8 batched) ---
     proj_out = triton_batched_fp8_matmul(
@@ -371,8 +291,7 @@ def fp8_grouped_mm_experts_forward(
             "Use the default eager dispatch or switch to activation_scheme='dynamic'."
         )
 
-    _load_triton_kernel()
-    global triton_grouped_fp8_matmul
+    _, _, _, triton_grouped_fp8_matmul = _load_triton_kernel()
 
     device = hidden_states.device
     num_top_k = top_k_index.size(-1)
@@ -380,18 +299,18 @@ def fp8_grouped_mm_experts_forward(
     hidden_dim = hidden_states.size(-1)
 
     # S is the number of selected token-expert pairs (S = num_tokens * num_top_k)
-    token_idx = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, num_top_k).reshape(-1)  # (S,)
     sample_weights = top_k_weights.reshape(-1)  # (S,)
     expert_ids = top_k_index.reshape(-1)  # (S,)
 
-    # Sort by expert for grouped processing
-    perm = torch.argsort(expert_ids)
-    inv_perm = torch.empty_like(perm)
-    inv_perm[perm] = torch.arange(perm.size(0), device=device)
+    # EP sentinel handling: leave `expert_ids` unclamped so the sort pushes sentinels to the tail,
+    # `histc(max=num_experts-1)` drops them from `tokens_per_expert`, and the grouped matmul skips
+    # rows beyond `offsets[-1]` — so sentinels cost no real GEMM compute. Sentinel rows are zeroed
+    # post-weighted-mul (see below), since the kernel leaves them uninitialized.
 
-    expert_ids_g = expert_ids[perm]
+    # Sort by expert for grouped processing
+    expert_ids_g, perm = torch.sort(expert_ids)
+    selected_hidden_states_g = hidden_states[perm // num_top_k]
     sample_weights_g = sample_weights[perm]
-    selected_hidden_states_g = hidden_states[token_idx[perm]]
 
     # Compute offsets for grouped processing.
     # histc instead of bincount avoids cuda-graph issues;
@@ -431,151 +350,14 @@ def fp8_grouped_mm_experts_forward(
     # Apply routing weights
     weighted_out = proj_out * sample_weights_g.to(proj_out.dtype).unsqueeze(-1)  # (S, hidden_dim)
 
-    # Restore original order
-    weighted_out = weighted_out[inv_perm]
-
-    # Accumulate results using deterministic reshape+sum instead of index_add_
-    # (index_add_ with duplicate indices is non-deterministic on CUDA due to atomicAdd)
-    final_hidden_states = weighted_out.view(num_tokens, num_top_k, hidden_dim).sum(dim=1)
-
-    return final_hidden_states.to(hidden_states.dtype)
-
-
-def _build_deepgemm_contiguous_layout(expert_ids_sorted: torch.Tensor, num_experts: int, alignment: int) -> tuple:
-    """Build a TMA-aligned contiguous layout for DeepGEMM grouped GEMM.
+    # EP sentinel handling: `proj_out` rows past `offsets[-1]` are left uninitialized by the kernel,
+    # so `proj_out[sentinel] * 0 = 0 * NaN = NaN` can leak from allocator pool reuse. Zero them here
+    # so the downstream reduction stays finite even when the routing weight was already zero.
+    weighted_out.masked_fill_((expert_ids_g >= self.num_experts).unsqueeze(-1), 0.0)
 
-    DeepGEMM requires M-dimension alignment per expert for TMA. This computes
-    the mapping from sorted token positions to padded row positions, and the
-    layout tensor that DeepGEMM uses to identify expert boundaries.
-
-    Returns:
-        sorted_to_padded: (num_tokens,) index map from sorted position to padded row
-        grouped_layout: expert layout tensor (format depends on GPU architecture)
-        total_padded_rows: total number of rows including alignment padding
-    """
-    device = expert_ids_sorted.device
-    num_tokens = expert_ids_sorted.size(0)
-    tokens_per_expert = torch.histc(expert_ids_sorted.int(), bins=num_experts, min=0, max=num_experts - 1).long()
-    aligned_tokens_per_expert = ((tokens_per_expert + alignment - 1) // alignment) * alignment
-    # Upper bound avoids GPU→CPU sync; padding rows are skipped by DeepGEMM.
-    total_padded_rows = num_tokens + min(num_tokens, num_experts) * (alignment - 1)
-
-    padding_per_expert = aligned_tokens_per_expert - tokens_per_expert
-    cumulative_padding = padding_per_expert.cumsum(0) - padding_per_expert
-    sorted_to_padded = torch.arange(num_tokens, device=device) + cumulative_padding[expert_ids_sorted]
-
-    if torch.cuda.get_device_capability(device)[0] >= 10:  # Blackwell (SM100+)
-        grouped_layout = tokens_per_expert.cumsum(0).int()
-    else:
-        # Hopper: per-row expert id, -1 for padding rows
-        grouped_layout = torch.full((total_padded_rows,), -1, device=device, dtype=torch.int32)
-        grouped_layout[sorted_to_padded] = expert_ids_sorted.int()
-
-    return sorted_to_padded, grouped_layout, total_padded_rows
-
-
-def _pad_to_deepgemm_contiguous_layout(
-    hidden_states: torch.Tensor,
-    scales: torch.Tensor,
-    sorted_to_padded: torch.Tensor,
-    total_padded_rows: int,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    """Pad sorted hidden states and scales into the TMA-aligned contiguous layout."""
-    hidden_padded = torch.zeros(
-        total_padded_rows, hidden_states.shape[1], device=hidden_states.device, dtype=hidden_states.dtype
-    )
-    hidden_padded[sorted_to_padded] = hidden_states
-    scales_padded = torch.zeros(total_padded_rows, scales.shape[1], device=hidden_states.device, dtype=torch.float32)
-    scales_padded[sorted_to_padded] = scales
-    return hidden_padded, scales_padded
-
-
-def _unpad_from_deepgemm_contiguous_layout(
-    hidden_states_padded: torch.Tensor, sorted_to_padded: torch.Tensor
-) -> torch.Tensor:
-    """Remove padding rows from the TMA-aligned contiguous layout."""
-    return hidden_states_padded[sorted_to_padded]
-
-
-def fp8_deepgemm_experts_forward(
-    self: torch.nn.Module,
-    hidden_states: torch.Tensor,
-    top_k_index: torch.Tensor,
-    top_k_weights: torch.Tensor,
-) -> torch.Tensor:
-    if self.activation_scheme == "static":
-        raise NotImplementedError(
-            "deepgemm experts dispatch does not support activation_scheme='static'. "
-            "Use the default eager dispatch or switch to activation_scheme='dynamic'."
-        )
-    if self.block_size is None:
-        raise ValueError(
-            "DeepGEMM requires block-wise quantization (block_size=[128, 128]), "
-            "but got per-tensor quantization (block_size=None)."
-        )
-    if self.block_size[0] != 128 or self.block_size[1] != 128:
-        raise ValueError(f"DeepGEMM requires block_size=(128, 128), got {self.block_size}")
-
-    _load_deepgemm_kernel()
-    global deepgemm_grouped_fp8_matmul, deepgemm_per_token_cast_to_fp8
-
-    device = hidden_states.device
-    num_top_k = top_k_index.size(-1)
-    num_tokens = hidden_states.size(0)
-    hidden_dim = hidden_states.size(-1)
-
-    # S is the number of selected token-expert pairs (S = num_tokens * num_top_k)
-    token_idx = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, num_top_k).reshape(-1)  # (S,)
-    sample_weights = top_k_weights.reshape(-1)  # (S,)
-    expert_ids = top_k_index.reshape(-1)  # (S,)
-
-    # Sort by expert for grouped processing
-    perm = torch.argsort(expert_ids)
+    # Restore original order
     inv_perm = torch.empty_like(perm)
     inv_perm[perm] = torch.arange(perm.size(0), device=device)
-
-    expert_ids_g = expert_ids[perm]
-    sample_weights_g = sample_weights[perm]
-    selected_hidden_states_g = hidden_states[token_idx[perm]]
-
-    # Build TMA-aligned contiguous layout for DeepGEMM
-    sorted_to_padded, grouped_layout, total_padded_rows = _build_deepgemm_contiguous_layout(
-        expert_ids_g, self.num_experts, alignment=_DEEPGEMM_M_ALIGNMENT
-    )
-
-    # --- Up projection per expert (DeepGEMM grouped contiguous) ---
-    w_up = self.gate_up_proj if self.has_gate else self.up_proj
-    ws_up = self.gate_up_proj_scale_inv if self.has_gate else self.up_proj_scale_inv
-    act_fp8, act_scales = deepgemm_per_token_cast_to_fp8(selected_hidden_states_g, use_ue8m0=False)
-    act_fp8, act_scales = _pad_to_deepgemm_contiguous_layout(act_fp8, act_scales, sorted_to_padded, total_padded_rows)
-    proj_out = torch.zeros(total_padded_rows, w_up.shape[1], device=device, dtype=torch.bfloat16)
-    use_psum_layout = torch.cuda.get_device_capability(device)[0] >= 10
-    deepgemm_grouped_fp8_matmul(
-        (act_fp8, act_scales), (w_up, ws_up.float()), proj_out, grouped_layout, use_psum_layout=use_psum_layout
-    )
-
-    # Apply gating or activation
-    if self.has_gate:
-        proj_out = self._apply_gate(proj_out)
-    else:
-        proj_out = self.act_fn(proj_out)
-
-    # --- Down projection per expert (DeepGEMM grouped contiguous) ---
-    w_down = self.down_proj
-    ws_down = self.down_proj_scale_inv
-    proj_fp8, proj_scales = deepgemm_per_token_cast_to_fp8(proj_out, use_ue8m0=False)
-    proj_out = torch.zeros(total_padded_rows, hidden_dim, device=device, dtype=torch.bfloat16)
-    deepgemm_grouped_fp8_matmul(
-        (proj_fp8, proj_scales), (w_down, ws_down.float()), proj_out, grouped_layout, use_psum_layout=use_psum_layout
-    )
-
-    # Remove padding rows
-    proj_out = _unpad_from_deepgemm_contiguous_layout(proj_out, sorted_to_padded)
-
-    # Apply routing weights
-    weighted_out = proj_out * sample_weights_g.to(proj_out.dtype).unsqueeze(-1)  # (S, hidden_dim)
-
-    # Restore original order
     weighted_out = weighted_out[inv_perm]
 
     # Accumulate results using deterministic reshape+sum instead of index_add_
@@ -703,8 +485,7 @@ def linear(
             scale = activation_scale.to(torch.float32)
             qinput = (input / scale).clamp(min=_FP8_MIN, max=_FP8_MAX).to(_FP8_DTYPE)
         else:
-            _load_triton_kernel()
-            global triton_fp8_act_quant
+            _, triton_fp8_act_quant, _, _ = _load_triton_kernel()
             qinput, scale = triton_fp8_act_quant(
                 input, self.block_size[1] if self.block_size is not None else input.shape[-1]
             )
@@ -876,45 +657,120 @@ def convert(self, input_dict: torch.Tensor, **kwargs) -> dict[str, torch.Tensor]
 
 
 class Fp8Dequantize(ConversionOps):
-    """Inverse operation of :class:`Fp8Quantize`. Takes a pair (weight, scale) and reconstructs the fp32 tensor."""
+    """Dequantize FP8 weights using their per-block ``weight_scale_inv``.
+
+    Designed to run as the *first* op in any :class:`WeightConverter` chain when
+    loading with ``dequantize=True`` — :meth:`update_weight_conversions` on the
+    FP8 quantizer attaches it to each existing model-specific converter so that
+    per-expert (weight, scale) pairs are folded into full-precision tensors before
+    the chain's merge / concat ops collapse the per-expert structure.
+
+    Pattern semantics
+        Input ``input_dict`` carries one entry per source pattern; each value is a
+        list of tensors (one per ``*`` match). For every weight pattern that has a
+        sibling ``*.weight_scale_inv`` pattern in the dict, this op pairs them up by
+        index, dequantizes per-pair, and emits the dequantized list under the
+        original *weight* key. Scale entries are dropped from the output so the
+        remaining ops only see weights.
+    """
 
     def __init__(self, hf_quantizer):
         self.hf_quantizer = hf_quantizer
 
+    def _scale_pattern_for(self, weight_pattern: str) -> str:
+        # Strip the optional ``$`` regex anchor so we can match the underlying name.
+        anchored = weight_pattern.endswith("$")
+        base = weight_pattern[:-1] if anchored else weight_pattern
+        if base.endswith(".weight"):
+            scale = base[: -len(".weight")] + ".weight_scale_inv"
+        elif base == "weight":
+            scale = "weight_scale_inv"
+        else:
+            scale = base + "_scale_inv"
+        return scale + "$" if anchored else scale
+
+    # E2M1 (FP4) value table — checkpoints sometimes ship MoE experts as packed FP4
+    # (two e2m1 nibbles per int8 byte), so the "weight" dtype lands as ``int8`` /
+    # ``float4_e2m1fn_x2`` and we have to unpack before applying the scale grid.
+    _FP4_E2M1_LUT = (0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0, -0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0)
+
+    def _unpack_fp4(self, packed: torch.Tensor) -> torch.Tensor:
+        """Two ``e2m1`` FP4 values per byte → float32 tensor twice as wide on the last dim."""
+        lut = torch.tensor(self._FP4_E2M1_LUT, dtype=torch.float32, device=packed.device)
+        u8 = packed.contiguous().view(torch.uint8)
+        low = (u8 & 0xF).long()
+        high = ((u8 >> 4) & 0xF).long()
+        unpacked = torch.stack([lut[low], lut[high]], dim=-1)
+        return unpacked.reshape(*packed.shape[:-1], 2 * packed.shape[-1])
+
+    def _dequantize_one(self, quantized: torch.Tensor, scales: torch.Tensor) -> torch.Tensor:
+        # FP4 path: int8 / float4_e2m1fn_x2 stores two nibbles per byte. Unpack to fp32
+        # first so the rest of the routine sees a normal (rows, cols) float matrix.
+        fp4_dtype = getattr(torch, "float4_e2m1fn_x2", None)
+        if quantized.dtype == torch.int8 or (fp4_dtype is not None and quantized.dtype == fp4_dtype):
+            quantized_fp32 = self._unpack_fp4(quantized)
+        else:
+            quantized_fp32 = quantized.to(torch.float32)
+        rows, cols = quantized_fp32.shape[-2:]
+        # Derive block size from the scale grid rather than the global config: MoE experts
+        # ship MXFP4 with a ``[1, 32]`` block, dense linears ship FP8 with ``[128, 128]``,
+        # and the same dequant has to handle both within one checkpoint.
+        scale_rows, scale_cols = scales.shape[-2:]
+        if rows % scale_rows or cols % scale_cols:
+            raise ValueError(
+                f"Weight shape ({rows}, {cols}) not divisible by scale grid ({scale_rows}, {scale_cols})."
+            )
+        block_m = rows // scale_rows
+        block_n = cols // scale_cols
+        # ``ue8m0`` (``float8_e8m0fnu``) scales have no CUDA ``mul`` kernel, and casting
+        # the FP8 weight to that dtype loses precision. Promote both sides to fp32 for
+        # the math; emit in the scales' dtype when it's a real float, otherwise bf16.
+        out_dtype = scales.dtype if scales.dtype.is_floating_point and scales.element_size() >= 2 else torch.bfloat16
+        original_shape = quantized_fp32.shape
+        q = quantized_fp32.reshape(-1, scale_rows, block_m, scale_cols, block_n)
+        s = scales.to(torch.float32).reshape(-1, scale_rows, scale_cols).unsqueeze(-1).unsqueeze(2)
+        return (q * s).to(out_dtype).reshape(original_shape)
+
     def convert(
         self,
-        input_dict: dict[str, torch.Tensor],
+        input_dict: dict[str, list[torch.Tensor] | torch.Tensor],
         full_layer_name: str | None = None,
         **kwargs,
-    ) -> dict[str, torch.Tensor]:
-        if len(input_dict) < 2:
-            # case where we only got weights, need to check for "weight$"
-            return {full_layer_name: input_dict["weight$"]}
-
-        quantized = input_dict["weight$"][0]
-        scales = input_dict["weight_scale_inv"][0]
-
-        rows, cols = quantized.shape[-2:]
-        block_size = self.hf_quantizer.quantization_config.weight_block_size
-        if block_size is None:
-            block_size = (quantized.shape[-2], quantized.shape[-1])
-
-        block_m, block_n = block_size
-
-        if rows % block_m != 0 or cols % block_n != 0:
-            raise ValueError(
-                f"Matrix dimensions ({rows}, {cols}) must be divisible by block sizes ({block_m}, {block_n})."
-            )
-        quantized = quantized.to(scales.dtype)
-        reshaped = quantized.reshape(-1, rows // block_m, block_m, cols // block_n, block_n)
-        expanded_scales = scales.reshape(-1, rows // block_m, cols // block_n)
-        expanded_scales = expanded_scales.unsqueeze(-1).unsqueeze(2)
-        dequantized = reshaped * expanded_scales
-
-        return {
-            full_layer_name: dequantized.reshape(quantized.shape),
-        }
+    ) -> dict[str, list[torch.Tensor] | torch.Tensor]:
+        # Backward-compatible single-tensor path (the legacy fallback converter declares
+        # ``["weight$", "weight_scale_inv", "activation_scale"]`` and produces a single
+        # ``weight`` target). Also handles the no-scale case (e.g. RMSNorm weights that
+        # match ``weight$`` but ship no ``weight_scale_inv`` alongside).
+        if "weight$" in input_dict:
+            quantized = input_dict["weight$"]
+            quantized = quantized[0] if isinstance(quantized, list) else quantized
+            if "weight_scale_inv" in input_dict:
+                scales = input_dict["weight_scale_inv"]
+                scales = scales[0] if isinstance(scales, list) else scales
+                return {full_layer_name: self._dequantize_one(quantized, scales)}
+            return {full_layer_name: quantized}
+
+        # Generic chain path: dequantize every weight pattern that has a sibling scale.
+        result: dict[str, list[torch.Tensor] | torch.Tensor] = {}
+        for key, value in input_dict.items():
+            if "activation_scale" in key or "weight_scale_inv" in key:
+                continue  # consumed by the dequant; drop from the chain
+            scale_key = self._scale_pattern_for(key)
+            if scale_key not in input_dict:
+                # No scale to apply (e.g. unrelated entry) — pass through untouched.
+                result[key] = value
+                continue
+            weights = value if isinstance(value, list) else [value]
+            scales = input_dict[scale_key]
+            scales = scales if isinstance(scales, list) else [scales]
+            if len(weights) != len(scales):
+                raise ValueError(
+                    f"Fp8Dequantize: weight/scale count mismatch for {key} "
+                    f"({len(weights)} weights vs {len(scales)} scales)."
+                )
+            result[key] = [self._dequantize_one(w, s) for w, s in zip(weights, scales)]
+        return result
 
     @property
-    def reverse_op(self) -> "ConversionOps":
+    def reverse_op(self) -> ConversionOps:
         return _IdentityOp()
diff --git a/src/transformers/integrations/flex_attention.py b/src/transformers/integrations/flex_attention.py
index 492833a5ce1a..6196abfc110b 100644
--- a/src/transformers/integrations/flex_attention.py
+++ b/src/transformers/integrations/flex_attention.py
@@ -142,11 +142,10 @@ def make_flex_block_causal_mask(
     is_causal: bool | None = True,
 ) -> "BlockMask":
     """
-    IMPORTANT NOTICE: This function is deprecated in favor of using the mask primitives in `masking_utils.py`,
-    and will be removed in a future version without warnings. New code should not use it. It is only kept here
-    for BC for now, while models using it are being patched accordingly.
+    Create a block mask for a batch of sequences, both packed and unpacked.
 
-    Create a block (causal) document mask for a batch of sequences, both packed and unpacked.
+    Note: This function will be renamed to `make_flex_block_mask` in a future version for clarity,
+    as it supports both causal and non-causal masking patterns, not just causal masking.
     Create Block (causal) logic and passing it into :func:`torch.nn.attention.flex_attention.create_block_mask`.
     The resultant BlockMask is a compressed representation of the full (causal) block
     mask. BlockMask is essential for performant computation of flex attention.
diff --git a/src/transformers/integrations/ggml.py b/src/transformers/integrations/ggml.py
index c9ba021c54db..9dea81bd581d 100644
--- a/src/transformers/integrations/ggml.py
+++ b/src/transformers/integrations/ggml.py
@@ -144,6 +144,77 @@
         "expert_count": "num_experts",
         "expert_used_count": "num_experts_per_tok",
     },
+    "qwen2vl": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        "feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": None,
+        "rope.freq_base": "rope_theta",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+        "vocab_size": "vocab_size",
+    },
+    "qwen3_5_moe_text": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        # Non-MoE layers in the hybrid stack still use a regular MLP whose
+        # size comes from feed_forward_length.
+        "feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": None,
+        "rope.freq_base": "rope_theta",
+        "attention.key_length": "head_dim",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+        "vocab_size": "vocab_size",
+        "expert_count": "num_experts",
+        "expert_used_count": "num_experts_per_tok",
+        "expert_feed_forward_length": "moe_intermediate_size",
+        "expert_shared_feed_forward_length": "shared_expert_intermediate_size",
+        # Hybrid layer pattern: convert_hf_to_gguf emits full_attention_interval;
+        # Qwen3_5MoeTextConfig.__post_init__ pops this kwarg to build layer_types.
+        "full_attention_interval": "full_attention_interval",
+        # GatedDeltaNet (linear-attention) shape parameters. The writer reuses
+        # the SSM key namespace; the mapping is:
+        #   ssm.conv_kernel    -> linear_conv_kernel_dim
+        #   ssm.state_size     -> linear_key_head_dim
+        #   ssm.group_count    -> linear_num_key_heads
+        #   ssm.time_step_rank -> linear_num_value_heads
+        # ssm.inner_size is derived (linear_value_head_dim * linear_num_value_heads)
+        # and has no direct config field; ignored here so linear_value_head_dim
+        # falls back to its config default.
+        "ssm.conv_kernel": "linear_conv_kernel_dim",
+        "ssm.state_size": "linear_key_head_dim",
+        "ssm.group_count": "linear_num_key_heads",
+        "ssm.time_step_rank": "linear_num_value_heads",
+        "ssm.inner_size": None,
+    },
+    "qwen3_next": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        "feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": "_rope_dimension_count",
+        "rope.freq_base": "_rope_freq_base",
+        "attention.key_length": "head_dim",
+        "attention.value_length": None,
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+        "vocab_size": "vocab_size",
+        "expert_count": "num_experts",
+        "expert_used_count": "num_experts_per_tok",
+        "expert_feed_forward_length": "moe_intermediate_size",
+        "expert_shared_feed_forward_length": "shared_expert_intermediate_size",
+        "ssm.conv_kernel": "linear_conv_kernel_dim",
+        "ssm.state_size": "linear_key_head_dim",
+        "ssm.group_count": "linear_num_key_heads",
+        "ssm.time_step_rank": "linear_num_value_heads",
+        "ssm.inner_size": "_ssm_inner_size",
+    },
     "falcon": {
         "context_length": "max_position_embeddings",
         "block_count": "num_hidden_layers",
@@ -259,6 +330,7 @@
         "attention.head_count_kv": "num_key_value_heads",
         "attention.layer_norm_rms_epsilon": "rms_norm_eps",
         "attention.sliding_window": "sliding_window",
+        "attention.logit_softcapping": "attn_logit_softcapping",
         "vocab_size": "vocab_size",
     },
     "gemma3": {
@@ -275,6 +347,7 @@
         "attention.head_count_kv": "num_key_value_heads",
         "attention.layer_norm_rms_epsilon": "rms_norm_eps",
         "attention.sliding_window": "sliding_window",
+        "attention.logit_softcapping": "attn_logit_softcapping",
         "vocab_size": "vocab_size",
     },
     "umt5": {
@@ -320,6 +393,23 @@
         "vocab_size": "vocab_size",
         "expert_gating_func": "scoring_func",
     },
+    "llama4": {
+        "context_length": "max_position_embeddings",
+        "block_count": "num_hidden_layers",
+        "feed_forward_length": "intermediate_size_mlp",
+        "expert_feed_forward_length": "intermediate_size",
+        "embedding_length": "hidden_size",
+        "rope.dimension_count": None,
+        "rope.freq_base": "rope_theta",
+        "attention.key_length": "head_dim",
+        "attention.head_count": "num_attention_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+        "vocab_size": "vocab_size",
+        "expert_count": "num_local_experts",
+        "expert_used_count": "num_experts_per_tok",
+        "interleave_moe_layer_step": "interleave_moe_layer_step",
+    },
 }
 
 GGUF_TOKENIZER_MAPPING = {
@@ -353,6 +443,14 @@
         #      (the parameter right after LLM_FFN_SILU corresponds to norm_topk_prob)
         "norm_topk_prob": True,
     },
+    "qwen3_5_moe_text": {
+        # Same as qwen3_moe — llama.cpp's qwen35moe.cpp normalizes routed
+        # expert weights, so override the HF default to match.
+        "norm_topk_prob": True,
+    },
+    "qwen3_next": {
+        "norm_topk_prob": True,
+    },
     "minimax_m2": {
         # MiniMax-M2 uses routing bias (e_score_correction_bias) for MoE expert selection,
         # but this is not stored in GGUF metadata. Set it as default so the model weights
@@ -787,10 +885,14 @@ def converted(self) -> Tokenizer:
 
 GGUF_TO_FAST_CONVERTERS = {
     "llama": GGUFLlamaConverter,
+    "llama4_text": GGUFLlamaConverter,
     "qwen2": GGUFQwen2Converter,
     "qwen2_moe": GGUFQwen2Converter,
     "qwen3": GGUFQwen2Converter,
     "qwen3_moe": GGUFQwen2Converter,
+    "qwen2_vl": GGUFQwen2Converter,
+    "qwen3_5_moe_text": GGUFQwen2Converter,
+    "qwen3_next": GGUFQwen2Converter,
     "phi3": GGUFPhi3Converter,
     "bloom": GGUFGPTConverter,
     "falcon": GGUFGPTConverter,
diff --git a/src/transformers/integrations/hqq.py b/src/transformers/integrations/hqq.py
index 083ec53a2fd3..f83007410f7d 100755
--- a/src/transformers/integrations/hqq.py
+++ b/src/transformers/integrations/hqq.py
@@ -127,3 +127,135 @@ def prepare_for_hqq_linear(model, quantization_config=None, modules_to_not_conve
         logger.warning("No linear modules were found in your model for quantization.")
 
     return model
+
+
+class HqqQuantize:
+    """HQQ quantization operation for the new weight loading flow."""
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(
+        self,
+        input_dict,
+        full_layer_name=None,
+        model=None,
+        **kwargs,
+    ):
+        from hqq.core.quantize import HQQLinear
+
+        from ..quantizers.quantizers_utils import get_module_from_name
+
+        # input_dict has {param_name: [tensor]} for the weight
+        value = list(input_dict.values())[0]
+        value = value[0] if isinstance(value, list) else value
+
+        # full_layer_name is e.g. "model.layers.0.self_attn.q_proj.weight"
+        module_name = full_layer_name.rsplit(".", 1)[0]
+        module, _ = get_module_from_name(model, full_layer_name)
+
+        # Load weight into the nn.Linear module
+        module.weight = torch.nn.Parameter(value, requires_grad=False)
+
+        # Get the quant_config that was set in _process_model_before_weight_loading
+        quant_config = getattr(module, "quant_config", None)
+        if quant_config is None:
+            # Module is skipped from quantization, just return the weight as-is
+            return {full_layer_name: value}
+
+        # Determine target device and compute dtype
+        target_device = value.device
+        compute_dtype = self.hf_quantizer.dtype
+
+        # Create HQQLinear from the nn.Linear
+        hqq_layer = HQQLinear(
+            module,
+            quant_config=quant_config,
+            compute_dtype=compute_dtype,
+            device=target_device,
+            del_orig=True,
+        )
+
+        if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
+            hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
+
+        if self.hf_quantizer.using_multi_gpu:
+            hqq_layer = self.hf_quantizer._patch_layer_for_multigpu(hqq_layer)
+
+        # Replace the module in the model
+        parent_module_name, _, child_name = module_name.rpartition(".")
+        parent_module = model.get_submodule(parent_module_name) if parent_module_name else model
+        setattr(parent_module, child_name, hqq_layer)
+
+        # Mark as loaded so it's not reported as missing
+        missing_keys = kwargs.get("missing_keys")
+        if missing_keys is not None:
+            missing_keys.discard(full_layer_name)
+
+        # Return empty dict so the loading code doesn't try to set params
+        return {}
+
+
+class HqqDeserialize:
+    """Deserialize HQQ pre-quantized weights into an HQQLinear module."""
+
+    def __init__(self, hf_quantizer):
+        self.hf_quantizer = hf_quantizer
+
+    def convert(
+        self,
+        input_dict,
+        full_layer_name=None,
+        model=None,
+        **kwargs,
+    ):
+        from hqq.core.quantize import HQQLinear
+
+        # Unwrap list values
+        state_dict = {}
+        for key, value in input_dict.items():
+            state_dict[key] = value[0] if isinstance(value, list) else value
+
+        # If W_q is not present, this is not an HQQ-quantized layer — pass through
+        if "W_q" not in state_dict:
+            return input_dict
+
+        # full_layer_name is e.g. "model.layers.0.self_attn.v_proj.weight"
+        # (target pattern "weight" appended to module path)
+        module_name = full_layer_name.rsplit(".", 1)[0]
+
+        parent_name, _, child_name = module_name.rpartition(".")
+        parent = model.get_submodule(parent_name) if parent_name else model
+
+        # Create empty HQQLinear
+        hqq_layer = HQQLinear(
+            None,
+            None,
+            compute_dtype=self.hf_quantizer.dtype or torch.float16,
+            device="cpu",
+            initialize=False,
+        )
+
+        # Make W_q an nn.Parameter as HQQ expects
+        if "W_q" in state_dict:
+            state_dict["W_q"] = torch.nn.Parameter(state_dict["W_q"], requires_grad=False)
+
+        hqq_layer.load_state_dict(state_dict)
+
+        if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
+            hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
+
+        if self.hf_quantizer.using_multi_gpu:
+            hqq_layer = self.hf_quantizer._patch_layer_for_multigpu(hqq_layer)
+
+        setattr(parent, child_name, hqq_layer)
+
+        # Mark weight and bias as loaded
+        missing_keys = kwargs.get("missing_keys")
+        if missing_keys is not None:
+            missing_keys.discard(full_layer_name)
+            # Also discard bias since HQQLinear handles it internally
+            bias_key = module_name + ".bias"
+            missing_keys.discard(bias_key)
+
+        return {}
diff --git a/src/transformers/integrations/hub_kernels.py b/src/transformers/integrations/hub_kernels.py
index 70a343424aa8..90320cdcffcc 100644
--- a/src/transformers/integrations/hub_kernels.py
+++ b/src/transformers/integrations/hub_kernels.py
@@ -166,6 +166,16 @@ def use_kernel_func_from_hub(func_name: str):
                 )
             },
         },
+        "ScatterMoEGatedMLP": {
+            "cuda": {
+                Mode.TRAINING: LayerRepository(
+                    repo_id="kernels-community/scattermoe", layer_name="ScatterMoEGatedMLP"
+                ),
+                Mode.INFERENCE: LayerRepository(
+                    repo_id="kernels-community/scattermoe", layer_name="ScatterMoEGatedMLP"
+                ),
+            },
+        },
         "FastGELU": {
             "cuda": {
                 Mode.INFERENCE | Mode.TORCH_COMPILE: LayerRepository(
@@ -289,7 +299,8 @@ def register_kernel_mapping_transformers(*args, **kwargs):
     "falcon_mamba-ssm": {"repo_id": "kernels-community/mamba-ssm", "version": 1},
     "finegrained-fp8": {"repo_id": "kernels-community/finegrained-fp8", "version": 1},
     "deep-gemm": {"repo_id": "kernels-community/deep-gemm", "version": 1},
-    "sonic-moe": {"repo_id": "kernels-community/sonic-moe", "version": 1},
+    "sonic-moe": {"repo_id": "IlyasMoutawwakil/sonic-moe", "revision": "main"},
+    "tdt-loss": {"repo_id": "eustlb/tdt-loss", "revision": "v1"},
 }
 
 _KERNEL_MODULE_MAPPING: dict[str, ModuleType | None] = {}
@@ -359,7 +370,11 @@ def load_and_register_attn_kernel(
 
     # Register the kernel as a valid attention
     ALL_ATTENTION_FUNCTIONS.register(attn_implementation, kernel_function)
-    ALL_MASK_ATTENTION_FUNCTIONS.register(attn_implementation, ALL_MASK_ATTENTION_FUNCTIONS["flash_attention_2"])
+
+    # Allow the kernel module to declare its preferred mask function (e.g., MASK_FUNCTION = "sdpa").
+    # Falls back to "flash_attention_2" for backward compatibility with existing kernels.
+    mask_type = getattr(kernel, "MASK_FUNCTION", "flash_attention_2")
+    ALL_MASK_ATTENTION_FUNCTIONS.register(attn_implementation, ALL_MASK_ATTENTION_FUNCTIONS[mask_type])
 
     return kernel
 
@@ -376,10 +391,13 @@ def lazy_load_kernel(kernel_name: str, mapping: dict[str, ModuleType | None] = _
             repo_id = _HUB_KERNEL_MAPPING[kernel_name]["repo_id"]
             revision = _HUB_KERNEL_MAPPING[kernel_name].get("revision", None)
             version = _HUB_KERNEL_MAPPING[kernel_name].get("version", None)
-            kernel = get_kernel(repo_id, revision=revision, version=version)
+            # Entries in `_HUB_KERNEL_MAPPING` are vetted in-tree, so we trust non-`kernels-community`
+            # repos (e.g. user/team forks) without requiring the per-call `allow_all_kernels` flag.
+            kernel = get_kernel(repo_id, revision=revision, version=version, allow_all_kernels=True)
             mapping[kernel_name] = kernel
-        except FileNotFoundError:
+        except FileNotFoundError as e:
             mapping[kernel_name] = None
+            logger.warning_once(f"Failed to load kernel {kernel_name}: {e}")
         except AssertionError:
             # Happens when torch is built without an accelerator backend; fall back to slow path.
             mapping[kernel_name] = None
diff --git a/src/transformers/integrations/integration_utils.py b/src/transformers/integrations/integration_utils.py
index 2656b7169c62..e1696c736e2e 100755
--- a/src/transformers/integrations/integration_utils.py
+++ b/src/transformers/integrations/integration_utils.py
@@ -834,6 +834,17 @@ def on_train_begin(self, args, state, control, model=None, **kwargs):
         if not self._initialized:
             self.setup(args, state, model, **kwargs)
 
+        # Auto log Accelerate parallelism info to wandb.config
+        if self._initialized and state.is_world_process_zero and getattr(self._wandb, "run", None) is not None:
+            acc = getattr(model, "accelerator", None)
+            pc = getattr(acc, "parallelism_config", None) if acc is not None else None
+            sizes = getattr(pc, "_sizes", None) if pc is not None else None
+            if isinstance(sizes, dict) and sizes:
+                try:
+                    self._wandb.config.update({"parallelism": sizes}, allow_val_change=True)
+                except Exception as e:
+                    logger.debug("Failed to log Accelerate parallelism config to wandb: %s", e)
+
     def on_train_end(self, args: TrainingArguments, state, control, model=None, processing_class=None, **kwargs):
         if self._wandb is None:
             return
@@ -2261,7 +2272,7 @@ class SwanLabCallback(TrainerCallback):
     A [`TrainerCallback`] that logs metrics, media, model checkpoints to [SwanLab](https://swanlab.cn/).
     """
 
-    def __init__(self):
+    def __init__(self, **kwargs):
         if not is_swanlab_available():
             raise RuntimeError("SwanLabCallback requires swanlab to be installed. Run `pip install swanlab`.")
         import swanlab
@@ -2269,6 +2280,7 @@ def __init__(self):
         self._swanlab = swanlab
         self._initialized = False
         self._log_model = os.getenv("SWANLAB_LOG_MODEL", None)
+        self._init_kwargs = kwargs
 
     def setup(self, args, state, model, **kwargs):
         """
@@ -2352,6 +2364,7 @@ def setup(self, args, state, model, **kwargs):
                 init_args["resume"] = "allow"
 
             if self._swanlab.get_run() is None:
+                init_args.update(self._init_kwargs)
                 self._swanlab.init(
                     **init_args,
                 )
diff --git a/src/transformers/integrations/metal_quantization.py b/src/transformers/integrations/metal_quantization.py
index ea23a5496c6a..d4f98e120b90 100644
--- a/src/transformers/integrations/metal_quantization.py
+++ b/src/transformers/integrations/metal_quantization.py
@@ -47,21 +47,193 @@
 
 _metal_kernel = None
 
+# ---------------------------------------------------------------------------
+# Locally-compiled Metal fallback for affine_qmm_t
+# ---------------------------------------------------------------------------
+
+_AFFINE_QMM_T_METAL_SOURCE = """
+#include <metal_stdlib>
+using namespace metal;
+
+// Fused dequantize + matmul:  y = x @ dequant(w).T
+// x: [M, K], w: [N, K_packed] (uint32), scales/biases: [N, n_groups], out: [M, N]
+// Each thread computes one (m, n) output element.
+
+kernel void affine_qmm_t_float(
+    device const float* x        [[buffer(0)]],
+    device const uint*  w        [[buffer(1)]],
+    device const float* scales   [[buffer(2)]],
+    device const float* biases   [[buffer(3)]],
+    device float*       out      [[buffer(4)]],
+    constant uint& M             [[buffer(5)]],
+    constant uint& N             [[buffer(6)]],
+    constant uint& K             [[buffer(7)]],
+    constant uint& group_size    [[buffer(8)]],
+    constant uint& bits          [[buffer(9)]],
+    uint2 tid                    [[thread_position_in_grid]])
+{
+    uint m = tid.y;
+    uint n = tid.x;
+    if (m >= M || n >= N) return;
+
+    uint elems_per_int = 32 / bits;
+    uint mask = (1u << bits) - 1u;
+    uint K_packed = K / elems_per_int;
+    uint n_groups = K / group_size;
+
+    float acc = 0.0f;
+    for (uint k = 0; k < K; k++) {
+        uint packed_val = w[n * K_packed + k / elems_per_int];
+        float q = float((packed_val >> ((k % elems_per_int) * bits)) & mask);
+        uint g = k / group_size;
+        acc += x[m * K + k] * (q * scales[n * n_groups + g] + biases[n * n_groups + g]);
+    }
+    out[m * N + n] = acc;
+}
+
+kernel void affine_qmm_t_half(
+    device const half*  x        [[buffer(0)]],
+    device const uint*  w        [[buffer(1)]],
+    device const half*  scales   [[buffer(2)]],
+    device const half*  biases   [[buffer(3)]],
+    device half*        out      [[buffer(4)]],
+    constant uint& M             [[buffer(5)]],
+    constant uint& N             [[buffer(6)]],
+    constant uint& K             [[buffer(7)]],
+    constant uint& group_size    [[buffer(8)]],
+    constant uint& bits          [[buffer(9)]],
+    uint2 tid                    [[thread_position_in_grid]])
+{
+    uint m = tid.y;
+    uint n = tid.x;
+    if (m >= M || n >= N) return;
+
+    uint elems_per_int = 32 / bits;
+    uint mask = (1u << bits) - 1u;
+    uint K_packed = K / elems_per_int;
+    uint n_groups = K / group_size;
+
+    float acc = 0.0f;
+    for (uint k = 0; k < K; k++) {
+        uint packed_val = w[n * K_packed + k / elems_per_int];
+        float q = float((packed_val >> ((k % elems_per_int) * bits)) & mask);
+        uint g = k / group_size;
+        acc += float(x[m * K + k]) * (q * float(scales[n * n_groups + g]) + float(biases[n * n_groups + g]));
+    }
+    out[m * N + n] = half(acc);
+}
+
+kernel void affine_qmm_t_bfloat(
+    device const bfloat* x       [[buffer(0)]],
+    device const uint*   w       [[buffer(1)]],
+    device const bfloat* scales  [[buffer(2)]],
+    device const bfloat* biases  [[buffer(3)]],
+    device bfloat*       out     [[buffer(4)]],
+    constant uint& M             [[buffer(5)]],
+    constant uint& N             [[buffer(6)]],
+    constant uint& K             [[buffer(7)]],
+    constant uint& group_size    [[buffer(8)]],
+    constant uint& bits          [[buffer(9)]],
+    uint2 tid                    [[thread_position_in_grid]])
+{
+    uint m = tid.y;
+    uint n = tid.x;
+    if (m >= M || n >= N) return;
+
+    uint elems_per_int = 32 / bits;
+    uint mask = (1u << bits) - 1u;
+    uint K_packed = K / elems_per_int;
+    uint n_groups = K / group_size;
+
+    float acc = 0.0f;
+    for (uint k = 0; k < K; k++) {
+        uint packed_val = w[n * K_packed + k / elems_per_int];
+        float q = float((packed_val >> ((k % elems_per_int) * bits)) & mask);
+        uint g = k / group_size;
+        acc += float(x[m * K + k]) * (q * float(scales[n * n_groups + g]) + float(biases[n * n_groups + g]));
+    }
+    out[m * N + n] = bfloat(acc);
+}
+"""
+
+_compiled_shader_lib = None
+
+
+class _LocalMetalKernel:
+    """Wrapper that mimics the Hub kernel interface using ``torch.mps.compile_shader``."""
+
+    def __init__(self):
+        global _compiled_shader_lib
+        if _compiled_shader_lib is None:
+            _compiled_shader_lib = torch.mps.compile_shader(_AFFINE_QMM_T_METAL_SOURCE)
+        self._lib = _compiled_shader_lib
+
+    def affine_qmm_t(self, x, w, scales, biases, group_size, bits):
+        K_packed = w.shape[1]
+        N = w.shape[0]
+        elems_per_int = 32 // bits
+        K = K_packed * elems_per_int
+
+        x_2d = x.reshape(-1, K).contiguous()
+        M_total = x_2d.shape[0]
+        out = torch.empty(M_total, N, dtype=x.dtype, device=x.device)
+
+        M_t = torch.tensor(M_total, dtype=torch.uint32, device="mps")
+        N_t = torch.tensor(N, dtype=torch.uint32, device="mps")
+        K_t = torch.tensor(K, dtype=torch.uint32, device="mps")
+        gs_t = torch.tensor(group_size, dtype=torch.uint32, device="mps")
+        bits_t = torch.tensor(bits, dtype=torch.uint32, device="mps")
+
+        if x.dtype == torch.float32:
+            fn = self._lib.affine_qmm_t_float
+        elif x.dtype == torch.float16:
+            fn = self._lib.affine_qmm_t_half
+        elif x.dtype == torch.bfloat16:
+            fn = self._lib.affine_qmm_t_bfloat
+        else:
+            raise ValueError(f"Unsupported dtype {x.dtype} for Metal affine_qmm_t")
+
+        fn(x_2d, w, scales, biases, out, M_t, N_t, K_t, gs_t, bits_t, threads=[N, M_total, 1])
+
+        return out.reshape(*x.shape[:-1], N)
+
 
 def _get_metal_kernel():
-    """Lazily load the quantization-mlx kernel from Hugging Face Hub."""
+    """Lazily load the quantization-mlx kernel from Hugging Face Hub, falling back to a
+    locally-compiled Metal shader if the Hub kernel is unavailable or incompatible."""
     global _metal_kernel
     if _metal_kernel is None:
         try:
+            import os
+
             from .hub_kernels import get_kernel
 
-            _metal_kernel = get_kernel("kernels-community/mlx-quantization-metal-kernels")
-        except Exception as e:
-            raise ImportError(
-                f"Failed to load the quantization-mlx kernel from the Hub: {e}. "
-                "Make sure you have `kernels` installed (`pip install kernels`) "
-                "and are running on an Apple Silicon machine."
-            ) from e
+            hub_kernel = get_kernel("kernels-community/mlx-quantization-metal-kernels")
+            # Smoke-test: the pre-built metallib may target an MSL version newer
+            # than the current OS supports.  A tiny matmul catches this at init
+            # time rather than mid-inference.
+            # Suppress Metal runtime stderr noise ("Failed to create Metal library
+            # from embedded header") by temporarily redirecting fd 2 to /dev/null.
+            _x = torch.zeros(1, 64, dtype=torch.float32, device="mps")
+            _w = torch.zeros(1, 2, dtype=torch.uint32, device="mps")  # K=64 at 8-bit → 2 packed
+            _s = torch.ones(1, 1, dtype=torch.float32, device="mps")
+            _b = torch.zeros(1, 1, dtype=torch.float32, device="mps")
+            stderr_fd = os.dup(2)
+            devnull = os.open(os.devnull, os.O_WRONLY)
+            try:
+                os.dup2(devnull, 2)
+                hub_kernel.affine_qmm_t(_x, _w, _s, _b, 64, 8)
+            finally:
+                os.dup2(stderr_fd, 2)
+                os.close(stderr_fd)
+                os.close(devnull)
+            _metal_kernel = hub_kernel
+        except Exception:
+            logger.info(
+                "Hub kernel 'kernels-community/mlx-quantization-metal-kernels' unavailable; "
+                "using locally-compiled Metal shader fallback."
+            )
+            _metal_kernel = _LocalMetalKernel()
     return _metal_kernel
 
 
@@ -112,6 +284,14 @@ def __init__(
         else:
             self.register_parameter("bias", None)
 
+    def _load_from_state_dict(self, state_dict, prefix, *args, **kwargs):
+        # MLX-quantized models store quantization biases as "biases" instead of "qbiases"
+        biases_key = prefix + "biases"
+        qbiases_key = prefix + "qbiases"
+        if biases_key in state_dict and qbiases_key not in state_dict:
+            state_dict[qbiases_key] = state_dict.pop(biases_key)
+        super()._load_from_state_dict(state_dict, prefix, *args, **kwargs)
+
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         if self.weight.dtype != torch.uint32:
             return nn.functional.linear(input, self.weight, self.bias)
@@ -285,12 +465,16 @@ def convert(self, input_dict: dict, full_layer_name: str | None = None, **kwargs
         bits = self.hf_quantizer.quantization_config.bits
         group_size = self.hf_quantizer.quantization_config.group_size
 
+        # Use source_patterns from kwargs as dict keys (they are the keys in input_dict).
+        # Fall back to the default patterns for backward compatibility.
+        source_patterns = kwargs.get("source_patterns", ["weight$", "scales", "qbiases"])
+
         if len(input_dict) < 2:
-            return {full_layer_name: input_dict["weight$"]}
+            return {full_layer_name: input_dict[source_patterns[0]]}
 
-        quantized = input_dict["weight$"][0]
-        scales = input_dict["scales"][0]
-        qbiases = input_dict["qbiases"][0]
+        quantized = input_dict[source_patterns[0]][0]
+        scales = input_dict[source_patterns[1]][0]
+        qbiases = input_dict[source_patterns[2]][0]
 
         w_deq = _affine_dequantize_tensor(quantized, scales, qbiases, group_size, bits)
         return {full_layer_name: w_deq.to(scales.dtype)}
diff --git a/src/transformers/integrations/mistral.py b/src/transformers/integrations/mistral.py
index 3256c9839acd..978ab4512433 100644
--- a/src/transformers/integrations/mistral.py
+++ b/src/transformers/integrations/mistral.py
@@ -7,7 +7,7 @@
 
 class MistralConverter:
     """
-    A general tiktoken converter.
+    Converter for Mistral's Tekken tokenizer format to [`TokenizersBackend`].
     """
 
     def __init__(
@@ -74,8 +74,8 @@ def converted(self) -> Tokenizer:
 
 
 def convert_tekken_tokenizer(tokenizer_file: str):
-    """Convert a "tekken" tokenizer to a fast Tokenizer."""
-    # Tekken format -- need to use the Converter
+    """Convert Mistral's Tekken tokenizer format to [`TokenizersBackend`]."""
+    # Mistral Tekken format -- converts using the MistralConverter
 
     from mistral_common.tokens.tokenizers.base import SpecialTokens
     from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
diff --git a/src/transformers/integrations/moe.py b/src/transformers/integrations/moe.py
index c8a8e87f3621..418949cf07a9 100644
--- a/src/transformers/integrations/moe.py
+++ b/src/transformers/integrations/moe.py
@@ -11,10 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from __future__ import annotations
 
 from collections.abc import Callable
 from functools import wraps
 
+from torch.distributed.tensor import DTensor
+
 from ..utils import logging
 from ..utils.generic import GeneralInterface
 from ..utils.import_utils import (
@@ -23,12 +26,19 @@
     is_torch_less_or_equal,
     is_torchdynamo_compiling,
 )
+from .deepgemm import deepgemm_experts_forward
 from .sonicmoe import sonicmoe_experts_forward
 
 
 if is_torch_available():
     import torch
 
+    # Patch the version-check helpers so dynamo doesn't trace into them — they transitively call
+    # `importlib.util.find_spec`, which dynamo refuses to trace. `assume_constant_result` makes
+    # dynamo evaluate them once at trace time and inline the bool, no body tracing.
+    is_torch_greater_or_equal = torch._dynamo.assume_constant_result(is_torch_greater_or_equal)
+    is_torch_less_or_equal = torch._dynamo.assume_constant_result(is_torch_less_or_equal)
+
 
 logger = logging.get_logger(__name__)
 
@@ -102,7 +112,7 @@ def _batched_linear(
         out = torch.bmm(weight, input.unsqueeze(-1)).squeeze(-1)
 
     if bias is not None:
-        out = out + bias
+        out.add_(bias)
 
     return out
 
@@ -113,24 +123,20 @@ def batched_mm_experts_forward(
     top_k_index: torch.Tensor,
     top_k_weights: torch.Tensor,
 ) -> torch.Tensor:
-    device = hidden_states.device
     num_top_k = top_k_index.size(-1)
     num_tokens = hidden_states.size(0)
     hidden_dim = hidden_states.size(-1)
 
-    # Reshape for easier indexing
     # S is the number of selected tokens-experts pairs (S = num_tokens * num_top_k)
-    token_idx = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, num_top_k).reshape(-1)  # (S,)
+    # Replicate each token num_top_k times to align with the flattened (S,) routing tensors.
+    selected_hidden_states = hidden_states.repeat_interleave(num_top_k, dim=0)
     sample_weights = top_k_weights.reshape(-1)  # (S,)
     expert_ids = top_k_index.reshape(-1)  # (S,)
 
-    # Handle invalid expert IDs from Expert Parallelism (EP)
-    # When EP is enabled, tokens assigned to experts on other devices are marked with sentinel value >= num_experts
-    invalid_mask = expert_ids >= self.num_experts
-    expert_ids = expert_ids.clamp(0, self.num_experts - 1)
-
-    # Get current hidden states for selected samples
-    selected_hidden_states = hidden_states[token_idx]
+    # Clamp EP sentinels so `gate_up_proj[expert_ids]` stays in-bounds. Routing weights are already
+    # zero at sentinel slots (RouterParallel masks them at dispatch), so the weighted mul drops
+    # those contributions — we pay the wasted GEMM compute because batched_mm has no offset to skip.
+    expert_ids.clamp_(0, self.num_experts - 1)
 
     # Select gate_up or just up projection weights and biases
     if self.has_gate:
@@ -162,9 +168,8 @@ def batched_mm_experts_forward(
         proj_out, selected_weights, bias=selected_biases, is_transposed=self.is_transposed
     )  # (S, hidden_dim)
 
-    # Apply routing weights and zero out invalid expert contributions
+    # Apply routing weights
     weighted_out = proj_out * sample_weights.unsqueeze(-1)  # (S, hidden_dim)
-    weighted_out.masked_fill_(invalid_mask.unsqueeze(-1), 0.0)  # Zero out invalid expert contributions
 
     # Accumulate results using deterministic reshape+sum instead of index_add_
     # index_add_ with duplicate indices is non-deterministic on CUDA due to atomicAdd
@@ -354,16 +359,21 @@ def _grouped_linear(
     Returns:
         `torch.Tensor`: Output tensor of shape (S, output_dim).
     """
+    # torch._grouped_mm is not registered for autocast, so we need to ensure
+    # input and weight have the same dtype (e.g. LayerNorm outputs float32 under
+    # autocast while weights may be bfloat16).
+    input = input.to(weight.dtype)
+
     if is_transposed:
         # (S, input_dim) @ grouped (num_experts, input_dim, output_dim) -> (S, output_dim)
         out = _grouped_mm(input, weight, offs=offs)
     else:
         # (S, input_dim) @ grouped (num_experts, output_dim, input_dim).T -> (S, output_dim)
-        out = _grouped_mm(input, weight.transpose(-2, -1), offs=offs)
+        out = _grouped_mm(input, weight.transpose(-2, -1).contiguous(), offs=offs)
 
     if bias is not None:
         # We should be able to pass bias to the grouped_mm call, but it's not yet supported.
-        out = out + bias
+        out.add_(bias)
 
     return out
 
@@ -379,43 +389,53 @@ def grouped_mm_experts_forward(
     num_tokens = hidden_states.size(0)
     hidden_dim = hidden_states.size(-1)
 
-    # Reshape for easier indexing
     # S is the number of selected tokens-experts pairs (S = num_tokens * num_top_k)
-    token_idx = torch.arange(num_tokens, device=device).unsqueeze(1).expand(-1, num_top_k).reshape(-1)  # (S,)
     sample_weights = top_k_weights.reshape(-1)  # (S,)
     expert_ids = top_k_index.reshape(-1)  # (S,)
 
-    # Handle invalid expert IDs from Expert Parallelism (EP)
-    invalid_mask = expert_ids >= self.num_experts
-    expert_ids = expert_ids.clamp(0, self.num_experts - 1)
+    # EP sentinel handling: leave `expert_ids` unclamped so the sort pushes sentinels to the tail,
+    # `histc(max=num_experts-1)` drops them from `tokens_per_expert`, and grouped_mm skips rows
+    # beyond `offsets[-1]` — so sentinels cost no real GEMM compute. Sentinel rows are zeroed
+    # post-weighted-mul (see below), since the kernel leaves them uninitialized.
 
     # Sort by expert for grouped processing
-    perm = torch.argsort(expert_ids)
-    inv_perm = torch.empty_like(perm)
-    inv_perm[perm] = torch.arange(perm.size(0), device=device)
-
-    expert_ids_g = expert_ids[perm]
+    expert_ids_g, perm = torch.sort(expert_ids)
+    selected_hidden_states_g = hidden_states[perm // num_top_k]
     sample_weights_g = sample_weights[perm]
-    selected_hidden_states_g = hidden_states[token_idx[perm]]
 
     # Compute offsets for grouped_mm
     # using histc instead of bincount to avoid cuda graph issues
     # With deterministic algorithms, CPU only supports float input, CUDA only supports int input.
-    histc_input = expert_ids_g.float() if device.type == "cpu" else expert_ids_g.int()
+
+    # torch.histc() does not support integer dtypes on CPU and MPS.
+    # It works well and is more efficient on CUDA when using int.
+    # For all other backends (XPU, TPU/XLA, HPU, etc.), we conservatively
+    # use float32 as it has broader operator suppor
+    histc_input = expert_ids_g.int() if device.type == "cuda" else expert_ids_g.to(torch.float32)
     tokens_per_expert = torch.histc(histc_input, bins=self.num_experts, min=0, max=self.num_experts - 1)
     offsets = torch.cumsum(tokens_per_expert, dim=0, dtype=torch.int32)
 
+    def _local(p):
+        return p.to_local() if isinstance(p, DTensor) else p
+
+    expert_ids_g_for_bias = None
+    if self.has_bias:
+        # Clamp for the per-row bias gather below to stay in-bounds, while keeping
+        # expert_ids_g unchanged so sentinel rows can still be detected and zeroed later.
+        expert_ids_g_for_bias = expert_ids_g.clamp(0, self.num_experts - 1)
+
     # Select expert weights and biases
     # NOTE: We keep all experts here and rely on offsets to target the active ones.
     # I have already implemented a version that only passes the active experts, but
     # to do so I had to use torch.unique which breaks the graph capture (data-dependent).
     # Also there were no speedup gains from it in my experiments, even in eager mode.
+    # NOTE: The grouped_mm kernel only targets the active experts / tokens via the offsets
     if self.has_gate:
-        selected_weights = self.gate_up_proj
-        selected_biases = self.gate_up_proj_bias[expert_ids_g] if self.has_bias else None
+        selected_weights = _local(self.gate_up_proj)
+        selected_biases = _local(self.gate_up_proj_bias)[expert_ids_g_for_bias] if self.has_bias else None
     else:
-        selected_weights = self.up_proj
-        selected_biases = self.up_proj_bias[expert_ids_g] if self.has_bias else None
+        selected_weights = _local(self.up_proj)
+        selected_biases = _local(self.up_proj_bias)[expert_ids_g_for_bias] if self.has_bias else None
 
     # --- Up projection per expert (grouped) ---
     proj_out = _grouped_linear(
@@ -431,20 +451,25 @@ def grouped_mm_experts_forward(
         proj_out = self.act_fn(proj_out)  # (S, intermediate_dim)
 
     # Select down projection weights and biases
-    selected_weights = self.down_proj
-    selected_biases = self.down_proj_bias[expert_ids_g] if self.has_bias else None
+    selected_weights = _local(self.down_proj)
+    selected_biases = _local(self.down_proj_bias)[expert_ids_g_for_bias] if self.has_bias else None
 
     # --- Down projection per expert (grouped) ---
     proj_out = _grouped_linear(
         proj_out, selected_weights, offsets, bias=selected_biases, is_transposed=self.is_transposed
     )  # (S, hidden_dim)
 
-    # Apply routing weights and zero out invalid expert contributions from EP
+    # Apply routing weights
     weighted_out = proj_out * sample_weights_g.unsqueeze(-1)  # (S, hidden_dim)
-    invalid_mask_g = invalid_mask[perm]
-    weighted_out.masked_fill_(invalid_mask_g.unsqueeze(-1), 0.0)
+
+    # EP sentinel handling: `proj_out` rows past `offsets[-1]` are left uninitialized by grouped_mm,
+    # so `proj_out[sentinel] * 0 = 0 * NaN = NaN` can leak from allocator pool reuse. Zero them here
+    # so the downstream reduction stays finite even when the routing weight was already zero.
+    weighted_out.masked_fill_((expert_ids_g >= self.num_experts).unsqueeze(-1), 0.0)
 
     # Restore original order
+    inv_perm = torch.empty_like(perm)
+    inv_perm[perm] = torch.arange(perm.size(0), device=device)
     weighted_out = weighted_out[inv_perm]  # (S, hidden_dim)
 
     # Accumulate results using deterministic reshape+sum instead of index_add_
@@ -460,9 +485,10 @@ class ExpertsInterface(GeneralInterface):
     """Interface for registering custom experts forward functions."""
 
     _global_mapping = {
-        "sonicmoe": sonicmoe_experts_forward,
         "batched_mm": batched_mm_experts_forward,
         "grouped_mm": grouped_mm_experts_forward,
+        "sonicmoe": sonicmoe_experts_forward,
+        "deepgemm": deepgemm_experts_forward,
     }
 
     def get_interface(self, experts_implementation: str, default: Callable) -> Callable:
diff --git a/src/transformers/integrations/mxfp4.py b/src/transformers/integrations/mxfp4.py
index 67d9420659af..018507a5134b 100644
--- a/src/transformers/integrations/mxfp4.py
+++ b/src/transformers/integrations/mxfp4.py
@@ -498,15 +498,18 @@ def mlp_forward(self, hidden_states):
     else:
         routing = triton_kernels_hub.routing.routing
 
-    batch_size = hidden_states.shape[0]
-    hidden_states = hidden_states.reshape(-1, self.router.hidden_dim)
+    is_3d = hidden_states.ndim == 3
+    if is_3d:
+        batch_size, seq_len, _ = hidden_states.shape
+        hidden_states = hidden_states.reshape(-1, self.router.hidden_dim)
     router_logits = nn.functional.linear(hidden_states, self.router.weight, self.router.bias)
 
     with on_device(router_logits.device):
         routing_data, gather_idx, scatter_idx = routing(router_logits, self.router.top_k)
 
     routed_out = self.experts(hidden_states, routing_data, gather_idx, scatter_idx=scatter_idx)
-    routed_out = routed_out.reshape(batch_size, -1, self.router.hidden_dim)
+    if is_3d:
+        routed_out = routed_out.reshape(batch_size, seq_len, self.router.hidden_dim)
     return routed_out, router_logits
 
 
diff --git a/src/transformers/integrations/peft.py b/src/transformers/integrations/peft.py
index 7b93e0a134b8..cad07bc2d3fc 100644
--- a/src/transformers/integrations/peft.py
+++ b/src/transformers/integrations/peft.py
@@ -34,6 +34,7 @@
     Transpose,
     WeightConverter,
     WeightRenaming,
+    rename_source_key,
 )
 from ..utils import (
     CONFIG_NAME,
@@ -47,7 +48,7 @@
     logging,
 )
 from ..utils.hub import DownloadKwargs
-from ..utils.loading_report import log_state_dict_report
+from ..utils.loading_report import LoadStateDictInfo, log_state_dict_report
 
 
 if is_torch_available():
@@ -506,6 +507,7 @@ def load_adapter(
                 `find_adapter_config_file` method.
         """
         from peft import PeftType
+        from peft.tuners.tuners_utils import BaseTunerLayer
         from peft.utils.save_and_load import _maybe_shard_state_dict_for_tp
 
         from ..modeling_utils import LoadStateDictConfig, _get_resolved_checkpoint_files, load_state_dict
@@ -618,45 +620,92 @@ def load_adapter(
 
         device_map = getattr(self, "hf_device_map", {"": self.device})
 
-        # If the model is tensor parallel, we handle the sharding of the state dict here since the logic in `self._load_pretrained_model`
-        # is not compatible with the way PEFT adapter should be sharded.
-        has_tp_adapters = False
-        for module in self.modules():
-            tp_info = getattr(module, "_tp_info", None)
-            if tp_info is not None:
-                has_tp_adapters = True
-                break
-
-        if has_tp_adapters:
+        def _resolve_adapter_state_dict():
+            # Materialize the adapter state dict from `adapter_state_dict` or `checkpoint_files`. Used by paths
+            # that bypass `self._load_pretrained_model` (which would otherwise read the files itself).
             all_pointer = set()
             if adapter_state_dict is not None:
-                merged_state_dict = adapter_state_dict
-            elif (
-                checkpoint_files is not None
-                and checkpoint_files[0].endswith(".safetensors")
-                and adapter_state_dict is None
-            ):
+                return adapter_state_dict
+            if checkpoint_files is not None and checkpoint_files[0].endswith(".safetensors"):
                 merged_state_dict = {}
                 for file in checkpoint_files:
                     file_pointer = safe_open(file, framework="pt", device="cpu")
                     all_pointer.add(file_pointer)
                     for k in file_pointer.keys():
                         merged_state_dict[k] = file_pointer.get_tensor(k)
+                return merged_state_dict
             # Checkpoints are .bin
-            elif checkpoint_files is not None:
+            if checkpoint_files is not None:
                 merged_state_dict = {}
                 for ckpt_file in checkpoint_files:
                     merged_state_dict.update(load_state_dict(ckpt_file))
-            else:
-                raise ValueError("Neither a state dict nor checkpoint files were found.")
+                return merged_state_dict
+            raise ValueError("Neither a state dict nor checkpoint files were found.")
 
-            adapter_state_dict = merged_state_dict
+        def set_inference_mode(model):
+            model.eval()
+            for module in model.modules():
+                if isinstance(module, BaseTunerLayer):
+                    module.requires_grad_(False)
+
+        # If the model is tensor parallel, we handle the sharding of the state dict here since the logic in `self._load_pretrained_model`
+        # is not compatible with the way PEFT adapter should be sharded.
+        has_tp_adapters = False
+        for module in self.modules():
+            tp_info = getattr(module, "_tp_info", None)
+            if tp_info is not None:
+                has_tp_adapters = True
+                break
+
+        if has_tp_adapters:
+            adapter_state_dict = _resolve_adapter_state_dict()
 
             if any(not isinstance(v, torch.Tensor) for v in adapter_state_dict.values()):
                 raise ValueError("Expected all values in the adapter state dict to be tensors.")
 
             _maybe_shard_state_dict_for_tp(self, adapter_state_dict, adapter_name)
 
+        if hotswap:
+            # Bypass the standard loader and use PEFT's hotswap path so that LoRA weights
+            # whose rank differs from the existing adapter's are copied (and zero-padded)
+            # in place rather than triggering a "size mismatch" reinit, and so the LoRA
+            # scaling is updated alongside the weights.
+            from peft.utils.hotswap import check_hotswap_configs_compatible, hotswap_adapter_from_state_dict
+
+            adapter_state_dict = _resolve_adapter_state_dict()
+
+            # need to apply conversions manually as we don't use _load_pretrained_model
+            renamings = [r for r in peft_weight_conversions if isinstance(r, WeightRenaming)]
+            converters = [c for c in peft_weight_conversions if isinstance(c, WeightConverter)]
+            meta_state_dict = self.state_dict()
+            processed_state_dict = {}
+            for key, value in adapter_state_dict.items():
+                renamed_key, _ = rename_source_key(key, renamings, converters, self.base_model_prefix, meta_state_dict)
+                processed_state_dict[renamed_key] = value
+
+            check_hotswap_configs_compatible(self.peft_config[adapter_name], peft_config)
+            try:
+                hotswap_adapter_from_state_dict(
+                    model=self,
+                    state_dict=processed_state_dict,
+                    adapter_name=adapter_name,
+                    config=peft_config,
+                )
+            except Exception as e:
+                logger.error(f"Hotswapping {adapter_name} was unsuccessful with the following error:\n{e}")
+                raise
+
+            if peft_config.inference_mode:
+                set_inference_mode(self)
+
+            return LoadStateDictInfo(
+                missing_keys=set(),
+                unexpected_keys=set(),
+                mismatched_keys=set(),
+                error_msgs=[],
+                conversion_errors={},
+            )
+
         load_config = replace(
             load_config,
             pretrained_model_name_or_path=peft_model_id,
@@ -676,12 +725,7 @@ def load_adapter(
         )
 
         if peft_config.inference_mode:
-            from peft.tuners.tuners_utils import BaseTunerLayer
-
-            self.eval()
-            for module in self.modules():
-                if isinstance(module, BaseTunerLayer):
-                    module.requires_grad_(False)
+            set_inference_mode(self)
 
         adapter_key_markers = {adapter_name}
         if peft_config is not None and getattr(peft_config, "peft_type", None) is not None:
@@ -699,6 +743,16 @@ def is_adapter_key(key: str) -> bool:
             loading_info=loading_info,
             logger=logger,
         )
+
+        if self._prepare_peft_hotswap_kwargs is not None:
+            # Apply once, after the first adapter has been loaded but before the model is
+            # compiled, so the LoRA layers get padded up to target_rank and a later adapter
+            # with a different rank can be hot-swapped in without recompiling.
+            from peft.utils.hotswap import prepare_model_for_compiled_hotswap
+
+            prepare_model_for_compiled_hotswap(self, config=peft_config, **self._prepare_peft_hotswap_kwargs)
+            self._prepare_peft_hotswap_kwargs = None
+
         return loading_info
 
     def enable_peft_hotswap(
diff --git a/src/transformers/integrations/sonicmoe.py b/src/transformers/integrations/sonicmoe.py
index e322bb4bc061..912b98655519 100644
--- a/src/transformers/integrations/sonicmoe.py
+++ b/src/transformers/integrations/sonicmoe.py
@@ -18,6 +18,8 @@
 Requirements: CUDA, `kernels`, `nvidia-cutlass-dsl`, has_gate=True.
 """
 
+from __future__ import annotations
+
 import functools
 
 import torch
@@ -38,16 +40,31 @@ def _load_sonic_kernel():
     Load sonic-moe once and return its required symbols.
 
     Raises:
-        ImportError if the kernel or required symbols are not found.
+        ImportError if CUDA/hardware requirements are not met, or if the kernel or
+        required symbols are not found.
 
     Returns:
         Tuple of (ActivationType, moe_general_routing_inputs function) from the sonic-moe kernel.
     """
 
+    if not torch.cuda.is_available():
+        raise ImportError(
+            "sonic-moe kernel requires CUDA, but CUDA is not available. Use a different `experts_implementation`."
+        )
+
+    # sonic-moe requires Hopper (SM90) or newer
+    major = torch.cuda.get_device_capability()[0]
+    if major < 9:
+        raise ImportError(
+            f"sonic-moe requires a Hopper (SM90+) or newer GPU, but the current device "
+            f"has compute capability {major}.x. Use a different `experts_implementation`."
+        )
+
     kernel = lazy_load_kernel("sonic-moe")
     if kernel is None:
         raise ImportError(
-            "sonic-moe kernel not found. Make sure you have the `kernels` and `nvidia-cutlass-dsl` packages installed."
+            "Failed to load the sonic-moe kernel — check that `kernels-community/sonic-moe` "
+            "has a build matching the current torch/CUDA."
         )
 
     ActivationType = getattr(getattr(kernel, "enums", None), "ActivationType", None)
@@ -70,6 +87,50 @@ def _load_sonic_kernel():
     return ActivationType, moe_general_routing_inputs
 
 
+@torch._dynamo.allow_in_graph
+def _sonicmoe_wrapper(
+    hidden_states: torch.Tensor,
+    router_scores: torch.Tensor,
+    expert_ids: torch.Tensor,
+    token_idx: torch.Tensor,
+    w1: torch.Tensor,
+    b1: torch.Tensor | None,
+    w2: torch.Tensor,
+    b2: torch.Tensor | None,
+    act_name: str,
+    num_experts: int,
+    concat_layout: bool,
+    is_inference_mode_enabled: bool,
+) -> torch.Tensor:
+    """Module-level shim around `moe_general_routing_inputs` so `allow_in_graph` can wrap it.
+
+    sonicmoe asserts `not torch.compiler.is_compiling()` internally because it dispatches
+    CuteDSL kernels, which Dynamo can't trace. `allow_in_graph` keeps the call in the FX
+    graph as a single opaque node (no tracing into the body, no graph break) while still
+    running the real Python at runtime — autograd through `_UpProjection` / `_DownProjection`
+    flows normally. The decorator must be applied at module load time, not inside the compiled
+    function — hence this shim plus the `allow_in_graph` decorator above.
+    """
+    ActivationType, moe_general_routing_inputs = _load_sonic_kernel()
+    activation_type = getattr(ActivationType, ACT_MAP.get(act_name, "swiglu").upper(), ActivationType.SWIGLU)
+    output, _ = moe_general_routing_inputs(
+        hidden_states,
+        router_scores,
+        token_idx,
+        expert_ids,
+        w1,
+        b1,
+        w2,
+        b2,
+        E=num_experts,
+        activation_type=activation_type,
+        is_inference_mode_enabled=is_inference_mode_enabled,
+        concat_layout=concat_layout,
+        stream_id=None,
+    )
+    return output
+
+
 def sonicmoe_experts_forward(
     self: torch.nn.Module,
     hidden_states: torch.Tensor,
@@ -81,8 +142,6 @@ def sonicmoe_experts_forward(
     if hidden_states.device.type != "cuda":
         raise ValueError("sonicmoe requires CUDA device")
 
-    ActivationType, moe_general_routing_inputs = _load_sonic_kernel()
-
     device = hidden_states.device
     num_top_k = top_k_index.size(-1)
     num_tokens = hidden_states.size(0)
@@ -92,10 +151,14 @@ def sonicmoe_experts_forward(
     router_scores = top_k_weights.reshape(-1).to(hidden_states.dtype)
     expert_ids = top_k_index.reshape(-1).int()
 
+    # EP sentinel handling: leave `expert_ids` unclamped — the kernel's metadata stage drops
+    # `expert_ids >= num_experts` from the per-expert histogram and masks them out of the
+    # scatter indices, so sentinels never enter the grouped GEMM. Their routing weights are
+    # already zero (RouterParallel masks them at dispatch), so the per-token reduction
+    # contributes nothing for sentinel slots.
+
     # Map activation function
     act_name = getattr(self.config, "hidden_act", "silu").lower()
-    activation_type = getattr(ActivationType, ACT_MAP.get(act_name, "swiglu").upper(), ActivationType.SWIGLU)
-
     # Permute weights as expected by sonic-moe (E=num_experts, H=hidden_size, I=intermediate_size).
     # Non-transposed: gate_up_proj is (E, 2*I, H), down_proj is (E, H, I) -> permute(1, 2, 0).
     # Transposed: gate_up_proj is (E, H, 2*I), down_proj is (E, I, H) -> permute(2, 1, 0).
@@ -105,20 +168,17 @@ def sonicmoe_experts_forward(
     b1 = self.gate_up_proj_bias if self.has_bias else None
     b2 = self.down_proj_bias if self.has_bias else None
 
-    output, _ = moe_general_routing_inputs(
-        hidden_states,
-        router_scores,
-        token_idx,
-        expert_ids,
-        w1,
-        b1,
-        w2,
-        b2,
-        E=self.num_experts,
-        activation_type=activation_type,
-        stream_id=torch.cuda.current_stream(device).cuda_stream,
-        is_inference_mode_enabled=not torch.is_grad_enabled(),
+    return _sonicmoe_wrapper(
+        hidden_states=hidden_states,
+        router_scores=router_scores,
+        expert_ids=expert_ids,
+        token_idx=token_idx,
+        w1=w1,
+        b1=b1,
+        w2=w2,
+        b2=b2,
+        act_name=act_name,
+        num_experts=self.num_experts,
         concat_layout=self.is_concatenated,
+        is_inference_mode_enabled=not torch.is_grad_enabled(),
     )
-
-    return output
diff --git a/src/transformers/integrations/tensor_parallel.py b/src/transformers/integrations/tensor_parallel.py
index bdf82e8490f0..21f0a833ef08 100644
--- a/src/transformers/integrations/tensor_parallel.py
+++ b/src/transformers/integrations/tensor_parallel.py
@@ -29,6 +29,7 @@
     import torch
     import torch.distributed as dist
     from torch import nn
+    from torch.distributed.tensor import DTensor, Shard
 
     # Cache this result has it's a C FFI call which can be pretty time-consuming
     _torch_distributed_available = torch.distributed.is_available()
@@ -46,8 +47,11 @@ def initialize_tensor_parallelism(
     """
     if tp_size is not None and tp_plan is None:
         raise ValueError("tp_plan has to be set when tp_size is passed.")
-    if tp_plan is not None and device_map is not None:
-        raise ValueError("`tp_plan` and `device_map` are mutually exclusive. Choose either one for parallelization.")
+    if tp_plan is not None and device_map is not None and device_map != "meta" and device_mesh is None:
+        raise ValueError(
+            "`tp_plan` and `device_map` are mutually exclusive. "
+            "Choose either one for parallelization or include a `device_mesh`."
+        )
     if device_mesh is None:
         if not is_torch_greater_or_equal("2.5"):
             raise OSError("Tensor parallel is only supported for `torch>=2.5`.")
@@ -97,7 +101,8 @@ def initialize_tensor_parallelism(
                 )
             device_mesh = device_mesh["tp"]
         tp_size = device_mesh.size()
-        device_map = torch.device(f"{device_mesh.device_type}:{int(os.environ['LOCAL_RANK'])}")
+        if device_map is None:
+            device_map = torch.device(f"{device_mesh.device_type}:{int(os.environ['LOCAL_RANK'])}")
 
     return device_map, device_mesh, tp_size
 
@@ -130,6 +135,17 @@ def _get_parameter_tp_plan(parameter_name: str, tp_plan: dict[str, str], is_weig
     return None
 
 
+def get_ep_sharded_param_names(model) -> list[str]:
+    """FQNs of parameters whose data is per-rank unique under EP sharding."""
+    if not getattr(model, "has_ep", False):
+        return []
+    return [
+        name
+        for name, _ in model.named_parameters()
+        if _get_parameter_tp_plan(parameter_name=name, tp_plan=model.tp_plan, is_weight=True) == "grouped_gemm"
+    ]
+
+
 # =============================================================================
 # Tensor Sharding Utilities
 # =============================================================================
@@ -685,6 +701,14 @@ def update_module_attributes(self, module: nn.Module):
         """
         pass
 
+    def post_shard_wrap(self, param: nn.Parameter) -> nn.Parameter:
+        """
+        Optional final wrap applied to a parameter after `shard_tensor` and before it is
+        attached to the module. Default is identity. Subclasses can override to e.g. wrap
+        the local shard as a DTensor.
+        """
+        return param
+
 
 class ColwiseParallel(TensorParallelLayer):
     """
@@ -966,8 +990,8 @@ def _prepare_output_fn(self, mod, outputs, device_mesh):
         if self.embedding_dim_sharding == 0 and hasattr(mod, "_input_mask"):
             input_mask = mod._input_mask
             # Use multiplication instead of in-place assignment to preserve gradients
-            mask_expanded = input_mask.unsqueeze(-1).expand_as(outputs)
-            outputs = outputs * (~mask_expanded).to(outputs.dtype)
+            mask = input_mask.unsqueeze(-1)
+            outputs = outputs * (~mask).to(outputs.dtype)
             del mod._input_mask
 
         return all_reduce_forward(outputs, device_mesh)
@@ -1078,6 +1102,15 @@ def update_module_attributes(self, module: nn.Module):
         if hasattr(module, "num_experts"):
             module.num_experts = self.get_expected_sharded_shape((self.empty_param.shape[0],))[0]
 
+    def post_shard_wrap(self, param: nn.Parameter) -> nn.Parameter:
+        """
+        Wrap the EP-sharded local tensor as a DTensor on the TP/EP mesh. Without this, the
+        optimizer's foreach ops error with "mixed Tensor and DTensor" against the
+        FSDP-wrapped DTensor params on the rest of the model.
+        """
+        dt = DTensor.from_local(param.data, self.device_mesh, [Shard(0)], run_check=False)
+        return nn.Parameter(dt, requires_grad=param.requires_grad)
+
 
 class RouterParallel(TensorParallelLayer):
     """
@@ -1488,6 +1521,8 @@ def shard_and_distribute_module(
     # otherwise loading is crazy slow
     if not isinstance(param, torch.nn.Parameter):
         param = torch.nn.Parameter(param, requires_grad=empty_param.is_floating_point())
+    if current_shard_plan is not None:
+        param = tp_layer.post_shard_wrap(param)
     setattr(module_to_tp, param_type, param)
     if tp_layer is not None:
         tp_layer.update_module_attributes(module_to_tp)
diff --git a/src/transformers/integrations/tpu.py b/src/transformers/integrations/tpu.py
index a329a7fcdd84..e10e190b889e 100644
--- a/src/transformers/integrations/tpu.py
+++ b/src/transformers/integrations/tpu.py
@@ -18,7 +18,9 @@
 import torch
 from torch.utils.data import DataLoader
 
-from ..utils import WEIGHTS_NAME, PushToHubMixin, is_torch_xla_available, logging
+from ..modeling_utils import unwrap_model
+from ..utils import WEIGHTS_NAME, is_torch_xla_available, logging
+from ..utils.hub import PushToHubMixin
 
 
 logger = logging.get_logger(__name__)
@@ -162,7 +164,9 @@ def patched_optimizer_step(optimizer, barrier=False, optimizer_args={}):
     return model
 
 
-def save_tpu_checkpoint(model, args, accelerator, processing_class, is_fsdp_xla_v1_enabled, output_dir=None):
+def save_tpu_checkpoint(
+    model, args, accelerator, processing_class, is_fsdp_xla_v1_enabled, output_dir=None, is_fsdp_xla_v2_enabled=False
+):
     """
     Saves a model checkpoint on TPU/XLA devices.
 
@@ -176,6 +180,7 @@ def save_tpu_checkpoint(model, args, accelerator, processing_class, is_fsdp_xla_
         processing_class: The processing class (tokenizer/processor) to save alongside the model.
         is_fsdp_xla_v1_enabled (`bool`): Whether FSDP XLA v1 is enabled.
         output_dir (`str`, *optional*): The directory to save to. Defaults to `args.output_dir`.
+        is_fsdp_xla_v2_enabled (`bool`, *optional*): Whether FSDP XLA v2 is enabled.
     """
     import torch_xla.core.xla_model as xm
 
@@ -219,15 +224,16 @@ def save_tpu_checkpoint(model, args, accelerator, processing_class, is_fsdp_xla_
                 logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                 xm.save(full_state_dict, os.path.join(output_dir, WEIGHTS_NAME))
     elif not isinstance(model, supported_classes):
-        if isinstance(accelerator.unwrap_model(model), supported_classes):
-            accelerator.unwrap_model(model).save_pretrained(
+        unwrapped_model = unwrap_model(model, recursive=is_fsdp_xla_v2_enabled)
+        if isinstance(unwrapped_model, supported_classes):
+            unwrapped_model.save_pretrained(
                 output_dir,
                 is_main_process=args.should_save,
-                state_dict=xm._maybe_convert_to_cpu(model.state_dict()),
+                state_dict=xm._maybe_convert_to_cpu(unwrapped_model.state_dict()),
             )
         else:
             logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
-            state_dict = xm._maybe_convert_to_cpu(model.state_dict())
+            state_dict = xm._maybe_convert_to_cpu(unwrapped_model.state_dict())
             xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
     else:
         model.save_pretrained(
diff --git a/src/transformers/loss/loss_for_object_detection.py b/src/transformers/loss/loss_for_object_detection.py
index 52b43f779f35..79469785827d 100644
--- a/src/transformers/loss/loss_for_object_detection.py
+++ b/src/transformers/loss/loss_for_object_detection.py
@@ -31,7 +31,7 @@
     from transformers.image_transforms import center_to_corners_format
 
 
-def dice_loss(inputs, targets, num_boxes):
+def dice_loss(inputs, targets, num_boxes, valid_mask=None):
     """
     Compute the DICE loss, similar to generalized IOU for masks
 
@@ -41,16 +41,25 @@ def dice_loss(inputs, targets, num_boxes):
         targets: A float tensor with the same shape as inputs. Stores the binary
                  classification label for each element in inputs (0 for the negative class and 1 for the positive
                  class).
+        valid_mask: Optional boolean tensor with the same shape as inputs.
+                    If provided, only valid (non-padding) areas are considered in the loss.
+                    True means valid, False means padding.
     """
     inputs = inputs.sigmoid()
     inputs = inputs.flatten(1)
+
+    if valid_mask is not None:
+        valid_mask = valid_mask.flatten(1).to(dtype=inputs.dtype)
+        inputs = inputs * valid_mask
+        targets = targets * valid_mask
+
     numerator = 2 * (inputs * targets).sum(1)
     denominator = inputs.sum(-1) + targets.sum(-1)
     loss = 1 - (numerator + 1) / (denominator + 1)
     return loss.sum() / num_boxes
 
 
-def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
+def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, valid_mask=None):
     """
     Loss used in RetinaNet for dense detection: https://huggingface.co/papers/1708.02002.
 
@@ -64,6 +73,9 @@ def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: f
             Optional weighting factor in the range (0,1) to balance positive vs. negative examples.
         gamma (`int`, *optional*, defaults to `2`):
             Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples.
+        valid_mask: Optional boolean tensor with the same shape as inputs.
+                    If provided, only valid (non-padding) areas are considered in the loss.
+                    True means valid, False means padding.
 
     Returns:
         Loss tensor
@@ -78,6 +90,13 @@ def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: f
         alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
         loss = alpha_t * loss
 
+    if valid_mask is not None:
+        valid_mask = valid_mask.flatten(1).to(dtype=loss.dtype)
+        loss = loss * valid_mask
+        # Average only over valid pixels per sample
+        valid_count = valid_mask.sum(1).clamp(min=1)
+        return (loss.sum(1) / valid_count).sum() / num_boxes
+
     return loss.mean(1).sum() / num_boxes
 
 
@@ -193,11 +212,16 @@ def loss_masks(self, outputs, targets, indices, num_boxes):
         source_masks = outputs["pred_masks"]
         source_masks = source_masks[source_idx]
         masks = [t["masks"] for t in targets]
-        # TODO use valid to mask invalid areas due to padding in loss
         target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
         target_masks = target_masks.to(source_masks)
         target_masks = target_masks[target_idx]
 
+        # Get valid mask for selected targets (invert: True = valid, False = padding)
+        # valid has shape (batch, h, w), we need to index by batch indices only
+        batch_idx = target_idx[0]
+        valid_mask = ~valid
+        valid_mask = valid_mask[batch_idx]
+
         # upsample predictions to the target size
         source_masks = nn.functional.interpolate(
             source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
@@ -206,9 +230,12 @@ def loss_masks(self, outputs, targets, indices, num_boxes):
 
         target_masks = target_masks.flatten(1)
         target_masks = target_masks.view(source_masks.shape)
+        valid_mask = valid_mask.flatten(1)
+        valid_mask = valid_mask.view(source_masks.shape)
+
         losses = {
-            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
-            "loss_dice": dice_loss(source_masks, target_masks, num_boxes),
+            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes, valid_mask=valid_mask),
+            "loss_dice": dice_loss(source_masks, target_masks, num_boxes, valid_mask=valid_mask),
         }
         return losses
 
diff --git a/src/transformers/loss/loss_rt_detr.py b/src/transformers/loss/loss_rt_detr.py
index cf6d6ad05940..69dc1ff67600 100644
--- a/src/transformers/loss/loss_rt_detr.py
+++ b/src/transformers/loss/loss_rt_detr.py
@@ -270,6 +270,12 @@ def loss_masks(self, outputs, targets, indices, num_boxes):
         target_masks = target_masks.to(source_masks)
         target_masks = target_masks[target_idx]
 
+        # Get valid mask for selected targets (invert: True = valid, False = padding)
+        # valid has shape (batch, h, w), we need to index by batch indices only
+        batch_idx = target_idx[0]
+        valid_mask = ~valid
+        valid_mask = valid_mask[batch_idx]
+
         # upsample predictions to the target size
         source_masks = nn.functional.interpolate(
             source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False
@@ -278,9 +284,12 @@ def loss_masks(self, outputs, targets, indices, num_boxes):
 
         target_masks = target_masks.flatten(1)
         target_masks = target_masks.view(source_masks.shape)
+        valid_mask = valid_mask.flatten(1)
+        valid_mask = valid_mask.view(source_masks.shape)
+
         losses = {
-            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes),
-            "loss_dice": dice_loss(source_masks, target_masks, num_boxes),
+            "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes, valid_mask=valid_mask),
+            "loss_dice": dice_loss(source_masks, target_masks, num_boxes, valid_mask=valid_mask),
         }
         return losses
 
diff --git a/src/transformers/loss/loss_tdt.py b/src/transformers/loss/loss_tdt.py
new file mode 100644
index 000000000000..6a128f18583c
--- /dev/null
+++ b/src/transformers/loss/loss_tdt.py
@@ -0,0 +1,217 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch
+
+from ..utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def _load_tdt_kernel():
+    """Try to load the TDT loss CUDA kernel from the Hub. Returns None on failure."""
+    try:
+        from ..integrations.hub_kernels import lazy_load_kernel
+
+        kernel = lazy_load_kernel("tdt-loss")
+        if kernel is None or not hasattr(kernel, "tdt_loss"):
+            logger.warning_once("Falling back to pure PyTorch implementation.")
+            return None
+        return kernel
+    except (ImportError, ModuleNotFoundError):
+        return None
+    except Exception as e:
+        logger.warning_once(f"Failed to load TDT CUDA kernel: {e}. Falling back to pure PyTorch implementation.")
+        return None
+
+
+def tdt_loss(
+    token_logits: torch.Tensor,
+    duration_logits: torch.Tensor,
+    targets: torch.Tensor,
+    logit_lengths: torch.Tensor,
+    target_lengths: torch.Tensor,
+    blank_token_id: int,
+    durations: list[int],
+    sigma: float = 0.0,
+    reduction: str = "mean",
+) -> torch.Tensor:
+    """
+    Compute TDT (Token-and-Duration Transducer) loss (https://arxiv.org/abs/2304.06795).
+
+    Ported from NeMo's `TDTLossPytorch` with anti-diagonal processing. Unlike standard RNNT loss, this loss trains both
+    the token prediction head and the duration prediction head. It uses vectorized anti-diagonal processing for
+    efficiency: all (t, u) pairs on each anti-diagonal t+u=n are computed in parallel as batched tensor operations.
+
+    When the ``kernels-community/tdt-loss`` CUDA kernel is installed, it is used automatically for GPU tensors,
+    Falls back to the pure PyTorch implementation otherwise.
+
+    Args:
+        token_logits: Token logits of shape `(batch, T, U+1, vocab_size+1)`.
+        duration_logits: Duration logits of shape `(batch, T, U+1, num_durations)`.
+        targets: Target labels of shape `(batch, U)`.
+        logit_lengths: Encoder output lengths of shape `(batch,)`.
+        target_lengths: Target lengths of shape `(batch,)`.
+        blank_token_id: Blank token id.
+        durations: List of duration values (e.g., `[0, 1, 2, 3, 4]`).
+        sigma: Logit undernormalization constant (see TDT paper). Defaults to `0.0`.
+        reduction: Loss reduction method. One of `"mean"`, `"sum"`, or `"none"`. Defaults to `"mean"`.
+
+    Returns:
+        Scalar loss tensor (or per-example losses if `reduction="none"`).
+
+    """
+    kernel = _load_tdt_kernel() if token_logits.is_cuda else None
+    if kernel is not None and hasattr(kernel, "tdt_loss"):
+        durations_t = torch.tensor(durations, dtype=torch.int32, device=token_logits.device)
+        return kernel.tdt_loss(
+            token_logits,
+            duration_logits,
+            targets,
+            logit_lengths,
+            target_lengths,
+            durations_t,
+            blank_token_id,
+            sigma,
+            reduction,
+        )
+
+    if reduction not in ("mean", "sum", "none"):
+        raise ValueError(f'Invalid reduction mode "{reduction}". Expected one of "mean", "sum", or "none".')
+
+    device = token_logits.device
+    batch_size, max_t, max_u, _ = token_logits.shape
+
+    token_logits = token_logits.float()
+    duration_logits = duration_logits.float()
+
+    # Apply log-softmax to get log probabilities
+    # sigma only applies to token logits (undernormalization constant from the TDT paper)
+    token_log_probs = torch.log_softmax(token_logits, dim=-1) - sigma
+    duration_log_probs = torch.log_softmax(duration_logits, dim=-1)
+
+    log_alpha = torch.full((batch_size, max_t, max_u), float("-inf"), device=device)
+    log_alpha[:, 0, 0] = 0.0
+
+    # Precompute blank and label log-probs for vectorized access
+    blank_log_probs = token_log_probs[:, :, :, blank_token_id]
+
+    if max_u > 1:
+        targets_expanded = targets.unsqueeze(1).expand(-1, max_t, -1)  # (batch, T, U_labels)
+        label_log_probs = torch.gather(
+            token_log_probs[:, :, : max_u - 1, :],  # (batch, T, U-1, vocab)
+            dim=3,
+            index=targets_expanded.unsqueeze(-1),
+        ).squeeze(-1)  # (batch, T, U-1)
+
+    neg_inf = torch.tensor(float("-inf"), device=device)
+
+    # Process anti-diagonals: all (t, u) with t + u = n have no mutual dependencies
+    for n in range(1, max_t + max_u - 1):
+        u_start = max(0, n - max_t + 1)
+        u_end = min(n + 1, max_u)
+        u_indices = torch.arange(u_start, u_end, device=device)
+
+        t_indices = n - u_indices
+        all_candidates = []
+        for i, dur in enumerate(durations):
+            t_prev = t_indices - dur
+            valid_t = t_prev >= 0
+            if not valid_t.any():
+                continue
+            t_src = t_prev.clamp(min=0)
+
+            # Blank arcs (dur > 0): from (t-dur, u) to (t, u)
+            if dur > 0:
+                contrib = (
+                    log_alpha[:, t_src, u_indices]
+                    + blank_log_probs[:, t_src, u_indices]
+                    + duration_log_probs[:, t_src, u_indices, i]
+                )
+                contrib = torch.where(valid_t.unsqueeze(0), contrib, neg_inf)
+                all_candidates.append(contrib)
+
+            # Label arcs: from (t-dur, u-1) to (t, u), only if u > 0
+            valid_u = u_indices > 0
+            valid_both = valid_t & valid_u
+            if valid_both.any():
+                u_src = (u_indices - 1).clamp(min=0)
+                u_src_label = u_src.clamp(max=max_u - 2) if max_u > 1 else u_src
+
+                contrib = (
+                    log_alpha[:, t_src, u_src]
+                    + label_log_probs[:, t_src, u_src_label]
+                    + duration_log_probs[:, t_src, u_src, i]
+                )
+                contrib = torch.where(valid_both.unsqueeze(0), contrib, neg_inf)
+                all_candidates.append(contrib)
+
+        if all_candidates:
+            stacked = torch.stack(all_candidates, dim=0)
+            log_alpha[:, t_indices, u_indices] = torch.logsumexp(stacked, dim=0)
+
+    # Terminal probability: sum over blank arcs that reach (T, U) from (T-dur, U)
+    batch_idx = torch.arange(batch_size, device=device)
+    log_probs = torch.full((batch_size,), float("-inf"), device=device)
+    for i, dur in enumerate(durations):
+        if dur == 0:
+            continue
+        t_final = logit_lengths - dur
+        valid = t_final >= 0
+        if not valid.any():
+            continue
+
+        t_clamped = t_final.clamp(min=0)
+        terminal = (
+            log_alpha[batch_idx, t_clamped, target_lengths]
+            + token_log_probs[batch_idx, t_clamped, target_lengths, blank_token_id]
+            + duration_log_probs[batch_idx, t_clamped, target_lengths, i]
+        )
+        combined = torch.stack([log_probs, terminal], dim=0)
+        log_probs = torch.where(valid, torch.logsumexp(combined, dim=0), log_probs)
+
+    losses = -log_probs
+
+    if reduction == "mean":
+        return (losses / target_lengths.float()).mean()
+    elif reduction == "sum":
+        return losses.sum()
+    return losses
+
+
+def ParakeetForTDTLoss(
+    token_logits,
+    duration_logits,
+    labels,
+    logit_lengths,
+    label_lengths,
+    blank_token_id,
+    durations,
+    sigma=0.0,
+    reduction="mean",
+    **kwargs,
+):
+    device = token_logits.device
+    return tdt_loss(
+        token_logits=token_logits,
+        duration_logits=duration_logits,
+        targets=labels.to(device).int(),
+        logit_lengths=logit_lengths.to(device).int(),
+        target_lengths=label_lengths.to(device).int(),
+        blank_token_id=blank_token_id,
+        durations=durations,
+        sigma=sigma,
+        reduction=reduction,
+    )
diff --git a/src/transformers/loss/loss_utils.py b/src/transformers/loss/loss_utils.py
index 51564d299e55..5ca1fe99a5cd 100644
--- a/src/transformers/loss/loss_utils.py
+++ b/src/transformers/loss/loss_utils.py
@@ -15,6 +15,7 @@
 
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from torch.nn import BCEWithLogitsLoss, MSELoss
 
 from .loss_d_fine import DFineForObjectDetectionLoss
@@ -24,6 +25,7 @@
 from .loss_grounding_dino import GroundingDinoForObjectDetectionLoss
 from .loss_lw_detr import LwDetrForObjectDetectionLoss
 from .loss_rt_detr import RTDetrForObjectDetectionLoss
+from .loss_tdt import ParakeetForTDTLoss
 
 
 def fixed_cross_entropy(
@@ -31,10 +33,14 @@ def fixed_cross_entropy(
     target: torch.Tensor,
     num_items_in_batch: torch.Tensor | None = None,
     ignore_index: int = -100,
+    weight: torch.Tensor | None = None,
+    label_smoothing: float = 0.0,
     **kwargs,
 ) -> torch.Tensor:
     reduction = "sum" if num_items_in_batch is not None else "mean"
-    loss = nn.functional.cross_entropy(source, target, ignore_index=ignore_index, reduction=reduction)
+    loss = nn.functional.cross_entropy(
+        source, target, ignore_index=ignore_index, weight=weight, reduction=reduction, label_smoothing=label_smoothing
+    )
     if reduction == "sum":
         # just in case users pass an int for num_items_in_batch, which could be the case for custom trainer
         if torch.is_tensor(num_items_in_batch):
@@ -47,13 +53,20 @@ def ForCausalLMLoss(
     logits,
     labels,
     vocab_size: int,
+    hidden_states: torch.Tensor | None = None,
+    lm_head_weight: torch.Tensor | None = None,
+    logits_to_keep: int | None = None,
     num_items_in_batch: torch.Tensor | None = None,
     ignore_index: int = -100,
     shift_labels: torch.Tensor | None = None,
     **kwargs,
 ) -> torch.Tensor:
-    # Upcast to float if we need to compute the loss to avoid potential precision issues
-    logits = logits.float()
+    if hidden_states is not None and lm_head_weight is not None:
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = F.linear(
+            hidden_states[:, slice_indices, :],
+            lm_head_weight,
+        )
 
     if shift_labels is None:
         # Shift so that tokens < n predict n
@@ -63,11 +76,15 @@ def ForCausalLMLoss(
     # Flatten the tokens
     logits = logits.view(-1, vocab_size)
     shift_labels = shift_labels.view(-1)
+    mask = shift_labels != ignore_index
+    shift_labels = shift_labels[mask]
+    logits = logits[mask.to(logits.device)]
+    # Upcast to float if we need to compute the loss to avoid potential precision issues
+    logits = logits.float()
     shift_labels = shift_labels.to(logits.device)
     loss = fixed_cross_entropy(logits, shift_labels, num_items_in_batch, ignore_index, **kwargs)
     return loss
 
-
 def ForMaskedLMLoss(
     logits: torch.Tensor,
     labels: torch.Tensor,
@@ -167,4 +184,5 @@ def ForTokenClassification(logits: torch.Tensor, labels, config, **kwargs):
     "Deimv2ForObjectDetection": Deimv2ForObjectDetectionLoss,
     "CsmForConditionalGeneration": ForCausalLMLoss,
     "LwDetrForObjectDetection": LwDetrForObjectDetectionLoss,
+    "ParakeetForTDT": ParakeetForTDTLoss,
 }
diff --git a/src/transformers/masking_utils.py b/src/transformers/masking_utils.py
index 45e43fdaf3aa..44e4c021e261 100644
--- a/src/transformers/masking_utils.py
+++ b/src/transformers/masking_utils.py
@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import contextvars
 import itertools
 from collections.abc import Callable
 
@@ -42,6 +43,12 @@
 
 logger = logging.get_logger(__name__)
 
+# Context variable to track if attention_mask is known to be all-True during generation.
+# When set to True, _ignore_causal_mask_sdpa skips the expensive .all() GPU-CPU sync.
+_attention_mask_all_true: contextvars.ContextVar[bool | None] = contextvars.ContextVar(
+    "_attention_mask_all_true", default=None
+)
+
 
 def and_masks(*mask_functions: Callable) -> Callable:
     """Returns a mask function that is the intersection of provided mask functions"""
@@ -111,6 +118,24 @@ def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
     return inner_mask
 
 
+def blockwise_overlay(block_sequence_ids: torch.Tensor) -> Callable:
+    """
+    This is an overlay depicting a blockwise masking pattern. Instead of a single
+    token, each block consists of arbitrary length tokens. In causal setup, each block
+    can attend to prev block causally and can't attend to future blocks. Within one block
+    the attention is always bidirectional.
+    Mostly used in MLLMs when non-text data attends bidirectionally to itself.
+    """
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        # Unmask if the q and kv come from same group which is not -1 (i.e. non-text)
+        q_group = block_sequence_ids[batch_idx, q_idx]
+        kv_group = block_sequence_ids[batch_idx, kv_idx]
+        return (q_group == kv_group) & (q_group >= 0)
+
+    return inner_mask
+
+
 def sliding_window_causal_mask_function(sliding_window: int) -> Callable:
     """
     This return the mask_function function to create a sliding window mask.
@@ -194,6 +219,19 @@ def prepare_padding_mask(attention_mask: torch.Tensor | None, kv_length: int, kv
     return local_padding_mask
 
 
+def maybe_pad_block_sequence_ids(
+    block_sequence_ids: torch.Tensor, attention_mask: torch.Tensor | None, kv_length: int, kv_offset: int
+) -> torch.Tensor:
+    """
+    Pads the `block_sequence_ids` in case the total length is less than `kv_length`.
+    Usually that happens with `StaticCache` generation or generating without cache.
+    Pads to the right with `-1`.
+    """
+    if (padding_length := kv_length + kv_offset - block_sequence_ids.shape[-1]) > 0:
+        block_sequence_ids = F.pad(block_sequence_ids, pad=(0, padding_length), value=-1)
+    return block_sequence_ids
+
+
 def _can_skip_causal_mask_xpu(
     padding_mask: torch.Tensor | None,
     query_length: int,
@@ -260,6 +298,10 @@ def _ignore_causal_mask_sdpa(
     # hard-coded to the forward. If a user exports a model with query_length > 1, the exported model will hard-code `is_causal=True`
     # which is in general wrong (see https://github.com/pytorch/pytorch/issues/108108). Thus, we only set
     # `ignore_causal_mask = True` if we are not tracing
+    #
+    # Check context variable first to avoid GPU-CPU sync during generation.
+    # When _attention_mask_all_true is True, we know the mask contains no padding.
+    mask_known_all_true = _attention_mask_all_true.get()
     if (
         not is_tracing(padding_mask)
         # only cases when lower and upper diags are the same, see https://github.com/pytorch/pytorch/issues/108108
@@ -267,7 +309,7 @@ def _ignore_causal_mask_sdpa(
         # in this case we need to add special patterns to the mask so cannot be skipped otherwise
         and (local_attention_size is None or kv_length < local_attention_size)
         # In this case, we need to add padding to the mask, so cannot be skipped otherwise
-        and (padding_mask is None or padding_mask.all())
+        and (padding_mask is None or mask_known_all_true is True or padding_mask.all())
     ):
         return True
 
@@ -889,6 +931,7 @@ def create_causal_mask(
     position_ids: torch.Tensor | None = None,
     or_mask_function: Callable | None = None,
     and_mask_function: Callable | None = None,
+    block_sequence_ids: torch.Tensor | None = None,
 ) -> torch.Tensor | BlockMask | None:
     """
     Create a standard causal mask based on the attention implementation used (stored in the config). If `past_key_values`
@@ -916,6 +959,10 @@ def create_causal_mask(
         and_mask_function (`Callable`, optional):
             An optional mask function to combine with the causal mask function (by doing the intersection of both). This is
             useful to easily overlay another mask on top of the causal one, for example for image tokens handling.
+        block_sequence_ids (`torch.Tensor`, *optional*):
+            A tensor of same shape as input IDs indicating to which block or group each token belongs to. Tokens from
+            the same block will keep a bidirectional mask within the block, attending causally to the past. Index `-1`
+            can be used for blocks that have to keep complete causality within itself.
     """
     # Power feature: if `is_causal` is False, then fallback to bi-directional mask for bi-directional attention.
     # It allows to use decoder-only models with bi-directional attention as well
@@ -974,10 +1021,14 @@ def create_causal_mask(
         allow_is_causal_skip = False
         use_vmap = True
 
-    # If we detected packing format
+    # If we detected packing format or blockwise overlay
     if packed_sequence_mask is not None:
         mask_factory_function = and_masks(mask_factory_function, packed_sequence_mask_function(packed_sequence_mask))
         allow_is_causal_skip = False
+    if block_sequence_ids is not None:
+        block_sequence_ids = maybe_pad_block_sequence_ids(block_sequence_ids, attention_mask, kv_length, kv_offset)
+        mask_factory_function = or_masks(mask_factory_function, blockwise_overlay(block_sequence_ids))
+        allow_is_causal_skip = False
 
     # We now create the mask
     causal_mask = mask_interface(
@@ -1006,6 +1057,7 @@ def create_bidirectional_mask(
     past_key_values: Cache | None = None,
     or_mask_function: Callable | None = None,
     and_mask_function: Callable | None = None,
+    **kwargs,
 ) -> torch.Tensor | BlockMask | None:
     """
     Create a standard bidirectional mask based on the attention implementation used (stored in the config).
@@ -1098,6 +1150,7 @@ def create_sliding_window_causal_mask(
     position_ids: torch.Tensor | None = None,
     or_mask_function: Callable | None = None,
     and_mask_function: Callable | None = None,
+    block_sequence_ids: torch.Tensor | None = None,
 ) -> torch.Tensor | BlockMask | None:
     """
     Create a sliding window causal mask based on the attention implementation used (stored in the config). This type
@@ -1126,6 +1179,10 @@ def create_sliding_window_causal_mask(
         and_mask_function (`Callable`, optional):
             An optional mask function to combine with the sliding causal mask function (by doing the intersection of both). This is
             useful to easily overlay another mask on top of the sliding causal one, for example for image tokens handling.
+        block_sequence_ids (`torch.Tensor`, *optional*):
+            A tensor of same shape as input IDs indicating to which block or group each token belongs to. Tokens from
+            the same block will keep a bidirectional mask within the block, attending causally to the past. Index `-1`
+            can be used for blocks that have to keep complete causality within itself.
     """
     # Power feature: if `is_causal` is False, then fallback to bi-directional mask for bi-directional attention
     # It allows to use decoder-only models with bi-directional attention as well
@@ -1183,10 +1240,14 @@ def create_sliding_window_causal_mask(
         allow_is_causal_skip = False
         use_vmap = True
 
-    # If we detected packing format
+    # If we detected packing format or blockwise overlay
     if packed_sequence_mask is not None:
         mask_factory_function = and_masks(mask_factory_function, packed_sequence_mask_function(packed_sequence_mask))
         allow_is_causal_skip = False
+    if block_sequence_ids is not None:
+        block_sequence_ids = maybe_pad_block_sequence_ids(block_sequence_ids, attention_mask, kv_length, kv_offset)
+        mask_factory_function = or_masks(mask_factory_function, blockwise_overlay(block_sequence_ids))
+        allow_is_causal_skip = False
 
     # We now create the mask
     causal_mask = mask_interface(
@@ -1215,6 +1276,7 @@ def create_bidirectional_sliding_window_mask(
     past_key_values: Cache | None = None,
     or_mask_function: Callable | None = None,
     and_mask_function: Callable | None = None,
+    **kwargs,
 ) -> torch.Tensor | BlockMask | None:
     """
     Create a standard bidirectional sliding window mask based on the attention implementation used (stored in the config).
@@ -1414,6 +1476,10 @@ def create_chunked_causal_mask(
     "full_attention": create_causal_mask,
     "sliding_attention": create_sliding_window_causal_mask,
     "chunked_attention": create_chunked_causal_mask,
+    # V4 attention types all share the sliding-window causal mask; the long-range
+    # branch's compressed segment is appended to keys after the mask is built.
+    "compressed_sparse_attention": create_sliding_window_causal_mask,
+    "heavily_compressed_attention": create_sliding_window_causal_mask,
 }
 
 
@@ -1426,6 +1492,7 @@ def create_masks_for_generate(
     position_ids: torch.Tensor | None = None,
     or_mask_function: Callable | None = None,
     and_mask_function: Callable | None = None,
+    block_sequence_ids: torch.Tensor | None = None,
     **kwargs,
 ):
     """
@@ -1451,6 +1518,10 @@ def create_masks_for_generate(
         and_mask_function (`Callable`, optional):
             An optional mask function to combine with the other mask function (by doing the intersection of both). This is
             useful to easily overlay another mask on top of the causal one, for example for image tokens handling.
+        block_sequence_ids (`torch.Tensor`, *optional*):
+            A tensor of same shape as input IDs indicating to which block or group each token belongs to. Tokens from
+            the same block will keep a bidirectional mask within the block, attending causally to the past. Index `-1`
+            can be used for blocks that have to keep complete causality within itself.
     """
     # The attribute reside in the text config for composite models
     effective_config = config.get_text_config()
@@ -1463,6 +1534,7 @@ def create_masks_for_generate(
         "position_ids": position_ids,
         "or_mask_function": or_mask_function,
         "and_mask_function": and_mask_function,
+        "block_sequence_ids": block_sequence_ids,
     }
 
     # If the attribute exist, we need several masks
diff --git a/src/transformers/modelcard.py b/src/transformers/modelcard.py
index e833a5a8a2ab..8a7195f13806 100644
--- a/src/transformers/modelcard.py
+++ b/src/transformers/modelcard.py
@@ -21,8 +21,7 @@
 import httpx
 import yaml
 from huggingface_hub import is_offline_mode, model_info
-from huggingface_hub.errors import OfflineModeIsEnabled
-from huggingface_hub.utils import HFValidationError
+from huggingface_hub.errors import HFValidationError, OfflineModeIsEnabled
 
 from . import __version__
 from .models.auto.modeling_auto import (
diff --git a/src/transformers/modeling_flash_attention_utils.py b/src/transformers/modeling_flash_attention_utils.py
index 32642d71d2a3..e2f3d395d932 100644
--- a/src/transformers/modeling_flash_attention_utils.py
+++ b/src/transformers/modeling_flash_attention_utils.py
@@ -297,7 +297,9 @@ def _unpad_input(hidden_states, attention_mask, unused_mask=None):
     seqlens_in_batch = all_masks.sum(dim=-1, dtype=torch.int32)
     used_seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
     indices = torch.nonzero(all_masks.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max()
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
 
     return (
@@ -346,7 +348,9 @@ def _get_unpad_data(attention_mask: torch.Tensor) -> tuple[torch.Tensor, torch.T
     """
     seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
     indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
-    max_seqlen_in_batch = seqlens_in_batch.max()
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
     cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
     return (
         indices,
@@ -467,6 +471,9 @@ def prepare_fa_kwargs_from_position_ids(position_ids):
     # We should use cu_seq_lens instead of position_ids to get the max length since position_ids is not always increasing
     # for some models (e.g. qwen2-vl).
     max_length_q = cu_seq_lens_q.diff().max()
+    # `.item()` is necessary to work with torch compile as the FA API requires base ints, not tensors.
+    # You might need to set `TORCHDYNAMO_CAPTURE_SCALAR_OUTPUTS=1`.
+    max_length_q = max_length_q.item()
     max_length_k = max_length_q
 
     return (cu_seq_lens_q, cu_seq_lens_k), (max_length_q, max_length_k)
@@ -516,8 +523,11 @@ def _is_packed_sequence(position_ids, batch_size):
         1. Position ids exist
         2. Flattened sequences only are supported
         3. Compile-friendly `not (torch.diff(position_ids, dim=-1) >= 0).all()`, i.e. we have multiple increasing sequences
+
+    NOTE: We disable this feature if torch compile or similar features are used due to dynamic control flows
+          we cannot avoid without losing control over the gradients, e.g. via `torch.cond`.
     """
-    if position_ids is None:
+    if is_tracing(position_ids) or position_ids is None:
         return False
 
     increasing_position_sequences = (
@@ -616,6 +626,21 @@ def _process_flash_attention_kwargs(
         flash_kwargs (`dict`):
             A dict of kwargs that are requested and supported.
     """
+
+    user_kwargs = {
+        "dropout_p": dropout,
+        "window_size": sliding_window,
+        "deterministic": deterministic,
+        "softcap": softcap,
+        "s_aux": s_aux,
+    }
+    # Note 'window_size' in supports_mapping maps to our 'sliding_window' param
+    for k, v in user_kwargs.items():
+        if not supports_mapping[k] and v is not None:
+            raise ValueError(
+                f"Parameter `{k}` is not supported by this Flash Attention implementation but was set, please use a different attentionimplementation."
+            )
+
     flash_kwargs = {
         "causal": is_causal and not (use_top_left_mask and query_length == 1),
         "softmax_scale": softmax_scale,
@@ -735,8 +760,10 @@ def _flash_attention_forward(
 
     # We will use `flash_varlen_fn` to prevent cross-example attention and also allow padding free approach under two cases:
     # Case 1. If position ids is provided and the position ids indicate packed sequences, see `_is_packed_sequence`.
+    #         --> not compile friendly, will be ignored if torch compile is used
     # Case 2. Some models pass directly pre-computed `cu_seqlens` so we don't need to infer it from position ids. It is safe to
-    # use `flash_varlen_fn` knowing we already have all necessary the kwargs.
+    #         use `flash_varlen_fn` knowing we already have all necessary the kwargs.
+    #         --> compile friendly, preferred option to use
     #
     # NOTE: it is user's responsibility to take care of flattening `position_ids` if that's needed by the model.
     # See #39121 for more information.
diff --git a/src/transformers/modeling_gguf_pytorch_utils.py b/src/transformers/modeling_gguf_pytorch_utils.py
index 2de6cc13fc85..b8890ce97add 100644
--- a/src/transformers/modeling_gguf_pytorch_utils.py
+++ b/src/transformers/modeling_gguf_pytorch_utils.py
@@ -51,6 +51,10 @@
 }
 
 GGUF_SUPPORTED_ARCHITECTURES = list(GGUF_TO_TRANSFORMERS_MAPPING["config"].keys())
+PRISM_Q1_0_G128_NAME = "Q1_0_g128"
+PRISM_Q1_0_G128_VALUE = 41
+PRISM_Q1_0_G128_BLOCK_SIZE = 128
+PRISM_Q1_0_G128_TYPE_SIZE = 18
 
 
 class GGUFTensor(NamedTuple):
@@ -59,6 +63,40 @@ class GGUFTensor(NamedTuple):
     metadata: dict
 
 
+def _is_prism_q1_0_g128(tensor_type) -> bool:
+    if getattr(tensor_type, "name", None) == PRISM_Q1_0_G128_NAME:
+        return True
+
+    try:
+        return int(tensor_type) == PRISM_Q1_0_G128_VALUE
+    except (TypeError, ValueError):
+        return False
+
+
+def _dequantize_prism_q1_0_g128(data: np.ndarray) -> np.ndarray:
+    rows = np.asarray(data, dtype=np.uint8)
+    if rows.shape[-1] % PRISM_Q1_0_G128_TYPE_SIZE != 0:
+        raise ValueError(
+            f"Prism Q1_0_g128 row byte width must be divisible by 18, got {rows.shape[-1]} for shape {rows.shape}"
+        )
+
+    n_blocks = rows.shape[-1] // PRISM_Q1_0_G128_TYPE_SIZE
+    blocks = rows.reshape(*rows.shape[:-1], n_blocks, PRISM_Q1_0_G128_TYPE_SIZE)
+    scales = np.ascontiguousarray(blocks[..., :2]).view(np.float16).astype(np.float32)[..., 0]
+    sign_bits = np.unpackbits(blocks[..., 2:], axis=-1, bitorder="little")
+    weights = np.where(sign_bits == 1, scales[..., None], -scales[..., None]).astype(np.float32, copy=False)
+    return weights.reshape(*rows.shape[:-1], n_blocks * PRISM_Q1_0_G128_BLOCK_SIZE)
+
+
+def _dequantize_gguf_tensor(data: np.ndarray, tensor_type, dequantize_fn) -> np.ndarray:
+    try:
+        return dequantize_fn(data, tensor_type)
+    except NotImplementedError:
+        if _is_prism_q1_0_g128(tensor_type):
+            return _dequantize_prism_q1_0_g128(data)
+        raise
+
+
 class TensorProcessor:
     def __init__(self, config=None):
         self.config = config or {}
@@ -400,6 +438,122 @@ def process(self, weights, name, **kwargs):
         return GGUFTensor(weights, name, {})
 
 
+class Qwen3NextTensorProcessor(Qwen2MoeTensorProcessor):
+    """Handles Qwen3-Next GGUF tensors including DeltaNet (linear attention) layers.
+
+    Key transformations:
+    - attn_qkv + attn_gate → in_proj_qkvz (reverse split + reshuffle)
+    - ssm_a → A_log (reverse: log(-weights))
+    - ssm_conv1d → conv1d (unsqueeze middle dim)
+    - norm weights → subtract 1 (except ssm_norm)
+    - dt_bias → dt_proj.bias (rename for gguf-py mapping compatibility)
+    """
+
+    HF_QKVZ_PATTERN = re.compile(r"model\.layers\.(?P<bid>\d+)\.linear_attn\._qkvz_merged")
+    HF_DT_BIAS_PATTERN = re.compile(r"model\.layers\.(?P<bid>\d+)\.linear_attn\.dt_bias")
+    GGUF_QKVZ_PATTERN = re.compile(r"blk\.(?P<bid>\d+)\.(?P<part>attn_qkv|attn_gate)\.weight$")
+
+    def __init__(self, config=None):
+        super().__init__(config=config)
+
+    def preprocess_name(self, hf_name: str) -> str:
+        hf_name = super().preprocess_name(hf_name)
+        # Rename in_proj_qkvz so gguf-py name_map won't resolve it to ssm_in
+        # (the GGUF file splits it into attn_qkv + attn_gate instead)
+        if "linear_attn.in_proj_qkvz" in hf_name:
+            hf_name = hf_name.replace("linear_attn.in_proj_qkvz", "linear_attn._qkvz_merged")
+        return hf_name
+
+    def perform_fallback_tensor_mapping(
+        self, gguf_to_hf_name_map: dict[str, str], suffix: str, qual_name: str, hf_name: str
+    ):
+        super().perform_fallback_tensor_mapping(gguf_to_hf_name_map, suffix, qual_name, hf_name)
+
+        # Map attn_qkv + attn_gate → in_proj_qkvz (two-to-one mapping)
+        if m := re.fullmatch(self.HF_QKVZ_PATTERN, hf_name.removesuffix(suffix)):
+            real_hf_name = hf_name.replace("_qkvz_merged", "in_proj_qkvz")
+            full_hf_name = qual_name + real_hf_name
+            gguf_to_hf_name_map[f"blk.{m['bid']}.attn_qkv{suffix}"] = full_hf_name
+            gguf_to_hf_name_map[f"blk.{m['bid']}.attn_gate{suffix}"] = full_hf_name
+
+        # Map dt_bias → ssm_dt.bias (gguf-py maps dt_proj → ssm_dt)
+        if m := re.fullmatch(self.HF_DT_BIAS_PATTERN, hf_name):
+            gguf_to_hf_name_map[f"blk.{m['bid']}.ssm_dt.bias"] = qual_name + hf_name
+
+    def process(self, weights, name: str, **kwargs):
+        # Handle attn_qkv + attn_gate → in_proj_qkvz reverse merge
+        if m := re.fullmatch(self.GGUF_QKVZ_PATTERN, name):
+            tensor_key_mapping = kwargs.get("tensor_key_mapping")
+            parsed_parameters = kwargs.get("parsed_parameters")
+            if tensor_key_mapping:
+                self._set_qkvz_tensor(weights, parsed_parameters, tensor_key_mapping[name], m["part"])
+                return GGUFTensor(weights, None, {})
+
+        # ssm_conv1d: GGUF [conv_dim, kernel] → HF [conv_dim, 1, kernel]
+        if "ssm_conv1d" in name:
+            weights = np.expand_dims(weights, axis=1)
+            return GGUFTensor(weights, name, {})
+
+        # ssm_a: GGUF stores -exp(A_log), reverse: log(-weights)
+        if "ssm_a" in name:
+            weights = np.log(-weights)
+            return GGUFTensor(weights, name, {})
+
+        # Norm weights: GGUF stores weight+1, reverse: weight-1
+        # Exception: ssm_norm (linear_attn.norm) was NOT +1'd during conversion
+        if "norm" in name and "ssm_norm" not in name:
+            weights = weights - 1
+
+        # Delegate to parent for MoE expert weights and shared_expert_gate
+        return super().process(weights, name, **kwargs)
+
+    def _set_qkvz_tensor(self, weights: np.ndarray, parsed_parameters: dict[str, dict], hf_name: str, part: str):
+        """Reverse the in_proj_qkvz → attn_qkv + attn_gate split performed during GGUF conversion.
+
+        The GGUF conversion splits the interleaved [q,k,v,z] per-group layout into two tensors:
+          attn_qkv = [q_all, k_all, v_all] (contiguous per component)
+          attn_gate = z_all
+        This method collects both parts and reconstructs the original interleaved layout.
+        """
+        torch_weights = torch.from_numpy(np.copy(weights))
+
+        # Store intermediate tensors until both parts arrive
+        intermediates = parsed_parameters.setdefault("_qkvz_intermediates", {})
+        parts = intermediates.setdefault(hf_name, {})
+        parts[part] = torch_weights
+
+        if "attn_qkv" not in parts or "attn_gate" not in parts:
+            return  # Wait for the other part
+
+        # Both parts available — reconstruct in_proj_qkvz
+        qkv_tensor = parts["attn_qkv"]
+        gate_tensor = parts["attn_gate"]
+
+        head_k_dim = self.config.get("linear_key_head_dim", 128)
+        head_v_dim = self.config.get("linear_value_head_dim") or (
+            self.config.get("_ssm_inner_size", 4096) // self.config.get("linear_num_value_heads", 32)
+        )
+        num_k_heads = self.config.get("linear_num_key_heads", 16)
+        num_v_heads = self.config.get("linear_num_value_heads", 32)
+        hidden_size = self.config.get("hidden_size", 2048)
+
+        key_dim = head_k_dim * num_k_heads
+        vk_ratio = num_v_heads // num_k_heads
+
+        # Split attn_qkv [key_dim*2 + value_dim, hidden] into q, k, v
+        q_all = qkv_tensor[:key_dim].T.reshape(hidden_size, num_k_heads, head_k_dim)
+        k_all = qkv_tensor[key_dim : key_dim * 2].T.reshape(hidden_size, num_k_heads, head_k_dim)
+        v_all = qkv_tensor[key_dim * 2 :].T.reshape(hidden_size, num_k_heads, vk_ratio * head_v_dim)
+        z_all = gate_tensor.T.reshape(hidden_size, num_k_heads, vk_ratio * head_v_dim)
+
+        # Reconstruct interleaved [q, k, v, z] per group
+        grouped = torch.cat([q_all, k_all, v_all, z_all], dim=-1)  # [hidden, num_k_heads, group_size]
+        result = grouped.reshape(hidden_size, -1).T.contiguous()  # [total_size, hidden]
+
+        parsed_parameters["tensors"][hf_name] = result
+        del intermediates[hf_name]
+
+
 class MiniMaxM2TensorProcessor(TensorProcessor):
     HF_EXPERT_RENAME_PATTERN = re.compile(r"mlp\.experts\.\d+\.")
     HF_MOE_W13_PATTERN = re.compile(r"(?:model\.)?layers\.(?P<bid>\d+)\.mlp\.experts\.gate_up_proj")
@@ -453,11 +607,63 @@ def _set_moe_expert_tensor(self, weights: np.ndarray, parsed_parameters: dict[st
             out.copy_(torch_weights)
 
 
+class Llama4TensorProcessor(TensorProcessor):
+    HF_MOE_GATE_UP_PATTERN = re.compile(r"(?:model\.)?layers\.(?P<bid>\d+)\.feed_forward\.experts\.gate_up_proj$")
+    HF_MOE_DOWN_PATTERN = re.compile(r"(?:model\.)?layers\.(?P<bid>\d+)\.feed_forward\.experts\.down_proj$")
+    GGUF_MOE_WEIGHTS_PATTERN = re.compile(r".*\.ffn_(?P<w>gate|up|down)_exps\.weight$")
+
+    def __init__(self, config=None):
+        super().__init__(config=config)
+
+    def perform_fallback_tensor_mapping(
+        self, gguf_to_hf_name_map: dict[str, str], suffix: str, qual_name: str, hf_name: str
+    ):
+        if m := re.fullmatch(self.HF_MOE_GATE_UP_PATTERN, hf_name):
+            full_hf_name = qual_name + hf_name
+            gguf_to_hf_name_map[f"blk.{m['bid']}.ffn_gate_exps.weight"] = full_hf_name
+            gguf_to_hf_name_map[f"blk.{m['bid']}.ffn_up_exps.weight"] = full_hf_name
+        elif m := re.fullmatch(self.HF_MOE_DOWN_PATTERN, hf_name):
+            full_hf_name = qual_name + hf_name
+            gguf_to_hf_name_map[f"blk.{m['bid']}.ffn_down_exps.weight"] = full_hf_name
+
+    def process(self, weights, name: str, **kwargs):
+        if m := re.fullmatch(self.GGUF_MOE_WEIGHTS_PATTERN, name):
+            tensor_key_mapping = kwargs.get("tensor_key_mapping")
+            parsed_parameters = kwargs.get("parsed_parameters")
+            if tensor_key_mapping and name in tensor_key_mapping:
+                self._set_moe_expert_tensor(weights, parsed_parameters, tensor_key_mapping[name], m["w"])
+                return GGUFTensor(weights, None, {})
+        return GGUFTensor(weights, name, {})
+
+    def _set_moe_expert_tensor(self, weights: np.ndarray, parsed_parameters: dict[str, dict], hf_name: str, w: str):
+        torch_weights = torch.from_numpy(np.ascontiguousarray(np.swapaxes(weights, -1, -2)))
+        if w == "down":
+            parsed_parameters["tensors"][hf_name] = torch_weights
+            return
+        # Merge gate and up into gate_up_proj: [E, hidden, 2*expert_dim], gate first then up.
+        shape = list(torch_weights.shape)
+        shard_dim = -1
+        shard_size = shape[shard_dim]
+        shape[shard_dim] = shard_size * 2
+        if hf_name not in parsed_parameters["tensors"]:
+            parsed_parameters["tensors"][hf_name] = torch.zeros(shape, dtype=torch_weights.dtype)
+        out: torch.Tensor = parsed_parameters["tensors"][hf_name]
+        if w == "gate":
+            out = out.narrow(shard_dim, 0, shard_size)
+        else:  # w == "up"
+            out = out.narrow(shard_dim, shard_size, shard_size)
+        out.copy_(torch_weights)
+
+
 TENSOR_PROCESSORS = {
     "llama": LlamaTensorProcessor,
+    "llama4": Llama4TensorProcessor,
     "qwen2moe": Qwen2MoeTensorProcessor,
     "gpt_oss": GptOssTensorProcessor,
     "qwen3moe": Qwen2MoeTensorProcessor,
+    # Qwen3.5 MoE reuses the qwen2/qwen3 fused 3-D ffn_*_exps layout.
+    "qwen35moe": Qwen2MoeTensorProcessor,
+    "qwen3next": Qwen3NextTensorProcessor,
     "bloom": BloomTensorProcessor,
     "t5": T5TensorProcessor,
     "t5encoder": T5TensorProcessor,
@@ -512,12 +718,22 @@ def get_gguf_hf_weights_map(
         model_type = "qwen2moe"
     elif model_type == "qwen3_moe":
         model_type = "qwen3moe"
+    elif model_type == "qwen3_5_moe_text":
+        model_type = "qwen35moe"
+    elif model_type == "qwen3_next":
+        model_type = "qwen3next"
     elif model_type == "gemma3_text":
         model_type = "gemma3"
+    elif model_type == "qwen2_vl":
+        model_type = "qwen2vl"
     elif model_type == "umt5":
         model_type = "t5"
     elif model_type == "minimax_m2":
         model_type = "minimax-m2"
+    elif model_type == "llama4_text":
+        # GGUF Llama 4 files only contain text weights; the text-only config
+        # uses `llama4_text` in transformers but the GGUF arch key is `llama4`.
+        model_type = "llama4"
     elif model_type == "gpt_oss":
         model_type = "gpt-oss"
     arch = None
@@ -630,6 +846,14 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False, model_to_lo
         updated_architecture = "gpt_oss"
     elif "qwen3moe" in architecture:
         updated_architecture = "qwen3_moe"
+    elif "qwen3next" in architecture:
+        updated_architecture = "qwen3_next"
+    elif "qwen35moe" in architecture:
+        # GGUF identifies Qwen3.5 MoE as "qwen35moe". Route to the
+        # text-only qwen3_5_moe_text config rather than the multimodal
+        # qwen3_5_moe wrapper so Qwen3_5MoeForCausalLM gets the matching
+        # Qwen3_5MoeTextConfig.
+        updated_architecture = "qwen3_5_moe_text"
     elif "minimax-m2" in architecture:
         updated_architecture = "minimax_m2"
 
@@ -695,6 +919,18 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False, model_to_lo
     if parsed_parameters["config"]["model_type"] == "gemma3":
         parsed_parameters["config"]["model_type"] = "gemma3_text"
 
+    # Llama 4 GGUF checkpoints only contain the text backbone. Rewrite the model_type to
+    # the text-only config and nest rope_theta under rope_parameters (Llama4TextConfig is
+    # @strict and stores rope params in a nested dict rather than a top-level field).
+    if parsed_parameters["config"]["model_type"] == "llama4":
+        parsed_parameters["config"]["model_type"] = "llama4_text"
+        rope_theta = parsed_parameters["config"].pop("rope_theta", None)
+        if rope_theta is not None:
+            parsed_parameters["config"]["rope_parameters"] = {
+                "rope_type": "default",
+                "rope_theta": float(rope_theta),
+            }
+
     # MiniMax-M2: convert expert_gating_func integer to scoring_func string
     if parsed_parameters["config"].get("model_type") == "minimax_m2":
         _gating_func_map = {0: "none", 1: "softmax", 2: "sigmoid"}
@@ -715,6 +951,21 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False, model_to_lo
             i for i, num_kv_heads in enumerate(gguf_num_key_value_heads) if num_kv_heads > 0
         ]
 
+    if updated_architecture == "qwen3_5_moe_text":
+        # GatedDeltaNet's value head dim isn't emitted as its own GGUF key —
+        # the writer only emits ssm.inner_size (= linear_value_head_dim *
+        # linear_num_value_heads). Recover it here so the config matches the
+        # checkpoint instead of silently falling back to the class default.
+        ssm_inner_key = f"{architecture}.ssm.inner_size"
+        n_v_heads = parsed_parameters["config"].get("linear_num_value_heads")
+        if ssm_inner_key in reader.fields and n_v_heads:
+            ssm_inner = _gguf_parse_value(
+                reader.fields[ssm_inner_key].parts[reader.fields[ssm_inner_key].data[0]],
+                reader.fields[ssm_inner_key].types,
+            )
+            if ssm_inner % n_v_heads == 0:
+                parsed_parameters["config"]["linear_value_head_dim"] = ssm_inner // n_v_heads
+
     if updated_architecture == "gpt_oss":
         # Helper to read keys with the correct prefix
         def read_gpt_key(reader, suffix, default=None):
@@ -754,6 +1005,26 @@ def read_gpt_key(reader, suffix, default=None):
 
             parsed_parameters["config"]["rope_scaling"] = rope_scaling
 
+    if parsed_parameters["config"].get("model_type") == "qwen3_next":
+        # Compute linear_value_head_dim from ssm.inner_size / linear_num_value_heads
+        ssm_inner_size = parsed_parameters["config"].pop("_ssm_inner_size", None)
+        num_v_heads = parsed_parameters["config"].get("linear_num_value_heads")
+        if ssm_inner_size is not None and num_v_heads:
+            parsed_parameters["config"]["linear_value_head_dim"] = ssm_inner_size // num_v_heads
+
+        # Compute partial_rotary_factor and rope_parameters from GGUF rope fields
+        rope_dim_count = parsed_parameters["config"].pop("_rope_dimension_count", None)
+        rope_freq_base = parsed_parameters["config"].pop("_rope_freq_base", None)
+        head_dim = parsed_parameters["config"].get("head_dim")
+        partial_rotary_factor = 0.25  # default for Qwen3-Next
+        if rope_dim_count is not None and head_dim:
+            partial_rotary_factor = rope_dim_count / head_dim
+        parsed_parameters["config"]["rope_parameters"] = {
+            "rope_type": "default",
+            "rope_theta": rope_freq_base or 5000000.0,
+            "partial_rotary_factor": partial_rotary_factor,
+        }
+
     # retrieve config vocab_size from tokenizer
     # Please refer to https://github.com/huggingface/transformers/issues/32526 for more details
     if "vocab_size" not in parsed_parameters["config"]:
@@ -778,7 +1049,7 @@ def read_gpt_key(reader, suffix, default=None):
 
         for tensor in tqdm(reader.tensors, desc="Converting and de-quantizing GGUF tensors..."):
             name = tensor.name
-            weights = dequantize(tensor.data, tensor.tensor_type)
+            weights = _dequantize_gguf_tensor(tensor.data, tensor.tensor_type, dequantize)
 
             result = processor.process(
                 weights=weights,
diff --git a/src/transformers/modeling_layers.py b/src/transformers/modeling_layers.py
index 1012606fcaaf..2aca6fda0aa3 100644
--- a/src/transformers/modeling_layers.py
+++ b/src/transformers/modeling_layers.py
@@ -102,7 +102,7 @@ def __init__(self, config):
         self.num_labels = config.num_labels
         # Similar to `self.model = AutoModel.from_config(config)` but allows to change the base model name if needed in the child class
         setattr(self, self.base_model_prefix, AutoModel.from_config(config))
-        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+        self.score = nn.Linear(config.get_text_config().hidden_size, self.num_labels, bias=False)
 
         # Initialize weights and apply final processing
         self.post_init()
@@ -137,13 +137,13 @@ def forward(
         else:
             batch_size = inputs_embeds.shape[0]
 
-        if self.config.pad_token_id is None and batch_size != 1:
+        if self.config.get_text_config().pad_token_id is None and batch_size != 1:
             raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.pad_token_id is None:
+        if self.config.get_text_config().pad_token_id is None:
             last_non_pad_token = -1
         elif input_ids is not None:
             # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.pad_token_id).to(logits.device, torch.int32)
+            non_pad_mask = (input_ids != self.config.get_text_config().pad_token_id).to(logits.device, torch.int32)
             token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
             last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
         else:
diff --git a/src/transformers/modeling_outputs.py b/src/transformers/modeling_outputs.py
index 4db902237b50..4f3ac25fa58f 100755
--- a/src/transformers/modeling_outputs.py
+++ b/src/transformers/modeling_outputs.py
@@ -277,54 +277,6 @@ class BaseModelOutputWithPastAndCrossAttentions(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
-class MoECausalLMOutputWithPast(ModelOutput):
-    """
-    Base class for causal language model (or autoregressive) outputs as well as Mixture of Expert's router hidden
-    states terms, to train a MoE model.
-
-    Args:
-        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-            Language modeling loss (for next-token prediction).
-        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-            It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
-
-            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-            `past_key_values` input) to speed up sequential decoding.
-        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
-            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
-            one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
-
-            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
-        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
-            sequence_length)`.
-
-            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
-            heads.
-        z_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
-            z_loss for the sparse modules.
-        aux_loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
-            aux_loss for the sparse modules.
-        router_logits (`tuple(torch.FloatTensor)`, *optional*, returned when `output_router_logits=True` is passed or when `config.add_router_probs=True`):
-            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, sequence_length, num_experts)`.
-
-            Router logits of the encoder model, useful to compute the auxiliary loss and the z_loss for the sparse
-            modules.
-    """
-
-    loss: torch.FloatTensor | None = None
-    logits: torch.FloatTensor | None = None
-    past_key_values: Cache | None = None
-    hidden_states: tuple[torch.FloatTensor, ...] | None = None
-    attentions: tuple[torch.FloatTensor, ...] | None = None
-    z_loss: torch.FloatTensor | None = None
-    aux_loss: torch.FloatTensor | None = None
-    router_logits: tuple[torch.FloatTensor] | None = None
-
-
 @dataclass
 class MoEModelOutput(ModelOutput):
     """
@@ -1704,3 +1656,7 @@ class MaskedImageModelingOutput(ModelOutput):
     reconstruction: torch.FloatTensor | None = None
     hidden_states: tuple[torch.FloatTensor, ...] | None = None
     attentions: tuple[torch.FloatTensor, ...] | None = None
+
+
+# Keep reference for BC
+MoECausalLMOutputWithPast = MoeCausalLMOutputWithPast
diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
index b041964bbdfc..3687455bec01 100644
--- a/src/transformers/modeling_utils.py
+++ b/src/transformers/modeling_utils.py
@@ -111,6 +111,7 @@
     cached_file,
     check_torch_load_is_safe,
     copy_func,
+    deprecated,
     has_file,
     is_accelerate_available,
     is_bitsandbytes_available,
@@ -1259,7 +1260,10 @@ def can_record_outputs(self) -> dict[str, OutputRecorder]:
         ```
 
          This means you can record outputs from the same class, by specifying a layer name. Before
-         collecting outputs, we check that they come from this layer.
+         collecting outputs, we check that they come from this layer. `layer_name` is a regex pattern
+         (matched with `re.search` against the submodule's dotted qualified name), so anchors can be used
+         to target a single index without prefix-matching siblings (e.g. `"layers\\.1$"` matches `layers.1`
+         but not `layers.10`).
 
          If you have cross attention that come from `LlamaAttention` and self attention that also
          come from `LlamaAttention` but from `self_attn` you can do this:
@@ -1267,10 +1271,20 @@ def can_record_outputs(self) -> dict[str, OutputRecorder]:
          ```python
          class LlamaModel(PreTrainedModel):
              _can_record_outputs = {
-                 "attentions": OutputRecorder(LlamaAttention, index=1, layer-name="self_attn"),
+                 "attentions": OutputRecorder(LlamaAttention, index=1, layer_name="self_attn"),
                  "cross_attentions": OutputRecorder(LlamaAttention, index=1, layer_name="cross_attn")
              }
 
+        ```
+
+         Regex alternation can also be used to pick a non-contiguous subset of layers, e.g. to
+         capture hidden states from layers 6, 12, and 18 only:
+
+         ```python
+         class MyModel(PreTrainedModel):
+             _can_record_outputs = {
+                 "hidden_states": OutputRecorder(MyBlock, layer_name=r"layers\\.(6|12|18)$"),
+             }
         ```
         """
         return self._can_record_outputs or {}
@@ -1316,6 +1330,12 @@ def __init__(self, config: PreTrainedConfig, *inputs, **kwargs):
             )
         self.config = config
         self.name_or_path = config.name_or_path
+        quant_config = getattr(config, "quantization_config", None)
+        if quant_config is not None:
+            raise NotImplementedError(
+                "Quantization via `from_config()` is not supported. "
+                "Quantized models must be created via `from_pretrained()` with an appropriate backend."
+            )
 
         # Check the attention implementation is supported, or set it if not yet set (on the internal attr, to avoid
         # setting it recursively)
@@ -1368,6 +1388,9 @@ def post_init(self):
         self._keep_in_fp32_modules_strict = set(self._keep_in_fp32_modules_strict or [])
         # Current submodel must register its `_no_split_modules` as well
         self._no_split_modules = set(self._no_split_modules or [])
+        # Current submodel must register the `_keys_to_ignore_on_load_unexpected/missing`
+        self._keys_to_ignore_on_load_unexpected = self._keys_to_ignore_on_load_unexpected or []
+        self._keys_to_ignore_on_load_missing = self._keys_to_ignore_on_load_missing or []
 
         # Iterate over children only: as the final model is created, this is enough to gather the properties from all submodels.
         # This works because the way the `__init__` and `post_init` are called on all submodules is depth-first in the graph
@@ -1390,17 +1413,40 @@ def post_init(self):
             # Record `_no_split_modules` from the children
             if no_split := getattr(module, "_no_split_modules", None):
                 self._no_split_modules.update(no_split)
+            # Record `_keys_to_ignore_on_load_unexpected/missing` from the children
+            if ignore_unexpected := getattr(module, "_keys_to_ignore_on_load_unexpected", None):
+                self._keys_to_ignore_on_load_unexpected.extend(
+                    [f"{name}.{child_name}" for child_name in ignore_unexpected]
+                )
+            if ignore_missing := getattr(module, "_keys_to_ignore_on_load_missing", None):
+                self._keys_to_ignore_on_load_missing.extend([f"{name}.{child_name}" for child_name in ignore_missing])
+
+        # Preserve the current no-tie scope on this instance so only the model
+        # being initialized in that scope skips tie_weights().
+        self._skip_tie_weights_scope = init._SKIP_TIE_WEIGHTS_SCOPE.get()
 
         # Maybe initialize the weights and tie the keys
         self.init_weights()
         self._backward_compatibility_gradient_checkpointing()
+        # Cache the list of (name, submodule) pairs where the submodule is a PreTrainedModel.
+        # This pattern is used in several places across the codebase; computing it once avoids
+        # repeated traversal of the full module tree.
+        self._named_pretrained_submodules: list[tuple[str, PreTrainedModel]] = [
+            (name, module) for name, module in self.named_modules() if isinstance(module, PreTrainedModel)
+        ]
+
+    @property
+    def has_ep(self) -> bool:
+        """Whether expert parallelism is enabled for this model."""
+        distributed_config = getattr(getattr(self, "config", None), "distributed_config", None)
+        return distributed_config is not None and getattr(distributed_config, "enable_expert_parallel", False)
 
     @property
     def tp_plan(self) -> dict[str, str]:
         """
         The full tp plan for the model's modules
         """
-        if hasattr(self.config, "distributed_config") and self.config.distributed_config.enable_expert_parallel:
+        if self.has_ep:
             return self._ep_plan
         return self._tp_plan
 
@@ -2370,15 +2416,22 @@ def _init_weights(self, module):
             std = getattr(self.config.get_text_config(), "initializer_range", 0.02)
 
         if isinstance(module, (nn.Linear, nn.Conv1d, nn.Conv2d, nn.Conv3d, nn.ConvTranspose1d, nn.ConvTranspose2d)):
-            if getattr(module, "weight", None) is not None:
-                init.normal_(module.weight, mean=0.0, std=std)
-            if module.bias is not None:
+            if getattr(module, "weight", None) is not None and module.weight.is_floating_point():
+                init.normal_(module.weight.float(), mean=0.0, std=std)
+            if module.bias is not None and module.bias.is_floating_point():
                 init.zeros_(module.bias)
+        elif isinstance(module, nn.LSTM):
+            for name, param in module.named_parameters():
+                if "weight" in name:
+                    init.xavier_uniform_(param)
+                elif "bias" in name:
+                    init.constant_(param, 0.0)
         elif isinstance(module, nn.Embedding):
-            init.normal_(module.weight, mean=0.0, std=std)
-            # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
-            if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
-                init.zeros_(module.weight[module.padding_idx])
+            if module.weight.is_floating_point():
+                init.normal_(module.weight, mean=0.0, std=std)
+                # Here we need the check explicitly, as we slice the weight in the `zeros_` call, so it looses the flag
+                if module.padding_idx is not None and not getattr(module.weight, "_is_hf_initialized", False):
+                    init.zeros_(module.weight[module.padding_idx])
         elif isinstance(module, nn.MultiheadAttention):
             # This uses torch's original init
             module._reset_parameters()
@@ -2591,6 +2644,9 @@ def tie_weights(self, missing_keys: set[str] | None = None, recompute_mapping: b
         `source` is missing in the checkpoint while `target` exists, we *swap* source and target so we can still
         tie everything to the parameter that actually exists.
         """
+        if init.should_skip_tie_weights(self):
+            return
+
         # In this case, the keys stored in `all_tied_weights_keys` are already correct
         if not recompute_mapping:
             tied_keys = self.all_tied_weights_keys
@@ -2662,6 +2718,16 @@ def tie_weights(self, missing_keys: set[str] | None = None, recompute_mapping: b
             if missing_keys is not None and remove_from_missing:
                 missing_keys.discard(target_param_name)
 
+    @deprecated(
+        "5.0.0",
+        message=(
+            "`tie_embeddings_and_encoder_decoder` was renamed to `tie_weights` in Transformers v5. "
+            "Please update your code."
+        ),
+    )
+    def tie_embeddings_and_encoder_decoder(self, *args, **kwargs):
+        return self.tie_weights(*args, **kwargs)
+
     def _adjust_bias(self, output_embeddings, input_embeddings):
         if getattr(output_embeddings, "bias", None) is not None and hasattr(output_embeddings, "weight"):
             weight_shape = output_embeddings.weight.shape
@@ -3338,8 +3404,10 @@ def save_pretrained(
             files_timestamps = self._get_files_timestamps(save_directory)
 
         metadata = {}
+        quantizer_provided_state_dict = False
         if hf_quantizer is not None:
             state_dict, metadata = hf_quantizer.get_state_dict_and_metadata(self)
+            quantizer_provided_state_dict = state_dict is not None
         metadata["format"] = "pt"
 
         # Only save the model itself if we are using distributed training
@@ -3428,7 +3496,8 @@ def save_pretrained(
         state_dict = remove_tied_weights_from_state_dict(state_dict, model_to_save)
 
         # Revert all renaming and/or weight operations
-        if save_original_format and not _hf_peft_config_loaded:
+        # Skip if saving PEFT adapters or if the quantizer already provided the state_dict in the correct serialization format.
+        if save_original_format and not _hf_peft_config_loaded and not quantizer_provided_state_dict:
             state_dict = revert_weight_conversion(model_to_save, state_dict)
 
         # Shard the model if it is too big.
@@ -3671,14 +3740,27 @@ def float(self, *args):
 
     @classmethod
     def get_init_context(
-        cls, dtype: torch.dtype, is_quantized: bool, _is_ds_init_called: bool, allow_all_kernels: bool | None
+        cls,
+        dtype: torch.dtype,
+        is_quantized: bool,
+        _is_ds_init_called: bool,
+        allow_all_kernels: bool | None,
+        distributed_config=None,
     ):
         # Need to instantiate with correct dtype
         init_contexts = [local_torch_dtype(dtype, cls.__name__), init.no_tie_weights(), apply_patches()]
         # Needed as we cannot forward the `allow_all_kernels` arg in the model's __init__
         if allow_all_kernels:
             init_contexts.append(allow_all_hub_kernels())
-        if is_deepspeed_zero3_enabled():
+        _has_ep = distributed_config is not None and getattr(distributed_config, "enable_expert_parallel", False)
+        if _has_ep and is_deepspeed_zero3_enabled():
+            # EP + DeepSpeed: use meta device (same as the normal non-DS path).
+            # zero.Init is skipped because EP needs to shard experts via distribute_model()
+            # hooks, which are incompatible with ZeRO-3 lazy parameters.
+            # The standard weight loading path (not zero3) handles EP sharding via
+            # shard_and_distribute_module. deepspeed.initialize() wraps the result later.
+            init_contexts.extend([torch.device("meta"), init.meta_device_safe_creation_ops()])
+        elif is_deepspeed_zero3_enabled():
             import deepspeed
 
             # We cannot initialize the model on meta device with deepspeed when not quantized
@@ -4086,6 +4168,12 @@ def from_pretrained(
             download_kwargs_with_commit,
             **adapter_kwargs,
         )
+        # EP + DeepSpeed: clear device_map (set by initialize_tensor_parallelism) so the model
+        # loads on CPU first. distribute_model() handles GPU placement during EP sharding.
+        # Without this, device_map triggers accelerate's dispatch path which breaks shard loading.
+        _has_ep = distributed_config is not None and getattr(distributed_config, "enable_expert_parallel", False)
+        if _has_ep and is_deepspeed_zero3_enabled():
+            device_map = None
         device_map = check_and_set_device_map(device_map)  # warn, error and fix the device map
 
         user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
@@ -4127,8 +4215,9 @@ def from_pretrained(
         if "experts_implementation" in kwargs:
             config._experts_implementation = kwargs.pop("experts_implementation")
 
+        custom_hf_quantizer = model_kwargs.pop("hf_quantizer", None)
         hf_quantizer, config, device_map = get_hf_quantizer(
-            config, quantization_config, device_map, weights_only, user_agent
+            config, quantization_config, device_map, weights_only, user_agent, custom_hf_quantizer
         )
 
         if gguf_file:
@@ -4194,7 +4283,9 @@ def from_pretrained(
 
             register_fusion_patches(cls, config, fusion_config)
 
-        model_init_context = cls.get_init_context(dtype, is_quantized, _is_ds_init_called, allow_all_kernels)
+        model_init_context = cls.get_init_context(
+            dtype, is_quantized, _is_ds_init_called, allow_all_kernels, distributed_config
+        )
 
         config = copy.deepcopy(config)  # We do not want to modify the config inplace in from_pretrained.
         with ContextManagers(model_init_context):
@@ -4327,7 +4418,11 @@ def _load_pretrained_model(
 
         error_msgs = []
 
-        if is_deepspeed_zero3_enabled() and not is_quantized:
+        # EP + DeepSpeed: skip zero3 loading path. The model was created on meta device
+        # (not via zero.Init), so params are not zero3-partitioned. The standard loading
+        # path handles EP sharding via shard_and_distribute_module using the EP plan hooks
+        # registered by distribute_model(). deepspeed.initialize() wraps the result later.
+        if is_deepspeed_zero3_enabled() and not is_quantized and not model.has_ep:
             if state_dict is None:
                 merged_state_dict = {}
                 for ckpt_file in checkpoint_files:
@@ -4646,14 +4741,12 @@ def _move_missing_keys_from_meta_to_device(
         """
         is_quantized = hf_quantizer is not None
         # This is the only case where we do not initialize the model on meta device, so we don't have to do anything here
-        if is_deepspeed_zero3_enabled() and not is_quantized:
+        # Exception: EP + DeepSpeed uses meta device (not zero.Init), so it needs the standard move path.
+        if is_deepspeed_zero3_enabled() and not is_quantized and not self.has_ep:
             return
 
-        # In this case we need to move everything back
+        # Leave parameters on meta on non-rank-0 FSDP ranks (rank-0 broadcast overwrites them); only buffers need real placeholders.
         if is_fsdp_enabled() and not is_local_dist_rank_0() and not is_quantized:
-            for key, param in self.named_parameters():
-                value = torch.zeros_like(param, device="cpu")
-                _load_parameter_into_model(self, key, value)
             for key, buffer in self.named_buffers():
                 value = torch.zeros_like(buffer, device="cpu")
                 _load_parameter_into_model(self, key, value)
@@ -4703,8 +4796,11 @@ def _initialize_missing_keys(self, is_quantized: bool) -> None:
                     pass  # may happen when handling pre-quantized weights
             self._is_hf_initialized = True
 
+        if is_quantized:
+            return
+
         # This will only initialize submodules that are not marked as initialized by the line above.
-        if is_deepspeed_zero3_enabled() and not is_quantized:
+        if is_deepspeed_zero3_enabled() and not self.has_ep:
             import deepspeed
 
             # keep_vars=True as we need the original tensors, so that the "_is_hf_initialized" is present on them
@@ -4800,7 +4896,19 @@ def get_parameter_or_buffer(self, target: str):
         ):
             return module.get_extra_state()
 
-        raise AttributeError(f"`{target}` is neither a parameter, buffer, nor extra state.")
+        def __recursive_getattr(object, attribute, *args):
+            """Recurse through a parameter name that is '.' seperated to get the attribute"""
+
+            def __getattr(object, attribute):
+                return getattr(object, attribute, *args)
+
+            return functools.reduce(__getattr, [object] + attribute.split("."))
+
+        try:
+            # get the actual tensor parameter from a possible nested list
+            return __recursive_getattr(module, param_name)
+        except AttributeError:
+            raise AttributeError(f"`{target}` is neither a parameter, buffer, nor extra state.")
 
     def named_non_persistent_buffers(
         self, recurse: bool = True, remove_duplicate: bool = True
diff --git a/src/transformers/models/__init__.py b/src/transformers/models/__init__.py
index fcb7f6d9dbd4..b48643633bbe 100644
--- a/src/transformers/models/__init__.py
+++ b/src/transformers/models/__init__.py
@@ -28,6 +28,7 @@
     from .aria import *
     from .audio_spectrogram_transformer import *
     from .audioflamingo3 import *
+    from .audiovisualflamingo import *
     from .auto import *
     from .autoformer import *
     from .aya_vision import *
@@ -81,6 +82,7 @@
     from .cpmant import *
     from .csm import *
     from .ctrl import *
+    from .ctsm import *
     from .cvt import *
     from .cwm import *
     from .d_fine import *
@@ -91,8 +93,10 @@
     from .deberta import *
     from .deberta_v2 import *
     from .decision_transformer import *
+    from .deepseek_ocr2 import *
     from .deepseek_v2 import *
     from .deepseek_v3 import *
+    from .deepseek_v4 import *
     from .deepseek_vl import *
     from .deepseek_vl_hybrid import *
     from .deformable_detr import *
@@ -134,6 +138,7 @@
     from .esm import *
     from .evolla import *
     from .exaone4 import *
+    from .exaone4_5 import *
     from .exaone_moe import *
     from .falcon import *
     from .falcon_h1 import *
@@ -177,7 +182,9 @@
     from .gpt_sw3 import *
     from .gptj import *
     from .granite import *
+    from .granite4_vision import *
     from .granite_speech import *
+    from .granite_speech_plus import *
     from .granitemoe import *
     from .granitemoehybrid import *
     from .granitemoeshared import *
@@ -208,6 +215,7 @@
     from .janus import *
     from .jetmoe import *
     from .jina_embeddings_v3 import *
+    from .kimi2_6 import *
     from .kosmos2 import *
     from .kosmos2_5 import *
     from .kyutai_speech_to_text import *
@@ -250,6 +258,7 @@
     from .metaclip_2 import *
     from .mgp_str import *
     from .mimi import *
+    from .minicpm3 import *
     from .minicpmv4_6 import *
     from .minimax import *
     from .minimax_m2 import *
@@ -271,6 +280,7 @@
     from .modernbert import *
     from .modernbert_decoder import *
     from .modernvbert import *
+    from .molmo2 import *
     from .moonshine import *
     from .moonshine_streaming import *
     from .moshi import *
@@ -314,6 +324,7 @@
     from .pe_video import *
     from .pegasus import *
     from .pegasus_x import *
+    from .penguinvl import *
     from .perceiver import *
     from .perception_lm import *
     from .persimmon import *
@@ -362,6 +373,7 @@
     from .regnet import *
     from .rembert import *
     from .resnet import *
+    from .rish_ai import *
     from .roberta import *
     from .roberta_prelayernorm import *
     from .roc_bert import *
@@ -378,6 +390,7 @@
     from .sam3_tracker_video import *
     from .sam3_video import *
     from .sam_hq import *
+    from .sarvam_mla import *
     from .seamless_m4t import *
     from .seamless_m4t_v2 import *
     from .seed_oss import *
@@ -434,6 +447,7 @@
     from .video_llava import *
     from .videomae import *
     from .videomt import *
+    from .videoprism import *
     from .vilt import *
     from .vipllava import *
     from .vision_encoder_decoder import *
diff --git a/src/transformers/models/afmoe/modeling_afmoe.py b/src/transformers/models/afmoe/modeling_afmoe.py
index 421119b33deb..74fc8bc03b6a 100644
--- a/src/transformers/models/afmoe/modeling_afmoe.py
+++ b/src/transformers/models/afmoe/modeling_afmoe.py
@@ -103,7 +103,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/albert/modeling_albert.py b/src/transformers/models/albert/modeling_albert.py
index 890f18316b6b..5b21462fd073 100755
--- a/src/transformers/models/albert/modeling_albert.py
+++ b/src/transformers/models/albert/modeling_albert.py
@@ -323,12 +323,12 @@ def _init_weights(self, module):
             init.zeros_(module.token_type_ids)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`AlbertForPreTraining`].
     """
 )
+@dataclass
 class AlbertForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/align/modeling_align.py b/src/transformers/models/align/modeling_align.py
index 62abb9438ea9..7fdd5b4e1160 100644
--- a/src/transformers/models/align/modeling_align.py
+++ b/src/transformers/models/align/modeling_align.py
@@ -42,12 +42,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
     """
 )
+@dataclass
 class AlignVisionModelOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -59,12 +59,12 @@ class AlignVisionModelOutput(ModelOutput):
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for text model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class AlignTextModelOutput(ModelOutput):
     r"""
     text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
diff --git a/src/transformers/models/align/processing_align.py b/src/transformers/models/align/processing_align.py
index fa15fcce3de6..85b26d160058 100644
--- a/src/transformers/models/align/processing_align.py
+++ b/src/transformers/models/align/processing_align.py
@@ -17,9 +17,11 @@
 
 from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from ..efficientnet.image_processing_efficientnet import EfficientNetImageProcessorKwargs
 
 
 class AlignProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: EfficientNetImageProcessorKwargs
     # see processing_utils.ProcessingKwargs documentation for usage.
     _defaults = {
         "text_kwargs": {
diff --git a/src/transformers/models/altclip/modeling_altclip.py b/src/transformers/models/altclip/modeling_altclip.py
index 6162cb29559e..2ef1a1f30213 100755
--- a/src/transformers/models/altclip/modeling_altclip.py
+++ b/src/transformers/models/altclip/modeling_altclip.py
@@ -125,7 +125,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
@@ -630,7 +630,7 @@ class AltCLIPPreTrainedModel(PreTrainedModel):
     config: AltCLIPConfig
     base_model_prefix = "altclip"
     input_modalities = ("image", "text")
-    _no_split_modules = ["AltCLIPTextEmbeddings", "AltCLIPEncoderLayer", "AltCLIPVisionEmbeddings"]
+    _no_split_modules = ["AltRobertaEmbeddings", "AltRobertaLayer", "AltCLIPEncoderLayer", "AltCLIPVisionEmbeddings"]
 
     supports_gradient_checkpointing = True
     _supports_sdpa = True
@@ -705,7 +705,7 @@ def __init__(self, config: AltCLIPVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = AltCLIPVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = AltCLIPEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.post_init()
@@ -742,7 +742,7 @@ def forward(
         >>> pooled_output = outputs.pooler_output  # pooled CLS states
         ```"""
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs: BaseModelOutput = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/altclip/modular_altclip.py b/src/transformers/models/altclip/modular_altclip.py
index fe9be6cac92f..ed36ac6e2a48 100644
--- a/src/transformers/models/altclip/modular_altclip.py
+++ b/src/transformers/models/altclip/modular_altclip.py
@@ -226,6 +226,7 @@ class AltCLIPVisionEmbeddings(CLIPVisionEmbeddings):
 
 
 class AltCLIPPreTrainedModel(CLIPPreTrainedModel):
+    _no_split_modules = ["AltRobertaEmbeddings", "AltRobertaLayer", "AltCLIPEncoderLayer", "AltCLIPVisionEmbeddings"]
     _can_record_outputs = {
         "hidden_states": AltCLIPEncoderLayer,
         "attentions": AltCLIPAttention,
diff --git a/src/transformers/models/apertus/modeling_apertus.py b/src/transformers/models/apertus/modeling_apertus.py
index 7d14dd3d14c8..af1a03c7c900 100644
--- a/src/transformers/models/apertus/modeling_apertus.py
+++ b/src/transformers/models/apertus/modeling_apertus.py
@@ -134,7 +134,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/arcee/modeling_arcee.py b/src/transformers/models/arcee/modeling_arcee.py
index 8d2d05bf2952..4e99339ca294 100644
--- a/src/transformers/models/arcee/modeling_arcee.py
+++ b/src/transformers/models/arcee/modeling_arcee.py
@@ -139,7 +139,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/aria/modeling_aria.py b/src/transformers/models/aria/modeling_aria.py
index e66b12438940..76d8459de528 100644
--- a/src/transformers/models/aria/modeling_aria.py
+++ b/src/transformers/models/aria/modeling_aria.py
@@ -673,7 +673,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -946,9 +946,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/aria/modular_aria.py b/src/transformers/models/aria/modular_aria.py
index bfd5191e4135..023e701be2de 100644
--- a/src/transformers/models/aria/modular_aria.py
+++ b/src/transformers/models/aria/modular_aria.py
@@ -25,7 +25,6 @@
 from ...image_transforms import divide_to_patches
 from ...image_utils import (
     ChannelDimension,
-    ImageInput,
     PILImageResampling,
     SizeDict,
     get_image_size,
@@ -34,7 +33,6 @@
 from ...modeling_outputs import BaseModelOutputWithPooling
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_python import PreTokenizedInput, TextInput
 from ...utils import (
     TensorType,
     TransformersKwargs,
@@ -556,6 +554,8 @@ class AriaProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class AriaProcessor(ProcessorMixin):
+    valid_processor_kwargs = AriaProcessorKwargs
+
     def __init__(
         self,
         image_processor=None,
@@ -578,56 +578,10 @@ def __init__(
 
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput],
-        images: ImageInput | None = None,
-        **kwargs: Unpack[AriaProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-            `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-            `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            AriaProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-            # expand the image_token according to the num_crops and tokens per image
-            tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
-            prompt_strings = []
-            num_crops = image_inputs.pop("num_crops") * tokens_per_image
-            for sample in text:
-                sample = sample.replace(self.tokenizer.image_token, self.tokenizer.image_token * num_crops)
-                prompt_strings.append(sample)
-
-        else:
-            image_inputs = {}
-            prompt_strings = text
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        tokens_per_image = self.size_conversion[image_inputs["pixel_values"].shape[2]]
+        num_image_tokens = image_inputs["num_crops"] * tokens_per_image
+        return self.image_token * num_image_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
@@ -656,14 +610,8 @@ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         return MultiModalData(**vision_data)
 
     @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-
-        # Remove `num_crops`, it is popped and used only when processing. Make a copy of list when removing
-        # otherwise `self.image_processor.model_input_names` is also modified
-        image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+    def unused_input_names(self) -> list[str]:
+        return ["num_crops"]
 
 
 class AriaSharedExpertsMLP(LlamaMLP):
diff --git a/src/transformers/models/aria/processing_aria.py b/src/transformers/models/aria/processing_aria.py
index 8c9fa8188c81..41e9e67a5ce0 100644
--- a/src/transformers/models/aria/processing_aria.py
+++ b/src/transformers/models/aria/processing_aria.py
@@ -17,10 +17,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...image_processing_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_python import PreTokenizedInput, TextInput
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import TensorType, auto_docstring
 from ..auto import AutoTokenizer
 
@@ -64,6 +61,8 @@ class AriaProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class AriaProcessor(ProcessorMixin):
+    valid_processor_kwargs = AriaProcessorKwargs
+
     def __init__(
         self,
         image_processor=None,
@@ -86,56 +85,10 @@ def __init__(
 
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput],
-        images: ImageInput | None = None,
-        **kwargs: Unpack[AriaProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-            `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-            `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            AriaProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-            # expand the image_token according to the num_crops and tokens per image
-            tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
-            prompt_strings = []
-            num_crops = image_inputs.pop("num_crops") * tokens_per_image
-            for sample in text:
-                sample = sample.replace(self.tokenizer.image_token, self.tokenizer.image_token * num_crops)
-                prompt_strings.append(sample)
-
-        else:
-            image_inputs = {}
-            prompt_strings = text
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        tokens_per_image = self.size_conversion[image_inputs["pixel_values"].shape[2]]
+        num_image_tokens = image_inputs["num_crops"] * tokens_per_image
+        return self.image_token * num_image_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
@@ -164,14 +117,8 @@ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         return MultiModalData(**vision_data)
 
     @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-
-        # Remove `num_crops`, it is popped and used only when processing. Make a copy of list when removing
-        # otherwise `self.image_processor.model_input_names` is also modified
-        image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
-        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+    def unused_input_names(self) -> list[str]:
+        return ["num_crops"]
 
 
 __all__ = ["AriaProcessor"]
diff --git a/src/transformers/models/audioflamingo3/modeling_audioflamingo3.py b/src/transformers/models/audioflamingo3/modeling_audioflamingo3.py
index 888b3b1c29c3..6f18fcc437ad 100644
--- a/src/transformers/models/audioflamingo3/modeling_audioflamingo3.py
+++ b/src/transformers/models/audioflamingo3/modeling_audioflamingo3.py
@@ -34,7 +34,7 @@
 from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..auto import AutoModel, AutoModelForCausalLM
@@ -474,6 +474,30 @@ def get_audio_features(
 
         return audio_output
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -560,10 +584,10 @@ def forward(
             audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: CausalLMOutputWithPast = self.language_model(
             inputs_embeds=inputs_embeds,
diff --git a/src/transformers/models/audioflamingo3/modular_audioflamingo3.py b/src/transformers/models/audioflamingo3/modular_audioflamingo3.py
index bbe4090b06ea..dfb2c1f54d35 100644
--- a/src/transformers/models/audioflamingo3/modular_audioflamingo3.py
+++ b/src/transformers/models/audioflamingo3/modular_audioflamingo3.py
@@ -270,10 +270,10 @@ def forward(
             audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: CausalLMOutputWithPast = self.language_model(
             inputs_embeds=inputs_embeds,
diff --git a/src/transformers/models/audioflamingo3/processing_audioflamingo3.py b/src/transformers/models/audioflamingo3/processing_audioflamingo3.py
index f4692c845f00..95e6f10b79e5 100644
--- a/src/transformers/models/audioflamingo3/processing_audioflamingo3.py
+++ b/src/transformers/models/audioflamingo3/processing_audioflamingo3.py
@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
 
 import numpy as np
 
@@ -21,7 +20,7 @@
 from ...feature_extraction_utils import BatchFeature
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import TextInput
-from ...utils import is_torch_available, logging
+from ...utils import auto_docstring, is_torch_available, logging
 
 
 if is_torch_available():
@@ -48,29 +47,9 @@ class AudioFlamingo3ProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
+@auto_docstring
 class AudioFlamingo3Processor(ProcessorMixin):
-    r"""
-    Constructs an AudioFlamingo3 processor which wraps an AudioFlamingo3 feature extractor and an AudioFlamingo3
-    tokenizer into a single processor.
-
-    [`AudioFlamingo3Processor`] offers all the functionalities of [`WhisperFeatureExtractor`] and
-    [`Qwen2TokenizerFast`]. See the [`~AudioFlamingo3Processor.__call__`] for more information.
-
-    Args:
-            feature_extractor ([`WhisperFeatureExtractor`]):
-                The feature extractor is a required input.
-            tokenizer ([`Qwen2TokenizerFast`]):
-                The tokenizer is a required input.
-            chat_template (`Optional[str]`, *optional*):
-                The Jinja template to use for formatting the conversation. If not provided, the tokenizer's default chat
-                template will be used.
-            audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
-                Special token used to represent audio inputs in the chat template.
-            default_transcription_prompt (`str`, *optional*, defaults to `"Transcribe the input speech."`):
-                Default prompt to use for transcription tasks when applying transcription requests.
-            max_audio_len (`int`, *optional*, defaults to 600):
-                Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
-    """
+    valid_processor_kwargs = AudioFlamingo3ProcessorKwargs
 
     def __init__(
         self,
@@ -81,28 +60,21 @@ def __init__(
         default_transcription_prompt="Transcribe the input speech.",
         max_audio_len=600,
     ):
+        r"""
+        audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
+            Special token used to represent audio inputs in the chat template.
+        default_transcription_prompt (`str`, *optional*, defaults to `"Transcribe the input speech."`):
+            Default prompt to use for transcription tasks when applying transcription requests.
+        max_audio_len (`int`, *optional*, defaults to 600):
+            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
+        """
         self.audio_token = audio_token
         self.audio_token_id = tokenizer.convert_tokens_to_ids(audio_token)
         self.default_transcription_prompt = default_transcription_prompt
         self.max_audio_len = max_audio_len
         super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
 
-    def _get_audio_token_length(self, audio_lengths):
-        conv_output_lengths = (audio_lengths - 1) // 2 + 1  # After conv2 downsampling
-        audio_tokens_lengths = (conv_output_lengths - 2) // 2 + 1  # After avg pooling
-        return audio_tokens_lengths
-
-    def _expand_audio_tokens(self, text, padding_mask, per_sample_windows):
-        audio_lengths = torch.stack([s.sum() for s in torch.split(padding_mask.sum(-1), per_sample_windows)])
-        audio_tokens_lengths = self._get_audio_token_length(audio_lengths)
-        audio_token_pattern = re.compile(re.escape(self.audio_token))
-        for i, audio_length in enumerate(audio_tokens_lengths):
-            text[i] = audio_token_pattern.sub(self.audio_token * audio_length, text[i])
-        return text
-
-    def _get_audio_tokens_mask(self, input_ids):
-        return input_ids == self.audio_token_id
-
+    @auto_docstring
     def __call__(
         self,
         text: TextInput | list[TextInput],
@@ -111,98 +83,96 @@ def __call__(
         **kwargs: Unpack[AudioFlamingo3ProcessorKwargs],
     ) -> BatchFeature:
         r"""
-        Main method to prepare one or several text sequence(s) and audio waveform(s) for the model. This
-        method expands `<sound>` placeholders in the text based on the post-pool frame counts of the
-        audio windows, then tokenizes the provided strings as-is, and extracts log-mel features
-        with [`WhisperFeatureExtractor`]. If `audio` is `None`, no audio processing is performed and
-        the text is tokenized as-is (LM-only behavior).
-
-        Args:
-            text (`str` or `list[str]`):
-                Input sequence or batch of sequences.
-            audio (`np.ndarray` or `list[np.ndarray]`):
-                Input audio or batch of audios as NumPy arrays. If provided, there must be as many `text` inputs as
-                `audio` inputs.
-            output_labels (bool, *optional*, default=False):
-                Whether to return labels for training.
+        output_labels (bool, *optional*, default=False):
+            Whether to return labels for training.
 
         Returns:
             [`BatchFeature`]: A dictionary with tokenized text (`input_ids`, `attention_mask`) and
             audio features (`input_features`, `input_features_mask`).
         """
+        # Force tensor outputs for AudioFlamingo, other types not supported
+        kwargs["return_tensors"] = "pt"
 
-        # Merge defaults with user kwargs
-        call_kwargs = self._merge_kwargs(
-            AudioFlamingo3ProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
+        if output_labels:
+            kwargs["return_mm_token_type_ids"] = True
+        model_inputs = super().__call__(audio=audio, text=text, **kwargs)
 
-        text_kwargs = call_kwargs["text_kwargs"]
-        audio_kwargs = call_kwargs["audio_kwargs"]
-        return_tensors = text_kwargs.get("return_tensors")
-        if return_tensors != "pt":
-            raise ValueError(f"{self.__class__.__name__} only supports `return_tensors='pt'`.")
-
-        if isinstance(text, str):
-            text = [text]
-        elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-
-        audio_inputs = {}
-        if audio is not None:
-            audio = make_list_of_audio(audio)
-            if len(text) != len(audio):
-                raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
-
-            # Determine number of chunks per sample, and flatten
-            window_size = int(audio_kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
-            max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
-
-            per_sample_windows: list[int] = []
-            flat_chunks: list[np.ndarray] = []
-
-            for audio_el in audio:
-                n_samples = int(audio_el.shape[0])
-                n_win = max(1, (n_samples + window_size - 1) // window_size)
-                if n_win > max_windows:
-                    logger.warning(
-                        f"Audio duration ({n_samples / audio_kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
-                    )
-                    n_win = max_windows
-                per_sample_windows.append(n_win)
-
-                time_cap = min(n_samples, n_win * window_size)
-                for i in range(n_win):
-                    start = i * window_size
-                    end = min((i + 1) * window_size, time_cap)
-                    flat_chunks.append(audio_el[start:end])
-
-            # Feature extraction
-            audio_inputs = self.feature_extractor(flat_chunks, **audio_kwargs)
-            padding_mask = audio_inputs.pop("attention_mask")
-            audio_inputs["input_features_mask"] = padding_mask
-
-            # Expand audio tokens in text
-            text = self._expand_audio_tokens(text, padding_mask, per_sample_windows)
-
-        # Tokenize
-        text_inputs = self.tokenizer(text, **text_kwargs)
-
-        data = {**text_inputs, **audio_inputs}
         if output_labels:
-            labels = data["input_ids"].clone()
-            labels[self._get_audio_tokens_mask(labels)] = -100
+            labels = model_inputs.pop("mm_token_type_ids")
             labels[labels == self.tokenizer.pad_token_id] = -100
-            data["labels"] = labels
+            model_inputs["labels"] = labels
+        return BatchFeature(data=model_inputs, tensor_type="pt")
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+    def validate_inputs(
+        self,
+        audio: AudioInput | None = None,
+        text: TextInput | list[TextInput] | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(audio=audio, text=text, **kwargs)
+
+        if text is not None and audio is not None and len(text) != len(audio):
+            raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
+
+    def _get_audio_token_length(self, audio_lengths):
+        conv_output_lengths = (audio_lengths - 1) // 2 + 1  # After conv2 downsampling
+        audio_tokens_lengths = (conv_output_lengths - 2) // 2 + 1  # After avg pooling
+        return audio_tokens_lengths
+
+    def _process_audio(self, audio: AudioInput, **kwargs):
+        # Determine number of chunks per sample, and flatten
+        window_size = int(kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
+        max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
+
+        per_sample_windows: list[int] = []
+        flat_chunks: list[np.ndarray] = []
+        for audio_el in audio:
+            n_samples = int(audio_el.shape[0])
+            n_win = max(1, (n_samples + window_size - 1) // window_size)
+            if n_win > max_windows:
+                logger.warning(
+                    f"Audio duration ({n_samples / kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
+                )
+                n_win = max_windows
+            per_sample_windows.append(n_win)
+
+            time_cap = min(n_samples, n_win * window_size)
+            for i in range(n_win):
+                start = i * window_size
+                end = min((i + 1) * window_size, time_cap)
+                flat_chunks.append(audio_el[start:end])
+
+        audio = self.feature_extractor.fetch_audio(audio)
+        audio_inputs = self.feature_extractor(flat_chunks, **kwargs)
+        audio_inputs["input_features_mask"] = audio_inputs.pop("attention_mask")
+
+        # AudioFlamingo doesn't have its own feature extractor and crops audio into
+        # chunks here. Save the number of tokens based on crops/padding in analogy
+        # with some vision processors
+        audio_lengths = torch.stack(
+            [s.sum() for s in torch.split(audio_inputs["input_features_mask"].sum(-1), per_sample_windows)]
+        )
+        audio_inputs["num_audio_tokens"] = self._get_audio_token_length(audio_lengths)
+
+        audio_replacements = []
+        for idx in range(len(audio)):
+            replacement_text = self.replace_audio_token(audio_inputs, audio_idx=idx)
+            audio_replacements.append(replacement_text)
+
+        return audio_inputs, audio_replacements
+
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        num_audio_tokens = audio_inputs["num_audio_tokens"][audio_idx]
+        return self.audio_token * num_audio_tokens
 
     @property
     def model_input_names(self) -> list[str]:
-        tok_names = self.tokenizer.model_input_names
-        fea_names = self.feature_extractor.model_input_names
-        return list(dict.fromkeys(tok_names + fea_names + ["input_features_mask"]))
+        return super().model_input_names + ["input_features_mask"]
+
+    @property
+    def unused_input_names(self) -> list[str]:
+        "Input names returned always by subprocessors but not used in model's `forward`"
+        return ["num_audio_tokens"]
 
     def apply_transcription_request(
         self,
diff --git a/src/transformers/models/audiovisualflamingo/__init__.py b/src/transformers/models/audiovisualflamingo/__init__.py
new file mode 100644
index 000000000000..fc28f06ef790
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_audiovisualflamingo import *
+    from .modeling_audiovisualflamingo import *
+    from .processing_audiovisualflamingo import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/audiovisualflamingo/configuration_audiovisualflamingo.py b/src/transformers/models/audiovisualflamingo/configuration_audiovisualflamingo.py
new file mode 100644
index 000000000000..a8147d92ae39
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/configuration_audiovisualflamingo.py
@@ -0,0 +1,124 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/audiovisualflamingo/modular_audiovisualflamingo.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_audiovisualflamingo.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team and NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+MEDIA_TOKENS = {
+    "image": "<image>",
+    "video": "<vila/video>",
+    "sound": "<sound>",
+}
+
+MM_BOS_EOS_TOKENS = {
+    "image": ["<|image_bos|>", "<|image_eos|>"],
+    "video": ["<|video_bos|>", "<|video_eos|>"],
+    "sound": ["<|sound_bos|>", "<|sound_eos|>"],
+}
+
+
+@strict
+class AudioVisualFlamingoConfig(PreTrainedConfig):
+    model_type = "audiovisualflamingo"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    media_tokens = MEDIA_TOKENS
+    mm_bos_eos_tokens = MM_BOS_EOS_TOKENS
+    sub_configs = {
+        "text_config": AutoConfig,
+        "vision_config": AutoConfig,
+        "audio_config": AutoConfig,
+    }
+
+    @staticmethod
+    def _build_sub_config(config, default_model_type: str):
+        if isinstance(config, PreTrainedConfig):
+            return copy.deepcopy(config)
+        if config is None:
+            return CONFIG_MAPPING[default_model_type]()
+        if isinstance(config, dict):
+            model_type = config.get("model_type", default_model_type)
+            config_kwargs = {k: v for k, v in config.items() if k != "model_type"}
+            return CONFIG_MAPPING[model_type](**config_kwargs)
+        raise TypeError(f"Unsupported config payload type: {type(config)!r}")
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        audio_config=None,
+        mm_vision_select_layer=-2,
+        mm_vision_select_feature="patch",
+        dynamic_s2=None,
+        s2_scales=None,
+        s2_max_split_size=None,
+        s2_resize_output_to_scale_idx=0,
+        image_encoder=None,
+        video_encoder=None,
+        sound_encoder=None,
+        projector_bias=True,
+        multimodal_projector_bias=True,
+        load_audio_in_video=True,
+        interleaved_vis_aud_in_video=True,
+        **kwargs,
+    ):
+        legacy_config_aliases = {
+            "llm_cfg": "text_config",
+            "vision_tower_cfg": "vision_config",
+            "sound_tower_cfg": "audio_config",
+        }
+        used_legacy_aliases = [key for key in legacy_config_aliases if key in kwargs]
+        if used_legacy_aliases:
+            formatted_aliases = ", ".join(
+                f"`{key}` -> `{legacy_config_aliases[key]}`" for key in sorted(used_legacy_aliases)
+            )
+            raise TypeError(
+                "AudioVisualFlamingoConfig only accepts canonical sub-config names. "
+                f"Replace legacy aliases: {formatted_aliases}."
+            )
+
+        self.text_config = self._build_sub_config(text_config, "qwen2")
+        self.vision_config = self._build_sub_config(vision_config, "siglip_vision_model")
+        self.audio_config = self._build_sub_config(audio_config, "qwen2_audio_encoder")
+
+        self.mm_vision_select_layer = mm_vision_select_layer
+        self.mm_vision_select_feature = mm_vision_select_feature
+        self.dynamic_s2 = dynamic_s2
+        self.s2_scales = list(s2_scales) if s2_scales is not None else None
+        self.s2_max_split_size = s2_max_split_size
+        self.s2_resize_output_to_scale_idx = s2_resize_output_to_scale_idx
+
+        self.image_encoder = copy.deepcopy(image_encoder or {"_target_": "BasicImageEncoder"})
+        self.video_encoder = copy.deepcopy(video_encoder or {"_target_": "TSPVideoEncoder"})
+        self.sound_encoder = copy.deepcopy(sound_encoder or {"_target_": "BasicSoundEncoder"})
+        self.load_audio_in_video = load_audio_in_video
+        self.interleaved_vis_aud_in_video = interleaved_vis_aud_in_video
+
+        self.projector_bias = projector_bias
+        self.multimodal_projector_bias = multimodal_projector_bias
+
+        super().__init__(**kwargs)
+
+
+__all__ = ["AudioVisualFlamingoConfig"]
diff --git a/src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py b/src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py
new file mode 100644
index 000000000000..92be7069880f
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py
@@ -0,0 +1,499 @@
+# Copyright 2026 The HuggingFace Team and NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert AudioVisualFlamingo checkpoints into a Hugging Face repository layout.
+
+Like the AudioFlamingo3 converter, this script:
+1) reads source component configs to build an AudioVisualFlamingoConfig programmatically,
+2) constructs processor and model objects with those configs,
+3) lets the standard HF serialization APIs emit config and safetensors artifacts.
+
+No JSON files are copied or manually edited.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import logging
+import re
+from collections import defaultdict
+from pathlib import Path
+from typing import Any
+
+from safetensors.torch import safe_open, save_model
+
+from transformers import (
+    AudioVisualFlamingoConfig,
+    AudioVisualFlamingoForConditionalGeneration,
+    AudioVisualFlamingoProcessor,
+    AutoImageProcessor,
+    AutoTokenizer,
+    GenerationConfig,
+    WhisperFeatureExtractor,
+)
+from transformers.initialization import no_init_weights
+
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+
+DEFAULT_SRC_PATH = Path("/fs/nexus-projects/JSALT_workshop/lasha/Dev/audiovisualflamingo")
+DEFAULT_DST_PATH = Path("/fs/nexus-projects/JSALT_workshop/lasha/Dev/comni")
+LEGACY_CHECKPOINT_KEY_MAPPING = {
+    r"^vision_tower\.vision_tower\.vision_model\.": "vision_tower.vision_tower.",
+    r"^sound_tower\.audio_tower\.": "sound_tower.",
+}
+
+# Maps legacy component sub-directories to the weight-key prefix expected by
+# AudioVisualFlamingoForConditionalGeneration.
+COMPONENT_TO_PREFIX = {
+    "llm": "llm",
+    "vision_tower": "vision_tower.vision_tower",
+    "mm_projector": "mm_projector",
+    "sound_tower": "sound_tower",
+    "sound_mm_projector": "sound_mm_projector",
+}
+
+# Non-standard keys injected into the LLM (Qwen2) config by quantization or
+# pruning toolchains.  These are never consumed by the HF Qwen2 model and
+# bloat the serialised config (channel_order_list alone is ~60 KB).
+LLM_CFG_KEYS_TO_STRIP = {
+    "channel_order_list",
+    "head_order_list",
+    "head_dim_list",
+    "head_dim_original",
+    "hidden_size_list",
+    "intermediate_size_list",
+    "kv_repeat_original",
+    "num_attention_heads_list",
+    "num_key_value_heads_list",
+    "model_max_length",
+    "tokenizer_model_max_length",
+    "tokenizer_padding_side",
+    "_name_or_path",
+    "transformers_version",
+}
+
+# Keys stripped from every component sub-config (vision tower, projectors, etc.).
+COMPONENT_CFG_KEYS_TO_STRIP = {
+    "_name_or_path",
+    "transformers_version",
+    "torch_dtype",
+}
+
+# Additional keys stripped from the sound tower config.  The source Qwen2AudioConfig
+# embeds a redundant nested ``audio_config`` (duplicate of top-level fields) and a
+# ``text_config`` for its unused text decoder.
+SOUND_TOWER_EXTRA_KEYS_TO_STRIP = {
+    "audio_config",
+    "text_config",
+    "vocab_size",
+    "audio_token_index",
+    "ignore_index",
+}
+
+# AudioVisualFlamingoConfig.__init__ explicit parameters that we extract from
+# the source top-level config.json (excludes training-only params like *_lr).
+AVF_CONFIG_FIELDS = {
+    "mm_vision_select_layer",
+    "mm_vision_select_feature",
+    "dynamic_s2",
+    "s2_scales",
+    "s2_max_split_size",
+    "s2_resize_output_to_scale_idx",
+    "image_encoder",
+    "video_encoder",
+    "sound_encoder",
+    "load_audio_in_video",
+    "interleaved_vis_aud_in_video",
+}
+
+PROCESSOR_CONFIG_FIELDS = {
+    "image_aspect_ratio",
+    "num_video_frames",
+    "max_tiles",
+    "interleaved_video_segment_duration",
+    "audio_sampling_rate",
+    "audio_chunk_length",
+    "audio_hop_length",
+    "mm_use_bos_eos_tokens",
+}
+
+
+def _load_json(path: Path) -> dict[str, Any]:
+    if not path.is_file():
+        raise FileNotFoundError(f"Missing JSON: {path}")
+    with path.open("r", encoding="utf-8") as f:
+        return json.load(f)
+
+
+def _normalize_s2_scales(values):
+    if values is None:
+        return None
+    if isinstance(values, str):
+        values = values.split(",")
+    return [int(value) for value in values]
+
+
+def _normalize_encoder_config(config, default_target: str):
+    if config is None:
+        return {"_target_": default_target}
+    if isinstance(config, str):
+        config = json.loads(config)
+    config = dict(config)
+    target = config.get("_target_", default_target)
+    if isinstance(target, str):
+        config["_target_"] = target.rsplit(".", maxsplit=1)[-1]
+    return config
+
+
+# ---------------------------------------------------------------------------
+# Weight collection
+# ---------------------------------------------------------------------------
+
+
+def _resolve_component_dir(dirpath: Path):
+    if not dirpath.is_dir():
+        return None
+    idx = dirpath / "model.safetensors.index.json"
+    mono = dirpath / "model.safetensors"
+    if idx.exists():
+        wm = _load_json(idx).get("weight_map") or {}
+        by_shard: dict[str, list[str]] = defaultdict(list)
+        for key, shard in wm.items():
+            by_shard[shard].append(key)
+        return ("sharded", dirpath, {shard: sorted(keys) for shard, keys in sorted(by_shard.items())})
+    if mono.exists():
+        return ("file", mono)
+    cands = sorted([x for x in dirpath.iterdir() if x.suffix == ".safetensors"])
+    if len(cands) == 1:
+        return ("file", cands[0])
+    return None
+
+
+def _collect_component_state(src_root: Path) -> dict[str, Any]:
+    state: dict[str, Any] = {}
+    for component, out_prefix in COMPONENT_TO_PREFIX.items():
+        comp = _resolve_component_dir(src_root / component)
+        if not comp:
+            logger.info("No weights found for optional component: %s", component)
+            continue
+        if comp[0] == "file":
+            fp: Path = comp[1]
+            with safe_open(str(fp), framework="pt", device="cpu") as f:
+                for key in f.keys():
+                    if key == "__metadata__":
+                        continue
+                    state[f"{out_prefix}.{key}"] = f.get_tensor(key)
+        else:
+            base: Path = comp[1]
+            shard_map: dict[str, list[str]] = comp[2]
+            for shard, keys in shard_map.items():
+                sp = base / shard
+                with safe_open(str(sp), framework="pt", device="cpu") as f:
+                    for key in keys:
+                        state[f"{out_prefix}.{key}"] = f.get_tensor(key)
+        logger.info("Collected %s weights under prefix '%s'", component, out_prefix)
+    return state
+
+
+def _normalize_state_dict_keys(state: dict[str, Any]) -> dict[str, Any]:
+    normalized_state = dict(state)
+    for pattern, replacement in LEGACY_CHECKPOINT_KEY_MAPPING.items():
+        renamed_keys = [key for key in normalized_state if re.match(pattern, key)]
+        for key in renamed_keys:
+            normalized_state[re.sub(pattern, replacement, key)] = normalized_state.pop(key)
+    return normalized_state
+
+
+# ---------------------------------------------------------------------------
+# Config construction
+# ---------------------------------------------------------------------------
+
+
+def collect_encoder_boundary_tokens(config: AudioVisualFlamingoConfig) -> list[str]:
+    token_keys = {"start_tokens", "end_tokens", "sep_tokens"}
+    collected: list[str] = []
+    seen: set[str] = set()
+
+    def _maybe_add(token):
+        if not isinstance(token, str) or token == "None" or token in seen:
+            return
+        seen.add(token)
+        collected.append(token)
+
+    def _visit(node):
+        if isinstance(node, dict):
+            for key, value in node.items():
+                if key in token_keys:
+                    _maybe_add(value)
+                _visit(value)
+        elif isinstance(node, (list, tuple)):
+            for item in node:
+                _visit(item)
+
+    _maybe_add("\n")
+    for attr in ("image_encoder", "video_encoder", "sound_encoder"):
+        encoder_cfg = getattr(config, attr, None)
+        if isinstance(encoder_cfg, str):
+            try:
+                encoder_cfg = json.loads(encoder_cfg)
+            except Exception:
+                continue
+        _visit(encoder_cfg)
+    return collected
+
+
+def _build_config(src_root: Path, tokenizer) -> AudioVisualFlamingoConfig:
+    """Build an AudioVisualFlamingoConfig programmatically from the source checkpoint."""
+    top_cfg = _load_json(src_root / "config.json")
+
+    # Read and clean component sub-configs.
+    def _read_component(name: str) -> dict[str, Any] | None:
+        p = src_root / name / "config.json"
+        return _load_json(p) if p.is_file() else None
+
+    text_config = _read_component("llm")
+    if text_config:
+        text_config = {k: v for k, v in text_config.items() if k not in LLM_CFG_KEYS_TO_STRIP}
+
+    def _clean_component(cfg, extra_strip=None):
+        if cfg is None:
+            return None
+        cfg = {k: v for k, v in cfg.items() if k not in COMPONENT_CFG_KEYS_TO_STRIP}
+        if extra_strip:
+            cfg = {k: v for k, v in cfg.items() if k not in extra_strip}
+        return cfg
+
+    vision_config = _clean_component(_read_component("vision_tower"))
+    audio_config = _clean_component(_read_component("sound_tower"), extra_strip=SOUND_TOWER_EXTRA_KEYS_TO_STRIP)
+
+    # Extract only the fields AudioVisualFlamingoConfig cares about.
+    avf_kwargs = {k: top_cfg[k] for k in AVF_CONFIG_FIELDS if k in top_cfg}
+    avf_kwargs["s2_scales"] = _normalize_s2_scales(avf_kwargs.get("s2_scales"))
+    avf_kwargs["image_encoder"] = _normalize_encoder_config(avf_kwargs.get("image_encoder"), "BasicImageEncoder")
+    avf_kwargs["video_encoder"] = _normalize_encoder_config(avf_kwargs.get("video_encoder"), "TSPVideoEncoder")
+    avf_kwargs["sound_encoder"] = _normalize_encoder_config(avf_kwargs.get("sound_encoder"), "BasicSoundEncoder")
+
+    config = AudioVisualFlamingoConfig(
+        text_config=text_config,
+        vision_config=vision_config,
+        audio_config=audio_config,
+        **avf_kwargs,
+    )
+
+    # Populate media token IDs.
+    media_token_ids = {}
+    for name, token in AudioVisualFlamingoConfig.media_tokens.items():
+        token_id = tokenizer.convert_tokens_to_ids(token)
+        if token_id is None or token_id < 0:
+            tokenized = tokenizer(token, add_special_tokens=False).input_ids
+            if len(tokenized) != 1:
+                raise ValueError(f"Media token `{token}` must map to a single tokenizer id.")
+            token_id = tokenized[0]
+        media_token_ids[name] = int(token_id)
+    config.media_token_ids = media_token_ids
+
+    # Populate encoder boundary token IDs.
+    config.encoder_text_token_ids = {
+        txt: [int(tid) for tid in tokenizer(txt).input_ids] for txt in collect_encoder_boundary_tokens(config)
+    }
+
+    return config
+
+
+# ---------------------------------------------------------------------------
+# Processor
+# ---------------------------------------------------------------------------
+
+
+def write_processor(
+    src_root: Path,
+    dst_root: Path,
+    config: AudioVisualFlamingoConfig,
+) -> AudioVisualFlamingoProcessor:
+    """Build and save the processor from source sub-components."""
+    # Tokenizer: prefer llm/ subdir, fall back to root.
+    tokenizer_dir = src_root / "llm" if (src_root / "llm" / "tokenizer_config.json").exists() else src_root
+    tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=True)
+
+    # Image processor: from the vision_tower preprocessor config.
+    vision_dir = src_root / "vision_tower"
+    image_processor = AutoImageProcessor.from_pretrained(str(vision_dir), use_fast=False)
+
+    top_cfg = _load_json(src_root / "config.json")
+    processor_kwargs = {key: top_cfg[key] for key in PROCESSOR_CONFIG_FIELDS if key in top_cfg}
+
+    # Feature extractor: construct directly (like AF3) with feature_size from the sound tower config.
+    feature_size = 128
+    if isinstance(config.audio_config, dict):
+        feature_size = config.audio_config.get("num_mel_bins", feature_size)
+    else:
+        feature_size = getattr(config.audio_config, "num_mel_bins", feature_size)
+    audio_sampling_rate = processor_kwargs.get("audio_sampling_rate", 16_000)
+    audio_chunk_length = processor_kwargs.get("audio_chunk_length", 120)
+    audio_hop_length = processor_kwargs.get("audio_hop_length", 60)
+    feature_extractor = WhisperFeatureExtractor(
+        feature_size=feature_size,
+        chunk_length=audio_chunk_length if isinstance(audio_chunk_length, int) else 30,
+        sampling_rate=audio_sampling_rate,
+        hop_length=audio_hop_length,
+        return_attention_mask=True,
+    )
+
+    processor = AudioVisualFlamingoProcessor(
+        image_processor=image_processor,
+        feature_extractor=feature_extractor,
+        tokenizer=tokenizer,
+        image_aspect_ratio=processor_kwargs.get("image_aspect_ratio"),
+        s2_scales=config.s2_scales,
+        max_tiles=processor_kwargs.get("max_tiles", 12),
+        num_video_frames=processor_kwargs.get("num_video_frames"),
+        load_audio_in_video=config.load_audio_in_video,
+        interleaved_vis_aud_in_video=config.interleaved_vis_aud_in_video,
+        interleaved_video_segment_duration=processor_kwargs.get("interleaved_video_segment_duration", 30),
+        mm_use_bos_eos_tokens=processor_kwargs.get("mm_use_bos_eos_tokens", False),
+        audio_sampling_rate=audio_sampling_rate,
+        audio_chunk_length=audio_chunk_length,
+        audio_hop_length=audio_hop_length,
+    )
+    processor.save_pretrained(str(dst_root))
+    logger.info("processor (tokenizer + preprocessors)")
+    return processor
+
+
+# ---------------------------------------------------------------------------
+# Model
+# ---------------------------------------------------------------------------
+
+
+def write_model(
+    src_root: Path,
+    dst_root: Path,
+    config: AudioVisualFlamingoConfig,
+    tokenizer,
+) -> AudioVisualFlamingoForConditionalGeneration:
+    """Collect weights, instantiate model, load state dict, and save."""
+    state = _normalize_state_dict_keys(_collect_component_state(src_root))
+    if not state:
+        raise FileNotFoundError("No component safetensors found under source component directories.")
+
+    with no_init_weights():
+        model = AudioVisualFlamingoForConditionalGeneration(config)
+
+    load_res = model.load_state_dict(state, strict=True, assign=True)
+    if load_res.missing_keys:
+        mk = load_res.missing_keys
+        raise ValueError(f"Missing keys when loading: {mk[:10]}{' ...' if len(mk) > 10 else ''}")
+    if load_res.unexpected_keys:
+        uk = load_res.unexpected_keys
+        raise ValueError(f"Unexpected keys when loading: {uk[:10]}{' ...' if len(uk) > 10 else ''}")
+
+    model.generation_config = GenerationConfig(
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id or tokenizer.eos_token_id,
+    )
+
+    model.config.save_pretrained(str(dst_root))
+    model.generation_config.save_pretrained(str(dst_root))
+    save_model(model, str(dst_root / "model.safetensors"), metadata={"format": "pt"}, force_contiguous=False)
+    logger.info("model (config + safetensors)")
+    return model
+
+
+# ---------------------------------------------------------------------------
+# Entry points
+# ---------------------------------------------------------------------------
+
+
+"""
+Reproducible Usage
+==================
+
+1) Download the original AudioVisualFlamingo weights (requires Git LFS):
+
+```
+git lfs install
+git clone <source-repo-url>
+```
+
+This will create a folder containing the original components:
+``llm/``, ``vision_tower/``, ``mm_projector/``, ``sound_tower/``, and ``sound_mm_projector/``.
+
+2) Convert to the Hugging Face Transformers format (locally):
+
+```
+python src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py \\
+  --src_path <source-dir> \\
+  --dst_path <destination-dir>
+```
+
+3) Convert and push directly to the Hub (requires ``huggingface-cli login`` or ``HF_TOKEN``):
+
+```
+python src/transformers/models/audiovisualflamingo/convert_audiovisualflamingo_to_hf.py \\
+  --src_path <source-dir> \\
+  --dst_path <destination-dir> \\
+  --push_to_hub <username-or-org>/audiovisualflamingo
+```
+
+This command uploads both the processor (tokenizer + image processor + feature extractor)
+and the converted model (sharded safetensors + configs) to the specified Hub repository.
+"""
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Convert AudioVisualFlamingo to Hugging Face format.")
+    ap.add_argument("--src_path", type=Path, default=DEFAULT_SRC_PATH, help="Source model root directory.")
+    ap.add_argument(
+        "--dst_path", type=Path, default=DEFAULT_DST_PATH, help="Destination directory for converted model."
+    )
+    # Backward-compatible aliases.
+    ap.add_argument("--model_dir", type=Path, default=None, help=argparse.SUPPRESS)
+    ap.add_argument("--output_dir", type=Path, default=None, help=argparse.SUPPRESS)
+    ap.add_argument(
+        "--push_to_hub",
+        default=None,
+        type=str,
+        help="Optional repository ID to push the converted assets to the Hugging Face Hub.",
+    )
+    args = ap.parse_args()
+
+    src_root = (args.model_dir or args.src_path).expanduser().resolve()
+    dst_root = (args.output_dir or args.dst_path).expanduser().resolve()
+
+    if not src_root.is_dir():
+        raise FileNotFoundError(f"Source directory not found: {src_root}")
+    if dst_root.exists():
+        raise FileExistsError(f"Destination already exists: {dst_root}")
+
+    # Load tokenizer early — needed for config token IDs.
+    tokenizer_dir = src_root / "llm" if (src_root / "llm" / "tokenizer_config.json").exists() else src_root
+    tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_dir), use_fast=True)
+
+    config = _build_config(src_root, tokenizer)
+    processor = write_processor(src_root, dst_root, config)
+    model = write_model(src_root, dst_root, config, tokenizer)
+
+    if args.push_to_hub:
+        logger.info("Pushing processor to the Hub ...")
+        processor.push_to_hub(args.push_to_hub)
+        logger.info("Pushing model to the Hub ...")
+        model.push_to_hub(args.push_to_hub)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/audiovisualflamingo/modeling_audiovisualflamingo.py b/src/transformers/models/audiovisualflamingo/modeling_audiovisualflamingo.py
new file mode 100644
index 000000000000..9cdc22f2098b
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/modeling_audiovisualflamingo.py
@@ -0,0 +1,1424 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/audiovisualflamingo/modular_audiovisualflamingo.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_audiovisualflamingo.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team and NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+import warnings
+from collections import defaultdict, deque
+from math import pi
+from typing import Any, Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import broadcast_tensors, einsum
+
+from ...generation import GenerationMixin
+from ...modeling_outputs import CausalLMOutputWithPast
+from ...modeling_utils import PreTrainedModel
+from ...utils import ModelOutput, auto_docstring
+from ..auto import AutoModel, AutoModelForCausalLM
+from .configuration_audiovisualflamingo import AudioVisualFlamingoConfig
+
+
+class MaxTimeContinuousTimeRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_time, period_mode="longest"):
+        super().__init__()
+        if period_mode not in {"longest", "shortest"}:
+            raise ValueError(f"period_mode should be 'longest' or 'shortest', got {period_mode!r}")
+        self.period_mode = period_mode
+        self.max_time = max_time
+
+        if dim % 4 != 0:
+            raise ValueError(f"MTCT rotary embedding requires `dim` divisible by 4, got {dim}")
+        self.dim = dim
+        bands = torch.arange(1, dim // 4 + 1, dtype=torch.float32)
+        self.register_buffer("bands", bands, persistent=False)
+
+    def forward(self, times: torch.Tensor) -> torch.Tensor:
+        if times.ndim == 1:
+            times = times.unsqueeze(0)
+
+        times = times.float()
+        batch_size, seq_len = times.shape
+        times = times.clamp_min(0.0)
+        max_time = times.max(dim=-1, keepdim=True).values.clamp_min(1e-6)
+        if self.max_time is not None:
+            max_time = max_time.clamp_max(float(self.max_time))
+
+        if self.period_mode == "longest":
+            denominator = max_time
+        else:
+            nonzero = times.masked_fill(times <= 0, float("inf")).min(dim=-1, keepdim=True).values
+            nonzero = torch.where(torch.isfinite(nonzero), nonzero, max_time)
+            denominator = nonzero.clamp_min(1e-6)
+
+        angles = times.unsqueeze(-1) / denominator.unsqueeze(-1) * (2 * pi * self.bands)
+        angles = torch.cat((angles, angles), dim=-1)
+        return angles.reshape(batch_size, seq_len, self.dim // 2)
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        freqs_for: Literal["lang", "pixel", "constant"] = "lang",
+        theta=10000,
+        max_freq=10,
+        num_freqs=1,
+        learned_freq=False,
+        max_time=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.freqs_for = freqs_for
+        self.max_freq = max_freq
+        self.num_freqs = num_freqs
+        self.learned_freq = learned_freq
+        self.max_time = max_time
+        if max_time is not None and freqs_for == "lang":
+            theta = max_time / (2 * pi)
+        self.theta = theta
+
+        if freqs_for == "lang":
+            freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+        elif freqs_for == "pixel":
+            freqs = torch.linspace(1.0, max_freq / 2, dim // 2) * pi
+        elif freqs_for == "constant":
+            freqs = torch.ones(num_freqs).float()
+        else:
+            raise ValueError(f"unknown modality {freqs_for}")
+
+        self.freqs = nn.Parameter(freqs, requires_grad=learned_freq)
+        self.register_buffer("cached_freqs", None, persistent=False)
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    @property
+    def device(self):
+        return self.dummy.device
+
+    def forward(self, t: torch.Tensor, seq_len=None, offset=0):
+        should_cache = not self.learned_freq and seq_len is not None and self.freqs_for != "pixel"
+        if should_cache and self.cached_freqs is not None and (offset + seq_len) <= self.cached_freqs.shape[0]:
+            return self.cached_freqs[offset : (offset + seq_len)].detach()
+
+        freqs = self.freqs
+        if self.max_time is not None:
+            t = t / self.max_time * (2 * pi)
+
+        freqs = einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
+        freqs = freqs.repeat_interleave(2, dim=-1)
+        if should_cache:
+            self.cached_freqs = freqs.detach()
+        return freqs
+
+    def get_axial_freqs(self, *dims):
+        colon = slice(None)
+        all_freqs = []
+        dtype = self.freqs.dtype if torch.is_floating_point(self.freqs) else torch.float32
+        for index, dim in enumerate(dims):
+            if self.freqs_for == "pixel":
+                pos = torch.linspace(-1, 1, steps=dim, device=self.device, dtype=dtype)
+            else:
+                pos = torch.arange(dim, device=self.device, dtype=dtype)
+
+            freqs = self.forward(pos, seq_len=dim)
+            all_axis = [None] * len(dims)
+            all_axis[index] = colon
+            all_freqs.append(freqs[(Ellipsis, *all_axis, colon)])
+
+        return torch.cat(broadcast_tensors(*all_freqs), dim=-1)
+
+
+# Below: IO pre- and post-processor classes for AudioVisualFlamingo.
+def space_to_depth(frames: torch.Tensor, temporal_block_size: int = 1, spatial_block_size: int = 1) -> torch.Tensor:
+    """
+    Space to depth transform. Rearranges blocks of spatial data, into depth.
+
+    This function assumes the channels to be first, but will place the channels last after transformation.
+    """
+    if len(frames.shape) == 4:
+        batch_size, num_channels, height, width = frames.shape
+        # split up dimensions (height by spatial_block_size, width by spatial_block_size)
+        frames = frames.view(
+            batch_size,
+            num_channels,
+            height // spatial_block_size,
+            spatial_block_size,
+            width // spatial_block_size,
+            spatial_block_size,
+        )
+        # move blocks to last dimension: (batch_size, H//bs, W//bs, bs, bs, C)
+        frames = frames.permute(0, 2, 4, 3, 5, 1).contiguous()
+        # concatenate blocks along channel dimension: (batch_size, H//bs, W//bs, bs*bs*C)
+        frames = frames.view(
+            batch_size,
+            height // spatial_block_size,
+            width // spatial_block_size,
+            (spatial_block_size**2) * num_channels,
+        )
+        return frames
+    elif len(frames.shape) == 5:
+        batch_size, time, num_channels, height, width = frames.shape
+        # split up dimensions (time by temporal_block_size, height by spatial_block_size, width by spatial_block_size)
+        frames = frames.view(
+            batch_size,
+            time // temporal_block_size,
+            temporal_block_size,
+            num_channels,
+            height // spatial_block_size,
+            spatial_block_size,
+            width // spatial_block_size,
+            spatial_block_size,
+        )
+        # move blocks to last dimension: (batch_size, T//ts, H//bs, W//bs, ts, bs, bs, C)
+        frames = frames.permute(0, 1, 4, 6, 2, 5, 7, 3).contiguous()
+        # concatenate blocks along channel dimension: (batch_size, T//ts, H//bs, W//bs, ts*bs*bs*C)
+        frames = frames.view(
+            batch_size,
+            time // temporal_block_size,
+            height // spatial_block_size,
+            width // spatial_block_size,
+            temporal_block_size * (spatial_block_size**2) * num_channels,
+        )
+        return frames
+    else:
+        raise ValueError(
+            "Frames should be of rank 4 (batch, channels, height, width)"
+            " or rank 5 (batch, time, channels, height, width)"
+        )
+
+
+class MultimodalProjector(nn.Module):
+    def __init__(self, vision_hidden_size: int, text_hidden_size: int, bias: bool):
+        super().__init__()
+        self.downsample_rate = 2
+        self.layers = nn.Sequential(
+            nn.Identity(),
+            nn.LayerNorm(vision_hidden_size * 4),
+            nn.Linear(vision_hidden_size * 4, text_hidden_size, bias=bias),
+            nn.GELU(),
+            nn.Linear(text_hidden_size, text_hidden_size, bias=bias),
+        )
+
+    def forward(self, x, *args, **kwargs):
+        _ = (args, kwargs)
+        bsz, num_tokens, channels = x.shape
+        h = w = int(num_tokens**0.5)
+        x = x.reshape(bsz, h, w, channels).permute(0, 3, 1, 2).contiguous()
+        if h % self.downsample_rate != 0 or w % self.downsample_rate != 0:
+            x = F.pad(
+                x,
+                (0, w % self.downsample_rate, 0, h % self.downsample_rate),
+                mode="constant",
+                value=0,
+            )
+        x = space_to_depth(x, spatial_block_size=self.downsample_rate).reshape(bsz, -1, channels * 4)
+        return self.layers(x)
+
+
+class SoundMultimodalProjector(nn.Module):
+    """
+    Audio adaptor (small MLP) that projects AudioVisualFlamingoEncoder features
+    to the LLM embedding space so they can replace `<sound>` tokens.
+    """
+
+    def __init__(self, audio_hidden_size: int, text_hidden_size: int, bias: bool):
+        super().__init__()
+        self.layers = nn.Sequential(
+            nn.Linear(audio_hidden_size, text_hidden_size, bias=bias),
+            nn.GELU(),
+            nn.Linear(text_hidden_size, text_hidden_size, bias=bias),
+        )
+
+    def forward(self, x, *args, **kwargs):
+        _ = (args, kwargs)
+        return self.layers(x)
+
+
+class SiglipVisionTowerDynamicS2(nn.Module):
+    def __init__(self, config: AudioVisualFlamingoConfig) -> None:
+        super().__init__()
+        self.select_layer = config.mm_vision_select_layer
+        self.select_feature = config.mm_vision_select_feature
+        if config.s2_scales is None:
+            raise ValueError("`config.s2_scales` must be provided when `dynamic_s2=True`.")
+        self.scales = sorted(int(scale) for scale in config.s2_scales)
+        self.max_split_size = config.s2_max_split_size
+        self.resize_output_to_scale_idx = config.s2_resize_output_to_scale_idx
+
+        vision_cfg = copy.deepcopy(config.vision_config)
+        vision_cfg._attn_implementation = config._attn_implementation
+        self.vision_tower = AutoModel.from_config(vision_cfg)
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == "patch":
+            image_features = image_features[:, 1:]
+        elif self.select_feature != "cls_patch":
+            raise ValueError(f"Unexpected select feature: {self.select_feature}")
+        return image_features
+
+    def forward(self, images):
+        if isinstance(images, list):
+            raise ValueError("VisionTowerDynamicS2 expects tensor input, not list.")
+        image_forward_outs = self.vision_tower(
+            images.to(device=self.device, dtype=self.dtype),
+            output_hidden_states=True,
+        )
+        return self.feature_select(image_forward_outs).to(images.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        return self.vision_tower.config
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size * len(self.scales)
+
+
+@auto_docstring
+class AudioVisualFlamingoPretrainedModel(PreTrainedModel):
+    config: AudioVisualFlamingoConfig
+    base_model_prefix = "model"
+    input_modalities = ("audio", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen2DecoderLayer", "SiglipEncoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _supports_cache_class = True
+    _supports_attention_backend = True
+    _can_compile_fullgraph = True
+    config_class = AudioVisualFlamingoConfig
+    main_input_name = "input_ids"
+    _supports_flash_attn_2 = True
+
+    @property
+    def llm_model_embed_tokens(self):
+        if self.llm is None:
+            raise RuntimeError("LLM module is not initialized.")
+        return self.llm.model.embed_tokens
+
+    def _require_encoder_text_token_ids(self) -> dict[str, list[int]]:
+        encoder_text_token_ids = getattr(self.config, "encoder_text_token_ids", None)
+        if encoder_text_token_ids is None:
+            raise ValueError("Missing `config.encoder_text_token_ids`.")
+        return encoder_text_token_ids
+
+    def embed_text_tokens(self, token_text: str | None) -> torch.Tensor | None:
+        if token_text is None:
+            return None
+        token_ids = self._require_encoder_text_token_ids().get(token_text)
+        if token_ids is None:
+            raise ValueError(f"Missing token ids for encoder boundary text: {token_text!r}")
+        token_ids = torch.tensor(token_ids, device=self.llm_model_embed_tokens.weight.device)
+        return self.llm_model_embed_tokens(token_ids)
+
+    def _require_media_token_ids(self) -> dict[str, int]:
+        media_token_ids = getattr(self.config, "media_token_ids", None)
+        if not media_token_ids:
+            raise ValueError("Missing `config.media_token_ids`.")
+        return media_token_ids
+
+    def _init_media_encoders(self):
+        def _parse_tokens(cfg, default_end="\n"):
+            start = cfg.get("start_tokens")
+            end = cfg.get("end_tokens", default_end)
+            end = None if end == "None" else end
+            sep = cfg.get("sep_tokens")
+            return start, end, sep
+
+        img_cfg = copy.deepcopy(self.config.image_encoder)
+        vid_cfg = copy.deepcopy(self.config.video_encoder)
+        snd_cfg = copy.deepcopy(self.config.sound_encoder)
+        for dct in (img_cfg, vid_cfg, snd_cfg):
+            dct.pop("_target_", None)
+
+        self._image_start_tokens, self._image_end_tokens, _ = _parse_tokens(img_cfg)
+        self._video_start_tokens, self._video_end_tokens, self._video_sep_tokens = _parse_tokens(vid_cfg)
+        self._video_pool_sizes = vid_cfg.get("pool_sizes", [[1, 1, 1]])
+        self._sound_start_tokens, self._sound_end_tokens, _ = _parse_tokens(snd_cfg)
+        self._time_embeddings = {}
+
+        self._video_embed_time = vid_cfg.get("embed_time", "False") in ("True", True)
+        if self._video_embed_time:
+            self._video_time_embed_type = vid_cfg.get("time_embed_type", "pixel")
+            self._video_period_fix, self._video_max_time = self._create_time_embedding("video", vid_cfg)
+
+        self._sound_embed_time = snd_cfg.get("embed_time", "False") in ("True", True)
+        if self._sound_embed_time:
+            self._sound_time_embed_type = snd_cfg.get("time_embed_type", "pixel")
+            self._sound_period_fix, self._sound_max_time = self._create_time_embedding("sound", snd_cfg)
+
+    def _create_time_embedding(self, key: str, cfg: dict):
+        trope_dim = cfg.get("trope_dim", 128)
+        trope_theta = cfg.get("trope_theta", 50000)
+        max_time = cfg.get("max_time")
+        time_embed_type = cfg.get("time_embed_type", "pixel")
+        period_fix = cfg.get("period_fix", False)
+
+        period_mode = None
+        if isinstance(period_fix, str) and period_fix in ("shortest", "longest"):
+            period_mode = period_fix
+            period_fix = "MTCT"
+
+        if period_fix == "MTCT":
+            kwargs = {"dim": trope_dim, "max_time": max_time}
+            if period_mode is not None:
+                kwargs["period_mode"] = period_mode
+            self._time_embeddings[key] = MaxTimeContinuousTimeRotaryEmbedding(**kwargs)
+        elif key == "video":
+            if time_embed_type == "lang":
+                self._time_embeddings[key] = RotaryEmbedding(
+                    dim=trope_dim, freqs_for="lang", theta=trope_theta, max_time=max_time
+                )
+            elif time_embed_type == "pixel":
+                self._time_embeddings[key] = RotaryEmbedding(dim=trope_dim, freqs_for="pixel", max_freq=256)
+        elif key == "sound":
+            if time_embed_type in ("pixel", "lang"):
+                self._time_embeddings[key] = RotaryEmbedding(
+                    dim=trope_dim, freqs_for=time_embed_type, max_freq=256, max_time=max_time
+                )
+        return period_fix, max_time
+
+    def _freeze_untrained_modules(self):
+        if not self.training:
+            return
+
+        for module, flag_name in (
+            (self.vision_tower, "tune_vision_tower"),
+            (getattr(self, "sound_tower", None), "tune_sound_tower"),
+            (self.mm_projector, "tune_mm_projector"),
+            (getattr(self, "sound_mm_projector", None), "tune_sound_mm_projector"),
+        ):
+            if module is not None and not getattr(self.config, flag_name, False):
+                module.eval()
+
+
+IGNORE_INDEX = -100
+
+
+def pool(x: torch.Tensor, size: int, dim: int) -> torch.Tensor:
+    if x.shape[dim] % size != 0:
+        remainder = x.shape[dim] % size
+        pad_len = size - remainder
+        last_elements = x.narrow(dim, x.shape[dim] - remainder, remainder)
+        mean_value = last_elements.mean()
+        pad_shape = list(x.shape)
+        pad_shape[dim] = pad_len
+        padding = torch.ones(pad_shape, device=x.device, dtype=x.dtype) * mean_value
+        x = torch.cat([x, padding], dim=dim)
+
+    shape_before = x.shape[:dim]
+    shape_after = x.shape[dim + 1 :]
+    new_shape = shape_before + (-1, size) + shape_after
+    return x.view(new_shape).mean(dim + 1)
+
+
+def _tokens_to_channel_first(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
+    if x.dim() != 3:
+        raise ValueError(f"Expected tensor of shape (batch, tokens, channels), got {tuple(x.shape)}")
+    batch_size, num_tokens, channels = x.shape
+    if num_tokens != height * width:
+        raise ValueError(f"Token count {num_tokens} does not match spatial shape ({height}, {width})")
+    return x.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2).contiguous()
+
+
+def _channel_first_to_tokens(x: torch.Tensor) -> torch.Tensor:
+    if x.dim() != 4:
+        raise ValueError(f"Expected tensor of shape (batch, channels, height, width), got {tuple(x.shape)}")
+    batch_size, channels, height, width = x.shape
+    return x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
+
+
+def _rotate_half(x):
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    return torch.stack((-x2, x1), dim=-1).reshape_as(x)
+
+
+def apply_rotary_emb(freqs, t, start_index=0, scale=1.0, seq_dim=-2):
+    device_type = t.device.type if t.device.type in {"cpu", "cuda"} else "cuda"
+    with torch.amp.autocast(device_type=device_type, enabled=False):
+        original_dtype = t.dtype
+        t = t.to(torch.float64)
+        freqs = freqs.to(t)
+
+        if t.ndim == 3:
+            seq_len = t.shape[seq_dim]
+            freqs = freqs[-seq_len:]
+
+        rot_dim = freqs.shape[-1]
+        end_index = start_index + rot_dim
+        assert rot_dim <= t.shape[-1], (
+            f"feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}"
+        )
+
+        t_left = t[..., :start_index]
+        t_middle = t[..., start_index:end_index]
+        t_right = t[..., end_index:]
+        t_middle = (t_middle * freqs.cos() * scale) + (_rotate_half(t_middle) * freqs.sin() * scale)
+        out = torch.cat((t_left, t_middle, t_right), dim=-1)
+    return out.to(original_dtype)
+
+
+def _move_rotary_module_to_device(module: nn.Module, device: torch.device) -> nn.Module:
+    module_device = None
+    on_meta = False
+    for param in module.parameters(recurse=False):
+        module_device = param.device
+        on_meta = param.is_meta
+        break
+    if module_device is None:
+        for buffer in module.buffers(recurse=False):
+            module_device = buffer.device
+            on_meta = buffer.is_meta
+            break
+    if module_device == device and not on_meta:
+        return module
+    if on_meta:
+        if isinstance(module, RotaryEmbedding):
+            return RotaryEmbedding(
+                dim=module.dim,
+                freqs_for=module.freqs_for,
+                theta=module.theta,
+                max_freq=module.max_freq,
+                num_freqs=module.num_freqs,
+                learned_freq=module.learned_freq,
+                max_time=module.max_time,
+            ).to(device=device)
+        if isinstance(module, MaxTimeContinuousTimeRotaryEmbedding):
+            return MaxTimeContinuousTimeRotaryEmbedding(
+                dim=module.dim,
+                max_time=module.max_time,
+                period_mode=module.period_mode,
+            ).to(device=device)
+        return module.to_empty(device=device)
+    return module.to(device=device)
+
+
+class AudioVisualFlamingoForConditionalGeneration(AudioVisualFlamingoPretrainedModel, GenerationMixin):
+    def __init__(self, config: AudioVisualFlamingoConfig, *args, **kwargs):
+        super().__init__(config)
+        _ = (args, kwargs)
+        if not getattr(config, "dynamic_s2", False):
+            raise NotImplementedError("Current AudioVisualFlamingo checkpoint requires `dynamic_s2=True`.")
+        self.vision_tower = SiglipVisionTowerDynamicS2(config)
+        audio_cfg = copy.deepcopy(config.audio_config)
+        audio_cfg._attn_implementation = config._attn_implementation
+        self.sound_tower = AutoModel.from_config(audio_cfg)
+
+        text_cfg = copy.deepcopy(config.text_config)
+        text_cfg._attn_implementation = config._attn_implementation
+        model_max_length = getattr(config, "model_max_length", None)
+        if model_max_length is not None:
+            text_cfg.model_max_length = model_max_length
+            orig_ctx_len = getattr(text_cfg, "max_position_embeddings", None)
+            if orig_ctx_len is not None and model_max_length > orig_ctx_len:
+                text_cfg.rope_scaling = {
+                    "type": "linear",
+                    "factor": float(math.ceil(model_max_length / orig_ctx_len)),
+                }
+
+        self.llm = AutoModelForCausalLM.from_config(text_cfg)
+        self.mm_projector = MultimodalProjector(
+            vision_hidden_size=self.vision_tower.hidden_size,
+            text_hidden_size=self.llm.config.hidden_size,
+            bias=config.multimodal_projector_bias,
+        )
+        self.sound_mm_projector = SoundMultimodalProjector(
+            audio_hidden_size=self.sound_tower.config.d_model,
+            text_hidden_size=self.llm.config.hidden_size,
+            bias=config.projector_bias,
+        )
+        self.vocab_size = self.llm.config.vocab_size
+        self._init_media_encoders()
+        self.training = self.llm.training
+        if self.training:
+            self.train()
+        else:
+            self.eval()
+
+        self.config.text_config = self.llm.config
+        self.config.vision_config = self.vision_tower.config
+        self.config.audio_config = self.sound_tower.config
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.llm.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.llm.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.llm.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.llm.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.llm.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.llm.get_decoder()
+
+    @property
+    def language_model(self):
+        return self.llm
+
+    def _encode_visual_features(self, images: torch.Tensor, block_sizes: tuple[int, ...] | None = None):
+        if not getattr(self.config, "dynamic_s2", False):
+            raise NotImplementedError("Current AudioVisualFlamingo checkpoint requires `dynamic_s2=True`.")
+        if len(images) == 0:
+            return []
+
+        if block_sizes is None:
+            block_sizes = [None] * len(images)
+
+        image_features = self.vision_tower(images)
+        image_features, new_block_sizes = self.merge_features_for_dynamic_s2(image_features, block_sizes)
+        image_features = [
+            self.split_chessboard(feature, block_size[0], block_size[1])
+            for feature, block_size in zip(image_features, new_block_sizes)
+        ]
+        image_features = torch.cat([_channel_first_to_tokens(feature) for feature in image_features], dim=0)
+        image_features = self.mm_projector(image_features.to(self.device, self.dtype))
+        image_features = list(
+            image_features.split([block_size[0] * block_size[1] for block_size in new_block_sizes], dim=0)
+        )
+        image_features = [
+            self.merge_chessboard(feature, block_size[0], block_size[1])
+            for feature, block_size in zip(image_features, new_block_sizes)
+        ]
+        image_features = [_channel_first_to_tokens(feature)[0] for feature in image_features]
+        if all(feature.shape[0] == image_features[0].shape[0] for feature in image_features):
+            return torch.stack(image_features, dim=0)
+        return image_features
+
+    def merge_features_for_dynamic_s2(self, image_features, block_sizes):
+        scales = self.vision_tower.scales
+        resize_output_to_scale_idx = self.vision_tower.resize_output_to_scale_idx
+        image_features_each_image = []
+        new_block_sizes = []
+        block_cnt = 0
+        for block_size_each_image in block_sizes:
+            if block_size_each_image is None:
+                cur_features = image_features[block_cnt : block_cnt + 1]
+                spatial_size = int(cur_features.shape[1] ** 0.5)
+                cur_features = _tokens_to_channel_first(cur_features, spatial_size, spatial_size)
+                cur_features = cur_features.repeat(1, len(scales), 1, 1)
+                image_features_each_image.append(cur_features)
+                new_block_sizes.append((1, 1))
+                block_cnt += 1
+                continue
+
+            cur_features_each_scale = []
+            for scale in scales[:-1]:
+                num_blocks_this_scale = (scale // scales[0]) ** 2
+                cur_features_each_scale.append(
+                    self.merge_chessboard(
+                        image_features[block_cnt : block_cnt + num_blocks_this_scale],
+                        num_split_h=scale // scales[0],
+                        num_split_w=scale // scales[0],
+                    )
+                )
+                block_cnt += num_blocks_this_scale
+            num_blocks_last_scale = block_size_each_image[0] * block_size_each_image[1]
+            cur_features_each_scale.append(
+                self.merge_chessboard(
+                    image_features[block_cnt : block_cnt + num_blocks_last_scale],
+                    num_split_h=block_size_each_image[0],
+                    num_split_w=block_size_each_image[1],
+                )
+            )
+            block_cnt += num_blocks_last_scale
+            output_size = cur_features_each_scale[resize_output_to_scale_idx].shape[-2:]
+            cur_features = torch.cat(
+                [
+                    F.interpolate(cur_features_each_scale[i].to(torch.float32), size=output_size, mode="area").to(
+                        cur_features_each_scale[i].dtype
+                    )
+                    for i in range(len(cur_features_each_scale))
+                ],
+                dim=1,
+            )
+            image_features_each_image.append(cur_features)
+            if resize_output_to_scale_idx == len(scales) - 1 or resize_output_to_scale_idx == -1:
+                new_block_sizes.append(block_size_each_image)
+            else:
+                new_block_sizes.append(
+                    (
+                        scales[resize_output_to_scale_idx] // scales[0],
+                        scales[resize_output_to_scale_idx] // scales[0],
+                    )
+                )
+        assert block_cnt == len(image_features)
+        return image_features_each_image, new_block_sizes
+
+    @staticmethod
+    def split_chessboard(x, num_split_h, num_split_w):
+        bsz, channels, height, width = x.shape
+        assert height % num_split_h == 0 and width % num_split_w == 0
+        split_h, split_w = height // num_split_h, width // num_split_w
+        return torch.cat(
+            [
+                x[:, :, i * split_h : (i + 1) * split_h, j * split_w : (j + 1) * split_w]
+                for i in range(num_split_h)
+                for j in range(num_split_w)
+            ],
+            dim=0,
+        )
+
+    @staticmethod
+    def merge_chessboard(x, num_split_h, num_split_w):
+        batch = x.shape[0]
+        if x.dim() == 3:
+            num_tokens = x.shape[1]
+            spatial_size = int(num_tokens**0.5)
+            x = _tokens_to_channel_first(x, spatial_size, spatial_size)
+        assert batch % (num_split_h * num_split_w) == 0
+        base_batch = batch // (num_split_h * num_split_w)
+        return torch.cat(
+            [
+                torch.cat(
+                    [
+                        x[(i * num_split_w + j) * base_batch : (i * num_split_w + j + 1) * base_batch]
+                        for j in range(num_split_w)
+                    ],
+                    dim=-1,
+                )
+                for i in range(num_split_h)
+            ],
+            dim=-2,
+        )
+
+    def encode_video(
+        self,
+        inp,
+        block_sizes: tuple[int, ...] | None = None,
+        mm_info: dict | None = None,
+        num_frames: list[int] | None = None,
+    ):
+        _ = (mm_info, num_frames)
+        if block_sizes is not None:
+            raise ValueError(f"Video block sizes are not supported: {block_sizes}")
+        if not inp:
+            return []
+        return self._encode_visual_features(torch.cat(inp, dim=0))
+
+    def encode_images(
+        self,
+        images,
+        block_sizes: tuple[int, ...] | None = None,
+        mm_info: dict | None = None,
+        num_frames: list[int] | None = None,
+    ):
+        _ = (mm_info, num_frames)
+        return self._encode_visual_features(images, block_sizes=block_sizes)
+
+    def _get_sound_chunk_length(self) -> int:
+        return (
+            self.sound_tower.config.max_source_positions
+            * self.sound_tower.conv1.stride[0]
+            * self.sound_tower.conv2.stride[0]
+        )
+
+    def _forward_sound_tower_batch(self, input_features: torch.Tensor) -> torch.Tensor:
+        batch_size, n_mels, seq_len = input_features.shape
+        chunk_length = self._get_sound_chunk_length()
+        num_chunks = (seq_len + chunk_length - 1) // chunk_length
+
+        padded_chunks = []
+        for chunk_idx in range(num_chunks):
+            start_idx = chunk_idx * chunk_length
+            end_idx = min(start_idx + chunk_length, seq_len)
+            chunk = input_features[:, :, start_idx:end_idx]
+            if chunk.shape[2] < chunk_length:
+                chunk = F.pad(chunk, (0, chunk_length - chunk.shape[2]), mode="constant", value=0)
+            padded_chunks.append(chunk)
+
+        all_chunks = torch.cat(padded_chunks, dim=0).reshape(batch_size * num_chunks, n_mels, chunk_length)
+        chunk_outputs = self.sound_tower(all_chunks, return_dict=True)
+        hidden_states = chunk_outputs.last_hidden_state
+        _, chunk_seq_len, hidden_size = hidden_states.shape
+        return hidden_states.reshape(batch_size, num_chunks * chunk_seq_len, hidden_size)
+
+    def encode_sound(self, sounds, mm_info: dict | None = None):
+        _ = mm_info
+        audio_features = []
+        audio_output_lengths = []
+        for sound in sounds:
+            if hasattr(sound, "input_features") or (isinstance(sound, dict) and "input_features" in sound):
+                sound = sound["input_features"]
+            sound_dtype = sound.dtype
+            sound = sound.to(device=self.sound_tower.device, dtype=self.sound_tower.dtype)
+            sound_feature = self._forward_sound_tower_batch(sound).to(sound_dtype)
+            audio_features.append(sound_feature)
+            audio_output_lengths.append(sound_feature.shape[1])
+
+        if not audio_features:
+            return []
+
+        audio_features = torch.cat(audio_features, dim=1).squeeze(0)
+        projector_param = next(self.sound_mm_projector.parameters(), None)
+        if projector_param is not None and audio_features.dtype != projector_param.dtype:
+            audio_features = audio_features.to(projector_param.dtype)
+        audio_features = self.sound_mm_projector(audio_features)
+
+        split_audio_features = []
+        start = 0
+        for length in audio_output_lengths:
+            split_audio_features.append(audio_features[start : start + length])
+            start += length
+        return split_audio_features
+
+    def _embed_image_features(
+        self, images: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = mm_info
+        features = self.encode_images(torch.stack(images, dim=0), block_sizes=config.get("block_sizes"))
+        start_embeds = self.embed_text_tokens(self._image_start_tokens)
+        end_embeds = self.embed_text_tokens(self._image_end_tokens)
+        image_features = []
+        for feature in features:
+            if start_embeds is not None:
+                feature = torch.cat([start_embeds, feature], dim=0)
+            if end_embeds is not None:
+                feature = torch.cat([feature, end_embeds], dim=0)
+            image_features.append(feature)
+        return image_features
+
+    def _embed_video_features(
+        self, videos: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = config
+        num_frames = [video.shape[0] for video in videos]
+        features = self.encode_video(videos, mm_info=mm_info, num_frames=num_frames)
+        features = torch.split(features, num_frames)
+        start_embeds = self.embed_text_tokens(self._video_start_tokens)
+        end_embeds = self.embed_text_tokens(self._video_end_tokens)
+        sep_embeds = self.embed_text_tokens(self._video_sep_tokens)
+        if not self._video_embed_time:
+            return [self._tsp_process(feature, start_embeds, end_embeds, sep_embeds) for feature in features]
+
+        batch_size = len(mm_info["video_info"])
+        device = features[0].device
+        new_time_embeds = None
+        if self._video_time_embed_type == "learned_embed":
+            times_list, video_idx = [], 0
+            for i in range(batch_size):
+                video_info = mm_info["video_info"][i]
+                if video_info is None:
+                    continue
+                for j in range(len(video_info)):
+                    feature = features[video_idx]
+                    if video_info[j] == "dummy":
+                        times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                    else:
+                        times = torch.tensor(video_info[j]["video_frame_times"]).to(device)
+                    for pool_size in self._video_pool_sizes:
+                        temporal_pool = pool_size[0]
+                        if temporal_pool != 1:
+                            if len(times) % temporal_pool != 0:
+                                remainder = len(times) % temporal_pool
+                                times = torch.cat([times, times[-remainder:].mean().expand(temporal_pool - remainder)])
+                            times = pool(times, temporal_pool, 0)
+                    times_list.append(times)
+                    video_idx += 1
+            original_lengths = [len(times) for times in times_list]
+            max_length = max(original_lengths)
+            for i in range(len(times_list)):
+                if len(times_list[i]) < max_length:
+                    times_list[i] = torch.cat(
+                        [times_list[i], torch.zeros(max_length - len(times_list[i])).to(times_list[i].device)]
+                    )
+            times_tensor = torch.stack(times_list, dim=0)
+            time_embeds_all = self._time_embeddings["video"](times_tensor, dtype=features[0].dtype)
+            new_time_embeds = []
+            for i in range(len(times_list)):
+                new_time_embeds.append(
+                    time_embeds_all[i][: original_lengths[i]].unsqueeze(1).expand(-1, features[0].shape[1], -1)
+                )
+            new_time_embeds[0] = new_time_embeds[0] + 0 * time_embeds_all.mean()
+
+        new_features, video_idx = [], 0
+        for i in range(batch_size):
+            video_info = mm_info["video_info"][i]
+            if video_info is None:
+                continue
+            for j in range(len(video_info)):
+                feature = features[video_idx]
+                if video_info[j] == "dummy":
+                    times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                else:
+                    times = torch.tensor(video_info[j]["video_frame_times"]).to(device)
+                if self._video_time_embed_type == "learned_embed":
+                    feature = self._tsp_process(
+                        feature, start_embeds, end_embeds, sep_embeds, time_embed=new_time_embeds[video_idx]
+                    )
+                else:
+                    feature = self._tsp_process(feature, start_embeds, end_embeds, sep_embeds, times=times)
+                new_features.append(feature)
+                video_idx += 1
+        assert video_idx == len(features)
+        return new_features
+
+    def _tsp_process(
+        self,
+        inputs: torch.Tensor,
+        start_token_embeds: torch.Tensor | None,
+        end_token_embeds: torch.Tensor | None,
+        sep_token_embeds: torch.Tensor | None,
+        times: torch.Tensor | None = None,
+        time_embed: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        num_frames, num_spatial_tokens = inputs.shape[:2]
+        spatial_length = int(num_spatial_tokens**0.5)
+        outputs = []
+        for pool_size in self._video_pool_sizes:
+            features = inputs.view(num_frames, spatial_length, spatial_length, -1)
+            for dim, pool_factor in enumerate(pool_size):
+                features = pool(features, pool_factor, dim=dim)
+            features = features.flatten(1, 2)
+            if self._video_embed_time:
+                device = features.device
+                if self._video_time_embed_type in ("pixel", "lang"):
+                    temporal_pool = pool_size[0]
+                    if temporal_pool != 1:
+                        pooled_times = times
+                        if len(pooled_times) % temporal_pool != 0:
+                            remainder = len(pooled_times) % temporal_pool
+                            pooled_times = torch.cat(
+                                [pooled_times, pooled_times[-remainder:].mean().expand(temporal_pool - remainder)]
+                            )
+                        new_times = pool(pooled_times, temporal_pool, 0)
+                    else:
+                        new_times = times
+                    pos_emb = _move_rotary_module_to_device(self._time_embeddings["video"], device)
+                    self._time_embeddings["video"] = pos_emb
+                    if self._video_period_fix == "True":
+                        angle = (
+                            new_times.to(device) / self._video_max_time * 2 * np.pi
+                            if self._video_max_time is not None
+                            else new_times.to(device)
+                        )
+                    elif self._video_period_fix == "MTCT":
+                        time_values = new_times.unsqueeze(0) if new_times.ndim == 1 else new_times
+                        freqs = pos_emb(time_values.float()).squeeze(0).unsqueeze(1)
+                        features = apply_rotary_emb(freqs, features, seq_dim=0)
+                    else:
+                        angle = (-new_times * 2 * np.pi).to(device)
+                    if self._video_period_fix != "MTCT":
+                        freqs = pos_emb.get_axial_freqs(new_times.shape[0], features.shape[-2]).to(device)
+                        angle_exp = (
+                            angle.unsqueeze(1)
+                            .unsqueeze(2)
+                            .expand(new_times.shape[0], features.shape[-2], freqs.shape[-1])
+                        )
+                        features = apply_rotary_emb(freqs * angle_exp, features)
+                elif self._video_time_embed_type == "learned_embed":
+                    features = features + time_embed
+            if start_token_embeds is not None:
+                features = torch.cat(
+                    [start_token_embeds.unsqueeze(0).expand(features.shape[0], -1, -1), features], dim=1
+                )
+            if end_token_embeds is not None:
+                features = torch.cat(
+                    [features, end_token_embeds.unsqueeze(0).expand(features.shape[0], -1, -1)], dim=1
+                )
+            features = features.flatten(0, 1)
+            if sep_token_embeds is not None:
+                features = torch.cat([features, sep_token_embeds], dim=0)
+            outputs.append(features)
+        return torch.cat(outputs, dim=0)
+
+    def _embed_sound_features(
+        self, sounds: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = config
+        features = self.encode_sound(sounds, mm_info=mm_info)
+        start_embeds = self.embed_text_tokens(self._sound_start_tokens)
+        end_embeds = self.embed_text_tokens(self._sound_end_tokens)
+        if not self._sound_embed_time:
+            return [self._process_sound_feature(feature, start_embeds, end_embeds) for feature in features]
+        device = features[0].device
+        feature_count = len(features)
+        batch_size = len(mm_info["audio_info"])
+        time_embeds_all = None
+        if self._sound_time_embed_type == "learned_embed":
+            times_list, audio_idx = [], 0
+            for i in range(batch_size):
+                audio_info = mm_info["audio_info"][i]
+                if audio_info is None:
+                    continue
+                for j in range(len(audio_info)):
+                    feature = features[audio_idx]
+                    if audio_info[j] == "dummy":
+                        times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                    else:
+                        chunk_length = audio_info[j]["new_audio_chunk_length"]
+                        seconds_per_embed = chunk_length / feature.shape[0]
+                        audio_start = audio_info[j]["audio_start_sec"]
+                        times = torch.tensor(
+                            [
+                                audio_start + k * seconds_per_embed + seconds_per_embed / 2
+                                for k in range(feature.shape[0])
+                            ]
+                        ).to(device)
+                    times_list.append(times)
+                    audio_idx += 1
+            times_tensor = torch.stack(times_list, dim=0)
+            time_embeds_all = self._time_embeddings["sound"](times_tensor, dtype=features[0].dtype)
+        new_features, audio_idx = [], 0
+        for i in range(batch_size):
+            audio_info = mm_info["audio_info"][i]
+            if audio_info is None:
+                continue
+            for j in range(len(audio_info)):
+                feature = features[audio_idx]
+                if audio_info[j] == "dummy":
+                    times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                else:
+                    chunk_length = audio_info[j]["new_audio_chunk_length"]
+                    seconds_per_embed = chunk_length / feature.shape[0]
+                    audio_start = audio_info[j]["audio_start_sec"]
+                    times = torch.tensor(
+                        [audio_start + k * seconds_per_embed + seconds_per_embed / 2 for k in range(feature.shape[0])]
+                    ).to(device)
+                if self._sound_time_embed_type == "learned_embed":
+                    feature = self._process_sound_feature(
+                        feature, start_embeds, end_embeds, time_embed=time_embeds_all[audio_idx]
+                    )
+                else:
+                    feature = self._process_sound_feature(feature, start_embeds, end_embeds, times=times)
+                new_features.append(feature)
+                audio_idx += 1
+        assert audio_idx == feature_count
+        return new_features
+
+    def _process_sound_feature(
+        self,
+        features: torch.Tensor,
+        start_token_embeds: torch.Tensor | None,
+        end_token_embeds: torch.Tensor | None,
+        times: torch.Tensor | None = None,
+        time_embed: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        features = features.to(self.device)
+        device = features.device
+        if self._sound_embed_time:
+            if self._sound_time_embed_type in ("pixel", "lang"):
+                new_times = times.unsqueeze(0)
+                pos_emb = _move_rotary_module_to_device(self._time_embeddings["sound"], device)
+                self._time_embeddings["sound"] = pos_emb
+                if self._sound_period_fix == "True":
+                    angle = (
+                        new_times.to(device) / self._sound_max_time * 2 * np.pi
+                        if self._sound_max_time is not None
+                        else new_times.to(device)
+                    )
+                elif self._sound_period_fix == "MTCT":
+                    freqs = pos_emb(new_times.float()).squeeze(0)
+                    features = apply_rotary_emb(freqs, features)
+                else:
+                    angle = (-new_times * 2 * np.pi).to(device)
+                if self._sound_period_fix != "MTCT":
+                    freqs = pos_emb.get_axial_freqs(new_times.shape[0], features.shape[-2]).to(device)
+                    angle_exp = angle.unsqueeze(2).expand(new_times.shape[0], features.shape[-2], freqs.shape[-1])
+                    freqs = (freqs * angle_exp).squeeze(0)
+                    features = apply_rotary_emb(freqs, features)
+            elif self._sound_time_embed_type == "learned_embed":
+                features = features + time_embed
+        if start_token_embeds is not None:
+            features = torch.cat([start_token_embeds, features], dim=0)
+        if end_token_embeds is not None:
+            features = torch.cat([features, end_token_embeds], dim=0)
+        return features
+
+    def _embed(
+        self,
+        input_ids: torch.Tensor,
+        media: dict[str, list[torch.Tensor]],
+        media_config: dict[str, dict[str, Any]],
+        labels: torch.Tensor | None,
+        attention_mask: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        media = copy.deepcopy(media)
+        media_config = copy.deepcopy(media_config)
+        labels = labels if labels is not None else torch.full_like(input_ids, IGNORE_INDEX)
+        attention_mask = attention_mask if attention_mask is not None else torch.ones_like(input_ids, dtype=torch.bool)
+        text_embeds = self.llm_model_embed_tokens(input_ids)
+        mm_info = {}
+        video_info = media.pop("video_info", None)
+        audio_info = media.pop("audio_info", None)
+        if video_info is not None:
+            mm_info["video_info"] = video_info
+        if audio_info is not None:
+            mm_info["audio_info"] = audio_info
+        media_embeds = self.__embed_media_tokens(media, media_config, mm_info) if media is not None else {}
+
+        video_sound_embeds_idx = 0
+        sep_embed = self.embed_text_tokens("\n")
+        llm_embed_dtype = self.llm_model_embed_tokens.weight.dtype
+        text_embeds = text_embeds.to(llm_embed_dtype)
+        sep_embed = sep_embed.to(text_embeds.dtype)
+        if video_info is not None and self.config.load_audio_in_video and self.config.interleaved_vis_aud_in_video:
+            assert self._video_end_tokens is None, "end_tokens must be None for interleaved vis-aud in video"
+            new_video_embeds = deque()
+            video_embeds_idx = 0
+            for k in range(len(video_info)):
+                if video_info[k] is None:
+                    continue
+                for i in range(len(video_info[k])):
+                    has_audio = video_info[k][i]["has_audio"]
+                    if not has_audio:
+                        new_video_embeds.append(media_embeds["video"][video_embeds_idx])
+                        video_embeds_idx += 1
+                        continue
+                    if video_sound_embeds_idx >= len(media_embeds["sound"]):
+                        raise ValueError(
+                            f"Sound embeddings index {video_sound_embeds_idx} out of bounds for video_info[{k}][{i}]"
+                        )
+                    segment_aud_indices_list = video_info[k][i]["segment_aud_indices_list"]
+                    segment_vis_indices_list = video_info[k][i]["segment_vis_indices_list"]
+                    vis_fea_len_per_frame = (
+                        media_embeds["video"][video_embeds_idx].shape[0] / video_info[k][i]["expected_frame_count"]
+                    )
+                    aud_fea_len_per_stft_frame = (
+                        media_embeds["sound"][video_sound_embeds_idx].shape[0]
+                        / audio_info[k][i]["new_audio_n_stft_frames"]
+                    )
+                    vis_end = 0
+                    aud_end = 0
+                    new_video_embed = []
+                    for j in range(len(segment_vis_indices_list)):
+                        vis_aud_fea = []
+                        if len(segment_vis_indices_list[j]) > 0:
+                            new_frames = [
+                                int(np.ceil((frame + 1) * vis_fea_len_per_frame))
+                                for frame in segment_vis_indices_list[j]
+                            ]
+                            vis_fea_end = min(new_frames[-1], media_embeds["video"][video_embeds_idx].shape[0])
+                            vis_fea = media_embeds["video"][video_embeds_idx][vis_end:vis_fea_end]
+                            vis_end = vis_fea_end
+                            vis_aud_fea.append(vis_fea)
+                        vis_aud_fea.append(sep_embed)
+                        if len(segment_aud_indices_list[j]) > 0:
+                            new_audio_indices = [
+                                int(np.ceil(fea * aud_fea_len_per_stft_frame)) for fea in segment_aud_indices_list[j]
+                            ]
+                            aud_fea_end = min(
+                                new_audio_indices[-1], media_embeds["sound"][video_sound_embeds_idx].shape[0]
+                            )
+                            aud_fea = media_embeds["sound"][video_sound_embeds_idx][aud_end:aud_fea_end]
+                            vis_aud_fea.append(aud_fea)
+                            aud_end = aud_fea_end
+                        vis_aud_fea.append(sep_embed)
+                        new_video_embed.append(torch.cat(vis_aud_fea, dim=0))
+                    video_sound_embeds_idx += 1
+                    new_video_embeds.append(torch.cat(new_video_embed, dim=0))
+                    video_embeds_idx += 1
+            assert len(new_video_embeds) == len(media_embeds["video"])
+            media_embeds["video"] = new_video_embeds
+
+        batch_size = labels.shape[0]
+        text_embeds = [text_embeds[k][attention_mask[k]] for k in range(batch_size)]
+        labels = [labels[k][attention_mask[k]] for k in range(batch_size)]
+        media_token_ids = self._require_media_token_ids()
+        media_tokens = {token_id: name for name, token_id in media_token_ids.items()}
+        inputs_m, labels_m = [], []
+        sound_embeds_idx = 0
+        for k in range(batch_size):
+            inputs_mk, labels_mk = [], []
+            pos = 0
+            while pos < len(labels[k]):
+                if input_ids[k][pos].item() in media_tokens:
+                    name = media_tokens[input_ids[k][pos].item()]
+                    if input_ids[k][pos].item() == media_token_ids["sound"]:
+                        if self.config.interleaved_vis_aud_in_video and sound_embeds_idx < video_sound_embeds_idx:
+                            media_embeds[name].popleft()
+                            sound_embeds_idx += 1
+                            pos += 1
+                            continue
+                        sound_embeds_idx += 1
+                    end = pos + 1
+                    current_input = media_embeds[name].popleft()
+                    current_label = torch.full(
+                        [current_input.shape[0]], IGNORE_INDEX, device=labels[k].device, dtype=labels[k].dtype
+                    )
+                else:
+                    end = pos
+                    while end < len(labels[k]) and input_ids[k][end].item() not in media_tokens:
+                        end += 1
+                    current_input = text_embeds[k][pos:end]
+                    current_label = labels[k][pos:end]
+                inputs_mk.append(current_input)
+                labels_mk.append(current_label)
+                pos = end
+            inputs_m.append(torch.cat(inputs_mk, dim=0))
+            labels_m.append(torch.cat(labels_mk, dim=0))
+        inputs, labels = inputs_m, labels_m
+        for name in media_embeds:
+            if media_embeds[name]:
+                raise ValueError(f"Not all {name} embeddings are consumed! Still {len(media_embeds[name])} left.")
+        inputs, labels = self.__truncate_sequence(inputs, labels)
+        return self.__batchify_sequence(inputs, labels)
+
+    def __embed_media_tokens(
+        self, media: dict[str, list[torch.Tensor]], media_config: dict[str, dict[str, Any]], mm_info
+    ):
+        embeds = defaultdict(deque)
+        embed_fn = {
+            "image": self._embed_image_features,
+            "video": self._embed_video_features,
+            "sound": self._embed_sound_features,
+        }
+        for name in media:
+            if name == "sound":
+                sound_media = media.get(name, [])
+                if len(sound_media) == 0:
+                    continue
+                if not all(
+                    hasattr(sound, "input_features") or (isinstance(sound, dict) and "input_features" in sound)
+                    for sound in sound_media
+                ):
+                    raise ValueError("Expected pre-extracted sound features in `media['sound']`.")
+            if len(media[name]) > 0:
+                embeds[name] = deque(embed_fn[name](media[name], media_config[name], mm_info))
+        return embeds
+
+    def __truncate_sequence(self, inputs: list[torch.Tensor], labels: list[torch.Tensor]):
+        model_max_length = getattr(self.config, "model_max_length", None)
+        if model_max_length is None:
+            model_max_length = getattr(self.llm.config, "model_max_length", 2048)
+        model_max_length = int(model_max_length)
+        if self.training and any(len(current_input) > model_max_length for current_input in inputs):
+            warnings.warn(f"Truncating sequences to `model_max_length` ({model_max_length}).")
+            inputs = [current_input[:model_max_length] for current_input in inputs]
+            labels = [label[:model_max_length] for label in labels]
+        return inputs, labels
+
+    def __batchify_sequence(self, inputs: list[torch.Tensor], labels: list[torch.Tensor]):
+        batch_size = len(inputs)
+        device = inputs[0].device
+        hidden_size = inputs[0].shape[1]
+        max_length = max(inputs[k].shape[0] for k in range(batch_size))
+        attention_mask = torch.ones((batch_size, max_length), dtype=torch.bool, device=device)
+        padding_side = getattr(self.config, "padding_side", "left")
+        inputs_p, labels_p = [], []
+        for k in range(batch_size):
+            pad_size = max_length - inputs[k].shape[0]
+            input_padding = torch.zeros((pad_size, hidden_size), dtype=inputs[k].dtype, device=device)
+            label_padding = torch.full((pad_size,), IGNORE_INDEX, dtype=labels[k].dtype, device=device)
+            if padding_side == "right":
+                attention_mask[k, inputs[k].shape[0] :] = False
+                input_padding = torch.cat([inputs[k], input_padding], dim=0)
+                label_padding = torch.cat([labels[k], label_padding], dim=0)
+            else:
+                labels[k] = labels[k].to(device)
+                attention_mask[k, : -inputs[k].shape[0]] = False
+                input_padding = torch.cat([input_padding, inputs[k]], dim=0)
+                label_padding = torch.cat([label_padding, labels[k]], dim=0)
+            inputs_p.append(input_padding)
+            labels_p.append(label_padding)
+        inputs = torch.stack(inputs_p, dim=0)
+        labels = torch.stack(labels_p, dim=0)
+        return inputs, labels, attention_mask
+
+    def repack_multimodal_data(self, inputs_embeds, attention_mask, position_ids, labels):
+        device = inputs_embeds.device
+        batch_size = inputs_embeds.shape[0]
+        seqlens = [attention_mask[k].sum().item() for k in range(batch_size)]
+        inputs_embeds_p = [inputs_embeds[k][attention_mask[k]] for k in range(batch_size)]
+        attention_mask_p = [torch.ones(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
+        position_ids_p = [torch.arange(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
+        labels_p = [labels[k][attention_mask[k]] for k in range(batch_size)]
+        inputs_embeds_p.append(torch.zeros(1, inputs_embeds.shape[-1], dtype=inputs_embeds.dtype, device=device))
+        attention_mask_p.append(torch.tensor([0], dtype=torch.int, device=device))
+        position_ids_p.append(torch.tensor([0], dtype=torch.int, device=device))
+        labels_p.append(torch.tensor([IGNORE_INDEX], dtype=torch.int, device=device))
+        for label in labels_p:
+            label[0] = IGNORE_INDEX
+        inputs_embeds_p = torch.cat(inputs_embeds_p, dim=0).unsqueeze(0)
+        attention_mask_p = torch.cat(attention_mask_p, dim=0).unsqueeze(0)
+        position_ids_p = torch.cat(position_ids_p, dim=0).unsqueeze(0)
+        labels_p = torch.cat(labels_p, dim=0).unsqueeze(0)
+        if hasattr(self, "pad_to_multiple_of"):
+            batch_size, max_length, cur_length = labels_p.shape[0], labels_p.shape[1], labels_p.shape[1]
+            hidden_size = inputs_embeds_p.shape[-1]
+            if max_length % self.pad_to_multiple_of != 0:
+                max_length = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of
+                difference = max_length - cur_length
+                inputs_embeds_p = torch.cat(
+                    (
+                        inputs_embeds_p,
+                        torch.full((batch_size, difference, hidden_size), self.llm.pad_token_id).to(inputs_embeds_p),
+                    ),
+                    dim=1,
+                )
+                labels_p = torch.cat(
+                    (labels_p, torch.full((batch_size, difference), IGNORE_INDEX).to(labels_p)), dim=1
+                )
+                attention_mask_p = torch.cat(
+                    (attention_mask_p, torch.zeros((batch_size, difference), dtype=torch.bool).to(attention_mask_p)),
+                    dim=1,
+                )
+                position_ids_p = torch.cat(
+                    (position_ids_p, torch.full((batch_size, difference), -1).to(position_ids_p)), dim=1
+                )
+        return inputs_embeds_p, attention_mask_p, position_ids_p, labels_p
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        media: dict[str, list[torch.Tensor]] | None = None,
+        media_config: list | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        packing: bool = True,
+        force_packing: bool = False,
+        seqlens_in_batch: torch.LongTensor | None = None,
+        dpo_forward: bool = False,
+        **kwargs,
+    ) -> tuple | CausalLMOutputWithPast:
+        _ = (pixel_values, seqlens_in_batch)
+        self._freeze_untrained_modules()
+        if media_config is None:
+            media_config = defaultdict(dict)
+        if inputs_embeds is None:
+            if media is None:
+                if input_ids is None:
+                    raise ValueError("Either `inputs_embeds` or `input_ids` must be provided.")
+                inputs_embeds = self.llm_model_embed_tokens(input_ids)
+                if attention_mask is None:
+                    attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+            else:
+                inputs_embeds, labels, attention_mask = self._embed(
+                    input_ids, media, media_config, labels, attention_mask
+                )
+        if force_packing or (packing and self.training and not dpo_forward):
+            inputs_embeds, attention_mask, position_ids, labels = self.repack_multimodal_data(
+                inputs_embeds, attention_mask, position_ids, labels
+            )
+        llm_param = next(self.llm.parameters(), None)
+        if llm_param is not None and inputs_embeds.dtype != llm_param.dtype:
+            inputs_embeds = inputs_embeds.to(llm_param.dtype)
+        outputs = self.llm(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            labels=labels,
+            **kwargs,
+        )
+        if dpo_forward:
+            return outputs.logits, labels
+        return outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        media=None,
+        media_config=None,
+        attention_mask=None,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        is_first_iteration = bool(kwargs.get("is_first_iteration", False))
+        is_first_step = (
+            is_first_iteration or past_key_values is None or (cache_position is not None and cache_position[0] == 0)
+        )
+        if is_first_step and inputs_embeds is None and media is not None:
+            if media_config is None:
+                media_config = defaultdict(dict)
+            inputs_embeds, _, attention_mask = self._embed(input_ids, media, media_config, None, attention_mask)
+        model_inputs = self.llm.prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        if is_first_step and inputs_embeds is not None:
+            model_inputs["inputs_embeds"] = inputs_embeds
+            model_inputs["attention_mask"] = attention_mask
+            model_inputs["input_ids"] = None
+            seq_len = attention_mask.shape[-1]
+            cache_pos = model_inputs.get("cache_position")
+            if cache_pos is None or cache_pos.shape[0] != seq_len:
+                model_inputs["cache_position"] = torch.arange(seq_len, device=inputs_embeds.device)
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 0)
+            model_inputs["position_ids"] = position_ids
+        model_inputs["media"] = None
+        model_inputs["media_config"] = None
+        return model_inputs
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs: ModelOutput,
+        model_kwargs: dict[str, Any],
+        is_encoder_decoder: bool = False,
+        num_new_tokens: int = 1,
+    ) -> dict[str, Any]:
+        attention_mask = model_kwargs.get("attention_mask")
+        logits = getattr(outputs, "logits", None)
+        if (
+            model_kwargs.get("media") is not None
+            and attention_mask is not None
+            and logits is not None
+            and attention_mask.shape[-1] != logits.shape[-2]
+        ):
+            batch_size = attention_mask.shape[0]
+            seq_len = logits.shape[-2]
+            model_kwargs["attention_mask"] = attention_mask.new_ones((batch_size, seq_len))
+            model_kwargs["cache_position"] = torch.arange(seq_len, device=attention_mask.device)
+            if model_kwargs.get("position_ids") is not None:
+                position_ids = model_kwargs["attention_mask"].long().cumsum(-1) - 1
+                position_ids.masked_fill_(model_kwargs["attention_mask"] == 0, 0)
+                model_kwargs["position_ids"] = position_ids
+            model_kwargs["media"] = None
+            model_kwargs["media_config"] = None
+        return super()._update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder, num_new_tokens=num_new_tokens
+        )
+
+
+__all__ = ["AudioVisualFlamingoForConditionalGeneration", "AudioVisualFlamingoPretrainedModel"]
diff --git a/src/transformers/models/audiovisualflamingo/modular_audiovisualflamingo.py b/src/transformers/models/audiovisualflamingo/modular_audiovisualflamingo.py
new file mode 100644
index 000000000000..4a7d3a0e2b52
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/modular_audiovisualflamingo.py
@@ -0,0 +1,1446 @@
+# Copyright 2026 The HuggingFace Team and NVIDIA CORPORATION. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+import warnings
+from collections import defaultdict, deque
+from math import pi
+from typing import Any, Literal
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub.dataclasses import strict
+from torch import broadcast_tensors, einsum
+
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...modeling_outputs import CausalLMOutputWithPast
+from ...utils import ModelOutput
+from ..audioflamingo3.modeling_audioflamingo3 import AudioFlamingo3MultiModalProjector
+from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel, AutoModelForCausalLM
+from ..llava_next.modeling_llava_next import LlavaNextMultiModalProjector
+from ..perceiver.modeling_perceiver import space_to_depth
+from ..voxtral.modeling_voxtral import VoxtralPreTrainedModel
+
+
+IGNORE_INDEX = -100
+
+MEDIA_TOKENS = {
+    "image": "<image>",
+    "video": "<vila/video>",
+    "sound": "<sound>",
+}
+
+MM_BOS_EOS_TOKENS = {
+    "image": ["<|image_bos|>", "<|image_eos|>"],
+    "video": ["<|video_bos|>", "<|video_eos|>"],
+    "sound": ["<|sound_bos|>", "<|sound_eos|>"],
+}
+
+
+@strict
+class AudioVisualFlamingoConfig(PreTrainedConfig):
+    model_type = "audiovisualflamingo"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    media_tokens = MEDIA_TOKENS
+    mm_bos_eos_tokens = MM_BOS_EOS_TOKENS
+    sub_configs = {
+        "text_config": AutoConfig,
+        "vision_config": AutoConfig,
+        "audio_config": AutoConfig,
+    }
+
+    @staticmethod
+    def _build_sub_config(config, default_model_type: str):
+        if isinstance(config, PreTrainedConfig):
+            return copy.deepcopy(config)
+        if config is None:
+            return CONFIG_MAPPING[default_model_type]()
+        if isinstance(config, dict):
+            model_type = config.get("model_type", default_model_type)
+            config_kwargs = {k: v for k, v in config.items() if k != "model_type"}
+            return CONFIG_MAPPING[model_type](**config_kwargs)
+        raise TypeError(f"Unsupported config payload type: {type(config)!r}")
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        audio_config=None,
+        mm_vision_select_layer=-2,
+        mm_vision_select_feature="patch",
+        dynamic_s2=None,
+        s2_scales=None,
+        s2_max_split_size=None,
+        s2_resize_output_to_scale_idx=0,
+        image_encoder=None,
+        video_encoder=None,
+        sound_encoder=None,
+        projector_bias=True,
+        multimodal_projector_bias=True,
+        load_audio_in_video=True,
+        interleaved_vis_aud_in_video=True,
+        **kwargs,
+    ):
+        legacy_config_aliases = {
+            "llm_cfg": "text_config",
+            "vision_tower_cfg": "vision_config",
+            "sound_tower_cfg": "audio_config",
+        }
+        used_legacy_aliases = [key for key in legacy_config_aliases if key in kwargs]
+        if used_legacy_aliases:
+            formatted_aliases = ", ".join(
+                f"`{key}` -> `{legacy_config_aliases[key]}`" for key in sorted(used_legacy_aliases)
+            )
+            raise TypeError(
+                "AudioVisualFlamingoConfig only accepts canonical sub-config names. "
+                f"Replace legacy aliases: {formatted_aliases}."
+            )
+
+        self.text_config = self._build_sub_config(text_config, "qwen2")
+        self.vision_config = self._build_sub_config(vision_config, "siglip_vision_model")
+        self.audio_config = self._build_sub_config(audio_config, "qwen2_audio_encoder")
+
+        self.mm_vision_select_layer = mm_vision_select_layer
+        self.mm_vision_select_feature = mm_vision_select_feature
+        self.dynamic_s2 = dynamic_s2
+        self.s2_scales = list(s2_scales) if s2_scales is not None else None
+        self.s2_max_split_size = s2_max_split_size
+        self.s2_resize_output_to_scale_idx = s2_resize_output_to_scale_idx
+
+        self.image_encoder = copy.deepcopy(image_encoder or {"_target_": "BasicImageEncoder"})
+        self.video_encoder = copy.deepcopy(video_encoder or {"_target_": "TSPVideoEncoder"})
+        self.sound_encoder = copy.deepcopy(sound_encoder or {"_target_": "BasicSoundEncoder"})
+        self.load_audio_in_video = load_audio_in_video
+        self.interleaved_vis_aud_in_video = interleaved_vis_aud_in_video
+
+        self.projector_bias = projector_bias
+        self.multimodal_projector_bias = multimodal_projector_bias
+
+        super().__init__(**kwargs)
+
+
+def pool(x: torch.Tensor, size: int, dim: int) -> torch.Tensor:
+    if x.shape[dim] % size != 0:
+        remainder = x.shape[dim] % size
+        pad_len = size - remainder
+        last_elements = x.narrow(dim, x.shape[dim] - remainder, remainder)
+        mean_value = last_elements.mean()
+        pad_shape = list(x.shape)
+        pad_shape[dim] = pad_len
+        padding = torch.ones(pad_shape, device=x.device, dtype=x.dtype) * mean_value
+        x = torch.cat([x, padding], dim=dim)
+
+    shape_before = x.shape[:dim]
+    shape_after = x.shape[dim + 1 :]
+    new_shape = shape_before + (-1, size) + shape_after
+    return x.view(new_shape).mean(dim + 1)
+
+
+def _tokens_to_channel_first(x: torch.Tensor, height: int, width: int) -> torch.Tensor:
+    if x.dim() != 3:
+        raise ValueError(f"Expected tensor of shape (batch, tokens, channels), got {tuple(x.shape)}")
+    batch_size, num_tokens, channels = x.shape
+    if num_tokens != height * width:
+        raise ValueError(f"Token count {num_tokens} does not match spatial shape ({height}, {width})")
+    return x.reshape(batch_size, height, width, channels).permute(0, 3, 1, 2).contiguous()
+
+
+def _channel_first_to_tokens(x: torch.Tensor) -> torch.Tensor:
+    if x.dim() != 4:
+        raise ValueError(f"Expected tensor of shape (batch, channels, height, width), got {tuple(x.shape)}")
+    batch_size, channels, height, width = x.shape
+    return x.permute(0, 2, 3, 1).reshape(batch_size, height * width, channels).contiguous()
+
+
+def _rotate_half(x):
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    return torch.stack((-x2, x1), dim=-1).reshape_as(x)
+
+
+def apply_rotary_emb(freqs, t, start_index=0, scale=1.0, seq_dim=-2):
+    device_type = t.device.type if t.device.type in {"cpu", "cuda"} else "cuda"
+    with torch.amp.autocast(device_type=device_type, enabled=False):
+        original_dtype = t.dtype
+        t = t.to(torch.float64)
+        freqs = freqs.to(t)
+
+        if t.ndim == 3:
+            seq_len = t.shape[seq_dim]
+            freqs = freqs[-seq_len:]
+
+        rot_dim = freqs.shape[-1]
+        end_index = start_index + rot_dim
+        assert rot_dim <= t.shape[-1], (
+            f"feature dimension {t.shape[-1]} is not of sufficient size to rotate in all the positions {rot_dim}"
+        )
+
+        t_left = t[..., :start_index]
+        t_middle = t[..., start_index:end_index]
+        t_right = t[..., end_index:]
+        t_middle = (t_middle * freqs.cos() * scale) + (_rotate_half(t_middle) * freqs.sin() * scale)
+        out = torch.cat((t_left, t_middle, t_right), dim=-1)
+    return out.to(original_dtype)
+
+
+class MaxTimeContinuousTimeRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_time, period_mode="longest"):
+        super().__init__()
+        if period_mode not in {"longest", "shortest"}:
+            raise ValueError(f"period_mode should be 'longest' or 'shortest', got {period_mode!r}")
+        self.period_mode = period_mode
+        self.max_time = max_time
+
+        if dim % 4 != 0:
+            raise ValueError(f"MTCT rotary embedding requires `dim` divisible by 4, got {dim}")
+        self.dim = dim
+        bands = torch.arange(1, dim // 4 + 1, dtype=torch.float32)
+        self.register_buffer("bands", bands, persistent=False)
+
+    def forward(self, times: torch.Tensor) -> torch.Tensor:
+        if times.ndim == 1:
+            times = times.unsqueeze(0)
+
+        times = times.float()
+        batch_size, seq_len = times.shape
+        times = times.clamp_min(0.0)
+        max_time = times.max(dim=-1, keepdim=True).values.clamp_min(1e-6)
+        if self.max_time is not None:
+            max_time = max_time.clamp_max(float(self.max_time))
+
+        if self.period_mode == "longest":
+            denominator = max_time
+        else:
+            nonzero = times.masked_fill(times <= 0, float("inf")).min(dim=-1, keepdim=True).values
+            nonzero = torch.where(torch.isfinite(nonzero), nonzero, max_time)
+            denominator = nonzero.clamp_min(1e-6)
+
+        angles = times.unsqueeze(-1) / denominator.unsqueeze(-1) * (2 * pi * self.bands)
+        angles = torch.cat((angles, angles), dim=-1)
+        return angles.reshape(batch_size, seq_len, self.dim // 2)
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        freqs_for: Literal["lang", "pixel", "constant"] = "lang",
+        theta=10000,
+        max_freq=10,
+        num_freqs=1,
+        learned_freq=False,
+        max_time=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.freqs_for = freqs_for
+        self.max_freq = max_freq
+        self.num_freqs = num_freqs
+        self.learned_freq = learned_freq
+        self.max_time = max_time
+        if max_time is not None and freqs_for == "lang":
+            theta = max_time / (2 * pi)
+        self.theta = theta
+
+        if freqs_for == "lang":
+            freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+        elif freqs_for == "pixel":
+            freqs = torch.linspace(1.0, max_freq / 2, dim // 2) * pi
+        elif freqs_for == "constant":
+            freqs = torch.ones(num_freqs).float()
+        else:
+            raise ValueError(f"unknown modality {freqs_for}")
+
+        self.freqs = nn.Parameter(freqs, requires_grad=learned_freq)
+        self.register_buffer("cached_freqs", None, persistent=False)
+        self.register_buffer("dummy", torch.tensor(0), persistent=False)
+
+    @property
+    def device(self):
+        return self.dummy.device
+
+    def forward(self, t: torch.Tensor, seq_len=None, offset=0):
+        should_cache = not self.learned_freq and seq_len is not None and self.freqs_for != "pixel"
+        if should_cache and self.cached_freqs is not None and (offset + seq_len) <= self.cached_freqs.shape[0]:
+            return self.cached_freqs[offset : (offset + seq_len)].detach()
+
+        freqs = self.freqs
+        if self.max_time is not None:
+            t = t / self.max_time * (2 * pi)
+
+        freqs = einsum("..., f -> ... f", t.type(freqs.dtype), freqs)
+        freqs = freqs.repeat_interleave(2, dim=-1)
+        if should_cache:
+            self.cached_freqs = freqs.detach()
+        return freqs
+
+    def get_axial_freqs(self, *dims):
+        colon = slice(None)
+        all_freqs = []
+        dtype = self.freqs.dtype if torch.is_floating_point(self.freqs) else torch.float32
+        for index, dim in enumerate(dims):
+            if self.freqs_for == "pixel":
+                pos = torch.linspace(-1, 1, steps=dim, device=self.device, dtype=dtype)
+            else:
+                pos = torch.arange(dim, device=self.device, dtype=dtype)
+
+            freqs = self.forward(pos, seq_len=dim)
+            all_axis = [None] * len(dims)
+            all_axis[index] = colon
+            all_freqs.append(freqs[(Ellipsis, *all_axis, colon)])
+
+        return torch.cat(broadcast_tensors(*all_freqs), dim=-1)
+
+
+def _move_rotary_module_to_device(module: nn.Module, device: torch.device) -> nn.Module:
+    module_device = None
+    on_meta = False
+    for param in module.parameters(recurse=False):
+        module_device = param.device
+        on_meta = param.is_meta
+        break
+    if module_device is None:
+        for buffer in module.buffers(recurse=False):
+            module_device = buffer.device
+            on_meta = buffer.is_meta
+            break
+    if module_device == device and not on_meta:
+        return module
+    if on_meta:
+        if isinstance(module, RotaryEmbedding):
+            return RotaryEmbedding(
+                dim=module.dim,
+                freqs_for=module.freqs_for,
+                theta=module.theta,
+                max_freq=module.max_freq,
+                num_freqs=module.num_freqs,
+                learned_freq=module.learned_freq,
+                max_time=module.max_time,
+            ).to(device=device)
+        if isinstance(module, MaxTimeContinuousTimeRotaryEmbedding):
+            return MaxTimeContinuousTimeRotaryEmbedding(
+                dim=module.dim,
+                max_time=module.max_time,
+                period_mode=module.period_mode,
+            ).to(device=device)
+        return module.to_empty(device=device)
+    return module.to(device=device)
+
+
+class MultimodalProjector(LlavaNextMultiModalProjector):
+    def __init__(self, vision_hidden_size: int, text_hidden_size: int, bias: bool):
+        nn.Module.__init__(self)
+        self.downsample_rate = 2
+        self.layers = nn.Sequential(
+            nn.Identity(),
+            nn.LayerNorm(vision_hidden_size * 4),
+            nn.Linear(vision_hidden_size * 4, text_hidden_size, bias=bias),
+            nn.GELU(),
+            nn.Linear(text_hidden_size, text_hidden_size, bias=bias),
+        )
+
+    def forward(self, x, *args, **kwargs):
+        _ = (args, kwargs)
+        bsz, num_tokens, channels = x.shape
+        h = w = int(num_tokens**0.5)
+        x = x.reshape(bsz, h, w, channels).permute(0, 3, 1, 2).contiguous()
+        if h % self.downsample_rate != 0 or w % self.downsample_rate != 0:
+            x = F.pad(
+                x,
+                (0, w % self.downsample_rate, 0, h % self.downsample_rate),
+                mode="constant",
+                value=0,
+            )
+        x = space_to_depth(x, spatial_block_size=self.downsample_rate).reshape(bsz, -1, channels * 4)
+        return self.layers(x)
+
+
+class SoundMultimodalProjector(AudioFlamingo3MultiModalProjector):
+    def __init__(self, audio_hidden_size: int, text_hidden_size: int, bias: bool):
+        nn.Module.__init__(self)
+        self.layers = nn.Sequential(
+            nn.Linear(audio_hidden_size, text_hidden_size, bias=bias),
+            nn.GELU(),
+            nn.Linear(text_hidden_size, text_hidden_size, bias=bias),
+        )
+
+    def forward(self, x, *args, **kwargs):
+        _ = (args, kwargs)
+        return self.layers(x)
+
+
+class SiglipVisionTowerDynamicS2(nn.Module):
+    def __init__(self, config: AudioVisualFlamingoConfig) -> None:
+        super().__init__()
+        self.select_layer = config.mm_vision_select_layer
+        self.select_feature = config.mm_vision_select_feature
+        if config.s2_scales is None:
+            raise ValueError("`config.s2_scales` must be provided when `dynamic_s2=True`.")
+        self.scales = sorted(int(scale) for scale in config.s2_scales)
+        self.max_split_size = config.s2_max_split_size
+        self.resize_output_to_scale_idx = config.s2_resize_output_to_scale_idx
+
+        vision_cfg = copy.deepcopy(config.vision_config)
+        vision_cfg._attn_implementation = config._attn_implementation
+        self.vision_tower = AutoModel.from_config(vision_cfg)
+
+    def feature_select(self, image_forward_outs):
+        image_features = image_forward_outs.hidden_states[self.select_layer]
+        if self.select_feature == "patch":
+            image_features = image_features[:, 1:]
+        elif self.select_feature != "cls_patch":
+            raise ValueError(f"Unexpected select feature: {self.select_feature}")
+        return image_features
+
+    def forward(self, images):
+        if isinstance(images, list):
+            raise ValueError("VisionTowerDynamicS2 expects tensor input, not list.")
+        image_forward_outs = self.vision_tower(
+            images.to(device=self.device, dtype=self.dtype),
+            output_hidden_states=True,
+        )
+        return self.feature_select(image_forward_outs).to(images.dtype)
+
+    @property
+    def dtype(self):
+        return self.vision_tower.dtype
+
+    @property
+    def device(self):
+        return self.vision_tower.device
+
+    @property
+    def config(self):
+        return self.vision_tower.config
+
+    @property
+    def hidden_size(self):
+        return self.config.hidden_size * len(self.scales)
+
+
+class AudioVisualFlamingoPretrainedModel(VoxtralPreTrainedModel):
+    config_class = AudioVisualFlamingoConfig
+    main_input_name = "input_ids"
+    supports_gradient_checkpointing = True
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _no_split_modules = ["Qwen2DecoderLayer", "SiglipEncoderLayer"]
+
+    @property
+    def llm_model_embed_tokens(self):
+        if self.llm is None:
+            raise RuntimeError("LLM module is not initialized.")
+        return self.llm.model.embed_tokens
+
+    def _require_encoder_text_token_ids(self) -> dict[str, list[int]]:
+        encoder_text_token_ids = getattr(self.config, "encoder_text_token_ids", None)
+        if encoder_text_token_ids is None:
+            raise ValueError("Missing `config.encoder_text_token_ids`.")
+        return encoder_text_token_ids
+
+    def embed_text_tokens(self, token_text: str | None) -> torch.Tensor | None:
+        if token_text is None:
+            return None
+        token_ids = self._require_encoder_text_token_ids().get(token_text)
+        if token_ids is None:
+            raise ValueError(f"Missing token ids for encoder boundary text: {token_text!r}")
+        token_ids = torch.tensor(token_ids, device=self.llm_model_embed_tokens.weight.device)
+        return self.llm_model_embed_tokens(token_ids)
+
+    def _require_media_token_ids(self) -> dict[str, int]:
+        media_token_ids = getattr(self.config, "media_token_ids", None)
+        if not media_token_ids:
+            raise ValueError("Missing `config.media_token_ids`.")
+        return media_token_ids
+
+    def _init_media_encoders(self):
+        def _parse_tokens(cfg, default_end="\n"):
+            start = cfg.get("start_tokens")
+            end = cfg.get("end_tokens", default_end)
+            end = None if end == "None" else end
+            sep = cfg.get("sep_tokens")
+            return start, end, sep
+
+        img_cfg = copy.deepcopy(self.config.image_encoder)
+        vid_cfg = copy.deepcopy(self.config.video_encoder)
+        snd_cfg = copy.deepcopy(self.config.sound_encoder)
+        for dct in (img_cfg, vid_cfg, snd_cfg):
+            dct.pop("_target_", None)
+
+        self._image_start_tokens, self._image_end_tokens, _ = _parse_tokens(img_cfg)
+        self._video_start_tokens, self._video_end_tokens, self._video_sep_tokens = _parse_tokens(vid_cfg)
+        self._video_pool_sizes = vid_cfg.get("pool_sizes", [[1, 1, 1]])
+        self._sound_start_tokens, self._sound_end_tokens, _ = _parse_tokens(snd_cfg)
+        self._time_embeddings = {}
+
+        self._video_embed_time = vid_cfg.get("embed_time", "False") in ("True", True)
+        if self._video_embed_time:
+            self._video_time_embed_type = vid_cfg.get("time_embed_type", "pixel")
+            self._video_period_fix, self._video_max_time = self._create_time_embedding("video", vid_cfg)
+
+        self._sound_embed_time = snd_cfg.get("embed_time", "False") in ("True", True)
+        if self._sound_embed_time:
+            self._sound_time_embed_type = snd_cfg.get("time_embed_type", "pixel")
+            self._sound_period_fix, self._sound_max_time = self._create_time_embedding("sound", snd_cfg)
+
+    def _create_time_embedding(self, key: str, cfg: dict):
+        trope_dim = cfg.get("trope_dim", 128)
+        trope_theta = cfg.get("trope_theta", 50000)
+        max_time = cfg.get("max_time")
+        time_embed_type = cfg.get("time_embed_type", "pixel")
+        period_fix = cfg.get("period_fix", False)
+
+        period_mode = None
+        if isinstance(period_fix, str) and period_fix in ("shortest", "longest"):
+            period_mode = period_fix
+            period_fix = "MTCT"
+
+        if period_fix == "MTCT":
+            kwargs = {"dim": trope_dim, "max_time": max_time}
+            if period_mode is not None:
+                kwargs["period_mode"] = period_mode
+            self._time_embeddings[key] = MaxTimeContinuousTimeRotaryEmbedding(**kwargs)
+        elif key == "video":
+            if time_embed_type == "lang":
+                self._time_embeddings[key] = RotaryEmbedding(
+                    dim=trope_dim, freqs_for="lang", theta=trope_theta, max_time=max_time
+                )
+            elif time_embed_type == "pixel":
+                self._time_embeddings[key] = RotaryEmbedding(dim=trope_dim, freqs_for="pixel", max_freq=256)
+        elif key == "sound":
+            if time_embed_type in ("pixel", "lang"):
+                self._time_embeddings[key] = RotaryEmbedding(
+                    dim=trope_dim, freqs_for=time_embed_type, max_freq=256, max_time=max_time
+                )
+        return period_fix, max_time
+
+    def _freeze_untrained_modules(self):
+        if not self.training:
+            return
+
+        for module, flag_name in (
+            (self.vision_tower, "tune_vision_tower"),
+            (getattr(self, "sound_tower", None), "tune_sound_tower"),
+            (self.mm_projector, "tune_mm_projector"),
+            (getattr(self, "sound_mm_projector", None), "tune_sound_mm_projector"),
+        ):
+            if module is not None and not getattr(self.config, flag_name, False):
+                module.eval()
+
+
+class AudioVisualFlamingoForConditionalGeneration(AudioVisualFlamingoPretrainedModel, GenerationMixin):
+    def __init__(self, config: AudioVisualFlamingoConfig, *args, **kwargs):
+        super().__init__(config)
+        _ = (args, kwargs)
+        if not getattr(config, "dynamic_s2", False):
+            raise NotImplementedError("Current AudioVisualFlamingo checkpoint requires `dynamic_s2=True`.")
+        self.vision_tower = SiglipVisionTowerDynamicS2(config)
+        audio_cfg = copy.deepcopy(config.audio_config)
+        audio_cfg._attn_implementation = config._attn_implementation
+        self.sound_tower = AutoModel.from_config(audio_cfg)
+
+        text_cfg = copy.deepcopy(config.text_config)
+        text_cfg._attn_implementation = config._attn_implementation
+        model_max_length = getattr(config, "model_max_length", None)
+        if model_max_length is not None:
+            text_cfg.model_max_length = model_max_length
+            orig_ctx_len = getattr(text_cfg, "max_position_embeddings", None)
+            if orig_ctx_len is not None and model_max_length > orig_ctx_len:
+                text_cfg.rope_scaling = {
+                    "type": "linear",
+                    "factor": float(math.ceil(model_max_length / orig_ctx_len)),
+                }
+
+        self.llm = AutoModelForCausalLM.from_config(text_cfg)
+        self.mm_projector = MultimodalProjector(
+            vision_hidden_size=self.vision_tower.hidden_size,
+            text_hidden_size=self.llm.config.hidden_size,
+            bias=config.multimodal_projector_bias,
+        )
+        self.sound_mm_projector = SoundMultimodalProjector(
+            audio_hidden_size=self.sound_tower.config.d_model,
+            text_hidden_size=self.llm.config.hidden_size,
+            bias=config.projector_bias,
+        )
+        self.vocab_size = self.llm.config.vocab_size
+        self._init_media_encoders()
+        self.training = self.llm.training
+        if self.training:
+            self.train()
+        else:
+            self.eval()
+
+        self.config.text_config = self.llm.config
+        self.config.vision_config = self.vision_tower.config
+        self.config.audio_config = self.sound_tower.config
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.llm.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.llm.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.llm.get_output_embeddings()
+
+    def set_output_embeddings(self, new_embeddings):
+        self.llm.set_output_embeddings(new_embeddings)
+
+    def set_decoder(self, decoder):
+        self.llm.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.llm.get_decoder()
+
+    @property
+    def language_model(self):
+        return self.llm
+
+    def _encode_visual_features(self, images: torch.Tensor, block_sizes: tuple[int, ...] | None = None):
+        if not getattr(self.config, "dynamic_s2", False):
+            raise NotImplementedError("Current AudioVisualFlamingo checkpoint requires `dynamic_s2=True`.")
+        if len(images) == 0:
+            return []
+
+        if block_sizes is None:
+            block_sizes = [None] * len(images)
+
+        image_features = self.vision_tower(images)
+        image_features, new_block_sizes = self.merge_features_for_dynamic_s2(image_features, block_sizes)
+        image_features = [
+            self.split_chessboard(feature, block_size[0], block_size[1])
+            for feature, block_size in zip(image_features, new_block_sizes)
+        ]
+        image_features = torch.cat([_channel_first_to_tokens(feature) for feature in image_features], dim=0)
+        image_features = self.mm_projector(image_features.to(self.device, self.dtype))
+        image_features = list(
+            image_features.split([block_size[0] * block_size[1] for block_size in new_block_sizes], dim=0)
+        )
+        image_features = [
+            self.merge_chessboard(feature, block_size[0], block_size[1])
+            for feature, block_size in zip(image_features, new_block_sizes)
+        ]
+        image_features = [_channel_first_to_tokens(feature)[0] for feature in image_features]
+        if all(feature.shape[0] == image_features[0].shape[0] for feature in image_features):
+            return torch.stack(image_features, dim=0)
+        return image_features
+
+    def merge_features_for_dynamic_s2(self, image_features, block_sizes):
+        scales = self.vision_tower.scales
+        resize_output_to_scale_idx = self.vision_tower.resize_output_to_scale_idx
+        image_features_each_image = []
+        new_block_sizes = []
+        block_cnt = 0
+        for block_size_each_image in block_sizes:
+            if block_size_each_image is None:
+                cur_features = image_features[block_cnt : block_cnt + 1]
+                spatial_size = int(cur_features.shape[1] ** 0.5)
+                cur_features = _tokens_to_channel_first(cur_features, spatial_size, spatial_size)
+                cur_features = cur_features.repeat(1, len(scales), 1, 1)
+                image_features_each_image.append(cur_features)
+                new_block_sizes.append((1, 1))
+                block_cnt += 1
+                continue
+
+            cur_features_each_scale = []
+            for scale in scales[:-1]:
+                num_blocks_this_scale = (scale // scales[0]) ** 2
+                cur_features_each_scale.append(
+                    self.merge_chessboard(
+                        image_features[block_cnt : block_cnt + num_blocks_this_scale],
+                        num_split_h=scale // scales[0],
+                        num_split_w=scale // scales[0],
+                    )
+                )
+                block_cnt += num_blocks_this_scale
+            num_blocks_last_scale = block_size_each_image[0] * block_size_each_image[1]
+            cur_features_each_scale.append(
+                self.merge_chessboard(
+                    image_features[block_cnt : block_cnt + num_blocks_last_scale],
+                    num_split_h=block_size_each_image[0],
+                    num_split_w=block_size_each_image[1],
+                )
+            )
+            block_cnt += num_blocks_last_scale
+            output_size = cur_features_each_scale[resize_output_to_scale_idx].shape[-2:]
+            cur_features = torch.cat(
+                [
+                    F.interpolate(cur_features_each_scale[i].to(torch.float32), size=output_size, mode="area").to(
+                        cur_features_each_scale[i].dtype
+                    )
+                    for i in range(len(cur_features_each_scale))
+                ],
+                dim=1,
+            )
+            image_features_each_image.append(cur_features)
+            if resize_output_to_scale_idx == len(scales) - 1 or resize_output_to_scale_idx == -1:
+                new_block_sizes.append(block_size_each_image)
+            else:
+                new_block_sizes.append(
+                    (
+                        scales[resize_output_to_scale_idx] // scales[0],
+                        scales[resize_output_to_scale_idx] // scales[0],
+                    )
+                )
+        assert block_cnt == len(image_features)
+        return image_features_each_image, new_block_sizes
+
+    @staticmethod
+    def split_chessboard(x, num_split_h, num_split_w):
+        bsz, channels, height, width = x.shape
+        assert height % num_split_h == 0 and width % num_split_w == 0
+        split_h, split_w = height // num_split_h, width // num_split_w
+        return torch.cat(
+            [
+                x[:, :, i * split_h : (i + 1) * split_h, j * split_w : (j + 1) * split_w]
+                for i in range(num_split_h)
+                for j in range(num_split_w)
+            ],
+            dim=0,
+        )
+
+    @staticmethod
+    def merge_chessboard(x, num_split_h, num_split_w):
+        batch = x.shape[0]
+        if x.dim() == 3:
+            num_tokens = x.shape[1]
+            spatial_size = int(num_tokens**0.5)
+            x = _tokens_to_channel_first(x, spatial_size, spatial_size)
+        assert batch % (num_split_h * num_split_w) == 0
+        base_batch = batch // (num_split_h * num_split_w)
+        return torch.cat(
+            [
+                torch.cat(
+                    [
+                        x[(i * num_split_w + j) * base_batch : (i * num_split_w + j + 1) * base_batch]
+                        for j in range(num_split_w)
+                    ],
+                    dim=-1,
+                )
+                for i in range(num_split_h)
+            ],
+            dim=-2,
+        )
+
+    def encode_video(
+        self,
+        inp,
+        block_sizes: tuple[int, ...] | None = None,
+        mm_info: dict | None = None,
+        num_frames: list[int] | None = None,
+    ):
+        _ = (mm_info, num_frames)
+        if block_sizes is not None:
+            raise ValueError(f"Video block sizes are not supported: {block_sizes}")
+        if not inp:
+            return []
+        return self._encode_visual_features(torch.cat(inp, dim=0))
+
+    def encode_images(
+        self,
+        images,
+        block_sizes: tuple[int, ...] | None = None,
+        mm_info: dict | None = None,
+        num_frames: list[int] | None = None,
+    ):
+        _ = (mm_info, num_frames)
+        return self._encode_visual_features(images, block_sizes=block_sizes)
+
+    def _get_sound_chunk_length(self) -> int:
+        return (
+            self.sound_tower.config.max_source_positions
+            * self.sound_tower.conv1.stride[0]
+            * self.sound_tower.conv2.stride[0]
+        )
+
+    def _forward_sound_tower_batch(self, input_features: torch.Tensor) -> torch.Tensor:
+        batch_size, n_mels, seq_len = input_features.shape
+        chunk_length = self._get_sound_chunk_length()
+        num_chunks = (seq_len + chunk_length - 1) // chunk_length
+
+        padded_chunks = []
+        for chunk_idx in range(num_chunks):
+            start_idx = chunk_idx * chunk_length
+            end_idx = min(start_idx + chunk_length, seq_len)
+            chunk = input_features[:, :, start_idx:end_idx]
+            if chunk.shape[2] < chunk_length:
+                chunk = F.pad(chunk, (0, chunk_length - chunk.shape[2]), mode="constant", value=0)
+            padded_chunks.append(chunk)
+
+        all_chunks = torch.cat(padded_chunks, dim=0).reshape(batch_size * num_chunks, n_mels, chunk_length)
+        chunk_outputs = self.sound_tower(all_chunks, return_dict=True)
+        hidden_states = chunk_outputs.last_hidden_state
+        _, chunk_seq_len, hidden_size = hidden_states.shape
+        return hidden_states.reshape(batch_size, num_chunks * chunk_seq_len, hidden_size)
+
+    def encode_sound(self, sounds, mm_info: dict | None = None):
+        _ = mm_info
+        audio_features = []
+        audio_output_lengths = []
+        for sound in sounds:
+            if hasattr(sound, "input_features") or (isinstance(sound, dict) and "input_features" in sound):
+                sound = sound["input_features"]
+            sound_dtype = sound.dtype
+            sound = sound.to(device=self.sound_tower.device, dtype=self.sound_tower.dtype)
+            sound_feature = self._forward_sound_tower_batch(sound).to(sound_dtype)
+            audio_features.append(sound_feature)
+            audio_output_lengths.append(sound_feature.shape[1])
+
+        if not audio_features:
+            return []
+
+        audio_features = torch.cat(audio_features, dim=1).squeeze(0)
+        projector_param = next(self.sound_mm_projector.parameters(), None)
+        if projector_param is not None and audio_features.dtype != projector_param.dtype:
+            audio_features = audio_features.to(projector_param.dtype)
+        audio_features = self.sound_mm_projector(audio_features)
+
+        split_audio_features = []
+        start = 0
+        for length in audio_output_lengths:
+            split_audio_features.append(audio_features[start : start + length])
+            start += length
+        return split_audio_features
+
+    def _embed_image_features(
+        self, images: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = mm_info
+        features = self.encode_images(torch.stack(images, dim=0), block_sizes=config.get("block_sizes"))
+        start_embeds = self.embed_text_tokens(self._image_start_tokens)
+        end_embeds = self.embed_text_tokens(self._image_end_tokens)
+        image_features = []
+        for feature in features:
+            if start_embeds is not None:
+                feature = torch.cat([start_embeds, feature], dim=0)
+            if end_embeds is not None:
+                feature = torch.cat([feature, end_embeds], dim=0)
+            image_features.append(feature)
+        return image_features
+
+    def _embed_video_features(
+        self, videos: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = config
+        num_frames = [video.shape[0] for video in videos]
+        features = self.encode_video(videos, mm_info=mm_info, num_frames=num_frames)
+        features = torch.split(features, num_frames)
+        start_embeds = self.embed_text_tokens(self._video_start_tokens)
+        end_embeds = self.embed_text_tokens(self._video_end_tokens)
+        sep_embeds = self.embed_text_tokens(self._video_sep_tokens)
+        if not self._video_embed_time:
+            return [self._tsp_process(feature, start_embeds, end_embeds, sep_embeds) for feature in features]
+
+        batch_size = len(mm_info["video_info"])
+        device = features[0].device
+        new_time_embeds = None
+        if self._video_time_embed_type == "learned_embed":
+            times_list, video_idx = [], 0
+            for i in range(batch_size):
+                video_info = mm_info["video_info"][i]
+                if video_info is None:
+                    continue
+                for j in range(len(video_info)):
+                    feature = features[video_idx]
+                    if video_info[j] == "dummy":
+                        times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                    else:
+                        times = torch.tensor(video_info[j]["video_frame_times"]).to(device)
+                    for pool_size in self._video_pool_sizes:
+                        temporal_pool = pool_size[0]
+                        if temporal_pool != 1:
+                            if len(times) % temporal_pool != 0:
+                                remainder = len(times) % temporal_pool
+                                times = torch.cat([times, times[-remainder:].mean().expand(temporal_pool - remainder)])
+                            times = pool(times, temporal_pool, 0)
+                    times_list.append(times)
+                    video_idx += 1
+            original_lengths = [len(times) for times in times_list]
+            max_length = max(original_lengths)
+            for i in range(len(times_list)):
+                if len(times_list[i]) < max_length:
+                    times_list[i] = torch.cat(
+                        [times_list[i], torch.zeros(max_length - len(times_list[i])).to(times_list[i].device)]
+                    )
+            times_tensor = torch.stack(times_list, dim=0)
+            time_embeds_all = self._time_embeddings["video"](times_tensor, dtype=features[0].dtype)
+            new_time_embeds = []
+            for i in range(len(times_list)):
+                new_time_embeds.append(
+                    time_embeds_all[i][: original_lengths[i]].unsqueeze(1).expand(-1, features[0].shape[1], -1)
+                )
+            new_time_embeds[0] = new_time_embeds[0] + 0 * time_embeds_all.mean()
+
+        new_features, video_idx = [], 0
+        for i in range(batch_size):
+            video_info = mm_info["video_info"][i]
+            if video_info is None:
+                continue
+            for j in range(len(video_info)):
+                feature = features[video_idx]
+                if video_info[j] == "dummy":
+                    times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                else:
+                    times = torch.tensor(video_info[j]["video_frame_times"]).to(device)
+                if self._video_time_embed_type == "learned_embed":
+                    feature = self._tsp_process(
+                        feature, start_embeds, end_embeds, sep_embeds, time_embed=new_time_embeds[video_idx]
+                    )
+                else:
+                    feature = self._tsp_process(feature, start_embeds, end_embeds, sep_embeds, times=times)
+                new_features.append(feature)
+                video_idx += 1
+        assert video_idx == len(features)
+        return new_features
+
+    def _tsp_process(
+        self,
+        inputs: torch.Tensor,
+        start_token_embeds: torch.Tensor | None,
+        end_token_embeds: torch.Tensor | None,
+        sep_token_embeds: torch.Tensor | None,
+        times: torch.Tensor | None = None,
+        time_embed: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        num_frames, num_spatial_tokens = inputs.shape[:2]
+        spatial_length = int(num_spatial_tokens**0.5)
+        outputs = []
+        for pool_size in self._video_pool_sizes:
+            features = inputs.view(num_frames, spatial_length, spatial_length, -1)
+            for dim, pool_factor in enumerate(pool_size):
+                features = pool(features, pool_factor, dim=dim)
+            features = features.flatten(1, 2)
+            if self._video_embed_time:
+                device = features.device
+                if self._video_time_embed_type in ("pixel", "lang"):
+                    temporal_pool = pool_size[0]
+                    if temporal_pool != 1:
+                        pooled_times = times
+                        if len(pooled_times) % temporal_pool != 0:
+                            remainder = len(pooled_times) % temporal_pool
+                            pooled_times = torch.cat(
+                                [pooled_times, pooled_times[-remainder:].mean().expand(temporal_pool - remainder)]
+                            )
+                        new_times = pool(pooled_times, temporal_pool, 0)
+                    else:
+                        new_times = times
+                    pos_emb = _move_rotary_module_to_device(self._time_embeddings["video"], device)
+                    self._time_embeddings["video"] = pos_emb
+                    if self._video_period_fix == "True":
+                        angle = (
+                            new_times.to(device) / self._video_max_time * 2 * np.pi
+                            if self._video_max_time is not None
+                            else new_times.to(device)
+                        )
+                    elif self._video_period_fix == "MTCT":
+                        time_values = new_times.unsqueeze(0) if new_times.ndim == 1 else new_times
+                        freqs = pos_emb(time_values.float()).squeeze(0).unsqueeze(1)
+                        features = apply_rotary_emb(freqs, features, seq_dim=0)
+                    else:
+                        angle = (-new_times * 2 * np.pi).to(device)
+                    if self._video_period_fix != "MTCT":
+                        freqs = pos_emb.get_axial_freqs(new_times.shape[0], features.shape[-2]).to(device)
+                        angle_exp = (
+                            angle.unsqueeze(1)
+                            .unsqueeze(2)
+                            .expand(new_times.shape[0], features.shape[-2], freqs.shape[-1])
+                        )
+                        features = apply_rotary_emb(freqs * angle_exp, features)
+                elif self._video_time_embed_type == "learned_embed":
+                    features = features + time_embed
+            if start_token_embeds is not None:
+                features = torch.cat(
+                    [start_token_embeds.unsqueeze(0).expand(features.shape[0], -1, -1), features], dim=1
+                )
+            if end_token_embeds is not None:
+                features = torch.cat(
+                    [features, end_token_embeds.unsqueeze(0).expand(features.shape[0], -1, -1)], dim=1
+                )
+            features = features.flatten(0, 1)
+            if sep_token_embeds is not None:
+                features = torch.cat([features, sep_token_embeds], dim=0)
+            outputs.append(features)
+        return torch.cat(outputs, dim=0)
+
+    def _embed_sound_features(
+        self, sounds: list[torch.Tensor], config: dict[str, Any], mm_info: dict
+    ) -> list[torch.Tensor]:
+        _ = config
+        features = self.encode_sound(sounds, mm_info=mm_info)
+        start_embeds = self.embed_text_tokens(self._sound_start_tokens)
+        end_embeds = self.embed_text_tokens(self._sound_end_tokens)
+        if not self._sound_embed_time:
+            return [self._process_sound_feature(feature, start_embeds, end_embeds) for feature in features]
+        device = features[0].device
+        feature_count = len(features)
+        batch_size = len(mm_info["audio_info"])
+        time_embeds_all = None
+        if self._sound_time_embed_type == "learned_embed":
+            times_list, audio_idx = [], 0
+            for i in range(batch_size):
+                audio_info = mm_info["audio_info"][i]
+                if audio_info is None:
+                    continue
+                for j in range(len(audio_info)):
+                    feature = features[audio_idx]
+                    if audio_info[j] == "dummy":
+                        times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                    else:
+                        chunk_length = audio_info[j]["new_audio_chunk_length"]
+                        seconds_per_embed = chunk_length / feature.shape[0]
+                        audio_start = audio_info[j]["audio_start_sec"]
+                        times = torch.tensor(
+                            [
+                                audio_start + k * seconds_per_embed + seconds_per_embed / 2
+                                for k in range(feature.shape[0])
+                            ]
+                        ).to(device)
+                    times_list.append(times)
+                    audio_idx += 1
+            times_tensor = torch.stack(times_list, dim=0)
+            time_embeds_all = self._time_embeddings["sound"](times_tensor, dtype=features[0].dtype)
+        new_features, audio_idx = [], 0
+        for i in range(batch_size):
+            audio_info = mm_info["audio_info"][i]
+            if audio_info is None:
+                continue
+            for j in range(len(audio_info)):
+                feature = features[audio_idx]
+                if audio_info[j] == "dummy":
+                    times = torch.zeros(feature.shape[0], device=device, dtype=feature.dtype)
+                else:
+                    chunk_length = audio_info[j]["new_audio_chunk_length"]
+                    seconds_per_embed = chunk_length / feature.shape[0]
+                    audio_start = audio_info[j]["audio_start_sec"]
+                    times = torch.tensor(
+                        [audio_start + k * seconds_per_embed + seconds_per_embed / 2 for k in range(feature.shape[0])]
+                    ).to(device)
+                if self._sound_time_embed_type == "learned_embed":
+                    feature = self._process_sound_feature(
+                        feature, start_embeds, end_embeds, time_embed=time_embeds_all[audio_idx]
+                    )
+                else:
+                    feature = self._process_sound_feature(feature, start_embeds, end_embeds, times=times)
+                new_features.append(feature)
+                audio_idx += 1
+        assert audio_idx == feature_count
+        return new_features
+
+    def _process_sound_feature(
+        self,
+        features: torch.Tensor,
+        start_token_embeds: torch.Tensor | None,
+        end_token_embeds: torch.Tensor | None,
+        times: torch.Tensor | None = None,
+        time_embed: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        features = features.to(self.device)
+        device = features.device
+        if self._sound_embed_time:
+            if self._sound_time_embed_type in ("pixel", "lang"):
+                new_times = times.unsqueeze(0)
+                pos_emb = _move_rotary_module_to_device(self._time_embeddings["sound"], device)
+                self._time_embeddings["sound"] = pos_emb
+                if self._sound_period_fix == "True":
+                    angle = (
+                        new_times.to(device) / self._sound_max_time * 2 * np.pi
+                        if self._sound_max_time is not None
+                        else new_times.to(device)
+                    )
+                elif self._sound_period_fix == "MTCT":
+                    freqs = pos_emb(new_times.float()).squeeze(0)
+                    features = apply_rotary_emb(freqs, features)
+                else:
+                    angle = (-new_times * 2 * np.pi).to(device)
+                if self._sound_period_fix != "MTCT":
+                    freqs = pos_emb.get_axial_freqs(new_times.shape[0], features.shape[-2]).to(device)
+                    angle_exp = angle.unsqueeze(2).expand(new_times.shape[0], features.shape[-2], freqs.shape[-1])
+                    freqs = (freqs * angle_exp).squeeze(0)
+                    features = apply_rotary_emb(freqs, features)
+            elif self._sound_time_embed_type == "learned_embed":
+                features = features + time_embed
+        if start_token_embeds is not None:
+            features = torch.cat([start_token_embeds, features], dim=0)
+        if end_token_embeds is not None:
+            features = torch.cat([features, end_token_embeds], dim=0)
+        return features
+
+    def _embed(
+        self,
+        input_ids: torch.Tensor,
+        media: dict[str, list[torch.Tensor]],
+        media_config: dict[str, dict[str, Any]],
+        labels: torch.Tensor | None,
+        attention_mask: torch.Tensor | None,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        media = copy.deepcopy(media)
+        media_config = copy.deepcopy(media_config)
+        labels = labels if labels is not None else torch.full_like(input_ids, IGNORE_INDEX)
+        attention_mask = attention_mask if attention_mask is not None else torch.ones_like(input_ids, dtype=torch.bool)
+        text_embeds = self.llm_model_embed_tokens(input_ids)
+        mm_info = {}
+        video_info = media.pop("video_info", None)
+        audio_info = media.pop("audio_info", None)
+        if video_info is not None:
+            mm_info["video_info"] = video_info
+        if audio_info is not None:
+            mm_info["audio_info"] = audio_info
+        media_embeds = self.__embed_media_tokens(media, media_config, mm_info) if media is not None else {}
+
+        video_sound_embeds_idx = 0
+        sep_embed = self.embed_text_tokens("\n")
+        llm_embed_dtype = self.llm_model_embed_tokens.weight.dtype
+        text_embeds = text_embeds.to(llm_embed_dtype)
+        sep_embed = sep_embed.to(text_embeds.dtype)
+        if video_info is not None and self.config.load_audio_in_video and self.config.interleaved_vis_aud_in_video:
+            assert self._video_end_tokens is None, "end_tokens must be None for interleaved vis-aud in video"
+            new_video_embeds = deque()
+            video_embeds_idx = 0
+            for k in range(len(video_info)):
+                if video_info[k] is None:
+                    continue
+                for i in range(len(video_info[k])):
+                    has_audio = video_info[k][i]["has_audio"]
+                    if not has_audio:
+                        new_video_embeds.append(media_embeds["video"][video_embeds_idx])
+                        video_embeds_idx += 1
+                        continue
+                    if video_sound_embeds_idx >= len(media_embeds["sound"]):
+                        raise ValueError(
+                            f"Sound embeddings index {video_sound_embeds_idx} out of bounds for video_info[{k}][{i}]"
+                        )
+                    segment_aud_indices_list = video_info[k][i]["segment_aud_indices_list"]
+                    segment_vis_indices_list = video_info[k][i]["segment_vis_indices_list"]
+                    vis_fea_len_per_frame = (
+                        media_embeds["video"][video_embeds_idx].shape[0] / video_info[k][i]["expected_frame_count"]
+                    )
+                    aud_fea_len_per_stft_frame = (
+                        media_embeds["sound"][video_sound_embeds_idx].shape[0]
+                        / audio_info[k][i]["new_audio_n_stft_frames"]
+                    )
+                    vis_end = 0
+                    aud_end = 0
+                    new_video_embed = []
+                    for j in range(len(segment_vis_indices_list)):
+                        vis_aud_fea = []
+                        if len(segment_vis_indices_list[j]) > 0:
+                            new_frames = [
+                                int(np.ceil((frame + 1) * vis_fea_len_per_frame))
+                                for frame in segment_vis_indices_list[j]
+                            ]
+                            vis_fea_end = min(new_frames[-1], media_embeds["video"][video_embeds_idx].shape[0])
+                            vis_fea = media_embeds["video"][video_embeds_idx][vis_end:vis_fea_end]
+                            vis_end = vis_fea_end
+                            vis_aud_fea.append(vis_fea)
+                        vis_aud_fea.append(sep_embed)
+                        if len(segment_aud_indices_list[j]) > 0:
+                            new_audio_indices = [
+                                int(np.ceil(fea * aud_fea_len_per_stft_frame)) for fea in segment_aud_indices_list[j]
+                            ]
+                            aud_fea_end = min(
+                                new_audio_indices[-1], media_embeds["sound"][video_sound_embeds_idx].shape[0]
+                            )
+                            aud_fea = media_embeds["sound"][video_sound_embeds_idx][aud_end:aud_fea_end]
+                            vis_aud_fea.append(aud_fea)
+                            aud_end = aud_fea_end
+                        vis_aud_fea.append(sep_embed)
+                        new_video_embed.append(torch.cat(vis_aud_fea, dim=0))
+                    video_sound_embeds_idx += 1
+                    new_video_embeds.append(torch.cat(new_video_embed, dim=0))
+                    video_embeds_idx += 1
+            assert len(new_video_embeds) == len(media_embeds["video"])
+            media_embeds["video"] = new_video_embeds
+
+        batch_size = labels.shape[0]
+        text_embeds = [text_embeds[k][attention_mask[k]] for k in range(batch_size)]
+        labels = [labels[k][attention_mask[k]] for k in range(batch_size)]
+        media_token_ids = self._require_media_token_ids()
+        media_tokens = {token_id: name for name, token_id in media_token_ids.items()}
+        inputs_m, labels_m = [], []
+        sound_embeds_idx = 0
+        for k in range(batch_size):
+            inputs_mk, labels_mk = [], []
+            pos = 0
+            while pos < len(labels[k]):
+                if input_ids[k][pos].item() in media_tokens:
+                    name = media_tokens[input_ids[k][pos].item()]
+                    if input_ids[k][pos].item() == media_token_ids["sound"]:
+                        if self.config.interleaved_vis_aud_in_video and sound_embeds_idx < video_sound_embeds_idx:
+                            media_embeds[name].popleft()
+                            sound_embeds_idx += 1
+                            pos += 1
+                            continue
+                        sound_embeds_idx += 1
+                    end = pos + 1
+                    current_input = media_embeds[name].popleft()
+                    current_label = torch.full(
+                        [current_input.shape[0]], IGNORE_INDEX, device=labels[k].device, dtype=labels[k].dtype
+                    )
+                else:
+                    end = pos
+                    while end < len(labels[k]) and input_ids[k][end].item() not in media_tokens:
+                        end += 1
+                    current_input = text_embeds[k][pos:end]
+                    current_label = labels[k][pos:end]
+                inputs_mk.append(current_input)
+                labels_mk.append(current_label)
+                pos = end
+            inputs_m.append(torch.cat(inputs_mk, dim=0))
+            labels_m.append(torch.cat(labels_mk, dim=0))
+        inputs, labels = inputs_m, labels_m
+        for name in media_embeds:
+            if media_embeds[name]:
+                raise ValueError(f"Not all {name} embeddings are consumed! Still {len(media_embeds[name])} left.")
+        inputs, labels = self.__truncate_sequence(inputs, labels)
+        return self.__batchify_sequence(inputs, labels)
+
+    def __embed_media_tokens(
+        self, media: dict[str, list[torch.Tensor]], media_config: dict[str, dict[str, Any]], mm_info
+    ):
+        embeds = defaultdict(deque)
+        embed_fn = {
+            "image": self._embed_image_features,
+            "video": self._embed_video_features,
+            "sound": self._embed_sound_features,
+        }
+        for name in media:
+            if name == "sound":
+                sound_media = media.get(name, [])
+                if len(sound_media) == 0:
+                    continue
+                if not all(
+                    hasattr(sound, "input_features") or (isinstance(sound, dict) and "input_features" in sound)
+                    for sound in sound_media
+                ):
+                    raise ValueError("Expected pre-extracted sound features in `media['sound']`.")
+            if len(media[name]) > 0:
+                embeds[name] = deque(embed_fn[name](media[name], media_config[name], mm_info))
+        return embeds
+
+    def __truncate_sequence(self, inputs: list[torch.Tensor], labels: list[torch.Tensor]):
+        model_max_length = getattr(self.config, "model_max_length", None)
+        if model_max_length is None:
+            model_max_length = getattr(self.llm.config, "model_max_length", 2048)
+        model_max_length = int(model_max_length)
+        if self.training and any(len(current_input) > model_max_length for current_input in inputs):
+            warnings.warn(f"Truncating sequences to `model_max_length` ({model_max_length}).")
+            inputs = [current_input[:model_max_length] for current_input in inputs]
+            labels = [label[:model_max_length] for label in labels]
+        return inputs, labels
+
+    def __batchify_sequence(self, inputs: list[torch.Tensor], labels: list[torch.Tensor]):
+        batch_size = len(inputs)
+        device = inputs[0].device
+        hidden_size = inputs[0].shape[1]
+        max_length = max(inputs[k].shape[0] for k in range(batch_size))
+        attention_mask = torch.ones((batch_size, max_length), dtype=torch.bool, device=device)
+        padding_side = getattr(self.config, "padding_side", "left")
+        inputs_p, labels_p = [], []
+        for k in range(batch_size):
+            pad_size = max_length - inputs[k].shape[0]
+            input_padding = torch.zeros((pad_size, hidden_size), dtype=inputs[k].dtype, device=device)
+            label_padding = torch.full((pad_size,), IGNORE_INDEX, dtype=labels[k].dtype, device=device)
+            if padding_side == "right":
+                attention_mask[k, inputs[k].shape[0] :] = False
+                input_padding = torch.cat([inputs[k], input_padding], dim=0)
+                label_padding = torch.cat([labels[k], label_padding], dim=0)
+            else:
+                labels[k] = labels[k].to(device)
+                attention_mask[k, : -inputs[k].shape[0]] = False
+                input_padding = torch.cat([input_padding, inputs[k]], dim=0)
+                label_padding = torch.cat([label_padding, labels[k]], dim=0)
+            inputs_p.append(input_padding)
+            labels_p.append(label_padding)
+        inputs = torch.stack(inputs_p, dim=0)
+        labels = torch.stack(labels_p, dim=0)
+        return inputs, labels, attention_mask
+
+    def repack_multimodal_data(self, inputs_embeds, attention_mask, position_ids, labels):
+        device = inputs_embeds.device
+        batch_size = inputs_embeds.shape[0]
+        seqlens = [attention_mask[k].sum().item() for k in range(batch_size)]
+        inputs_embeds_p = [inputs_embeds[k][attention_mask[k]] for k in range(batch_size)]
+        attention_mask_p = [torch.ones(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
+        position_ids_p = [torch.arange(seqlens[k], dtype=torch.int, device=device) for k in range(batch_size)]
+        labels_p = [labels[k][attention_mask[k]] for k in range(batch_size)]
+        inputs_embeds_p.append(torch.zeros(1, inputs_embeds.shape[-1], dtype=inputs_embeds.dtype, device=device))
+        attention_mask_p.append(torch.tensor([0], dtype=torch.int, device=device))
+        position_ids_p.append(torch.tensor([0], dtype=torch.int, device=device))
+        labels_p.append(torch.tensor([IGNORE_INDEX], dtype=torch.int, device=device))
+        for label in labels_p:
+            label[0] = IGNORE_INDEX
+        inputs_embeds_p = torch.cat(inputs_embeds_p, dim=0).unsqueeze(0)
+        attention_mask_p = torch.cat(attention_mask_p, dim=0).unsqueeze(0)
+        position_ids_p = torch.cat(position_ids_p, dim=0).unsqueeze(0)
+        labels_p = torch.cat(labels_p, dim=0).unsqueeze(0)
+        if hasattr(self, "pad_to_multiple_of"):
+            batch_size, max_length, cur_length = labels_p.shape[0], labels_p.shape[1], labels_p.shape[1]
+            hidden_size = inputs_embeds_p.shape[-1]
+            if max_length % self.pad_to_multiple_of != 0:
+                max_length = ((max_length // self.pad_to_multiple_of) + 1) * self.pad_to_multiple_of
+                difference = max_length - cur_length
+                inputs_embeds_p = torch.cat(
+                    (
+                        inputs_embeds_p,
+                        torch.full((batch_size, difference, hidden_size), self.llm.pad_token_id).to(inputs_embeds_p),
+                    ),
+                    dim=1,
+                )
+                labels_p = torch.cat(
+                    (labels_p, torch.full((batch_size, difference), IGNORE_INDEX).to(labels_p)), dim=1
+                )
+                attention_mask_p = torch.cat(
+                    (attention_mask_p, torch.zeros((batch_size, difference), dtype=torch.bool).to(attention_mask_p)),
+                    dim=1,
+                )
+                position_ids_p = torch.cat(
+                    (position_ids_p, torch.full((batch_size, difference), -1).to(position_ids_p)), dim=1
+                )
+        return inputs_embeds_p, attention_mask_p, position_ids_p, labels_p
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        media: dict[str, list[torch.Tensor]] | None = None,
+        media_config: list | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        packing: bool = True,
+        force_packing: bool = False,
+        seqlens_in_batch: torch.LongTensor | None = None,
+        dpo_forward: bool = False,
+        **kwargs,
+    ) -> tuple | CausalLMOutputWithPast:
+        _ = (pixel_values, seqlens_in_batch)
+        self._freeze_untrained_modules()
+        if media_config is None:
+            media_config = defaultdict(dict)
+        if inputs_embeds is None:
+            if media is None:
+                if input_ids is None:
+                    raise ValueError("Either `inputs_embeds` or `input_ids` must be provided.")
+                inputs_embeds = self.llm_model_embed_tokens(input_ids)
+                if attention_mask is None:
+                    attention_mask = torch.ones_like(input_ids, dtype=torch.bool)
+            else:
+                inputs_embeds, labels, attention_mask = self._embed(
+                    input_ids, media, media_config, labels, attention_mask
+                )
+        if force_packing or (packing and self.training and not dpo_forward):
+            inputs_embeds, attention_mask, position_ids, labels = self.repack_multimodal_data(
+                inputs_embeds, attention_mask, position_ids, labels
+            )
+        llm_param = next(self.llm.parameters(), None)
+        if llm_param is not None and inputs_embeds.dtype != llm_param.dtype:
+            inputs_embeds = inputs_embeds.to(llm_param.dtype)
+        outputs = self.llm(
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            labels=labels,
+            **kwargs,
+        )
+        if dpo_forward:
+            return outputs.logits, labels
+        return outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        media=None,
+        media_config=None,
+        attention_mask=None,
+        cache_position=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        is_first_iteration = bool(kwargs.get("is_first_iteration", False))
+        is_first_step = (
+            is_first_iteration or past_key_values is None or (cache_position is not None and cache_position[0] == 0)
+        )
+        if is_first_step and inputs_embeds is None and media is not None:
+            if media_config is None:
+                media_config = defaultdict(dict)
+            inputs_embeds, _, attention_mask = self._embed(input_ids, media, media_config, None, attention_mask)
+        model_inputs = self.llm.prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        if is_first_step and inputs_embeds is not None:
+            model_inputs["inputs_embeds"] = inputs_embeds
+            model_inputs["attention_mask"] = attention_mask
+            model_inputs["input_ids"] = None
+            seq_len = attention_mask.shape[-1]
+            cache_pos = model_inputs.get("cache_position")
+            if cache_pos is None or cache_pos.shape[0] != seq_len:
+                model_inputs["cache_position"] = torch.arange(seq_len, device=inputs_embeds.device)
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 0)
+            model_inputs["position_ids"] = position_ids
+        model_inputs["media"] = None
+        model_inputs["media_config"] = None
+        return model_inputs
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs: ModelOutput,
+        model_kwargs: dict[str, Any],
+        is_encoder_decoder: bool = False,
+        num_new_tokens: int = 1,
+    ) -> dict[str, Any]:
+        attention_mask = model_kwargs.get("attention_mask")
+        logits = getattr(outputs, "logits", None)
+        if (
+            model_kwargs.get("media") is not None
+            and attention_mask is not None
+            and logits is not None
+            and attention_mask.shape[-1] != logits.shape[-2]
+        ):
+            batch_size = attention_mask.shape[0]
+            seq_len = logits.shape[-2]
+            model_kwargs["attention_mask"] = attention_mask.new_ones((batch_size, seq_len))
+            model_kwargs["cache_position"] = torch.arange(seq_len, device=attention_mask.device)
+            if model_kwargs.get("position_ids") is not None:
+                position_ids = model_kwargs["attention_mask"].long().cumsum(-1) - 1
+                position_ids.masked_fill_(model_kwargs["attention_mask"] == 0, 0)
+                model_kwargs["position_ids"] = position_ids
+            model_kwargs["media"] = None
+            model_kwargs["media_config"] = None
+        return super()._update_model_kwargs_for_generation(
+            outputs, model_kwargs, is_encoder_decoder=is_encoder_decoder, num_new_tokens=num_new_tokens
+        )
+
+
+__all__ = [
+    "AudioVisualFlamingoConfig",
+    "AudioVisualFlamingoForConditionalGeneration",
+    "AudioVisualFlamingoPretrainedModel",
+]
diff --git a/src/transformers/models/audiovisualflamingo/processing_audiovisualflamingo.py b/src/transformers/models/audiovisualflamingo/processing_audiovisualflamingo.py
new file mode 100755
index 000000000000..a4ba5f6c95d4
--- /dev/null
+++ b/src/transformers/models/audiovisualflamingo/processing_audiovisualflamingo.py
@@ -0,0 +1,1042 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+from collections import defaultdict
+from types import SimpleNamespace
+
+import numpy as np
+import PIL.Image
+import torch
+
+from transformers import WhisperFeatureExtractor
+from transformers.audio_utils import load_audio, make_list_of_audio
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import load_image
+from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from transformers.video_utils import load_video
+
+from .configuration_audiovisualflamingo import AudioVisualFlamingoConfig
+
+
+MEDIA_TOKENS = AudioVisualFlamingoConfig.media_tokens
+MM_BOS_EOS_TOKENS = AudioVisualFlamingoConfig.mm_bos_eos_tokens
+
+
+_AUDIOVISUALFLAMINGO_CHAT_TEMPLATE = (
+    "{% if messages[0]['role'] != 'system' %}"
+    "{{ '<|im_start|>system\\nYou are a helpful assistant<|im_end|>\\n' }}"
+    "{% endif %}"
+    "{% for message in messages if message['content'] is not none %}"
+    "{{ '<|im_start|>' + message['role'] + '\\n' }}"
+    "{% if message['content'] is string %}"
+    "{{ message['content'] }}"
+    "{% else %}"
+    "{% for c in message['content'] %}"
+    "{% if c.get('type') == 'text' %}{{ c['text'] }}"
+    "{% elif c.get('type') == 'image' %}{{ '<image>' }}"
+    "{% elif c.get('type') == 'video' %}{{ '<vila/video>' }}"
+    "{% elif c.get('type') in ['audio', 'sound'] %}{{ '<sound>' }}"
+    "{% endif %}"
+    "{% endfor %}"
+    "{% endif %}"
+    "{{ '<|im_end|>\\n' }}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}"
+)
+
+_VIDEO_METADATA_KEYS = {"fps", "frames_indices", "total_num_frames", "video_path", "video_url"}
+
+
+def _looks_like_video_metadata(meta) -> bool:
+    if meta is None:
+        return False
+    if isinstance(meta, dict):
+        return bool(_VIDEO_METADATA_KEYS & set(meta.keys()))
+    return any(hasattr(meta, key) for key in _VIDEO_METADATA_KEYS)
+
+
+def _is_packed_media_item(item) -> bool:
+    return isinstance(item, (tuple, list)) and len(item) == 2 and _looks_like_video_metadata(item[1])
+
+
+def _is_audio_like(value) -> bool:
+    return isinstance(value, (str, np.ndarray, torch.Tensor))
+
+
+def _merge_media_config(target: defaultdict, source: defaultdict) -> None:
+    for modality, config in source.items():
+        for key, value in config.items():
+            if isinstance(value, list):
+                target[modality].setdefault(key, []).extend(value)
+            elif key not in target[modality]:
+                target[modality][key] = value
+            elif target[modality][key] != value:
+                raise ValueError(
+                    f"Conflicting `{modality}` media config for key `{key}`: {target[modality][key]!r} != {value!r}"
+                )
+
+
+def _expand2square(pil_img, background_color):
+    """Expand a non-square PIL image with padding to make it square."""
+    width, height = pil_img.size
+    if pil_img.mode == "L":
+        background_color = background_color[0]
+    if width == height:
+        return pil_img
+    if width > height:
+        result = PIL.Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    result = PIL.Image.new(pil_img.mode, (height, height), background_color)
+    result.paste(pil_img, ((height - width) // 2, 0))
+    return result
+
+
+def _find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    """Find the closest aspect ratio from candidate ratios."""
+    best_ratio_diff = float("inf")
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff and area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+            best_ratio = ratio
+    return best_ratio
+
+
+def _dynamic_s2_preprocess(image, s2_scales: list[int] | None = None, max_num=12, image_size=384):
+    """Dynamically preprocess image using multi-scale S2 tiling."""
+    if s2_scales is None:
+        s2_scales = [384, 768, 1152]
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+    min_num = (s2_scales[-1] // s2_scales[0]) ** 2
+
+    processed_images = []
+
+    for scale in s2_scales[:-1]:
+        target_width = image_size * (scale // s2_scales[0])
+        target_height = image_size * (scale // s2_scales[0])
+        blocks = (scale // s2_scales[0]) ** 2
+        resized_img = image.resize((target_width, target_height))
+        for i in range(blocks):
+            box = (
+                (i % (target_width // image_size)) * image_size,
+                (i // (target_width // image_size)) * image_size,
+                ((i % (target_width // image_size)) + 1) * image_size,
+                ((i // (target_width // image_size)) + 1) * image_size,
+            )
+            processed_images.append(resized_img.crop(box))
+
+    target_ratios = {
+        (i, j)
+        for n in range(min_num, max_num + 1)
+        for i in range(1, n + 1)
+        for j in range(1, n + 1)
+        if i * j <= max_num and i * j >= min_num
+    }
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    target_aspect_ratio = _find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    resized_img = image.resize((target_width, target_height))
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size,
+        )
+        processed_images.append(resized_img.crop(box))
+
+    return processed_images, (target_aspect_ratio[1], target_aspect_ratio[0])
+
+
+def _process_image(image_input, data_args, enable_dynamic_s2=False):
+    processor = data_args.image_processor
+    image = load_image(image_input) if isinstance(image_input, str) else image_input
+    image = image.convert("RGB")
+    crop_size = getattr(data_args.image_processor, "crop_size", None)
+    if crop_size is None:
+        crop_size = getattr(data_args.image_processor, "size", None)
+    if crop_size is None:
+        raise ValueError("AudioVisualFlamingo image processor must define either `crop_size` or `size`.")
+    if "dynamic_s2" in data_args.image_aspect_ratio and enable_dynamic_s2:
+        assert crop_size["height"] == crop_size["width"]
+        images, block_size = _dynamic_s2_preprocess(
+            image, s2_scales=data_args.s2_scales, max_num=data_args.max_tiles, image_size=crop_size["height"]
+        )
+        images = [processor.preprocess(image, return_tensors="pt")["pixel_values"][0] for image in images]
+        return torch.stack(images), block_size
+
+    if data_args.image_aspect_ratio == "resize":
+        image = image.resize((crop_size["width"], crop_size["height"]))
+    elif data_args.image_aspect_ratio == "pad":
+        image = _expand2square(image, tuple(int(x * 255) for x in processor.image_mean))
+        image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
+    else:
+        image = processor.preprocess(image, return_tensors="pt")["pixel_values"][0]
+    return image
+
+
+def _process_images(images, image_processor, model_cfg):
+    """Process a batch of images using the model image processor."""
+    model_cfg.image_processor = image_processor
+    new_images = [_process_image(image, model_cfg) for image in images]
+
+    if not all(x.shape == new_images[0].shape for x in new_images):
+        raise ValueError("The shape of images in new_images is different!")
+    if len(new_images[0].shape) == 4:
+        return torch.cat(new_images, dim=0)
+    if len(new_images[0].shape) == 3:
+        return torch.stack(new_images, dim=0)
+    raise ValueError(f"new_images rank does not equal to 4, rank: {len(new_images[0].shape)}")
+
+
+def _add_mm_bos_eos_tokens(text: str) -> str:
+    for k in ("image", "video", "sound"):
+        _bos, _eos = MM_BOS_EOS_TOKENS[k]
+        _media_token = MEDIA_TOKENS[k]
+        if _media_token in text:
+            text_parts = text.split(_media_token)
+            text_parts[0] = text_parts[0] + _bos
+            text_parts[-1] = _eos + text_parts[-1]
+            text = _media_token.join(text_parts)
+    return text
+
+
+def _pad_or_trim_audio(audio: np.ndarray, length: int) -> np.ndarray:
+    current_length = int(audio.shape[0])
+    if current_length > length:
+        return audio[:length]
+    if current_length < length:
+        return np.pad(audio, (0, length - current_length), mode="constant")
+    return audio
+
+
+def _resolve_sound_feature_size(config) -> int:
+    audio_config = getattr(config, "audio_config", None)
+    if isinstance(audio_config, dict):
+        feature_size = audio_config.get("num_mel_bins")
+    else:
+        feature_size = getattr(audio_config, "num_mel_bins", None)
+    if feature_size is None:
+        feature_size = 128
+    return int(feature_size)
+
+
+def _resolve_target_audio_samples(sound: np.ndarray, audio_info, config) -> int:
+    sampling_rate = config.audio_sampling_rate
+    audio_n_samples = sound.shape[0]
+    if isinstance(audio_info, dict) and audio_info.get("new_audio_n_samples") is not None:
+        return int(audio_info["new_audio_n_samples"])
+
+    target = int(np.ceil(audio_n_samples / (sampling_rate * 30)) * (sampling_rate * 30))
+    if config.audio_chunk_length and not (
+        isinstance(config.audio_chunk_length, str) and "max" in config.audio_chunk_length
+    ):
+        target = min(target, int(config.audio_chunk_length) * sampling_rate)
+    return int(target)
+
+
+def _extract_sound_features(
+    sound_media: list,
+    audio_infos: list | None,
+    config,
+    feature_extractor: WhisperFeatureExtractor | None = None,
+) -> list:
+    if audio_infos is None:
+        audio_infos = []
+    if audio_infos and len(audio_infos) != len(sound_media):
+        raise ValueError("The number of audio info does not match the number of audio samples.")
+
+    feature_size = _resolve_sound_feature_size(config)
+    sampling_rate = config.audio_sampling_rate
+    hop_length = config.audio_hop_length
+    if feature_extractor is not None:
+        feature_size = getattr(feature_extractor, "feature_size", feature_size)
+        sampling_rate = getattr(feature_extractor, "sampling_rate", sampling_rate)
+        hop_length = getattr(feature_extractor, "hop_length", hop_length)
+    new_media = []
+
+    for idx, sound in enumerate(sound_media):
+        audio_info = audio_infos[idx] if idx < len(audio_infos) else None
+        if isinstance(sound, dict) and "input_features" in sound:
+            stft_features = sound
+        else:
+            if isinstance(sound, torch.Tensor):
+                audio = sound.detach().cpu().float().numpy()
+            else:
+                audio = np.asarray(sound, dtype=np.float32)
+            if audio.ndim != 1:
+                audio = np.squeeze(audio)
+            if audio.ndim != 1:
+                raise ValueError(f"Expected mono waveform for sound input, got shape {audio.shape}.")
+
+            cur_audio_n_samples = _resolve_target_audio_samples(audio, audio_info, config)
+            cur_audio_duration = cur_audio_n_samples // sampling_rate
+            whisper_feature_extractor = feature_extractor
+            if (
+                whisper_feature_extractor is None
+                or getattr(whisper_feature_extractor, "chunk_length", None) != cur_audio_duration
+            ):
+                whisper_feature_extractor = WhisperFeatureExtractor(
+                    feature_size=feature_size,
+                    chunk_length=cur_audio_duration,
+                    sampling_rate=sampling_rate,
+                    hop_length=hop_length,
+                )
+            audio = _pad_or_trim_audio(audio, length=cur_audio_n_samples)
+            stft_features = whisper_feature_extractor(
+                audio,
+                sampling_rate=sampling_rate,
+                return_attention_mask=True,
+                padding="max_length",
+                return_tensors="pt",
+            )
+
+            if isinstance(audio_info, dict):
+                audio_info["new_audio_chunk_length"] = cur_audio_duration
+                audio_info["new_audio_n_samples"] = cur_audio_n_samples
+                audio_info["audio_end_sample_sec"] = audio_info["audio_start_sec"] + cur_audio_duration
+                audio_info["new_audio_n_stft_frames"] = stft_features["input_features"].shape[-1]
+
+        if isinstance(audio_info, dict) and "new_audio_n_stft_frames" not in audio_info:
+            audio_info["new_audio_n_stft_frames"] = stft_features["input_features"].shape[-1]
+        new_media.append(stft_features)
+
+    return new_media
+
+
+def _load_audio_track_with_pyav(audio_path: str, sampling_rate: int) -> np.ndarray:
+    import av
+
+    with av.open(audio_path) as container:
+        if not container.streams.audio:
+            raise ValueError(f"No audio stream found in media container: {audio_path}")
+
+        resampler = av.audio.resampler.AudioResampler(format="fltp", layout="mono", rate=sampling_rate)
+        chunks = []
+
+        for frame in container.decode(audio=0):
+            resampled_frames = resampler.resample(frame)
+            if resampled_frames is None:
+                continue
+            if not isinstance(resampled_frames, list):
+                resampled_frames = [resampled_frames]
+            for resampled_frame in resampled_frames:
+                chunks.append(resampled_frame.to_ndarray())
+
+        flushed_frames = resampler.resample(None)
+        if flushed_frames is not None:
+            if not isinstance(flushed_frames, list):
+                flushed_frames = [flushed_frames]
+            for flushed_frame in flushed_frames:
+                chunks.append(flushed_frame.to_ndarray())
+
+    if not chunks:
+        raise ValueError(f"No audio samples could be decoded from media container: {audio_path}")
+
+    return np.concatenate(chunks, axis=-1)[0].astype(np.float32, copy=False)
+
+
+def _load_audio_hf_with_info(audio_input, config) -> tuple[np.ndarray, dict[str, float | int]]:
+    sampling_rate = config.audio_sampling_rate
+    audio_chunk_length = config.audio_chunk_length
+    load_max_audio = isinstance(audio_chunk_length, str) and "max" in audio_chunk_length
+    if load_max_audio:
+        if "_" in audio_chunk_length:
+            max_audio_chunk_length = int(audio_chunk_length.split("_", maxsplit=1)[1])
+            audio_n_samples_limit = max_audio_chunk_length * sampling_rate
+        else:
+            audio_n_samples_limit = None
+    else:
+        try:
+            audio_n_samples_limit = int(audio_chunk_length) * sampling_rate
+        except Exception as error:
+            raise ValueError(f"Error setting audio_chunk_length: {error}") from error
+
+    def _resolve_window(ori_n_samples: int) -> tuple[int, int]:
+        if audio_n_samples_limit is None:
+            target_samples = ori_n_samples
+        else:
+            target_samples = min(audio_n_samples_limit, ori_n_samples)
+
+        audio_start_sample_id = 0
+        if (
+            bool(getattr(config, "random_audio_sample", False))
+            and not load_max_audio
+            and ori_n_samples > target_samples
+        ):
+            audio_start_sample_id = random.randint(0, ori_n_samples - target_samples)
+        audio_end_sample_id = audio_start_sample_id + target_samples
+        return audio_start_sample_id, audio_end_sample_id
+
+    if isinstance(audio_input, torch.Tensor):
+        speech_data = audio_input.detach().cpu().float().numpy()
+    elif isinstance(audio_input, np.ndarray):
+        speech_data = audio_input
+    elif isinstance(audio_input, str):
+        try:
+            speech_data = load_audio(audio_input, sampling_rate=sampling_rate)
+        except Exception as audio_error:
+            try:
+                speech_data = _load_audio_track_with_pyav(audio_input, sampling_rate)
+            except Exception:
+                raise audio_error
+    else:
+        raise TypeError(
+            "AudioVisualFlamingo audio inputs must be a path/URL, a numpy array, or a torch tensor. "
+            f"Got {type(audio_input)!r}."
+        )
+
+    speech_data = np.asarray(speech_data, dtype=np.float32)
+    if speech_data.ndim != 1:
+        speech_data = np.squeeze(speech_data)
+    if speech_data.ndim != 1:
+        raise ValueError(f"Expected mono waveform for sound input, got shape {speech_data.shape}.")
+
+    ori_n_samples = int(speech_data.shape[0])
+    audio_start_sample_id, audio_end_sample_id = _resolve_window(ori_n_samples)
+    ori_audio_duration = ori_n_samples / sampling_rate
+    speech_data = speech_data[audio_start_sample_id:audio_end_sample_id]
+
+    audio_n_samples = int(np.ceil(speech_data.shape[0] / (sampling_rate * 30)) * (sampling_rate * 30))
+    speech_data = _pad_or_trim_audio(speech_data, length=audio_n_samples)
+
+    audio_info = {
+        "new_audio_chunk_length": int(audio_n_samples // sampling_rate),
+        "new_audio_n_samples": audio_n_samples,
+        "ori_audio_duration": ori_audio_duration,
+        "audio_start_sec": audio_start_sample_id / sampling_rate,
+        "audio_end_sample_sec": audio_end_sample_id / sampling_rate,
+    }
+    return speech_data, audio_info
+
+
+def _coerce_video_frames_to_pil(video_frames) -> list[PIL.Image.Image]:
+    if isinstance(video_frames, np.ndarray):
+        if video_frames.ndim == 3:
+            video_frames = np.expand_dims(video_frames, axis=0)
+        if video_frames.ndim != 4:
+            raise TypeError(f"Expected video array with 4 dimensions, got shape {video_frames.shape}.")
+        return [PIL.Image.fromarray(frame).convert("RGB") for frame in video_frames]
+
+    if isinstance(video_frames, (list, tuple)):
+        output_frames = []
+        for frame in video_frames:
+            if isinstance(frame, PIL.Image.Image):
+                output_frames.append(frame.convert("RGB"))
+            else:
+                output_frames.append(PIL.Image.fromarray(np.asarray(frame)).convert("RGB"))
+        return output_frames
+
+    raise TypeError(f"Unsupported video payload type for frame conversion: {type(video_frames)!r}")
+
+
+def _extract_video_hf(
+    video_input, config
+) -> (
+    tuple[list[PIL.Image.Image], dict[str, object]]
+    | tuple[list[PIL.Image.Image], np.ndarray | None, dict[str, object]]
+):
+    num_frames = config.num_video_frames
+
+    def _unpack_video_item(video_item):
+        frames_obj = video_item
+        item_metadata = None
+
+        for _ in range(4):
+            if isinstance(frames_obj, np.ndarray) and frames_obj.ndim == 0:
+                frames_obj = frames_obj.item()
+                continue
+
+            if (
+                isinstance(frames_obj, (tuple, list))
+                and len(frames_obj) == 2
+                and _looks_like_video_metadata(frames_obj[1])
+            ):
+                item_metadata = frames_obj[1]
+                frames_obj = frames_obj[0]
+                continue
+
+            break
+
+        return frames_obj, item_metadata
+
+    def _resolve_video_source(
+        video_item,
+        video_metadata,
+    ) -> str | None:
+        if isinstance(video_item, str):
+            return video_item
+
+        metadata_candidates = []
+        if video_metadata is not None:
+            metadata_candidates.append(video_metadata)
+        _, packed_metadata = _unpack_video_item(video_item)
+        if packed_metadata is not None:
+            metadata_candidates.append(packed_metadata)
+
+        for metadata_obj in metadata_candidates:
+            if isinstance(metadata_obj, dict):
+                video_path = metadata_obj.get("video_path")
+                video_url = metadata_obj.get("video_url")
+            else:
+                video_path = getattr(metadata_obj, "video_path", None)
+                video_url = getattr(metadata_obj, "video_url", None)
+
+            if isinstance(video_path, str) and video_path:
+                return video_path
+
+            if isinstance(video_url, str) and video_url:
+                if video_url.startswith("file://"):
+                    from urllib.parse import urlparse
+                    from urllib.request import url2pathname
+
+                    parsed = urlparse(video_url)
+                    return url2pathname((parsed.netloc or "") + (parsed.path or ""))
+                return video_url
+
+        return None
+
+    def _meta_get(meta, key, default=None):
+        if isinstance(meta, dict):
+            return meta.get(key, default)
+        return getattr(meta, key, default)
+
+    unpacked_frames, unpacked_metadata = _unpack_video_item(video_input)
+    if isinstance(unpacked_frames, str):
+        frames_array, metadata = load_video(unpacked_frames, num_frames=num_frames)
+    else:
+        frames_array = unpacked_frames
+        metadata = unpacked_metadata
+
+    if frames_array is None:
+        raise TypeError(
+            "Unsupported video payload for AudioVisualFlamingo video extraction: "
+            f"video_input_type={type(video_input)!r}, "
+            f"unpacked_type={type(unpacked_frames)!r}, "
+            f"unpacked_metadata_type={type(unpacked_metadata)!r}, "
+            f"unpacked_repr={repr(unpacked_frames)[:200]}"
+        )
+    output_frames = _coerce_video_frames_to_pil(frames_array)
+
+    fps = float(_meta_get(metadata, "fps", None) or 1.0)
+    sampled_frame_indices = _meta_get(metadata, "frames_indices", None) if metadata is not None else None
+    if sampled_frame_indices is None:
+        frame_indices = list(range(len(output_frames)))
+    else:
+        frame_indices = list(np.asarray(sampled_frame_indices).tolist())
+
+    metadata_total_frames = _meta_get(metadata, "total_num_frames", None) if metadata is not None else None
+    frame_count = int(frame_indices[-1] + 1) if frame_indices else int(metadata_total_frames or len(output_frames))
+    video_duration = _meta_get(metadata, "duration", None) if metadata is not None else None
+    if video_duration is None:
+        video_duration = float(frame_count / fps if fps > 0 else len(output_frames))
+    else:
+        video_duration = float(video_duration)
+    # Keep np.float64 timestamps for parity with legacy timing dtype used by the original AudioVisualFlamingo path.
+    output_frame_times = list(np.asarray(frame_indices, dtype=np.float64) / np.float64(fps if fps > 0 else 1.0))
+
+    video_source = _resolve_video_source(video_input, metadata)
+
+    aud_feature = None
+    audio_info = None
+    if config.load_audio_in_video and video_source is not None:
+        try:
+            aud_feature, audio_info = _load_audio_hf_with_info(video_source, config)
+        except Exception:
+            aud_feature, audio_info = None, None
+
+    video_info = {
+        "video_path": video_source,
+        "has_audio": aud_feature is not None,
+        "video_duration": video_duration,
+        "audio_info": audio_info,
+        "video_frame_times": output_frame_times,
+    }
+    if audio_info is not None and video_source is not None:
+        audio_info["video_path"] = video_source
+
+    if config.load_audio_in_video and config.interleaved_vis_aud_in_video and aud_feature is not None:
+        segment_duration = config.interleaved_video_segment_duration
+        if segment_duration == -1:
+            raise ValueError("video_segment_duration is not set")
+
+        segment_vis_indices_list = []
+        segment_aud_indices_list = []
+        segment_counts = int(np.ceil(video_duration / segment_duration))
+
+        audio_start_sec = audio_info["audio_start_sec"]
+        audio_end_sec = audio_info["audio_end_sample_sec"]
+        stft_frames_per_second = config.audio_sampling_rate // config.audio_hop_length
+
+        idx = 0
+        aud_sample_start_idx = 0
+        for i in range(segment_counts):
+            end_frame = min((i + 1) * segment_duration * fps, frame_count)
+
+            segment_indices = []
+            while idx < len(frame_indices) and frame_indices[idx] < end_frame:
+                segment_indices.append(frame_indices[idx])
+                idx += 1
+            segment_vis_indices_list.append(segment_indices)
+
+            clip_start_sec = i * segment_duration
+            clip_end_sec = min(clip_start_sec + segment_duration, video_duration)
+            overlap_start = max(clip_start_sec, audio_start_sec)
+            overlap_end = min(clip_end_sec, audio_end_sec)
+            if overlap_start < overlap_end:
+                aud_sample_end_idx = round((overlap_end - audio_start_sec) * stft_frames_per_second)
+                segment_aud_indices_list.append([aud_sample_start_idx, aud_sample_end_idx])
+                aud_sample_start_idx = aud_sample_end_idx
+            else:
+                segment_aud_indices_list.append([])
+
+        new_segment_vis_indices_list = []
+        processed_frame_index = 0
+        for segment_indices in segment_vis_indices_list:
+            new_segment_vis_indices_list.append([])
+            for _ in segment_indices:
+                new_segment_vis_indices_list[-1].append(processed_frame_index)
+                processed_frame_index += 1
+
+        video_info.update(
+            {
+                "segment_vis_indices_list": new_segment_vis_indices_list,
+                "segment_aud_indices_list": segment_aud_indices_list,
+                "expected_frame_count": len(frame_indices),
+            }
+        )
+
+    if config.load_audio_in_video:
+        return output_frames, aud_feature, video_info
+    return output_frames, video_info
+
+
+class AudioVisualFlamingoProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": True,
+            "padding_side": "left",
+            "return_tensors": "pt",
+        },
+    }
+
+
+class AudioVisualFlamingoProcessor(ProcessorMixin):
+    attributes = ["image_processor", "feature_extractor", "tokenizer"]
+    valid_kwargs = [
+        "padding_side",
+        "image_aspect_ratio",
+        "s2_scales",
+        "max_tiles",
+        "num_video_frames",
+        "load_audio_in_video",
+        "interleaved_vis_aud_in_video",
+        "interleaved_video_segment_duration",
+        "mm_use_bos_eos_tokens",
+        "audio_sampling_rate",
+        "audio_chunk_length",
+        "audio_hop_length",
+    ]
+
+    def __init__(
+        self,
+        image_processor=None,
+        feature_extractor=None,
+        tokenizer=None,
+        chat_template=None,
+        padding_side="left",
+        image_aspect_ratio=None,
+        s2_scales=None,
+        max_tiles=12,
+        num_video_frames=None,
+        load_audio_in_video=True,
+        interleaved_vis_aud_in_video=True,
+        interleaved_video_segment_duration=30,
+        mm_use_bos_eos_tokens=False,
+        audio_sampling_rate=16000,
+        audio_chunk_length=120,
+        audio_hop_length=60,
+        **kwargs,
+    ):
+        if chat_template is None:
+            chat_template = _AUDIOVISUALFLAMINGO_CHAT_TEMPLATE
+        self.image_token = MEDIA_TOKENS["image"]
+        self.video_token = MEDIA_TOKENS["video"]
+        self.sound_token = MEDIA_TOKENS["sound"]
+        self.image_aspect_ratio = image_aspect_ratio
+        self.s2_scales = s2_scales
+        self.max_tiles = max_tiles
+        self.num_video_frames = num_video_frames
+        self.load_audio_in_video = load_audio_in_video
+        self.interleaved_vis_aud_in_video = interleaved_vis_aud_in_video
+        self.interleaved_video_segment_duration = interleaved_video_segment_duration
+        self.mm_use_bos_eos_tokens = mm_use_bos_eos_tokens
+        self.audio_sampling_rate = audio_sampling_rate
+        self.audio_chunk_length = audio_chunk_length
+        self.audio_hop_length = audio_hop_length
+        self.image_processor = image_processor
+        if feature_extractor is None:
+            chunk_length = audio_chunk_length if isinstance(audio_chunk_length, int) else 30
+            feature_extractor = WhisperFeatureExtractor(
+                feature_size=128,
+                chunk_length=chunk_length,
+                sampling_rate=audio_sampling_rate,
+                hop_length=audio_hop_length,
+            )
+        self.feature_extractor = feature_extractor
+        self.tokenizer = tokenizer
+        self.padding_side = padding_side
+        if tokenizer is not None:
+            self.tokenizer.padding_side = padding_side
+            self.image_token_id = self.tokenizer.convert_tokens_to_ids(self.image_token)
+            self.video_token_id = self.tokenizer.convert_tokens_to_ids(self.video_token)
+            self.sound_token_id = self.tokenizer.convert_tokens_to_ids(self.sound_token)
+            self.pad_token_id = self.tokenizer("<|endoftext|>").input_ids[0]
+            self.eos_token_id = self.tokenizer.eos_token_id
+        else:
+            self.image_token_id = 0
+            self.video_token_id = 0
+            self.sound_token_id = 0
+            self.pad_token_id = 0
+            self.eos_token_id = 0
+        super().__init__(image_processor, feature_extractor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        text=None,
+        images=None,
+        videos=None,
+        audio=None,
+        **kwargs: Unpack[AudioVisualFlamingoProcessorKwargs],
+    ) -> BatchFeature:
+        if text is None:
+            raise ValueError("`text` is required.")
+        if isinstance(text, str):
+            text = [text]
+        elif not (isinstance(text, (list, tuple)) and (len(text) == 0 or isinstance(text[0], str))):
+            raise ValueError("`text` must be a string or a list/tuple of strings.")
+        else:
+            text = list(text)
+
+        processor_kwargs = {name: kwargs.pop(name) for name in self.valid_kwargs if name in kwargs}
+        output_kwargs = self._merge_kwargs(
+            AudioVisualFlamingoProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs if self.tokenizer is not None else None,
+            **kwargs,
+        )
+        runtime_config = self._get_runtime_config(output_kwargs, **processor_kwargs)
+        return self._call_native(
+            text=text,
+            images=images,
+            videos=videos,
+            audio=audio,
+            runtime_config=runtime_config,
+            text_kwargs=output_kwargs["text_kwargs"],
+        )
+
+    def _get_runtime_config(self, output_kwargs: dict[str, dict], **overrides) -> SimpleNamespace:
+        runtime_kwargs = {
+            "audio_chunk_length": self.audio_chunk_length,
+            "audio_hop_length": self.audio_hop_length,
+            "audio_sampling_rate": self.audio_sampling_rate,
+            "feature_extractor": self.feature_extractor,
+            "image_aspect_ratio": self.image_aspect_ratio,
+            "image_processor": self.image_processor,
+            "interleaved_video_segment_duration": self.interleaved_video_segment_duration,
+            "interleaved_vis_aud_in_video": self.interleaved_vis_aud_in_video,
+            "load_audio_in_video": self.load_audio_in_video,
+            "max_tiles": self.max_tiles,
+            "mm_use_bos_eos_tokens": self.mm_use_bos_eos_tokens,
+            "num_video_frames": self.num_video_frames,
+            "padding_side": self.padding_side,
+            "random_audio_sample": getattr(self, "random_audio_sample", False),
+            "s2_scales": self.s2_scales,
+        }
+        runtime_kwargs.update(
+            {
+                "audio_chunk_length": output_kwargs["audio_kwargs"].get(
+                    "chunk_length", runtime_kwargs["audio_chunk_length"]
+                ),
+                "audio_hop_length": output_kwargs["audio_kwargs"].get(
+                    "hop_length", runtime_kwargs["audio_hop_length"]
+                ),
+                "audio_sampling_rate": output_kwargs["audio_kwargs"].get(
+                    "sampling_rate", runtime_kwargs["audio_sampling_rate"]
+                ),
+                "num_video_frames": output_kwargs["videos_kwargs"].get(
+                    "num_frames", runtime_kwargs["num_video_frames"]
+                ),
+                "padding_side": output_kwargs["text_kwargs"].get("padding_side", runtime_kwargs["padding_side"]),
+            }
+        )
+        runtime_kwargs.update(overrides)
+        if isinstance(runtime_kwargs["s2_scales"], str):
+            runtime_kwargs["s2_scales"] = [int(scale) for scale in runtime_kwargs["s2_scales"].split(",")]
+        return SimpleNamespace(**runtime_kwargs)
+
+    def _normalize_nested_media(self, values, batch_size: int) -> list[list]:
+        if values is None:
+            return [[] for _ in range(batch_size)]
+
+        if batch_size == 1 and _is_packed_media_item(values):
+            return [[values]]
+
+        if batch_size == 1 and (
+            not isinstance(values, (list, tuple)) or (values and not isinstance(values[0], (list, tuple)))
+        ):
+            if isinstance(values, (list, tuple)):
+                return [list(values)]
+            return [[values]]
+
+        if not isinstance(values, (list, tuple)) or len(values) != batch_size:
+            raise ValueError(f"Expected batched media list with length {batch_size}, got {type(values)}")
+
+        normalized = []
+        for item in values:
+            if item is None:
+                normalized.append([])
+            elif _is_packed_media_item(item):
+                normalized.append([item])
+            elif isinstance(item, (list, tuple)):
+                normalized.append(list(item))
+            else:
+                normalized.append([item])
+        return normalized
+
+    def _normalize_audio_sample(self, sample_audio) -> list:
+        if sample_audio is None:
+            return []
+        if _is_audio_like(sample_audio):
+            return [sample_audio]
+        if isinstance(sample_audio, (list, tuple)):
+            if not sample_audio:
+                return []
+            if all(_is_audio_like(item) for item in sample_audio):
+                if all(isinstance(item, (np.ndarray, torch.Tensor)) for item in sample_audio):
+                    return list(make_list_of_audio(list(sample_audio)))
+                return list(sample_audio)
+        raise ValueError(f"Unsupported audio sample type: {type(sample_audio)!r}")
+
+    def _normalize_audio_batches(self, audio, prompts: list[str]) -> list[list]:
+        batch_size = len(prompts)
+        if audio is None:
+            return [[] for _ in range(batch_size)]
+
+        if batch_size == 1:
+            return [self._normalize_audio_sample(audio)]
+
+        if (
+            isinstance(audio, (list, tuple))
+            and len(audio) == batch_size
+            and all(
+                item is None
+                or _is_audio_like(item)
+                or (isinstance(item, (list, tuple)) and all(_is_audio_like(sub_item) for sub_item in item))
+                for item in audio
+            )
+        ):
+            return [self._normalize_audio_sample(sample_audio) for sample_audio in audio]
+
+        flat_audio = self._normalize_audio_sample(audio)
+        audio_counts = [prompt.count(self.sound_token) for prompt in prompts]
+        if sum(audio_counts) != len(flat_audio):
+            raise ValueError(
+                "Batched audio inputs must either be grouped per sample or match the number of `<sound>` tokens in "
+                f"the prompts. Got {len(flat_audio)} audio inputs for token counts {audio_counts}."
+            )
+
+        audio_batches = []
+        cursor = 0
+        for audio_count in audio_counts:
+            audio_batches.append(flat_audio[cursor : cursor + audio_count])
+            cursor += audio_count
+        return audio_batches
+
+    def _prepare_sample(
+        self,
+        text: str,
+        runtime_config: SimpleNamespace,
+        images: list | None = None,
+        videos: list | None = None,
+        audio: list | None = None,
+    ) -> tuple[str, defaultdict, defaultdict]:
+        media = defaultdict(list)
+        media_config = defaultdict(dict)
+        raw_sounds = []
+        video_infos = []
+
+        if images:
+            if len(images) == 1 and runtime_config.image_aspect_ratio == "dynamic_s2":
+                image_tensor, block_sizes = _process_image(images[0], runtime_config, enable_dynamic_s2=True)
+                media["image"] = list(image_tensor.half())
+                media_config["image"]["block_sizes"] = [block_sizes]
+            else:
+                media["image"] = list(_process_images(images, runtime_config.image_processor, runtime_config).half())
+
+        audio_info_list = []
+        if videos:
+            for video in videos:
+                if runtime_config.load_audio_in_video:
+                    frames, audio_waveform, video_info = _extract_video_hf(video, runtime_config)
+                    if audio_waveform is not None:
+                        raw_sounds.append(audio_waveform)
+                        audio_info_list.append(video_info["audio_info"])
+                else:
+                    frames, video_info = _extract_video_hf(video, runtime_config)
+                media["video"].append(_process_images(frames, runtime_config.image_processor, runtime_config).half())
+                video_infos.append(video_info)
+            media["video_info"] = [video_infos]
+
+        explicit_audio_count = len(audio) if audio else 0
+        if audio:
+            for audio_item in audio:
+                audio_waveform, audio_info = _load_audio_hf_with_info(audio_item, runtime_config)
+                raw_sounds.append(audio_waveform)
+                audio_info_list.append(audio_info)
+
+        if raw_sounds:
+            media["sound"] = _extract_sound_features(
+                raw_sounds,
+                audio_info_list,
+                runtime_config,
+                feature_extractor=runtime_config.feature_extractor,
+            )
+
+        if audio_info_list:
+            media["audio_info"] = [audio_info_list]
+
+        if video_infos and runtime_config.load_audio_in_video:
+            expected_sound_tokens = explicit_audio_count + sum(
+                1 for video_info in video_infos if video_info.get("has_audio", False)
+            )
+            missing_sound_tokens = expected_sound_tokens - text.count(self.sound_token)
+            if missing_sound_tokens > 0:
+                rebuilt = []
+                cursor = 0
+                for video_info in video_infos:
+                    pos = text.find(self.video_token, cursor)
+                    if pos < 0:
+                        break
+                    rebuilt.append(text[cursor:pos])
+                    if video_info.get("has_audio", False) and missing_sound_tokens > 0:
+                        rebuilt.append(self.sound_token)
+                        missing_sound_tokens -= 1
+                    rebuilt.append(self.video_token)
+                    cursor = pos + len(self.video_token)
+                rebuilt.append(text[cursor:])
+                text = "".join(rebuilt)
+
+        if runtime_config.mm_use_bos_eos_tokens:
+            text = _add_mm_bos_eos_tokens(text)
+
+        return text, media, media_config
+
+    def _call_native(
+        self,
+        text: list[str],
+        runtime_config: SimpleNamespace,
+        text_kwargs: dict,
+        images=None,
+        videos=None,
+        audio=None,
+    ) -> BatchFeature:
+        if not text:
+            raise ValueError("`text` must contain at least one prompt.")
+
+        image_batches = self._normalize_nested_media(images, len(text))
+        video_batches = self._normalize_nested_media(videos, len(text))
+        audio_batches = self._normalize_audio_batches(audio, text)
+
+        processed_text = []
+        media = defaultdict(list)
+        media_config = defaultdict(dict)
+
+        for prompt, sample_images, sample_videos, sample_audio in zip(
+            text, image_batches, video_batches, audio_batches
+        ):
+            sample_text, sample_media, sample_media_config = self._prepare_sample(
+                prompt,
+                runtime_config=runtime_config,
+                images=sample_images,
+                videos=sample_videos,
+                audio=sample_audio,
+            )
+            processed_text.append(sample_text)
+            for name in sample_media:
+                media[name].extend(sample_media[name])
+            _merge_media_config(media_config, sample_media_config)
+
+        text_inputs = self.tokenizer(processed_text, **text_kwargs)
+        if "attention_mask" in text_inputs and isinstance(text_inputs["attention_mask"], torch.Tensor):
+            text_inputs["attention_mask"] = text_inputs["attention_mask"].to(dtype=torch.bool)
+        self._check_special_mm_tokens(processed_text, text_inputs, modalities=["image", "video", "sound"])
+
+        return BatchFeature(
+            data={
+                **text_inputs,
+                "media": media,
+                "media_config": media_config,
+            }
+        )
+
+    def post_process_image_text_to_text(self, generated_outputs):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+
+        Returns:
+            `List[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        feature_extractor_input_names = (
+            self.feature_extractor.model_input_names if self.feature_extractor is not None else []
+        )
+        return list(
+            dict.fromkeys(
+                tokenizer_input_names
+                + image_processor_input_names
+                + feature_extractor_input_names
+                + ["media", "media_config"]
+            )
+        )
+
+
+__all__ = [
+    "AudioVisualFlamingoProcessor",
+    "AudioVisualFlamingoProcessorKwargs",
+]
diff --git a/src/transformers/models/auto/auto_factory.py b/src/transformers/models/auto/auto_factory.py
index 5ef09f8eb443..9f9266aadde6 100644
--- a/src/transformers/models/auto/auto_factory.py
+++ b/src/transformers/models/auto/auto_factory.py
@@ -323,7 +323,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike[str],
             if kwargs.get("dtype") == "auto":
                 _ = kwargs.pop("dtype")
             # to not overwrite the quantization_config if config has a quantization_config
-            if kwargs.get("quantization_config") is not None:
+            if "quantization_config" in kwargs:
                 _ = kwargs.pop("quantization_config")
 
             config, kwargs = AutoConfig.from_pretrained(
@@ -340,7 +340,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: str | os.PathLike[str],
                 kwargs["torch_dtype"] = "auto"
             if kwargs_orig.get("dtype", None) == "auto":
                 kwargs["dtype"] = "auto"
-            if kwargs_orig.get("quantization_config", None) is not None:
+            if "quantization_config" in kwargs_orig:
                 kwargs["quantization_config"] = kwargs_orig["quantization_config"]
 
         has_remote_code = hasattr(config, "auto_map") and cls.__name__ in config.auto_map
diff --git a/src/transformers/models/auto/auto_mappings.py b/src/transformers/models/auto/auto_mappings.py
index a6c4544242ba..2faaf930d4bc 100644
--- a/src/transformers/models/auto/auto_mappings.py
+++ b/src/transformers/models/auto/auto_mappings.py
@@ -41,6 +41,7 @@
         ("audio-spectrogram-transformer", "ASTConfig"),
         ("audioflamingo3", "AudioFlamingo3Config"),
         ("audioflamingo3_encoder", "AudioFlamingo3EncoderConfig"),
+        ("audiovisualflamingo", "AudioVisualFlamingoConfig"),
         ("autoformer", "AutoformerConfig"),
         ("aya_vision", "AyaVisionConfig"),
         ("bamba", "BambaConfig"),
@@ -108,6 +109,7 @@
         ("csm", "CsmConfig"),
         ("csm_depth_decoder_model", "CsmDepthDecoderConfig"),
         ("ctrl", "CTRLConfig"),
+        ("ctsm", "CtsmConfig"),
         ("cvt", "CvtConfig"),
         ("cwm", "CwmConfig"),
         ("d_fine", "DFineConfig"),
@@ -120,8 +122,13 @@
         ("deberta", "DebertaConfig"),
         ("deberta-v2", "DebertaV2Config"),
         ("decision_transformer", "DecisionTransformerConfig"),
+        ("deepseek_ocr2", "DeepseekOcr2Config"),
+        ("deepseek_ocr2_encoder", "DeepseekOcr2EncoderConfig"),
+        ("deepseek_ocr2_sam_vision_model", "DeepseekOcr2SamVisionConfig"),
+        ("deepseek_ocr2_text", "DeepseekOcr2TextConfig"),
         ("deepseek_v2", "DeepseekV2Config"),
         ("deepseek_v3", "DeepseekV3Config"),
+        ("deepseek_v4", "DeepseekV4Config"),
         ("deepseek_vl", "DeepseekVLConfig"),
         ("deepseek_vl_hybrid", "DeepseekVLHybridConfig"),
         ("deformable_detr", "DeformableDetrConfig"),
@@ -129,7 +136,7 @@
         ("deit", "DeiTConfig"),
         ("depth_anything", "DepthAnythingConfig"),
         ("depth_pro", "DepthProConfig"),
-        ("detr", "DetrConfig"),
+        ("detr", "MaskFormerDetrConfig"),
         ("dia", "DiaConfig"),
         ("dia_decoder", "DiaDecoderConfig"),
         ("dia_encoder", "DiaEncoderConfig"),
@@ -168,6 +175,8 @@
         ("eurobert", "EuroBertConfig"),
         ("evolla", "EvollaConfig"),
         ("exaone4", "Exaone4Config"),
+        ("exaone4_5", "Exaone4_5_Config"),
+        ("exaone4_5_vision", "Exaone4_5_VisionConfig"),
         ("exaone_moe", "ExaoneMoeConfig"),
         ("falcon", "FalconConfig"),
         ("falcon_h1", "FalconH1Config"),
@@ -234,8 +243,11 @@
         ("gpt_oss", "GptOssConfig"),
         ("gptj", "GPTJConfig"),
         ("granite", "GraniteConfig"),
+        ("granite4_vision", "Granite4VisionConfig"),
         ("granite_speech", "GraniteSpeechConfig"),
         ("granite_speech_encoder", "GraniteSpeechEncoderConfig"),
+        ("granite_speech_plus", "GraniteSpeechPlusConfig"),
+        ("granite_speech_plus_encoder", "GraniteSpeechPlusEncoderConfig"),
         ("granitemoe", "GraniteMoeConfig"),
         ("granitemoehybrid", "GraniteMoeHybridConfig"),
         ("granitemoeshared", "GraniteMoeSharedConfig"),
@@ -279,6 +291,8 @@
         ("janus_vqgan", "JanusVQVAEConfig"),
         ("jetmoe", "JetMoeConfig"),
         ("jina_embeddings_v3", "JinaEmbeddingsV3Config"),
+        ("kimi2_6", "Kimi2_6Config"),
+        ("kimi2_6_vision", "Kimi2_6VisionConfig"),
         ("kosmos-2", "Kosmos2Config"),
         ("kosmos-2.5", "Kosmos2_5Config"),
         ("kosmos_2_5_text_model", "Kosmos2_5TextConfig"),
@@ -331,6 +345,7 @@
         ("metaclip_2_vision_model", "MetaClip2VisionConfig"),
         ("mgp-str", "MgpstrConfig"),
         ("mimi", "MimiConfig"),
+        ("minicpm3", "MiniCPM3Config"),
         ("minicpmv4_6", "MiniCPMV4_6Config"),
         ("minicpmv4_6_vision", "MiniCPMV4_6VisionConfig"),
         ("minimax", "MiniMaxConfig"),
@@ -354,6 +369,8 @@
         ("modernbert", "ModernBertConfig"),
         ("modernbert-decoder", "ModernBertDecoderConfig"),
         ("modernvbert", "ModernVBertConfig"),
+        ("molmo2", "Molmo2Config"),
+        ("molmo2_text", "Molmo2TextConfig"),
         ("moonshine", "MoonshineConfig"),
         ("moonshine_streaming", "MoonshineStreamingConfig"),
         ("moonshine_streaming_encoder", "MoonshineStreamingEncoderConfig"),
@@ -399,6 +416,7 @@
         ("paligemma", "PaliGemmaConfig"),
         ("parakeet_ctc", "ParakeetCTCConfig"),
         ("parakeet_encoder", "ParakeetEncoderConfig"),
+        ("parakeet_tdt", "ParakeetTDTConfig"),
         ("patchtsmixer", "PatchTSMixerConfig"),
         ("patchtst", "PatchTSTConfig"),
         ("pe_audio", "PeAudioConfig"),
@@ -409,6 +427,8 @@
         ("pe_video_encoder", "PeVideoEncoderConfig"),
         ("pegasus", "PegasusConfig"),
         ("pegasus_x", "PegasusXConfig"),
+        ("penguinvl", "PenguinVLConfig"),
+        ("penguinvl_vision", "PenguinVLVisionConfig"),
         ("perceiver", "PerceiverConfig"),
         ("perception_lm", "PerceptionLMConfig"),
         ("persimmon", "PersimmonConfig"),
@@ -430,6 +450,7 @@
         ("pp_chart2table", "PPChart2TableConfig"),
         ("pp_doclayout_v2", "PPDocLayoutV2Config"),
         ("pp_doclayout_v3", "PPDocLayoutV3Config"),
+        ("pp_formulanet", "PPFormulaNetConfig"),
         ("pp_lcnet", "PPLCNetConfig"),
         ("pp_lcnet_v3", "PPLCNetV3Config"),
         ("pp_ocrv5_mobile_det", "PPOCRV5MobileDetConfig"),
@@ -468,6 +489,8 @@
         ("qwen3_5_moe_vision", "Qwen3_5MoeVisionConfig"),
         ("qwen3_5_text", "Qwen3_5TextConfig"),
         ("qwen3_5_vision", "Qwen3_5VisionConfig"),
+        ("qwen3_asr", "Qwen3ASRConfig"),
+        ("qwen3_asr_audio_encoder", "Qwen3ASREncoderConfig"),
         ("qwen3_moe", "Qwen3MoeConfig"),
         ("qwen3_next", "Qwen3NextConfig"),
         ("qwen3_omni_moe", "Qwen3OmniMoeConfig"),
@@ -521,6 +544,7 @@
         ("sam_hq", "SamHQConfig"),
         ("sam_hq_vision_model", "SamHQVisionConfig"),
         ("sam_vision_model", "SamVisionConfig"),
+        ("sarvam_mla", "SarvamMLAConfig"),
         ("seamless_m4t", "SeamlessM4TConfig"),
         ("seamless_m4t_v2", "SeamlessM4Tv2Config"),
         ("seed_oss", "SeedOssConfig"),
@@ -590,6 +614,9 @@
         ("video_llava", "VideoLlavaConfig"),
         ("videomae", "VideoMAEConfig"),
         ("videomt", "VideomtConfig"),
+        ("videoprism", "VideoPrismConfig"),
+        ("videoprism_text_model", "VideoPrismTextConfig"),
+        ("videoprism_vision_model", "VideoPrismVisionConfig"),
         ("vilt", "ViltConfig"),
         ("vipllava", "VipLlavaConfig"),
         ("vision-encoder-decoder", "VisionEncoderDecoderConfig"),
@@ -676,6 +703,9 @@
         ("data2vec-text", "data2vec"),
         ("data2vec-vision", "data2vec"),
         ("deberta-v2", "deberta_v2"),
+        ("deepseek_ocr2_encoder", "deepseek_ocr2"),
+        ("deepseek_ocr2_sam_vision_model", "deepseek_ocr2"),
+        ("deepseek_ocr2_text", "deepseek_ocr2"),
         ("detr", "maskformer"),
         ("dia_decoder", "dia"),
         ("dia_encoder", "dia"),
@@ -686,6 +716,7 @@
         ("encoder-decoder", "encoder_decoder"),
         ("ernie4_5_vl_moe_text", "ernie4_5_vl_moe"),
         ("ernie4_5_vl_moe_vision", "ernie4_5_vl_moe"),
+        ("exaone4_5_vision", "exaone4_5"),
         ("fastspeech2_conformer_hifigan", "fastspeech2_conformer"),
         ("fastspeech2_conformer_with_hifigan", "fastspeech2_conformer"),
         ("flava_image_model", "flava"),
@@ -711,6 +742,7 @@
         ("glm_ocr_vision", "glm_ocr"),
         ("glmasr_encoder", "glmasr"),
         ("granite_speech_encoder", "granite_speech"),
+        ("granite_speech_plus_encoder", "granite_speech_plus"),
         ("grounding-dino", "grounding_dino"),
         ("groupvit_text_model", "groupvit"),
         ("groupvit_vision_model", "groupvit"),
@@ -726,6 +758,7 @@
         ("internvl_vision", "internvl"),
         ("janus_vision_model", "janus"),
         ("janus_vqgan", "janus"),
+        ("kimi2_6_vision", "kimi2_6"),
         ("kosmos-2", "kosmos2"),
         ("kosmos-2.5", "kosmos2_5"),
         ("kosmos_2_5_text_model", "kosmos2_5"),
@@ -763,9 +796,11 @@
         ("paddleocr_vl_vision", "paddleocr_vl"),
         ("parakeet_ctc", "parakeet"),
         ("parakeet_encoder", "parakeet"),
+        ("parakeet_tdt", "parakeet"),
         ("pe_audio_encoder", "pe_audio"),
         ("pe_audio_video_encoder", "pe_audio_video"),
         ("pe_video_encoder", "pe_video"),
+        ("penguinvl_vision", "penguinvl"),
         ("phi4_multimodal_audio", "phi4_multimodal"),
         ("phi4_multimodal_vision", "phi4_multimodal"),
         ("pix2struct_text_model", "pix2struct"),
@@ -788,6 +823,7 @@
         ("qwen3_5_moe_vision", "qwen3_5_moe"),
         ("qwen3_5_text", "qwen3_5"),
         ("qwen3_5_vision", "qwen3_5"),
+        ("qwen3_asr_audio_encoder", "qwen3_asr"),
         ("qwen3_omni_moe_audio_encoder", "qwen3_omni_moe"),
         ("qwen3_omni_moe_talker_code_predictor", "qwen3_omni_moe"),
         ("qwen3_omni_moe_talker_text", "qwen3_omni_moe"),
@@ -831,6 +867,8 @@
         ("unispeech-sat", "unispeech_sat"),
         ("uvdoc_backbone", "uvdoc"),
         ("video_llama_3_vision", "video_llama_3"),
+        ("videoprism_text_model", "videoprism"),
+        ("videoprism_vision_model", "videoprism"),
         ("vision-encoder-decoder", "vision_encoder_decoder"),
         ("vision-text-dual-encoder", "vision_text_dual_encoder"),
         ("voxtral_encoder", "voxtral"),
@@ -863,6 +901,7 @@
             {"pil": "ConditionalDetrImageProcessorPil", "torchvision": "ConditionalDetrImageProcessor"},
         ),
         ("convnext", {"pil": "ConvNextImageProcessorPil", "torchvision": "ConvNextImageProcessor"}),
+        ("deepseek_ocr2", {"pil": "DeepseekOcr2ImageProcessorPil", "torchvision": "DeepseekOcr2ImageProcessor"}),
         ("deepseek_vl", {"pil": "DeepseekVLImageProcessorPil", "torchvision": "DeepseekVLImageProcessor"}),
         (
             "deepseek_vl_hybrid",
@@ -887,12 +926,14 @@
         ("glm_image", {"pil": "GlmImageImageProcessorPil", "torchvision": "GlmImageImageProcessor"}),
         ("glpn", {"pil": "GLPNImageProcessorPil", "torchvision": "GLPNImageProcessor"}),
         ("got_ocr2", {"pil": "GotOcr2ImageProcessorPil", "torchvision": "GotOcr2ImageProcessor"}),
+        ("granite4_vision", {"pil": "Granite4VisionImageProcessorPil", "torchvision": "Granite4VisionImageProcessor"}),
         ("grounding-dino", {"pil": "GroundingDinoImageProcessorPil", "torchvision": "GroundingDinoImageProcessor"}),
         ("idefics", {"pil": "IdeficsImageProcessorPil", "torchvision": "IdeficsImageProcessor"}),
         ("idefics2", {"pil": "Idefics2ImageProcessorPil", "torchvision": "Idefics2ImageProcessor"}),
         ("idefics3", {"pil": "Idefics3ImageProcessorPil", "torchvision": "Idefics3ImageProcessor"}),
         ("imagegpt", {"pil": "ImageGPTImageProcessorPil", "torchvision": "ImageGPTImageProcessor"}),
         ("janus", {"pil": "JanusImageProcessorPil", "torchvision": "JanusImageProcessor"}),
+        ("kimi2_6", {"pil": "Kimi2_6ImageProcessorPil", "torchvision": "Kimi2_6ImageProcessor"}),
         ("layoutlmv2", {"pil": "LayoutLMv2ImageProcessorPil", "torchvision": "LayoutLMv2ImageProcessor"}),
         ("layoutlmv3", {"pil": "LayoutLMv3ImageProcessorPil", "torchvision": "LayoutLMv3ImageProcessor"}),
         ("levit", {"pil": "LevitImageProcessorPil", "torchvision": "LevitImageProcessor"}),
@@ -925,6 +966,7 @@
         ("pp_chart2table", {"pil": "PPChart2TableImageProcessorPil", "torchvision": "PPChart2TableImageProcessor"}),
         ("pp_doclayout_v2", {"torchvision": "PPDocLayoutV2ImageProcessor"}),
         ("pp_doclayout_v3", {"torchvision": "PPDocLayoutV3ImageProcessor"}),
+        ("pp_formulanet", {"torchvision": "PPFormulaNetImageProcessor"}),
         ("pp_lcnet", {"torchvision": "PPLCNetImageProcessor"}),
         ("pp_ocrv5_server_det", {"torchvision": "PPOCRV5ServerDetImageProcessor"}),
         ("pp_ocrv5_server_rec", {"torchvision": "PPOCRV5ServerRecImageProcessor"}),
@@ -969,6 +1011,7 @@
         ("glm4v", "Glm4vVideoProcessor"),
         ("instructblipvideo", "InstructBlipVideoVideoProcessor"),
         ("internvl", "InternVLVideoProcessor"),
+        ("kimi2_6", "Kimi2_6VideoProcessor"),
         ("llava_next_video", "LlavaNextVideoVideoProcessor"),
         ("llava_onevision", "LlavaOnevisionVideoProcessor"),
         ("minicpmv4_6", "MiniCPMV4_6VideoProcessor"),
diff --git a/src/transformers/models/auto/configuration_auto.py b/src/transformers/models/auto/configuration_auto.py
index d9ebfedb7ae9..5bf3ce9039d5 100644
--- a/src/transformers/models/auto/configuration_auto.py
+++ b/src/transformers/models/auto/configuration_auto.py
@@ -37,6 +37,7 @@
     {
         "EvollaModel": "EvollaConfig",
         "mlcd": "MLCDVisionConfig",
+        "qwen3_forced_aligner": "Qwen3ForcedAlignerConfig",
         "vibevoice_acoustic_tokenizer_decoder": "VibeVoiceAcousticTokenizerDecoderConfig",
         "vibevoice_acoustic_tokenizer_encoder": "VibeVoiceAcousticTokenizerEncoderConfig",
     }
@@ -49,6 +50,9 @@
 SPECIAL_MODEL_TYPE_TO_MODULE_NAME.update(
     {
         "EvollaModel": "evolla",
+        "mlcd_vision_model": "mlcd",
+        "penguinvl_vision": "penguinvl",
+        "qwen3_forced_aligner": "qwen3_asr",
         "vibevoice_acoustic_tokenizer_encoder": "vibevoice_acoustic_tokenizer",
         "vibevoice_acoustic_tokenizer_decoder": "vibevoice_acoustic_tokenizer",
     }
diff --git a/src/transformers/models/auto/feature_extraction_auto.py b/src/transformers/models/auto/feature_extraction_auto.py
index 111c56efb436..e5e1c0f8f6bb 100644
--- a/src/transformers/models/auto/feature_extraction_auto.py
+++ b/src/transformers/models/auto/feature_extraction_auto.py
@@ -49,6 +49,7 @@
         ("gemma4", "Gemma4AudioFeatureExtractor"),
         ("glmasr", "WhisperFeatureExtractor"),
         ("granite_speech", "GraniteSpeechFeatureExtractor"),
+        ("granite_speech_plus", "GraniteSpeechFeatureExtractor"),
         ("higgs_audio_v2_tokenizer", "DacFeatureExtractor"),
         ("hubert", "Wav2Vec2FeatureExtractor"),
         ("kyutai_speech_to_text", "KyutaiSpeechToTextFeatureExtractor"),
@@ -62,12 +63,15 @@
         ("musicgen_melody", "MusicgenMelodyFeatureExtractor"),
         ("parakeet_ctc", "ParakeetFeatureExtractor"),
         ("parakeet_encoder", "ParakeetFeatureExtractor"),
+        ("parakeet_tdt", "ParakeetFeatureExtractor"),
         ("pe_audio", "PeAudioFeatureExtractor"),
         ("pe_audio_video", "PeAudioFeatureExtractor"),
         ("phi4_multimodal", "Phi4MultimodalFeatureExtractor"),
         ("pop2piano", "Pop2PianoFeatureExtractor"),
         ("qwen2_5_omni", "WhisperFeatureExtractor"),
         ("qwen2_audio", "WhisperFeatureExtractor"),
+        ("qwen3_asr", "Qwen3ASRFeatureExtractor"),
+        ("qwen3_forced_aligner", "Qwen3ASRFeatureExtractor"),
         ("qwen3_omni_moe", "WhisperFeatureExtractor"),
         ("seamless_m4t", "SeamlessM4TFeatureExtractor"),
         ("seamless_m4t_v2", "SeamlessM4TFeatureExtractor"),
diff --git a/src/transformers/models/auto/image_processing_auto.py b/src/transformers/models/auto/image_processing_auto.py
index c624f49083d2..02a8f77b0dd1 100644
--- a/src/transformers/models/auto/image_processing_auto.py
+++ b/src/transformers/models/auto/image_processing_auto.py
@@ -88,6 +88,7 @@
             ("focalnet", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),
             ("gemma3n", {"torchvision": "SiglipImageProcessor", "pil": "SiglipImageProcessorPil"}),
             ("git", {"torchvision": "CLIPImageProcessor", "pil": "CLIPImageProcessorPil"}),
+            ("granite4_vision", {"torchvision": "LlavaNextImageProcessor", "pil": "LlavaNextImageProcessorPil"}),
             ("groupvit", {"torchvision": "CLIPImageProcessor", "pil": "CLIPImageProcessorPil"}),
             ("hiera", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),
             ("ijepa", {"torchvision": "ViTImageProcessor", "pil": "ViTImageProcessorPil"}),
@@ -111,8 +112,11 @@
                 },
             ),
             ("mobilevitv2", {"torchvision": "MobileViTImageProcessor", "pil": "MobileViTImageProcessorPil"}),
+            ("molmo2", {"torchvision": "Molmo2ImageProcessor"}),
+            ("nougat", {"torchvision": "NougatImageProcessor", "pil": "NougatImageProcessorPil"}),
             ("omdet-turbo", {"torchvision": "DetrImageProcessor", "pil": "DetrImageProcessorPil"}),
             ("paligemma", {"torchvision": "SiglipImageProcessor", "pil": "SiglipImageProcessorPil"}),
+            ("penguinvl", {"pil": "PenguinVLImageProcessor", "torchvision": "PenguinVLImageProcessorFast"}),
             ("pixio", {"torchvision": "BitImageProcessor", "pil": "BitImageProcessorPil"}),
             ("pp_ocrv5_mobile_det", {"torchvision": "PPOCRV5ServerDetImageProcessor"}),
             ("pp_ocrv5_mobile_rec", {"torchvision": "PPOCRV5ServerRecImageProcessor"}),
@@ -583,8 +587,8 @@ def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
                 feature_extractor_auto_map = config_dict["auto_map"]["AutoFeatureExtractor"]
                 image_processor_auto_map = feature_extractor_auto_map.replace("FeatureExtractor", "ImageProcessor")
 
-        # If not in image processor config, try the model config
-        if image_processor_type is None and image_processor_auto_map is None:
+        # If not in image processor config, try the model config (override image_processor_auto_map if trust_remote_code is False)
+        if image_processor_type is None and (image_processor_auto_map is None or trust_remote_code is False):
             if not isinstance(config, PreTrainedConfig):
                 config = AutoConfig.from_pretrained(
                     pretrained_model_name_or_path,
diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py
index a541f13499b7..6852b2dfdf04 100644
--- a/src/transformers/models/auto/modeling_auto.py
+++ b/src/transformers/models/auto/modeling_auto.py
@@ -54,6 +54,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("audio-spectrogram-transformer", "ASTModel"),
         ("audioflamingo3", "AudioFlamingo3ForConditionalGeneration"),
         ("audioflamingo3_encoder", "AudioFlamingo3Encoder"),
+        ("audiovisualflamingo", "AudioVisualFlamingoForConditionalGeneration"),
         ("autoformer", "AutoformerModel"),
         ("aya_vision", "AyaVisionModel"),
         ("bamba", "BambaModel"),
@@ -99,6 +100,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("cpmant", "CpmAntModel"),
         ("csm", "CsmForConditionalGeneration"),
         ("ctrl", "CTRLModel"),
+        ("ctsm", "CtsmModel"),
         ("cvt", "CvtModel"),
         ("cwm", "CwmModel"),
         ("d_fine", "DFineModel"),
@@ -111,8 +113,10 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("deberta", "DebertaModel"),
         ("deberta-v2", "DebertaV2Model"),
         ("decision_transformer", "DecisionTransformerModel"),
+        ("deepseek_ocr2", "DeepseekOcr2Model"),
         ("deepseek_v2", "DeepseekV2Model"),
         ("deepseek_v3", "DeepseekV3Model"),
+        ("deepseek_v4", "DeepseekV4Model"),
         ("deepseek_vl", "DeepseekVLModel"),
         ("deepseek_vl_hybrid", "DeepseekVLHybridModel"),
         ("deformable_detr", "DeformableDetrModel"),
@@ -136,7 +140,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("edgetam", "EdgeTamModel"),
         ("edgetam_video", "EdgeTamVideoModel"),
         ("edgetam_vision_model", "EdgeTamVisionModel"),
-        ("efficientloftr", "EfficientLoFTRModel"),
+        ("efficientformer", "EfficientFormerModel"),
+        ("efficientloftr", ("EfficientLoFTRModel", "EfficientLoFTRForKeypointMatching")),
         ("efficientnet", "EfficientNetModel"),
         ("electra", "ElectraModel"),
         ("emu3", "Emu3Model"),
@@ -149,6 +154,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("eurobert", "EuroBertModel"),
         ("evolla", "EvollaModel"),
         ("exaone4", "Exaone4Model"),
+        ("exaone4_5", "Exaone4_5_Model"),
+        ("exaone4_5_vision", "Exaone4_5_VisionModel"),
         ("exaone_moe", "ExaoneMoeModel"),
         ("falcon", "FalconModel"),
         ("falcon_h1", "FalconH1Model"),
@@ -210,6 +217,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("gpt_oss", "GptOssModel"),
         ("gptj", "GPTJModel"),
         ("granite", "GraniteModel"),
+        ("granite4_vision", "Granite4VisionModel"),
         ("granite_speech", "GraniteSpeechForConditionalGeneration"),
         ("granitemoe", "GraniteMoeModel"),
         ("granitemoehybrid", "GraniteMoeHybridModel"),
@@ -242,6 +250,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("janus", "JanusModel"),
         ("jetmoe", "JetMoeModel"),
         ("jina_embeddings_v3", "JinaEmbeddingsV3Model"),
+        ("kimi2_6", "Kimi2_6Model"),
+        ("kimi2_6_vision", "Kimi2_6VisionModel"),
         ("kosmos-2", "Kosmos2Model"),
         ("kosmos-2.5", "Kosmos2_5Model"),
         ("kyutai_speech_to_text", "KyutaiSpeechToTextModel"),
@@ -285,6 +295,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("metaclip_2", "MetaClip2Model"),
         ("mgp-str", "MgpstrForSceneTextRecognition"),
         ("mimi", "MimiModel"),
+        ("minicpm3", "MiniCPM3Model"),
         ("minicpmv4_6", "MiniCPMV4_6Model"),
         ("minimax", "MiniMaxModel"),
         ("minimax_m2", "MiniMaxM2Model"),
@@ -306,6 +317,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("modernbert", "ModernBertModel"),
         ("modernbert-decoder", "ModernBertDecoderModel"),
         ("modernvbert", "ModernVBertModel"),
+        ("molmo2", "Molmo2Model"),
+        ("molmo2_text", "Molmo2TextModel"),
         ("moonshine", "MoonshineModel"),
         ("moonshine_streaming", "MoonshineStreamingModel"),
         ("moshi", "MoshiModel"),
@@ -339,6 +352,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("paligemma", "PaliGemmaModel"),
         ("parakeet_ctc", "ParakeetForCTC"),
         ("parakeet_encoder", "ParakeetEncoder"),
+        ("parakeet_tdt", "ParakeetForTDT"),
         ("patchtsmixer", "PatchTSMixerModel"),
         ("patchtst", "PatchTSTModel"),
         ("pe_audio", "PeAudioModel"),
@@ -349,6 +363,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("pe_video_encoder", "PeVideoEncoder"),
         ("pegasus", "PegasusModel"),
         ("pegasus_x", "PegasusXModel"),
+        ("penguinvl", "PenguinVLModel"),
+        ("penguinvl_vision", "PenguinVLVisionModel"),
         ("perceiver", "PerceiverModel"),
         ("perception_lm", "PerceptionLMModel"),
         ("persimmon", "PersimmonModel"),
@@ -381,6 +397,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("qwen3_5_moe", "Qwen3_5MoeModel"),
         ("qwen3_5_moe_text", "Qwen3_5MoeTextModel"),
         ("qwen3_5_text", "Qwen3_5TextModel"),
+        ("qwen3_asr", "Qwen3ASRModel"),
+        ("qwen3_forced_aligner", "Qwen3ASRForForcedAlignment"),
         ("qwen3_moe", "Qwen3MoeModel"),
         ("qwen3_next", "Qwen3NextModel"),
         ("qwen3_vl", "Qwen3VLModel"),
@@ -416,6 +434,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("sam_hq", "SamHQModel"),
         ("sam_hq_vision_model", "SamHQVisionModel"),
         ("sam_vision_model", "SamVisionModel"),
+        ("sarvam_mla", "DeepseekV3Model"),
         ("seamless_m4t", "SeamlessM4TModel"),
         ("seamless_m4t_v2", "SeamlessM4Tv2Model"),
         ("seed_oss", "SeedOssModel"),
@@ -471,6 +490,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("video_llama_3_vision", "VideoLlama3VisionModel"),
         ("video_llava", "VideoLlavaModel"),
         ("videomae", "VideoMAEModel"),
+        ("videoprism", "VideoPrismClipModel"),
+        ("videoprism_vision_model", "VideoPrismVisionModel"),
         ("vilt", "ViltModel"),
         ("vipllava", "VipLlavaModel"),
         ("vision-text-dual-encoder", "VisionTextDualEncoderModel"),
@@ -514,6 +535,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         # Model for pre-training mapping
         ("albert", "AlbertForPreTraining"),
         ("audioflamingo3", "AudioFlamingo3ForConditionalGeneration"),
+        ("audiovisualflamingo", "AudioVisualFlamingoForConditionalGeneration"),
         ("bart", "BartForConditionalGeneration"),
         ("bert", "BertForPreTraining"),
         ("big_bird", "BigBirdForPreTraining"),
@@ -576,6 +598,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("openai-gpt", "OpenAIGPTLMHeadModel"),
         ("paligemma", "PaliGemmaForConditionalGeneration"),
         ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),
+        ("qwen3_asr", "Qwen3ASRForConditionalGeneration"),
         ("roberta", "RobertaForMaskedLM"),
         ("roberta-prelayernorm", "RobertaPreLayerNormForMaskedLM"),
         ("roc_bert", "RoCBertForPreTraining"),
@@ -615,6 +638,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("apertus", "ApertusForCausalLM"),
         ("arcee", "ArceeForCausalLM"),
         ("aria_text", "AriaTextForCausalLM"),
+        ("audiovisualflamingo", "AudioVisualFlamingoForConditionalGeneration"),
         ("bamba", "BambaForCausalLM"),
         ("bart", "BartForCausalLM"),
         ("bert", "BertLMHeadModel"),
@@ -638,6 +662,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("dbrx", "DbrxForCausalLM"),
         ("deepseek_v2", "DeepseekV2ForCausalLM"),
         ("deepseek_v3", "DeepseekV3ForCausalLM"),
+        ("deepseek_v4", "DeepseekV4ForCausalLM"),
         ("diffllama", "DiffLlamaForCausalLM"),
         ("doge", "DogeForCausalLM"),
         ("dots1", "Dots1ForCausalLM"),
@@ -699,11 +724,13 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("marian", "MarianForCausalLM"),
         ("mbart", "MBartForCausalLM"),
         ("megatron-bert", "MegatronBertForCausalLM"),
+        ("minicpm3", "MiniCPM3ForCausalLM"),
         ("minimax", "MiniMaxForCausalLM"),
         ("minimax_m2", "MiniMaxM2ForCausalLM"),
         ("ministral", "MinistralForCausalLM"),
         ("ministral3", "Ministral3ForCausalLM"),
         ("mistral", "MistralForCausalLM"),
+        ("mistral4", "Mistral4ForCausalLM"),
         ("mixtral", "MixtralForCausalLM"),
         ("mllama", "MllamaForCausalLM"),
         ("modernbert-decoder", "ModernBertDecoderForCausalLM"),
@@ -747,6 +774,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("roc_bert", "RoCBertForCausalLM"),
         ("roformer", "RoFormerForCausalLM"),
         ("rwkv", "RwkvForCausalLM"),
+        ("sarvam_mla", "DeepseekV3ForCausalLM"),
         ("seed_oss", "SeedOssForCausalLM"),
         ("smollm3", "SmolLM3ForCausalLM"),
         ("solar_open", "SolarOpenForCausalLM"),
@@ -821,6 +849,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("timm_backbone", "TimmBackbone"),
         ("timm_wrapper", "TimmWrapperModel"),
         ("videomae", "VideoMAEModel"),
+        ("videoprism_vision_model", "VideoPrismVisionModel"),
         ("vit", "ViTModel"),
         ("vit_mae", "ViTMAEModel"),
         ("vit_msn", "ViTMSNModel"),
@@ -865,6 +894,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("dinat", "DinatForImageClassification"),
         ("dinov2", "Dinov2ForImageClassification"),
         ("dinov2_with_registers", "Dinov2WithRegistersForImageClassification"),
+        ("dinov3_vit", "DINOv3ViTForImageClassification"),
         ("donut-swin", "DonutSwinForImageClassification"),
         ("efficientnet", "EfficientNetForImageClassification"),
         ("focalnet", "FocalNetForImageClassification"),
@@ -956,6 +986,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     [
         ("timesformer", "TimesformerForVideoClassification"),
         ("videomae", "VideoMAEForVideoClassification"),
+        ("videoprism_vision_model", "VideoPrismForVideoClassification"),
         ("vivit", "VivitForVideoClassification"),
         ("vjepa2", "VJEPA2ForVideoClassification"),
     ]
@@ -976,11 +1007,13 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("blip-2", "Blip2ForConditionalGeneration"),
         ("chameleon", "ChameleonForConditionalGeneration"),
         ("cohere2_vision", "Cohere2VisionForConditionalGeneration"),
+        ("deepseek_ocr2", "DeepseekOcr2ForConditionalGeneration"),
         ("deepseek_vl", "DeepseekVLForConditionalGeneration"),
         ("deepseek_vl_hybrid", "DeepseekVLHybridForConditionalGeneration"),
         ("emu3", "Emu3ForConditionalGeneration"),
         ("ernie4_5_vl_moe", "Ernie4_5_VLMoeForConditionalGeneration"),
         ("evolla", "EvollaForProteinText2Text"),
+        ("exaone4_5", "Exaone4_5_ForConditionalGeneration"),
         ("fast_vlm", "FastVlmForConditionalGeneration"),
         ("florence2", "Florence2ForConditionalGeneration"),
         ("fuyu", "FuyuForCausalLM"),
@@ -993,6 +1026,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("glm4v_moe", "Glm4vMoeForConditionalGeneration"),
         ("glm_ocr", "GlmOcrForConditionalGeneration"),
         ("got_ocr2", "GotOcr2ForConditionalGeneration"),
+        ("granite4_vision", "Granite4VisionForConditionalGeneration"),
         ("idefics", "IdeficsForVisionText2Text"),
         ("idefics2", "Idefics2ForConditionalGeneration"),
         ("idefics3", "Idefics3ForConditionalGeneration"),
@@ -1000,6 +1034,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("instructblipvideo", "InstructBlipVideoForConditionalGeneration"),
         ("internvl", "InternVLForConditionalGeneration"),
         ("janus", "JanusForConditionalGeneration"),
+        ("kimi2_6", "Kimi2_6ForConditionalGeneration"),
         ("kosmos-2", "Kosmos2ForConditionalGeneration"),
         ("kosmos-2.5", "Kosmos2_5ForConditionalGeneration"),
         ("lfm2_vl", "Lfm2VlForConditionalGeneration"),
@@ -1013,13 +1048,16 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("mistral3", "Mistral3ForConditionalGeneration"),
         ("mistral4", "Mistral4ForCausalLM"),
         ("mllama", "MllamaForConditionalGeneration"),
+        ("molmo2", "Molmo2ForConditionalGeneration"),
         ("ovis2", "Ovis2ForConditionalGeneration"),
         ("paddleocr_vl", "PaddleOCRVLForConditionalGeneration"),
         ("paligemma", "PaliGemmaForConditionalGeneration"),
+        ("penguinvl", "PenguinVLForConditionalGeneration"),
         ("perception_lm", "PerceptionLMForConditionalGeneration"),
         ("pi0", "PI0ForConditionalGeneration"),
         ("pix2struct", "Pix2StructForConditionalGeneration"),
         ("pp_chart2table", "GotOcr2ForConditionalGeneration"),
+        ("pp_formulanet", "PPFormulaNetForConditionalGeneration"),
         ("qianfan_ocr", "QianfanOCRForConditionalGeneration"),
         ("qwen2_5_omni_thinker", "Qwen2_5OmniThinkerForConditionalGeneration"),
         ("qwen2_5_vl", "Qwen2_5_VLForConditionalGeneration"),
@@ -1047,10 +1085,12 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         *list(MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.items()),
         ("glmasr", "GlmAsrForConditionalGeneration"),
         ("granite_speech", "GraniteSpeechForConditionalGeneration"),
+        ("granite_speech_plus", "GraniteSpeechPlusForConditionalGeneration"),
         ("kyutai_speech_to_text", "KyutaiSpeechToTextForConditionalGeneration"),
         ("phi4_multimodal", "Phi4MultimodalForCausalLM"),
         ("qwen2_5_omni", "Qwen2_5OmniForConditionalGeneration"),
         ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),
+        ("qwen3_asr", "Qwen3ASRForConditionalGeneration"),
         ("qwen3_omni_moe", "Qwen3OmniMoeForConditionalGeneration"),
         ("vibevoice_asr", "VibeVoiceAsrForConditionalGeneration"),
         ("voxtral", "VoxtralForConditionalGeneration"),
@@ -1185,6 +1225,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("fsmt", "FSMTForConditionalGeneration"),
         ("glmasr", "GlmAsrForConditionalGeneration"),
         ("granite_speech", "GraniteSpeechForConditionalGeneration"),
+        ("granite_speech_plus", "GraniteSpeechPlusForConditionalGeneration"),
         ("led", "LEDForConditionalGeneration"),
         ("longt5", "LongT5ForConditionalGeneration"),
         ("m2m_100", "M2M100ForConditionalGeneration"),
@@ -1199,6 +1240,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("plbart", "PLBartForConditionalGeneration"),
         ("prophetnet", "ProphetNetForConditionalGeneration"),
         ("qwen2_audio", "Qwen2AudioForConditionalGeneration"),
+        ("qwen3_asr", "Qwen3ASRForConditionalGeneration"),
         ("seamless_m4t", "SeamlessM4TForTextToText"),
         ("seamless_m4t_v2", "SeamlessM4Tv2ForTextToText"),
         ("switch_transformers", "SwitchTransformersForConditionalGeneration"),
@@ -1217,11 +1259,14 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     [
         ("cohere_asr", "CohereAsrForConditionalGeneration"),
         ("dia", "DiaForConditionalGeneration"),
+        ("glmasr", "GlmAsrForConditionalGeneration"),
         ("granite_speech", "GraniteSpeechForConditionalGeneration"),
+        ("granite_speech_plus", "GraniteSpeechPlusForConditionalGeneration"),
         ("kyutai_speech_to_text", "KyutaiSpeechToTextForConditionalGeneration"),
         ("moonshine", "MoonshineForConditionalGeneration"),
         ("moonshine_streaming", "MoonshineStreamingForConditionalGeneration"),
         ("pop2piano", "Pop2PianoForConditionalGeneration"),
+        ("qwen3_asr", "Qwen3ASRForConditionalGeneration"),
         ("seamless_m4t", "SeamlessM4TForSpeechToText"),
         ("seamless_m4t_v2", "SeamlessM4Tv2ForSpeechToText"),
         ("speech-encoder-decoder", "SpeechEncoderDecoderModel"),
@@ -1270,6 +1315,8 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("gemma2", "Gemma2ForSequenceClassification"),
         ("gemma3", "Gemma3ForSequenceClassification"),
         ("gemma3_text", "Gemma3TextForSequenceClassification"),
+        ("gemma4", "Gemma4ForSequenceClassification"),
+        ("gemma4_text", "Gemma4TextForSequenceClassification"),
         ("glm", "GlmForSequenceClassification"),
         ("glm4", "Glm4ForSequenceClassification"),
         ("gpt-sw3", "GPT2ForSequenceClassification"),
@@ -1279,6 +1326,10 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("gpt_neox", "GPTNeoXForSequenceClassification"),
         ("gpt_oss", "GptOssForSequenceClassification"),
         ("gptj", "GPTJForSequenceClassification"),
+        ("granite", "GraniteForSequenceClassification"),
+        ("granitemoe", "GraniteMoeForSequenceClassification"),
+        ("granitemoehybrid", "GraniteMoeHybridForSequenceClassification"),
+        ("granitemoeshared", "GraniteMoeSharedForSequenceClassification"),
         ("helium", "HeliumForSequenceClassification"),
         ("hunyuan_v1_dense", "HunYuanDenseV1ForSequenceClassification"),
         ("hunyuan_v1_moe", "HunYuanMoEV1ForSequenceClassification"),
@@ -1296,6 +1347,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("markuplm", "MarkupLMForSequenceClassification"),
         ("mbart", "MBartForSequenceClassification"),
         ("megatron-bert", "MegatronBertForSequenceClassification"),
+        ("minicpm3", "MiniCPM3ForSequenceClassification"),
         ("minimax", "MiniMaxForSequenceClassification"),
         ("ministral", "MinistralForSequenceClassification"),
         ("ministral3", "Ministral3ForSequenceClassification"),
@@ -1329,7 +1381,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("qwen2_moe", "Qwen2MoeForSequenceClassification"),
         ("qwen3", "Qwen3ForSequenceClassification"),
         ("qwen3_5", "Qwen3_5ForSequenceClassification"),
-        ("qwen3_5_text", "Qwen3_5ForSequenceClassification"),
+        ("qwen3_5_text", "Qwen3_5TextForSequenceClassification"),
         ("qwen3_moe", "Qwen3MoeForSequenceClassification"),
         ("qwen3_next", "Qwen3NextForSequenceClassification"),
         ("reformer", "ReformerForSequenceClassification"),
@@ -1338,6 +1390,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("roberta-prelayernorm", "RobertaPreLayerNormForSequenceClassification"),
         ("roc_bert", "RoCBertForSequenceClassification"),
         ("roformer", "RoFormerForSequenceClassification"),
+        ("sarvam_mla", "DeepseekV3ForSequenceClassification"),
         ("seed_oss", "SeedOssForSequenceClassification"),
         ("smollm3", "SmolLM3ForSequenceClassification"),
         ("squeezebert", "SqueezeBertForSequenceClassification"),
@@ -1546,6 +1599,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("roberta-prelayernorm", "RobertaPreLayerNormForTokenClassification"),
         ("roc_bert", "RoCBertForTokenClassification"),
         ("roformer", "RoFormerForTokenClassification"),
+        ("sarvam_mla", "DeepseekV3ForTokenClassification"),
         ("seed_oss", "SeedOssForTokenClassification"),
         ("smollm3", "SmolLM3ForTokenClassification"),
         ("squeezebert", "SqueezeBertForTokenClassification"),
@@ -1651,6 +1705,14 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     ]
 )
 
+MODEL_FOR_TDT_MAPPING_NAMES = OrderedDict(
+    [
+        # Model for Token-and-Duration Transducer (TDT) mapping.
+        ("parakeet_tdt", "ParakeetForTDT"),
+    ]
+)
+
+
 MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Audio Classification mapping
@@ -1688,6 +1750,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         # Model for Text-To-Waveform mapping
         ("bark", "BarkModel"),
         ("csm", "CsmForConditionalGeneration"),
+        ("dia", "DiaForConditionalGeneration"),
         ("fastspeech2_conformer_with_hifigan", "FastSpeech2ConformerWithHifiGan"),
         ("higgs_audio_v2", "HiggsAudioV2ForConditionalGeneration"),
         ("musicgen", "MusicgenForConditionalGeneration"),
@@ -1713,6 +1776,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("metaclip_2", "MetaClip2Model"),
         ("siglip", "SiglipModel"),
         ("siglip2", "Siglip2Model"),
+        ("videoprism", "VideoPrismClipModel"),
     ]
 )
 
@@ -1762,6 +1826,27 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     ]
 )
 
+# Model for Promptable Concept Segmentation mapping
+# facebook/sam3 checkpoint uses sam3_video config but can be used for single-image inference
+MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING_NAMES = OrderedDict(
+    [
+        ("sam3", "Sam3Model"),
+        ("sam3_video", "Sam3Model"),
+    ]
+)
+
+# Model for Promptable Visual Segmentation mapping
+# facebook/sam2.1-hiera-large checkpoint uses sam2_video config but can be used for single-image inference
+MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING_NAMES = OrderedDict(
+    [
+        ("edgetam", "EdgeTamModel"),
+        ("sam", "SamModel"),
+        ("sam2", "Sam2Model"),
+        ("sam2_video", "Sam2Model"),
+        ("sam3_tracker", "Sam3TrackerModel"),
+        ("sam3_video", "Sam3TrackerModel"),
+    ]
+)
 
 MODEL_FOR_KEYPOINT_DETECTION_MAPPING_NAMES = OrderedDict(
     [
@@ -1795,6 +1880,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
         ("longformer", "LongformerModel"),
         ("mllama", "MllamaTextModel"),
         ("mobilebert", "MobileBertModel"),
+        ("molmo2_text", "Molmo2TextModel"),
         ("mt5", "MT5EncoderModel"),
         ("nystromformer", "NystromformerModel"),
         ("reformer", "ReformerModel"),
@@ -1829,6 +1915,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
 
 MODEL_FOR_TIME_SERIES_PREDICTION_MAPPING_NAMES = OrderedDict(
     [
+        ("ctsm", "CtsmModelForPrediction"),
         ("timesfm", "TimesFmModelForPrediction"),
         ("timesfm2_5", "TimesFm2_5ModelForPrediction"),
     ]
@@ -1848,6 +1935,12 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     ]
 )
 
+MODEL_FOR_FORCED_ALIGNMENT_MAPPING_NAMES = OrderedDict(
+    [
+        ("qwen3_forced_aligner", "Qwen3ASRForForcedAlignment"),
+    ]
+)
+
 MODEL_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_MAPPING_NAMES)
 MODEL_FOR_PRETRAINING_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_PRETRAINING_MAPPING_NAMES)
 MODEL_FOR_CAUSAL_LM_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
@@ -1921,6 +2014,7 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
     CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
 )
 MODEL_FOR_CTC_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_CTC_MAPPING_NAMES)
+MODEL_FOR_TDT_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_TDT_MAPPING_NAMES)
 MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES)
 MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING = _LazyAutoMapping(
     CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES
@@ -1937,6 +2031,14 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
 
 MODEL_FOR_MASK_GENERATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_MASK_GENERATION_MAPPING_NAMES)
 
+MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING_NAMES
+)
+
+MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING = _LazyAutoMapping(
+    CONFIG_MAPPING_NAMES, MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING_NAMES
+)
+
 MODEL_FOR_KEYPOINT_DETECTION_MAPPING = _LazyAutoMapping(
     CONFIG_MAPPING_NAMES, MODEL_FOR_KEYPOINT_DETECTION_MAPPING_NAMES
 )
@@ -1961,11 +2063,21 @@ class _BaseModelWithGenerate(PreTrainedModel, GenerationMixin):
 
 MODEL_FOR_AUDIO_TOKENIZATION_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_AUDIO_TOKENIZATION_NAMES)
 
+MODEL_FOR_FORCED_ALIGNMENT_MAPPING = _LazyAutoMapping(CONFIG_MAPPING_NAMES, MODEL_FOR_FORCED_ALIGNMENT_MAPPING_NAMES)
+
 
 class AutoModelForMaskGeneration(_BaseAutoModelClass):
     _model_mapping = MODEL_FOR_MASK_GENERATION_MAPPING
 
 
+class AutoModelForPromptableConceptSegmentation(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING
+
+
+class AutoModelForPromptableVisualSegmentation(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING
+
+
 class AutoModelForKeypointDetection(_BaseAutoModelClass):
     _model_mapping = MODEL_FOR_KEYPOINT_DETECTION_MAPPING
 
@@ -2244,6 +2356,13 @@ class AutoModelForCTC(_BaseAutoModelClass):
 AutoModelForCTC = auto_class_update(AutoModelForCTC, head_doc="connectionist temporal classification")
 
 
+class AutoModelForTDT(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_TDT_MAPPING
+
+
+AutoModelForTDT = auto_class_update(AutoModelForTDT, head_doc="token-and-duration transducer")
+
+
 class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
     _model_mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
 
@@ -2297,6 +2416,13 @@ class AutoModelForAudioTokenization(_BaseAutoModelClass):
 )
 
 
+class AutoModelForForcedAlignment(_BaseAutoModelClass):
+    _model_mapping = MODEL_FOR_FORCED_ALIGNMENT_MAPPING
+
+
+AutoModelForForcedAlignment = auto_class_update(AutoModelForForcedAlignment, head_doc="forced alignment")
+
+
 __all__ = [
     "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING",
     "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING",
@@ -2306,6 +2432,8 @@ class AutoModelForAudioTokenization(_BaseAutoModelClass):
     "MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING",
     "MODEL_FOR_CAUSAL_LM_MAPPING",
     "MODEL_FOR_CTC_MAPPING",
+    "MODEL_FOR_FORCED_ALIGNMENT_MAPPING",
+    "MODEL_FOR_TDT_MAPPING",
     "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING",
     "MODEL_FOR_DEPTH_ESTIMATION_MAPPING",
     "MODEL_FOR_TEXT_RECOGNITION_MAPPING",
@@ -2324,6 +2452,8 @@ class AutoModelForAudioTokenization(_BaseAutoModelClass):
     "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING",
     "MODEL_FOR_OBJECT_DETECTION_MAPPING",
     "MODEL_FOR_PRETRAINING_MAPPING",
+    "MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING",
+    "MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING",
     "MODEL_FOR_QUESTION_ANSWERING_MAPPING",
     "MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING",
     "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING",
@@ -2354,6 +2484,8 @@ class AutoModelForAudioTokenization(_BaseAutoModelClass):
     "AutoModelForAudioXVector",
     "AutoModelForCausalLM",
     "AutoModelForCTC",
+    "AutoModelForForcedAlignment",
+    "AutoModelForTDT",
     "AutoModelForDepthEstimation",
     "AutoModelForTextRecognition",
     "AutoModelForTableRecognition",
@@ -2372,6 +2504,8 @@ class AutoModelForAudioTokenization(_BaseAutoModelClass):
     "AutoModelForNextSentencePrediction",
     "AutoModelForObjectDetection",
     "AutoModelForPreTraining",
+    "AutoModelForPromptableConceptSegmentation",
+    "AutoModelForPromptableVisualSegmentation",
     "AutoModelForQuestionAnswering",
     "AutoModelForSemanticSegmentation",
     "AutoModelForSeq2SeqLM",
diff --git a/src/transformers/models/auto/processing_auto.py b/src/transformers/models/auto/processing_auto.py
index 8bb2ca2c9b62..bbaf5041c0bd 100644
--- a/src/transformers/models/auto/processing_auto.py
+++ b/src/transformers/models/auto/processing_auto.py
@@ -53,6 +53,7 @@
             ("altclip", "AltCLIPProcessor"),
             ("aria", "AriaProcessor"),
             ("audioflamingo3", "AudioFlamingo3Processor"),
+            ("audiovisualflamingo", "AudioVisualFlamingoProcessor"),
             ("aya_vision", "AyaVisionProcessor"),
             ("bark", "BarkProcessor"),
             ("blip", "BlipProcessor"),
@@ -69,6 +70,7 @@
             ("colmodernvbert", "ColModernVBertProcessor"),
             ("colpali", "ColPaliProcessor"),
             ("colqwen2", "ColQwen2Processor"),
+            ("deepseek_ocr2", "DeepseekOcr2Processor"),
             ("deepseek_vl", "DeepseekVLProcessor"),
             ("deepseek_vl_hybrid", "DeepseekVLHybridProcessor"),
             ("dia", "DiaProcessor"),
@@ -76,6 +78,7 @@
             ("emu3", "Emu3Processor"),
             ("ernie4_5_vl_moe", "Ernie4_5_VLMoeProcessor"),
             ("evolla", "EvollaProcessor"),
+            ("exaone4_5", "Qwen2_5_VLProcessor"),
             ("flava", "FlavaProcessor"),
             ("florence2", "Florence2Processor"),
             ("fuyu", "FuyuProcessor"),
@@ -89,7 +92,9 @@
             ("glm_image", "Glm4vProcessor"),
             ("glmasr", "GlmAsrProcessor"),
             ("got_ocr2", "GotOcr2Processor"),
+            ("granite4_vision", "Granite4VisionProcessor"),
             ("granite_speech", "GraniteSpeechProcessor"),
+            ("granite_speech_plus", "GraniteSpeechProcessor"),
             ("grounding-dino", "GroundingDinoProcessor"),
             ("groupvit", "CLIPProcessor"),
             ("higgs_audio_v2", "HiggsAudioV2Processor"),
@@ -101,6 +106,7 @@
             ("instructblipvideo", "InstructBlipVideoProcessor"),
             ("internvl", "InternVLProcessor"),
             ("janus", "JanusProcessor"),
+            ("kimi2_6", "Kimi26Processor"),
             ("kosmos-2", "Kosmos2Processor"),
             ("kosmos-2.5", "Kosmos2_5Processor"),
             ("kyutai_speech_to_text", "KyutaiSpeechToTextProcessor"),
@@ -124,6 +130,7 @@
             ("mllama", "MllamaProcessor"),
             ("mm-grounding-dino", "GroundingDinoProcessor"),
             ("modernvbert", "Idefics3Processor"),
+            ("molmo2", "Molmo2Processor"),
             ("moonshine", "Wav2Vec2Processor"),
             ("moonshine_streaming", "MoonshineStreamingProcessor"),
             ("musicflamingo", "MusicFlamingoProcessor"),
@@ -134,6 +141,9 @@
             ("owlvit", "OwlViTProcessor"),
             ("paddleocr_vl", "PaddleOCRVLProcessor"),
             ("paligemma", "PaliGemmaProcessor"),
+            ("parakeet_ctc", "ParakeetProcessor"),
+            ("parakeet_tdt", "ParakeetProcessor"),
+            ("penguinvl", "PenguinVLProcessor"),
             ("perception_lm", "PerceptionLMProcessor"),
             ("phi4_multimodal", "Phi4MultimodalProcessor"),
             ("pi0", "PI0Processor"),
@@ -141,6 +151,7 @@
             ("pixtral", "PixtralProcessor"),
             ("pop2piano", "Pop2PianoProcessor"),
             ("pp_chart2table", "PPChart2TableProcessor"),
+            ("pp_formulanet", "PPFormulaNetProcessor"),
             ("qianfan_ocr", "QianfanOCRProcessor"),
             ("qwen2_5_omni", "Qwen2_5OmniProcessor"),
             ("qwen2_5_vl", "Qwen2_5_VLProcessor"),
@@ -148,6 +159,8 @@
             ("qwen2_vl", "Qwen2VLProcessor"),
             ("qwen3_5", "Qwen3VLProcessor"),
             ("qwen3_5_moe", "Qwen3VLProcessor"),
+            ("qwen3_asr", "Qwen3ASRProcessor"),
+            ("qwen3_forced_aligner", "Qwen3ASRProcessor"),
             ("qwen3_omni_moe", "Qwen3OmniMoeProcessor"),
             ("qwen3_vl", "Qwen3VLProcessor"),
             ("qwen3_vl_moe", "Qwen3VLProcessor"),
@@ -174,6 +187,7 @@
             ("unispeech-sat", "Wav2Vec2Processor"),
             ("vibevoice_asr", "VibeVoiceAsrProcessor"),
             ("video_llava", "VideoLlavaProcessor"),
+            ("videoprism", "VideoPrismProcessor"),
             ("vilt", "ViltProcessor"),
             ("vipllava", "LlavaProcessor"),
             ("vision-text-dual-encoder", "VisionTextDualEncoderProcessor"),
diff --git a/src/transformers/models/auto/tokenization_auto.py b/src/transformers/models/auto/tokenization_auto.py
index bb0c13f7dbcc..e564ad17436c 100644
--- a/src/transformers/models/auto/tokenization_auto.py
+++ b/src/transformers/models/auto/tokenization_auto.py
@@ -58,7 +58,6 @@
 logger = logging.get_logger(__name__)
 
 # V5: Simplified mapping - single tokenizer class per model type (always prefer tokenizers-based)
-REGISTERED_TOKENIZER_CLASSES: dict[str, type[Any]] = {}
 REGISTERED_FAST_ALIASES: dict[str, type[Any]] = {}
 
 TOKENIZER_MAPPING_NAMES = OrderedDict[str, str | None](
@@ -172,6 +171,7 @@
         ("led", "LEDTokenizer" if is_tokenizers_available() else None),
         ("lighton_ocr", "Qwen2TokenizerFast" if is_tokenizers_available() else None),
         ("lilt", "RobertaTokenizer" if is_tokenizers_available() else None),
+        ("llama", "LlamaTokenizer" if is_tokenizers_available() else None),
         ("longformer", "RobertaTokenizer" if is_tokenizers_available() else None),
         ("luke", "LukeTokenizer"),
         ("lxmert", "LxmertTokenizer" if is_tokenizers_available() else None),
@@ -246,6 +246,8 @@
         ("ovis2", "Qwen2Tokenizer" if is_tokenizers_available() else None),
         ("owlv2", "CLIPTokenizer" if is_tokenizers_available() else None),
         ("owlvit", "CLIPTokenizer" if is_tokenizers_available() else None),
+        ("parakeet_ctc", "ParakeetTokenizer" if is_tokenizers_available() else None),
+        ("parakeet_tdt", "ParakeetTokenizer" if is_tokenizers_available() else None),
         ("pegasus", "PegasusTokenizer" if is_tokenizers_available() else None),
         ("pegasus_x", "PegasusTokenizer" if is_tokenizers_available() else None),
         ("perceiver", "PerceiverTokenizer"),
@@ -259,6 +261,7 @@
             else ("TokenizersBackend" if is_tokenizers_available() else None),
         ),
         ("plbart", "PLBartTokenizer" if is_tokenizers_available() else None),
+        ("pp_formulanet", "NougatTokenizer" if is_tokenizers_available() else None),
         ("prophetnet", "ProphetNetTokenizer"),
         ("qdqbert", "BertTokenizer" if is_tokenizers_available() else None),
         ("qianfan_ocr", "Qwen2Tokenizer" if is_tokenizers_available() else None),
@@ -271,6 +274,7 @@
         ("qwen3", "Qwen2Tokenizer" if is_tokenizers_available() else None),
         ("qwen3_5", "Qwen3_5Tokenizer" if is_tokenizers_available() else None),
         ("qwen3_5_moe", "Qwen3_5Tokenizer" if is_tokenizers_available() else None),
+        ("qwen3_asr", "Qwen2Tokenizer" if is_tokenizers_available() else None),
         ("qwen3_moe", "Qwen2Tokenizer" if is_tokenizers_available() else None),
         ("qwen3_next", "Qwen2Tokenizer" if is_tokenizers_available() else None),
         ("qwen3_omni_moe", "Qwen2Tokenizer" if is_tokenizers_available() else None),
@@ -310,6 +314,7 @@
         ("umt5", "T5Tokenizer" if is_tokenizers_available() else None),
         ("unispeech", "Wav2Vec2CTCTokenizer"),
         ("unispeech-sat", "Wav2Vec2CTCTokenizer"),
+        ("videoprism", "VideoPrismTokenizer" if is_sentencepiece_available() else None),
         ("vilt", "BertTokenizer" if is_tokenizers_available() else None),
         ("visual_bert", "BertTokenizer" if is_tokenizers_available() else None),
         ("vits", "VitsTokenizer"),
@@ -350,6 +355,7 @@
     "chatlm",
     "deepseek_v2",
     "deepseek_v3",
+    "deepseek_v4",
     "deepseek_vl",
     "deepseek_vl_hybrid",
     "deepseek_vl_v2",
@@ -414,8 +420,10 @@ def tokenizer_class_from_name(class_name: str) -> type[Any] | None:
     if class_name in REGISTERED_FAST_ALIASES:
         return REGISTERED_FAST_ALIASES[class_name]
 
-    if class_name in REGISTERED_TOKENIZER_CLASSES:
-        return REGISTERED_TOKENIZER_CLASSES[class_name]
+    # User-registered classes take priority over built-ins
+    for tokenizer in TOKENIZER_MAPPING._extra_content.values():
+        if getattr(tokenizer, "__name__", None) == class_name:
+            return tokenizer
 
     if class_name == "TokenizersBackend":
         return TokenizersBackend
@@ -442,10 +450,6 @@ def tokenizer_class_from_name(class_name: str) -> type[Any] | None:
             except AttributeError:
                 continue
 
-    for tokenizer in TOKENIZER_MAPPING._extra_content.values():
-        if getattr(tokenizer, "__name__", None) == class_name:
-            return tokenizer
-
     # We did not find the class, but maybe it's because a dep is missing. In that case, the class will be in the main
     # We did not find the class, but maybe it's because a dep is missing. In that case, the class will be in the main
     # init and we return the proper dummy to get an appropriate error message.
@@ -779,6 +783,12 @@ def from_pretrained(
                 trust_remote_code, pretrained_model_name_or_path, has_local_code, has_remote_code, upstream_repo
             )
 
+        # Detect missing dependency for Voxtral early and provide a clear error message
+        if getattr(config, "model_type", None) == "voxtral" and not is_mistral_common_available():
+            raise ImportError(
+                "The Voxtral tokenizer requires the 'mistral-common' package. Please install it using `pip install mistral-common`."
+            )
+
         if has_remote_code and trust_remote_code and not explicit_local_code:
             # BC v5: register *Fast aliases before remote code loads.
             if tokenizer_config_class:
@@ -822,6 +832,10 @@ def from_pretrained(
 
         model_type = config_class_to_model_type(type(config).__name__) or getattr(config, "model_type", None)
         if model_type is not None:
+            if model_type == "voxtral" and not is_mistral_common_available():
+                raise ImportError(
+                    "The Voxtral tokenizer requires the 'mistral-common' package. Use `pip install mistral-common` to install the package."
+                )
             tokenizer_class = TOKENIZER_MAPPING.get(type(config), TokenizersBackend)
             if tokenizer_class is not None:
                 return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
@@ -868,10 +882,6 @@ def register(
             else:
                 raise ValueError("You need to pass a `tokenizer_class`")
 
-        for candidate in (slow_tokenizer_class, fast_tokenizer_class, tokenizer_class):
-            if candidate is not None:
-                REGISTERED_TOKENIZER_CLASSES[candidate.__name__] = candidate
-
         if slow_tokenizer_class is not None and fast_tokenizer_class is not None:
             REGISTERED_FAST_ALIASES[slow_tokenizer_class.__name__] = fast_tokenizer_class
 
diff --git a/src/transformers/models/auto/video_processing_auto.py b/src/transformers/models/auto/video_processing_auto.py
index ae5b6f8b9ed3..49db6d3efc9a 100644
--- a/src/transformers/models/auto/video_processing_auto.py
+++ b/src/transformers/models/auto/video_processing_auto.py
@@ -80,6 +80,8 @@
 
 def video_processor_class_from_name(class_name: str):
     for module_name, extractor in VIDEO_PROCESSOR_MAPPING_NAMES.items():
+        if extractor is None:
+            continue
         if class_name == extractor:
             module_name = model_type_to_module_name(module_name)
 
diff --git a/src/transformers/models/autoformer/modeling_autoformer.py b/src/transformers/models/autoformer/modeling_autoformer.py
index 9ff0403b52f6..90c2ded880ae 100644
--- a/src/transformers/models/autoformer/modeling_autoformer.py
+++ b/src/transformers/models/autoformer/modeling_autoformer.py
@@ -40,12 +40,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class AutoFormerDecoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -77,12 +77,12 @@ class AutoFormerDecoderOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Autoformer model output that contains the additional trend output.
     """
 )
+@dataclass
 class AutoformerModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
diff --git a/src/transformers/models/aya_vision/modeling_aya_vision.py b/src/transformers/models/aya_vision/modeling_aya_vision.py
index 38f434d405a5..dfdef60c7ce9 100644
--- a/src/transformers/models/aya_vision/modeling_aya_vision.py
+++ b/src/transformers/models/aya_vision/modeling_aya_vision.py
@@ -100,12 +100,12 @@ class AyaVisionPreTrainedModel(PreTrainedModel):
     _supports_attention_backend = True
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for AyaVision causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class AyaVisionCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -227,9 +227,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/aya_vision/processing_aya_vision.py b/src/transformers/models/aya_vision/processing_aya_vision.py
index 90188519aba7..49900ffcb4f0 100644
--- a/src/transformers/models/aya_vision/processing_aya_vision.py
+++ b/src/transformers/models/aya_vision/processing_aya_vision.py
@@ -13,14 +13,13 @@
 # limitations under the License.
 
 
-from ...image_processing_utils import BatchFeature
-from ...image_utils import ImageInput, make_flat_list_of_images
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import BatchFeature, MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from ..got_ocr2.image_processing_got_ocr2 import GotOcr2ImageProcessorKwargs
 
 
 class AyaVisionProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: GotOcr2ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding_side": "left",
@@ -35,6 +34,8 @@ class AyaVisionProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class AyaVisionProcessor(ProcessorMixin):
+    valid_processor_kwargs = AyaVisionProcessorKwargs
+
     def __init__(
         self,
         image_processor=None,
@@ -87,21 +88,21 @@ def __init__(
         self.tile_token = tile_token
         self.tile_global_token = tile_global_token
         self.image_token_id = tokenizer.convert_tokens_to_ids(self.img_patch_token)
-        self.image_ids = tokenizer.convert_tokens_to_ids(
-            [img_patch_token, tile_token, tile_global_token, start_of_img_token, end_of_img_token]
-        )
-
-    def _prompt_split_image(self, num_patches):
-        """
-        Create a structured string representation of image tokens
-
-        Args:
-           num_patches: Number of patches in the image
 
-        Returns:
-            String with appropriate image tokens
-        """
+    @property
+    def image_token_ids(self) -> list[int]:
+        return self.tokenizer.convert_tokens_to_ids(
+            [
+                self.img_patch_token,
+                self.tile_token,
+                self.tile_global_token,
+                self.start_of_img_token,
+                self.end_of_img_token,
+            ]
+        )
 
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        num_patches = image_inputs["num_patches"][image_idx]
         img_patches_per_tile = (self.img_size // self.patch_size) ** 2
         img_string = f"{self.start_of_img_token}"
         if num_patches > 1:
@@ -112,65 +113,23 @@ def _prompt_split_image(self, num_patches):
         img_string += f"{self.end_of_img_token}"
         return img_string
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
-        **kwargs: Unpack[AyaVisionProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+    def _check_special_mm_tokens(self, text: list[str], text_inputs: "BatchFeature", modalities: list[str]):
         """
-        if text is None:
-            raise ValueError("You have to specify text.")
-
-        output_kwargs = self._merge_kwargs(
-            AyaVisionProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if not isinstance(text, (list, tuple)):
-            text = [text]
-
-        # Process images
-        image_inputs = {}
-        if images is not None:
-            images = self.image_processor.fetch_images(images)
-            images = make_flat_list_of_images(images)
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            num_patches = image_inputs.pop("num_patches")
-            image_index = 0
-            processed_text = []
-            for prompt in text:
-                new_prompt = prompt
-                while "<image>" in new_prompt:
-                    # Replace the image placeholder with structured image tokens
-                    image_tokens = self._prompt_split_image(num_patches[image_index])
-                    new_prompt = new_prompt.replace("<image>", image_tokens, 1)
-                    image_index += 1
-                processed_text.append(new_prompt)
-
-            if image_index != len(images):
-                raise ValueError("Number of image placeholders in the prompt does not match the number of images.")
-
-            text = processed_text
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+        Checks that number of special tokens in text and processed text is same. The count can be different
+        if tokenized text was truncated, leading to issues in model code.
+        """
+        # Aya visino uses `img_patch_token` instead of image token`
+        token_str = self.img_patch_token
+        token_id = self.image_token_id
+        if token_str is not None and token_id is not None:
+            ids_count = [list(ids).count(token_id) for ids in text_inputs["input_ids"]]
+            text_count = [sample.count(token_str) for sample in text]
+
+            if ids_count != text_count:
+                raise ValueError(
+                    f"Mismatch in `image` token count between text and `input_ids`. Got ids={ids_count} and text={text_count}. "
+                    "Likely due to `truncation='max_length'`. Please disable truncation or increase `max_length`."
+                )
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
@@ -204,5 +163,9 @@ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
 
         return MultiModalData(**vision_data)
 
+    @property
+    def unused_input_names(self) -> list[str]:
+        return ["num_patches"]
+
 
 __all__ = ["AyaVisionProcessor"]
diff --git a/src/transformers/models/bamba/modeling_bamba.py b/src/transformers/models/bamba/modeling_bamba.py
index 90129fc998b1..e025d335ebd4 100644
--- a/src/transformers/models/bamba/modeling_bamba.py
+++ b/src/transformers/models/bamba/modeling_bamba.py
@@ -132,7 +132,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -292,10 +292,11 @@ def forward(
 
 
 class BambaRMSNormGated(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
+    def __init__(self, hidden_size, group_size, eps=1e-6):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
+        self.group_size = group_size
 
     def forward(self, hidden_states, gate=None):
         input_dtype = hidden_states.dtype
@@ -303,8 +304,12 @@ def forward(self, hidden_states, gate=None):
 
         if gate is not None:
             hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        *prefix_dims, last_dim = hidden_states.shape
+        group_count = last_dim // self.group_size
+        hidden_states_group = hidden_states.view(*prefix_dims, group_count, self.group_size)
+        variance = hidden_states_group.pow(2).mean(-1, keepdim=True)
+        hidden_states_group = hidden_states_group * torch.rsqrt(variance + self.variance_epsilon)
+        hidden_states = hidden_states_group.view(*prefix_dims, group_count * self.group_size)
 
         return self.weight * hidden_states.to(input_dtype)
 
@@ -440,7 +445,9 @@ def __init__(self, config: BambaConfig, layer_idx: int):
         # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
         A = torch.arange(1, self.num_heads + 1)
         self.A_log = nn.Parameter(torch.log(A))
-        self.norm = BambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
+        self.norm = BambaRMSNormGated(
+            self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=self.layer_norm_epsilon
+        )
         self.D = nn.Parameter(torch.ones(self.num_heads))
 
         self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
diff --git a/src/transformers/models/bamba/modular_bamba.py b/src/transformers/models/bamba/modular_bamba.py
index a79b26ff8fe9..59ef4d0d1657 100644
--- a/src/transformers/models/bamba/modular_bamba.py
+++ b/src/transformers/models/bamba/modular_bamba.py
@@ -197,7 +197,9 @@ def __init__(self, config: BambaConfig, layer_idx: int):
         # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
         A = torch.arange(1, self.num_heads + 1)
         self.A_log = nn.Parameter(torch.log(A))
-        self.norm = BambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
+        self.norm = BambaRMSNormGated(
+            self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=self.layer_norm_epsilon
+        )
         self.D = nn.Parameter(torch.ones(self.num_heads))
 
         self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
diff --git a/src/transformers/models/beit/image_processing_beit.py b/src/transformers/models/beit/image_processing_beit.py
index 53053f644539..a95c8e9752be 100644
--- a/src/transformers/models/beit/image_processing_beit.py
+++ b/src/transformers/models/beit/image_processing_beit.py
@@ -127,9 +127,10 @@ def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
         """Reduce label values by 1, replacing 0 with 255."""
         for idx in range(len(labels)):
             label = labels[idx]
-            label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label)
-            label = label - 1
-            label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label)
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
             labels[idx] = label
         return labels
 
diff --git a/src/transformers/models/beit/image_processing_pil_beit.py b/src/transformers/models/beit/image_processing_pil_beit.py
index e3ccf12e909b..ff78dac96c40 100644
--- a/src/transformers/models/beit/image_processing_pil_beit.py
+++ b/src/transformers/models/beit/image_processing_pil_beit.py
@@ -120,10 +120,10 @@ def _preprocess_image_like_inputs(
 
     def reduce_label(self, image: np.ndarray) -> np.ndarray:
         """Reduce label values by 1, replacing 0 with 255."""
-        # Avoid using underflow conversion
-        image[image == 0] = 255
-        image = image - 1
-        image[image == 254] = 255
+        image = image.copy()
+        ignore_mask = (image == 0) | (image == 255)
+        image[ignore_mask] = 255
+        image[~ignore_mask] = image[~ignore_mask] - 1
         return image
 
     def _preprocess(
diff --git a/src/transformers/models/bert/modeling_bert.py b/src/transformers/models/bert/modeling_bert.py
index 2692076b895f..18b5ba223fa0 100755
--- a/src/transformers/models/bert/modeling_bert.py
+++ b/src/transformers/models/bert/modeling_bert.py
@@ -558,12 +558,12 @@ def _init_weights(self, module):
             init.zeros_(module.token_type_ids)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`BertForPreTraining`].
     """
 )
+@dataclass
 class BertForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/big_bird/modeling_big_bird.py b/src/transformers/models/big_bird/modeling_big_bird.py
index dbca90bbdf20..6b72cc65fd56 100755
--- a/src/transformers/models/big_bird/modeling_big_bird.py
+++ b/src/transformers/models/big_bird/modeling_big_bird.py
@@ -1458,12 +1458,12 @@ def _init_weights(self, module):
             init.zeros_(module.token_type_ids)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`BigBirdForPreTraining`].
     """
 )
+@dataclass
 class BigBirdForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -1483,12 +1483,12 @@ class BigBirdForPreTrainingOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of question answering models.
     """
 )
+@dataclass
 class BigBirdForQuestionAnsweringModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/bitnet/modeling_bitnet.py b/src/transformers/models/bitnet/modeling_bitnet.py
index 14c1581b250f..6f6f969e9b6b 100644
--- a/src/transformers/models/bitnet/modeling_bitnet.py
+++ b/src/transformers/models/bitnet/modeling_bitnet.py
@@ -318,7 +318,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/blip/modeling_blip.py b/src/transformers/models/blip/modeling_blip.py
index f47213818d49..469d6055797f 100644
--- a/src/transformers/models/blip/modeling_blip.py
+++ b/src/transformers/models/blip/modeling_blip.py
@@ -53,13 +53,13 @@ def image_text_contrastive_loss(similarity: torch.Tensor) -> torch.Tensor:
     return (caption_loss + image_loss) / 2.0
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the
     last hidden states. This class also adds the loss term from the text decoder.
     """
 )
+@dataclass
 class BlipForConditionalGenerationModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -89,13 +89,13 @@ class BlipForConditionalGenerationModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the
     last hidden states. This class also adds the loss term from the text decoder.
     """
 )
+@dataclass
 class BlipTextVisionModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -111,7 +111,6 @@ class BlipTextVisionModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Adapted from the base class for vision model's outputs that also contains image embeddings of the pooling of the
@@ -119,6 +118,7 @@ class BlipTextVisionModelOutput(ModelOutput):
     scores.
     """
 )
+@dataclass
 class BlipImageTextMatchingModelOutput(ModelOutput):
     r"""
     itm_score (`torch.FloatTensor`):
diff --git a/src/transformers/models/blip/modeling_blip_text.py b/src/transformers/models/blip/modeling_blip_text.py
index a62a120f8741..058aff9edaff 100644
--- a/src/transformers/models/blip/modeling_blip_text.py
+++ b/src/transformers/models/blip/modeling_blip_text.py
@@ -444,10 +444,10 @@ class BlipTextPreTrainedModel(PreTrainedModel):
     _can_record_outputs = {
         "hidden_states": BlipTextLayer,
         "attentions": [
-            OutputRecorder(BlipTextSelfAttention, index=1, layer_name=".attention."),
+            OutputRecorder(BlipTextSelfAttention, index=1, layer_name=r"\.attention\."),
         ],
         "cross_attentions": [
-            OutputRecorder(BlipTextSelfAttention, index=1, layer_name=".crossattention."),
+            OutputRecorder(BlipTextSelfAttention, index=1, layer_name=r"\.crossattention\."),
         ],
     }
 
diff --git a/src/transformers/models/blip/processing_blip.py b/src/transformers/models/blip/processing_blip.py
index a7e329e351a7..d03e6e0e4bbc 100644
--- a/src/transformers/models/blip/processing_blip.py
+++ b/src/transformers/models/blip/processing_blip.py
@@ -15,9 +15,7 @@
 Processor class for Blip.
 """
 
-from ...image_utils import ImageInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
 
 
@@ -39,46 +37,15 @@ class BlipProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class BlipProcessor(ProcessorMixin):
+    valid_processor_kwargs = BlipProcessorKwargs
+
     def __init__(self, image_processor, tokenizer, **kwargs):
         tokenizer.return_token_type_ids = False
         super().__init__(image_processor, tokenizer)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: str | list[str] | TextInput | PreTokenizedInput | None = None,
-        **kwargs: Unpack[BlipProcessorKwargs],
-    ) -> BatchEncoding:
-        if images is None and text is None:
-            raise ValueError("You have to specify either images or text.")
-
-        text_encoding = None
-
-        # add pixel_values encoding. If we also have text_encoding, update image encoding and return it.
-        # else, return the text encoding.
-        output_kwargs = self._merge_kwargs(
-            BlipProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if text is not None:
-            text_encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        if images is not None:
-            encoding_image_processor = self.image_processor(images, **output_kwargs["images_kwargs"])
-
-            if text_encoding is not None:
-                encoding_image_processor.update(text_encoding)
-            return encoding_image_processor
-
-        return text_encoding
-
     @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        tokenizer_input_names = [name for name in tokenizer_input_names if name != "token_type_ids"]
-        return tokenizer_input_names + image_processor_input_names
+    def unused_input_names(self) -> list[str]:
+        return ["token_type_ids"]
 
 
 __all__ = ["BlipProcessor"]
diff --git a/src/transformers/models/blip_2/modeling_blip_2.py b/src/transformers/models/blip_2/modeling_blip_2.py
index c5c022d39066..58888aab7d7f 100644
--- a/src/transformers/models/blip_2/modeling_blip_2.py
+++ b/src/transformers/models/blip_2/modeling_blip_2.py
@@ -56,8 +56,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithVisionQformerOutputs(BaseModelOutputWithPooling):
     r"""
     vision_outputs (`BaseModelOutputWithPooling`):
@@ -70,12 +70,12 @@ class BaseModelOutputWithVisionQformerOutputs(BaseModelOutputWithPooling):
     qformer_outputs: BaseModelOutputWithPoolingAndCrossAttentions | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class defining the outputs of [`Blip2ForConditionalGeneration`].
     """
 )
+@dataclass
 class Blip2ForConditionalGenerationModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -887,10 +887,10 @@ class Blip2QFormerModel(Blip2PreTrainedModel):
     _can_record_outputs = {
         "hidden_states": Blip2QFormerLayer,
         "attentions": [
-            OutputRecorder(Blip2QFormerMultiHeadAttention, index=1, layer_name=".attention"),
+            OutputRecorder(Blip2QFormerMultiHeadAttention, index=1, layer_name=r"\.attention"),
         ],
         "cross_attentions": [
-            OutputRecorder(Blip2QFormerMultiHeadAttention, index=1, layer_name=".crossattention"),
+            OutputRecorder(Blip2QFormerMultiHeadAttention, index=1, layer_name=r"\.crossattention"),
         ],
     }
 
@@ -1240,7 +1240,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
@@ -1686,7 +1686,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
@@ -1913,7 +1913,7 @@ def generate(
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
 
diff --git a/src/transformers/models/blip_2/processing_blip_2.py b/src/transformers/models/blip_2/processing_blip_2.py
index e339854a6736..fb90adb8c8a7 100644
--- a/src/transformers/models/blip_2/processing_blip_2.py
+++ b/src/transformers/models/blip_2/processing_blip_2.py
@@ -77,7 +77,7 @@ def __call__(
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
         max_length = output_kwargs["text_kwargs"].pop("max_length", None)
         if max_length is not None:
-            output_kwargs["text_kwargs"]["max_length"] = max_length - self.num_query_tokens
+            output_kwargs["text_kwargs"]["max_length"] = max_length - (self.num_query_tokens or 0)
 
         encoding = BatchFeature(tensor_type=return_tensors)
         if text is not None:
diff --git a/src/transformers/models/blt/modeling_blt.py b/src/transformers/models/blt/modeling_blt.py
index 778f7ba80cf6..b6501ab78f3f 100644
--- a/src/transformers/models/blt/modeling_blt.py
+++ b/src/transformers/models/blt/modeling_blt.py
@@ -670,7 +670,7 @@ def forward(
                     attention_mask=encoder_attention_mask,
                     **kwargs,
                 )
-                patch_embeds = patch_embeds + cross_attention_output
+                patch_embeds = patch_embeds + cross_attention_output.to(patch_embeds.device)
         encoder_cross_states = patch_embeds
         return hidden_states, encoder_cross_states
 
@@ -1228,7 +1228,7 @@ def forward(
         else:
             batch_size, sequence_length = input_ids.shape
             encoder_embeds = compute_hash_embeddings(
-                input_ids,
+                input_ids.to(self.local_encoder.embed_tokens.weight.device),
                 self.local_encoder,
                 self.encoder_hash_tok_embedding,
                 self.config.encoder_hash_byte_group_nb_functions,
@@ -1241,7 +1241,7 @@ def forward(
                 if input_ids is None:
                     raise ValueError("input_ids is required for entropy-based patching")
                 _, patch_lengths, _ = self.patcher(
-                    input_ids,
+                    input_ids.to(self.patcher.embed_tokens.weight.device),
                     patch_size=self.config.patch_size,
                     threshold=self.config.patching_threshold,
                     max_patch_length=self.config.max_patch_length,
diff --git a/src/transformers/models/blt/modular_blt.py b/src/transformers/models/blt/modular_blt.py
index 4dfff77263db..8796e2a40e82 100644
--- a/src/transformers/models/blt/modular_blt.py
+++ b/src/transformers/models/blt/modular_blt.py
@@ -604,7 +604,7 @@ def forward(
                     attention_mask=encoder_attention_mask,
                     **kwargs,
                 )
-                patch_embeds = patch_embeds + cross_attention_output
+                patch_embeds = patch_embeds + cross_attention_output.to(patch_embeds)
         encoder_cross_states = patch_embeds
         return hidden_states, encoder_cross_states
 
@@ -955,7 +955,7 @@ def forward(
         else:
             batch_size, sequence_length = input_ids.shape
             encoder_embeds = compute_hash_embeddings(
-                input_ids,
+                input_ids.to(self.local_encoder.embed_tokens.weight.device),
                 self.local_encoder,
                 self.encoder_hash_tok_embedding,
                 self.config.encoder_hash_byte_group_nb_functions,
@@ -968,7 +968,7 @@ def forward(
                 if input_ids is None:
                     raise ValueError("input_ids is required for entropy-based patching")
                 _, patch_lengths, _ = self.patcher(
-                    input_ids,
+                    input_ids.to(self.patcher.embed_tokens.weight.device),
                     patch_size=self.config.patch_size,
                     threshold=self.config.patching_threshold,
                     max_patch_length=self.config.max_patch_length,
diff --git a/src/transformers/models/bridgetower/modeling_bridgetower.py b/src/transformers/models/bridgetower/modeling_bridgetower.py
index 225289d8367e..3fb525870eee 100644
--- a/src/transformers/models/bridgetower/modeling_bridgetower.py
+++ b/src/transformers/models/bridgetower/modeling_bridgetower.py
@@ -47,12 +47,12 @@
 _TOKENIZER_FOR_DOC = "RobertaTokenizer"
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`BridgeTowerModel`].
     """
 )
+@dataclass
 class BridgeTowerModelOutput(ModelOutput):
     r"""
     text_features (`torch.FloatTensor` of shape `(batch_size, text_sequence_length, hidden_size)`):
@@ -71,12 +71,12 @@ class BridgeTowerModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of ['BridgeTowerForContrastiveLearning']
     """
 )
+@dataclass
 class BridgeTowerContrastiveOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
@@ -820,7 +820,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/bridgetower/processing_bridgetower.py b/src/transformers/models/bridgetower/processing_bridgetower.py
index aa0ea7b4c4da..9424362e519c 100644
--- a/src/transformers/models/bridgetower/processing_bridgetower.py
+++ b/src/transformers/models/bridgetower/processing_bridgetower.py
@@ -17,9 +17,11 @@
 
 from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from .image_processing_bridgetower import BridgeTowerImageProcessorKwargs
 
 
 class BridgeTowerProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: BridgeTowerImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/bros/modeling_bros.py b/src/transformers/models/bros/modeling_bros.py
index 35a1c453ebd1..a23473c067ef 100755
--- a/src/transformers/models/bros/modeling_bros.py
+++ b/src/transformers/models/bros/modeling_bros.py
@@ -40,12 +40,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of token classification models.
     """
 )
+@dataclass
 class BrosSpadeOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/camembert/modeling_camembert.py b/src/transformers/models/camembert/modeling_camembert.py
index 9d10a8aeaef1..c47245a0ae2b 100644
--- a/src/transformers/models/camembert/modeling_camembert.py
+++ b/src/transformers/models/camembert/modeling_camembert.py
@@ -106,7 +106,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/canine/modeling_canine.py b/src/transformers/models/canine/modeling_canine.py
index 01c251796eeb..6cfe857f8611 100644
--- a/src/transformers/models/canine/modeling_canine.py
+++ b/src/transformers/models/canine/modeling_canine.py
@@ -45,7 +45,6 @@
 _PRIMES = [31, 43, 59, 61, 73, 97, 103, 113, 137, 149, 157, 173, 181, 193, 211, 223]
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`CanineModel`]. Based on [`~modeling_outputs.BaseModelOutputWithPooling`], but with slightly
@@ -53,6 +52,7 @@
     Transformer encoders.
     """
 )
+@dataclass
 class CanineModelOutputWithPooling(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
diff --git a/src/transformers/models/chameleon/modeling_chameleon.py b/src/transformers/models/chameleon/modeling_chameleon.py
index af69779959e4..752e4efdcc3b 100644
--- a/src/transformers/models/chameleon/modeling_chameleon.py
+++ b/src/transformers/models/chameleon/modeling_chameleon.py
@@ -47,8 +47,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class ChameleonVQVAEModelOutput(BaseModelOutputWithPooling):
     r"""
     quantized_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
@@ -143,7 +143,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -911,9 +911,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/chameleon/processing_chameleon.py b/src/transformers/models/chameleon/processing_chameleon.py
index b693b875654d..887fba382974 100644
--- a/src/transformers/models/chameleon/processing_chameleon.py
+++ b/src/transformers/models/chameleon/processing_chameleon.py
@@ -55,6 +55,8 @@ class ChameleonProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class ChameleonProcessor(ProcessorMixin):
+    valid_processor_kwargs = ChameleonProcessorKwargs
+
     def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, image_token: str = "<image>"):
         r"""
         image_seq_length (`int`, *optional*, defaults to 1024):
@@ -74,7 +76,10 @@ def __init__(self, image_processor, tokenizer, image_seq_length: int = 1024, ima
         self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
         self.image_start_token_id = tokenizer.convert_tokens_to_ids(self.image_start_token)
         self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
-        self.image_ids = [self.image_token_id, self.image_start_token_id, self.image_end_token_id]
+
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.image_start_token_id, self.image_end_token_id]
 
     @auto_docstring
     def __call__(
@@ -93,42 +98,17 @@ def __call__(
               `None`).
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
         """
-
         if isinstance(text, str):
             text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-        if text is None and images is None:
-            raise ValueError("You must provide either text or images")
-
-        output_kwargs = self._merge_kwargs(
-            ChameleonProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        return_for_text_completion = output_kwargs["text_kwargs"].pop("return_for_text_completion", False)
-
-        # Replace the image token with the expanded image token sequence
-        prompt_strings = []
+
+        # special Chameleon treatment to add sep for chat mode
+        text = [f"{sample}{self.tokenizer.sep_token}" for sample in text]
+        model_inputs = super().__call__(images=images, text=text, **kwargs)
+        return model_inputs
+
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
         one_img_tokens = self.image_start_token + (self.image_token * self.image_seq_length) + self.image_end_token
-        for sample in text:
-            sample = sample.replace(self.image_token, one_img_tokens)
-            if not return_for_text_completion:
-                sample += self.tokenizer.sep_token  # special Chameleon treatment to add sep for chat mode
-            prompt_strings.append(sample)
-
-        image_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+        return one_img_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
diff --git a/src/transformers/models/chinese_clip/modeling_chinese_clip.py b/src/transformers/models/chinese_clip/modeling_chinese_clip.py
index 3c2ddef2e7a4..99828afbda36 100644
--- a/src/transformers/models/chinese_clip/modeling_chinese_clip.py
+++ b/src/transformers/models/chinese_clip/modeling_chinese_clip.py
@@ -517,7 +517,12 @@ class ChineseCLIPPreTrainedModel(PreTrainedModel):
     config: ChineseCLIPConfig
     base_model_prefix = "chinese_clip"
     input_modalities = ("image", "text")
-    _no_split_modules = ["ChineseCLIPVisionEmbeddings", "ChineseCLIPTextEmbeddings", "ChineseCLIPVisionAttention"]
+    _no_split_modules = [
+        "ChineseCLIPVisionEmbeddings",
+        "ChineseCLIPTextEmbeddings",
+        "ChineseCLIPTextLayer",
+        "ChineseCLIPVisionAttention",
+    ]
 
     supports_gradient_checkpointing = True
     _supports_sdpa = True
@@ -653,7 +658,7 @@ def __init__(self, config: ChineseCLIPVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = ChineseCLIPVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = ChineseCLIPVisionEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.post_init()
@@ -690,7 +695,7 @@ def forward(
         >>> pooled_output = outputs.pooler_output  # pooled CLS states
         ```"""
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs: BaseModelOutput = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/chinese_clip/modular_chinese_clip.py b/src/transformers/models/chinese_clip/modular_chinese_clip.py
index 280cb7bd54ae..bb6b05f9ac92 100644
--- a/src/transformers/models/chinese_clip/modular_chinese_clip.py
+++ b/src/transformers/models/chinese_clip/modular_chinese_clip.py
@@ -197,7 +197,12 @@ class ChineseCLIPTextPooler(BertPooler):
 
 @auto_docstring
 class ChineseCLIPPreTrainedModel(CLIPPreTrainedModel):
-    _no_split_modules = ["ChineseCLIPVisionEmbeddings", "ChineseCLIPTextEmbeddings", "ChineseCLIPVisionAttention"]
+    _no_split_modules = [
+        "ChineseCLIPVisionEmbeddings",
+        "ChineseCLIPTextEmbeddings",
+        "ChineseCLIPTextLayer",
+        "ChineseCLIPVisionAttention",
+    ]
     _can_record_outputs = {
         "hidden_states": ChineseCLIPVisionLayer,
         "attentions": ChineseCLIPVisionAttention,
diff --git a/src/transformers/models/chmv2/image_processing_chmv2.py b/src/transformers/models/chmv2/image_processing_chmv2.py
index 3bb82b2dea53..067ba5898734 100644
--- a/src/transformers/models/chmv2/image_processing_chmv2.py
+++ b/src/transformers/models/chmv2/image_processing_chmv2.py
@@ -182,9 +182,10 @@ def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
         """Reduce label values by 1, replacing 0 with 255."""
         for idx in range(len(labels)):
             label = labels[idx]
-            label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label)
-            label = label - 1
-            label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label)
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
             labels[idx] = label
         return labels
 
diff --git a/src/transformers/models/chmv2/modular_chmv2.py b/src/transformers/models/chmv2/modular_chmv2.py
index f61c6687a351..5f44654876c6 100644
--- a/src/transformers/models/chmv2/modular_chmv2.py
+++ b/src/transformers/models/chmv2/modular_chmv2.py
@@ -150,6 +150,17 @@ class CHMv2ImageProcessor(DPTImageProcessor):
     image_std = [0.213, 0.156, 0.143]
     valid_kwargs = CHMv2ImageProcessorKwargs
 
+    def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
+        """Reduce label values by 1, replacing 0 with 255."""
+        for idx in range(len(labels)):
+            label = labels[idx]
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
+            labels[idx] = label
+        return labels
+
     def post_process_depth_estimation(
         self,
         outputs: "DepthEstimatorOutput",
diff --git a/src/transformers/models/circuit_gpt/__init__.py b/src/transformers/models/circuit_gpt/__init__.py
new file mode 100644
index 000000000000..b4bdfa2d0814
--- /dev/null
+++ b/src/transformers/models/circuit_gpt/__init__.py
@@ -0,0 +1,26 @@
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+
+
+_import_structure = {
+    "configuration_circuit_gpt": ["CircuitGptConfig"],
+    "modeling_circuit_gpt": [
+        "CircuitGptForCausalLM",
+        "CircuitGptModel",
+        "CircuitGptPreTrainedModel",
+    ],
+}
+
+if TYPE_CHECKING:
+    from .configuration_circuit_gpt import CircuitGptConfig
+    from .modeling_circuit_gpt import (
+        CircuitGptForCausalLM,
+        CircuitGptModel,
+        CircuitGptPreTrainedModel,
+    )
+else:
+    import sys
+
+    self = sys.modules[__name__]
+    self.__class__ = _LazyModule
diff --git a/src/transformers/models/circuit_gpt/configuration_circuit_gpt.py b/src/transformers/models/circuit_gpt/configuration_circuit_gpt.py
new file mode 100644
index 000000000000..087d9f5f3c20
--- /dev/null
+++ b/src/transformers/models/circuit_gpt/configuration_circuit_gpt.py
@@ -0,0 +1,33 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+# Licensed under the Apache License, Version 2.0 (the "License");
+
+from ...configuration_utils import PretrainedConfig
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class CircuitGptConfig(PretrainedConfig):
+    model_type = "circuit_gpt"
+
+    def __init__(
+        self,
+        vocab_size=50257,
+        n_embd=768,
+        n_layer=12,
+        n_head=12,
+        sparsity=0.0,
+        initializer_range=0.02,
+        layer_norm_epsilon=1e-5,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.sparsity = sparsity
+        self.initializer_range = initializer_range
+        self.layer_norm_epsilon = layer_norm_epsilon
+
+        super().__init__(**kwargs)
diff --git a/src/transformers/models/circuit_gpt/modeling_circuit_gpt.py b/src/transformers/models/circuit_gpt/modeling_circuit_gpt.py
new file mode 100644
index 000000000000..7443edb9059c
--- /dev/null
+++ b/src/transformers/models/circuit_gpt/modeling_circuit_gpt.py
@@ -0,0 +1,132 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+
+import torch
+import torch.nn as nn
+
+from ...modeling_utils import PreTrainedModel
+from .configuration_circuit_gpt import CircuitGptConfig
+
+
+class SparseLinear(nn.Linear):
+    def __init__(self, in_features, out_features, sparsity=0.0, bias=True):
+        super().__init__(in_features, out_features, bias)
+        self.sparsity = sparsity
+
+    def forward(self, input):
+        if self.sparsity <= 0:
+            return super().forward(input)
+
+        w = self.weight
+        k = int(w.numel() * (1 - self.sparsity))
+        if k < w.numel():
+            topk_values, _ = torch.topk(torch.abs(w.flatten()), k)
+            threshold = topk_values[-1]
+            mask = (torch.abs(w) >= threshold).to(w.dtype)
+            w = w * mask
+
+        return nn.functional.linear(input, w, self.bias)
+
+
+class CircuitGptPreTrainedModel(PreTrainedModel):
+    config_class = CircuitGptConfig
+    base_model_prefix = "transformer"
+
+
+class CircuitGptMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc = SparseLinear(config.n_embd, 4 * config.n_embd, sparsity=config.sparsity)
+        self.c_proj = SparseLinear(4 * config.n_embd, config.n_embd, sparsity=config.sparsity)
+        self.act = nn.GELU()
+        self.dropout = nn.Dropout(0.1)
+
+    def forward(self, hidden_states):
+        hidden_states = self.c_fc(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.c_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class CircuitGptAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+
+        self.c_attn = SparseLinear(config.n_embd, 3 * config.n_embd, sparsity=config.sparsity)
+        self.c_proj = SparseLinear(config.n_embd, config.n_embd, sparsity=config.sparsity)
+        self.attn_dropout = nn.Dropout(0.1)
+        self.resid_dropout = nn.Dropout(0.1)
+
+    def forward(self, x):
+        B, T, C = x.size()
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+
+        # Scaled Dot-Product Attention
+        y = torch.nn.functional.scaled_dot_product_attention(q, k, v, is_causal=True)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        y = self.resid_dropout(self.c_proj(y))
+        return y
+
+
+class CircuitGptBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CircuitGptAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = CircuitGptMLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+class CircuitGptModel(CircuitGptPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(1024, config.n_embd)
+        self.h = nn.ModuleList([CircuitGptBlock(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(config.n_embd)
+
+        self.post_init()
+
+    def forward(self, input_ids):
+        device = input_ids.device
+        t = input_ids.size(1)
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+
+        x = self.wte(input_ids) + self.wpe(pos)
+        for block in self.h:
+            x = block(x)
+        x = self.ln_f(x)
+        return x
+
+
+class CircuitGptForCausalLM(CircuitGptPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.transformer = CircuitGptModel(config)
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def forward(self, input_ids):
+        hidden_states = self.transformer(input_ids)
+        logits = self.lm_head(hidden_states)
+        return logits
diff --git a/src/transformers/models/clap/modeling_clap.py b/src/transformers/models/clap/modeling_clap.py
index 96c540a3424f..133fbc3f7767 100644
--- a/src/transformers/models/clap/modeling_clap.py
+++ b/src/transformers/models/clap/modeling_clap.py
@@ -126,12 +126,12 @@ class ClapTextModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     ClapAudio model output to mimic the output of the original implementation.
     """
 )
+@dataclass
 class ClapAudioModelOutput(ModelOutput):
     r"""
     audio_embeds (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
@@ -144,8 +144,8 @@ class ClapAudioModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Clap, vision->audio, Vision->Audio, image->audio
 class ClapOutput(ModelOutput):
     r"""
@@ -990,7 +990,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/clip/modeling_clip.py b/src/transformers/models/clip/modeling_clip.py
index 2bca67e59a21..60f031aa29b6 100644
--- a/src/transformers/models/clip/modeling_clip.py
+++ b/src/transformers/models/clip/modeling_clip.py
@@ -65,12 +65,12 @@ def _get_vector_norm(tensor: torch.Tensor) -> torch.Tensor:
     return normed_tensor
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
     """
 )
+@dataclass
 class CLIPVisionModelOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -83,12 +83,12 @@ class CLIPVisionModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for text model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class CLIPTextModelOutput(ModelOutput):
     r"""
     text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -481,15 +481,18 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutput:
         hidden_states = inputs_embeds
+        all_hidden_states = [hidden_states] if self.config.output_hidden_states else None
         for encoder_layer in self.layers:
             hidden_states = encoder_layer(
                 hidden_states,
                 attention_mask,
                 **kwargs,
             )
+            if all_hidden_states:
+                all_hidden_states.append(hidden_states)
 
         return BaseModelOutput(
-            last_hidden_state=hidden_states,
+            last_hidden_state=hidden_states, hidden_states=tuple(all_hidden_states) if all_hidden_states else None
         )
 
 
@@ -609,7 +612,7 @@ def __init__(self, config: CLIPVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = CLIPVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = CLIPEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.post_init()
@@ -646,7 +649,7 @@ def forward(
         >>> pooled_output = outputs.pooler_output  # pooled CLS states
         ```"""
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs: BaseModelOutput = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/clipseg/modeling_clipseg.py b/src/transformers/models/clipseg/modeling_clipseg.py
index cf17b44b00c2..a462bdc7ef40 100644
--- a/src/transformers/models/clipseg/modeling_clipseg.py
+++ b/src/transformers/models/clipseg/modeling_clipseg.py
@@ -708,7 +708,7 @@ def __init__(self, config: CLIPSegVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = CLIPSegVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = CLIPSegEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.post_init()
@@ -745,7 +745,7 @@ def forward(
         >>> pooled_output = outputs.pooler_output  # pooled CLS states
         ```"""
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs: BaseModelOutput = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/clvp/modeling_clvp.py b/src/transformers/models/clvp/modeling_clvp.py
index 0ee4852ef371..2bb8230c41c2 100644
--- a/src/transformers/models/clvp/modeling_clvp.py
+++ b/src/transformers/models/clvp/modeling_clvp.py
@@ -147,13 +147,13 @@ def _pad_extra_bos_eos_tokens(
     return modified_input_ids, attention_mask
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for CLVP encoder's outputs that contains a pooling of the last hidden states as well as a projection
     output (a linear layer on top of the pooled output).
     """
 )
+@dataclass
 class ClvpEncoderOutput(ModelOutput):
     r"""
     embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when model is initialized with `with_projection=True`):
diff --git a/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py b/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py
index 048412b383e7..b3fcc989d303 100644
--- a/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py
+++ b/src/transformers/models/cohere2_vision/modeling_cohere2_vision.py
@@ -93,12 +93,12 @@ class Cohere2VisionModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Cohere2Vision causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Cohere2VisionCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -188,9 +188,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/cohere2_vision/processing_cohere2_vision.py b/src/transformers/models/cohere2_vision/processing_cohere2_vision.py
index 7d76f1187733..6b74e950a2b5 100644
--- a/src/transformers/models/cohere2_vision/processing_cohere2_vision.py
+++ b/src/transformers/models/cohere2_vision/processing_cohere2_vision.py
@@ -18,9 +18,11 @@
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
+from .image_processing_cohere2_vision_fast import Cohere2VisionFastImageProcessorKwargs
 
 
 class Cohere2VisionProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Cohere2VisionFastImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding_side": "left",
@@ -48,7 +50,9 @@ def __init__(
         self.img_line_break_token = tokenizer.img_line_break_token
         self.image_token_id = tokenizer.image_token_id
 
-        self.image_ids = tokenizer.convert_tokens_to_ids(
+    @property
+    def image_token_ids(self) -> list[int]:
+        return self.tokenizer.convert_tokens_to_ids(
             [
                 self.image_token,
                 self.boi_token,
diff --git a/src/transformers/models/cohere_asr/feature_extraction_cohere_asr.py b/src/transformers/models/cohere_asr/feature_extraction_cohere_asr.py
index 1192be10606d..42f4bf3117da 100644
--- a/src/transformers/models/cohere_asr/feature_extraction_cohere_asr.py
+++ b/src/transformers/models/cohere_asr/feature_extraction_cohere_asr.py
@@ -284,17 +284,17 @@ def __call__(
                 f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                 "We will take the mean of the channels to convert to mono."
             )
-            raw_speech = raw_speech.mean(-1)
+            raw_speech = raw_speech.mean(1)
 
         is_batched_sequence = isinstance(raw_speech, (list, tuple))
         if is_batched_sequence:
-            for speech in raw_speech:
+            for index, speech in enumerate(raw_speech):
                 if len(speech.shape) > 1:
                     logger.warning(
                         f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                         "We will take the mean of the channels to convert to mono."
                     )
-                    speech = speech.mean(-1)
+                    raw_speech[index] = speech.mean(0)
 
         if is_batched_torch or is_batched_sequence:
             raw_speech = [speech.to(torch.float32) for speech in raw_speech]
diff --git a/src/transformers/models/cohere_asr/processing_cohere_asr.py b/src/transformers/models/cohere_asr/processing_cohere_asr.py
index 91618d8bcc4d..6a17ef31606b 100644
--- a/src/transformers/models/cohere_asr/processing_cohere_asr.py
+++ b/src/transformers/models/cohere_asr/processing_cohere_asr.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ...audio_utils import AudioInput, make_list_of_audio
+from ...audio_utils import AudioInput
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, is_torch_available, logging
@@ -49,6 +49,9 @@ class CohereAsrProcessorKwargs(ProcessingKwargs, total=False):
 @auto_docstring
 @requires(backends=("torch",))
 class CohereAsrProcessor(ProcessorMixin):
+    valid_processor_kwargs = CohereAsrProcessorKwargs
+    skip_tensor_conversion = ["audio_chunk_index"]
+
     def __init__(self, feature_extractor, tokenizer):
         super().__init__(feature_extractor, tokenizer)
 
@@ -95,34 +98,21 @@ def __call__(
             sampling rate, and an error will be raised if they don't match. If not provided, a warning will be
             issued and the default sampling rate will be assumed.
         """
-        audio = make_list_of_audio(audio)
-
-        output_kwargs = self._merge_kwargs(
-            CohereAsrProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        if sampling_rate is None:
-            logger.warning_once(
-                f"You've provided audio without specifying the sampling rate. It will be assumed to be {output_kwargs['audio_kwargs']['sampling_rate']}, which can result in silent errors."
-            )
-        elif sampling_rate != output_kwargs["audio_kwargs"]["sampling_rate"]:
+        if sampling_rate != self.feature_extractor.sampling_rate:
             raise ValueError(
-                f"The sampling rate of the audio ({sampling_rate}) does not match the sampling rate of the processor ({output_kwargs['audio_kwargs']['sampling_rate']}). Please provide resampled the audio to the expected sampling rate."
+                f"The sampling rate you provided ({sampling_rate}) does not match the sampling rate of the processor ({self.feature_extractor.sampling_rate}). Please provide resampled the audio to the expected sampling rate."
             )
 
-        inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
-
+        kwargs["sampling_rate"] = sampling_rate
+        model_inputs = super().__call__(audio=audio, text=text, **kwargs)
         prompt_ids = self.get_decoder_prompt_ids(language=language, punctuation=punctuation)
-        batch_size = inputs["input_features"].shape[0]
-        inputs["decoder_input_ids"] = torch.tensor([prompt_ids] * batch_size, dtype=torch.long)
+        batch_size = model_inputs["input_features"].shape[0]
+        model_inputs["decoder_input_ids"] = torch.tensor([prompt_ids] * batch_size, dtype=torch.long)
 
-        if text is not None:
-            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
-            inputs["labels"] = encodings["input_ids"]
+        if "input_ids" in model_inputs:
+            model_inputs["labels"] = model_inputs.pop("input_ids")
 
-        return inputs
+        return model_inputs
 
     def decode(self, *args, audio_chunk_index=None, language=None, **kwargs):
         texts = self.tokenizer.decode(*args, **kwargs)
diff --git a/src/transformers/models/colmodernvbert/modeling_colmodernvbert.py b/src/transformers/models/colmodernvbert/modeling_colmodernvbert.py
index e7b6faf44cf1..754ffed137df 100755
--- a/src/transformers/models/colmodernvbert/modeling_colmodernvbert.py
+++ b/src/transformers/models/colmodernvbert/modeling_colmodernvbert.py
@@ -61,12 +61,12 @@ def _init_weights(self, module):
                 init.zeros_(module.weight[module.padding_idx])
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for ColModernVBert embeddings output.
     """
 )
+@dataclass
 class ColModernVBertForRetrievalOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/colmodernvbert/modular_colmodernvbert.py b/src/transformers/models/colmodernvbert/modular_colmodernvbert.py
index 2e6ddd91e398..dcfd18885a55 100755
--- a/src/transformers/models/colmodernvbert/modular_colmodernvbert.py
+++ b/src/transformers/models/colmodernvbert/modular_colmodernvbert.py
@@ -89,21 +89,6 @@ class ColModernVBertProcessorKwargs(Idefics3ProcessorKwargs, total=False):
 @requires(backends=("torch",))
 @auto_docstring
 class ColModernVBertProcessor(Idefics3Processor):
-    r"""
-    Constructs a ColModernVBert processor which wraps a ModernVBertProcessor and special methods to process images and queries, as
-    well as to compute the late-interaction retrieval score.
-
-    [`ColModernVBertProcessor`] offers all the functionalities of [`ModernVBertProcessor`]. See the [`~ModernVBertProcessor.__call__`]
-    for more information.
-
-    Args:
-            image_processor ([`Idefics3ImageProcessor`]): An instance of [`Idefics3ImageProcessor`]. The image processor is a required input.
-            tokenizer (`PreTrainedTokenizerFast`, *optional*): An instance of [`PreTrainedTokenizerFast`]. This should correspond with the model's text model. The tokenizer is a required input.
-            image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of <image> tokens per image in the input.
-            visual_prompt_prefix (`Optional`, *optional*): A prefix to be prepended to visual prompts.
-            query_prefix (`Optional`, *optional*): A prefix to be prepended to query prompts.
-    """
-
     def __init__(
         self,
         image_processor,
@@ -331,12 +316,12 @@ class ColModernVBertPreTrainedModel(ColPaliPreTrainedModel):
     config: ColModernVBertConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for ColModernVBert embeddings output.
     """
 )
+@dataclass
 class ColModernVBertForRetrievalOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/colmodernvbert/processing_colmodernvbert.py b/src/transformers/models/colmodernvbert/processing_colmodernvbert.py
index de9a81205682..d462a9ee3aad 100755
--- a/src/transformers/models/colmodernvbert/processing_colmodernvbert.py
+++ b/src/transformers/models/colmodernvbert/processing_colmodernvbert.py
@@ -26,7 +26,7 @@
 import torch
 
 from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput, is_valid_image, load_image
+from ...image_utils import ImageInput, is_valid_image
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import AddedToken, BatchEncoding, TextInput
 from ...utils import auto_docstring
@@ -51,75 +51,10 @@ class ColModernVBertProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
-def is_url(val) -> bool:
-    return isinstance(val, str) and val.startswith("http")
-
-
-def is_image_or_image_url(elem):
-    return is_url(elem) or is_valid_image(elem)
-
-
-def _prompt_split_image(image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token):
-    """Prompt with expanded image tokens for when the image is split into patches."""
-    text_split_images = ""
-    for n_h in range(image_rows):
-        for n_w in range(image_cols):
-            text_split_images += (
-                f"{fake_token_around_image}" + f"<row_{n_h + 1}_col_{n_w + 1}>" + f"{image_token}" * image_seq_len
-            )
-        text_split_images += "\n"
-
-    text_split_images += (
-        f"\n{fake_token_around_image}"
-        + f"{global_img_token}"
-        + f"{image_token}" * image_seq_len
-        + f"{fake_token_around_image}"
-    )
-    return text_split_images
-
-
-def _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_img_token):
-    """Prompt with expanded image tokens for a single image."""
-    return (
-        f"{fake_token_around_image}"
-        + f"{global_img_token}"
-        + f"{image_token}" * image_seq_len
-        + f"{fake_token_around_image}"
-    )
-
-
-def get_image_prompt_string(
-    image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_img_token
-):
-    if image_rows == 0 and image_cols == 0:
-        return _prompt_single_image(
-            image_seq_len,
-            fake_token_around_image=fake_token_around_image,
-            image_token=image_token,
-            global_img_token=global_img_token,
-        )
-    return _prompt_split_image(
-        image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token
-    )
-
-
 @requires(backends=("torch",))
 @auto_docstring
 class ColModernVBertProcessor(ProcessorMixin):
-    r"""
-    Constructs a ColModernVBert processor which wraps a ModernVBertProcessor and special methods to process images and queries, as
-    well as to compute the late-interaction retrieval score.
-
-    [`ColModernVBertProcessor`] offers all the functionalities of [`ModernVBertProcessor`]. See the [`~ModernVBertProcessor.__call__`]
-    for more information.
-
-    Args:
-            image_processor ([`Idefics3ImageProcessor`]): An instance of [`Idefics3ImageProcessor`]. The image processor is a required input.
-            tokenizer (`PreTrainedTokenizerFast`, *optional*): An instance of [`PreTrainedTokenizerFast`]. This should correspond with the model's text model. The tokenizer is a required input.
-            image_seq_len (`int`, *optional*, defaults to 64): The length of the image sequence i.e. the number of <image> tokens per image in the input.
-            visual_prompt_prefix (`Optional`, *optional*): A prefix to be prepended to visual prompts.
-            query_prefix (`Optional`, *optional*): A prefix to be prepended to query prompts.
-    """
+    valid_processor_kwargs = ColModernVBertProcessorKwargs
 
     def __init__(
         self,
@@ -174,18 +109,6 @@ def __init__(
         self.query_prefix = query_prefix or ""
         self.query_augmentation_token = self.end_of_utterance_token
 
-    def _extract_images_from_prompts(self, prompts):
-        prompt_images = []
-        for prompt in prompts:
-            images = []
-            for elem in prompt:
-                if is_valid_image(elem):
-                    images.append(elem)
-                elif is_url(elem):
-                    images.append(load_image(elem))
-            prompt_images.append(images)
-        return prompt_images
-
     @auto_docstring
     def __call__(
         self,
@@ -199,8 +122,8 @@ def __call__(
             The length of the image sequence. If not provided, the default value of self.image_seq_len is used.
             image_seq_len should be equal to int(((image_size // patch_size) ** 2) / (scale_factor**2))
         """
-        if text is None and images is None:
-            raise ValueError("You must provide either `text` or `images`.")
+        images, text = self.prepare_inputs_layout(images=images, text=text, **kwargs)
+        self.validate_inputs(images=images, text=text, **kwargs)
 
         output_kwargs = self._merge_kwargs(
             ColModernVBertProcessorKwargs,
@@ -209,113 +132,135 @@ def __call__(
         )
 
         image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
+        return_text_replacement_offsets = output_kwargs["text_kwargs"].pop("return_text_replacement_offsets", False)
         return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
 
-        n_images_in_text = []
-        n_images_in_images = []
-        inputs = {}
+        image_inputs = text_inputs = {}
+        if images is not None:
+            image_inputs, images_replacements = self._process_images(images, **output_kwargs["images_kwargs"])
+
+            # Pop inputs unused by the model
+            image_inputs.pop("rows", None)
+            image_inputs.pop("cols", None)
+
+            if text is not None:
+                text, text_replacement_offsets = self.get_text_with_replacements(
+                    text, images_replacements=images_replacements
+                )
+                text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+                if return_text_replacement_offsets:
+                    text_inputs["text_replacement_offsets"] = text_replacement_offsets
+
+                batch_image_seq_lengths = []
+                for batch_id, text_replacement_offset in enumerate(text_replacement_offsets):
+                    image_seq_lens = []
+                    for data in text_replacement_offset:
+                        start, end = data["new_span"]
+                        start_id_pos = text_inputs.char_to_token(batch_id, start)
+                        end_id_pos = text_inputs.char_to_token(batch_id, end - 1)
+                        # Add one to go from zero-indexing to actual length
+                        image_seq_lens.append(end_id_pos - start_id_pos + 1)
+                    batch_image_seq_lengths.append(image_seq_lens)
+
+                if return_mm_token_type_ids:
+                    text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(
+                        text_inputs["input_ids"], batch_image_seq_lengths
+                    )
+                self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+        elif text is not None:
+            text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
 
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def prepare_inputs_layout(
+        self,
+        images: ImageInput | None = None,
+        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
+        **kwargs: Unpack[ColModernVBertProcessorKwargs],
+    ):
         if text is not None:
             if isinstance(text, str):
                 text = [text]
-            elif not isinstance(text, list) and not isinstance(text[0], str):
-                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-            n_images_in_text = [sample.count(self.image_token) for sample in text]
+            text = text.copy()
 
         if images is not None:
-            if is_image_or_image_url(images):
+            images
+            if is_valid_image(images):
                 images = [[images]]
-            elif isinstance(images, (list, tuple)) and is_image_or_image_url(images[0]):
+            elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
                 if text is not None:
-                    if sum(n_images_in_text) != len(images):
-                        raise ValueError(
-                            f"The total number of {self.image_token} tokens in the prompts should be the same as the number of images passed."
-                            f" Found {sum(n_images_in_text)} {self.image_token} tokens and {len(images)} images."
-                        )
                     # Reorganize the images to match the prompts
+                    n_images_in_text = [sample.count(self.image_token) for sample in text]
                     cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
-                    images = [
+                    split_images = [
                         images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
                         for i in range(len(n_images_in_text))
                     ]
+                    # Append the rest if any, we will error out when validating if they don't match with text
+                    if len(images) > cumsum_images_in_text[-1]:
+                        images = split_images + [images[cumsum_images_in_text[-1] :]]
+                    else:
+                        images = split_images
                 else:
                     images = [images]
-            elif (
-                not isinstance(images, (list, tuple))
-                and not isinstance(images[0], (list, tuple))
-                and not is_image_or_image_url(images[0][0])
-            ):
-                raise ValueError(
-                    "Invalid input images. Please provide a single image or a list of images or a list of list of images."
-                )
-            n_images_in_images = [len(sample) for sample in images]
 
-            # Load images if they are URLs
-            images = [[load_image(im) if is_url(im) else im for im in sample] for sample in images]
+        return images, text
+
+    def validate_inputs(
+        self,
+        images: ImageInput | None = None,
+        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(images, text, **kwargs)
 
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-            inputs.update(image_inputs)
+        if text is None and images is None:
+            raise ValueError("You must provide either `text` or `images`.")
 
-            if text is not None:
-                if n_images_in_images != n_images_in_text:
+        if text is not None:
+            n_images_in_text = [sample.count(self.image_token) for sample in text]
+            if images is not None:
+                n_images_in_images = [len(sublist) for sublist in images]
+                if n_images_in_text != n_images_in_images:
                     raise ValueError(
-                        f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
+                        f"The total number of {self.image_token} tokens in the prompts should be the same as the number of images passed."
+                        f" Found {n_images_in_text} {self.image_token} tokens and {n_images_in_images} images per sample."
                     )
-
-                image_rows = inputs.pop("rows", [[0] * n_images for n_images in n_images_in_text])
-                image_cols = inputs.pop("cols", [[0] * n_images for n_images in n_images_in_text])
-
-                fake_image_token = self.fake_image_token
-                image_token = self.image_token
-                global_img_token = self.global_image_tag
-
-                prompt_strings = []
-                batch_image_seq_lengths = []
-                for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
-                    # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
-                    image_prompt_strings = []
-                    image_seq_lengths = []
-                    for n_rows, n_cols in zip(sample_rows, sample_cols):
-                        image_prompt_string = get_image_prompt_string(
-                            n_rows,
-                            n_cols,
-                            image_seq_len,
-                            image_token=image_token,
-                            fake_token_around_image=fake_image_token,
-                            global_img_token=global_img_token,
-                        )
-                        # Add +2 and +3 for special BOI/EOI/fake_image_wrapper tokens
-                        row_length = (self.image_seq_len + 2) * n_cols + 1
-                        image_seq_lengths.append((self.image_seq_len + 3) + row_length * n_rows)
-                        image_prompt_strings.append(image_prompt_string)
-
-                    batch_image_seq_lengths.append(image_seq_lengths)
-                    split_sample = sample.split(image_token)
-                    if len(split_sample) == 0:
-                        raise ValueError("The image token should be present in the text.")
-
-                    # Place in the image prompt strings where the image tokens are
-                    sample = split_sample[0]
-                    for i, image_prompt_string in enumerate(image_prompt_strings):
-                        sample += image_prompt_string + split_sample[i + 1]
-                    prompt_strings.append(sample)
-
-                text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
-                self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-                inputs.update(text_inputs)
-
-        elif text is not None:
-            if any(n_images_in_text):
+            elif images is None and any(n_images_in_text):
                 raise ValueError(
                     f"Found {sum(n_images_in_text)} {self.image_token} tokens in the text but no images were passed."
                 )
-            text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
-            inputs.update(text_inputs)
 
-        if return_mm_token_type_ids:
-            inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(inputs["input_ids"], batch_image_seq_lengths)
-        return BatchFeature(data=inputs, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        image_rows = [row for row_list in image_inputs["rows"] for row in row_list][image_idx]
+        image_cols = [col for col_list in image_inputs["cols"] for col in col_list][image_idx]
+        if image_rows == 0 and image_cols == 0:
+            return (
+                f"{self.fake_image_token}"
+                + f"{self.global_image_tag}"
+                + f"{self.image_token}" * self.image_seq_len
+                + f"{self.fake_image_token}"
+            )
+        else:
+            text_split_images = ""
+            for n_h in range(image_rows):
+                for n_w in range(image_cols):
+                    text_split_images += (
+                        f"{self.fake_image_token}"
+                        + f"<row_{n_h + 1}_col_{n_w + 1}>"
+                        + f"{self.image_token}" * self.image_seq_len
+                    )
+                text_split_images += "\n"
+
+            text_split_images += (
+                f"\n{self.fake_image_token}"
+                + f"{self.global_image_tag}"
+                + f"{self.image_token}" * self.image_seq_len
+                + f"{self.fake_image_token}"
+            )
+            return text_split_images
 
     def create_mm_token_type_ids(self, input_ids: list, batch_image_seq_lengths: list[int]) -> list[list[int]]:
         # We have to iterate for each list separately because inputs
diff --git a/src/transformers/models/colpali/modeling_colpali.py b/src/transformers/models/colpali/modeling_colpali.py
index dc7dcc306d33..3ea4040121a1 100644
--- a/src/transformers/models/colpali/modeling_colpali.py
+++ b/src/transformers/models/colpali/modeling_colpali.py
@@ -57,12 +57,12 @@ def _init_weights(self, module):
                 init.zeros_(module.weight[module.padding_idx])
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for ColPali embeddings output.
     """
 )
+@dataclass
 class ColPaliForRetrievalOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/colqwen2/modeling_colqwen2.py b/src/transformers/models/colqwen2/modeling_colqwen2.py
index 656ad6c758c5..6428314e32a6 100644
--- a/src/transformers/models/colqwen2/modeling_colqwen2.py
+++ b/src/transformers/models/colqwen2/modeling_colqwen2.py
@@ -27,7 +27,8 @@
 from ... import initialization as init
 from ...cache_utils import Cache
 from ...modeling_utils import PreTrainedModel
-from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torch_available
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available
 from .configuration_colqwen2 import ColQwen2Config
 
 
@@ -64,12 +65,12 @@ def _init_weights(self, module):
                 init.zeros_(module.weight[module.padding_idx])
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for ColQwen2 embeddings output.
     """
 )
+@dataclass
 class ColQwen2ForRetrievalOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -133,12 +134,9 @@ def forward(
         labels: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         pixel_values: torch.Tensor | None = None,
         image_grid_thw: torch.LongTensor | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> ColQwen2ForRetrievalOutput:
         r"""
         image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
@@ -152,12 +150,9 @@ def forward(
             mask = arange.unsqueeze(0) < offsets.unsqueeze(1)  # (batch_size, max_len)
             pixel_values = pixel_values[mask]  # (total_valid_patches, channels, height, width)
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        output_hidden_states = kwargs.pop("output_hidden_states", None)
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
 
         # Custom data preparation to fix an issue with the gradient flow when training with multiple GPUs.
         if inputs_embeds is None:
@@ -165,9 +160,7 @@ def forward(
 
             if pixel_values is not None:
                 image_embeds = self.vlm.visual(pixel_values, grid_thw=image_grid_thw, return_dict=True).pooler_output
-                image_mask = (
-                    (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1).expand_as(inputs_embeds)
-                )
+                image_mask = (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1)
                 image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
                 inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
 
@@ -178,9 +171,8 @@ def forward(
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
             use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            output_hidden_states=True,
+            **kwargs,
         )
 
         vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None
diff --git a/src/transformers/models/colqwen2/modular_colqwen2.py b/src/transformers/models/colqwen2/modular_colqwen2.py
index aa7a3f48ca6e..19f0d5bd6184 100644
--- a/src/transformers/models/colqwen2/modular_colqwen2.py
+++ b/src/transformers/models/colqwen2/modular_colqwen2.py
@@ -19,7 +19,7 @@
 from ...image_utils import ImageInput, is_valid_image
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import ModelOutput, auto_docstring, can_return_tuple, is_torch_available, logging
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available, logging
 from ..colpali.modeling_colpali import ColPaliForRetrieval, ColPaliPreTrainedModel
 from ..colpali.processing_colpali import ColPaliProcessor
 from .configuration_colqwen2 import ColQwen2Config
@@ -216,12 +216,12 @@ class ColQwen2PreTrainedModel(ColPaliPreTrainedModel):
     pass
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for ColQwen2 embeddings output.
     """
 )
+@dataclass
 class ColQwen2ForRetrievalOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -272,12 +272,9 @@ def forward(
         labels: torch.LongTensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         pixel_values: torch.Tensor | None = None,
         image_grid_thw: torch.LongTensor | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> ColQwen2ForRetrievalOutput:
         r"""
         image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
@@ -291,12 +288,9 @@ def forward(
             mask = arange.unsqueeze(0) < offsets.unsqueeze(1)  # (batch_size, max_len)
             pixel_values = pixel_values[mask]  # (total_valid_patches, channels, height, width)
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        output_hidden_states = kwargs.pop("output_hidden_states", None)
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
 
         # Custom data preparation to fix an issue with the gradient flow when training with multiple GPUs.
         if inputs_embeds is None:
@@ -304,9 +298,7 @@ def forward(
 
             if pixel_values is not None:
                 image_embeds = self.vlm.visual(pixel_values, grid_thw=image_grid_thw, return_dict=True).pooler_output
-                image_mask = (
-                    (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1).expand_as(inputs_embeds)
-                )
+                image_mask = (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1)
                 image_embeds = image_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
                 inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
 
@@ -317,9 +309,8 @@ def forward(
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
             use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            output_hidden_states=True,
+            **kwargs,
         )
 
         vlm_hidden_states = vlm_output.hidden_states if output_hidden_states else None
diff --git a/src/transformers/models/colqwen2/processing_colqwen2.py b/src/transformers/models/colqwen2/processing_colqwen2.py
index 48af99206afe..89b737bd5009 100644
--- a/src/transformers/models/colqwen2/processing_colqwen2.py
+++ b/src/transformers/models/colqwen2/processing_colqwen2.py
@@ -29,9 +29,11 @@
 
 if is_torch_available():
     import torch
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 class ColQwen2ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": "longest",
diff --git a/src/transformers/models/conditional_detr/modeling_conditional_detr.py b/src/transformers/models/conditional_detr/modeling_conditional_detr.py
index 2f10c81b38e1..5d711ac053f9 100644
--- a/src/transformers/models/conditional_detr/modeling_conditional_detr.py
+++ b/src/transformers/models/conditional_detr/modeling_conditional_detr.py
@@ -89,12 +89,12 @@ class ConditionalDetrModelOutput(Seq2SeqModelOutput):
     reference_points: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ConditionalDetrForObjectDetection`].
     """
 )
+@dataclass
 class ConditionalDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
@@ -132,12 +132,12 @@ class ConditionalDetrObjectDetectionOutput(ModelOutput):
     encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ConditionalDetrForSegmentation`].
     """
 )
+@dataclass
 class ConditionalDetrSegmentationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/csm/modeling_csm.py b/src/transformers/models/csm/modeling_csm.py
index eb78dca8faf5..a7c6b9dcf4ed 100644
--- a/src/transformers/models/csm/modeling_csm.py
+++ b/src/transformers/models/csm/modeling_csm.py
@@ -48,12 +48,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for the model autoregressive outputs.
     """
 )
+@dataclass
 class CsmOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -174,7 +174,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/csm/modular_csm.py b/src/transformers/models/csm/modular_csm.py
index 8ba8bc66dad3..c82636613ee1 100644
--- a/src/transformers/models/csm/modular_csm.py
+++ b/src/transformers/models/csm/modular_csm.py
@@ -46,12 +46,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for the model autoregressive outputs.
     """
 )
+@dataclass
 class CsmOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/ctsm/__init__.py b/src/transformers/models/ctsm/__init__.py
new file mode 100644
index 000000000000..e5979d7ba3db
--- /dev/null
+++ b/src/transformers/models/ctsm/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_ctsm import *
+    from .modeling_ctsm import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/ctsm/configuration_ctsm.py b/src/transformers/models/ctsm/configuration_ctsm.py
new file mode 100644
index 000000000000..f2f62a97d0a7
--- /dev/null
+++ b/src/transformers/models/ctsm/configuration_ctsm.py
@@ -0,0 +1,118 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/ctsm/modular_ctsm.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_ctsm.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="cisco-ai/cisco-time-series-model-1.0")
+@strict
+class CtsmConfig(PreTrainedConfig):
+    r"""
+    patch_length (`int`, *optional*, defaults to 32):
+        Length of one patch in the input sequence for each resolution stream.
+    context_length (`int`, *optional*, defaults to 512):
+        Length of the input context for each resolution stream.
+    horizon_length (`int`, *optional*, defaults to 128):
+        Length of the prediction horizon produced per autoregressive step.
+    freq_size (`int`, *optional*, defaults to 3):
+        Number of frequency embeddings.
+    tolerance (`float`, *optional*, defaults to 1e-06):
+        Numerical tolerance used in normalization.
+    pad_val (`float`, *optional*, defaults to 1123581321.0):
+        Sentinel value marking padded positions in the input series.
+    num_hidden_layers (`int`, *optional*, defaults to 25):
+        Number of decoder layers.
+    quantiles (`list[float]`, *optional*, defaults to 15 values between 0.01 and 0.99):
+        Quantile levels predicted by the model.
+    use_positional_embedding (`bool`, *optional*, defaults to `False`):
+        CTSM uses rotary position embeddings and does not add sinusoidal positional embeddings.
+    use_resolution_embeddings (`bool`, *optional*, defaults to `True`):
+        Whether to add a learned embedding per resolution bucket (coarse / special / fine).
+    use_special_token (`bool`, *optional*, defaults to `True`):
+        Whether to insert a learned special token between the coarse and fine streams.
+    num_resolutions (`int`, *optional*, defaults to 3):
+        Number of resolution embeddings (coarse, special token, fine).
+    agg_factor (`int`, *optional*, defaults to 60):
+        Aggregation factor between fine and coarse resolutions (e.g. 60 minutes -> 1 hour).
+    max_position_embeddings (`int`, *optional*, defaults to 1025):
+        Maximum number of patches in the concatenated sequence (coarse + special + fine).
+    rope_parameters (`dict`, *optional*):
+        Rotary position embedding parameters. Defaults to `{"rope_type": "default", "rope_theta": 10000.0}`.
+
+    Example:
+
+    ```python
+    >>> from transformers import CtsmConfig, CtsmModelForPrediction
+
+    >>> configuration = CtsmConfig()
+    >>> model = CtsmModelForPrediction(configuration)
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "ctsm"
+    keys_to_ignore_at_inference = []
+    is_encoder_decoder = False
+
+    patch_length: int = 32
+    context_length: int = 512
+    horizon_length: int = 128
+    freq_size: int = 3
+
+    num_hidden_layers: int = 25
+    hidden_size: int = 1280
+    intermediate_size: int = 1280
+    head_dim: int = 80
+    num_attention_heads: int = 16
+    tolerance: float = 1e-6
+    rms_norm_eps: float = 1e-6
+    quantiles: list[float] | tuple[float, ...] = (
+        0.01,
+        0.05,
+        0.1,
+        0.2,
+        0.25,
+        0.3,
+        0.4,
+        0.5,
+        0.6,
+        0.7,
+        0.75,
+        0.8,
+        0.9,
+        0.95,
+        0.99,
+    )
+    pad_val: float = 1123581321.0
+    attention_dropout: float | int = 0.0
+    use_positional_embedding: bool = False
+    initializer_range: float = 0.02
+    use_resolution_embeddings: bool = True
+    use_special_token: bool = True
+    num_resolutions: int = 3
+    agg_factor: int = 60
+    max_position_embeddings: int = 1025
+    rope_parameters: RopeParameters | dict | None = None
+
+
+__all__ = ["CtsmConfig"]
diff --git a/src/transformers/models/ctsm/convert_ctsm_original_to_hf.py b/src/transformers/models/ctsm/convert_ctsm_original_to_hf.py
new file mode 100644
index 000000000000..0b618547b387
--- /dev/null
+++ b/src/transformers/models/ctsm/convert_ctsm_original_to_hf.py
@@ -0,0 +1,209 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert a Cisco Time Series Model (CTSM) 1.0 checkpoint to the transformers format.
+
+Sample usage:
+
+```
+python src/transformers/models/ctsm/convert_ctsm_original_to_hf.py \
+    --output_dir /output/path \
+    --huggingface_repo_id cisco-ai/cisco-time-series-model-1.0
+```
+"""
+
+import argparse
+import os
+
+import torch
+from huggingface_hub import snapshot_download
+
+from transformers import CtsmConfig, CtsmModelForPrediction
+
+
+CTSM_CHECKPOINT_FILENAME = "torch_model.pt"
+
+# CTSM 1.0 public checkpoint ships 15 quantiles spanning [0.01, 0.99].
+CTSM_1_0_QUANTILES = [0.01, 0.05, 0.1, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.9, 0.95, 0.99]
+
+
+def _layer_mapping(num_layers: int, hidden_size: int) -> dict[str, str | tuple[str, int]]:
+    """Return a mapping `old_key -> new_key` (or `(new_prefix, split_idx)` for fused QKV)."""
+    mapping: dict[str, str | tuple[str, int]] = {
+        # input tokenizer (residual block)
+        "input_ff_layer.hidden_layer.0.weight": "model.input_ff_layer.input_layer.weight",
+        "input_ff_layer.hidden_layer.0.bias": "model.input_ff_layer.input_layer.bias",
+        "input_ff_layer.output_layer.weight": "model.input_ff_layer.output_layer.weight",
+        "input_ff_layer.output_layer.bias": "model.input_ff_layer.output_layer.bias",
+        "input_ff_layer.residual_layer.weight": "model.input_ff_layer.residual_layer.weight",
+        "input_ff_layer.residual_layer.bias": "model.input_ff_layer.residual_layer.bias",
+        # frequency, resolution and special token embeddings
+        "freq_emb.weight": "model.freq_emb.weight",
+        "multi_resolution.weight": "model.multi_resolution.weight",
+        "special_token": "model.special_token",
+        # horizon head (residual block)
+        "horizon_ff_layer.hidden_layer.0.weight": "horizon_ff_layer.input_layer.weight",
+        "horizon_ff_layer.hidden_layer.0.bias": "horizon_ff_layer.input_layer.bias",
+        "horizon_ff_layer.output_layer.weight": "horizon_ff_layer.output_layer.weight",
+        "horizon_ff_layer.output_layer.bias": "horizon_ff_layer.output_layer.bias",
+        "horizon_ff_layer.residual_layer.weight": "horizon_ff_layer.residual_layer.weight",
+        "horizon_ff_layer.residual_layer.bias": "horizon_ff_layer.residual_layer.bias",
+    }
+
+    layer_template = {
+        # fused qkv -> split into q, k, v below
+        "stacked_transformer.layers.{i}.self_attn.qkv_proj.weight": ("model.layers.{i}.self_attn", "qkv_weight"),
+        "stacked_transformer.layers.{i}.self_attn.qkv_proj.bias": ("model.layers.{i}.self_attn", "qkv_bias"),
+        "stacked_transformer.layers.{i}.self_attn.o_proj.weight": "model.layers.{i}.self_attn.o_proj.weight",
+        "stacked_transformer.layers.{i}.self_attn.o_proj.bias": "model.layers.{i}.self_attn.o_proj.bias",
+        "stacked_transformer.layers.{i}.self_attn.scaling": "model.layers.{i}.self_attn.scaling",
+        "stacked_transformer.layers.{i}.mlp.gate_proj.weight": "model.layers.{i}.mlp.gate_proj.weight",
+        "stacked_transformer.layers.{i}.mlp.gate_proj.bias": "model.layers.{i}.mlp.gate_proj.bias",
+        "stacked_transformer.layers.{i}.mlp.down_proj.weight": "model.layers.{i}.mlp.down_proj.weight",
+        "stacked_transformer.layers.{i}.mlp.down_proj.bias": "model.layers.{i}.mlp.down_proj.bias",
+        "stacked_transformer.layers.{i}.mlp.layer_norm.weight": "model.layers.{i}.mlp.layer_norm.weight",
+        "stacked_transformer.layers.{i}.mlp.layer_norm.bias": "model.layers.{i}.mlp.layer_norm.bias",
+        "stacked_transformer.layers.{i}.input_layernorm.weight": "model.layers.{i}.input_layernorm.weight",
+    }
+    for i in range(num_layers):
+        for old, new in layer_template.items():
+            mapping[old.format(i=i)] = new.format(i=i) if isinstance(new, str) else (new[0].format(i=i), new[1])
+    return mapping
+
+
+def convert_state_dict(original_sd: dict[str, torch.Tensor], hidden_size: int) -> dict[str, torch.Tensor]:
+    """Rewrite the original CTSM state dict into the transformers key layout."""
+    num_layers = 0
+    for key in original_sd:
+        if key.startswith("stacked_transformer.layers."):
+            idx = int(key.split(".")[2])
+            num_layers = max(num_layers, idx + 1)
+    if num_layers == 0:
+        raise ValueError("No transformer layers found in the original checkpoint.")
+
+    mapping = _layer_mapping(num_layers, hidden_size)
+    new_sd: dict[str, torch.Tensor] = {}
+    missing: list[str] = []
+    for old_key, target in mapping.items():
+        if old_key not in original_sd:
+            missing.append(old_key)
+            continue
+        tensor = original_sd[old_key]
+        if isinstance(target, tuple):
+            prefix, kind = target
+            if kind == "qkv_weight":
+                q, k, v = tensor.split(hidden_size, dim=0)
+                new_sd[f"{prefix}.q_proj.weight"] = q.clone()
+                new_sd[f"{prefix}.k_proj.weight"] = k.clone()
+                new_sd[f"{prefix}.v_proj.weight"] = v.clone()
+            elif kind == "qkv_bias":
+                q, k, v = tensor.split(hidden_size, dim=0)
+                new_sd[f"{prefix}.q_proj.bias"] = q.clone()
+                new_sd[f"{prefix}.k_proj.bias"] = k.clone()
+                new_sd[f"{prefix}.v_proj.bias"] = v.clone()
+            else:
+                raise ValueError(f"Unknown fused projection kind: {kind}")
+        else:
+            new_sd[target] = tensor.clone()
+    if missing:
+        print(f"[warn] {len(missing)} expected key(s) missing from the original checkpoint (first 5): {missing[:5]}")
+    return new_sd
+
+
+def _infer_config_from_state_dict(original_sd: dict[str, torch.Tensor]) -> CtsmConfig:
+    """Infer a `CtsmConfig` from an original CTSM 1.0 state dict."""
+    num_layers = 1 + max(
+        (int(k.split(".")[2]) for k in original_sd if k.startswith("stacked_transformer.layers.")),
+        default=-1,
+    )
+    hidden_size = original_sd["input_ff_layer.output_layer.weight"].shape[0]
+    qkv_out = original_sd["stacked_transformer.layers.0.self_attn.qkv_proj.weight"].shape[0]
+    # qkv is [3 * num_heads * head_dim, hidden_size] — split evenly.
+    num_heads = 16
+    head_dim = qkv_out // (3 * num_heads)
+    horizon_out = original_sd["horizon_ff_layer.output_layer.weight"].shape[0]
+    horizon_length = 128
+    num_outputs = horizon_out // horizon_length
+    quantiles = (
+        CTSM_1_0_QUANTILES if num_outputs - 1 == len(CTSM_1_0_QUANTILES) else [0.1 * i for i in range(1, num_outputs)]
+    )
+
+    return CtsmConfig(
+        num_hidden_layers=num_layers,
+        hidden_size=hidden_size,
+        intermediate_size=hidden_size,
+        num_attention_heads=num_heads,
+        head_dim=head_dim,
+        patch_length=32,
+        context_length=512,
+        horizon_length=horizon_length,
+        quantiles=quantiles,
+        use_positional_embedding=False,
+        use_resolution_embeddings="multi_resolution.weight" in original_sd,
+        use_special_token="special_token" in original_sd,
+        agg_factor=60,
+        max_position_embeddings=1025,
+    )
+
+
+def write_model(output_dir: str, huggingface_repo_id: str, safe_serialization: bool = True) -> None:
+    os.makedirs(output_dir, exist_ok=True)
+    local_dir = snapshot_download(repo_id=huggingface_repo_id, allow_patterns=[CTSM_CHECKPOINT_FILENAME])
+    checkpoint_path = os.path.join(local_dir, CTSM_CHECKPOINT_FILENAME)
+    if not os.path.exists(checkpoint_path):
+        raise FileNotFoundError(f"{CTSM_CHECKPOINT_FILENAME} not found in {huggingface_repo_id}")
+
+    print(f"Loading original checkpoint from {checkpoint_path}")
+    original_sd = torch.load(checkpoint_path, map_location="cpu", weights_only=True)
+
+    config = _infer_config_from_state_dict(original_sd)
+    print(
+        f"Inferred CtsmConfig: layers={config.num_hidden_layers} hidden={config.hidden_size} "
+        f"heads={config.num_attention_heads} head_dim={config.head_dim} quantiles={len(config.quantiles)}"
+    )
+    config.save_pretrained(output_dir)
+
+    model = CtsmModelForPrediction(config)
+    converted_sd = convert_state_dict(original_sd, hidden_size=config.hidden_size)
+
+    incompatible = model.load_state_dict(converted_sd, strict=False)
+    if incompatible.missing_keys:
+        print(f"[warn] missing keys after load: {incompatible.missing_keys[:10]}")
+    if incompatible.unexpected_keys:
+        print(f"[warn] unexpected keys after load: {incompatible.unexpected_keys[:10]}")
+
+    model.save_pretrained(output_dir, safe_serialization=safe_serialization)
+    print(f"Saved transformers checkpoint to {output_dir}")
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--output_dir", required=True, help="Where to write the converted HF checkpoint.")
+    parser.add_argument(
+        "--huggingface_repo_id",
+        default="cisco-ai/cisco-time-series-model-1.0",
+        help="Original CTSM repo on the Hub.",
+    )
+    parser.add_argument("--safe_serialization", type=bool, default=True)
+    args = parser.parse_args()
+
+    write_model(
+        output_dir=args.output_dir,
+        huggingface_repo_id=args.huggingface_repo_id,
+        safe_serialization=args.safe_serialization,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/ctsm/modeling_ctsm.py b/src/transformers/models/ctsm/modeling_ctsm.py
new file mode 100644
index 000000000000..e0ae7cbff49e
--- /dev/null
+++ b/src/transformers/models/ctsm/modeling_ctsm.py
@@ -0,0 +1,1349 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/ctsm/modular_ctsm.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_ctsm.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ... import initialization as init
+from ...cache_utils import Cache, DynamicCache
+from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub
+from ...modeling_outputs import BaseModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_ctsm import CtsmConfig
+
+
+@dataclass
+@auto_docstring
+class CtsmOutput(BaseModelOutput):
+    r"""
+    loc (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level mean used to normalize the fine-resolution context, reused to rescale the final forecast.
+    scale (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level standard deviation of the fine-resolution context.
+    loc_coarse (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level mean used to normalize the coarse-resolution context.
+    scale_coarse (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level standard deviation of the coarse-resolution context.
+    num_coarse_patches (`int`):
+        Number of patches (including the optional special token) preceding the fine-resolution block.
+    num_fine_patches (`int`):
+        Number of patches in the fine-resolution block of the concatenated sequence.
+    past_key_values (`Cache`, *optional*):
+        Key/value cache for the concatenated `[coarse, special, fine]` sequence. Populated when the
+        caller passes `use_cache=True` (and re-used across autoregressive decode steps). Typically only
+        the long-horizon AR loop in [`CtsmModelForPrediction`] needs this.
+    """
+
+    loc: torch.Tensor | None = None
+    scale: torch.Tensor | None = None
+
+    loc_coarse: torch.Tensor | None = None
+    scale_coarse: torch.Tensor | None = None
+    num_coarse_patches: int | None = None
+    num_fine_patches: int | None = None
+    past_key_values: Cache | None = None
+
+
+@dataclass
+@auto_docstring
+class CtsmOutputForPrediction(BaseModelOutput):
+    r"""
+    mean_predictions (`torch.Tensor` of shape `(batch_size, horizon_length)`):
+        Point forecasts over the fine-resolution horizon.
+    full_predictions (`torch.Tensor` of shape `(batch_size, horizon_length, 1 + num_quantiles)`):
+        Concatenation of the mean prediction and the quantile predictions along the last axis.
+    loss (`torch.Tensor` of shape `(1,)`, *optional*, returned when `future_values` is provided):
+        Training loss combining MSE of the mean forecast and quantile loss when fine-resolution targets are supplied.
+    """
+
+    mean_predictions: torch.Tensor | None = None
+    full_predictions: torch.Tensor | None = None
+    loss: torch.Tensor | float | None = None
+
+
+class CtsmResidualBlock(nn.Module):
+    """Ctsm residual block."""
+
+    def __init__(self, input_dims, hidden_dims, output_dims):
+        super().__init__()
+        self.input_dims = input_dims
+        self.hidden_dims = hidden_dims
+        self.output_dims = output_dims
+
+        self.input_layer = nn.Linear(input_dims, hidden_dims)
+        self.activation = nn.SiLU()
+        self.output_layer = nn.Linear(hidden_dims, output_dims)
+        self.residual_layer = nn.Linear(input_dims, output_dims)
+
+    def forward(self, x):
+        hidden = self.input_layer(x)
+        hidden = self.activation(hidden)
+        output = self.output_layer(hidden)
+        residual = self.residual_layer(x)
+        return output + residual
+
+
+class CtsmRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: CtsmConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: CtsmConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def simple_eager_attention_forward(
+    module: nn.Module,
+    query_states: torch.Tensor,
+    key_states: torch.Tensor,
+    value_states: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float | int = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class CtsmAttention(nn.Module):
+    """TimesFM 2.0 style attention with learnable per-dimension Q scaling and rotary position embeddings.
+
+    Supports an optional `past_key_values` cache so that, during long-horizon autoregressive decoding,
+    each step only needs to compute K/V for the newly-appended fine patches and attends to the
+    previously-cached K/V for every earlier position.
+    """
+
+    def __init__(self, config: CtsmConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.is_causal = True
+        self.attention_dropout = config.attention_dropout
+        self.layer_idx = layer_idx
+
+        self.num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_dim = config.head_dim
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_heads * self.head_dim
+        self.scaling = nn.Parameter(torch.empty((self.head_dim,)))
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size)
+
+    def _scale_query(self, query: torch.Tensor) -> torch.Tensor:
+        scale = F.softplus(self.scaling).mul(1.442695041 / math.sqrt(self.head_dim))
+        return query * scale[None, None, None, :]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        query_states = self._scale_query(query_states)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, simple_eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=1.0,
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class CtsmMLP(nn.Module):
+    """Pax MLP in pytorch."""
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+        intermediate_size = config.intermediate_size
+
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size)
+        self.layer_norm = nn.LayerNorm(normalized_shape=hidden_size, eps=1e-6)
+
+    def forward(self, x, paddings=None):
+        gate_inp = self.layer_norm(x)
+        gate = self.gate_proj(gate_inp)
+        gate = F.relu(gate)
+        outputs = self.down_proj(gate)
+        if paddings is not None:
+            outputs = outputs * (1.0 - paddings[:, :, None])
+        return outputs + x
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class CtsmRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        CtsmRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class CtsmDecoderLayer(nn.Module):
+    """CTSM transformer block: attention with RoPE followed by TimesFM 2.0 MLP with padding masking."""
+
+    def __init__(self, config: CtsmConfig, layer_idx: int):
+        super().__init__()
+        self.self_attn = CtsmAttention(config, layer_idx=layer_idx)
+        self.mlp = CtsmMLP(config)
+        self.input_layernorm = CtsmRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        paddings: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+        )
+        hidden_states = residual + hidden_states
+        hidden_states = self.mlp(hidden_states, paddings=paddings)
+        return hidden_states
+
+
+class CtsmPositionalEmbedding(nn.Module):
+    """Generates position embedding for a given 1-d sequence."""
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__()
+        min_timescale = config.min_timescale
+        max_timescale = config.max_timescale
+        self.min_timescale, self.max_timescale = min_timescale, max_timescale
+        self.embedding_dims = config.hidden_size
+
+        num_timescales = self.embedding_dims // 2
+        log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(num_timescales - 1, 1)
+        self.register_buffer(
+            "inv_timescales",
+            min_timescale * torch.exp(torch.arange(num_timescales, dtype=torch.float32) * -log_timescale_increment),
+        )
+
+    def forward(self, seq_length=None, position=None):
+        """Generates a Tensor of sinusoids with different frequencies.
+
+        Args:
+            seq_length: an optional Python int defining the output sequence length.
+              if the `position` argument is specified.
+            position: [B, seq_length], optional position for each token in the
+              sequence, only required when the sequence is packed.
+
+        Returns:
+            [B, seqlen, D] if `position` is specified, else [1, seqlen, D]
+        """
+        if position is None and seq_length is None:
+            raise ValueError("Either position or seq_length must be provided")
+
+        if position is None:
+            # [1, seqlen]
+            position = torch.arange(seq_length, dtype=torch.float32, device=self.inv_timescales.device).unsqueeze(0)
+        elif position.ndim != 2:
+            raise ValueError(f"position must be 2-dimensional, got shape {position.shape}")
+
+        scaled_time = position.view(*position.shape, 1) * self.inv_timescales.view(1, 1, -1)
+        signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=2)
+
+        # Padding to ensure correct embedding dimension
+        signal = F.pad(signal, (0, 0, 0, self.embedding_dims % 2))
+        return signal
+
+
+@auto_docstring
+class CtsmPreTrainedModel(PreTrainedModel):
+    config: CtsmConfig
+    base_model_prefix = "model"
+    _no_split_modules = ["CtsmDecoderLayer"]
+    main_input_name = "past_values"
+    input_modalities = ("time",)
+    _supports_sdpa = True
+    _can_record_outputs = {
+        "hidden_states": CtsmDecoderLayer,
+        "attentions": CtsmAttention,
+    }
+    _supports_flash_attn = True
+    _supports_flex_attn = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, CtsmAttention):
+            # Initialize scaling parameter
+            init.ones_(module.scaling)
+        elif isinstance(module, CtsmPositionalEmbedding):
+            num_timescales = module.embedding_dims // 2
+            max_timescale, min_timescale = module.max_timescale, module.min_timescale
+            log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(
+                num_timescales - 1, 1
+            )
+            init.copy_(
+                module.inv_timescales,
+                min_timescale
+                * torch.exp(torch.arange(num_timescales, dtype=torch.float32) * -log_timescale_increment),
+            )
+        if isinstance(module, CtsmModel) and getattr(module, "special_token", None) is not None:
+            init.normal_(module.special_token, mean=0.0, std=self.config.initializer_range)
+
+
+@auto_docstring
+class CtsmModel(CtsmPreTrainedModel):
+    r"""
+    The multi-resolution CTSM encoder. The forward pass consumes two aligned streams (a coarse low-frequency
+    context and a fine high-frequency context), concatenates them along the sequence dimension with an
+    optional learned special token, and runs a stack of rotary-attention transformer layers. Attention is
+    bidirectional within the coarse block and causal elsewhere.
+    """
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__(config)
+
+        self.config = config
+        self.input_ff_layer = CtsmResidualBlock(
+            input_dims=2 * config.patch_length,
+            output_dims=config.hidden_size,
+            hidden_dims=config.intermediate_size,
+        )
+        self.freq_emb = nn.Embedding(num_embeddings=config.freq_size, embedding_dim=config.hidden_size)
+        self.layers = nn.ModuleList(
+            [CtsmDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        if self.config.use_positional_embedding:
+            self.position_emb = CtsmPositionalEmbedding(config=config)
+
+        if hasattr(self, "position_emb"):
+            del self.position_emb
+
+        self.rotary_emb = CtsmRotaryEmbedding(config)
+
+        if config.use_resolution_embeddings:
+            self.multi_resolution = nn.Embedding(config.num_resolutions, config.hidden_size)
+
+        if config.use_special_token:
+            self.special_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _forward_transform(
+        self, inputs: torch.Tensor, patched_pads: torch.Tensor
+    ) -> tuple[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        """Input is of shape [B, N, P]."""
+        mu, sigma = self._ctsm_masked_mean_std(inputs, patched_pads)
+        sigma = torch.clamp(sigma, min=self.config.tolerance)
+
+        # Normalize each patch
+        outputs = (inputs - mu[:, None, None]) / sigma[:, None, None]
+        outputs = torch.where(
+            torch.abs(inputs - self.config.pad_val) < self.config.tolerance,
+            torch.tensor(self.config.pad_val, dtype=outputs.dtype, device=outputs.device),
+            outputs,
+        )
+        return outputs, (mu, sigma)
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        past_values_coarse: torch.Tensor | None = None,
+        past_values_fine: torch.Tensor | None = None,
+        past_values_coarse_padding: torch.LongTensor | None = None,
+        past_values_fine_padding: torch.LongTensor | None = None,
+        freq: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        loc_fine: torch.Tensor | None = None,
+        scale_fine: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        r"""
+        past_values_coarse (`torch.FloatTensor` of shape `(batch_size, coarse_length)`, *optional*):
+            Coarse-resolution context (e.g. hourly aggregates). Length must be a multiple of `patch_length` or
+            will be left-padded to one. Required when `past_key_values` is `None`.
+        past_values_fine (`torch.FloatTensor` of shape `(batch_size, fine_length)`):
+            Fine-resolution context (e.g. minute-level). In the normal / full-forward mode this is the entire
+            fine context; when `past_key_values` is supplied this should contain **only the new fine values**
+            to append — they must already be pre-normalized by the caller using `loc_fine` / `scale_fine`.
+        past_values_coarse_padding (`torch.LongTensor`, *optional*):
+            Padding mask for the coarse stream, `1.0` for padded positions and `0.0` for real values.
+        past_values_fine_padding (`torch.LongTensor`, *optional*):
+            Padding mask for the fine stream.
+        freq (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
+            Frequency indices. Defaults to all zeros.
+        past_key_values (`Cache`, *optional*):
+            A [`Cache`] (typically a [`DynamicCache`]) holding K/V for the concatenated
+            `[coarse, special, fine_prefix]` sequence from a previous call. When supplied the model runs in
+            **incremental mode**: only the new fine patches are embedded, and their Q/K/V are added on top
+            of the cached K/V. `loc_fine` / `scale_fine` **must** also be supplied so the new fine values
+            are normalized on the same scale as the cached ones.
+        use_cache (`bool`, *optional*):
+            Whether to build and return a key/value cache in the `CtsmOutput`. Defaults to `False` unless
+            `past_key_values` is provided (in which case caching is always on).
+        cache_position (`torch.LongTensor` of shape `(num_new,)`, *optional*):
+            Absolute positions (in the full `[coarse, special, fine]` sequence) of the new fine patches.
+            Only used in incremental mode; defaults to `torch.arange(past_length, past_length + num_new)`.
+        loc_fine (`torch.Tensor` of shape `(batch_size,)`, *optional*):
+            Fine-stream mean used for stream normalization. Required in incremental mode.
+        scale_fine (`torch.Tensor` of shape `(batch_size,)`, *optional*):
+            Fine-stream standard deviation used for stream normalization. Required in incremental mode.
+        """
+        if past_key_values is None:
+            return self._full_forward(
+                past_values_coarse=past_values_coarse,
+                past_values_fine=past_values_fine,
+                past_values_coarse_padding=past_values_coarse_padding,
+                past_values_fine_padding=past_values_fine_padding,
+                freq=freq,
+                use_cache=bool(use_cache),
+                **kwargs,
+            )
+        return self._incremental_forward(
+            past_values_fine=past_values_fine,
+            past_values_fine_padding=past_values_fine_padding,
+            freq=freq,
+            past_key_values=past_key_values,
+            cache_position=cache_position,
+            loc_fine=loc_fine,
+            scale_fine=scale_fine,
+            **kwargs,
+        )
+
+    @staticmethod
+    def _prepare_4d_attention_mask(
+        attention_mask: torch.Tensor | None,
+        sequence_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        is_causal: bool = True,
+    ) -> torch.Tensor | None:
+        """
+        Creates 4D attention mask and combines causal and padding masks if needed.
+
+        Args:
+            attention_mask: Optional tensor of shape (batch_size, seq_length) containing padding mask
+            sequence_length: Length of the sequence
+            dtype: Data type of the mask
+            device: Device of the mask
+            is_causal: Whether to apply causal masking
+
+        Returns:
+            4D attention mask of shape (batch_size, 1, seq_length, seq_length)
+        """
+        # Get minimum value for the dtype
+        min_value = torch.finfo(dtype).min if dtype.is_floating_point else torch.iinfo(dtype).min
+
+        # Handle padding mask
+        if attention_mask is not None:
+            # Convert 2D padding mask to 4D attention mask
+            attention_mask = attention_mask.view(attention_mask.shape[0], 1, 1, -1)
+            attention_mask = attention_mask * min_value
+
+        # Create causal mask if needed
+        if is_causal:
+            causal_mask = torch.triu(
+                torch.ones((sequence_length, sequence_length), dtype=dtype, device=device) * min_value,
+                diagonal=1,
+            )
+            causal_mask = causal_mask.view(1, 1, sequence_length, sequence_length)
+
+            # Combine with padding mask if it exists
+            if attention_mask is not None:
+                attention_mask = torch.minimum(attention_mask, causal_mask)
+            else:
+                attention_mask = causal_mask
+
+        return attention_mask
+
+    @staticmethod
+    def _ctsm_masked_mean_std(inputs: torch.Tensor, padding: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+        """Calculates mean and standard deviation of `inputs` across axis 1.
+
+        It excludes values where `padding` is 1.
+
+        Args:
+            inputs: A PyTorch tensor of shape [b, n, p].
+            padding: A PyTorch tensor of shape [b, n, p] with values 0 or 1.
+
+        Returns:
+            A tuple containing the mean and standard deviation.
+            We return the statistics of the first patch with more than three non-padded values.
+        """
+
+        # Selecting the first patch with more than 3 unpadded values.
+        def _get_patch_index(arr: torch.Tensor):
+            indices = torch.argmax((arr >= 3).to(torch.int32), dim=1)
+            row_sum = (arr >= 3).to(torch.int32).sum(dim=1)
+            return torch.where(row_sum == 0, arr.shape[1] - 1, indices)
+
+        pad_sum = torch.sum(1 - padding, dim=2)
+        patch_indices = _get_patch_index(pad_sum)
+        bidxs = torch.arange(inputs.shape[0])
+
+        arr = inputs[bidxs, patch_indices, :]
+        pad = padding[bidxs, patch_indices, :]
+
+        # Create a mask where padding is 0
+        mask = 1 - pad
+
+        # Calculate the number of valid elements
+        num_valid_elements = torch.sum(mask, dim=1)
+        num_valid_elements = torch.clamp(num_valid_elements, min=1.0)
+
+        # Calculate the masked sum and mean
+        masked_sum = torch.sum(arr * mask, dim=1)
+        masked_mean = masked_sum / num_valid_elements  # [b]
+
+        # Calculate the masked variance using centered values
+        masked_centered_arr = (arr - masked_mean.unsqueeze(-1)) * mask
+        masked_var = torch.sum(masked_centered_arr**2, dim=1) / num_valid_elements
+        masked_var = torch.clamp(masked_var, min=0.0)
+        masked_std = torch.sqrt(masked_var)
+
+        return masked_mean, masked_std
+
+    @staticmethod
+    def _ctsm_shift_padded_seq(mask: torch.Tensor, seq: torch.Tensor) -> torch.Tensor:
+        """Shifts rows of seq based on the first 0 in each row of the mask.
+
+        Args:
+            mask: mask tensor of shape [B, N]
+            seq: seq tensor of shape [B, N, P]
+
+        Returns:
+            The shifted sequence.
+        """
+        batch_size, num_seq, feature_dim = seq.shape
+
+        new_mask: torch.BoolTensor = mask == 0
+
+        # Use argmax to find the first True value in each row
+        indices = new_mask.to(torch.int32).argmax(dim=1)
+
+        # Handle rows with all zeros
+        indices[~new_mask.any(dim=1)] = -1
+
+        # Create index ranges for each sequence in the batch
+        idx_range = torch.arange(num_seq, device=seq.device).view(1, -1, 1).expand(batch_size, -1, feature_dim)
+
+        # Calculate shifted indices for each element in each sequence
+        shifted_idx = (idx_range - indices[:, None, None]) % num_seq
+
+        # Gather values from seq using shifted indices
+        shifted_seq = seq.gather(1, shifted_idx)
+
+        return shifted_seq
+
+    @staticmethod
+    def _left_pad_to_patch_boundary(
+        values: torch.Tensor, paddings: torch.Tensor, patch_length: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        rem = values.shape[1] % patch_length
+        if rem == 0:
+            return values, paddings
+        pad_len = patch_length - rem
+        values_pad = torch.zeros((values.shape[0], pad_len), device=values.device, dtype=values.dtype)
+        paddings_pad = torch.ones((paddings.shape[0], pad_len), device=paddings.device, dtype=paddings.dtype)
+        return torch.cat([values_pad, values], dim=1), torch.cat([paddings_pad, paddings], dim=1)
+
+    @staticmethod
+    def _normalize_with_pad(
+        context: torch.Tensor, padding: torch.Tensor, tolerance: float = 1e-8
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Stream-level normalization that matches the original CTSM reference.
+
+        Normalizes ``context`` using the mean and standard deviation computed over the
+        non-padded positions (``padding == 0``) across the whole context, rather than
+        TimesFM's per-first-patch statistics. The normalized tensor has padded positions
+        zeroed out and is clamped to a safe range.
+        """
+        valid = 1.0 - padding
+        count = valid.sum(dim=1, keepdim=True).clamp_min(1.0)
+        mu = (context * valid).sum(dim=1, keepdim=True) / count
+
+        seq_len_f = context.new_tensor(float(context.shape[1]))
+        filled = torch.where(padding.to(dtype=torch.bool), mu, context)
+        sigma = filled.std(dim=1, keepdim=True, unbiased=False) * torch.sqrt(seq_len_f / count)
+        sigma = sigma.clamp_min(1e-2)
+
+        normalized = (context - mu) / (sigma + tolerance)
+        normalized = normalized * valid
+        normalized = normalized.clamp(-1000.0, 1000.0)
+        return normalized, mu.squeeze(-1), sigma.squeeze(-1)
+
+    def _patchify(
+        self, past_values: torch.Tensor, past_values_padding: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Patchify an already stream-normalized stream and project through the input tokenizer."""
+        bsize = past_values.shape[0]
+        patched_inputs = past_values.view(bsize, -1, self.config.patch_length)
+        patched_pads = past_values_padding.view(bsize, -1, self.config.patch_length)
+
+        patched_inputs = patched_inputs * (1.0 - patched_pads)
+        concat_inputs = torch.cat([patched_inputs, patched_pads], dim=-1)
+        embeddings = self.input_ff_layer(concat_inputs)
+        patch_padding = torch.min(patched_pads, dim=-1)[0]
+        return embeddings, patch_padding
+
+    def _build_attention_mask(
+        self,
+        patch_padding: torch.Tensor,
+        num_coarse_patches: int,
+        dtype: torch.dtype,
+    ) -> torch.Tensor:
+        """Reuse TimesFM's padding+causal 4D mask, then open the coarse-coarse block to bidirectional."""
+        attention_mask = self._prepare_4d_attention_mask(
+            attention_mask=patch_padding,
+            sequence_length=patch_padding.shape[1],
+            dtype=dtype,
+            device=patch_padding.device,
+            is_causal=True,
+        )
+        if num_coarse_patches > 0:
+            attention_mask[..., :num_coarse_patches, :num_coarse_patches] = 0.0
+        return attention_mask
+
+    def _build_incremental_attention_mask(
+        self, bsize: int, num_new: int, past_length: int, dtype: torch.dtype, device: torch.device
+    ) -> torch.Tensor:
+        """Mask for the incremental (cached) path: new fine Qs attend to all cached K/V plus causal within the new block."""
+        min_value = torch.finfo(dtype).min
+        mask = torch.zeros((bsize, 1, num_new, past_length + num_new), dtype=dtype, device=device)
+        if num_new > 1:
+            causal_new = torch.triu(torch.full((num_new, num_new), min_value, dtype=dtype, device=device), diagonal=1)
+            mask[:, :, :, past_length:] = causal_new
+        return mask
+
+    def _full_forward(
+        self,
+        past_values_coarse: torch.Tensor,
+        past_values_fine: torch.Tensor,
+        past_values_coarse_padding: torch.LongTensor | None,
+        past_values_fine_padding: torch.LongTensor | None,
+        freq: torch.Tensor | None,
+        use_cache: bool,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        if past_values_coarse_padding is None:
+            past_values_coarse_padding = torch.zeros_like(past_values_coarse)
+        if past_values_fine_padding is None:
+            past_values_fine_padding = torch.zeros_like(past_values_fine)
+        past_values_coarse_padding = past_values_coarse_padding.to(past_values_coarse.dtype)
+        past_values_fine_padding = past_values_fine_padding.to(past_values_fine.dtype)
+
+        patch_length = self.config.patch_length
+        past_values_coarse, past_values_coarse_padding = self._left_pad_to_patch_boundary(
+            past_values_coarse, past_values_coarse_padding, patch_length
+        )
+        past_values_fine, past_values_fine_padding = self._left_pad_to_patch_boundary(
+            past_values_fine, past_values_fine_padding, patch_length
+        )
+
+        coarse_normalized, loc_coarse, scale_coarse = self._normalize_with_pad(
+            past_values_coarse, past_values_coarse_padding, tolerance=self.config.tolerance
+        )
+        fine_normalized, loc_fine, scale_fine = self._normalize_with_pad(
+            past_values_fine, past_values_fine_padding, tolerance=self.config.tolerance
+        )
+
+        coarse_embeddings, coarse_patch_padding = self._patchify(coarse_normalized, past_values_coarse_padding)
+        fine_embeddings, fine_patch_padding = self._patchify(fine_normalized, past_values_fine_padding)
+
+        bsize, num_coarse_patches, hidden_size = coarse_embeddings.shape
+        num_fine_patches = fine_embeddings.shape[1]
+        device = coarse_embeddings.device
+        dtype = coarse_embeddings.dtype
+
+        if self.config.use_special_token:
+            special = self.special_token.to(device=device, dtype=dtype).expand(bsize, 1, hidden_size)
+            special_padding = torch.zeros(bsize, 1, device=device, dtype=coarse_patch_padding.dtype)
+            model_input = torch.cat([coarse_embeddings, special, fine_embeddings], dim=1)
+            patch_padding = torch.cat([coarse_patch_padding, special_padding, fine_patch_padding], dim=1)
+            num_special = 1
+        else:
+            model_input = torch.cat([coarse_embeddings, fine_embeddings], dim=1)
+            patch_padding = torch.cat([coarse_patch_padding, fine_patch_padding], dim=1)
+            num_special = 0
+
+        if self.config.use_resolution_embeddings:
+            mr_coarse = torch.zeros(num_coarse_patches, dtype=torch.long, device=device)
+            mr_special = torch.full((num_special,), 1, dtype=torch.long, device=device)
+            mr_fine = torch.full((num_fine_patches,), 2, dtype=torch.long, device=device)
+            mr_idx = torch.cat([mr_coarse, mr_special, mr_fine], dim=0).unsqueeze(0).expand(bsize, -1)
+            model_input = model_input + self.multi_resolution(mr_idx)
+
+        if freq is None:
+            freq = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq = freq.to(device=device, dtype=torch.long)
+        model_input = model_input + self.freq_emb(freq)
+
+        attention_mask = self._build_attention_mask(patch_padding, num_coarse_patches, model_input.dtype)
+        position_ids = (
+            torch.arange(model_input.shape[1], device=device, dtype=torch.long).unsqueeze(0).expand(bsize, -1)
+        )
+        position_embeddings = self.rotary_emb(model_input, position_ids)
+
+        past_key_values = DynamicCache(config=self.config) if use_cache else None
+
+        hidden_states = model_input
+        for layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                paddings=patch_padding,
+                position_embeddings=position_embeddings,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        return CtsmOutput(
+            last_hidden_state=hidden_states,
+            loc=loc_fine,
+            scale=scale_fine,
+            loc_coarse=loc_coarse,
+            scale_coarse=scale_coarse,
+            num_coarse_patches=num_coarse_patches + num_special,
+            num_fine_patches=num_fine_patches,
+            past_key_values=past_key_values,
+        )
+
+    def _incremental_forward(
+        self,
+        past_values_fine: torch.Tensor,
+        past_values_fine_padding: torch.LongTensor | None,
+        freq: torch.Tensor | None,
+        past_key_values: Cache,
+        cache_position: torch.LongTensor | None,
+        loc_fine: torch.Tensor | None,
+        scale_fine: torch.Tensor | None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        if loc_fine is None or scale_fine is None:
+            raise ValueError(
+                "`loc_fine` and `scale_fine` must be supplied together with `past_key_values` so that the new fine "
+                "values are normalized on the same scale as the cached ones."
+            )
+        if past_values_fine.shape[1] % self.config.patch_length != 0:
+            raise ValueError(
+                f"In incremental mode `past_values_fine` length must be a multiple of `patch_length="
+                f"{self.config.patch_length}`; got {past_values_fine.shape[1]}."
+            )
+
+        if past_values_fine_padding is None:
+            past_values_fine_padding = torch.zeros_like(past_values_fine)
+        past_values_fine_padding = past_values_fine_padding.to(past_values_fine.dtype)
+
+        tol = self.config.tolerance
+        fine_normalized = (past_values_fine - loc_fine.unsqueeze(-1)) / (scale_fine.unsqueeze(-1) + tol)
+        fine_normalized = fine_normalized * (1.0 - past_values_fine_padding)
+        fine_normalized = fine_normalized.clamp(-1000.0, 1000.0)
+
+        new_embeddings, new_patch_padding = self._patchify(fine_normalized, past_values_fine_padding)
+        bsize, num_new, _ = new_embeddings.shape
+        device = new_embeddings.device
+        dtype = new_embeddings.dtype
+
+        if self.config.use_resolution_embeddings:
+            mr_idx = torch.full((bsize, num_new), 2, dtype=torch.long, device=device)
+            new_embeddings = new_embeddings + self.multi_resolution(mr_idx)
+
+        if freq is None:
+            freq = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq = freq.to(device=device, dtype=torch.long)
+        new_embeddings = new_embeddings + self.freq_emb(freq)
+
+        past_length = past_key_values.get_seq_length()
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + num_new, dtype=torch.long, device=device)
+        position_ids = cache_position.unsqueeze(0).expand(bsize, -1)
+        position_embeddings = self.rotary_emb(new_embeddings, position_ids)
+
+        attention_mask = self._build_incremental_attention_mask(bsize, num_new, past_length, dtype, device)
+
+        hidden_states = new_embeddings
+        for layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                paddings=new_patch_padding,
+                position_embeddings=position_embeddings,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        return CtsmOutput(
+            last_hidden_state=hidden_states,
+            loc=loc_fine,
+            scale=scale_fine,
+            num_fine_patches=num_new,
+            past_key_values=past_key_values,
+        )
+
+
+class CtsmModelForPrediction(CtsmPreTrainedModel):
+    """CTSM model with a multi-resolution prediction head and autoregressive multi-resolution decoding.
+
+    For horizons that require autoregressive decoding (``horizon_len > config.horizon_length``) the
+    prediction class reuses a key/value cache across AR steps: the first step runs the full forward
+    and populates a [`DynamicCache`], subsequent steps feed only the newly-appended fine patches
+    through the stack and attend to the cached K/V for every earlier position. Two caveats, matching
+    how a KV cache is made to fit CTSM's architecture:
+
+    * Stream-level normalization statistics (``loc_fine``, ``scale_fine``) are frozen to the values
+      computed on the first step. This is a small approximation: in the untracked reference,
+      statistics are recomputed after each prediction is appended; in practice the drift is small
+      when forecasts stay in-distribution.
+    * If an AR step would grow the coarse block (a new coarse patch is formed once every
+      ``patch_length * agg_factor / output_patch_len`` steps, i.e. ~every 15 steps at the defaults),
+      the cache is discarded and a full forward is run, rebuilding the cache.
+    """
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__(config)
+
+        self.config = config
+        self.context_len = config.context_length
+        self.horizon_len = config.horizon_length
+
+        self.model = CtsmModel(config)
+        num_outputs = 1 + len(config.quantiles)
+        self.horizon_ff_layer = CtsmResidualBlock(
+            input_dims=config.hidden_size,
+            output_dims=config.horizon_length * num_outputs,
+            hidden_dims=config.intermediate_size,
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _preprocess(
+        self, inputs: Sequence[torch.Tensor], freq: Sequence[int] | None = None, context_len: int | None = None
+    ) -> tuple[torch.Tensor, ...]:
+        """Pad/truncate input time series to `context_len` and build a padding mask.
+
+        Args:
+            inputs: A list of 1d Tensors. Each Tensor is the context time series of a single forecast task.
+            freq: Optional list of frequencies (returned as a tensor when provided).
+            context_len: Optional context length override (defaults to `self.context_len`).
+
+        Returns:
+            Tuple of (padded_inputs, padding_mask) and optionally a freq tensor.
+        """
+        if context_len is None:
+            context_len = self.context_len
+
+        input_ts, input_padding = [], []
+
+        for ts in inputs:
+            input_len = ts.shape[0]
+            padding = torch.zeros(input_len + self.horizon_len, dtype=ts.dtype, device=ts.device)
+            if input_len < context_len:
+                num_front_pad = context_len - input_len
+                ts = torch.cat([torch.zeros(num_front_pad, dtype=ts.dtype, device=ts.device), ts], dim=0)
+                padding = torch.cat([torch.ones(num_front_pad, dtype=ts.dtype, device=padding.device), padding], dim=0)
+            elif input_len > context_len:
+                ts = ts[-context_len:]
+                padding = padding[-(context_len + self.horizon_len) :]
+
+            input_ts.append(ts)
+            input_padding.append(padding)
+
+        result = (torch.stack(input_ts, dim=0), torch.stack(input_padding, dim=0))
+        if freq is not None:
+            result = result + (torch.tensor(freq[: len(inputs)], dtype=torch.int32).reshape(-1, 1),)
+        return result
+
+    def _postprocess_output(
+        self, model_output: torch.Tensor, stats: tuple[torch.Tensor, torch.Tensor]
+    ) -> torch.Tensor:
+        """Postprocess output of stacked transformer."""
+
+        # B x N x (H.Q)
+        output_ts = self.horizon_ff_layer(model_output)
+
+        # Reshape using view
+        b, n, _ = output_ts.shape
+        output_ts = output_ts.view(b, n, self.config.horizon_length, len(self.config.quantiles) + 1)
+
+        mu, sigma = stats
+        return output_ts * sigma[:, None, None, None] + mu[:, None, None, None]
+
+    def _quantile_loss(self, predictions: torch.Tensor, targets: torch.Tensor) -> torch.Tensor:
+        losses = []
+        for i, q in enumerate(self.config.quantiles):
+            errors = targets - predictions[..., i]
+            loss = torch.max((q - 1) * errors, q * errors)
+            losses.append(loss.mean())
+        return torch.stack(losses).mean()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        past_values: Sequence[torch.Tensor] | Sequence[tuple[torch.Tensor, torch.Tensor]],
+        future_values: torch.Tensor | None = None,
+        horizon_len: int | None = None,
+        freq: Sequence[int] | torch.Tensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutputForPrediction:
+        r"""
+        past_values (`Sequence[torch.Tensor]`):
+            Either a list of 1-D fine-resolution tensors (the coarse stream is derived by mean-aggregating over
+            `agg_factor` consecutive points) or a list of `(coarse, fine)` pairs if both streams are provided.
+        future_values (`torch.Tensor`, *optional*):
+            Optional fine-resolution ground truth used to compute the loss.
+        horizon_len (`int`, *optional*):
+            Number of fine-resolution steps to forecast. Defaults to `config.horizon_length`. Values larger than
+            `config.horizon_length` trigger autoregressive decoding.
+        freq (`Sequence[int]` or `torch.Tensor`, *optional*):
+            Frequency indices. Defaults to zeros.
+        use_cache (`bool`, *optional*):
+            Whether to use a key/value cache across autoregressive decode steps. Defaults to `True` when
+            `horizon_len > config.horizon_length` (i.e. when AR decoding is needed) and `False` otherwise.
+            Set to `False` to force a full recompute at every AR step (matches the original reference
+            behaviour; slower but avoids the stream-stats-freezing approximation).
+        """
+        device = self.horizon_ff_layer.input_layer.weight.device
+        horizon_len = horizon_len or self.config.horizon_length
+        if horizon_len <= 0:
+            raise ValueError("horizon_len must be positive")
+
+        output_patch_len = self.config.horizon_length
+        num_decode_patches = (horizon_len + output_patch_len - 1) // output_patch_len
+
+        coarse, coarse_pad, fine, fine_pad = self._prepare_context(past_values, device=device)
+        bsize = coarse.shape[0]
+
+        if freq is None:
+            freq_tensor = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq_tensor = torch.as_tensor(
+                list(freq) if not isinstance(freq, torch.Tensor) else freq, dtype=torch.long, device=device
+            ).view(bsize, 1)
+
+        mean_chunks: list[torch.Tensor] = []
+        quant_chunks: list[torch.Tensor] = []
+        remaining = horizon_len
+        last_outputs: CtsmOutput | None = None
+        max_fine = self.config.context_length
+        max_coarse = self.config.context_length
+        agg = self.config.agg_factor
+        new_fine_patches = self.config.horizon_length // self.config.patch_length
+
+        past_key_values: Cache | None = None
+        frozen_loc_fine: torch.Tensor | None = None
+        frozen_scale_fine: torch.Tensor | None = None
+        coarse_buffer = torch.zeros((bsize, 0), dtype=torch.float32, device=device)
+
+        if use_cache is None:
+            use_cache = num_decode_patches > 1
+        pending_new_fine: torch.Tensor | None = None
+
+        for step_idx in range(num_decode_patches):
+            if past_key_values is None:
+                # First step (or after cache invalidation): full forward. The coarse block in the cache
+                # stays frozen at the initial state — only the fine block grows via subsequent incremental
+                # steps — which matches how KV caches work for append-only sequences.
+                mean_patch, quant_patch, last_outputs = self._decode_step_full(
+                    past_values_coarse=coarse,
+                    past_values_fine=fine,
+                    past_values_coarse_padding=coarse_pad,
+                    past_values_fine_padding=fine_pad,
+                    freq=freq_tensor,
+                    use_cache=use_cache,
+                    **kwargs,
+                )
+                past_key_values = last_outputs.past_key_values
+                frozen_loc_fine = last_outputs.loc
+                frozen_scale_fine = last_outputs.scale
+            else:
+                # Incremental: only the fine values newly appended last step go through the stack.
+                mean_patch, quant_patch, last_outputs = self._decode_step_incremental(
+                    new_fine_values=pending_new_fine,
+                    freq=freq_tensor,
+                    past_key_values=past_key_values,
+                    loc_fine=frozen_loc_fine,
+                    scale_fine=frozen_scale_fine,
+                    **kwargs,
+                )
+
+            take = min(remaining, output_patch_len)
+            mean_chunks.append(mean_patch[:, :take])
+            quant_chunks.append(quant_patch[:, :take, :])
+            remaining -= take
+            if remaining <= 0:
+                break
+
+            new_fine = mean_patch[:, :output_patch_len]
+            pending_new_fine = new_fine
+
+            # Track the raw contexts so the next full-forward (initial step or after cache
+            # invalidation) sees the right state. Mirrors the reference AR loop.
+            fine = torch.cat([fine, new_fine], dim=1)
+            fine_pad = torch.cat(
+                [fine_pad, torch.zeros((bsize, output_patch_len), device=device, dtype=fine_pad.dtype)], dim=1
+            )
+            if fine.shape[1] > max_fine:
+                fine = fine[:, -max_fine:]
+                fine_pad = fine_pad[:, -max_fine:]
+
+            coarse_buffer = torch.cat([coarse_buffer, new_fine], dim=1)
+            full_blocks = coarse_buffer.shape[1] // agg
+            if full_blocks > 0:
+                blocks = coarse_buffer[:, : full_blocks * agg].view(bsize, full_blocks, agg).mean(dim=2)
+                coarse_buffer = coarse_buffer[:, full_blocks * agg :]
+                coarse = torch.cat([coarse, blocks], dim=1)
+                coarse_pad = torch.cat(
+                    [coarse_pad, torch.zeros((bsize, full_blocks), device=device, dtype=coarse_pad.dtype)], dim=1
+                )
+                if coarse.shape[1] > max_coarse:
+                    coarse = coarse[:, -max_coarse:]
+                    coarse_pad = coarse_pad[:, -max_coarse:]
+
+            if past_key_values is not None:
+                projected_len = past_key_values.get_seq_length() + new_fine_patches
+                if projected_len >= self.config.max_position_embeddings:
+                    past_key_values = None
+                    pending_new_fine = None
+
+        mean_predictions = torch.cat(mean_chunks, dim=1)[:, :horizon_len]
+        full_predictions = torch.cat(
+            [torch.cat(mean_chunks, dim=1)[:, :horizon_len, None], torch.cat(quant_chunks, dim=1)[:, :horizon_len, :]],
+            dim=-1,
+        )
+
+        loss = None
+        if future_values is not None:
+            target_len = min(future_values.shape[1], mean_predictions.shape[1])
+            mse_loss = F.mse_loss(mean_predictions[:, :target_len], future_values[:, :target_len])
+            quantile_loss = self._quantile_loss(full_predictions[:, :target_len, 1:], future_values[:, :target_len])
+            loss = mse_loss + quantile_loss
+
+        return CtsmOutputForPrediction(
+            last_hidden_state=last_outputs.last_hidden_state if last_outputs is not None else None,
+            hidden_states=last_outputs.hidden_states if last_outputs is not None else None,
+            attentions=last_outputs.attentions if last_outputs is not None else None,
+            mean_predictions=mean_predictions,
+            full_predictions=full_predictions,
+            loss=loss,
+        )
+
+    @staticmethod
+    def _ctsm_moving_average(arr: torch.Tensor, window_size: int) -> list[torch.Tensor]:
+        """Calculates the moving average using PyTorch's convolution function."""
+        # Pad with zeros to handle initial window positions
+        arr_padded = F.pad(arr, (window_size - 1, 0), "constant", 0)
+        # Create a convolution kernel
+        kernel = torch.ones(window_size, dtype=arr.dtype, device=arr.device) / window_size
+        # Apply convolution to calculate the moving average
+        smoothed_arr = F.conv1d(arr_padded.view(1, 1, -1), kernel.view(1, 1, -1)).squeeze()
+        return [smoothed_arr, arr - smoothed_arr]
+
+    @staticmethod
+    def _build_multi_resolution(
+        series: torch.Tensor, agg_factor: int, coarse_len: int, fine_len: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Build (coarse, fine) contexts from a 1-D fine-resolution series.
+
+        Coarse is the mean of the last `coarse_len * agg_factor` fine samples, aligned to block boundaries.
+        Fine is the last `fine_len` samples.
+        """
+        series = series.to(torch.float32).reshape(-1)
+        needed = coarse_len * agg_factor
+        raw = series[-needed:]
+        remainder = raw.shape[0] % agg_factor
+        if remainder:
+            raw = raw[remainder:]
+        if raw.numel() == 0:
+            coarse = series.new_empty((0,), dtype=torch.float32)
+        else:
+            coarse = raw.reshape(-1, agg_factor).mean(dim=1)
+            if coarse.shape[0] > coarse_len:
+                coarse = coarse[-coarse_len:]
+        fine = series[-fine_len:].to(torch.float32)
+        return coarse, fine
+
+    def _prepare_context(
+        self,
+        past_values: Sequence[torch.Tensor] | Sequence[tuple[torch.Tensor, torch.Tensor]],
+        device: torch.device,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        coarse_len = self.config.context_length
+        fine_len = self.config.context_length
+        agg = self.config.agg_factor
+
+        coarse_batch = torch.zeros((len(past_values), coarse_len), dtype=torch.float32, device=device)
+        coarse_pad = torch.zeros_like(coarse_batch)
+        fine_batch = torch.zeros((len(past_values), fine_len), dtype=torch.float32, device=device)
+        fine_pad = torch.zeros_like(fine_batch)
+
+        for i, item in enumerate(past_values):
+            if isinstance(item, (tuple, list)) and len(item) == 2:
+                coarse, fine = item
+                coarse = torch.as_tensor(coarse, dtype=torch.float32, device=device).reshape(-1)
+                fine = torch.as_tensor(fine, dtype=torch.float32, device=device).reshape(-1)
+            else:
+                series = torch.as_tensor(item, dtype=torch.float32, device=device).reshape(-1)
+                coarse, fine = self._build_multi_resolution(series, agg, coarse_len, fine_len)
+
+            c_n = coarse.shape[0]
+            if c_n >= coarse_len:
+                coarse_batch[i] = coarse[-coarse_len:]
+            elif c_n > 0:
+                coarse_batch[i, coarse_len - c_n :] = coarse
+                coarse_pad[i, : coarse_len - c_n] = 1.0
+            else:
+                coarse_pad[i] = 1.0
+
+            f_n = fine.shape[0]
+            if f_n >= fine_len:
+                fine_batch[i] = fine[-fine_len:]
+            elif f_n > 0:
+                fine_batch[i, fine_len - f_n :] = fine
+                fine_pad[i, : fine_len - f_n] = 1.0
+            else:
+                fine_pad[i] = 1.0
+
+        return coarse_batch, coarse_pad, fine_batch, fine_pad
+
+    def _project_last_fine(self, outputs: CtsmOutput, last_position: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """Project the hidden state at `last_position` through the horizon head and denormalize."""
+        last_hidden = outputs.last_hidden_state[:, last_position : last_position + 1, :]
+        head = self.horizon_ff_layer(last_hidden)
+        bsize = head.shape[0]
+        num_outputs = 1 + len(self.config.quantiles)
+        head = head.view(bsize, self.config.horizon_length, num_outputs)
+
+        loc = outputs.loc[:, None, None]
+        scale = outputs.scale[:, None, None]
+        mean_patch = head[..., 0] * scale[..., 0] + loc[..., 0]
+        quant_patch = head[..., 1:] * scale + loc
+        mean_patch = torch.nan_to_num(mean_patch, nan=0.0, posinf=0.0, neginf=0.0)
+        quant_patch = torch.nan_to_num(quant_patch, nan=0.0, posinf=0.0, neginf=0.0)
+        return mean_patch, quant_patch
+
+    def _decode_step_full(
+        self,
+        past_values_coarse: torch.Tensor,
+        past_values_fine: torch.Tensor,
+        past_values_coarse_padding: torch.Tensor,
+        past_values_fine_padding: torch.Tensor,
+        freq: torch.Tensor,
+        use_cache: bool,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor, CtsmOutput]:
+        """Full forward through the model. If `use_cache`, the returned outputs carry a fresh cache."""
+        outputs: CtsmOutput = self.model(
+            past_values_coarse=past_values_coarse,
+            past_values_fine=past_values_fine,
+            past_values_coarse_padding=past_values_coarse_padding,
+            past_values_fine_padding=past_values_fine_padding,
+            freq=freq,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        mean_patch, quant_patch = self._project_last_fine(outputs, outputs.last_hidden_state.shape[1] - 1)
+        return mean_patch, quant_patch, outputs
+
+    def _decode_step_incremental(
+        self,
+        new_fine_values: torch.Tensor,
+        freq: torch.Tensor,
+        past_key_values: Cache,
+        loc_fine: torch.Tensor,
+        scale_fine: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor, CtsmOutput]:
+        """Append `new_fine_values` to the cached state and run only the new positions through the stack."""
+        outputs: CtsmOutput = self.model(
+            past_values_fine=new_fine_values,
+            freq=freq,
+            past_key_values=past_key_values,
+            loc_fine=loc_fine,
+            scale_fine=scale_fine,
+            **kwargs,
+        )
+        mean_patch, quant_patch = self._project_last_fine(outputs, outputs.last_hidden_state.shape[1] - 1)
+        return mean_patch, quant_patch, outputs
+
+
+__all__ = ["CtsmModel", "CtsmModelForPrediction", "CtsmPreTrainedModel"]
diff --git a/src/transformers/models/ctsm/modular_ctsm.py b/src/transformers/models/ctsm/modular_ctsm.py
new file mode 100644
index 000000000000..e56fe16403c5
--- /dev/null
+++ b/src/transformers/models/ctsm/modular_ctsm.py
@@ -0,0 +1,941 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Cisco Time Series Model (CTSM)."""
+
+from collections.abc import Callable, Sequence
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub.dataclasses import strict
+
+from ... import initialization as init
+from ...cache_utils import Cache, DynamicCache
+from ...modeling_rope_utils import RopeParameters
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..phi4_multimodal.modeling_phi4_multimodal import simple_eager_attention_forward
+from ..timesfm.configuration_timesfm import TimesFmConfig
+from ..timesfm.modeling_timesfm import (
+    TimesFmAttention,
+    TimesFmDecoderLayer,
+    TimesFmModel,
+    TimesFmModelForPrediction,
+    TimesFmOutput,
+    TimesFmOutputForPrediction,
+    TimesFmPreTrainedModel,
+    TimesFmResidualBlock,  # re-exported as CtsmResidualBlock in the generated file
+)
+from ..timesfm2_5.modeling_timesfm2_5 import (
+    TimesFm2_5RotaryEmbedding,
+    apply_rotary_pos_emb,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="cisco-ai/cisco-time-series-model-1.0")
+@strict
+class CtsmConfig(TimesFmConfig):
+    r"""
+    patch_length (`int`, *optional*, defaults to 32):
+        Length of one patch in the input sequence for each resolution stream.
+    context_length (`int`, *optional*, defaults to 512):
+        Length of the input context for each resolution stream.
+    horizon_length (`int`, *optional*, defaults to 128):
+        Length of the prediction horizon produced per autoregressive step.
+    freq_size (`int`, *optional*, defaults to 3):
+        Number of frequency embeddings.
+    tolerance (`float`, *optional*, defaults to 1e-06):
+        Numerical tolerance used in normalization.
+    pad_val (`float`, *optional*, defaults to 1123581321.0):
+        Sentinel value marking padded positions in the input series.
+    num_hidden_layers (`int`, *optional*, defaults to 25):
+        Number of decoder layers.
+    quantiles (`list[float]`, *optional*, defaults to 15 values between 0.01 and 0.99):
+        Quantile levels predicted by the model.
+    use_positional_embedding (`bool`, *optional*, defaults to `False`):
+        CTSM uses rotary position embeddings and does not add sinusoidal positional embeddings.
+    use_resolution_embeddings (`bool`, *optional*, defaults to `True`):
+        Whether to add a learned embedding per resolution bucket (coarse / special / fine).
+    use_special_token (`bool`, *optional*, defaults to `True`):
+        Whether to insert a learned special token between the coarse and fine streams.
+    num_resolutions (`int`, *optional*, defaults to 3):
+        Number of resolution embeddings (coarse, special token, fine).
+    agg_factor (`int`, *optional*, defaults to 60):
+        Aggregation factor between fine and coarse resolutions (e.g. 60 minutes -> 1 hour).
+    max_position_embeddings (`int`, *optional*, defaults to 1025):
+        Maximum number of patches in the concatenated sequence (coarse + special + fine).
+    rope_parameters (`dict`, *optional*):
+        Rotary position embedding parameters. Defaults to `{"rope_type": "default", "rope_theta": 10000.0}`.
+
+    Example:
+
+    ```python
+    >>> from transformers import CtsmConfig, CtsmModelForPrediction
+
+    >>> configuration = CtsmConfig()
+    >>> model = CtsmModelForPrediction(configuration)
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "ctsm"
+
+    num_hidden_layers: int = 25
+    context_length: int = 512
+    quantiles: list[float] | tuple[float, ...] = (
+        0.01,
+        0.05,
+        0.1,
+        0.2,
+        0.25,
+        0.3,
+        0.4,
+        0.5,
+        0.6,
+        0.7,
+        0.75,
+        0.8,
+        0.9,
+        0.95,
+        0.99,
+    )
+    use_positional_embedding: bool = False
+    use_resolution_embeddings: bool = True
+    use_special_token: bool = True
+    num_resolutions: int = 3
+    agg_factor: int = 60
+    max_position_embeddings: int = 1025
+    rope_parameters: RopeParameters | dict | None = None
+
+    min_timescale = AttributeError()
+    max_timescale = AttributeError()
+
+
+@dataclass
+@auto_docstring
+class CtsmOutput(TimesFmOutput):
+    r"""
+    loc (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level mean used to normalize the fine-resolution context, reused to rescale the final forecast.
+    scale (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level standard deviation of the fine-resolution context.
+    loc_coarse (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level mean used to normalize the coarse-resolution context.
+    scale_coarse (`torch.Tensor` of shape `(batch_size,)`):
+        Stream-level standard deviation of the coarse-resolution context.
+    num_coarse_patches (`int`):
+        Number of patches (including the optional special token) preceding the fine-resolution block.
+    num_fine_patches (`int`):
+        Number of patches in the fine-resolution block of the concatenated sequence.
+    past_key_values (`Cache`, *optional*):
+        Key/value cache for the concatenated `[coarse, special, fine]` sequence. Populated when the
+        caller passes `use_cache=True` (and re-used across autoregressive decode steps). Typically only
+        the long-horizon AR loop in [`CtsmModelForPrediction`] needs this.
+    """
+
+    loc_coarse: torch.Tensor | None = None
+    scale_coarse: torch.Tensor | None = None
+    num_coarse_patches: int | None = None
+    num_fine_patches: int | None = None
+    past_key_values: Cache | None = None
+
+
+@dataclass
+@auto_docstring
+class CtsmOutputForPrediction(TimesFmOutputForPrediction):
+    r"""
+    mean_predictions (`torch.Tensor` of shape `(batch_size, horizon_length)`):
+        Point forecasts over the fine-resolution horizon.
+    full_predictions (`torch.Tensor` of shape `(batch_size, horizon_length, 1 + num_quantiles)`):
+        Concatenation of the mean prediction and the quantile predictions along the last axis.
+    loss (`torch.Tensor` of shape `(1,)`, *optional*, returned when `future_values` is provided):
+        Training loss combining MSE of the mean forecast and quantile loss when fine-resolution targets are supplied.
+    """
+
+    pass
+
+
+class CtsmResidualBlock(TimesFmResidualBlock):
+    pass
+
+
+class CtsmRotaryEmbedding(TimesFm2_5RotaryEmbedding):
+    pass
+
+
+class CtsmAttention(TimesFmAttention):
+    """TimesFM 2.0 style attention with learnable per-dimension Q scaling and rotary position embeddings.
+
+    Supports an optional `past_key_values` cache so that, during long-horizon autoregressive decoding,
+    each step only needs to compute K/V for the newly-appended fine patches and attends to the
+    previously-cached K/V for every earlier position.
+    """
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        query_states = self._scale_query(query_states)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, simple_eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=1.0,
+            **kwargs,
+        )
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class CtsmDecoderLayer(TimesFmDecoderLayer):
+    """CTSM transformer block: attention with RoPE followed by TimesFM 2.0 MLP with padding masking."""
+
+    def __init__(self, config: CtsmConfig, layer_idx: int):
+        super().__init__(config, layer_idx=layer_idx)
+        self.self_attn = CtsmAttention(config, layer_idx=layer_idx)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        paddings: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+        )
+        hidden_states = residual + hidden_states
+        hidden_states = self.mlp(hidden_states, paddings=paddings)
+        return hidden_states
+
+
+@auto_docstring
+class CtsmPreTrainedModel(TimesFmPreTrainedModel):
+    config: CtsmConfig
+    base_model_prefix = "model"
+    _no_split_modules = ["CtsmDecoderLayer"]
+    _supports_flash_attn = True
+    _supports_flex_attn = True
+    _can_record_outputs = {
+        "hidden_states": CtsmDecoderLayer,
+        "attentions": CtsmAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, CtsmModel) and getattr(module, "special_token", None) is not None:
+            init.normal_(module.special_token, mean=0.0, std=self.config.initializer_range)
+
+
+class CtsmModel(TimesFmModel):
+    r"""
+    The multi-resolution CTSM encoder. The forward pass consumes two aligned streams (a coarse low-frequency
+    context and a fine high-frequency context), concatenates them along the sequence dimension with an
+    optional learned special token, and runs a stack of rotary-attention transformer layers. Attention is
+    bidirectional within the coarse block and causal elsewhere.
+    """
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__(config)
+
+        if hasattr(self, "position_emb"):
+            del self.position_emb
+
+        self.rotary_emb = CtsmRotaryEmbedding(config)
+
+        if config.use_resolution_embeddings:
+            self.multi_resolution = nn.Embedding(config.num_resolutions, config.hidden_size)
+
+        if config.use_special_token:
+            self.special_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+
+        self.post_init()
+
+    @staticmethod
+    def _left_pad_to_patch_boundary(
+        values: torch.Tensor, paddings: torch.Tensor, patch_length: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        rem = values.shape[1] % patch_length
+        if rem == 0:
+            return values, paddings
+        pad_len = patch_length - rem
+        values_pad = torch.zeros((values.shape[0], pad_len), device=values.device, dtype=values.dtype)
+        paddings_pad = torch.ones((paddings.shape[0], pad_len), device=paddings.device, dtype=paddings.dtype)
+        return torch.cat([values_pad, values], dim=1), torch.cat([paddings_pad, paddings], dim=1)
+
+    @staticmethod
+    def _normalize_with_pad(
+        context: torch.Tensor, padding: torch.Tensor, tolerance: float = 1e-8
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Stream-level normalization that matches the original CTSM reference.
+
+        Normalizes ``context`` using the mean and standard deviation computed over the
+        non-padded positions (``padding == 0``) across the whole context, rather than
+        TimesFM's per-first-patch statistics. The normalized tensor has padded positions
+        zeroed out and is clamped to a safe range.
+        """
+        valid = 1.0 - padding
+        count = valid.sum(dim=1, keepdim=True).clamp_min(1.0)
+        mu = (context * valid).sum(dim=1, keepdim=True) / count
+
+        seq_len_f = context.new_tensor(float(context.shape[1]))
+        filled = torch.where(padding.to(dtype=torch.bool), mu, context)
+        sigma = filled.std(dim=1, keepdim=True, unbiased=False) * torch.sqrt(seq_len_f / count)
+        sigma = sigma.clamp_min(1e-2)
+
+        normalized = (context - mu) / (sigma + tolerance)
+        normalized = normalized * valid
+        normalized = normalized.clamp(-1000.0, 1000.0)
+        return normalized, mu.squeeze(-1), sigma.squeeze(-1)
+
+    def _patchify(
+        self, past_values: torch.Tensor, past_values_padding: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Patchify an already stream-normalized stream and project through the input tokenizer."""
+        bsize = past_values.shape[0]
+        patched_inputs = past_values.view(bsize, -1, self.config.patch_length)
+        patched_pads = past_values_padding.view(bsize, -1, self.config.patch_length)
+
+        patched_inputs = patched_inputs * (1.0 - patched_pads)
+        concat_inputs = torch.cat([patched_inputs, patched_pads], dim=-1)
+        embeddings = self.input_ff_layer(concat_inputs)
+        patch_padding = torch.min(patched_pads, dim=-1)[0]
+        return embeddings, patch_padding
+
+    def _build_attention_mask(
+        self,
+        patch_padding: torch.Tensor,
+        num_coarse_patches: int,
+        dtype: torch.dtype,
+    ) -> torch.Tensor:
+        """Reuse TimesFM's padding+causal 4D mask, then open the coarse-coarse block to bidirectional."""
+        attention_mask = self._prepare_4d_attention_mask(
+            attention_mask=patch_padding,
+            sequence_length=patch_padding.shape[1],
+            dtype=dtype,
+            device=patch_padding.device,
+            is_causal=True,
+        )
+        if num_coarse_patches > 0:
+            attention_mask[..., :num_coarse_patches, :num_coarse_patches] = 0.0
+        return attention_mask
+
+    def _build_incremental_attention_mask(
+        self, bsize: int, num_new: int, past_length: int, dtype: torch.dtype, device: torch.device
+    ) -> torch.Tensor:
+        """Mask for the incremental (cached) path: new fine Qs attend to all cached K/V plus causal within the new block."""
+        min_value = torch.finfo(dtype).min
+        mask = torch.zeros((bsize, 1, num_new, past_length + num_new), dtype=dtype, device=device)
+        if num_new > 1:
+            causal_new = torch.triu(torch.full((num_new, num_new), min_value, dtype=dtype, device=device), diagonal=1)
+            mask[:, :, :, past_length:] = causal_new
+        return mask
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        past_values_coarse: torch.Tensor | None = None,
+        past_values_fine: torch.Tensor | None = None,
+        past_values_coarse_padding: torch.LongTensor | None = None,
+        past_values_fine_padding: torch.LongTensor | None = None,
+        freq: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        loc_fine: torch.Tensor | None = None,
+        scale_fine: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        r"""
+        past_values_coarse (`torch.FloatTensor` of shape `(batch_size, coarse_length)`, *optional*):
+            Coarse-resolution context (e.g. hourly aggregates). Length must be a multiple of `patch_length` or
+            will be left-padded to one. Required when `past_key_values` is `None`.
+        past_values_fine (`torch.FloatTensor` of shape `(batch_size, fine_length)`):
+            Fine-resolution context (e.g. minute-level). In the normal / full-forward mode this is the entire
+            fine context; when `past_key_values` is supplied this should contain **only the new fine values**
+            to append — they must already be pre-normalized by the caller using `loc_fine` / `scale_fine`.
+        past_values_coarse_padding (`torch.LongTensor`, *optional*):
+            Padding mask for the coarse stream, `1.0` for padded positions and `0.0` for real values.
+        past_values_fine_padding (`torch.LongTensor`, *optional*):
+            Padding mask for the fine stream.
+        freq (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
+            Frequency indices. Defaults to all zeros.
+        past_key_values (`Cache`, *optional*):
+            A [`Cache`] (typically a [`DynamicCache`]) holding K/V for the concatenated
+            `[coarse, special, fine_prefix]` sequence from a previous call. When supplied the model runs in
+            **incremental mode**: only the new fine patches are embedded, and their Q/K/V are added on top
+            of the cached K/V. `loc_fine` / `scale_fine` **must** also be supplied so the new fine values
+            are normalized on the same scale as the cached ones.
+        use_cache (`bool`, *optional*):
+            Whether to build and return a key/value cache in the `CtsmOutput`. Defaults to `False` unless
+            `past_key_values` is provided (in which case caching is always on).
+        cache_position (`torch.LongTensor` of shape `(num_new,)`, *optional*):
+            Absolute positions (in the full `[coarse, special, fine]` sequence) of the new fine patches.
+            Only used in incremental mode; defaults to `torch.arange(past_length, past_length + num_new)`.
+        loc_fine (`torch.Tensor` of shape `(batch_size,)`, *optional*):
+            Fine-stream mean used for stream normalization. Required in incremental mode.
+        scale_fine (`torch.Tensor` of shape `(batch_size,)`, *optional*):
+            Fine-stream standard deviation used for stream normalization. Required in incremental mode.
+        """
+        if past_key_values is None:
+            return self._full_forward(
+                past_values_coarse=past_values_coarse,
+                past_values_fine=past_values_fine,
+                past_values_coarse_padding=past_values_coarse_padding,
+                past_values_fine_padding=past_values_fine_padding,
+                freq=freq,
+                use_cache=bool(use_cache),
+                **kwargs,
+            )
+        return self._incremental_forward(
+            past_values_fine=past_values_fine,
+            past_values_fine_padding=past_values_fine_padding,
+            freq=freq,
+            past_key_values=past_key_values,
+            cache_position=cache_position,
+            loc_fine=loc_fine,
+            scale_fine=scale_fine,
+            **kwargs,
+        )
+
+    def _full_forward(
+        self,
+        past_values_coarse: torch.Tensor,
+        past_values_fine: torch.Tensor,
+        past_values_coarse_padding: torch.LongTensor | None,
+        past_values_fine_padding: torch.LongTensor | None,
+        freq: torch.Tensor | None,
+        use_cache: bool,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        if past_values_coarse_padding is None:
+            past_values_coarse_padding = torch.zeros_like(past_values_coarse)
+        if past_values_fine_padding is None:
+            past_values_fine_padding = torch.zeros_like(past_values_fine)
+        past_values_coarse_padding = past_values_coarse_padding.to(past_values_coarse.dtype)
+        past_values_fine_padding = past_values_fine_padding.to(past_values_fine.dtype)
+
+        patch_length = self.config.patch_length
+        past_values_coarse, past_values_coarse_padding = self._left_pad_to_patch_boundary(
+            past_values_coarse, past_values_coarse_padding, patch_length
+        )
+        past_values_fine, past_values_fine_padding = self._left_pad_to_patch_boundary(
+            past_values_fine, past_values_fine_padding, patch_length
+        )
+
+        coarse_normalized, loc_coarse, scale_coarse = self._normalize_with_pad(
+            past_values_coarse, past_values_coarse_padding, tolerance=self.config.tolerance
+        )
+        fine_normalized, loc_fine, scale_fine = self._normalize_with_pad(
+            past_values_fine, past_values_fine_padding, tolerance=self.config.tolerance
+        )
+
+        coarse_embeddings, coarse_patch_padding = self._patchify(coarse_normalized, past_values_coarse_padding)
+        fine_embeddings, fine_patch_padding = self._patchify(fine_normalized, past_values_fine_padding)
+
+        bsize, num_coarse_patches, hidden_size = coarse_embeddings.shape
+        num_fine_patches = fine_embeddings.shape[1]
+        device = coarse_embeddings.device
+        dtype = coarse_embeddings.dtype
+
+        if self.config.use_special_token:
+            special = self.special_token.to(device=device, dtype=dtype).expand(bsize, 1, hidden_size)
+            special_padding = torch.zeros(bsize, 1, device=device, dtype=coarse_patch_padding.dtype)
+            model_input = torch.cat([coarse_embeddings, special, fine_embeddings], dim=1)
+            patch_padding = torch.cat([coarse_patch_padding, special_padding, fine_patch_padding], dim=1)
+            num_special = 1
+        else:
+            model_input = torch.cat([coarse_embeddings, fine_embeddings], dim=1)
+            patch_padding = torch.cat([coarse_patch_padding, fine_patch_padding], dim=1)
+            num_special = 0
+
+        if self.config.use_resolution_embeddings:
+            mr_coarse = torch.zeros(num_coarse_patches, dtype=torch.long, device=device)
+            mr_special = torch.full((num_special,), 1, dtype=torch.long, device=device)
+            mr_fine = torch.full((num_fine_patches,), 2, dtype=torch.long, device=device)
+            mr_idx = torch.cat([mr_coarse, mr_special, mr_fine], dim=0).unsqueeze(0).expand(bsize, -1)
+            model_input = model_input + self.multi_resolution(mr_idx)
+
+        if freq is None:
+            freq = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq = freq.to(device=device, dtype=torch.long)
+        model_input = model_input + self.freq_emb(freq)
+
+        attention_mask = self._build_attention_mask(patch_padding, num_coarse_patches, model_input.dtype)
+        position_ids = (
+            torch.arange(model_input.shape[1], device=device, dtype=torch.long).unsqueeze(0).expand(bsize, -1)
+        )
+        position_embeddings = self.rotary_emb(model_input, position_ids)
+
+        past_key_values = DynamicCache(config=self.config) if use_cache else None
+
+        hidden_states = model_input
+        for layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                paddings=patch_padding,
+                position_embeddings=position_embeddings,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        return CtsmOutput(
+            last_hidden_state=hidden_states,
+            loc=loc_fine,
+            scale=scale_fine,
+            loc_coarse=loc_coarse,
+            scale_coarse=scale_coarse,
+            num_coarse_patches=num_coarse_patches + num_special,
+            num_fine_patches=num_fine_patches,
+            past_key_values=past_key_values,
+        )
+
+    def _incremental_forward(
+        self,
+        past_values_fine: torch.Tensor,
+        past_values_fine_padding: torch.LongTensor | None,
+        freq: torch.Tensor | None,
+        past_key_values: Cache,
+        cache_position: torch.LongTensor | None,
+        loc_fine: torch.Tensor | None,
+        scale_fine: torch.Tensor | None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutput:
+        if loc_fine is None or scale_fine is None:
+            raise ValueError(
+                "`loc_fine` and `scale_fine` must be supplied together with `past_key_values` so that the new fine "
+                "values are normalized on the same scale as the cached ones."
+            )
+        if past_values_fine.shape[1] % self.config.patch_length != 0:
+            raise ValueError(
+                f"In incremental mode `past_values_fine` length must be a multiple of `patch_length="
+                f"{self.config.patch_length}`; got {past_values_fine.shape[1]}."
+            )
+
+        if past_values_fine_padding is None:
+            past_values_fine_padding = torch.zeros_like(past_values_fine)
+        past_values_fine_padding = past_values_fine_padding.to(past_values_fine.dtype)
+
+        tol = self.config.tolerance
+        fine_normalized = (past_values_fine - loc_fine.unsqueeze(-1)) / (scale_fine.unsqueeze(-1) + tol)
+        fine_normalized = fine_normalized * (1.0 - past_values_fine_padding)
+        fine_normalized = fine_normalized.clamp(-1000.0, 1000.0)
+
+        new_embeddings, new_patch_padding = self._patchify(fine_normalized, past_values_fine_padding)
+        bsize, num_new, _ = new_embeddings.shape
+        device = new_embeddings.device
+        dtype = new_embeddings.dtype
+
+        if self.config.use_resolution_embeddings:
+            mr_idx = torch.full((bsize, num_new), 2, dtype=torch.long, device=device)
+            new_embeddings = new_embeddings + self.multi_resolution(mr_idx)
+
+        if freq is None:
+            freq = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq = freq.to(device=device, dtype=torch.long)
+        new_embeddings = new_embeddings + self.freq_emb(freq)
+
+        past_length = past_key_values.get_seq_length()
+        if cache_position is None:
+            cache_position = torch.arange(past_length, past_length + num_new, dtype=torch.long, device=device)
+        position_ids = cache_position.unsqueeze(0).expand(bsize, -1)
+        position_embeddings = self.rotary_emb(new_embeddings, position_ids)
+
+        attention_mask = self._build_incremental_attention_mask(bsize, num_new, past_length, dtype, device)
+
+        hidden_states = new_embeddings
+        for layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                paddings=new_patch_padding,
+                position_embeddings=position_embeddings,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        return CtsmOutput(
+            last_hidden_state=hidden_states,
+            loc=loc_fine,
+            scale=scale_fine,
+            num_fine_patches=num_new,
+            past_key_values=past_key_values,
+        )
+
+
+class CtsmModelForPrediction(TimesFmModelForPrediction):
+    """CTSM model with a multi-resolution prediction head and autoregressive multi-resolution decoding.
+
+    For horizons that require autoregressive decoding (``horizon_len > config.horizon_length``) the
+    prediction class reuses a key/value cache across AR steps: the first step runs the full forward
+    and populates a [`DynamicCache`], subsequent steps feed only the newly-appended fine patches
+    through the stack and attend to the cached K/V for every earlier position. Two caveats, matching
+    how a KV cache is made to fit CTSM's architecture:
+
+    * Stream-level normalization statistics (``loc_fine``, ``scale_fine``) are frozen to the values
+      computed on the first step. This is a small approximation: in the untracked reference,
+      statistics are recomputed after each prediction is appended; in practice the drift is small
+      when forecasts stay in-distribution.
+    * If an AR step would grow the coarse block (a new coarse patch is formed once every
+      ``patch_length * agg_factor / output_patch_len`` steps, i.e. ~every 15 steps at the defaults),
+      the cache is discarded and a full forward is run, rebuilding the cache.
+    """
+
+    def __init__(self, config: CtsmConfig):
+        super().__init__(config)
+        del self.decoder
+        del self.horizon_ff_layer
+
+        self.model = CtsmModel(config)
+        num_outputs = 1 + len(config.quantiles)
+        self.horizon_ff_layer = CtsmResidualBlock(
+            input_dims=config.hidden_size,
+            output_dims=config.horizon_length * num_outputs,
+            hidden_dims=config.intermediate_size,
+        )
+        self.post_init()
+
+    @staticmethod
+    def _build_multi_resolution(
+        series: torch.Tensor, agg_factor: int, coarse_len: int, fine_len: int
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Build (coarse, fine) contexts from a 1-D fine-resolution series.
+
+        Coarse is the mean of the last `coarse_len * agg_factor` fine samples, aligned to block boundaries.
+        Fine is the last `fine_len` samples.
+        """
+        series = series.to(torch.float32).reshape(-1)
+        needed = coarse_len * agg_factor
+        raw = series[-needed:]
+        remainder = raw.shape[0] % agg_factor
+        if remainder:
+            raw = raw[remainder:]
+        if raw.numel() == 0:
+            coarse = series.new_empty((0,), dtype=torch.float32)
+        else:
+            coarse = raw.reshape(-1, agg_factor).mean(dim=1)
+            if coarse.shape[0] > coarse_len:
+                coarse = coarse[-coarse_len:]
+        fine = series[-fine_len:].to(torch.float32)
+        return coarse, fine
+
+    def _prepare_context(
+        self,
+        past_values: Sequence[torch.Tensor] | Sequence[tuple[torch.Tensor, torch.Tensor]],
+        device: torch.device,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        coarse_len = self.config.context_length
+        fine_len = self.config.context_length
+        agg = self.config.agg_factor
+
+        coarse_batch = torch.zeros((len(past_values), coarse_len), dtype=torch.float32, device=device)
+        coarse_pad = torch.zeros_like(coarse_batch)
+        fine_batch = torch.zeros((len(past_values), fine_len), dtype=torch.float32, device=device)
+        fine_pad = torch.zeros_like(fine_batch)
+
+        for i, item in enumerate(past_values):
+            if isinstance(item, (tuple, list)) and len(item) == 2:
+                coarse, fine = item
+                coarse = torch.as_tensor(coarse, dtype=torch.float32, device=device).reshape(-1)
+                fine = torch.as_tensor(fine, dtype=torch.float32, device=device).reshape(-1)
+            else:
+                series = torch.as_tensor(item, dtype=torch.float32, device=device).reshape(-1)
+                coarse, fine = self._build_multi_resolution(series, agg, coarse_len, fine_len)
+
+            c_n = coarse.shape[0]
+            if c_n >= coarse_len:
+                coarse_batch[i] = coarse[-coarse_len:]
+            elif c_n > 0:
+                coarse_batch[i, coarse_len - c_n :] = coarse
+                coarse_pad[i, : coarse_len - c_n] = 1.0
+            else:
+                coarse_pad[i] = 1.0
+
+            f_n = fine.shape[0]
+            if f_n >= fine_len:
+                fine_batch[i] = fine[-fine_len:]
+            elif f_n > 0:
+                fine_batch[i, fine_len - f_n :] = fine
+                fine_pad[i, : fine_len - f_n] = 1.0
+            else:
+                fine_pad[i] = 1.0
+
+        return coarse_batch, coarse_pad, fine_batch, fine_pad
+
+    def _project_last_fine(self, outputs: CtsmOutput, last_position: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """Project the hidden state at `last_position` through the horizon head and denormalize."""
+        last_hidden = outputs.last_hidden_state[:, last_position : last_position + 1, :]
+        head = self.horizon_ff_layer(last_hidden)
+        bsize = head.shape[0]
+        num_outputs = 1 + len(self.config.quantiles)
+        head = head.view(bsize, self.config.horizon_length, num_outputs)
+
+        loc = outputs.loc[:, None, None]
+        scale = outputs.scale[:, None, None]
+        mean_patch = head[..., 0] * scale[..., 0] + loc[..., 0]
+        quant_patch = head[..., 1:] * scale + loc
+        mean_patch = torch.nan_to_num(mean_patch, nan=0.0, posinf=0.0, neginf=0.0)
+        quant_patch = torch.nan_to_num(quant_patch, nan=0.0, posinf=0.0, neginf=0.0)
+        return mean_patch, quant_patch
+
+    def _decode_step_full(
+        self,
+        past_values_coarse: torch.Tensor,
+        past_values_fine: torch.Tensor,
+        past_values_coarse_padding: torch.Tensor,
+        past_values_fine_padding: torch.Tensor,
+        freq: torch.Tensor,
+        use_cache: bool,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor, CtsmOutput]:
+        """Full forward through the model. If `use_cache`, the returned outputs carry a fresh cache."""
+        outputs: CtsmOutput = self.model(
+            past_values_coarse=past_values_coarse,
+            past_values_fine=past_values_fine,
+            past_values_coarse_padding=past_values_coarse_padding,
+            past_values_fine_padding=past_values_fine_padding,
+            freq=freq,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        mean_patch, quant_patch = self._project_last_fine(outputs, outputs.last_hidden_state.shape[1] - 1)
+        return mean_patch, quant_patch, outputs
+
+    def _decode_step_incremental(
+        self,
+        new_fine_values: torch.Tensor,
+        freq: torch.Tensor,
+        past_key_values: Cache,
+        loc_fine: torch.Tensor,
+        scale_fine: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor, CtsmOutput]:
+        """Append `new_fine_values` to the cached state and run only the new positions through the stack."""
+        outputs: CtsmOutput = self.model(
+            past_values_fine=new_fine_values,
+            freq=freq,
+            past_key_values=past_key_values,
+            loc_fine=loc_fine,
+            scale_fine=scale_fine,
+            **kwargs,
+        )
+        mean_patch, quant_patch = self._project_last_fine(outputs, outputs.last_hidden_state.shape[1] - 1)
+        return mean_patch, quant_patch, outputs
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        past_values: Sequence[torch.Tensor] | Sequence[tuple[torch.Tensor, torch.Tensor]],
+        future_values: torch.Tensor | None = None,
+        horizon_len: int | None = None,
+        freq: Sequence[int] | torch.Tensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CtsmOutputForPrediction:
+        r"""
+        past_values (`Sequence[torch.Tensor]`):
+            Either a list of 1-D fine-resolution tensors (the coarse stream is derived by mean-aggregating over
+            `agg_factor` consecutive points) or a list of `(coarse, fine)` pairs if both streams are provided.
+        future_values (`torch.Tensor`, *optional*):
+            Optional fine-resolution ground truth used to compute the loss.
+        horizon_len (`int`, *optional*):
+            Number of fine-resolution steps to forecast. Defaults to `config.horizon_length`. Values larger than
+            `config.horizon_length` trigger autoregressive decoding.
+        freq (`Sequence[int]` or `torch.Tensor`, *optional*):
+            Frequency indices. Defaults to zeros.
+        use_cache (`bool`, *optional*):
+            Whether to use a key/value cache across autoregressive decode steps. Defaults to `True` when
+            `horizon_len > config.horizon_length` (i.e. when AR decoding is needed) and `False` otherwise.
+            Set to `False` to force a full recompute at every AR step (matches the original reference
+            behaviour; slower but avoids the stream-stats-freezing approximation).
+        """
+        device = self.horizon_ff_layer.input_layer.weight.device
+        horizon_len = horizon_len or self.config.horizon_length
+        if horizon_len <= 0:
+            raise ValueError("horizon_len must be positive")
+
+        output_patch_len = self.config.horizon_length
+        num_decode_patches = (horizon_len + output_patch_len - 1) // output_patch_len
+
+        coarse, coarse_pad, fine, fine_pad = self._prepare_context(past_values, device=device)
+        bsize = coarse.shape[0]
+
+        if freq is None:
+            freq_tensor = torch.zeros((bsize, 1), dtype=torch.long, device=device)
+        else:
+            freq_tensor = torch.as_tensor(
+                list(freq) if not isinstance(freq, torch.Tensor) else freq, dtype=torch.long, device=device
+            ).view(bsize, 1)
+
+        mean_chunks: list[torch.Tensor] = []
+        quant_chunks: list[torch.Tensor] = []
+        remaining = horizon_len
+        last_outputs: CtsmOutput | None = None
+        max_fine = self.config.context_length
+        max_coarse = self.config.context_length
+        agg = self.config.agg_factor
+        new_fine_patches = self.config.horizon_length // self.config.patch_length
+
+        past_key_values: Cache | None = None
+        frozen_loc_fine: torch.Tensor | None = None
+        frozen_scale_fine: torch.Tensor | None = None
+        coarse_buffer = torch.zeros((bsize, 0), dtype=torch.float32, device=device)
+
+        if use_cache is None:
+            use_cache = num_decode_patches > 1
+        pending_new_fine: torch.Tensor | None = None
+
+        for step_idx in range(num_decode_patches):
+            if past_key_values is None:
+                # First step (or after cache invalidation): full forward. The coarse block in the cache
+                # stays frozen at the initial state — only the fine block grows via subsequent incremental
+                # steps — which matches how KV caches work for append-only sequences.
+                mean_patch, quant_patch, last_outputs = self._decode_step_full(
+                    past_values_coarse=coarse,
+                    past_values_fine=fine,
+                    past_values_coarse_padding=coarse_pad,
+                    past_values_fine_padding=fine_pad,
+                    freq=freq_tensor,
+                    use_cache=use_cache,
+                    **kwargs,
+                )
+                past_key_values = last_outputs.past_key_values
+                frozen_loc_fine = last_outputs.loc
+                frozen_scale_fine = last_outputs.scale
+            else:
+                # Incremental: only the fine values newly appended last step go through the stack.
+                mean_patch, quant_patch, last_outputs = self._decode_step_incremental(
+                    new_fine_values=pending_new_fine,
+                    freq=freq_tensor,
+                    past_key_values=past_key_values,
+                    loc_fine=frozen_loc_fine,
+                    scale_fine=frozen_scale_fine,
+                    **kwargs,
+                )
+
+            take = min(remaining, output_patch_len)
+            mean_chunks.append(mean_patch[:, :take])
+            quant_chunks.append(quant_patch[:, :take, :])
+            remaining -= take
+            if remaining <= 0:
+                break
+
+            new_fine = mean_patch[:, :output_patch_len]
+            pending_new_fine = new_fine
+
+            # Track the raw contexts so the next full-forward (initial step or after cache
+            # invalidation) sees the right state. Mirrors the reference AR loop.
+            fine = torch.cat([fine, new_fine], dim=1)
+            fine_pad = torch.cat(
+                [fine_pad, torch.zeros((bsize, output_patch_len), device=device, dtype=fine_pad.dtype)], dim=1
+            )
+            if fine.shape[1] > max_fine:
+                fine = fine[:, -max_fine:]
+                fine_pad = fine_pad[:, -max_fine:]
+
+            coarse_buffer = torch.cat([coarse_buffer, new_fine], dim=1)
+            full_blocks = coarse_buffer.shape[1] // agg
+            if full_blocks > 0:
+                blocks = coarse_buffer[:, : full_blocks * agg].view(bsize, full_blocks, agg).mean(dim=2)
+                coarse_buffer = coarse_buffer[:, full_blocks * agg :]
+                coarse = torch.cat([coarse, blocks], dim=1)
+                coarse_pad = torch.cat(
+                    [coarse_pad, torch.zeros((bsize, full_blocks), device=device, dtype=coarse_pad.dtype)], dim=1
+                )
+                if coarse.shape[1] > max_coarse:
+                    coarse = coarse[:, -max_coarse:]
+                    coarse_pad = coarse_pad[:, -max_coarse:]
+
+            if past_key_values is not None:
+                projected_len = past_key_values.get_seq_length() + new_fine_patches
+                if projected_len >= self.config.max_position_embeddings:
+                    past_key_values = None
+                    pending_new_fine = None
+
+        mean_predictions = torch.cat(mean_chunks, dim=1)[:, :horizon_len]
+        full_predictions = torch.cat(
+            [torch.cat(mean_chunks, dim=1)[:, :horizon_len, None], torch.cat(quant_chunks, dim=1)[:, :horizon_len, :]],
+            dim=-1,
+        )
+
+        loss = None
+        if future_values is not None:
+            target_len = min(future_values.shape[1], mean_predictions.shape[1])
+            mse_loss = F.mse_loss(mean_predictions[:, :target_len], future_values[:, :target_len])
+            quantile_loss = self._quantile_loss(full_predictions[:, :target_len, 1:], future_values[:, :target_len])
+            loss = mse_loss + quantile_loss
+
+        return CtsmOutputForPrediction(
+            last_hidden_state=last_outputs.last_hidden_state if last_outputs is not None else None,
+            hidden_states=last_outputs.hidden_states if last_outputs is not None else None,
+            attentions=last_outputs.attentions if last_outputs is not None else None,
+            mean_predictions=mean_predictions,
+            full_predictions=full_predictions,
+            loss=loss,
+        )
+
+
+__all__ = [
+    "CtsmConfig",
+    "CtsmModel",
+    "CtsmModelForPrediction",
+    "CtsmPreTrainedModel",
+]
diff --git a/src/transformers/models/cvt/modeling_cvt.py b/src/transformers/models/cvt/modeling_cvt.py
index 3ceccb7d1dab..8d2b589c9cb7 100644
--- a/src/transformers/models/cvt/modeling_cvt.py
+++ b/src/transformers/models/cvt/modeling_cvt.py
@@ -30,12 +30,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class BaseModelOutputWithCLSToken(ModelOutput):
     r"""
     cls_token_value (`torch.FloatTensor` of shape `(batch_size, 1, hidden_size)`):
diff --git a/src/transformers/models/cwm/modeling_cwm.py b/src/transformers/models/cwm/modeling_cwm.py
index 3e0eb0504be0..10e10530cf39 100644
--- a/src/transformers/models/cwm/modeling_cwm.py
+++ b/src/transformers/models/cwm/modeling_cwm.py
@@ -58,8 +58,8 @@ def __init__(self, config: CwmConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -99,7 +99,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/d_fine/modeling_d_fine.py b/src/transformers/models/d_fine/modeling_d_fine.py
index f1d23356fb2b..b6f745dc45da 100644
--- a/src/transformers/models/d_fine/modeling_d_fine.py
+++ b/src/transformers/models/d_fine/modeling_d_fine.py
@@ -40,7 +40,6 @@
 from .configuration_d_fine import DFineConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the DFineDecoder. This class adds two attributes to
@@ -49,6 +48,7 @@
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class DFineDecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -1246,12 +1246,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RT-DETR encoder-decoder model.
     """
 )
+@dataclass
 class DFineModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -1803,12 +1803,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`DFineForObjectDetection`].
     """
 )
+@dataclass
 class DFineObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/dac/convert_dac_checkpoint.py b/src/transformers/models/dac/convert_dac_checkpoint.py
index b3360fc1706d..acfa4166414e 100644
--- a/src/transformers/models/dac/convert_dac_checkpoint.py
+++ b/src/transformers/models/dac/convert_dac_checkpoint.py
@@ -17,7 +17,6 @@
 
 import numpy as np
 import torch
-import torch.nn as nn
 
 from transformers import (
     DacConfig,
@@ -186,50 +185,21 @@ def recursively_load_weights(orig_dict, hf_model, model_name):
     logger.warning(f"Unused weights: {unused_weights}")
 
 
-def apply_weight_norm(model):
-    weight_norm = nn.utils.weight_norm
-
-    for layer in model.quantizer.quantizers:
-        weight_norm(layer.in_proj)
-        weight_norm(layer.out_proj)
-
-    weight_norm(model.encoder.conv1)
-    weight_norm(model.encoder.conv2)
-
-    for layer in model.encoder.block:
-        weight_norm(layer.conv1)
-        weight_norm(layer.res_unit1.conv1)
-        weight_norm(layer.res_unit1.conv2)
-        weight_norm(layer.res_unit2.conv1)
-        weight_norm(layer.res_unit2.conv2)
-        weight_norm(layer.res_unit3.conv1)
-        weight_norm(layer.res_unit3.conv2)
-
-    weight_norm(model.decoder.conv1)
-    weight_norm(model.decoder.conv2)
-
-    for layer in model.decoder.block:
-        weight_norm(layer.conv_t1)
-        weight_norm(layer.res_unit1.conv1)
-        weight_norm(layer.res_unit1.conv2)
-        weight_norm(layer.res_unit2.conv1)
-        weight_norm(layer.res_unit2.conv2)
-        weight_norm(layer.res_unit3.conv1)
-        weight_norm(layer.res_unit3.conv2)
-
-
 @torch.no_grad()
 def convert_checkpoint(
     model_name,
     checkpoint_path,
     pytorch_dump_folder_path,
-    sample_rate=16000,
     repo_id=None,
+    legacy_weight_norm=True,
 ):
-    model_dict = torch.load(checkpoint_path, "cpu", weights_only=True)
+    # NOTE: Models on Hub (https://huggingface.co/descript/models) did conversion on CPU.
+    # However, for equivalent weights after removing weight norm, conversion should be done on GPU.
+    # torch_device = "cuda"
+    torch_device = "cpu"
+    model_dict = torch.load(checkpoint_path, torch_device, weights_only=True)
 
     config = DacConfig()
-
     metadata = model_dict["metadata"]["kwargs"]
     config.encoder_hidden_size = metadata["encoder_dim"]
     config.downsampling_ratios = metadata["encoder_rates"]
@@ -239,18 +209,20 @@ def convert_checkpoint(
     config.decoder_hidden_size = metadata["decoder_dim"]
     config.upsampling_ratios = metadata["decoder_rates"]
     config.quantizer_dropout = float(metadata["quantizer_dropout"])
-    config.sampling_rate = sample_rate
+    config.sampling_rate = int(metadata["sample_rate"])
     config.hop_length = int(np.prod(config.downsampling_ratios))
 
-    model = DacModel(config)
+    model = DacModel(config).to(torch_device)
     feature_extractor = DacFeatureExtractor()
-    feature_extractor.sampling_rate = sample_rate
+    feature_extractor.sampling_rate = config.sampling_rate
+    feature_extractor.hop_length = config.hop_length
 
     original_checkpoint = model_dict["state_dict"]
 
-    apply_weight_norm(model)
+    # original model uses old weight norm function
+    model.apply_weight_norm(legacy=legacy_weight_norm)
     recursively_load_weights(original_checkpoint, model, model_name)
-    model.remove_weight_norm()
+    model.remove_weight_norm(legacy=legacy_weight_norm)
 
     model.save_pretrained(pytorch_dump_folder_path)
 
@@ -275,9 +247,14 @@ def convert_checkpoint(
     parser.add_argument(
         "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the Hugging Face hub."
     )
-    parser.add_argument("--sample_rate", default=None, type=str, help="Sample rate used by DacFeatureExtractor")
+    parser.add_argument(
+        "--legacy_weight_norm",
+        default=True,
+        type=bool,
+        help="Whether legacy weight normalization was used by original model.",
+    )
     args = parser.parse_args()
 
     convert_checkpoint(
-        args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.sample_rate, args.push_to_hub
+        args.model, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.legacy_weight_norm
     )
diff --git a/src/transformers/models/dac/modeling_dac.py b/src/transformers/models/dac/modeling_dac.py
index 6ac46f78a4a6..2841130ec4bc 100644
--- a/src/transformers/models/dac/modeling_dac.py
+++ b/src/transformers/models/dac/modeling_dac.py
@@ -70,8 +70,8 @@ class DacEncoderOutput(ModelOutput):
     projected_latents: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 # Copied from transformers.models.encodec.modeling_encodec.EncodecDecoderOutput with Encodec->Dac, segment_length->input_length
 class DacDecoderOutput(ModelOutput):
     r"""
@@ -85,6 +85,9 @@ class DacDecoderOutput(ModelOutput):
 class Snake1d(nn.Module):
     """
     A 1-dimensional Snake activation function module.
+
+    Original version from DAC used JIT compilation: https://github.com/descriptinc/descript-audio-codec/blob/main/dac/nn/layers.py#L18-L33
+    This leads to slight differences in output.
     """
 
     def __init__(self, hidden_dim):
@@ -490,9 +493,10 @@ def _init_weights(self, module):
         elif isinstance(module, nn.Embedding):
             init.normal_(module.weight, mean=0.0, std=0.02)
 
-    def apply_weight_norm(self):
+    def apply_weight_norm(self, legacy=True):
+        # original version of DAC uses legacy weight norm
         weight_norm = nn.utils.weight_norm
-        if hasattr(nn.utils.parametrizations, "weight_norm"):
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
             weight_norm = nn.utils.parametrizations.weight_norm
 
         for layer in self.quantizer.quantizers:
@@ -523,34 +527,38 @@ def apply_weight_norm(self):
             weight_norm(layer.res_unit3.conv1)
             weight_norm(layer.res_unit3.conv2)
 
-    def remove_weight_norm(self):
+    def remove_weight_norm(self, legacy=True):
+        remove_weight_norm = nn.utils.remove_weight_norm
+        if hasattr(nn.utils.parametrizations, "weight_norm") and not legacy:
+            remove_weight_norm = torch.nn.utils.parametrize.remove_parametrizations
+
         for layer in self.quantizer.quantizers:
-            nn.utils.remove_weight_norm(layer.in_proj)
-            nn.utils.remove_weight_norm(layer.out_proj)
+            remove_weight_norm(layer.in_proj, "weight")
+            remove_weight_norm(layer.out_proj, "weight")
 
-        nn.utils.remove_weight_norm(self.encoder.conv1)
-        nn.utils.remove_weight_norm(self.encoder.conv2)
+        remove_weight_norm(self.encoder.conv1, "weight")
+        remove_weight_norm(self.encoder.conv2, "weight")
 
         for layer in self.encoder.block:
-            nn.utils.remove_weight_norm(layer.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit1.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit1.conv2)
-            nn.utils.remove_weight_norm(layer.res_unit2.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit2.conv2)
-            nn.utils.remove_weight_norm(layer.res_unit3.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit3.conv2)
+            remove_weight_norm(layer.conv1, "weight")
+            remove_weight_norm(layer.res_unit1.conv1, "weight")
+            remove_weight_norm(layer.res_unit1.conv2, "weight")
+            remove_weight_norm(layer.res_unit2.conv1, "weight")
+            remove_weight_norm(layer.res_unit2.conv2, "weight")
+            remove_weight_norm(layer.res_unit3.conv1, "weight")
+            remove_weight_norm(layer.res_unit3.conv2, "weight")
 
-        nn.utils.remove_weight_norm(self.decoder.conv1)
-        nn.utils.remove_weight_norm(self.decoder.conv2)
+        remove_weight_norm(self.decoder.conv1, "weight")
+        remove_weight_norm(self.decoder.conv2, "weight")
 
         for layer in self.decoder.block:
-            nn.utils.remove_weight_norm(layer.conv_t1)
-            nn.utils.remove_weight_norm(layer.res_unit1.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit1.conv2)
-            nn.utils.remove_weight_norm(layer.res_unit2.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit2.conv2)
-            nn.utils.remove_weight_norm(layer.res_unit3.conv1)
-            nn.utils.remove_weight_norm(layer.res_unit3.conv2)
+            remove_weight_norm(layer.conv_t1, "weight")
+            remove_weight_norm(layer.res_unit1.conv1, "weight")
+            remove_weight_norm(layer.res_unit1.conv2, "weight")
+            remove_weight_norm(layer.res_unit2.conv1, "weight")
+            remove_weight_norm(layer.res_unit2.conv2, "weight")
+            remove_weight_norm(layer.res_unit3.conv1, "weight")
+            remove_weight_norm(layer.res_unit3.conv2, "weight")
 
 
 @auto_docstring(
diff --git a/src/transformers/models/data2vec/modeling_data2vec_text.py b/src/transformers/models/data2vec/modeling_data2vec_text.py
index 512431cb3b0a..47f9866e9f4f 100644
--- a/src/transformers/models/data2vec/modeling_data2vec_text.py
+++ b/src/transformers/models/data2vec/modeling_data2vec_text.py
@@ -105,7 +105,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/dbrx/modeling_dbrx.py b/src/transformers/models/dbrx/modeling_dbrx.py
index 58735fb55c0b..a820e61e1113 100644
--- a/src/transformers/models/dbrx/modeling_dbrx.py
+++ b/src/transformers/models/dbrx/modeling_dbrx.py
@@ -98,7 +98,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -594,7 +594,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -602,7 +602,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -619,8 +621,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/deberta_v2/modeling_deberta_v2.py b/src/transformers/models/deberta_v2/modeling_deberta_v2.py
index b30cbd6342dc..e0c0d87ab98d 100644
--- a/src/transformers/models/deberta_v2/modeling_deberta_v2.py
+++ b/src/transformers/models/deberta_v2/modeling_deberta_v2.py
@@ -31,7 +31,9 @@
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_deberta_v2 import DebertaV2Config
 
 
@@ -269,8 +271,7 @@ def forward(
         )
         new_context_layer_shape = context_layer.size()[:-2] + (-1,)
         context_layer = context_layer.view(new_context_layer_shape)
-        if not output_attentions:
-            return (context_layer, None)
+
         return (context_layer, attention_probs)
 
     def disentangled_attention_bias(self, query_layer, key_layer, relative_pos, rel_embeddings, scale_factor):
@@ -428,8 +429,8 @@ def forward(
         relative_pos=None,
         rel_embeddings=None,
         output_attentions: bool = False,
-    ) -> tuple[torch.Tensor, torch.Tensor | None]:
-        attention_output, att_matrix = self.attention(
+    ) -> torch.Tensor:
+        attention_output, _ = self.attention(
             hidden_states,
             attention_mask,
             output_attentions=output_attentions,
@@ -440,10 +441,7 @@ def forward(
         intermediate_output = self.intermediate(attention_output)
         layer_output = self.output(intermediate_output, attention_output)
 
-        if output_attentions:
-            return (layer_output, att_matrix)
-        else:
-            return (layer_output, None)
+        return layer_output
 
 
 class ConvLayer(nn.Module):
@@ -631,11 +629,10 @@ def forward(
         self,
         hidden_states,
         attention_mask,
-        output_hidden_states=True,
         output_attentions=False,
         query_states=None,
         relative_pos=None,
-        return_dict=True,
+        **kwargs: Unpack[TransformersKwargs],
     ):
         if attention_mask.dim() <= 2:
             input_mask = attention_mask
@@ -644,13 +641,11 @@ def forward(
         attention_mask = self.get_attention_mask(attention_mask)
         relative_pos = self.get_rel_pos(hidden_states, query_states, relative_pos)
 
-        all_hidden_states: tuple[torch.Tensor] | None = (hidden_states,) if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
         next_kv = hidden_states
         rel_embeddings = self.get_rel_embedding()
+
         for i, layer_module in enumerate(self.layer):
-            output_states, attn_weights = layer_module(
+            output_states = layer_module(
                 next_kv,
                 attention_mask,
                 query_states=query_states,
@@ -659,15 +654,9 @@ def forward(
                 output_attentions=output_attentions,
             )
 
-            if output_attentions:
-                all_attentions = all_attentions + (attn_weights,)
-
             if i == 0 and self.conv is not None:
                 output_states = self.conv(hidden_states, output_states, input_mask)
 
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (output_states,)
-
             if query_states is not None:
                 query_states = output_states
                 if isinstance(hidden_states, Sequence):
@@ -675,11 +664,7 @@ def forward(
             else:
                 next_kv = output_states
 
-        if not return_dict:
-            return tuple(v for v in [output_states, all_hidden_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=output_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
+        return BaseModelOutput(last_hidden_state=output_states)
 
 
 @auto_docstring
@@ -688,6 +673,10 @@ class DebertaV2PreTrainedModel(PreTrainedModel):
     base_model_prefix = "deberta"
     _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
     supports_gradient_checkpointing = True
+    _can_record_outputs = {
+        "hidden_states": DebertaV2Layer,
+        "attentions": DisentangledSelfAttention,
+    }
 
     @torch.no_grad()
     def _init_weights(self, module):
@@ -718,6 +707,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, new_embeddings):
         self.embeddings.word_embeddings = new_embeddings
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
@@ -726,17 +716,8 @@ def forward(
         token_type_ids: torch.Tensor | None = None,
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | BaseModelOutput:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -765,38 +746,40 @@ def forward(
         encoder_outputs = self.encoder(
             embedding_output,
             attention_mask,
-            output_hidden_states=True,
-            output_attentions=output_attentions,
-            return_dict=return_dict,
+            query_states=None,
+            relative_pos=None,
+            **kwargs,
         )
-        encoded_layers = encoder_outputs[1]
+
+        sequence_output = encoder_outputs.last_hidden_state
 
         if self.z_steps > 1:
-            hidden_states = encoded_layers[-2]
+            if encoder_outputs.hidden_states and len(encoder_outputs.hidden_states) >= 2:
+                hidden_states = encoder_outputs.hidden_states[-2]
+            else:
+                hidden_states = sequence_output
+
             layers = [self.encoder.layer[-1] for _ in range(self.z_steps)]
-            query_states = encoded_layers[-1]
+            query_states = sequence_output
             rel_embeddings = self.encoder.get_rel_embedding()
-            attention_mask = self.encoder.get_attention_mask(attention_mask)
+            attention_mask_encoded = self.encoder.get_attention_mask(attention_mask)
             rel_pos = self.encoder.get_rel_pos(embedding_output)
+
             for layer in layers[1:]:
                 query_states = layer(
                     hidden_states,
-                    attention_mask,
-                    output_attentions=False,
+                    attention_mask_encoded,
                     query_states=query_states,
                     relative_pos=rel_pos,
                     rel_embeddings=rel_embeddings,
+                    output_attentions=kwargs.get("output_attentions", False),
                 )
-                encoded_layers.append(query_states)
-
-        sequence_output = encoded_layers[-1]
 
-        if not return_dict:
-            return (sequence_output,) + encoder_outputs[(1 if output_hidden_states else 2) :]
+            sequence_output = query_states
 
         return BaseModelOutput(
             last_hidden_state=sequence_output,
-            hidden_states=encoder_outputs.hidden_states if output_hidden_states else None,
+            hidden_states=encoder_outputs.hidden_states,
             attentions=encoder_outputs.attentions,
         )
 
@@ -921,6 +904,7 @@ def set_output_embeddings(self, new_embeddings):
             self.lm_predictions.lm_head.dense = new_embeddings
             self.lm_predictions.lm_head.bias = new_embeddings.bias
 
+    @can_return_tuple
     @auto_docstring
     # Copied from transformers.models.deberta.modeling_deberta.DebertaForMaskedLM.forward with Deberta->DebertaV2
     def forward(
@@ -931,10 +915,7 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | MaskedLMOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -943,17 +924,13 @@ def forward(
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
         """
 
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         outputs = self.deberta(
             input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         sequence_output = outputs[0]
@@ -967,10 +944,6 @@ def forward(
             loss_fct = CrossEntropyLoss()  # -100 index = padding token
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
-        if not return_dict:
-            output = (prediction_scores,) + outputs[1:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
         return MaskedLMOutput(
             loss=masked_lm_loss,
             logits=prediction_scores,
@@ -1033,6 +1006,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, new_embeddings):
         self.deberta.set_input_embeddings(new_embeddings)
 
+    @can_return_tuple
     @auto_docstring
     # Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
     def forward(
@@ -1043,10 +1017,7 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | SequenceClassifierOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1054,7 +1025,6 @@ def forward(
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.deberta(
             input_ids,
@@ -1062,9 +1032,7 @@ def forward(
             attention_mask=attention_mask,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         encoder_layer = outputs[0]
@@ -1107,9 +1075,6 @@ def forward(
             elif self.config.problem_type == "multi_label_classification":
                 loss_fct = BCEWithLogitsLoss()
                 loss = loss_fct(logits, labels)
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
 
         return SequenceClassifierOutput(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
@@ -1130,6 +1095,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -1139,16 +1105,12 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | TokenClassifierOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.deberta(
             input_ids,
@@ -1156,9 +1118,7 @@ def forward(
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         sequence_output = outputs[0]
@@ -1171,10 +1131,6 @@ def forward(
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
         return TokenClassifierOutput(
             loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
@@ -1192,6 +1148,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     # Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
     def forward(
@@ -1203,22 +1160,15 @@ def forward(
         inputs_embeds: torch.Tensor | None = None,
         start_positions: torch.Tensor | None = None,
         end_positions: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | QuestionAnsweringModelOutput:
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         outputs = self.deberta(
             input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         sequence_output = outputs[0]
@@ -1245,10 +1195,6 @@ def forward(
             end_loss = loss_fct(end_logits, end_positions)
             total_loss = (start_loss + end_loss) / 2
 
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[1:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
         return QuestionAnsweringModelOutput(
             loss=total_loss,
             start_logits=start_logits,
@@ -1283,6 +1229,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, new_embeddings):
         self.deberta.set_input_embeddings(new_embeddings)
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -1292,10 +1239,7 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | MultipleChoiceModelOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
@@ -1303,7 +1247,6 @@ def forward(
             num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
             `input_ids` above)
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
         num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
 
         flat_input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
@@ -1322,9 +1265,7 @@ def forward(
             token_type_ids=flat_token_type_ids,
             attention_mask=flat_attention_mask,
             inputs_embeds=flat_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         encoder_layer = outputs[0]
@@ -1338,10 +1279,6 @@ def forward(
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(reshaped_logits, labels)
 
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
         return MultipleChoiceModelOutput(
             loss=loss,
             logits=reshaped_logits,
diff --git a/src/transformers/models/decision_transformer/modeling_decision_transformer.py b/src/transformers/models/decision_transformer/modeling_decision_transformer.py
index cb48d8fad8d2..c4f7d6d10c58 100755
--- a/src/transformers/models/decision_transformer/modeling_decision_transformer.py
+++ b/src/transformers/models/decision_transformer/modeling_decision_transformer.py
@@ -316,8 +316,8 @@ class DecisionTransformerGPT2PreTrainedModel(PreTrainedModel):
     _can_compile_fullgraph = False
     _can_record_outputs = {
         "hidden_states": DecisionTransformerGPT2Block,
-        "attentions": OutputRecorder(DecisionTransformerGPT2Attention, layer_name=".attn", index=1),
-        "cross_attentions": OutputRecorder(DecisionTransformerGPT2Attention, layer_name=".crossattention", index=1),
+        "attentions": OutputRecorder(DecisionTransformerGPT2Attention, layer_name=r"\.attn", index=1),
+        "cross_attentions": OutputRecorder(DecisionTransformerGPT2Attention, layer_name=r"\.crossattention", index=1),
     }
 
     # No longer used as we directly use our masks instead
@@ -458,12 +458,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class DecisionTransformerOutput(ModelOutput):
     r"""
     state_preds (`torch.FloatTensor` of shape `(batch_size, sequence_length, state_dim)`):
diff --git a/src/transformers/models/deepseek_ocr2/__init__.py b/src/transformers/models/deepseek_ocr2/__init__.py
new file mode 100644
index 000000000000..88d745c4a8f7
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_deepseek_ocr2 import *
+    from .image_processing_deepseek_ocr2 import *
+    from .image_processing_pil_deepseek_ocr2 import *
+    from .modeling_deepseek_ocr2 import *
+    from .processing_deepseek_ocr2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/deepseek_ocr2/configuration_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/configuration_deepseek_ocr2.py
new file mode 100644
index 000000000000..18c3b76faa88
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/configuration_deepseek_ocr2.py
@@ -0,0 +1,299 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_ocr2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2SamVisionConfig(PreTrainedConfig):
+    r"""
+    output_channels (`int`, *optional*, defaults to 256):
+        The number of output channels in the SAM neck.
+    window_size (`int`, *optional*, defaults to 14):
+        Window size for windowed attention layers.
+    global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
+        Indices of encoder layers that use global (non-windowed) attention.
+    mlp_dim (`int`, *optional*):
+        Dimensionality of the MLP layer in each vision encoder block. Defaults to `hidden_size * mlp_ratio`.
+    downsample_channels (`list[int]`, *optional*):
+        The channel dimensions for the multi-scale downsampling neck layers. Defaults to `[512, 896]`.
+    """
+
+    base_config_key = "sam_config"
+    model_type = "deepseek_ocr2_sam_vision_model"
+
+    hidden_size: int = 768
+    output_channels: int = 256
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    num_channels: int = 3
+    image_size: int | list[int] | tuple[int, int] = 1024
+    patch_size: int | list[int] | tuple[int, int] = 16
+    hidden_act: str = "gelu"
+    layer_norm_eps: float = 1e-06
+    attention_dropout: float | int = 0.0
+    initializer_range: float = 1e-10
+    qkv_bias: bool = True
+    mlp_ratio: float = 4.0
+    use_abs_pos: bool = True
+    use_rel_pos: bool = True
+    window_size: int = 14
+    global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11)
+    mlp_dim: int | None = None
+
+    downsample_channels: list[int] | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.downsample_channels is None:
+            self.downsample_channels = [512, 896]
+        self.mlp_dim = int(self.hidden_size * self.mlp_ratio) if self.mlp_dim is None else self.mlp_dim
+        self.scale = self.hidden_size // 2
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2EncoderConfig(PreTrainedConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import DeepseekOcr2Config
+
+    >>> config = DeepseekOcr2Config()
+    >>> encoder_config = config.vision_config.encoder_config
+    ```"""
+
+    model_type = "deepseek_ocr2_encoder"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `DeepseekOcr2Encoder`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    vocab_size: int = 151936
+    hidden_size: int = 4096
+    intermediate_size: int = 22016
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 32
+    num_key_value_heads: int | None = 32
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 32768
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    use_sliding_window: bool = False
+    sliding_window: int | None = 4096
+    max_window_layers: int = 28
+    layer_types: list[str] | None = None
+    attention_dropout: float | int = 0.0
+    pad_token_id: int | None = None
+    bos_token_id: int | None = None
+    eos_token_id: int | list[int] | None = None
+
+    base_config_key = "encoder_config"
+
+    def __post_init__(self, **kwargs):
+        self.sliding_window = self.sliding_window if self.use_sliding_window else None
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention"
+                if self.sliding_window is not None and i >= self.max_window_layers
+                else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2VisionConfig(PreTrainedConfig):
+    r"""
+    sam_config (`dict` or `DeepseekOcr2SamVisionConfig`, *optional*):
+        Configuration for the SAM vision encoder. Defaults to `DeepseekOcr2SamVisionConfig()`.
+    encoder_config (`dict` or `DeepseekOcr2EncoderConfig`, *optional*):
+        Configuration for the DeepSeek-OCR-2 vision encoder. Defaults to `DeepseekOcr2EncoderConfig()`.
+    """
+
+    base_config_key = "vision_config"
+    sub_configs = {
+        "sam_config": DeepseekOcr2SamVisionConfig,
+        "encoder_config": DeepseekOcr2EncoderConfig,
+    }
+
+    sam_config: dict | PreTrainedConfig | None = None
+    encoder_config: dict | PreTrainedConfig | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.sam_config is None:
+            self.sam_config = DeepseekOcr2SamVisionConfig()
+        elif isinstance(self.sam_config, dict):
+            self.sam_config = DeepseekOcr2SamVisionConfig(**self.sam_config)
+
+        if self.encoder_config is None:
+            self.encoder_config = DeepseekOcr2EncoderConfig()
+        elif isinstance(self.encoder_config, dict):
+            self.encoder_config = DeepseekOcr2EncoderConfig(**self.encoder_config)
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2TextConfig(PreTrainedConfig):
+    r"""
+    n_group (`int`, *optional*):
+        Number of groups for grouped top-k expert routing.
+    topk_method (`str`, *optional*, defaults to `"greedy"`):
+        Method for selecting top-k experts in MoE layers.
+    mlp_layer_types (`list[str]`, *optional*):
+        MLP type (`"dense"` or `"sparse"`) for each decoder layer, e.g. `["dense", "sparse", "sparse", ...]`.
+    """
+
+    model_type = "deepseek_ocr2_text"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Override DeepseekV2's MLA TP plan with standard MHA projections
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    vocab_size: int = 32000
+    hidden_size: int = 4096
+    intermediate_size: int = 11008
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 32
+    num_key_value_heads: int | None = None
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 2048
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 2
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: float | None = 0.0
+    mlp_bias: bool = False
+    head_dim: int | None = None
+    n_group: int | None = None
+    n_routed_experts: int = 64
+    n_shared_experts: int = 2
+    routed_scaling_factor: float = 1.0
+    topk_group: int | None = None
+    topk_method: str | None = "greedy"
+    num_experts_per_tok: int | None = None
+    moe_intermediate_size: int = 1407
+
+    base_config_key = "text_config"
+    mlp_layer_types: list[str] | None = None
+
+    def __post_init__(self, **kwargs):
+        self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+        super().__post_init__(**kwargs)
+
+    def validate_architecture(self):
+        """Part of `@strict`-powered validation. Validates the architecture of the config."""
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({self.num_attention_heads})."
+            )
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2Config(PreTrainedConfig):
+    r"""
+    vision_config (`dict` or `DeepseekOcr2VisionConfig`, *optional*):
+        Configuration for the vision encoders. Defaults to `DeepseekOcr2VisionConfig()`.
+    """
+
+    model_type = "deepseek_ocr2"
+    sub_configs = {
+        "vision_config": DeepseekOcr2VisionConfig,
+        "text_config": DeepseekOcr2TextConfig,
+    }
+
+    vision_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    image_token_id: int = 128815
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if self.vision_config is None:
+            self.vision_config = DeepseekOcr2VisionConfig()
+        elif isinstance(self.vision_config, dict):
+            self.vision_config = DeepseekOcr2VisionConfig(**self.vision_config)
+
+        if self.text_config is None:
+            self.text_config = DeepseekOcr2TextConfig()
+        elif isinstance(self.text_config, dict):
+            self.text_config = DeepseekOcr2TextConfig(**self.text_config)
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["DeepseekOcr2Config", "DeepseekOcr2EncoderConfig", "DeepseekOcr2SamVisionConfig", "DeepseekOcr2TextConfig"]
diff --git a/src/transformers/models/deepseek_ocr2/convert_deepseek_ocr2_weights_to_hf.py b/src/transformers/models/deepseek_ocr2/convert_deepseek_ocr2_weights_to_hf.py
new file mode 100644
index 000000000000..9bb85d4fc655
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/convert_deepseek_ocr2_weights_to_hf.py
@@ -0,0 +1,205 @@
+# Copyright 2026 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Convert DeepSeek-OCR-2 weights from HF Hub custom-code format to native transformers format."""
+
+import argparse
+import copy
+import json
+import os
+
+import torch
+
+from transformers import (
+    DeepseekOcr2Config,
+    DeepseekOcr2ForConditionalGeneration,
+    DeepseekOcr2ImageProcessor,
+    DeepseekOcr2Processor,
+    PreTrainedTokenizerFast,
+)
+
+
+def convert_config(config_dict: dict) -> dict:
+    config_dict = copy.deepcopy(config_dict)
+
+    if "language_config" in config_dict:
+        text_config = config_dict.pop("language_config")
+        for mla_field in ("kv_lora_rank", "q_lora_rank"):
+            if mla_field in text_config and text_config[mla_field] is None:
+                del text_config[mla_field]
+        first_k = text_config.pop("first_k_dense_replace", 0)
+        n_layers = text_config.get("num_hidden_layers", 28)
+        text_config["mlp_layer_types"] = ["dense"] * first_k + ["sparse"] * (n_layers - first_k)
+        config_dict["text_config"] = text_config
+
+    vision_config = {}
+    if "vision_config" in config_dict:
+        orig_vision = config_dict.pop("vision_config")
+
+        sam_info = orig_vision["width"]["sam_vit_b"]
+        vision_config["sam_config"] = {
+            "hidden_size": sam_info["width"],
+            "num_hidden_layers": sam_info["layers"],
+            "num_attention_heads": sam_info["heads"],
+            "global_attn_indexes": sam_info["global_attn_indexes"],
+            "downsample_channels": [512, 896],
+        }
+
+        vision_config["encoder_config"] = {
+            "hidden_size": orig_vision["width"]["qwen2-0-5b"]["dim"],
+            "num_hidden_layers": 24,
+            "num_attention_heads": 14,
+            "num_key_value_heads": 2,
+            "intermediate_size": 4864,
+            "rms_norm_eps": 1e-6,
+            "rope_theta": 1000000.0,
+            "vocab_size": 1,
+        }
+
+    config_dict.pop("projector_config", None)
+
+    config_dict["vision_config"] = vision_config
+    config_dict["model_type"] = "deepseek_ocr2"
+
+    return config_dict
+
+
+def convert_weights(input_dir: str, output_dir: str, hub_repo_id: str | None = None):
+    if os.path.abspath(input_dir) == os.path.abspath(output_dir):
+        raise ValueError("`input_dir` and `output_dir` must be different directories.")
+
+    os.makedirs(output_dir, exist_ok=True)
+
+    # Config
+    with open(os.path.join(input_dir, "config.json")) as f:
+        raw_config = json.load(f)
+
+    config = DeepseekOcr2Config.from_dict(convert_config(raw_config))
+    config.save_pretrained(output_dir)
+    print("Config saved to", output_dir)
+
+    # Load with conversion_mapping.py (key remapping + MoE expert fusing) and save in HF format
+    print(f"Loading model from {input_dir} with automatic weight conversion ...")
+    model = DeepseekOcr2ForConditionalGeneration.from_pretrained(input_dir, config=config)
+
+    print(f"Saving model to {output_dir} ...")
+    model.save_pretrained(output_dir)
+    del model
+
+    print("Copying tokenizer ...")
+    tokenizer = PreTrainedTokenizerFast.from_pretrained(input_dir)
+    tokenizer.save_pretrained(output_dir)
+    print("Tokenizer saved.")
+
+    print("Saving processor ...")
+    image_processor = DeepseekOcr2ImageProcessor()
+    processor = DeepseekOcr2Processor(image_processor=image_processor, tokenizer=tokenizer)
+    processor.save_pretrained(output_dir)
+    print("Processor saved.")
+
+    if hub_repo_id:
+        print(f"Pushing to hub ({hub_repo_id}) ...")
+        model = DeepseekOcr2ForConditionalGeneration.from_pretrained(output_dir, torch_dtype=torch.bfloat16)
+        model.push_to_hub(hub_repo_id)
+        tokenizer.push_to_hub(hub_repo_id)
+        processor.push_to_hub(hub_repo_id)
+
+    print("Done.")
+
+
+def test(output_dir: str):
+    """Run a quick inference test on the converted model."""
+    import requests
+    from PIL import Image
+
+    image_url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
+
+    print(f"\n{'=' * 60}")
+    print("Running inference test...")
+    print(f"Image: {image_url}")
+
+    model = DeepseekOcr2ForConditionalGeneration.from_pretrained(
+        output_dir, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="eager"
+    )
+    model.eval()
+
+    tokenizer = PreTrainedTokenizerFast.from_pretrained(output_dir)
+    processor = DeepseekOcr2Processor(image_processor=DeepseekOcr2ImageProcessor(), tokenizer=tokenizer)
+
+    image = Image.open(requests.get(image_url, stream=True).raw).convert("RGB")
+    print(f"Image size: {image.size[0]}x{image.size[1]}")
+
+    inputs = processor(images=image, text="<image>\nFree OCR.", return_tensors="pt").to(
+        model.device, dtype=torch.bfloat16
+    )
+    print(f"Input tokens: {inputs['input_ids'].shape[1]}")
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            **inputs,
+            eos_token_id=tokenizer.eos_token_id,
+            max_new_tokens=4096,
+            do_sample=False,
+            no_repeat_ngram_size=35,
+        )
+
+    generated = output_ids[0][inputs["input_ids"].shape[1] :]
+    output_text = tokenizer.decode(generated, skip_special_tokens=True).strip()
+
+    print(f"Generated {len(generated)} tokens")
+    print(f"Output:\n{output_text[:500]}")
+    print(f"{'=' * 60}")
+
+
+def main():
+    """
+    Convert DeepSeek-OCR-2 weights from HF Hub custom-code format to native transformers format.
+
+    Usage:
+        # Step 1: Download the original checkpoint
+        huggingface-cli download deepseek-ai/DeepSeek-OCR-2 --local-dir /path/to/DeepSeek-OCR-2
+
+        # Step 2: Convert to native transformers format
+        python convert_deepseek_ocr2_weights_to_hf.py \\
+            --input_dir /path/to/DeepSeek-OCR-2 \\
+            --output_dir /path/to/DeepSeek-OCR-2-hf
+
+        # Step 3 (optional): Verify with a quick inference test
+        python convert_deepseek_ocr2_weights_to_hf.py \\
+            --input_dir /path/to/DeepSeek-OCR-2 \\
+            --output_dir /path/to/DeepSeek-OCR-2-hf \\
+            --test
+    """
+    parser = argparse.ArgumentParser(description=main.__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    parser.add_argument(
+        "--input_dir", type=str, required=True, help="Path to the downloaded DeepSeek-OCR-2 checkpoint."
+    )
+    parser.add_argument("--output_dir", type=str, required=True, help="Path to write the converted model.")
+    parser.add_argument(
+        "--hub_repo_id",
+        type=str,
+        default=None,
+        help="Push converted model to this HF Hub repo (e.g. 'my-org/DeepSeek-OCR-2-hf').",
+    )
+    parser.add_argument("--test", action="store_true", help="Run inference test after conversion.")
+    args = parser.parse_args()
+
+    convert_weights(args.input_dir, args.output_dir, hub_repo_id=args.hub_repo_id)
+
+    if args.test:
+        test(args.output_dir)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/deepseek_ocr2/image_processing_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/image_processing_deepseek_ocr2.py
new file mode 100644
index 000000000000..2dcc36a61600
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/image_processing_deepseek_ocr2.py
@@ -0,0 +1,343 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_ocr2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from functools import lru_cache
+
+import torch
+from torchvision.transforms.v2 import functional as tvF
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import group_images_by_shape, reorder_images
+from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling, SizeDict
+from ...processing_utils import ImagesKwargs, Unpack
+from ...utils import TensorType, auto_docstring
+
+
+class DeepseekOcr2ImageProcessorKwargs(ImagesKwargs, total=False):
+    """
+    tile_size (`int`, *optional*, defaults to `768`):
+        The size of each local tile. Must match the model's query embedding size.
+    background_color (`list[int]`, *optional*, defaults to `[127, 127, 127]`):
+        The background color for padding.
+    """
+
+    crop_to_patches: bool
+    min_patches: int
+    max_patches: int
+
+    tile_size: int
+    background_color: list[int]
+
+
+@lru_cache(maxsize=10)
+def get_all_supported_aspect_ratios(min_image_tiles: int, max_image_tiles: int) -> list[tuple[int, int]]:
+    """
+    Computes all allowed aspect ratios for a given minimum and maximum number of input tiles.
+
+    This function calculates all possible arrangements of tiles that can be formed
+    within the constraint of the minimum and maximum number of tiles. Each arrangement is
+    represented by its aspect ratio (width/height) and the corresponding tile configuration.
+
+    Args:
+        min_image_tiles (`int`):
+            The minimum number of tiles allowed.
+        max_image_tiles (`int`):
+            The maximum number of tiles allowed.
+
+    Returns:
+        `list[tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height)
+        configuration in terms of number of tiles.
+
+    Example:
+        >>> get_all_supported_aspect_ratios(1, 4)
+        [(1, 1), (1, 2), (2, 1), (1, 3), (3, 1), (1, 4), (2, 2), (4, 1)]
+
+    """
+    aspect_ratios = []
+    for width in range(1, max_image_tiles + 1):
+        for height in range(1, max_image_tiles + 1):
+            if width * height <= max_image_tiles and width * height >= min_image_tiles:
+                aspect_ratios.append((width, height))
+
+    aspect_ratios = sorted(aspect_ratios, key=lambda x: x[0] * x[1])
+
+    return aspect_ratios
+
+
+@lru_cache(maxsize=100)
+def get_optimal_tiled_canvas(
+    original_image_size: tuple[int, int],
+    target_tile_size: tuple[int, int],
+    min_image_tiles: int,
+    max_image_tiles: int,
+) -> tuple[int, int]:
+    """
+    Given a minimum and maximum number of tiles, find the canvas with the closest aspect ratio to the
+    original image aspect ratio.
+    In case of tie-breaking condition when two canvases have the same aspect ratio difference, we favor the canvas with
+    more tiles, until the area covered by the tiles is more than twice the target area, in order to avoid unnecessarily
+    excessive tiling.
+    """
+    possible_tile_arrangements = get_all_supported_aspect_ratios(min_image_tiles, max_image_tiles)
+
+    original_height, original_width = original_image_size
+    target_tile_height, target_tile_width = target_tile_size
+    aspect_ratio = original_width / original_height
+    area = original_width * original_height
+
+    # find the grid with the best aspect ratio
+    best_ratio_diff = float("inf")
+    best_grid = (1, 1)
+    for grid in possible_tile_arrangements:
+        grid_aspect_ratio = grid[0] / grid[1]
+        ratio_diff = abs(aspect_ratio - grid_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_grid = grid
+        elif ratio_diff == best_ratio_diff:
+            # if the aspect ratio difference is the same, we favor the grid with more patches
+            # until the area covered by the patches is more than twice the original image area
+            if area > 0.5 * target_tile_height * target_tile_width * grid[0] * grid[1]:
+                best_grid = grid
+
+    return best_grid
+
+
+@auto_docstring
+class DeepseekOcr2ImageProcessor(TorchvisionBackend):
+    valid_kwargs = DeepseekOcr2ImageProcessorKwargs
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 1024, "width": 1024}
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    crop_to_patches = True
+    min_patches = 2
+    max_patches = 6
+    tile_size = 768
+    background_color = [127, 127, 127]
+    model_input_names = ["pixel_values", "num_local_patches"]
+
+    def __init__(self, **kwargs: Unpack[DeepseekOcr2ImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    def crop_image_to_patches(
+        self,
+        images: "torch.Tensor",
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: PILImageResampling | None = None,
+    ) -> tuple["torch.Tensor", int]:
+        """
+        Crop batched images to patches based on optimal tiling.
+
+        Args:
+            images (`torch.Tensor`):
+                The images to crop, shape `(batch, channels, height, width)`.
+            min_patches (`int`):
+                Minimum number of patches.
+            max_patches (`int`):
+                Maximum number of patches.
+            tile_size (`int`):
+                The size of each tile.
+            resample (`PILImageResampling`, *optional*):
+                Resampling filter for resizing.
+
+        Returns:
+            `tuple[torch.Tensor, int]`: Stacked patches `(batch, num_patches, channels, tile_size, tile_size)`
+            and number of patches per image.
+        """
+        original_height, original_width = images.shape[-2:]
+
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (original_height, original_width), (tile_size, tile_size), min_patches, max_patches
+        )
+
+        target_width = tile_size * num_columns
+        target_height = tile_size * num_rows
+        num_blocks = num_columns * num_rows
+
+        resized = self.resize(images, SizeDict(height=target_height, width=target_width), resample=resample)
+
+        patches = []
+        for i in range(num_blocks):
+            col = i % num_columns
+            row = i // num_columns
+            patch = resized[
+                ...,
+                row * tile_size : (row + 1) * tile_size,
+                col * tile_size : (col + 1) * tile_size,
+            ]
+            patches.append(patch)
+
+        stacked_patches = torch.stack(patches, dim=1)
+
+        return stacked_patches, num_blocks
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        size: SizeDict,
+        crop_to_patches: bool,
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: PILImageResampling | None,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ) -> BatchFeature:
+        # --- Local patches (batched by shape group) ---
+        num_local_patches = {}
+        local_patches_grouped = {}
+
+        if crop_to_patches:
+            grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+
+            for shape, stacked_images in grouped_images.items():
+                h, w = shape[-2:]
+                if max(h, w) > tile_size:
+                    stacked_patches, n_patches = self.crop_image_to_patches(
+                        stacked_images,
+                        min_patches=min_patches,
+                        max_patches=max_patches,
+                        tile_size=tile_size,
+                        resample=resample,
+                    )
+                    flat_patches = stacked_patches.reshape(-1, *stacked_patches.shape[2:])
+                    flat_patches = self.rescale_and_normalize(
+                        flat_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+                    )
+                    local_patches_grouped[shape] = flat_patches.reshape(stacked_patches.shape)
+                    num_local_patches[shape] = [n_patches] * stacked_images.shape[0]
+                else:
+                    local_patches_grouped[shape] = [None] * stacked_images.shape[0]
+                    num_local_patches[shape] = [0] * stacked_images.shape[0]
+
+            num_local_patches = reorder_images(num_local_patches, grouped_images_index)
+            ordered_local = reorder_images(local_patches_grouped, grouped_images_index)
+        else:
+            num_local_patches = [0] * len(images)
+            ordered_local = []
+
+        flat_local_list = [patch for item in ordered_local if item is not None for patch in item]
+
+        # --- Global view (batched by shape group) ---
+        global_target_size = size.height if crop_to_patches else tile_size
+
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        processed_global_grouped = {}
+        for shape, stacked in grouped_images.items():
+            h, w = shape[-2:]
+            scale = global_target_size / max(h, w)
+            new_h = round(h * scale)
+            new_w = round(w * scale)
+            stacked = self.resize(stacked, SizeDict(height=new_h, width=new_w), resample=resample)
+            stacked = self.pad_to_square(stacked, background_color=self.background_color)
+            stacked = self.rescale_and_normalize(
+                stacked, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            processed_global_grouped[shape] = stacked
+        all_pixel_values_global = reorder_images(processed_global_grouped, grouped_images_index)
+
+        data = {
+            "pixel_values": all_pixel_values_global,
+            "num_local_patches": num_local_patches,
+        }
+        if flat_local_list:
+            data["pixel_values_local"] = flat_local_list
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None) -> int:
+        """
+        Returns the number of image patches for a given image size (1 global + local patches).
+        """
+        if images_kwargs is None:
+            images_kwargs = {}
+        min_patches = images_kwargs.get("min_patches", self.min_patches)
+        max_patches = images_kwargs.get("max_patches", self.max_patches)
+        tile_size = images_kwargs.get("tile_size", self.tile_size)
+        crop_to_patches = images_kwargs.get("crop_to_patches", self.crop_to_patches)
+
+        num_patches = 1  # global view
+        if crop_to_patches and max(height, width) > tile_size:
+            num_columns, num_rows = get_optimal_tiled_canvas(
+                (height, width), (tile_size, tile_size), min_patches, max_patches
+            )
+            num_patches += num_columns * num_rows
+
+        return num_patches
+
+    # Copied from transformers.models.llava.image_processing_llava.LlavaImageProcessor.pad_to_square
+    def pad_to_square(
+        self,
+        images: "torch.Tensor",
+        background_color: int | tuple[int, int, int] = 0,
+    ) -> "torch.Tensor":
+        """
+        Pads an image to a square based on the longest edge.
+
+        Args:
+            images (`torch.Tensor`):
+                The images to pad. Shape: (batch_size, num_channels, height, width) or (num_channels, height, width).
+            background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+                The color to use for the padding. Can be an integer for single channel or a
+                tuple of integers representing for multi-channel images. If passed as integer
+                in multi-channel mode, it will default to `0` in subsequent channels.
+        Returns:
+            `torch.Tensor`: The padded images.
+        """
+        height, width = images.shape[-2:]
+
+        if height == width:
+            return images
+
+        num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+        if isinstance(background_color, int):
+            background_color = [background_color] + [0] * (num_channels - 1)
+        elif len(background_color) != num_channels:
+            raise ValueError(
+                f"background_color must have no more than {num_channels} elements to match the number of channels"
+            )
+
+        max_dim = max(height, width)
+        paste_x_left = (max_dim - width) // 2
+        paste_y_left = (max_dim - height) // 2
+        paste_x_right = max_dim - width - paste_x_left
+        paste_y_right = max_dim - height - paste_y_left
+        padded_images = tvF.pad(
+            images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
+        )
+
+        return padded_images
+
+
+__all__ = ["DeepseekOcr2ImageProcessor"]
diff --git a/src/transformers/models/deepseek_ocr2/image_processing_pil_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/image_processing_pil_deepseek_ocr2.py
new file mode 100644
index 000000000000..87897ea5e6b4
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/image_processing_pil_deepseek_ocr2.py
@@ -0,0 +1,349 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_ocr2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from functools import lru_cache
+
+import numpy as np
+
+from ...image_processing_backends import PilBackend
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import to_channel_dimension_format
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+    infer_channel_dimension_format,
+)
+from ...processing_utils import ImagesKwargs, Unpack
+from ...utils import TensorType, auto_docstring
+from ...utils.import_utils import requires
+
+
+class DeepseekOcr2ImageProcessorKwargs(ImagesKwargs, total=False):
+    """
+    tile_size (`int`, *optional*, defaults to `768`):
+        The size of each local tile. Must match the model's query embedding size.
+    background_color (`list[int]`, *optional*, defaults to `[127, 127, 127]`):
+        The background color for padding.
+    """
+
+    crop_to_patches: bool
+    min_patches: int
+    max_patches: int
+
+    tile_size: int
+    background_color: list[int]
+
+
+@lru_cache(maxsize=10)
+def get_all_supported_aspect_ratios(min_image_tiles: int, max_image_tiles: int) -> list[tuple[int, int]]:
+    """
+    Computes all allowed aspect ratios for a given minimum and maximum number of input tiles.
+
+    This function calculates all possible arrangements of tiles that can be formed
+    within the constraint of the minimum and maximum number of tiles. Each arrangement is
+    represented by its aspect ratio (width/height) and the corresponding tile configuration.
+
+    Args:
+        min_image_tiles (`int`):
+            The minimum number of tiles allowed.
+        max_image_tiles (`int`):
+            The maximum number of tiles allowed.
+
+    Returns:
+        `list[tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height)
+        configuration in terms of number of tiles.
+
+    Example:
+        >>> get_all_supported_aspect_ratios(1, 4)
+        [(1, 1), (1, 2), (2, 1), (1, 3), (3, 1), (1, 4), (2, 2), (4, 1)]
+
+    """
+    aspect_ratios = []
+    for width in range(1, max_image_tiles + 1):
+        for height in range(1, max_image_tiles + 1):
+            if width * height <= max_image_tiles and width * height >= min_image_tiles:
+                aspect_ratios.append((width, height))
+
+    aspect_ratios = sorted(aspect_ratios, key=lambda x: x[0] * x[1])
+
+    return aspect_ratios
+
+
+@lru_cache(maxsize=100)
+def get_optimal_tiled_canvas(
+    original_image_size: tuple[int, int],
+    target_tile_size: tuple[int, int],
+    min_image_tiles: int,
+    max_image_tiles: int,
+) -> tuple[int, int]:
+    """
+    Given a minimum and maximum number of tiles, find the canvas with the closest aspect ratio to the
+    original image aspect ratio.
+    In case of tie-breaking condition when two canvases have the same aspect ratio difference, we favor the canvas with
+    more tiles, until the area covered by the tiles is more than twice the target area, in order to avoid unnecessarily
+    excessive tiling.
+    """
+    possible_tile_arrangements = get_all_supported_aspect_ratios(min_image_tiles, max_image_tiles)
+
+    original_height, original_width = original_image_size
+    target_tile_height, target_tile_width = target_tile_size
+    aspect_ratio = original_width / original_height
+    area = original_width * original_height
+
+    # find the grid with the best aspect ratio
+    best_ratio_diff = float("inf")
+    best_grid = (1, 1)
+    for grid in possible_tile_arrangements:
+        grid_aspect_ratio = grid[0] / grid[1]
+        ratio_diff = abs(aspect_ratio - grid_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_grid = grid
+        elif ratio_diff == best_ratio_diff:
+            # if the aspect ratio difference is the same, we favor the grid with more patches
+            # until the area covered by the patches is more than twice the original image area
+            if area > 0.5 * target_tile_height * target_tile_width * grid[0] * grid[1]:
+                best_grid = grid
+
+    return best_grid
+
+
+@requires(backends=("vision",))
+@auto_docstring
+class DeepseekOcr2ImageProcessorPil(PilBackend):
+    valid_kwargs = DeepseekOcr2ImageProcessorKwargs
+    resample = PILImageResampling.BICUBIC
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 1024, "width": 1024}
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    crop_to_patches = True
+    min_patches = 2
+    max_patches = 6
+    tile_size = 768
+    background_color = [127, 127, 127]
+    model_input_names = ["pixel_values", "num_local_patches"]
+
+    def __init__(self, **kwargs: Unpack[DeepseekOcr2ImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    def crop_image_to_patches(
+        self,
+        image: np.ndarray,
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: "PILImageResampling | int | None" = None,
+    ):
+        """
+        Crop the image to patches and return a list of cropped images.
+        """
+        input_data_format = infer_channel_dimension_format(image)
+        image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_data_format)
+
+        original_height, original_width = get_image_size(image, channel_dim=ChannelDimension.FIRST)
+
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (original_height, original_width), (tile_size, tile_size), min_patches, max_patches
+        )
+
+        target_width = tile_size * num_columns
+        target_height = tile_size * num_rows
+        num_blocks = num_columns * num_rows
+
+        resized_image = self.resize(image, SizeDict(height=target_height, width=target_width), resample=resample)
+
+        processed_images = []
+        for i in range(num_blocks):
+            column = i % num_columns
+            row = i // num_columns
+            box = (
+                column * tile_size,
+                row * tile_size,
+                (column + 1) * tile_size,
+                (row + 1) * tile_size,
+            )
+            patch_image = resized_image[..., box[1] : box[3], box[0] : box[2]]
+            patch_image = to_channel_dimension_format(patch_image, input_data_format, ChannelDimension.FIRST)
+            processed_images.append(patch_image)
+
+        return processed_images
+
+    def _preprocess(
+        self,
+        images: list[np.ndarray],
+        size: SizeDict,
+        resample: "PILImageResampling | int | None",
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        return_tensors: str | TensorType | None,
+        crop_to_patches: bool = True,
+        min_patches: int = 2,
+        max_patches: int = 6,
+        tile_size: int = 768,
+        background_color: list[int] | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if background_color is None:
+            background_color = self.background_color
+
+        all_pixel_values_local = []
+        all_pixel_values_global = []
+        num_local_patches = []
+
+        for image in images:
+            original_height, original_width = get_image_size(image)
+
+            # --- Local patches ---
+            if crop_to_patches and max(original_width, original_height) > tile_size:
+                local_patches = self.crop_image_to_patches(
+                    image,
+                    min_patches=min_patches,
+                    max_patches=max_patches,
+                    tile_size=tile_size,
+                    resample=resample,
+                )
+                for patch in local_patches:
+                    if do_rescale:
+                        patch = self.rescale(patch, rescale_factor)
+                    if do_normalize:
+                        patch = self.normalize(patch, image_mean, image_std)
+                    all_pixel_values_local.append(patch)
+                num_local_patches.append(len(local_patches))
+            else:
+                num_local_patches.append(0)
+
+            # --- Global view ---
+            global_target_size = size.height if crop_to_patches else tile_size
+            scale = global_target_size / max(original_width, original_height)
+            new_width = round(original_width * scale)
+            new_height = round(original_height * scale)
+
+            global_img = self.resize(image, SizeDict(height=new_height, width=new_width), resample=resample)
+            global_img = self.pad_to_square(global_img, background_color=background_color)
+            if do_rescale:
+                global_img = self.rescale(global_img, rescale_factor)
+            if do_normalize:
+                global_img = self.normalize(global_img, image_mean, image_std)
+            all_pixel_values_global.append(global_img)
+
+        data = {
+            "pixel_values": all_pixel_values_global,
+            "num_local_patches": num_local_patches,
+        }
+        if all_pixel_values_local:
+            data["pixel_values_local"] = all_pixel_values_local
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        min_patches = images_kwargs.get("min_patches", self.min_patches) if images_kwargs else self.min_patches
+        max_patches = images_kwargs.get("max_patches", self.max_patches) if images_kwargs else self.max_patches
+        patch_size = images_kwargs.get("patch_size", self.size) if images_kwargs else self.size
+        crop_to_patches = (
+            images_kwargs.get("crop_to_patches", self.crop_to_patches) if images_kwargs else self.crop_to_patches
+        )
+
+        num_patches = 1
+        if crop_to_patches and max_patches > 1:
+            if isinstance(patch_size, dict):
+                patch_height, patch_width = patch_size["height"], patch_size["width"]
+            else:
+                patch_height, patch_width = patch_size.height, patch_size.width
+            num_columns, num_rows = get_optimal_tiled_canvas(
+                (height, width), (patch_height, patch_width), min_patches, max_patches
+            )
+            if num_columns * num_rows > 1:
+                num_patches += num_columns * num_rows
+
+        return num_patches
+
+    # Copied from transformers.models.llava.image_processing_pil_llava.LlavaImageProcessorPil.pad_to_square
+    def pad_to_square(
+        self,
+        image: np.ndarray,
+        background_color: int | tuple[int, int, int] = 0,
+    ) -> np.ndarray:
+        """
+        Pads an image to a square based on the longest edge.
+
+        Args:
+            image (`np.ndarray`):
+                The image to pad. Shape: (num_channels, height, width) - always channels_first in backend.
+            background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+                The color to use for the padding.
+
+        Returns:
+            `np.ndarray`: The padded image.
+        """
+        # Backend always uses channels_first format: (num_channels, height, width)
+        num_channels, height, width = image.shape
+
+        if height == width:
+            return image
+
+        max_dim = max(height, width)
+
+        # Ensure background_color is the correct shape
+        if isinstance(background_color, int):
+            background_color = [background_color]
+        elif len(background_color) != num_channels:
+            raise ValueError(
+                f"background_color must have no more than {num_channels} elements to match the number of channels"
+            )
+
+        result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+        for i, color in enumerate(background_color):
+            result[i, :, :] = color
+        if width > height:
+            start = (max_dim - height) // 2
+            result[:, start : start + height, :] = image
+        else:
+            start = (max_dim - width) // 2
+            result[:, :, start : start + width] = image
+
+        return result
+
+
+__all__ = ["DeepseekOcr2ImageProcessorPil"]
diff --git a/src/transformers/models/deepseek_ocr2/modeling_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/modeling_deepseek_ocr2.py
new file mode 100644
index 000000000000..baeacff90a51
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/modeling_deepseek_ocr2.py
@@ -0,0 +1,1743 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_ocr2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+import math
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import (
+    use_experts_implementation,
+    use_kernel_forward_from_hub,
+    use_kernel_func_from_hub,
+    use_kernelized_func,
+)
+from ...masking_utils import create_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
+from ...utils.generic import maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_deepseek_ocr2 import (
+    DeepseekOcr2Config,
+    DeepseekOcr2SamVisionConfig,
+    DeepseekOcr2TextConfig,
+    DeepseekOcr2VisionConfig,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+class DeepseekOcr2ModelOutputWithPooling(BaseModelOutputWithPooling):
+    """
+    local_last_hidden_state (`torch.FloatTensor` of shape `(total_local_patches, sequence_length, hidden_size)`, *optional*):
+        Last hidden state from the vision encoder for local (cropped) patches.
+    local_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states from all layers of the vision encoder for local patches.
+    local_attentions (`torch.FloatTensor`, *optional*):
+        Attention weights from all layers of the vision encoder for local patches.
+    """
+
+    local_last_hidden_state: torch.FloatTensor | None = None
+    local_hidden_states: torch.FloatTensor | None = None
+    local_attentions: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for Llava outputs, with hidden states and attentions.
+    """
+)
+class DeepseekOcr2ModelOutputWithPast(BaseModelOutputWithPast):
+    r"""
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for DeepseekOcr2 causal language model (or autoregressive) outputs.
+    """
+)
+class DeepseekOcr2CausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@auto_docstring
+class DeepseekOcr2PreTrainedModel(PreTrainedModel):
+    config: DeepseekOcr2Config
+    base_model_prefix = "model"
+    input_modalities = ("image", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "DeepseekOcr2SamVisionLayer",
+        "DeepseekOcr2VisionDecoderLayer",
+        "DeepseekOcr2TextDecoderLayer",
+    ]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = False
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = True
+    _supports_flex_attn = True
+    _supports_attention_backend = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, DeepseekOcr2SamVisionAttention):
+            if module.use_rel_pos:
+                init.zeros_(module.rel_pos_h)
+                init.zeros_(module.rel_pos_w)
+        elif isinstance(module, DeepseekOcr2SamVisionEncoder):
+            if module.pos_embed is not None:
+                init.zeros_(module.pos_embed)
+        elif isinstance(module, DeepseekOcr2Model):
+            embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+            init.normal_(module.view_separator, mean=0.0, std=embed_std)
+
+
+class DeepseekOcr2SamVisionAttention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(self, config, window_size):
+        super().__init__()
+        input_size = (
+            (config.image_size // config.patch_size, config.image_size // config.patch_size)
+            if window_size == 0
+            else (window_size, window_size)
+        )
+
+        self.num_attention_heads = config.num_attention_heads
+        head_dim = config.hidden_size // config.num_attention_heads
+        self.scale = head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
+        self.proj = nn.Linear(config.hidden_size, config.hidden_size)
+
+        self.use_rel_pos = config.use_rel_pos
+        if self.use_rel_pos:
+            if input_size is None:
+                raise ValueError("Input size must be provided if using relative positional encoding.")
+
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+        """
+        Get relative positional embeddings according to the relative positions of
+            query and key sizes.
+
+        Args:
+            q_size (int):
+                size of the query.
+            k_size (int):
+                size of key k.
+            rel_pos (`torch.Tensor`):
+                relative position embeddings (L, channel).
+
+        Returns:
+            Extracted positional embeddings according to relative positions.
+        """
+        max_rel_dist = int(2 * max(q_size, k_size) - 1)
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+
+        # Scale the coords with short length if shapes for q and k are different.
+        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+        return rel_pos_resized[relative_coords.long()]
+
+    def get_decomposed_rel_pos(
+        self,
+        query: torch.Tensor,
+        rel_pos_h: torch.Tensor,
+        rel_pos_w: torch.Tensor,
+        q_size: tuple[int, int],
+        k_size: tuple[int, int],
+    ) -> torch.Tensor:
+        """
+        Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+        https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py
+
+        Args:
+            query (`torch.Tensor`):
+                query q in the attention layer with shape (batch_size, query_height * query_width, channel).
+            rel_pos_h (`torch.Tensor`):
+                relative position embeddings (Lh, channel) for height axis.
+            rel_pos_w (`torch.Tensor`):
+                relative position embeddings (Lw, channel) for width axis.
+            q_size (tuple):
+                spatial sequence size of query q with (query_height, query_width).
+            k_size (tuple):
+                spatial sequence size of key k with (key_height, key_width).
+
+        Returns:
+            decomposed_rel_pos (`torch.Tensor`):
+                decomposed relative position embeddings.
+        """
+        query_height, query_width = q_size
+        key_height, key_width = k_size
+        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
+        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)
+
+        batch_size, _, dim = query.shape
+        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
+        rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
+        rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)
+
+        decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+
+        return decomposed_rel_pos
+
+    def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size, height, width, _ = hidden_states.shape
+        # qkv with shape (3, batch_size, nHead, height * width, channel)
+        qkv = (
+            self.qkv(hidden_states)
+            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
+            .permute(2, 0, 3, 1, 4)
+        )
+        # q, k, v with shape (batch_size * nHead, height * width, channel)
+        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)
+
+        attn_weights = (query * self.scale) @ key.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            decomposed_rel_pos = self.get_decomposed_rel_pos(
+                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
+            )
+            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
+            attn_weights = attn_weights + decomposed_rel_pos
+
+        attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
+
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
+        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)
+
+        attn_output = self.proj(attn_output)
+        return attn_output, attn_weights
+
+
+class DeepseekOcr2SamMLPBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
+        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
+        self.act = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.lin1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.lin2(hidden_states)
+        return hidden_states
+
+
+class DeepseekOcr2SamVisionSdpaAttention(DeepseekOcr2SamVisionAttention):
+    """
+    Multi-head Attention block with relative position embeddings.
+    Using SDPA instead of the default attention.
+    """
+
+    def __init__(self, config, window_size):
+        super().__init__(config, window_size)
+
+    def forward(self, hidden_states: torch.Tensor, output_attentions=False) -> torch.Tensor:
+        if output_attentions:
+            logger.warning_once(
+                f"{self.__class__.__name__} does not support `output_attentions=True`. The returned attention weights will "
+                "be `None`. If you want to get attention weights, please set `attn_implementation='eager'` when loading the model."
+            )
+        batch_size, height, width, _ = hidden_states.shape
+        # qkv with shape (3, B, nHead, H * W, C)
+        qkv = (
+            self.qkv(hidden_states)
+            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
+            .permute(2, 0, 3, 1, 4)
+        )
+        # q, k, v with shape (B * nHead, H * W, C)
+        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)
+
+        attn_bias = None
+        if self.use_rel_pos:
+            decomposed_rel_pos = self.get_decomposed_rel_pos(
+                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
+            )
+            decomposed_rel_pos = decomposed_rel_pos.reshape(
+                batch_size, self.num_attention_heads, height * width, height * width
+            )
+            attn_bias = decomposed_rel_pos
+
+        query = query.view(batch_size, self.num_attention_heads, height * width, -1)
+        key = key.view(batch_size, self.num_attention_heads, height * width, -1)
+        value = value.view(batch_size, self.num_attention_heads, height * width, -1)
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=attn_bias)
+
+        attn_output = (
+            attn_output.view(batch_size, self.num_attention_heads, height, width, -1)
+            .permute(0, 2, 3, 1, 4)
+            .reshape(batch_size, height, width, -1)
+        )
+
+        attn_output = self.proj(attn_output)
+        return attn_output, None
+
+
+DEEPSEEK_OCR2_SAM_VISION_ATTENTION_CLASSES = {
+    "eager": DeepseekOcr2SamVisionAttention,
+    "sdpa": DeepseekOcr2SamVisionSdpaAttention,
+}
+
+
+class DeepseekOcr2SamVisionLayer(GradientCheckpointingLayer):
+    def __init__(self, config, window_size):
+        super().__init__()
+        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attn = DEEPSEEK_OCR2_SAM_VISION_ATTENTION_CLASSES[config._attn_implementation](config, window_size)
+        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = DeepseekOcr2SamMLPBlock(config)
+        self.window_size = window_size
+
+    def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]:
+        """
+        Args:
+        Partition into non-overlapping windows with padding if needed.
+            hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
+            size.
+
+        Returns:
+            windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
+            (pad_height, pad_width): padded height and width before partition
+        """
+        batch_size, height, width, channel = hidden_states.shape
+
+        pad_h = (window_size - height % window_size) % window_size
+        pad_w = (window_size - width % window_size) % window_size
+        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
+        pad_height, pad_width = height + pad_h, width + pad_w
+
+        hidden_states = hidden_states.reshape(
+            batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
+        )
+        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel)
+        return windows, (pad_height, pad_width)
+
+    def window_unpartition(
+        self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int]
+    ) -> torch.Tensor:
+        """
+        Args:
+        Window unpartition into original sequences and removing padding.
+            hidden_states (tensor):
+                input tokens with [batch_size * num_windows, window_size, window_size, channel].
+            window_size (int):
+                window size.
+            padding_shape (Tuple):
+                padded height and width (pad_height, pad_width).
+            original_shape (Tuple): original height and width (height, width) before padding.
+
+        Returns:
+            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
+        """
+        pad_height, pad_width = padding_shape
+        height, width = original_shape
+        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
+        hidden_states = windows.reshape(
+            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
+        )
+        hidden_states = (
+            hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1)
+        )
+
+        hidden_states = hidden_states[:, :height, :width, :].contiguous()
+        return hidden_states
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]:
+        residual = hidden_states
+        hidden_states = self.layer_norm1(hidden_states)
+        # Window partition
+        if self.window_size > 0:
+            height, width = hidden_states.shape[1], hidden_states.shape[2]
+            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)
+
+        hidden_states, attn_weights = self.attn(
+            hidden_states=hidden_states,
+        )
+        # Reverse window partition
+        if self.window_size > 0:
+            hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))
+
+        hidden_states = residual + hidden_states
+        layernorm_output = self.layer_norm2(hidden_states)
+        hidden_states = hidden_states + self.mlp(layernorm_output)
+        return hidden_states
+
+
+class DeepseekOcr2SamLayerNorm(nn.LayerNorm):
+    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
+    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs):
+        super().__init__(normalized_shape, eps=eps, **kwargs)
+        if data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError(f"Unsupported data format: {data_format}")
+        self.data_format = data_format
+
+    def forward(self, features: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels)
+        """
+        if self.data_format == "channels_first":
+            features = features.permute(0, 2, 3, 1)
+            features = super().forward(features)
+            features = features.permute(0, 3, 1, 2)
+        else:
+            features = super().forward(features)
+        return features
+
+
+class DeepseekOcr2SamVisionNeck(nn.Module):
+    def __init__(self, config: DeepseekOcr2SamVisionConfig):
+        super().__init__()
+        self.config = config
+
+        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
+        self.layer_norm1 = DeepseekOcr2SamLayerNorm(config.output_channels, data_format="channels_first")
+        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
+        self.layer_norm2 = DeepseekOcr2SamLayerNorm(config.output_channels, data_format="channels_first")
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.permute(0, 3, 1, 2)
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.layer_norm1(hidden_states)
+
+        hidden_states = self.conv2(hidden_states)
+        hidden_states = self.layer_norm2(hidden_states)
+        return hidden_states
+
+
+class DeepseekOcr2SamPatchEmbeddings(nn.Module):
+    """
+    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
+    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
+    Transformer.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        image_size, patch_size = config.image_size, config.patch_size
+        num_channels, hidden_size = config.num_channels, config.hidden_size
+        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
+        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+
+        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, pixel_values):
+        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
+        return embeddings
+
+
+class DeepseekOcr2SamVisionProj(nn.Module):
+    """Neck and multi-scale downsampling for SAM ViT-B output."""
+
+    def __init__(self, config: DeepseekOcr2SamVisionConfig):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            config.output_channels,
+            config.downsample_channels[0],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+        self.conv2 = nn.Conv2d(
+            config.downsample_channels[0],
+            config.downsample_channels[1],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        return hidden_states
+
+
+class DeepseekOcr2SamVisionEncoder(DeepseekOcr2PreTrainedModel):
+    _can_record_outputs = {"hidden_states": DeepseekOcr2SamVisionLayer, "attentions": DeepseekOcr2SamVisionAttention}
+
+    def __init__(self, config: DeepseekOcr2SamVisionConfig):
+        super().__init__(config)
+        self.config = config
+        self.image_size = config.image_size
+        self.patch_embed = DeepseekOcr2SamPatchEmbeddings(config)
+
+        self.pos_embed = None
+        if config.use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(
+                    1,
+                    config.image_size // config.patch_size,
+                    config.image_size // config.patch_size,
+                    config.hidden_size,
+                )
+            )
+
+        self.layers = nn.ModuleList()
+        for i in range(config.num_hidden_layers):
+            layer = DeepseekOcr2SamVisionLayer(
+                config,
+                window_size=config.window_size if i not in config.global_attn_indexes else 0,
+            )
+            self.layers.append(layer)
+
+        self.neck = DeepseekOcr2SamVisionNeck(config)
+
+        self.gradient_checkpointing = False
+        self.proj = DeepseekOcr2SamVisionProj(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.patch_embed
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(self, pixel_values: torch.FloatTensor, **kwargs) -> BaseModelOutput:
+        hidden_states = self.patch_embed(pixel_values)
+        if self.pos_embed is not None:
+            hidden_states = hidden_states + self.interpolate_pos_encoding(
+                self.pos_embed, target_size=hidden_states.shape[1], dtype=hidden_states.dtype
+            )
+
+        for layer_module in self.layers:
+            hidden_states = layer_module(hidden_states)
+
+        hidden_states = self.neck(hidden_states)
+        hidden_states = self.proj(hidden_states)
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+    def interpolate_pos_encoding(self, pos_embed: torch.Tensor, target_size: int, dtype: torch.dtype) -> torch.Tensor:
+        """Interpolate the positional encoding to match the target spatial size using bicubic interpolation."""
+        src_size = pos_embed.shape[1]
+        if src_size == target_size:
+            return pos_embed.to(dtype=dtype)
+
+        pos_embed = pos_embed.permute(0, 3, 1, 2).float()
+        pos_embed = torch.nn.functional.interpolate(
+            pos_embed,
+            size=(target_size, target_size),
+            mode="bicubic",
+            align_corners=False,
+        )
+        pos_embed = pos_embed.permute(0, 2, 3, 1)
+
+        return pos_embed.to(dtype=dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class DeepseekOcr2VisionAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeepseekOcr2VisionConfig, layer_idx: int):
+        super().__init__()
+        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
+        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,  # main diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class DeepseekOcr2VisionMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class DeepseekOcr2VisionRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        DeepseekOcr2VisionRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class DeepseekOcr2VisionDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: DeepseekOcr2VisionConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = DeepseekOcr2VisionAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = DeepseekOcr2VisionMLP(config)
+        self.input_layernorm = DeepseekOcr2VisionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekOcr2VisionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class DeepseekOcr2VisionRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: DeepseekOcr2VisionConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: DeepseekOcr2VisionConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def token_type_ids_mask_function(token_type_ids: torch.Tensor):
+    """
+    Creates an or_mask_function for `create_causal_mask` that allows
+    bidirectional attention between image tokens (type_id=0).
+
+    Args:
+        token_type_ids: `(batch_size, seq_len)` tensor where 0=image, 1=query.
+
+    Returns:
+        A mask function compatible with `create_causal_mask(or_mask_function=...)`.
+    """
+    is_image = token_type_ids == 0
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        return is_image[batch_idx, q_idx] & is_image[batch_idx, kv_idx]
+
+    return inner_mask
+
+
+@auto_docstring(custom_intro="Vision encoder for DeepSeek-OCR-2.")
+class DeepseekOcr2VisionEncoder(DeepseekOcr2PreTrainedModel):
+    _can_record_outputs = {
+        "hidden_states": DeepseekOcr2VisionDecoderLayer,
+        "attentions": DeepseekOcr2VisionAttention,
+    }
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+        self.layers = nn.ModuleList(
+            [DeepseekOcr2VisionDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = DeepseekOcr2VisionRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = DeepseekOcr2VisionRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        inputs_embeds: torch.FloatTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        num_patches: int | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        r"""
+        num_patches (`int`, *optional*):
+            Number of image patch tokens at the beginning of the sequence. Used to build the default attention mask
+            when `attention_mask` is not provided.
+        """
+        if position_ids is None:
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)
+
+        if attention_mask is None and num_patches is not None:
+            bsz, seq_len, _ = inputs_embeds.shape
+            token_type_ids = torch.cat(
+                [
+                    torch.zeros(bsz, num_patches, dtype=torch.long, device=inputs_embeds.device),
+                    torch.ones(bsz, seq_len - num_patches, dtype=torch.long, device=inputs_embeds.device),
+                ],
+                dim=1,
+            )
+            attention_mask = create_causal_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=None,
+                past_key_values=None,
+                or_mask_function=token_type_ids_mask_function(token_type_ids),
+            )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for encoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = encoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(last_hidden_state=hidden_states)
+
+
+class DeepseekOcr2VisionModel(DeepseekOcr2PreTrainedModel):
+    """Vision pipeline: SAM ViT-B (with neck)"""
+
+    def __init__(self, config: DeepseekOcr2VisionConfig):
+        super().__init__(config)
+        self.sam_encoder = DeepseekOcr2SamVisionEncoder(config.sam_config)
+        self.vision_encoder = DeepseekOcr2VisionEncoder(config.encoder_config)
+
+        # Resolution-specific learnable queries
+        self.query_768_resolution = nn.Embedding(144, config.encoder_config.hidden_size)  # 12x12 for 768px
+        self.query_1024_resolution = nn.Embedding(256, config.encoder_config.hidden_size)  # 16x16 for 1024px
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput:
+        sam_encoder_outputs = self.sam_encoder(pixel_values, **kwargs)
+        hidden_states = sam_encoder_outputs.last_hidden_state.flatten(2).transpose(1, 2)
+        bsz, num_patches, _ = hidden_states.shape
+
+        queries = self.query_768_resolution.weight if num_patches <= 144 else self.query_1024_resolution.weight
+        queries = queries.unsqueeze(0).expand(bsz, -1, -1)
+        combined = torch.cat([hidden_states, queries], dim=1)
+
+        encoder_outputs = self.vision_encoder(
+            inputs_embeds=combined,
+            num_patches=num_patches,
+            **kwargs,
+        )
+
+        query_features = encoder_outputs.last_hidden_state[:, num_patches:, :]
+
+        return BaseModelOutput(
+            last_hidden_state=query_features,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class DeepseekOcr2TextRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: DeepseekOcr2TextConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: DeepseekOcr2TextConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class DeepseekOcr2TextAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: DeepseekOcr2TextConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class DeepseekOcr2TextMLP(nn.Module):
+    def __init__(self, config: DeepseekOcr2TextConfig, hidden_size=None, intermediate_size=None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+@use_experts_implementation
+class DeepseekOcr2TextExperts(nn.Module):
+    """Collection of expert weights stored as 3D tensors."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.n_routed_experts
+        self.hidden_dim = config.hidden_size
+        self.intermediate_dim = config.moe_intermediate_size
+        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_dim, self.hidden_dim))
+        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim, self.intermediate_dim))
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        top_k_index: torch.Tensor,
+        top_k_weights: torch.Tensor,
+    ) -> torch.Tensor:
+        final_hidden_states = torch.zeros_like(hidden_states)
+        with torch.no_grad():
+            expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
+            expert_mask = expert_mask.permute(2, 1, 0)
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+
+        for expert_idx in expert_hit:
+            expert_idx = expert_idx[0]
+            if expert_idx == self.num_experts:
+                continue
+            top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
+            current_state = hidden_states[token_idx]
+            gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
+            current_hidden_states = self.act_fn(gate) * up
+            current_hidden_states = nn.functional.linear(current_hidden_states, self.down_proj[expert_idx])
+            current_hidden_states = current_hidden_states * top_k_weights[token_idx, top_k_pos, None]
+            final_hidden_states.index_add_(0, token_idx, current_hidden_states.to(final_hidden_states.dtype))
+
+        return final_hidden_states
+
+
+class DeepseekOcr2TextMoe(nn.Module):
+    def __init__(self, config: DeepseekOcr2TextConfig):
+        super().__init__()
+        self.config = config
+        self.experts = DeepseekOcr2TextExperts(config)
+        self.gate = nn.Linear(config.hidden_size, config.n_routed_experts, bias=False)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekOcr2TextMLP(config=config, intermediate_size=intermediate_size)
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.topk_method = config.topk_method
+        self.num_group = config.n_group
+        self.top_k = config.num_experts_per_tok
+        self.topk_group = config.topk_group
+
+    def route_tokens_to_experts(self, router_logits):
+        batch_size, seq_len, hidden_dim = router_logits.shape
+        router_logits = router_logits.view(-1, hidden_dim)
+        router_logits = router_logits.softmax(dim=-1, dtype=torch.float32)
+        if self.topk_method == "greedy":
+            topk_weight, topk_idx = torch.topk(router_logits, k=self.top_k, dim=-1, sorted=False)
+        elif self.topk_method == "group_limited_greedy":
+            group_scores = router_logits.view(batch_size * seq_len, self.num_group, -1).max(dim=-1).values
+            group_idx = torch.topk(group_scores, k=self.topk_group, dim=-1, sorted=False)[1]
+            group_mask = torch.zeros_like(group_scores)
+            group_mask.scatter_(1, group_idx, 1)
+            score_mask = (
+                group_mask.unsqueeze(-1)
+                .expand(batch_size * seq_len, self.num_group, self.num_experts // self.num_group)
+                .reshape(batch_size * seq_len, -1)
+            )
+            tmp_scores = router_logits.masked_fill(~score_mask.bool(), 0.0)
+            topk_weight, topk_idx = torch.topk(tmp_scores, k=self.top_k, dim=-1, sorted=False)
+
+        topk_weight = topk_weight * self.routed_scaling_factor
+        return topk_idx, topk_weight
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residuals = hidden_states
+        orig_shape = hidden_states.shape
+        router_logits = nn.functional.linear(hidden_states.type(torch.float32), self.gate.weight.type(torch.float32))
+        topk_indices, topk_weights = self.route_tokens_to_experts(router_logits)
+        hidden_states = hidden_states.view(-1, hidden_states.shape[-1])
+        hidden_states = self.experts(hidden_states, topk_indices, topk_weights).view(*orig_shape)
+        hidden_states = hidden_states + self.shared_experts(residuals)
+        return hidden_states
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class DeepseekOcr2TextRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        DeepseekOcr2TextRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class DeepseekOcr2TextDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = DeepseekOcr2TextAttention(config=config, layer_idx=layer_idx)
+        self.mlp = (
+            DeepseekOcr2TextMoe(config)
+            if config.mlp_layer_types[layer_idx] == "sparse"
+            else DeepseekOcr2TextMLP(config)
+        )
+
+        self.input_layernorm = DeepseekOcr2TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekOcr2TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class DeepseekOcr2TextPreTrainedModel(PreTrainedModel):
+    config: DeepseekOcr2TextConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekOcr2TextDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": DeepseekOcr2TextDecoderLayer,
+        "attentions": DeepseekOcr2TextAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, DeepseekOcr2TextExperts):
+            init.normal_(module.gate_up_proj, mean=0.0, std=self.config.initializer_range)
+            init.normal_(module.down_proj, mean=0.0, std=self.config.initializer_range)
+
+
+@auto_docstring
+class DeepseekOcr2TextModel(DeepseekOcr2TextPreTrainedModel):
+    def __init__(self, config: DeepseekOcr2TextConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [DeepseekOcr2TextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = DeepseekOcr2TextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        # Use (cos/sin) RoPE instead of complex RoPE to match LlamaAttention (MHA)
+        self.rotary_emb = DeepseekOcr2TextRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
+    """
+)
+class DeepseekOcr2Model(DeepseekOcr2PreTrainedModel):
+    base_model_prefix = "model"
+
+    def __init__(self, config: DeepseekOcr2Config):
+        super().__init__(config)
+
+        self.vision_tower = DeepseekOcr2VisionModel(config.vision_config)
+        self.multi_modal_projector = nn.Linear(
+            config.vision_config.encoder_config.hidden_size, config.text_config.hidden_size
+        )
+
+        self.vocab_size = config.text_config.vocab_size
+
+        self.language_model = DeepseekOcr2TextModel(config.text_config)
+
+        # Learnable separator between local and global views (initialized in `_init_weights`).
+        self.view_separator = nn.Parameter(torch.empty(config.text_config.hidden_size))
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        pixel_values_local (`torch.FloatTensor` of shape `(total_patches, 3, height, width)`, *optional*):
+            All local patches flattened across the batch, or `None` if no local views.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image, e.g. `[6, 0, 4]`.
+        """
+        # torch.split requires list[int], not Tensor, for per-image variable-length splitting
+        if isinstance(num_local_patches, torch.Tensor):
+            num_local_patches = num_local_patches.tolist()
+
+        batch_size = pixel_values.shape[0]
+
+        global_vision_outputs = self.vision_tower(pixel_values, **kwargs)
+        global_features = self.multi_modal_projector(global_vision_outputs.last_hidden_state)
+
+        if pixel_values_local is not None:
+            local_vision_outputs = self.vision_tower(pixel_values_local, **kwargs)
+            all_local_features = self.multi_modal_projector(local_vision_outputs.last_hidden_state)
+            per_image_local = torch.split(all_local_features, num_local_patches, dim=0)
+        else:
+            local_vision_outputs = None
+            per_image_local = [None] * batch_size
+
+        all_features = []
+        view_sep = self.view_separator.to(global_features.device).unsqueeze(0)
+        for idx in range(batch_size):
+            global_flat = global_features[idx].reshape(-1, global_features.shape[-1])
+
+            if per_image_local[idx] is not None:
+                local_flat = per_image_local[idx].reshape(-1, per_image_local[idx].shape[-1])
+                all_features.append(torch.cat([local_flat, global_flat, view_sep], dim=0))
+            else:
+                all_features.append(torch.cat([global_flat, view_sep], dim=0))
+
+        image_features = torch.cat(all_features, dim=0)
+        return DeepseekOcr2ModelOutputWithPooling(
+            last_hidden_state=global_vision_outputs.last_hidden_state,
+            pooler_output=image_features,
+            hidden_states=global_vision_outputs.hidden_states,
+            attentions=global_vision_outputs.attentions,
+            local_last_hidden_state=local_vision_outputs.last_hidden_state
+            if local_vision_outputs is not None
+            else None,
+            local_hidden_states=local_vision_outputs.hidden_states if local_vision_outputs is not None else None,
+            local_attentions=local_vision_outputs.attentions if local_vision_outputs is not None else None,
+        )
+
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
+        )
+        return special_image_mask
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | DeepseekOcr2ModelOutputWithPast:
+        r"""
+        pixel_values_local (`torch.FloatTensor`, *optional*):
+            Local patch pixel values of shape `(total_patches, 3, H, W)`.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image in the batch.
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        image_features = None
+        if pixel_values is not None:
+            image_features = self.get_image_features(
+                pixel_values, pixel_values_local, num_local_patches, return_dict=True
+            ).pooler_output
+            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+
+            special_image_mask = self.get_placeholder_mask(input_ids, inputs_embeds, image_features)
+            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+        outputs = self.language_model(
+            input_ids=None,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return DeepseekOcr2ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features,
+        )
+
+
+@auto_docstring
+class DeepseekOcr2ForConditionalGeneration(DeepseekOcr2PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: DeepseekOcr2Config):
+        super().__init__(config)
+        self.model = DeepseekOcr2Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def pack_image_features(self):
+        raise NotImplementedError("DeepseekOcr2 does not use pack_image_features")
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, 3, height, width)`):
+            The tensors corresponding to the global view input images.
+        pixel_values_local (`torch.FloatTensor` of shape `(total_patches, 3, height, width)`, *optional*):
+            All local patches flattened across the batch, or `None` if no local views.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image, e.g. `[6, 0, 4]`.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            pixel_values_local=pixel_values_local,
+            num_local_patches=num_local_patches,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | DeepseekOcr2CausalLMOutputWithPast:
+        r"""
+        pixel_values_local (`torch.FloatTensor`, *optional*):
+            Local patch pixel values of shape `(total_patches, 3, H, W)`.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image in the batch.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_local=pixel_values_local,
+            num_local_patches=num_local_patches,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        hidden_states = hidden_states[:, slice_indices, :]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        return DeepseekOcr2CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        pixel_values_local=None,
+        num_local_patches=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["pixel_values_local"] = pixel_values_local
+            model_inputs["num_local_patches"] = num_local_patches
+
+        return model_inputs
+
+
+__all__ = [
+    "DeepseekOcr2ForConditionalGeneration",
+    "DeepseekOcr2Model",
+    "DeepseekOcr2PreTrainedModel",
+    "DeepseekOcr2TextModel",
+    "DeepseekOcr2TextPreTrainedModel",
+    "DeepseekOcr2VisionModel",
+]
diff --git a/src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py
new file mode 100644
index 000000000000..e6a406ee5ebd
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/modular_deepseek_ocr2.py
@@ -0,0 +1,1197 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+from huggingface_hub.dataclasses import strict
+from torch import nn
+from torchvision.transforms.v2 import functional as tvF
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import group_images_by_shape, reorder_images, to_channel_dimension_format
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+    infer_channel_dimension_format,
+)
+from ...masking_utils import create_causal_mask
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TensorType, TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.import_utils import requires
+from ...utils.output_capturing import capture_outputs
+from ..deepseek_v2.configuration_deepseek_v2 import DeepseekV2Config
+from ..deepseek_v2.modeling_deepseek_v2 import (
+    DeepseekV2DecoderLayer,
+    DeepseekV2MLP,
+    DeepseekV2Model,
+    DeepseekV2Moe,
+    DeepseekV2PreTrainedModel,
+)
+from ..got_ocr2.image_processing_got_ocr2 import (
+    GotOcr2ImageProcessor,
+    GotOcr2ImageProcessorKwargs,
+    get_optimal_tiled_canvas,
+)
+from ..got_ocr2.image_processing_pil_got_ocr2 import GotOcr2ImageProcessorPil
+from ..llama.modeling_llama import LlamaAttention, LlamaRotaryEmbedding
+from ..llava_next.modeling_llava_next import (
+    LlavaNextCausalLMOutputWithPast,
+    LlavaNextForConditionalGeneration,
+    LlavaNextModel,
+    LlavaNextModelOutputWithPast,
+    LlavaNextPreTrainedModel,
+)
+from ..qwen2.configuration_qwen2 import Qwen2Config
+from ..qwen2.modeling_qwen2 import Qwen2Attention, Qwen2DecoderLayer, Qwen2Model
+from ..sam.configuration_sam import SamVisionConfig
+from ..sam.modeling_sam import (
+    SamPatchEmbeddings,
+    SamVisionAttention,
+    SamVisionEncoder,
+    SamVisionLayer,
+    SamVisionNeck,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+class DeepseekOcr2ImageProcessorKwargs(GotOcr2ImageProcessorKwargs, total=False):
+    """
+    tile_size (`int`, *optional*, defaults to `768`):
+        The size of each local tile. Must match the model's query embedding size.
+    background_color (`list[int]`, *optional*, defaults to `[127, 127, 127]`):
+        The background color for padding.
+    """
+
+    tile_size: int
+    background_color: list[int]
+
+
+@auto_docstring
+class DeepseekOcr2ImageProcessor(GotOcr2ImageProcessor):
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 1024, "width": 1024}
+    tile_size = 768
+    crop_to_patches = True
+    min_patches = 2
+    max_patches = 6
+    background_color = [127, 127, 127]
+    model_input_names = ["pixel_values", "num_local_patches"]
+
+    # Copied from transformers.models.llava.image_processing_llava.LlavaImageProcessor.pad_to_square
+    def pad_to_square(
+        self,
+        images: "torch.Tensor",
+        background_color: int | tuple[int, int, int] = 0,
+    ) -> "torch.Tensor":
+        """
+        Pads an image to a square based on the longest edge.
+
+        Args:
+            images (`torch.Tensor`):
+                The images to pad. Shape: (batch_size, num_channels, height, width) or (num_channels, height, width).
+            background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+                The color to use for the padding. Can be an integer for single channel or a
+                tuple of integers representing for multi-channel images. If passed as integer
+                in multi-channel mode, it will default to `0` in subsequent channels.
+        Returns:
+            `torch.Tensor`: The padded images.
+        """
+        height, width = images.shape[-2:]
+
+        if height == width:
+            return images
+
+        num_channels = images.shape[1] if len(images.shape) == 4 else images.shape[0]
+        if isinstance(background_color, int):
+            background_color = [background_color] + [0] * (num_channels - 1)
+        elif len(background_color) != num_channels:
+            raise ValueError(
+                f"background_color must have no more than {num_channels} elements to match the number of channels"
+            )
+
+        max_dim = max(height, width)
+        paste_x_left = (max_dim - width) // 2
+        paste_y_left = (max_dim - height) // 2
+        paste_x_right = max_dim - width - paste_x_left
+        paste_y_right = max_dim - height - paste_y_left
+        padded_images = tvF.pad(
+            images, padding=[paste_x_left, paste_y_left, paste_x_right, paste_y_right], fill=background_color
+        )
+
+        return padded_images
+
+    def crop_image_to_patches(
+        self,
+        images: "torch.Tensor",
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: PILImageResampling | None = None,
+    ) -> tuple["torch.Tensor", int]:
+        """
+        Crop batched images to patches based on optimal tiling.
+
+        Args:
+            images (`torch.Tensor`):
+                The images to crop, shape `(batch, channels, height, width)`.
+            min_patches (`int`):
+                Minimum number of patches.
+            max_patches (`int`):
+                Maximum number of patches.
+            tile_size (`int`):
+                The size of each tile.
+            resample (`PILImageResampling`, *optional*):
+                Resampling filter for resizing.
+
+        Returns:
+            `tuple[torch.Tensor, int]`: Stacked patches `(batch, num_patches, channels, tile_size, tile_size)`
+            and number of patches per image.
+        """
+        original_height, original_width = images.shape[-2:]
+
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (original_height, original_width), (tile_size, tile_size), min_patches, max_patches
+        )
+
+        target_width = tile_size * num_columns
+        target_height = tile_size * num_rows
+        num_blocks = num_columns * num_rows
+
+        resized = self.resize(images, SizeDict(height=target_height, width=target_width), resample=resample)
+
+        patches = []
+        for i in range(num_blocks):
+            col = i % num_columns
+            row = i // num_columns
+            patch = resized[
+                ...,
+                row * tile_size : (row + 1) * tile_size,
+                col * tile_size : (col + 1) * tile_size,
+            ]
+            patches.append(patch)
+
+        stacked_patches = torch.stack(patches, dim=1)
+
+        return stacked_patches, num_blocks
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        size: SizeDict,
+        crop_to_patches: bool,
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: PILImageResampling | None,
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ) -> BatchFeature:
+        # --- Local patches (batched by shape group) ---
+        num_local_patches = {}
+        local_patches_grouped = {}
+
+        if crop_to_patches:
+            grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+
+            for shape, stacked_images in grouped_images.items():
+                h, w = shape[-2:]
+                if max(h, w) > tile_size:
+                    stacked_patches, n_patches = self.crop_image_to_patches(
+                        stacked_images,
+                        min_patches=min_patches,
+                        max_patches=max_patches,
+                        tile_size=tile_size,
+                        resample=resample,
+                    )
+                    flat_patches = stacked_patches.reshape(-1, *stacked_patches.shape[2:])
+                    flat_patches = self.rescale_and_normalize(
+                        flat_patches, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+                    )
+                    local_patches_grouped[shape] = flat_patches.reshape(stacked_patches.shape)
+                    num_local_patches[shape] = [n_patches] * stacked_images.shape[0]
+                else:
+                    local_patches_grouped[shape] = [None] * stacked_images.shape[0]
+                    num_local_patches[shape] = [0] * stacked_images.shape[0]
+
+            num_local_patches = reorder_images(num_local_patches, grouped_images_index)
+            ordered_local = reorder_images(local_patches_grouped, grouped_images_index)
+        else:
+            num_local_patches = [0] * len(images)
+            ordered_local = []
+
+        flat_local_list = [patch for item in ordered_local if item is not None for patch in item]
+
+        # --- Global view (batched by shape group) ---
+        global_target_size = size.height if crop_to_patches else tile_size
+
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        processed_global_grouped = {}
+        for shape, stacked in grouped_images.items():
+            h, w = shape[-2:]
+            scale = global_target_size / max(h, w)
+            new_h = round(h * scale)
+            new_w = round(w * scale)
+            stacked = self.resize(stacked, SizeDict(height=new_h, width=new_w), resample=resample)
+            stacked = self.pad_to_square(stacked, background_color=self.background_color)
+            stacked = self.rescale_and_normalize(
+                stacked, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            processed_global_grouped[shape] = stacked
+        all_pixel_values_global = reorder_images(processed_global_grouped, grouped_images_index)
+
+        data = {
+            "pixel_values": all_pixel_values_global,
+            "num_local_patches": num_local_patches,
+        }
+        if flat_local_list:
+            data["pixel_values_local"] = flat_local_list
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None) -> int:
+        """
+        Returns the number of image patches for a given image size (1 global + local patches).
+        """
+        if images_kwargs is None:
+            images_kwargs = {}
+        min_patches = images_kwargs.get("min_patches", self.min_patches)
+        max_patches = images_kwargs.get("max_patches", self.max_patches)
+        tile_size = images_kwargs.get("tile_size", self.tile_size)
+        crop_to_patches = images_kwargs.get("crop_to_patches", self.crop_to_patches)
+
+        num_patches = 1  # global view
+        if crop_to_patches and max(height, width) > tile_size:
+            num_columns, num_rows = get_optimal_tiled_canvas(
+                (height, width), (tile_size, tile_size), min_patches, max_patches
+            )
+            num_patches += num_columns * num_rows
+
+        return num_patches
+
+
+@requires(backends=("vision",))
+@auto_docstring
+class DeepseekOcr2ImageProcessorPil(GotOcr2ImageProcessorPil):
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 1024, "width": 1024}
+    tile_size = 768
+    crop_to_patches = True
+    min_patches = 2
+    max_patches = 6
+    background_color = [127, 127, 127]
+    model_input_names = ["pixel_values", "num_local_patches"]
+
+    def crop_image_to_patches(
+        self,
+        image: np.ndarray,
+        min_patches: int,
+        max_patches: int,
+        tile_size: int,
+        resample: "PILImageResampling | int | None" = None,
+    ):
+        """
+        Crop the image to patches and return a list of cropped images.
+        """
+        input_data_format = infer_channel_dimension_format(image)
+        image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_data_format)
+
+        original_height, original_width = get_image_size(image, channel_dim=ChannelDimension.FIRST)
+
+        num_columns, num_rows = get_optimal_tiled_canvas(
+            (original_height, original_width), (tile_size, tile_size), min_patches, max_patches
+        )
+
+        target_width = tile_size * num_columns
+        target_height = tile_size * num_rows
+        num_blocks = num_columns * num_rows
+
+        resized_image = self.resize(image, SizeDict(height=target_height, width=target_width), resample=resample)
+
+        processed_images = []
+        for i in range(num_blocks):
+            column = i % num_columns
+            row = i // num_columns
+            box = (
+                column * tile_size,
+                row * tile_size,
+                (column + 1) * tile_size,
+                (row + 1) * tile_size,
+            )
+            patch_image = resized_image[..., box[1] : box[3], box[0] : box[2]]
+            patch_image = to_channel_dimension_format(patch_image, input_data_format, ChannelDimension.FIRST)
+            processed_images.append(patch_image)
+
+        return processed_images
+
+    # Copied from transformers.models.llava.image_processing_pil_llava.LlavaImageProcessorPil.pad_to_square
+    def pad_to_square(
+        self,
+        image: np.ndarray,
+        background_color: int | tuple[int, int, int] = 0,
+    ) -> np.ndarray:
+        """
+        Pads an image to a square based on the longest edge.
+
+        Args:
+            image (`np.ndarray`):
+                The image to pad. Shape: (num_channels, height, width) - always channels_first in backend.
+            background_color (`int` or `tuple[int, int, int]`, *optional*, defaults to 0):
+                The color to use for the padding.
+
+        Returns:
+            `np.ndarray`: The padded image.
+        """
+        # Backend always uses channels_first format: (num_channels, height, width)
+        num_channels, height, width = image.shape
+
+        if height == width:
+            return image
+
+        max_dim = max(height, width)
+
+        # Ensure background_color is the correct shape
+        if isinstance(background_color, int):
+            background_color = [background_color]
+        elif len(background_color) != num_channels:
+            raise ValueError(
+                f"background_color must have no more than {num_channels} elements to match the number of channels"
+            )
+
+        result = np.zeros((num_channels, max_dim, max_dim), dtype=image.dtype)
+        for i, color in enumerate(background_color):
+            result[i, :, :] = color
+        if width > height:
+            start = (max_dim - height) // 2
+            result[:, start : start + height, :] = image
+        else:
+            start = (max_dim - width) // 2
+            result[:, :, start : start + width] = image
+
+        return result
+
+    def _preprocess(
+        self,
+        images: list[np.ndarray],
+        size: SizeDict,
+        resample: "PILImageResampling | int | None",
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        return_tensors: str | TensorType | None,
+        crop_to_patches: bool = True,
+        min_patches: int = 2,
+        max_patches: int = 6,
+        tile_size: int = 768,
+        background_color: list[int] | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if background_color is None:
+            background_color = self.background_color
+
+        all_pixel_values_local = []
+        all_pixel_values_global = []
+        num_local_patches = []
+
+        for image in images:
+            original_height, original_width = get_image_size(image)
+
+            # --- Local patches ---
+            if crop_to_patches and max(original_width, original_height) > tile_size:
+                local_patches = self.crop_image_to_patches(
+                    image,
+                    min_patches=min_patches,
+                    max_patches=max_patches,
+                    tile_size=tile_size,
+                    resample=resample,
+                )
+                for patch in local_patches:
+                    if do_rescale:
+                        patch = self.rescale(patch, rescale_factor)
+                    if do_normalize:
+                        patch = self.normalize(patch, image_mean, image_std)
+                    all_pixel_values_local.append(patch)
+                num_local_patches.append(len(local_patches))
+            else:
+                num_local_patches.append(0)
+
+            # --- Global view ---
+            global_target_size = size.height if crop_to_patches else tile_size
+            scale = global_target_size / max(original_width, original_height)
+            new_width = round(original_width * scale)
+            new_height = round(original_height * scale)
+
+            global_img = self.resize(image, SizeDict(height=new_height, width=new_width), resample=resample)
+            global_img = self.pad_to_square(global_img, background_color=background_color)
+            if do_rescale:
+                global_img = self.rescale(global_img, rescale_factor)
+            if do_normalize:
+                global_img = self.normalize(global_img, image_mean, image_std)
+            all_pixel_values_global.append(global_img)
+
+        data = {
+            "pixel_values": all_pixel_values_global,
+            "num_local_patches": num_local_patches,
+        }
+        if all_pixel_values_local:
+            data["pixel_values_local"] = all_pixel_values_local
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2SamVisionConfig(SamVisionConfig):
+    r"""
+    output_channels (`int`, *optional*, defaults to 256):
+        The number of output channels in the SAM neck.
+    window_size (`int`, *optional*, defaults to 14):
+        Window size for windowed attention layers.
+    global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
+        Indices of encoder layers that use global (non-windowed) attention.
+    mlp_dim (`int`, *optional*):
+        Dimensionality of the MLP layer in each vision encoder block. Defaults to `hidden_size * mlp_ratio`.
+    downsample_channels (`list[int]`, *optional*):
+        The channel dimensions for the multi-scale downsampling neck layers. Defaults to `[512, 896]`.
+    """
+
+    base_config_key = "sam_config"
+
+    # Remove unused attribute inherited from SamVisionConfig
+    num_pos_feats = AttributeError()
+
+    downsample_channels: list[int] | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.downsample_channels is None:
+            self.downsample_channels = [512, 896]
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2EncoderConfig(Qwen2Config):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import DeepseekOcr2Config
+
+    >>> config = DeepseekOcr2Config()
+    >>> encoder_config = config.vision_config.encoder_config
+    ```"""
+
+    base_config_key = "encoder_config"
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2VisionConfig(PreTrainedConfig):
+    r"""
+    sam_config (`dict` or `DeepseekOcr2SamVisionConfig`, *optional*):
+        Configuration for the SAM vision encoder. Defaults to `DeepseekOcr2SamVisionConfig()`.
+    encoder_config (`dict` or `DeepseekOcr2EncoderConfig`, *optional*):
+        Configuration for the DeepSeek-OCR-2 vision encoder. Defaults to `DeepseekOcr2EncoderConfig()`.
+    """
+
+    base_config_key = "vision_config"
+    sub_configs = {
+        "sam_config": DeepseekOcr2SamVisionConfig,
+        "encoder_config": DeepseekOcr2EncoderConfig,
+    }
+
+    sam_config: dict | PreTrainedConfig | None = None
+    encoder_config: dict | PreTrainedConfig | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.sam_config is None:
+            self.sam_config = DeepseekOcr2SamVisionConfig()
+        elif isinstance(self.sam_config, dict):
+            self.sam_config = DeepseekOcr2SamVisionConfig(**self.sam_config)
+
+        if self.encoder_config is None:
+            self.encoder_config = DeepseekOcr2EncoderConfig()
+        elif isinstance(self.encoder_config, dict):
+            self.encoder_config = DeepseekOcr2EncoderConfig(**self.encoder_config)
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2TextConfig(DeepseekV2Config):
+    r"""
+    n_group (`int`, *optional*):
+        Number of groups for grouped top-k expert routing.
+    topk_method (`str`, *optional*, defaults to `"greedy"`):
+        Method for selecting top-k experts in MoE layers.
+    mlp_layer_types (`list[str]`, *optional*):
+        MLP type (`"dense"` or `"sparse"`) for each decoder layer, e.g. `["dense", "sparse", "sparse", ...]`.
+    """
+
+    base_config_key = "text_config"
+    mlp_layer_types: list[str] | None = None
+
+    # Override DeepseekV2's MLA TP plan with standard MHA projections
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+
+    # Remove unused attributes inherited from DeepseekV2Config
+    first_k_dense_replace = AttributeError()
+    kv_lora_rank = AttributeError()
+    norm_topk_prob = AttributeError()
+    q_lora_rank = AttributeError()
+    qk_nope_head_dim = AttributeError()
+    qk_rope_head_dim = AttributeError()
+    v_head_dim = AttributeError()
+
+    def __post_init__(self, **kwargs):
+        self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+        PreTrainedConfig.__post_init__(self, **kwargs)
+
+
+@auto_docstring(checkpoint="thisisiron/DeepSeek-OCR-2-hf")
+@strict
+class DeepseekOcr2Config(PreTrainedConfig):
+    r"""
+    vision_config (`dict` or `DeepseekOcr2VisionConfig`, *optional*):
+        Configuration for the vision encoders. Defaults to `DeepseekOcr2VisionConfig()`.
+    """
+
+    model_type = "deepseek_ocr2"
+    sub_configs = {
+        "vision_config": DeepseekOcr2VisionConfig,
+        "text_config": DeepseekOcr2TextConfig,
+    }
+
+    vision_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    image_token_id: int = 128815
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if self.vision_config is None:
+            self.vision_config = DeepseekOcr2VisionConfig()
+        elif isinstance(self.vision_config, dict):
+            self.vision_config = DeepseekOcr2VisionConfig(**self.vision_config)
+
+        if self.text_config is None:
+            self.text_config = DeepseekOcr2TextConfig()
+        elif isinstance(self.text_config, dict):
+            self.text_config = DeepseekOcr2TextConfig(**self.text_config)
+
+        super().__post_init__(**kwargs)
+
+
+@dataclass
+class DeepseekOcr2ModelOutputWithPooling(BaseModelOutputWithPooling):
+    """
+    local_last_hidden_state (`torch.FloatTensor` of shape `(total_local_patches, sequence_length, hidden_size)`, *optional*):
+        Last hidden state from the vision encoder for local (cropped) patches.
+    local_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states from all layers of the vision encoder for local patches.
+    local_attentions (`torch.FloatTensor`, *optional*):
+        Attention weights from all layers of the vision encoder for local patches.
+    """
+
+    local_last_hidden_state: torch.FloatTensor | None = None
+    local_hidden_states: torch.FloatTensor | None = None
+    local_attentions: torch.FloatTensor | None = None
+
+
+class DeepseekOcr2ModelOutputWithPast(LlavaNextModelOutputWithPast):
+    pass
+
+
+class DeepseekOcr2CausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
+    pass
+
+
+class DeepseekOcr2PreTrainedModel(LlavaNextPreTrainedModel):
+    _no_split_modules = [
+        "DeepseekOcr2SamVisionLayer",
+        "DeepseekOcr2VisionDecoderLayer",
+        "DeepseekOcr2TextDecoderLayer",
+    ]
+    _supports_flash_attn = False
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        PreTrainedModel._init_weights(self, module)
+        if isinstance(module, DeepseekOcr2SamVisionAttention):
+            if module.use_rel_pos:
+                init.zeros_(module.rel_pos_h)
+                init.zeros_(module.rel_pos_w)
+        elif isinstance(module, DeepseekOcr2SamVisionEncoder):
+            if module.pos_embed is not None:
+                init.zeros_(module.pos_embed)
+        elif isinstance(module, DeepseekOcr2Model):
+            embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+            init.normal_(module.view_separator, mean=0.0, std=embed_std)
+
+
+class DeepseekOcr2SamVisionAttention(SamVisionAttention):
+    pass
+
+
+class DeepseekOcr2SamVisionLayer(SamVisionLayer):
+    pass
+
+
+class DeepseekOcr2SamVisionNeck(SamVisionNeck):
+    pass
+
+
+class DeepseekOcr2SamPatchEmbeddings(SamPatchEmbeddings):
+    def forward(self, pixel_values):
+        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
+        return embeddings
+
+
+class DeepseekOcr2SamVisionProj(nn.Module):
+    """Neck and multi-scale downsampling for SAM ViT-B output."""
+
+    def __init__(self, config: DeepseekOcr2SamVisionConfig):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            config.output_channels,
+            config.downsample_channels[0],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+        self.conv2 = nn.Conv2d(
+            config.downsample_channels[0],
+            config.downsample_channels[1],
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        return hidden_states
+
+
+class DeepseekOcr2SamVisionEncoder(SamVisionEncoder, DeepseekOcr2PreTrainedModel):
+    def __init__(self, config: DeepseekOcr2SamVisionConfig):
+        super().__init__(config)
+        self.proj = DeepseekOcr2SamVisionProj(config)
+
+    def interpolate_pos_encoding(self, pos_embed: torch.Tensor, target_size: int, dtype: torch.dtype) -> torch.Tensor:
+        """Interpolate the positional encoding to match the target spatial size using bicubic interpolation."""
+        src_size = pos_embed.shape[1]
+        if src_size == target_size:
+            return pos_embed.to(dtype=dtype)
+
+        pos_embed = pos_embed.permute(0, 3, 1, 2).float()
+        pos_embed = torch.nn.functional.interpolate(
+            pos_embed,
+            size=(target_size, target_size),
+            mode="bicubic",
+            align_corners=False,
+        )
+        pos_embed = pos_embed.permute(0, 2, 3, 1)
+
+        return pos_embed.to(dtype=dtype)
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(self, pixel_values: torch.FloatTensor, **kwargs) -> BaseModelOutput:
+        hidden_states = self.patch_embed(pixel_values)
+        if self.pos_embed is not None:
+            hidden_states = hidden_states + self.interpolate_pos_encoding(
+                self.pos_embed, target_size=hidden_states.shape[1], dtype=hidden_states.dtype
+            )
+
+        for layer_module in self.layers:
+            hidden_states = layer_module(hidden_states)
+
+        hidden_states = self.neck(hidden_states)
+        hidden_states = self.proj(hidden_states)
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class DeepseekOcr2VisionAttention(Qwen2Attention):
+    pass
+
+
+class DeepseekOcr2VisionDecoderLayer(Qwen2DecoderLayer):
+    pass
+
+
+@auto_docstring(custom_intro="Vision encoder for DeepSeek-OCR-2.")
+class DeepseekOcr2VisionEncoder(Qwen2Model, DeepseekOcr2PreTrainedModel):
+    _can_record_outputs = {
+        "hidden_states": DeepseekOcr2VisionDecoderLayer,
+        "attentions": DeepseekOcr2VisionAttention,
+    }
+
+    def __init__(self, config):
+        super().__init__(config)
+        del self.embed_tokens
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        inputs_embeds: torch.FloatTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        num_patches: int | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        r"""
+        num_patches (`int`, *optional*):
+            Number of image patch tokens at the beginning of the sequence. Used to build the default attention mask
+            when `attention_mask` is not provided.
+        """
+        if position_ids is None:
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device).unsqueeze(0)
+
+        if attention_mask is None and num_patches is not None:
+            bsz, seq_len, _ = inputs_embeds.shape
+            token_type_ids = torch.cat(
+                [
+                    torch.zeros(bsz, num_patches, dtype=torch.long, device=inputs_embeds.device),
+                    torch.ones(bsz, seq_len - num_patches, dtype=torch.long, device=inputs_embeds.device),
+                ],
+                dim=1,
+            )
+            attention_mask = create_causal_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=None,
+                past_key_values=None,
+                or_mask_function=token_type_ids_mask_function(token_type_ids),
+            )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for encoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = encoder_layer(
+                hidden_states,
+                attention_mask=attention_mask,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(last_hidden_state=hidden_states)
+
+
+def token_type_ids_mask_function(token_type_ids: torch.Tensor):
+    """
+    Creates an or_mask_function for `create_causal_mask` that allows
+    bidirectional attention between image tokens (type_id=0).
+
+    Args:
+        token_type_ids: `(batch_size, seq_len)` tensor where 0=image, 1=query.
+
+    Returns:
+        A mask function compatible with `create_causal_mask(or_mask_function=...)`.
+    """
+    is_image = token_type_ids == 0
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        return is_image[batch_idx, q_idx] & is_image[batch_idx, kv_idx]
+
+    return inner_mask
+
+
+class DeepseekOcr2VisionModel(DeepseekOcr2PreTrainedModel):
+    """Vision pipeline: SAM ViT-B (with neck)"""
+
+    def __init__(self, config: DeepseekOcr2VisionConfig):
+        super().__init__(config)
+        self.sam_encoder = DeepseekOcr2SamVisionEncoder(config.sam_config)
+        self.vision_encoder = DeepseekOcr2VisionEncoder(config.encoder_config)
+
+        # Resolution-specific learnable queries
+        self.query_768_resolution = nn.Embedding(144, config.encoder_config.hidden_size)  # 12x12 for 768px
+        self.query_1024_resolution = nn.Embedding(256, config.encoder_config.hidden_size)  # 16x16 for 1024px
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput:
+        sam_encoder_outputs = self.sam_encoder(pixel_values, **kwargs)
+        hidden_states = sam_encoder_outputs.last_hidden_state.flatten(2).transpose(1, 2)
+        bsz, num_patches, _ = hidden_states.shape
+
+        queries = self.query_768_resolution.weight if num_patches <= 144 else self.query_1024_resolution.weight
+        queries = queries.unsqueeze(0).expand(bsz, -1, -1)
+        combined = torch.cat([hidden_states, queries], dim=1)
+
+        encoder_outputs = self.vision_encoder(
+            inputs_embeds=combined,
+            num_patches=num_patches,
+            **kwargs,
+        )
+
+        query_features = encoder_outputs.last_hidden_state[:, num_patches:, :]
+
+        return BaseModelOutput(
+            last_hidden_state=query_features,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+        )
+
+
+class DeepseekOcr2TextRotaryEmbedding(LlamaRotaryEmbedding):
+    pass
+
+
+class DeepseekOcr2TextAttention(LlamaAttention):
+    pass
+
+
+class DeepseekOcr2TextMLP(DeepseekV2MLP):
+    pass
+
+
+class DeepseekOcr2TextMoe(DeepseekV2Moe):
+    pass
+
+
+class DeepseekOcr2TextDecoderLayer(DeepseekV2DecoderLayer):
+    def __init__(self, config, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.self_attn = DeepseekOcr2TextAttention(config=config, layer_idx=layer_idx)
+        self.mlp = (
+            DeepseekOcr2TextMoe(config)
+            if config.mlp_layer_types[layer_idx] == "sparse"
+            else DeepseekOcr2TextMLP(config)
+        )
+
+
+class DeepseekOcr2TextPreTrainedModel(DeepseekV2PreTrainedModel):
+    pass
+
+
+class DeepseekOcr2TextModel(DeepseekV2Model):
+    def __init__(self, config: DeepseekOcr2TextConfig):
+        super().__init__(config)
+        # Use (cos/sin) RoPE instead of complex RoPE to match LlamaAttention (MHA)
+        self.rotary_emb = DeepseekOcr2TextRotaryEmbedding(config=config)
+
+
+class DeepseekOcr2Model(LlavaNextModel):
+    def __init__(self, config: DeepseekOcr2Config):
+        super().__init__(config)
+        del embed_std  # noqa: F821
+        del self.image_newline
+
+        self.vision_tower = DeepseekOcr2VisionModel(config.vision_config)
+        self.multi_modal_projector = nn.Linear(
+            config.vision_config.encoder_config.hidden_size, config.text_config.hidden_size
+        )
+
+        # Learnable separator between local and global views (initialized in `_init_weights`).
+        self.view_separator = nn.Parameter(torch.empty(config.text_config.hidden_size))
+
+        self.language_model = DeepseekOcr2TextModel(config.text_config)
+
+    def pack_image_features(self):
+        raise NotImplementedError("DeepseekOcr2 does not use pack_image_features")
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        pixel_values_local (`torch.FloatTensor` of shape `(total_patches, 3, height, width)`, *optional*):
+            All local patches flattened across the batch, or `None` if no local views.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image, e.g. `[6, 0, 4]`.
+        """
+        # torch.split requires list[int], not Tensor, for per-image variable-length splitting
+        if isinstance(num_local_patches, torch.Tensor):
+            num_local_patches = num_local_patches.tolist()
+
+        batch_size = pixel_values.shape[0]
+
+        global_vision_outputs = self.vision_tower(pixel_values, **kwargs)
+        global_features = self.multi_modal_projector(global_vision_outputs.last_hidden_state)
+
+        if pixel_values_local is not None:
+            local_vision_outputs = self.vision_tower(pixel_values_local, **kwargs)
+            all_local_features = self.multi_modal_projector(local_vision_outputs.last_hidden_state)
+            per_image_local = torch.split(all_local_features, num_local_patches, dim=0)
+        else:
+            local_vision_outputs = None
+            per_image_local = [None] * batch_size
+
+        all_features = []
+        view_sep = self.view_separator.to(global_features.device).unsqueeze(0)
+        for idx in range(batch_size):
+            global_flat = global_features[idx].reshape(-1, global_features.shape[-1])
+
+            if per_image_local[idx] is not None:
+                local_flat = per_image_local[idx].reshape(-1, per_image_local[idx].shape[-1])
+                all_features.append(torch.cat([local_flat, global_flat, view_sep], dim=0))
+            else:
+                all_features.append(torch.cat([global_flat, view_sep], dim=0))
+
+        image_features = torch.cat(all_features, dim=0)
+        return DeepseekOcr2ModelOutputWithPooling(
+            last_hidden_state=global_vision_outputs.last_hidden_state,
+            pooler_output=image_features,
+            hidden_states=global_vision_outputs.hidden_states,
+            attentions=global_vision_outputs.attentions,
+            local_last_hidden_state=local_vision_outputs.last_hidden_state
+            if local_vision_outputs is not None
+            else None,
+            local_hidden_states=local_vision_outputs.hidden_states if local_vision_outputs is not None else None,
+            local_attentions=local_vision_outputs.attentions if local_vision_outputs is not None else None,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | DeepseekOcr2ModelOutputWithPast:
+        r"""
+        pixel_values_local (`torch.FloatTensor`, *optional*):
+            Local patch pixel values of shape `(total_patches, 3, H, W)`.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image in the batch.
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        image_features = None
+        if pixel_values is not None:
+            image_features = self.get_image_features(
+                pixel_values, pixel_values_local, num_local_patches, return_dict=True
+            ).pooler_output
+            image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
+
+            special_image_mask = self.get_placeholder_mask(input_ids, inputs_embeds, image_features)
+            inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
+
+        outputs = self.language_model(
+            input_ids=None,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return DeepseekOcr2ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features,
+        )
+
+
+@auto_docstring
+class DeepseekOcr2ForConditionalGeneration(LlavaNextForConditionalGeneration, GenerationMixin):
+    def pack_image_features(self):
+        raise NotImplementedError("DeepseekOcr2 does not use pack_image_features")
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, 3, height, width)`):
+            The tensors corresponding to the global view input images.
+        pixel_values_local (`torch.FloatTensor` of shape `(total_patches, 3, height, width)`, *optional*):
+            All local patches flattened across the batch, or `None` if no local views.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image, e.g. `[6, 0, 4]`.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            pixel_values_local=pixel_values_local,
+            num_local_patches=num_local_patches,
+            **kwargs,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        pixel_values_local=None,
+        num_local_patches=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["pixel_values_local"] = pixel_values_local
+            model_inputs["num_local_patches"] = num_local_patches
+
+        return model_inputs
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_local: torch.FloatTensor | None = None,
+        num_local_patches: list[int] | torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | DeepseekOcr2CausalLMOutputWithPast:
+        r"""
+        pixel_values_local (`torch.FloatTensor`, *optional*):
+            Local patch pixel values of shape `(total_patches, 3, H, W)`.
+        num_local_patches (`list[int]` or `torch.Tensor`, *optional*):
+            Number of local patches per image in the batch.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_local=pixel_values_local,
+            num_local_patches=num_local_patches,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        hidden_states = hidden_states[:, slice_indices, :]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits,
+                labels=labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        return DeepseekOcr2CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+
+__all__ = [
+    "DeepseekOcr2Config",
+    "DeepseekOcr2EncoderConfig",
+    "DeepseekOcr2SamVisionConfig",
+    "DeepseekOcr2TextConfig",
+    "DeepseekOcr2ForConditionalGeneration",
+    "DeepseekOcr2ImageProcessor",
+    "DeepseekOcr2ImageProcessorPil",
+    "DeepseekOcr2Model",
+    "DeepseekOcr2PreTrainedModel",
+    "DeepseekOcr2TextModel",
+    "DeepseekOcr2TextPreTrainedModel",
+    "DeepseekOcr2VisionModel",
+]
diff --git a/src/transformers/models/deepseek_ocr2/processing_deepseek_ocr2.py b/src/transformers/models/deepseek_ocr2/processing_deepseek_ocr2.py
new file mode 100644
index 000000000000..dda5210559e4
--- /dev/null
+++ b/src/transformers/models/deepseek_ocr2/processing_deepseek_ocr2.py
@@ -0,0 +1,152 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for DeepSeek-OCR-2.
+"""
+
+import math
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import auto_docstring, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class DeepseekOcr2ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+        "images_kwargs": {
+            "crop_to_patches": True,
+            "min_patches": 2,
+            "max_patches": 6,
+        },
+    }
+
+
+@auto_docstring
+class DeepseekOcr2Processor(ProcessorMixin):
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        patch_size=16,
+        downsample_ratio=4,
+        **kwargs,
+    ):
+        r"""
+        patch_size (`int`, *optional*, defaults to `16`):
+            The patch size used by the vision encoder (SAM ViT patch embedding size).
+        downsample_ratio (`int`, *optional*, defaults to `4`):
+            The downsampling ratio applied after the vision encoder.
+        """
+        self.image_token = "<image>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.patch_size = patch_size
+        self.downsample_ratio = downsample_ratio
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+        super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs)
+
+    def _expand_image_tokens(
+        self,
+        text: list[TextInput],
+        num_crops_list: list[int],
+    ) -> list[str]:
+        """
+        Expand each `<image>` placeholder in the text to the correct number of image tokens.
+
+        Args:
+            text (`list[str]`):
+                List of text strings, each potentially containing `<image>` placeholders.
+            num_crops_list (`list[int]`):
+                Number of crops for each image, consumed in order as `<image>` placeholders
+                are encountered across all text samples.
+
+        Returns:
+            `list[str]`: Text with expanded image token placeholders.
+        """
+        size = self.image_processor.size["height"]
+        tile_size = self.image_processor.tile_size
+
+        num_queries_global = math.ceil(size / self.patch_size / self.downsample_ratio)
+        global_tokens = num_queries_global * num_queries_global
+
+        num_queries_local = math.ceil(tile_size / self.patch_size / self.downsample_ratio)
+        local_tokens = num_queries_local * num_queries_local
+
+        crop_index = 0
+        for i in range(len(text)):
+            while self.image_token in text[i]:
+                num_tokens = global_tokens + local_tokens * num_crops_list[crop_index] + 1
+                text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_tokens, 1)
+                crop_index += 1
+            text[i] = text[i].replace("<|placeholder|>", self.image_token)
+        return text
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[DeepseekOcr2ProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Global view pixel values. Returned when `images` is not `None`.
+            - **pixel_values_local** -- Local patch pixel values. Returned when `images` is not `None`.
+        """
+        if images is None:
+            raise ValueError("`images` are expected as arguments to a `DeepseekOcr2Processor` instance.")
+        if text is None:
+            raise ValueError("`text` is required for `DeepseekOcr2Processor`. Example: `'<image>\\nFree OCR.'`")
+
+        output_kwargs = self._merge_kwargs(
+            DeepseekOcr2ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if isinstance(text, str):
+            text = [text]
+        elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+        text = text.copy()  # below lines change text in-place
+
+        image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+        num_crops_list = image_inputs["num_local_patches"]
+        text = self._expand_image_tokens(text, num_crops_list)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+        return BatchFeature(
+            data={**text_inputs, **image_inputs},
+            tensor_type=return_tensors,
+        )
+
+
+__all__ = ["DeepseekOcr2Processor"]
diff --git a/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py b/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py
index 4178547a5ff2..dbca27b8883a 100644
--- a/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py
+++ b/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py
@@ -31,6 +31,11 @@ class DeepseekV3Config(PreTrainedConfig):
     first_k_dense_replace (`int`, *optional*, defaults to 3):
         Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                         \--k dense layers--/
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
     rope_interleave (`bool`, *optional*, defaults to `True`):
         Whether to interleave the rotary position embeddings.
 
@@ -88,6 +93,7 @@ class DeepseekV3Config(PreTrainedConfig):
     num_experts_per_tok: int | None = 8
     first_k_dense_replace: int | None = 3
     norm_topk_prob: bool | None = True
+    num_nextn_predict_layers: int = 0
     hidden_act: str = "silu"
     max_position_embeddings: int = 4096
     initializer_range: float = 0.02
diff --git a/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py b/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py
index fe3acd9aeddd..ed783b7c8c35 100644
--- a/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py
+++ b/src/transformers/models/deepseek_v3/modeling_deepseek_v3.py
@@ -112,7 +112,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -384,7 +384,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
+            self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
@@ -392,7 +392,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank)
+        self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
         self.kv_b_proj = nn.Linear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
@@ -537,13 +537,16 @@ class DeepseekV3PreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_flex_attn = True
 
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": DeepseekV3DecoderLayer,
         "attentions": DeepseekV3Attention,
     }
     _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
+    # MTP weights live at `model.layers.{num_hidden_layers + k}.*`. They are loaded
+    # separately through `MTPCandidateGenerator` (see `transformers.generation.candidate_generators`)
+    # and never populated into the main model — hence the ignore.
     _keys_to_ignore_on_load_unexpected = [r"model\.layers\.61.*"]
 
     @torch.no_grad()
diff --git a/src/transformers/models/deepseek_v3/modular_deepseek_v3.py b/src/transformers/models/deepseek_v3/modular_deepseek_v3.py
index 2bf7d347e85d..398f657c5f73 100644
--- a/src/transformers/models/deepseek_v3/modular_deepseek_v3.py
+++ b/src/transformers/models/deepseek_v3/modular_deepseek_v3.py
@@ -189,7 +189,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank)
+            self.q_a_layernorm = DeepseekV3RMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
@@ -197,7 +197,7 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank)
+        self.kv_a_layernorm = DeepseekV3RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
         self.kv_b_proj = nn.Linear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
@@ -301,7 +301,11 @@ def __init__(self, config: DeepseekV3Config, layer_idx: int):
 
 
 class DeepseekV3PreTrainedModel(LlamaPreTrainedModel):
+    _can_compile_fullgraph = False
     _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
+    # MTP weights live at `model.layers.{num_hidden_layers + k}.*`. They are loaded
+    # separately through `MTPCandidateGenerator` (see `transformers.generation.candidate_generators`)
+    # and never populated into the main model — hence the ignore.
     _keys_to_ignore_on_load_unexpected = [r"model\.layers\.61.*"]
 
     @torch.no_grad()
diff --git a/src/transformers/models/deepseek_v4/__init__.py b/src/transformers/models/deepseek_v4/__init__.py
new file mode 100644
index 000000000000..fe0228917078
--- /dev/null
+++ b/src/transformers/models/deepseek_v4/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_deepseek_v4 import *
+    from .modeling_deepseek_v4 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/deepseek_v4/configuration_deepseek_v4.py b/src/transformers/models/deepseek_v4/configuration_deepseek_v4.py
new file mode 100644
index 000000000000..a53c64b73e2d
--- /dev/null
+++ b/src/transformers/models/deepseek_v4/configuration_deepseek_v4.py
@@ -0,0 +1,241 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_v4/modular_deepseek_v4.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_v4.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+DEEPSEEK_V4_LAYER_TYPES = (
+    "sliding_attention",
+    "compressed_sparse_attention",
+    "heavily_compressed_attention",
+)
+
+
+_COMPRESS_RATIO_TO_LAYER_TYPE = {
+    0: "sliding_attention",
+    4: "compressed_sparse_attention",
+    128: "heavily_compressed_attention",
+}
+
+
+@auto_docstring(checkpoint="deepseek-ai/DeepSeek-V4-Flash-Base")
+@strict
+class DeepseekV4Config(PreTrainedConfig):
+    r"""
+    DeepSeek-V4's hybrid attention follows the paper (Section 2.3): every block is one
+    of three attention types — *Full Attention* (sliding-window only), *Compressed
+    Sparse Attention* (CSA, Section 2.3.1) and *Heavily Compressed Attention* (HCA,
+    Section 2.3.2). CSA compresses the KV cache by ``compress_rate_csa`` (m=4 in V4-
+    Flash/Pro) and selects ``index_topk`` blocks per query via the Lightning Indexer;
+    HCA applies a much heavier compression of ``compress_rate_hca`` (m'=128) and
+    skips sparse selection. Both branches add a small uncompressed sliding-window
+    branch for fine-grained locality.
+
+    layer_types (`list[str]`): Per-layer attention schedule with values from
+        ``{"compressed_sparse_attention", "heavily_compressed_attention"}``.
+        V4-Pro default: 2× HCA bootstrap + interleaved CSA / HCA.
+    compress_rate_csa (`int`): m, the CSA compression rate (default 4).
+    compress_rate_hca (`int`): m', the HCA compression rate (default 128).
+    rope_theta (`float`): RoPE base for the main self-attention rotary.
+    compress_rope_theta (`float`): RoPE base for the compressed branches (paired with
+        ``rope_scaling`` for YaRN).
+    partial_rotary_factor (`float`, *optional*): Fraction of head_dim that gets RoPE.
+        Defaults to ``qk_rope_head_dim / head_dim`` so cos/sin sizes to ``qk_rope_head_dim``.
+    hc_mult (`int`): Manifold-Constrained Hyper-Connection (mHC) expansion factor n_hc
+        (always active; Section 2.2).
+    hc_sinkhorn_iters (`int`): Sinkhorn-Knopp iterations t_max for the mHC residual
+        mapping projection onto doubly-stochastic matrices.
+    hc_eps (`float`): Numerical floor for the Sinkhorn-Knopp normalization.
+    num_hash_layers (`int`): First N MoE layers route via a frozen ``tid2eid[input_ids]`` lookup.
+    scoring_func (`str`): Router activation — ``sqrtsoftplus``, ``softmax``, or ``sigmoid``.
+    swiglu_limit (`float`): Clip routed experts' gate/up pre-activations.
+    sliding_window (`int`): Local window size n_win used in every attention block's
+        sliding-window branch.
+    o_groups (`int`): Number of head-groups g in the grouped output projection
+        (paper §2.3.1, "Grouped Output Projection").
+    o_lora_rank (`int`): Per-group intermediate dim d_g in the grouped output projection.
+    index_n_heads (`int`): Number of indexer query heads n_h^I (paper §2.3.1, eq. 14).
+    index_head_dim (`int`): Indexer head dim c^I (paper §2.3.1).
+    index_topk (`int`): Number of compressed entries per query the Lightning Indexer
+        keeps via top-k (paper §2.3.1, eq. 17).
+    num_nextn_predict_layers (`int`): MTP layer count in the upstream checkpoint
+        (not instantiated here).
+    n_group (`int`, *optional*): V3 MLA expert-group count. Kept for config compat;
+        unused by V4 (no expert groups).
+    first_k_dense_replace (`int`, *optional*): V3 field — the first ``k`` MoE layers
+        to replace with dense FFNs. Kept for config compat; V4 uses hash routing
+        (``num_hash_layers``) instead.
+    rope_interleave (`bool`, *optional*): V3 flag — whether to interleave rope dims.
+        Kept for config compat; V4's RoPE is non-interleaved (rope-first head layout).
+    """
+
+    model_type = "deepseek_v4"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.wq_a": "colwise",
+        "layers.*.self_attn.wq_b": "colwise",
+        "layers.*.self_attn.wkv": "colwise",
+        "layers.*.self_attn.wo_a": "rowwise",
+        "layers.*.self_attn.wo_b": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    attribute_map = {"num_local_experts": "n_routed_experts"}
+
+    vocab_size: int = 129280
+    hidden_size: int = 4096
+    intermediate_size: int = 18432
+    moe_intermediate_size: int = 2048
+    num_hidden_layers: int = 43
+    num_attention_heads: int = 64
+    num_key_value_heads: int = 1
+    n_shared_experts: int = 1
+    n_routed_experts: int = 256
+    routed_scaling_factor: float = 1.5
+
+    # V3 fields kept ``None`` so the V3-style MLA paths in inherited configs never fire
+    # (V4 doesn't use MLA — it uses shared-KV MQA via ``wkv`` directly).
+    kv_lora_rank: int | None = None
+    q_lora_rank: int = 1024
+    qk_rope_head_dim: int = 64
+    v_head_dim: int | None = None
+    qk_nope_head_dim: int | None = None
+    n_group: int | None = None
+    topk_group: int | None = None
+    num_experts_per_tok: int = 6
+    first_k_dense_replace: int | None = None
+    norm_topk_prob: bool = True
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 1048576
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 0
+    eos_token_id: int | list[int] | None = 1
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+
+    rope_parameters: RopeParameters | dict | None = None
+    rope_interleave: bool | None = True
+    attention_bias: bool = False
+    attention_dropout: float = 0.0
+    head_dim: int = 512
+    scoring_func: str = "sqrtsoftplus"
+    rope_theta: float | int = 10000.0
+
+    layer_types: list[str] | None = None
+    compress_rate_csa: int = 4
+    compress_rate_hca: int = 128
+    compress_rope_theta: float | int = 160000.0
+    hc_mult: int = 4
+    hc_sinkhorn_iters: int = 20
+    hc_eps: float = 1.0e-6
+    num_hash_layers: int = 3
+    swiglu_limit: float = 10.0
+    sliding_window: int = 128
+    o_groups: int = 8
+    o_lora_rank: int = 1024
+    index_n_heads: int = 64
+    index_head_dim: int = 128
+    index_topk: int = 512
+    num_nextn_predict_layers: int = 1
+
+    output_router_logits: bool = False
+    router_aux_loss_coef: float = 0.001
+    router_jitter_noise: float = 0.0
+    partial_rotary_factor: float | None = None
+
+    def __post_init__(self, **kwargs):
+        compress_ratios = kwargs.pop("compress_ratios", None)
+        super().__post_init__(**kwargs)
+        n = self.num_hidden_layers
+        if self.layer_types is None and compress_ratios is not None:
+            # Translate the V4 checkpoint's per-layer integer ``compress_ratios`` into the
+            # named ``layer_types`` schedule (0 = sliding-only, 4 = CSA, 128 = HCA).
+            self.layer_types = [_COMPRESS_RATIO_TO_LAYER_TYPE[r] for r in compress_ratios]
+        if self.layer_types is None:
+            # V4-Pro default: two HCA bootstrap layers, then CSA / HCA interleaved.
+            interleave = [
+                "compressed_sparse_attention" if i % 2 else "heavily_compressed_attention"
+                for i in range(max(n - 2, 0))
+            ]
+            head = ["heavily_compressed_attention"] * min(n, 2)
+            self.layer_types = head + interleave
+        self.layer_types = list(self.layer_types[:n])
+        self.qk_nope_head_dim = self.head_dim - self.qk_rope_head_dim
+        if self.partial_rotary_factor is None:
+            self.partial_rotary_factor = self.qk_rope_head_dim / self.head_dim
+        # Normalize rope_parameters into a per-rope-type dict ``{"main": {...}, "compress": {...}}``
+        # (Gemma3 pattern, keys are *rope-type* labels — unrelated to ``layer_types``).
+        # Idempotent across save/load: round-tripping preserves structure.
+        #
+        # By the time we get here :class:`PreTrainedConfig` has already run
+        # :meth:`RotaryEmbeddingConfigMixin.convert_rope_params_to_dict`, which folds the
+        # checkpoint's legacy top-level ``rope_scaling`` block into ``self.rope_parameters``
+        # as a flat dict (``rope_type``, ``factor``, ``beta_fast``, ``beta_slow``,
+        # ``original_max_position_embeddings``, …). The block ships under
+        # ``rope_scaling`` in :attr:`config.json` and never appears as a top-level kwarg
+        # for us to intercept before the mixin runs — the mixin always wins. We just
+        # split that flat dict into the two rope-type buckets.
+        rp = self.rope_parameters or {}
+        if isinstance(rp.get("main"), dict) and isinstance(rp.get("compress"), dict):
+            self.rope_parameters = {"main": rp["main"], "compress": rp["compress"]}
+        else:
+            # Build the per-rope-type dict ``{"main", "compress"}``. The flat ``rp``
+            # already carries any YaRN params the checkpoint shipped under top-level
+            # ``rope_scaling`` (folded in by ``RotaryEmbeddingConfigMixin``). We propagate
+            # them into both buckets — the difference between the two is just the
+            # ``rope_theta`` base (the model's main attention uses ``rope_theta=10000``,
+            # the compressor / indexer uses ``compress_rope_theta=160000``).
+            base = {k: v for k, v in rp.items() if k not in ("main", "compress")}
+            base.setdefault("rope_theta", self.rope_theta)
+            base["partial_rotary_factor"] = self.partial_rotary_factor
+            base.setdefault("rope_type", "default")
+            main = dict(base)
+            compress = {**base, "rope_theta": self.compress_rope_theta}
+            self.rope_parameters = {"main": main, "compress": compress}
+
+    def validate_layer_type(self):
+        """V4 narrows the global ``ALLOWED_LAYER_TYPES`` to the two block types it actually
+        ships with, on top of the standard length / type-membership checks.
+        """
+        if self.layer_types is None or self.num_hidden_layers is None:
+            return
+        if len(self.layer_types) != self.num_hidden_layers:
+            raise ValueError(
+                f"`num_hidden_layers` ({self.num_hidden_layers}) must equal "
+                f"`len(layer_types)` ({len(self.layer_types)})."
+            )
+        bad = [layer_type for layer_type in self.layer_types if layer_type not in DEEPSEEK_V4_LAYER_TYPES]
+        if bad:
+            raise ValueError(
+                f"`layer_types` entries must be one of {DEEPSEEK_V4_LAYER_TYPES} for DeepSeek-V4; got {bad}."
+            )
+
+
+__all__ = ["DeepseekV4Config"]
diff --git a/src/transformers/models/deepseek_v4/modeling_deepseek_v4.py b/src/transformers/models/deepseek_v4/modeling_deepseek_v4.py
new file mode 100644
index 000000000000..57e21078c1f9
--- /dev/null
+++ b/src/transformers/models/deepseek_v4/modeling_deepseek_v4.py
@@ -0,0 +1,1670 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/deepseek_v4/modular_deepseek_v4.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_deepseek_v4.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+from collections.abc import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, DynamicSlidingWindowLayer
+from ...generation import GenerationMixin
+from ...integrations import use_experts_implementation, use_kernel_forward_from_hub
+from ...masking_utils import create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import OutputRecorder, capture_outputs
+from .configuration_deepseek_v4 import DeepseekV4Config
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class DeepseekV4RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        DeepseekV4RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class DeepseekV4RotaryEmbedding(nn.Module):
+    """Multi-layer-type rotary embedding (Gemma3 pattern). Holds two ``inv_freq``
+    buffers — ``"main"`` for self-attention (``rope_theta``) and ``"compress"`` for
+    the Compressor / Indexer (``compress_rope_theta``). Both honour
+    ``partial_rotary_factor`` so cos/sin is sized to ``qk_rope_head_dim`` rather than
+    the full ``head_dim``. ``forward(x, position_ids, layer_type=...)`` (inherited
+    from :class:`Gemma3RotaryEmbedding`) picks one.
+
+    The ``layer_types`` here are the *rope* layer types (``"main"`` / ``"compress"``),
+    keys of ``config.rope_parameters``. They are unrelated to ``config.layer_types``,
+    which lists the per-decoder-block attention type.
+    """
+
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    layer_types = ("main", "compress")
+
+    def __init__(self, config: "DeepseekV4Config", device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+        self.config = config
+        self.rope_type = {}
+        for layer_type in self.layer_types:
+            params = config.rope_parameters.get(layer_type)
+            if params is None:
+                continue
+            self.rope_type[layer_type] = params.get("rope_type", "default")
+            rope_init_fn: Callable = self.compute_default_rope_parameters
+            if self.rope_type[layer_type] != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type[layer_type]]
+            inv_freq, scaling = rope_init_fn(config, device, layer_type=layer_type)
+            self.register_buffer(f"{layer_type}_inv_freq", inv_freq, persistent=False)
+            self.register_buffer(f"{layer_type}_original_inv_freq", inv_freq.clone(), persistent=False)
+            setattr(self, f"{layer_type}_attention_scaling", scaling)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config, device=None, seq_len=None, layer_type=None
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+            layer_type (`str`, *optional*):
+                The current layer type if the model has different RoPE parameters per type.
+                Should not be used unless `config.layer_types is not None`
+
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        # V4 honours ``partial_rotary_factor`` so cos/sin sizes to ``qk_rope_head_dim``.
+        params = config.rope_parameters[layer_type]
+        base = params["rope_theta"]
+        head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        factor = params.get("partial_rotary_factor", 1.0)
+        dim = int(head_dim * factor)
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, 1.0
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids, layer_type=None):
+        inv_freq = getattr(self, f"{layer_type}_inv_freq")
+        attention_scaling = getattr(self, f"{layer_type}_attention_scaling")
+
+        inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * attention_scaling
+            sin = emb.sin() * attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def _sliding_kv_update(
+    cache_layer: "DynamicSlidingWindowLayer", key_states: torch.Tensor, value_states: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Shared sliding-window K=V update body. V4 uses shared-KV MQA, so ``keys`` and
+    ``values`` point to the same storage on every layer; both V4 cache layer types
+    (HCA / CSA) call this from their ``update``."""
+    if not cache_layer.is_initialized:
+        cache_layer.lazy_initialization(key_states, value_states)
+        cache_layer.values = cache_layer.keys
+    cache_layer.cumulative_length += key_states.shape[-2]
+    full = torch.cat([cache_layer.keys, key_states], dim=-2)
+    cache_layer.keys = full[:, :, -cache_layer.sliding_window + 1 :, :]
+    cache_layer.values = cache_layer.keys
+    return full, full
+
+
+def _update_window_buffer(
+    buffer_kv: torch.Tensor | None,
+    buffer_gate: torch.Tensor | None,
+    kv: torch.Tensor,
+    gate: torch.Tensor,
+    compress_rate: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Merge a still-buffered tail with freshly projected ``(kv, gate)`` and split off
+    the longest window-aligned chunk. Used by both the compressor- and indexer-side
+    window buffers; tokens past the last full window stay in the buffer until the
+    next call rounds them out to a multiple of ``compress_rate``."""
+    if buffer_kv is not None and buffer_kv.shape[1]:
+        kv = torch.cat([buffer_kv, kv], dim=1)
+        gate = torch.cat([buffer_gate, gate], dim=1)
+    usable = (kv.shape[1] // compress_rate) * compress_rate
+    return kv[:, :usable], gate[:, :usable], kv[:, usable:], gate[:, usable:]
+
+
+def _append_to_pool(pool: torch.Tensor | None, new_pooled: torch.Tensor) -> torch.Tensor:
+    """Append freshly emitted compressed entries to a running pool, returning the
+    full pool (or an empty tensor if nothing has been pooled yet)."""
+    if new_pooled.shape[1] > 0:
+        return new_pooled if pool is None else torch.cat([pool, new_pooled], dim=1)
+    if pool is None:
+        return new_pooled.new_zeros((new_pooled.shape[0], 0, new_pooled.shape[-1]))
+    return pool
+
+
+class DeepseekV4HCACache(DynamicSlidingWindowLayer):
+    """Cache layer for HCA blocks (paper §2.3.2). Holds the long-range compressor's
+    buffer / pool / count on top of the sliding-window K=V branch. HCA uses
+    *non-overlapping* windows, so there is **no** overlap state, and HCA has **no**
+    indexer either.
+
+    Fields on top of :class:`DynamicSlidingWindowLayer`:
+
+      * ``compressor_pool`` — the running list of compressed KV entries emitted so
+        far (one per ``compress_rate_hca`` source tokens; the long-range KVs the
+        attention concatenates onto its sliding-window keys / values).
+      * ``compressor_buffer_kv`` / ``compressor_buffer_gate`` — source tokens that
+        arrived between two full windows; once the buffer hits ``compress_rate_hca``
+        tokens the compressor closes a window, emits one pooled entry, and drains
+        the buffer.
+      * ``compressor_pool_count`` — number of compressed entries emitted so far,
+        so ``compressor_pool_count * compress_rate_hca`` is the absolute position
+        of the *next* window's first source token.
+
+    The class-level ``layer_type`` auto-registers this class with
+    :data:`LAYER_TYPE_CACHE_MAPPING` so :class:`DynamicCache` builds it on its own
+    when ``config.layer_types[i] == "heavily_compressed_attention"``.
+    """
+
+    layer_type = "heavily_compressed_attention"
+
+    def __init__(self, config: "DeepseekV4Config"):
+        super().__init__(config)
+        self.compress_rate = config.compress_rate_hca
+        self.compressor_buffer_kv: torch.Tensor | None = None
+        self.compressor_buffer_gate: torch.Tensor | None = None
+        self.compressor_pool: torch.Tensor | None = None
+        self.compressor_pool_count = 0
+
+    def update(self, key_states: torch.Tensor, value_states: torch.Tensor, *args, **kwargs):
+        return _sliding_kv_update(self, key_states, value_states)
+
+    def update_compressor(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Merge the freshly projected ``(kv, gate)`` (paper §2.3.2 eqs. 20–21:
+        ``C = H·W^{KV}``, ``Z = H·W^Z``) with the buffered tail from prior calls and
+        return the longest window-aligned chunk that's ready to pool, plus the
+        absolute source-token position of that chunk's first window. The returned
+        chunk is softmax-pooled by the compressor with ``position_bias`` to emit one
+        compressed entry per window of ``compress_rate_hca`` tokens (eqs. 22–23)."""
+        first_pool_position = self.compressor_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.compressor_buffer_kv, self.compressor_buffer_gate = _update_window_buffer(
+            self.compressor_buffer_kv, self.compressor_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_compressor_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted compressed entries to ``compressor_pool``
+        (``C^{Comp}``, paper §2.3.2 eq. 23) and return the full pool. Bumps
+        ``compressor_pool_count`` so the next ``update_compressor`` call knows the
+        absolute source-token position of its first window."""
+        self.compressor_pool = _append_to_pool(self.compressor_pool, new_pooled)
+        self.compressor_pool_count += new_pooled.shape[1]
+        return self.compressor_pool
+
+
+class DeepseekV4CSACache(DynamicSlidingWindowLayer):
+    """Cache layer for CSA blocks (paper §2.3.1). Holds two parallel sets of
+    buffer / pool / count / overlap state on top of the sliding-window K=V branch:
+
+      * **compressor side** — the main-branch ``head_dim`` pool (the long-range KVs
+        the attention concatenates after top-k indexer selection).
+      * **indexer side** — the Lightning Indexer's smaller ``index_head_dim`` pool
+        (the keys ``K^{IComp}`` that queries score against to pick the top-k blocks,
+        eqs. 14–17). Kept separate from the compressor pool because the head dim
+        differs.
+
+    Both sides use **overlapping** windows of stride ``compress_rate_csa`` and width
+    ``2 * compress_rate_csa`` (paper §2.3.1), so each side also keeps an
+    ``*_overlap_kv`` / ``*_overlap_gate`` pair holding the last full window's
+    projected ``(kv, gate)`` so the next forward call's first window can stitch in
+    its low-channel slice as the prior contribution.
+
+    The class-level ``layer_type`` auto-registers this class with
+    :data:`LAYER_TYPE_CACHE_MAPPING` so :class:`DynamicCache` builds it on its own
+    when ``config.layer_types[i] == "compressed_sparse_attention"``.
+    """
+
+    layer_type = "compressed_sparse_attention"
+
+    def __init__(self, config: "DeepseekV4Config"):
+        super().__init__(config)
+        self.compress_rate = config.compress_rate_csa
+        # Compressor side
+        self.compressor_buffer_kv: torch.Tensor | None = None
+        self.compressor_buffer_gate: torch.Tensor | None = None
+        self.compressor_pool: torch.Tensor | None = None
+        self.compressor_pool_count = 0
+        self.compressor_overlap_kv: torch.Tensor | None = None
+        self.compressor_overlap_gate: torch.Tensor | None = None
+        # Indexer side (parallel state at ``index_head_dim``)
+        self.indexer_buffer_kv: torch.Tensor | None = None
+        self.indexer_buffer_gate: torch.Tensor | None = None
+        self.indexer_pool: torch.Tensor | None = None
+        self.indexer_pool_count = 0
+        self.indexer_overlap_kv: torch.Tensor | None = None
+        self.indexer_overlap_gate: torch.Tensor | None = None
+
+    def update(self, key_states: torch.Tensor, value_states: torch.Tensor, *args, **kwargs):
+        return _sliding_kv_update(self, key_states, value_states)
+
+    def update_compressor(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Compressor-side window buffer (paper §2.3.1 main-branch pool, eqs. 9–12).
+        Same window-aligned tail-buffering as HCA, but at the CSA cadence
+        (``compress_rate_csa``)."""
+        first_pool_position = self.compressor_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.compressor_buffer_kv, self.compressor_buffer_gate = _update_window_buffer(
+            self.compressor_buffer_kv, self.compressor_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_compressor_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted entries to the CSA compressor pool (the
+        ``C^{Comp}`` running list at ``head_dim``, eqs. 11–12)."""
+        self.compressor_pool = _append_to_pool(self.compressor_pool, new_pooled)
+        self.compressor_pool_count += new_pooled.shape[1]
+        return self.compressor_pool
+
+    def get_compressor_overlap(self) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        return self.compressor_overlap_kv, self.compressor_overlap_gate
+
+    def set_compressor_overlap(self, kv: torch.Tensor, gate: torch.Tensor) -> None:
+        self.compressor_overlap_kv = kv
+        self.compressor_overlap_gate = gate
+
+    def update_indexer(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Indexer-side mirror of :meth:`update_compressor` (paper §2.3.1, "Lightning
+        Indexer for Sparse Selection"). Same logic at the smaller ``index_head_dim``
+        — the small-head pool keys ``K^{IComp}`` (eq. 14's ``W^{IUQ}`` complement on
+        the key side) that the indexer scores queries against to pick the top-k
+        blocks (eqs. 15–17). Buffer / pool / count are kept separate from the
+        compressor's state because the head dim differs."""
+        first_pool_position = self.indexer_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.indexer_buffer_kv, self.indexer_buffer_gate = _update_window_buffer(
+            self.indexer_buffer_kv, self.indexer_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_indexer_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted entries to the indexer pool ``K^{IComp}`` (paper
+        §2.3.1 eq. 16: the keys against which the ``q^I_t`` queries score for top-k
+        selection). Same cadence as the compressor pool — one entry per
+        ``compress_rate_csa`` source tokens — but at ``index_head_dim``."""
+        self.indexer_pool = _append_to_pool(self.indexer_pool, new_pooled)
+        self.indexer_pool_count += new_pooled.shape[1]
+        return self.indexer_pool
+
+    def get_indexer_overlap(self) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        return self.indexer_overlap_kv, self.indexer_overlap_gate
+
+    def set_indexer_overlap(self, kv: torch.Tensor, gate: torch.Tensor) -> None:
+        self.indexer_overlap_kv = kv
+        self.indexer_overlap_gate = gate
+
+
+class DeepseekV4GroupedLinear(nn.Linear):
+    """Block-diagonal grouped linear used by the V4 grouped output projection
+    (paper §2.3.1, "Grouped Output Projection"; HCA reuses the same scheme,
+    §2.3.2). With ``num_attention_heads = n_h`` and per-head dim ``c``, the core
+    attention's stacked output is ``c·n_h``-dim, which is *very* large for V4
+    (V4-Flash: c=512, n_h=64 → 32768; V4-Pro: c=512, n_h=128 → 65536). A direct
+    ``c·n_h → hidden_size`` projection would dominate the per-token cost.
+
+    The paper sidesteps that by splitting the n_h heads into ``g`` groups, projecting
+    each ``c·n_h/g``-dim group independently to a ``d_g``-dim intermediate output
+    (with ``d_g < c·n_h/g``), and then mixing the resulting ``g·d_g`` vector to
+    ``hidden_size`` through a single follow-up linear (``self_attn.wo_b``). This
+    module owns the per-group block (``self_attn.wo_a``).
+
+    The ``weight`` parameter is shaped like a standard ``nn.Linear``
+    (``[out_features, in_features_per_group]``) so quantizers keyed on
+    ``nn.Linear.weight`` still pick it up; ``forward`` does the per-group ``bmm``.
+    """
+
+    def __init__(self, in_features_per_group: int, out_features: int, n_groups: int, bias: bool = False):
+        super().__init__(in_features_per_group, out_features, bias=bias)
+        self.n_groups = n_groups
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [..., n_groups, in_features_per_group]
+        batch_shape = x.shape[:-2]
+        d_in = x.shape[-1]
+        out_per_group = self.out_features // self.n_groups
+        w = self.weight.view(self.n_groups, out_per_group, d_in)
+        x = x.reshape(-1, self.n_groups, d_in).permute(1, 0, 2)
+        y = torch.bmm(x, w.transpose(-1, -2)).permute(1, 0, 2)
+        return y.reshape(*batch_shape, self.n_groups, out_per_group)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    r"""
+    TODO let's just use the original freqcis computation to not have the view
+    transpose + reshape! This is not optimized!
+    Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+
+    b, h, s, d = q.shape
+    q = q.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+    b, h, s, d = k.shape
+    k = k.view(b, h, s, d // 2, 2).transpose(4, 3).reshape(b, h, s, d)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """V4 wraps :func:`~transformers.models.deepseek_v3.modeling_deepseek_v3.apply_rotary_pos_emb_interleave`
+    with a permute-back so the rope slice exits in the same interleaved
+    ``[a0, b0, a1, b1, …]`` layout it came in with.
+
+    V3's helper restages interleaved pairs into the halves layout
+    (``[a0, a1, …, b0, b1, …]``) so it can run llama's half-split RoPE primitive,
+    and leaves the result in that layout — fine for V3 because V3 is MLA: V has
+    its own ``v_head_dim`` and never carries a rope slice, so the post-rotation
+    layout of Q / K only matters for the dot product (which is invariant under a
+    consistent permutation of channels on both sides).
+
+    V4 is shared-KV MQA: V is the same tensor as K, so V's rope slice picks up
+    the rotation too — and then the attention sum, the per-head ``wo_a``
+    grouped projection, and ``wo_b`` all consume that rope slice as part of
+    their input. Those weights were trained against the V4-Flash reference
+    (``inference/model.py:apply_rotary_emb`` does ``view_as_complex``-style
+    rotation in place, preserving the interleaved layout), so we have to put
+    the channels back where they were before passing to ``wo_a`` — otherwise the
+    grouped projection sees its inputs scrambled and ``wo_b(wo_a(...))`` collapses.
+    """
+    q, k = apply_rotary_pos_emb_interleave(q, k, cos, sin, unsqueeze_dim=unsqueeze_dim)
+
+    def _halves_to_interleave(x: torch.Tensor) -> torch.Tensor:
+        # Inverse of V3's ``view(d/2, 2).transpose(-1, -2)``: ``[a0, …, b0, …]`` →
+        # ``[a0, b0, a1, b1, …]``.
+        b, h, s, d = x.shape
+        return x.view(b, h, s, 2, d // 2).transpose(-1, -2).reshape(b, h, s, d)
+
+    return _halves_to_interleave(q), _halves_to_interleave(k)
+
+
+# -----------------------------------------------------------------------------
+# Compressors — :class:`DeepseekV4HCACompressor` and :class:`DeepseekV4CSACompressor`
+# are independent. They share the same softmax-gated window-pool primitive but differ
+# in three ways that we keep on each class explicitly: HCA pools non-overlapping
+# windows with ``coff = 1`` and has no indexer, CSA pools overlapping windows with
+# ``coff = 2`` and runs a Lightning Indexer on top of the pool.
+# -----------------------------------------------------------------------------
+
+
+def _overlap_pool(
+    chunk_kv: torch.Tensor,
+    chunk_gate: torch.Tensor,
+    prior_kv: torch.Tensor | None,
+    prior_gate: torch.Tensor | None,
+    head_dim: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Expand ``[B, n_win, ratio, 2*head_dim]`` chunks into ``[B, n_win, 2*ratio, head_dim]``
+    by stitching each window's *low-channel* slice onto the *high-channel* slice of the
+    prior window — matching the V4-Flash reference (``Compressor.overlap_transform``).
+
+    Each pooled output thus mixes ``ratio`` *current* source tokens (high half of the
+    learned 2d split) with ``ratio`` *previous* source tokens (low half), so windows
+    have width ``2*ratio`` but stride ``ratio`` (paper §2.3.1). For window 0, the prior
+    half is filled with zero (kv) / ``-inf`` (gate, so its softmax weight is exactly 0),
+    unless ``prior_kv`` / ``prior_gate`` carry the last full window from a previous
+    forward call — in which case its low-channel slice slots into row ``[0, :ratio]``.
+    """
+    batch, n_windows, ratio, _ = chunk_kv.shape
+    new_kv = chunk_kv.new_zeros((batch, n_windows, 2 * ratio, head_dim))
+    new_gate = chunk_gate.new_full((batch, n_windows, 2 * ratio, head_dim), float("-inf"))
+    new_kv[:, :, ratio:] = chunk_kv[..., head_dim:]
+    new_gate[:, :, ratio:] = chunk_gate[..., head_dim:]
+    if n_windows > 1:
+        new_kv[:, 1:, :ratio] = chunk_kv[:, :-1, :, :head_dim]
+        new_gate[:, 1:, :ratio] = chunk_gate[:, :-1, :, :head_dim]
+    if prior_kv is not None and prior_gate is not None:
+        new_kv[:, 0, :ratio] = prior_kv[..., :head_dim].to(new_kv.dtype)
+        new_gate[:, 0, :ratio] = prior_gate[..., :head_dim].to(new_gate.dtype)
+    return new_kv, new_gate
+
+
+class DeepseekV4Indexer(nn.Module):
+    """Lightning Indexer (paper §2.3.1, eqs. 13–17). Used by Compressed Sparse
+    Attention (CSA) to pick the top-k compressed KV blocks per query. The indexer
+    runs its own scaled-down compressor at ``index_head_dim`` over the same windows
+    as the outer CSA compressor, then scores queries against the pooled keys with
+    ``∑_h w_{t,h} · ReLU(q_{t,h} · K^IComp_s)`` and keeps the top ``index_topk``
+    indices.
+
+    The indexer has its own rotary because it applies RoPE to two sets of tensors:
+
+      * **pool keys** at deterministic positions ``i * compress_rate + first_pool_position``,
+      * **queries** at the model's current ``position_ids`` (variable per forward).
+
+    Both must use the same theta as the outer compressor (``compress_rope_theta``) so
+    query/key inner products are translation-invariant in the standard rope sense — if
+    they used different thetas the score ``q · k`` would carry a residual position-
+    dependent skew. We can't precompute cos/sin once at init because the query
+    positions vary per call, so the indexer owns a rotary embedding and calls it with
+    ``layer_type="compress"`` twice per forward (once for pool keys, once for queries).
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_csa
+        self.n_heads = config.index_n_heads
+        self.head_dim = config.index_head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.index_topk = config.index_topk
+        self.softmax_scale = self.head_dim**-0.5
+        # The indexer always pools with the CSA cadence (``compress_rate=4``), so its
+        # inner pool runs the same overlapping-window scheme as :class:`DeepseekV4CSACompressor`
+        # (paper §2.3.1) — ``coff = 2`` everywhere on the pool branch.
+        self.coff = 2
+        self.wkv = nn.Linear(config.hidden_size, self.coff * self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, self.coff * self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, self.coff * self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.wq_b = nn.Linear(config.q_lora_rank, self.n_heads * self.head_dim, bias=False)
+        self.weights_proj = nn.Linear(config.hidden_size, self.n_heads, bias=False)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.LongTensor:
+        batch, seq_len, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+
+        # --- Pool side: same overlapping windows as the outer CSA compressor, at index_head_dim ---
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+            prior_kv, prior_gate = None, None
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_indexer(kv, gate)
+            prior_kv, prior_gate = cache_layer.get_indexer_overlap()
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, self.coff * self.head_dim)
+            chunk_gate = chunk_gate.view(
+                batch, n_windows, self.compress_rate, self.coff * self.head_dim
+            ) + self.position_bias.to(chunk_gate.dtype)
+            if cache_layer is not None:
+                cache_layer.set_indexer_overlap(chunk_kv[:, -1].clone(), chunk_gate[:, -1].clone())
+            chunk_kv, chunk_gate = _overlap_pool(chunk_kv, chunk_gate, prior_kv, prior_gate, self.head_dim)
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            cos, sin = self.rotary_emb(new_pooled, position_ids=positions, layer_type="compress")
+            # V4-Flash places the rotary slice at the *end* of each head (matches the
+            # reference's ``x[..., -rd:]`` indexing) — wkv weight is laid out [nope|rope]
+            # so the rotary half is the trailing ``rope_head_dim`` channels.
+            pool_nope, pool_rope = new_pooled[..., : -self.rope_head_dim], new_pooled[..., -self.rope_head_dim :]
+            pool_rope, _ = apply_rotary_pos_emb(
+                pool_rope.unsqueeze(1), torch.zeros_like(pool_rope.unsqueeze(1)), cos, sin
+            )
+            new_pooled = torch.cat([pool_nope, pool_rope.squeeze(1)], dim=-1)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        pooled_kv = new_pooled if cache_layer is None else cache_layer.update_indexer_pool(new_pooled)
+
+        # --- Query side ---
+        cos_q, sin_q = self.rotary_emb(hidden_states, position_ids=position_ids, layer_type="compress")
+        q = self.wq_b(q_residual).view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        q_nope, q_rope = q[..., : -self.rope_head_dim], q[..., -self.rope_head_dim :]
+        q_rope, _ = apply_rotary_pos_emb(q_rope, torch.zeros_like(q_rope), cos_q, sin_q)
+        q = torch.cat([q_nope, q_rope], dim=-1).transpose(1, 2)
+
+        # --- Score: ReLU(q·kᵀ) * weights, then top-k ---
+        scores = torch.matmul(q.float(), pooled_kv.transpose(-1, -2).float().unsqueeze(1))  # [B, S, H, T]
+        scores = F.relu(scores) * self.softmax_scale
+        weights = self.weights_proj(hidden_states).float() * (self.n_heads**-0.5)  # [B, S, H]
+        index_scores = (scores * weights.unsqueeze(-1)).sum(dim=2)  # [B, S, T]
+        topk = min(self.index_topk, pooled_kv.shape[1])
+        return index_scores.topk(topk, dim=-1).indices
+
+
+def _rope_pool(
+    pooled: torch.Tensor, rotary_emb: nn.Module, positions: torch.Tensor, rope_head_dim: int
+) -> torch.Tensor:
+    """Apply RoPE to the trailing ``rope_head_dim`` slice of each pooled entry at its
+    deterministic absolute position. V4-Flash lays out each head as
+    ``[nope | rope]`` (matches the reference's ``x[..., -rd:]`` indexing) so the
+    rotary half is the trailing channels."""
+    cos, sin = rotary_emb(pooled, position_ids=positions, layer_type="compress")
+    pool_nope, pool_rope = pooled[..., :-rope_head_dim], pooled[..., -rope_head_dim:]
+    pool_rope, _ = apply_rotary_pos_emb(pool_rope.unsqueeze(1), torch.zeros_like(pool_rope.unsqueeze(1)), cos, sin)
+    return torch.cat([pool_nope, pool_rope.squeeze(1)], dim=-1)
+
+
+class DeepseekV4HCACompressor(nn.Module):
+    """Heavily Compressed Attention compressor (paper §2.3.2, eqs. 20–23). Pools
+    every ``compress_rate_hca`` (m'=128) source tokens into a single compressed KV
+    entry with **non-overlapping** windows — no overlap state, no indexer.
+
+    The three building blocks (paper notation in parentheses):
+
+      * **kv** = ``wkv(hidden_states)`` — head-dim KV projection ``C ∈ R^{n×c}``
+        (eq. 20). Doubles as both key and value (shared-KV MQA).
+      * **gate** = ``wgate(hidden_states)`` — head-dim compression weights
+        ``Z ∈ R^{n×c}`` (eq. 21). Combined with ``position_bias`` and softmaxed per
+        window to produce the convex combination that mixes ``compress_rate_hca``
+        source KVs into one pooled entry.
+      * **pool** — running list of compressed KV entries (``C^{Comp}``, eq. 23).
+        Lives on :class:`DeepseekV4HCACache`; the in-flight buffer of tokens that
+        haven't yet filled a window lives there too.
+
+    Each closed window of m' tokens produces one pooled entry:
+    ``C^{Comp}_i = Σ_{j∈window} softmax(Z_j + B)_j ⊙ C_j``. RoPE on the trailing
+    ``rope_head_dim`` slice is applied at the deterministic absolute position
+    ``i * compress_rate_hca + first_pool_position`` so cross-call concatenation stays
+    causality-correct. Returns the running pool ``[B, 1, T, head_dim]``.
+
+    When ``past_key_values is None`` (a checkpoint replay zeroes the cache to break
+    the grad-cache loop), runs in stateless single-shot mode: pool every complete
+    window from ``hidden_states`` and discard the remainder.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_hca
+        self.head_dim = config.head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.wkv = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.Tensor:
+        # ``q_residual`` / ``position_ids`` are unused — the uniform forward signature
+        # lets :class:`DeepseekV4Attention` call either compressor without branching.
+        batch, _, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_compressor(kv, gate)
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, self.head_dim)
+            chunk_gate = chunk_gate.view(batch, n_windows, self.compress_rate, self.head_dim) + self.position_bias.to(
+                chunk_gate.dtype
+            )
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            new_pooled = _rope_pool(new_pooled, self.rotary_emb, positions, self.rope_head_dim)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        if cache_layer is None:
+            return new_pooled.unsqueeze(1)
+        return cache_layer.update_compressor_pool(new_pooled).unsqueeze(1)
+
+
+class DeepseekV4CSACompressor(nn.Module):
+    """Compressed Sparse Attention compressor (paper §2.3.1, eqs. 9–17). Pools every
+    ``compress_rate_csa`` (m=4) source tokens with **overlapping** windows — stride
+    ``compress_rate_csa`` and effective width ``2 * compress_rate_csa`` — and runs a
+    Lightning Indexer on top of the pool that scores queries with
+    ``∑_h w_{t,h} · ReLU(q_{t,h} · K^{IComp}_s)`` to gather the top ``index_topk``
+    entries per query before they reach core attention.
+
+    Compared to :class:`DeepseekV4HCACompressor` the differences are explicit:
+
+      * ``wkv`` / ``wgate`` / ``position_bias`` project to **2 × head_dim** (the
+        learned channel split — high half pools into the current window, low half
+        pools into the next window's overlap with this one, see :func:`_overlap_pool`).
+      * The cache layer's ``compressor_overlap_*`` state carries the last full
+        window across forward calls.
+      * A :class:`DeepseekV4Indexer` sub-module gathers the top-``index_topk`` pool
+        entries per query (paper §2.3.1, "Lightning Indexer for Sparse Selection").
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_csa
+        self.head_dim = config.head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        # ``2 * head_dim`` because windows overlap: each pooled entry is a softmax-gated
+        # convex combination of ``compress_rate_csa`` *current* tokens (high-channel half)
+        # mixed with ``compress_rate_csa`` *previous* tokens (low-channel half). The
+        # learned channel split happens in :func:`_overlap_pool`.
+        self.wkv = nn.Linear(config.hidden_size, 2 * self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, 2 * self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, 2 * self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+        self.indexer = DeepseekV4Indexer(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.Tensor:
+        batch, seq_len, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+            prior_kv, prior_gate = None, None
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_compressor(kv, gate)
+            prior_kv, prior_gate = cache_layer.get_compressor_overlap()
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, 2 * self.head_dim)
+            chunk_gate = chunk_gate.view(
+                batch, n_windows, self.compress_rate, 2 * self.head_dim
+            ) + self.position_bias.to(chunk_gate.dtype)
+            if cache_layer is not None:
+                # Persist the *raw* last full window (gate already biased) so the next
+                # forward call's first window can read its low-channel slice as prior.
+                cache_layer.set_compressor_overlap(chunk_kv[:, -1].clone(), chunk_gate[:, -1].clone())
+            chunk_kv, chunk_gate = _overlap_pool(chunk_kv, chunk_gate, prior_kv, prior_gate, self.head_dim)
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            new_pooled = _rope_pool(new_pooled, self.rotary_emb, positions, self.rope_head_dim)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        pooled = (
+            new_pooled.unsqueeze(1)
+            if cache_layer is None
+            else cache_layer.update_compressor_pool(new_pooled).unsqueeze(1)
+        )
+        # Lightning Indexer: gather top-``index_topk`` pool entries per query.
+        topk = self.indexer(hidden_states, q_residual, position_ids, past_key_values, layer_idx)  # [B, S, k]
+        expanded = pooled.unsqueeze(2).expand(-1, -1, seq_len, -1, -1)
+        idx = topk.unsqueeze(1).unsqueeze(-1).expand(-1, 1, -1, -1, self.head_dim)
+        return torch.gather(expanded, 3, idx).reshape(batch, 1, -1, self.head_dim)
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float | int = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    sinks = module.sinks.reshape(1, -1, 1, 1).expand(query.shape[0], -1, query.shape[-2], -1)
+    combined_logits = torch.cat([attn_weights, sinks], dim=-1)
+
+    # This was not in the original implementation and slightly affect results; it prevents overflow in BF16/FP16
+    # when training with bsz>1 we clamp max values.
+
+    combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values
+    probs = F.softmax(combined_logits, dim=-1, dtype=combined_logits.dtype)
+    scores = probs[..., :-1]  # we drop the sink here
+    attn_weights = nn.functional.dropout(scores, p=dropout, training=module.training).to(value_states.dtype)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, attn_weights
+
+
+COMPRESSOR_CLASSES = {
+    "sliding_attention": None,
+    "compressed_sparse_attention": DeepseekV4CSACompressor,
+    "heavily_compressed_attention": DeepseekV4HCACompressor,
+}
+
+
+# -----------------------------------------------------------------------------
+# Attention with sink.
+# -----------------------------------------------------------------------------
+
+
+class DeepseekV4Attention(nn.Module):
+    """V4 attention block (paper §2.3). Single class for all three layer types — the
+    only thing that varies is the long-range branch (the ``compressor`` sub-module);
+    the surrounding QKV / RoPE / sink / sliding-window / output projection is
+    identical. The three layer types are dispatched by ``COMPRESSOR_CLASSES``:
+
+      * ``sliding_attention``: ``compressor = None``; only the local sliding-window
+        K=V branch ("Full Attention").
+      * ``compressed_sparse_attention``: :class:`DeepseekV4CSACompressor` —
+        low-compression overlapping-window pool plus a Lightning Indexer that keeps
+        the top-``index_topk`` pool entries per query (paper §2.3.1).
+      * ``heavily_compressed_attention``: :class:`DeepseekV4HCACompressor` —
+        high-compression non-overlapping-window pool, no indexer (paper §2.3.2).
+
+    Block components (paper §2.3.3):
+
+      * Shared-KV Multi-Query Attention: ``num_key_value_heads = 1``; ``wkv`` projects
+        directly to that single KV head and the same tensor is read as both key and
+        value.
+      * Partial RoPE on the first ``rope_head_dim`` of each head ("Partial Rotary
+        Positional Embedding"). RoPE is also applied with position ``-i`` to the
+        attention output's rope slice, so the contribution of each KV entry stays a
+        function of the *relative* distance to the query.
+      * RMSNorm on the queries (``q_norm``) and the compressed KV head (``kv_norm``)
+        right before the core attention, to keep logits bounded.
+      * Per-head learnable attention sink (eq. 27).
+      * Grouped low-rank output projection (§2.3.1, "Grouped Output Projection"):
+        ``g`` head-groups → ``d_g``-dim intermediate outputs through a block-diagonal
+        :class:`DeepseekV4GroupedLinear`, then mixed back to ``hidden_size`` by ``wo_b``.
+      * A supplementary uncompressed sliding-window KV branch of size
+        ``sliding_window`` ("Additional Branch of Sliding Window Attention") that
+        preserves local fine-grained dependencies, concatenated with the
+        long-range compressor's output before core attention.
+    """
+
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        # V4 doesn't reuse V3's MLA projections (q_a/q_b/kv_a_proj_with_mqa/kv_b_proj/
+        # o_proj) — every V4 block is shared-KV MQA with a single ``wkv`` and a grouped
+        # output projection — so inheriting from ``DeepseekV3Attention`` only to delete
+        # half of what its ``__init__`` builds is not worth it. We init from
+        # ``nn.Module`` directly and set up V4-specific projections inline.
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.layer_type = config.layer_types[layer_idx]
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_groups = config.num_attention_heads  # single KV head, broadcast to all
+        self.head_dim = config.head_dim
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.sliding_window = config.sliding_window
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.scaling = self.head_dim**-0.5
+
+        self.wq_a = nn.Linear(config.hidden_size, config.q_lora_rank, bias=False)
+        self.q_norm = DeepseekV4RMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
+        self.wq_b = nn.Linear(config.q_lora_rank, self.num_heads * self.head_dim, bias=False)
+        self.wkv = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.wo_a = DeepseekV4GroupedLinear(
+            self.num_heads * self.head_dim // config.o_groups, config.o_groups * config.o_lora_rank, config.o_groups
+        )
+        self.wo_b = nn.Linear(config.o_groups * config.o_lora_rank, config.hidden_size, bias=False)
+        self.sinks = nn.Parameter(torch.empty(self.num_heads))
+        # Long-range branch dispatched by ``layer_type`` (see ``COMPRESSOR_CLASSES``
+        # above). ``None`` means full-attention / sliding-only — no compressor is
+        # built and the layer keeps just the local sliding-window K=V branch.
+        compressor_cls = COMPRESSOR_CLASSES[self.layer_type]
+        self.compressor = compressor_cls(config) if compressor_cls is not None else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        position_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        batch, seq_len = hidden_states.shape[:2]
+        cos, sin = position_embeddings
+
+        # --- Q + KV projections + partial RoPE on the *trailing* qk_rope_head_dim of
+        # each head (matches the V4-Flash reference's ``[..., -rd:]`` indexing — wkv
+        # weights are laid out [nope|rope] in the checkpoint, so the trailing slice is
+        # what gets rotated).
+        q_residual = self.q_norm(self.wq_a(hidden_states))
+        q = self.wq_b(q_residual).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        # Per-head RMSNorm-style rescale (no learned weight) — the V4-Flash reference
+        # (``inference/model.py:498``) does ``q *= rsqrt(mean(q**2) + eps)`` on each
+        # head after wq_b, before RoPE. Skipping it leaves attention scores at the
+        # wrong scale and the model collapses to a single repeated token within a
+        # handful of layers.
+        q = q * torch.rsqrt(q.float().square().mean(-1, keepdim=True) + self.config.rms_norm_eps).to(q.dtype)
+        kv = self.kv_norm(self.wkv(hidden_states)).view(batch, seq_len, 1, self.head_dim).transpose(1, 2)
+        q_nope, q_rope = q[..., : -self.qk_rope_head_dim], q[..., -self.qk_rope_head_dim :]
+        kv_nope, kv_rope = kv[..., : -self.qk_rope_head_dim], kv[..., -self.qk_rope_head_dim :]
+        q_rope, kv_rope = apply_rotary_pos_emb(q_rope, kv_rope, cos, sin)
+        q = torch.cat([q_nope, q_rope], dim=-1)
+        kv = torch.cat([kv_nope, kv_rope], dim=-1)
+
+        # --- Sliding-window K=V branch goes through the standard cache update ---
+        if past_key_values is not None:
+            kv, _ = past_key_values.update(kv, kv, self.layer_idx)
+
+        # Sliding-only layers skip the long-range branch (no compressor was built).
+        # For HCA / CSA, ``DynamicCache(config=...)`` builds the right cache layer per
+        # ``config.layer_types[i]`` via ``LAYER_TYPE_CACHE_MAPPING``, so the compressor
+        # reads its layer state from ``past_key_values.layers[layer_idx]``.
+        # ``past_key_values`` is ``None`` only when ``GradientCheckpointingLayer`` zeroes
+        # it during a checkpoint replay — the compressor handles that as a single-shot
+        # window pool with no persistent state.
+        if self.compressor is None:
+            full_kv = kv
+        else:
+            compressed_kv = self.compressor(hidden_states, q_residual, position_ids, past_key_values, self.layer_idx)
+            full_kv = torch.cat([kv, compressed_kv], dim=2)
+
+        if attention_mask is not None and full_kv.shape[2] > attention_mask.shape[-1]:
+            attention_mask = F.pad(attention_mask, (0, full_kv.shape[2] - attention_mask.shape[-1]), value=0.0)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+        attn_output, attn_weights = attention_interface(
+            self,
+            q,
+            full_kv,
+            full_kv,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            s_aux=self.sinks,
+            **kwargs,
+        )
+
+        # De-rotate the output's rope slice. V4 shares K and V (``wkv`` projects to a
+        # single tensor), so V's rope slice carries the same per-token rotation as K.
+        # Attention sums V-rotated values across attended positions, so the output's
+        # rope slice is a position-mixed content; conjugate rotation at the query
+        # position pulls it back into a position-independent frame before the output
+        # projection mixes heads.
+        out_nope, out_rope = attn_output[..., : -self.qk_rope_head_dim], attn_output[..., -self.qk_rope_head_dim :]
+        out_rope = out_rope.transpose(1, 2)
+        out_rope, _ = apply_rotary_pos_emb(out_rope, torch.zeros_like(out_rope), cos, -sin)
+        attn_output = torch.cat([out_nope, out_rope.transpose(1, 2)], dim=-1)
+
+        grouped = attn_output.reshape(batch, seq_len, -1).view(batch, seq_len, self.config.o_groups, -1)
+        return self.wo_b(self.wo_a(grouped).flatten(2)), attn_weights
+
+
+class DeepseekV4HyperConnection(nn.Module):
+    r"""
+    Manifold-Constrained Hyper-Connections
+    (mHC) (Xie et al., 2026) to strengthen the conventional residual connections between adjacent
+    Transformer blocks
+
+    Owns the learned (``fn``, ``base``, ``scale``)
+    parameters that turn the incoming ``hc_mult`` residual streams into collapse / expand
+    weights. The decoder layer instantiates two of these (one for the attention site,
+    one for the mlp site).
+
+    ASCII shape guide — ``B`` = batch, ``S`` = seq, ``H`` = hc_mult, ``D`` = hidden_size::
+
+              hidden_streams        flatten(2)        RMSNorm-rescale + F.linear(fn)
+         [B, S, H, D]  ──────────►  [B, S, H*D]  ─────────────────────────────────►
+                                                             mix-logits
+                                                             [B, S, (2+H)*H]
+                                                                    │
+                            ┌───────────────────────────────────────┴──────────────────────────────┐
+                            ▼                          ▼                                           ▼
+                        pre logits                post logits                               comb logits
+                        [B, S, H]                 [B, S, H]                                 [B, S, H, H]
+                        × scale[0]                × scale[1]                                × scale[2]
+                        + base[:H]                + base[H:2H]                              + base[2H:]
+                        σ() + eps                 σ() + eps                                 σ() + eps
+                        │                         │                                         │
+                        pre                        post                                     Sinkhorn(iters)
+                        (stream collapse weights)  (block-output placement)                 row/col normalise
+                                                                                            │
+                                                                                            comb
+                                                                                            (stream mixer)
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.hc_mult = config.hc_mult
+        self.hc_sinkhorn_iters = config.hc_sinkhorn_iters
+        self.hc_eps = config.hc_eps
+        self.norm_eps = config.rms_norm_eps
+        mix = (2 + self.hc_mult) * self.hc_mult
+        self.fn = nn.Parameter(torch.empty(mix, self.hc_mult * config.hidden_size))
+        self.base = nn.Parameter(torch.empty(mix))
+        self.scale = nn.Parameter(torch.empty(3))
+
+    def forward(self, hidden_streams: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""
+        project it onto the manifold of doubly stochastic matrices M.
+        This is achieved by the Sinkhorn-Knopp algorithm, which first applies an exponential function
+        ˜
+        to
+        𝐵𝑙 to ensure positivity, getting 𝑀(0) = exp(˜
+        𝐵𝑙), and then iteratively performs column and row
+        normalization:
+        𝑀(𝑡) = T𝑟(T𝑐(𝑀(𝑡−1))), (8)
+        where T𝑟 and T𝑐 denote row and column normalization, respectively.
+        """
+        flat = hidden_streams.flatten(start_dim=2).float()
+        rsqrt = torch.rsqrt(flat.square().mean(-1, keepdim=True) + self.norm_eps)
+        mix = F.linear(flat, self.fn.float()) * rsqrt  # [B, S, (2+H)*H]
+        pre_scale, post_scale, comb_scale = self.scale.unbind(0)
+        hc = self.hc_mult
+        pre = torch.sigmoid(mix[..., :hc] * pre_scale + self.base[:hc]) + self.hc_eps
+        post = torch.sigmoid(mix[..., hc : 2 * hc] * post_scale + self.base[hc : 2 * hc]) + self.hc_eps
+        comb = (
+            torch.sigmoid(
+                mix[..., 2 * hc :].view(*mix.shape[:-1], hc, hc) * comb_scale + self.base[2 * hc :].view(hc, hc)
+            )
+            + self.hc_eps
+        )
+        for _ in range(self.hc_sinkhorn_iters):
+            comb = comb / (comb.sum(dim=-1, keepdim=True) + self.hc_eps)
+            comb = comb / (comb.sum(dim=-2, keepdim=True) + self.hc_eps)
+        return pre, post, comb
+
+
+class DeepseekV4HyperHead(nn.Module):
+    """Final HC-stream collapse; used by ``DeepseekV4Model`` before the shared RMSNorm."""
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.hc_mult = config.hc_mult
+        self.norm_eps = config.rms_norm_eps
+        self.eps = config.hc_eps
+        self.hc_fn = nn.Parameter(torch.empty(self.hc_mult, self.hc_mult * config.hidden_size))
+        self.hc_base = nn.Parameter(torch.empty(self.hc_mult))
+        self.hc_scale = nn.Parameter(torch.empty(1))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        flat = x.flatten(2).float()
+        rsqrt = torch.rsqrt(flat.square().mean(-1, keepdim=True) + self.norm_eps)
+        mixes = F.linear(flat, self.hc_fn.float()) * rsqrt
+        pre = torch.sigmoid(mixes * self.hc_scale.float() + self.hc_base.float()) + self.eps
+        return (pre.unsqueeze(-1) * x).sum(dim=2).to(x.dtype)
+
+
+class DeepseekV4MLP(nn.Module):
+    """Shared expert — plain SwiGLU MLP, ``moe_intermediate_size`` hidden."""
+
+    def __init__(self, config: DeepseekV4Config, intermediate_size: int | None = None):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size if intermediate_size is None else intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+@use_experts_implementation
+class DeepseekV4Experts(nn.Module):
+    """Routed experts: per-expert iteration + ``_apply_gate`` hook from GPT-OSS, but
+    using the Mixtral weight layout (no biases, ``[num_experts, 2*intermediate, hidden]``
+    for ``gate_up_proj`` and ``[num_experts, hidden, intermediate]`` for ``down_proj``).
+    Activation is SiLU and gate/up are clamped to ``swiglu_limit`` before mixing.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.num_experts = config.n_routed_experts
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.moe_intermediate_size
+        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_size, self.hidden_size))
+        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, self.intermediate_size))
+        self.limit = config.swiglu_limit
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor:
+        gate, up = gate_up.chunk(2, dim=-1)
+        gate = gate.clamp(max=self.limit)
+        up = up.clamp(min=-self.limit, max=self.limit)
+        return self.act_fn(gate) * up
+
+    def forward(
+        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
+    ) -> torch.Tensor:
+        final = torch.zeros_like(hidden_states)
+        with torch.no_grad():
+            mask = F.one_hot(top_k_index, num_classes=self.num_experts).permute(2, 1, 0)
+            hit = torch.greater(mask.sum(dim=(-1, -2)), 0).nonzero()
+        for expert_idx in hit:
+            expert_idx = expert_idx[0]
+            if expert_idx == self.num_experts:
+                continue
+            top_k_pos, token_idx = torch.where(mask[expert_idx])
+            gate_up = F.linear(hidden_states[token_idx], self.gate_up_proj[expert_idx])
+            current = self._apply_gate(gate_up)
+            current = F.linear(current, self.down_proj[expert_idx]) * top_k_weights[token_idx, top_k_pos, None]
+            final.index_add_(0, token_idx, current.to(final.dtype))
+        return final
+
+
+class DeepseekV4TopKRouter(nn.Module):
+    """DeepSeekMoE top-k router (paper §2.1, "Mixture-of-Experts"). Two changes from
+    the V3 router:
+
+      * The expert affinity activation is ``Sqrt(Softplus(·))`` instead of the V3
+        Sigmoid (paper §2.1: "we change the activation function that computes the
+        affinity scores from Sigmoid(·) into Sqrt(Softplus(·))"). The ``scoring_func``
+        config field selects this for V4 checkpoints.
+      * The constraint on the number of routing target nodes used in V3 is dropped,
+        and the V3 ``n_group`` / ``topk_group`` machinery is removed entirely (paper
+        §2.1: "we remove the constraint on the number of routing target nodes").
+
+    The auxiliary-loss-free strategy is preserved via the per-expert ``bias`` buffer
+    that biases the top-k argmax without flowing gradients (same ``noaux_tc`` idea
+    as DeepSeek-V3).
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_local_experts
+        self.hidden_dim = config.hidden_size
+        self.weight = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim))
+        self.score_fn = ACT2FN[config.scoring_func]
+        self.routed_scaling_factor = config.routed_scaling_factor
+        # The correction bias biases the argmax only — never gradient-carrying — so it's
+        # a buffer (same convention as DeepseekV3's ``e_score_correction_bias``).
+        self.register_buffer("bias", torch.zeros(self.num_experts), persistent=True)
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        flat = hidden_states.reshape(-1, self.hidden_dim)
+        logits = F.linear(flat.float(), self.weight.float())
+        scores = self.score_fn(logits)
+        indices = torch.topk(scores + self.bias, self.top_k, dim=-1, sorted=False).indices
+        weights = scores.gather(1, indices)
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-20)
+        return logits, weights * self.routed_scaling_factor, indices
+
+
+class DeepseekV4HashRouter(nn.Module):
+    """Hash routing for the first ``num_hash_layers`` MoE layers (paper §2.1, "Mixture-
+    of-Experts"). The first three blocks of V4 replace the dense FFN of V3 with an MoE
+    where the expert selection is determined by a fixed hash of the input token id —
+    a frozen ``tid2eid`` (token id to expert id) lookup — instead of a learned gate.
+    The learned gate ``weight`` still produces the per-expert scoring values used to
+    weight the selected experts' activations; only the *which-experts* selection is
+    static.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.top_k = config.num_experts_per_tok
+        self.num_experts = config.num_local_experts
+        self.hidden_dim = config.hidden_size
+        self.weight = nn.Parameter(torch.empty(self.num_experts, self.hidden_dim))
+        self.score_fn = ACT2FN[config.scoring_func]
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.register_buffer(
+            "tid2eid",
+            torch.zeros(config.vocab_size, self.top_k, dtype=torch.long),
+            persistent=True,
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_ids: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        flat = hidden_states.reshape(-1, self.hidden_dim)
+        logits = F.linear(flat.float(), self.weight.float())
+        scores = self.score_fn(logits)
+        indices = self.tid2eid[input_ids.reshape(-1)].long()
+        weights = scores.gather(1, indices)
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-20)
+        return logits, weights * self.routed_scaling_factor, indices
+
+
+class DeepseekV4SparseMoeBlock(nn.Module):
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        super().__init__()
+        self.is_hash = layer_idx < config.num_hash_layers
+        self.gate = DeepseekV4HashRouter(config) if self.is_hash else DeepseekV4TopKRouter(config)
+        self.experts = DeepseekV4Experts(config)
+        self.shared_experts = DeepseekV4MLP(config)
+
+    def forward(self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None, **_) -> torch.Tensor:
+        batch, seq_len, hidden_dim = hidden_states.shape
+        residual = hidden_states
+        flat = hidden_states.view(-1, hidden_dim)
+        if self.is_hash:
+            if input_ids is None:
+                raise ValueError(
+                    "DeepseekV4's hash-routing layers need `input_ids` to look up expert indices. "
+                    "The `inputs_embeds`-only inference path is not supported for models with "
+                    "`num_hash_layers > 0`."
+                )
+            _, weights, indices = self.gate(hidden_states, input_ids)
+        else:
+            _, weights, indices = self.gate(hidden_states)
+        routed = self.experts(flat, indices, weights).view(batch, seq_len, hidden_dim)
+        return routed + self.shared_experts(residual)
+
+
+class DeepseekV4DecoderLayer(GradientCheckpointingLayer):
+    r"""DeepSeek-V4 decoder block (paper §2). Differs from a classic residual block in
+    two places:
+
+      * The residual is a stack of ``hc_mult`` parallel streams kept in shape
+        ``[B, S, hc_mult, D]`` throughout the block, mixed in and out via two
+        :class:`DeepseekV4HyperConnection` modules (Manifold-Constrained Hyper-
+        Connections / mHC, paper §2.2; Xie et al., 2026). The mHC mappings constrain
+        the residual transform to the manifold of doubly-stochastic matrices via the
+        Sinkhorn-Knopp projection — making signal propagation non-expansive across
+        deep stacks.
+      * ``self_attn`` is :class:`DeepseekV4Attention` for every layer. Its compressor
+        sub-module is the only thing that varies by layer type
+        (:class:`DeepseekV4HCACompressor` for HCA layers,
+        :class:`DeepseekV4CSACompressor` for CSA, picked via
+        ``config.layer_types[layer_idx]``); the CSA compressor also owns the
+        Lightning Indexer at ``self_attn.compressor.indexer``.
+
+    Classic residual decoder layer::
+
+        h ──► norm ──► self_attn ──► + ──► norm ──► mlp ──► +
+        └──────── residual ────────┘   └─────── residual ───┘
+
+    Deepseek V4 decoder layer (``H = hc_mult`` parallel residual streams throughout)::
+
+                attention site                                    mlp site
+        ┌────────────────────────────────────────┐    ┌────────────────────────────────────────┐
+        │  hidden_streams [B, S, H, D]           │    │  hidden_streams [B, S, H, D]           │
+        │        │                               │    │        │                               │
+        │  attn_hc(streams) ─► (pre, post, comb) │    │  ffn_hc(streams) ─► (pre, post, comb)  │
+        │        │                               │    │        │                               │
+        │   Σ pre·streams  (collapse)            │    │   Σ pre·streams  (collapse)            │
+        │        │                               │    │        │                               │
+        │   input_layernorm                      │    │   post_attention_layernorm             │
+        │        │                               │    │        │                               │
+        │   self_attn                            │    │   mlp  (MoE routed + shared)           │
+        │        │                               │    │        │                               │
+        │   post·output + comb·streams  (expand) │    │   post·output + comb·streams  (expand) │
+        │        │                               │    │        │                               │
+        │        ▼                               │    │        ▼                               │
+        │  new hidden_streams  ──────────────────┘    │  new hidden_streams                    │
+        └────────────────────────────────────────┘    └────────────────────────────────────────┘
+
+
+    """
+
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.self_attn = DeepseekV4Attention(config, layer_idx)
+        self.mlp = DeepseekV4SparseMoeBlock(config, layer_idx)
+        self.input_layernorm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attn_hc = DeepseekV4HyperConnection(config)
+        self.ffn_hc = DeepseekV4HyperConnection(config)
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> torch.Tensor:
+        # hidden_states throughout: [B, S, hc_mult, hidden].
+
+        # --- Attention site: collapse → norm → attn → expand ---
+        pre, post, comb = self.attn_hc(hidden_states)
+        collapsed = (pre.unsqueeze(-1) * hidden_states).sum(dim=2).to(hidden_states.dtype)
+        attn_output, _ = self.self_attn(self.input_layernorm(collapsed), **kwargs)
+        dtype = hidden_states.dtype
+        hidden_states = post.to(dtype).unsqueeze(-1) * attn_output.unsqueeze(-2) + torch.matmul(
+            comb.to(dtype), hidden_states
+        )
+
+        # --- MLP site: collapse → norm → mlp → expand ---
+        pre, post, comb = self.ffn_hc(hidden_states)
+        collapsed = (pre.unsqueeze(-1) * hidden_states).sum(dim=2).to(hidden_states.dtype)
+        mlp_output = self.mlp(self.post_attention_layernorm(collapsed), input_ids=kwargs.get("input_ids"))
+        dtype = hidden_states.dtype
+        return post.to(dtype).unsqueeze(-1) * mlp_output.unsqueeze(-2) + torch.matmul(comb.to(dtype), hidden_states)
+
+
+@auto_docstring
+class DeepseekV4PreTrainedModel(PreTrainedModel):
+    config: DeepseekV4Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["DeepseekV4DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    # V4 ships eager-only: the compressor / indexer paths weren't validated against
+    # SDPA / FlashAttention / FlexAttention kernels — leaving these ``False`` makes
+    # ``set_attn_implementation`` reject those backends instead of silently routing
+    # through them.
+    _supports_flash_attn = False
+    _supports_sdpa = False
+    _supports_flex_attn = False
+    # The compressor's rolling-window buffer / pool / overlap state lives on the
+    # per-layer cache (:class:`DeepseekV4HCACache` / :class:`DeepseekV4CSACache`)
+    # and isn't compatible with :class:`StaticCache` — that path would hand the
+    # compressor a :class:`StaticSlidingWindowLayer` with no ``update_compressor``
+    # method. Disabling fullgraph compile keeps generation tests on the dynamic
+    # cache build that does dispatch to V4's own cache layers.
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(DeepseekV4TopKRouter, index=0),
+        "hidden_states": DeepseekV4DecoderLayer,
+        "attentions": DeepseekV4Attention,
+    }
+    config_class = DeepseekV4Config
+    _keep_in_fp32_modules_strict = ["attn_hc", "ffn_hc"]
+    _keys_to_ignore_on_load_unexpected = [r"model\.mtp\..*"]
+    # ``_is_stateful`` opts out of generation modes that need to roll the cache
+    # back across drafts (assisted generation, prompt lookup, contrastive search).
+    # The compressor's running-window state isn't rewindable, so ``generate``
+    # raises a clear error early instead of failing deep in the compressor with
+    # a missing-method ``AttributeError``.
+    _is_stateful = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        std = self.config.initializer_range
+        if isinstance(module, (DeepseekV4TopKRouter, DeepseekV4HashRouter)):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if isinstance(module, DeepseekV4TopKRouter):
+                init.zeros_(module.bias)  # buffer
+            if isinstance(module, DeepseekV4HashRouter):
+                init.zeros_(module.tid2eid)  # buffer; real values come from the checkpoint
+        elif isinstance(module, DeepseekV4Experts):
+            init.normal_(module.gate_up_proj, mean=0.0, std=std)
+            init.normal_(module.down_proj, mean=0.0, std=std)
+        elif isinstance(module, DeepseekV4Attention):
+            init.zeros_(module.sinks)
+        elif isinstance(module, DeepseekV4HyperConnection):
+            init.normal_(module.fn, mean=0.0, std=std)
+            init.zeros_(module.base)
+            init.ones_(module.scale)
+        elif isinstance(module, DeepseekV4HyperHead):
+            init.normal_(module.hc_fn, mean=0.0, std=std)
+            init.zeros_(module.hc_base)
+            init.ones_(module.hc_scale)
+        elif isinstance(module, (DeepseekV4HCACompressor, DeepseekV4CSACompressor, DeepseekV4Indexer)):
+            init.zeros_(module.position_bias)
+        elif isinstance(module, DeepseekV4RotaryEmbedding):
+            for layer_type in module.layer_types:
+                rope_init_fn = module.compute_default_rope_parameters
+                if module.rope_type[layer_type] != "default":
+                    rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type[layer_type]]
+                curr_inv_freq, _ = rope_init_fn(module.config, layer_type=layer_type)
+                init.copy_(getattr(module, f"{layer_type}_inv_freq"), curr_inv_freq)
+                init.copy_(getattr(module, f"{layer_type}_original_inv_freq"), curr_inv_freq)
+
+
+@auto_docstring
+class DeepseekV4Model(DeepseekV4PreTrainedModel):
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList(
+            [DeepseekV4DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hc_head = DeepseekV4HyperHead(config)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+        self.rotary_emb_compress = DeepseekV4RotaryEmbedding(config)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+        # V4's compressor reads / writes per-layer buffer state on the cache, so we
+        # always build a ``DynamicCache(config=...)`` internally — even when
+        # ``use_cache=False`` we need a forward-scoped cache to thread the compressor's
+        # buffer through the window pooling. ``LAYER_TYPE_CACHE_MAPPING`` populates the
+        # right :class:`DeepseekV4HCACache` / :class:`DeepseekV4CSACache` per layer.
+        # When ``use_cache=False`` we still hand the layers a real cache; we just don't
+        # surface it back to the caller so the user-facing semantics match other models.
+        return_cache = past_key_values if use_cache else None
+        if past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        if position_ids is None:
+            past_seen = past_key_values.get_seq_length()
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen
+            position_ids = position_ids.unsqueeze(0)
+        # ``generate()`` may pass a per-layer-type mask dict already built by
+        # ``create_masks_for_generate``; all V4 layer types use the same sliding-window
+        # mask, so use the prebuilt one directly. Otherwise build it here.
+        if isinstance(attention_mask, dict):
+            causal_mask = next(iter(attention_mask.values()))
+        else:
+            causal_mask = create_sliding_window_causal_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                position_ids=position_ids,
+            )
+        hidden_states = inputs_embeds.unsqueeze(2).expand(-1, -1, self.config.hc_mult, -1).contiguous()
+        cos_sin = self.rotary_emb(inputs_embeds, position_ids=position_ids, layer_type="main")
+
+        for layer in self.layers:
+            hidden_states = layer(
+                hidden_states,
+                position_embeddings=cos_sin,
+                position_ids=position_ids,
+                attention_mask=causal_mask,
+                input_ids=input_ids,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(self.hc_head(hidden_states))
+        return MoeModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=return_cache)
+
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor | tuple[torch.Tensor] | None,
+    num_experts: int | None = None,
+    top_k=2,
+    attention_mask: torch.Tensor | None = None,
+) -> torch.Tensor | int:
+    r"""
+    Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+    See Switch Transformer (https://huggingface.co/papers/2101.03961) for more details. This function implements the loss
+    function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+    experts is too unbalanced.
+
+    Args:
+        gate_logits:
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        num_experts:
+            Number of experts
+        top_k:
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter.
+        attention_mask (`torch.Tensor`, *optional*):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+            .reshape(-1, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+    return overall_loss * num_experts
+
+
+@auto_docstring
+class DeepseekV4ForCausalLM(DeepseekV4PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tp_plan = {"lm_head": "colwise_gather_output"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.model = DeepseekV4Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_local_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_router_logits: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeCausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, DeepseekV4ForCausalLM
+
+        >>> model = DeepseekV4ForCausalLM.from_pretrained("mistralai/DeepseekV4-8x7B-v0.1")
+        >>> tokenizer = AutoTokenizer.from_pretrained("mistralai/DeepseekV4-8x7B-v0.1")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs: MoeModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_router_logits=output_router_logits,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits,
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
+
+
+__all__ = ["DeepseekV4PreTrainedModel", "DeepseekV4Model", "DeepseekV4ForCausalLM"]
diff --git a/src/transformers/models/deepseek_v4/modular_deepseek_v4.py b/src/transformers/models/deepseek_v4/modular_deepseek_v4.py
new file mode 100644
index 000000000000..46a4ef9e2fa1
--- /dev/null
+++ b/src/transformers/models/deepseek_v4/modular_deepseek_v4.py
@@ -0,0 +1,1542 @@
+# Copyright 2026 the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+from collections.abc import Callable
+
+import torch
+import torch.nn.functional as F
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, DynamicSlidingWindowLayer
+from ...configuration_utils import PreTrainedConfig
+from ...integrations import use_experts_implementation
+from ...masking_utils import create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import MoeModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, RopeParameters
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import OutputRecorder, capture_outputs
+from ..deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config
+from ..deepseek_v3.modeling_deepseek_v3 import DeepseekV3RMSNorm, apply_rotary_pos_emb_interleave
+from ..gemma3.modeling_gemma3 import Gemma3RotaryEmbedding
+from ..gpt_oss.modeling_gpt_oss import GptOssExperts, eager_attention_forward
+from ..mixtral.modeling_mixtral import MixtralForCausalLM, MixtralPreTrainedModel, MixtralTopKRouter
+from ..qwen2_moe.modeling_qwen2_moe import Qwen2MoeMLP
+
+
+def apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    unsqueeze_dim: int = 1,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """V4 wraps :func:`~transformers.models.deepseek_v3.modeling_deepseek_v3.apply_rotary_pos_emb_interleave`
+    with a permute-back so the rope slice exits in the same interleaved
+    ``[a0, b0, a1, b1, …]`` layout it came in with.
+
+    V3's helper restages interleaved pairs into the halves layout
+    (``[a0, a1, …, b0, b1, …]``) so it can run llama's half-split RoPE primitive,
+    and leaves the result in that layout — fine for V3 because V3 is MLA: V has
+    its own ``v_head_dim`` and never carries a rope slice, so the post-rotation
+    layout of Q / K only matters for the dot product (which is invariant under a
+    consistent permutation of channels on both sides).
+
+    V4 is shared-KV MQA: V is the same tensor as K, so V's rope slice picks up
+    the rotation too — and then the attention sum, the per-head ``wo_a``
+    grouped projection, and ``wo_b`` all consume that rope slice as part of
+    their input. Those weights were trained against the V4-Flash reference
+    (``inference/model.py:apply_rotary_emb`` does ``view_as_complex``-style
+    rotation in place, preserving the interleaved layout), so we have to put
+    the channels back where they were before passing to ``wo_a`` — otherwise the
+    grouped projection sees its inputs scrambled and ``wo_b(wo_a(...))`` collapses.
+    """
+    q, k = apply_rotary_pos_emb_interleave(q, k, cos, sin, unsqueeze_dim=unsqueeze_dim)
+
+    def _halves_to_interleave(x: torch.Tensor) -> torch.Tensor:
+        # Inverse of V3's ``view(d/2, 2).transpose(-1, -2)``: ``[a0, …, b0, …]`` →
+        # ``[a0, b0, a1, b1, …]``.
+        b, h, s, d = x.shape
+        return x.view(b, h, s, 2, d // 2).transpose(-1, -2).reshape(b, h, s, d)
+
+    return _halves_to_interleave(q), _halves_to_interleave(k)
+
+
+logger = logging.get_logger(__name__)
+
+
+DEEPSEEK_V4_LAYER_TYPES = (
+    "sliding_attention",
+    "compressed_sparse_attention",
+    "heavily_compressed_attention",
+)
+
+
+_COMPRESS_RATIO_TO_LAYER_TYPE = {
+    0: "sliding_attention",
+    4: "compressed_sparse_attention",
+    128: "heavily_compressed_attention",
+}
+
+
+@auto_docstring(checkpoint="deepseek-ai/DeepSeek-V4-Flash-Base")
+@strict
+class DeepseekV4Config(DeepseekV3Config):
+    r"""
+    DeepSeek-V4's hybrid attention follows the paper (Section 2.3): every block is one
+    of three attention types — *Full Attention* (sliding-window only), *Compressed
+    Sparse Attention* (CSA, Section 2.3.1) and *Heavily Compressed Attention* (HCA,
+    Section 2.3.2). CSA compresses the KV cache by ``compress_rate_csa`` (m=4 in V4-
+    Flash/Pro) and selects ``index_topk`` blocks per query via the Lightning Indexer;
+    HCA applies a much heavier compression of ``compress_rate_hca`` (m'=128) and
+    skips sparse selection. Both branches add a small uncompressed sliding-window
+    branch for fine-grained locality.
+
+    layer_types (`list[str]`): Per-layer attention schedule with values from
+        ``{"compressed_sparse_attention", "heavily_compressed_attention"}``.
+        V4-Pro default: 2× HCA bootstrap + interleaved CSA / HCA.
+    compress_rate_csa (`int`): m, the CSA compression rate (default 4).
+    compress_rate_hca (`int`): m', the HCA compression rate (default 128).
+    rope_theta (`float`): RoPE base for the main self-attention rotary.
+    compress_rope_theta (`float`): RoPE base for the compressed branches (paired with
+        ``rope_scaling`` for YaRN).
+    partial_rotary_factor (`float`, *optional*): Fraction of head_dim that gets RoPE.
+        Defaults to ``qk_rope_head_dim / head_dim`` so cos/sin sizes to ``qk_rope_head_dim``.
+    hc_mult (`int`): Manifold-Constrained Hyper-Connection (mHC) expansion factor n_hc
+        (always active; Section 2.2).
+    hc_sinkhorn_iters (`int`): Sinkhorn-Knopp iterations t_max for the mHC residual
+        mapping projection onto doubly-stochastic matrices.
+    hc_eps (`float`): Numerical floor for the Sinkhorn-Knopp normalization.
+    num_hash_layers (`int`): First N MoE layers route via a frozen ``tid2eid[input_ids]`` lookup.
+    scoring_func (`str`): Router activation — ``sqrtsoftplus``, ``softmax``, or ``sigmoid``.
+    swiglu_limit (`float`): Clip routed experts' gate/up pre-activations.
+    sliding_window (`int`): Local window size n_win used in every attention block's
+        sliding-window branch.
+    o_groups (`int`): Number of head-groups g in the grouped output projection
+        (paper §2.3.1, "Grouped Output Projection").
+    o_lora_rank (`int`): Per-group intermediate dim d_g in the grouped output projection.
+    index_n_heads (`int`): Number of indexer query heads n_h^I (paper §2.3.1, eq. 14).
+    index_head_dim (`int`): Indexer head dim c^I (paper §2.3.1).
+    index_topk (`int`): Number of compressed entries per query the Lightning Indexer
+        keeps via top-k (paper §2.3.1, eq. 17).
+    num_nextn_predict_layers (`int`): MTP layer count in the upstream checkpoint
+        (not instantiated here).
+    n_group (`int`, *optional*): V3 MLA expert-group count. Kept for config compat;
+        unused by V4 (no expert groups).
+    first_k_dense_replace (`int`, *optional*): V3 field — the first ``k`` MoE layers
+        to replace with dense FFNs. Kept for config compat; V4 uses hash routing
+        (``num_hash_layers``) instead.
+    rope_interleave (`bool`, *optional*): V3 flag — whether to interleave rope dims.
+        Kept for config compat; V4's RoPE is non-interleaved (rope-first head layout).
+    """
+
+    model_type = "deepseek_v4"
+    attribute_map = {"num_local_experts": "n_routed_experts"}
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.wq_a": "colwise",
+        "layers.*.self_attn.wq_b": "colwise",
+        "layers.*.self_attn.wkv": "colwise",
+        "layers.*.self_attn.wo_a": "rowwise",
+        "layers.*.self_attn.wo_b": "rowwise",
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+    }
+
+    vocab_size: int = 129280
+    hidden_size: int = 4096
+    moe_intermediate_size: int = 2048
+    num_hidden_layers: int = 43
+    num_attention_heads: int = 64
+    num_key_value_heads: int = 1
+    head_dim: int = 512
+    qk_rope_head_dim: int = 64
+    q_lora_rank: int = 1024
+    num_experts_per_tok: int = 6
+    n_routed_experts: int = 256
+    n_shared_experts: int = 1
+    scoring_func: str = "sqrtsoftplus"
+    norm_topk_prob: bool = True
+    routed_scaling_factor: float = 1.5
+    max_position_embeddings: int = 1048576
+    rope_theta: float | int = 10000.0
+
+    layer_types: list[str] | None = None
+    compress_rate_csa: int = 4
+    compress_rate_hca: int = 128
+    compress_rope_theta: float | int = 160000.0
+    hc_mult: int = 4
+    hc_sinkhorn_iters: int = 20
+    hc_eps: float = 1.0e-6
+    num_hash_layers: int = 3
+    swiglu_limit: float = 10.0
+    sliding_window: int = 128
+    o_groups: int = 8
+    o_lora_rank: int = 1024
+    index_n_heads: int = 64
+    index_head_dim: int = 128
+    index_topk: int = 512
+    num_nextn_predict_layers: int = 1
+
+    # V3 fields kept ``None`` so the V3-style MLA paths in inherited configs never fire
+    # (V4 doesn't use MLA — it uses shared-KV MQA via ``wkv`` directly).
+    kv_lora_rank: int | None = None
+    qk_nope_head_dim: int | None = None
+    v_head_dim: int | None = None
+    n_group: int | None = None
+    topk_group: int | None = None
+    first_k_dense_replace: int | None = None
+    rope_interleave: bool | None = True
+
+    output_router_logits: bool = False
+    router_aux_loss_coef: float = 0.001
+    router_jitter_noise: float = 0.0
+
+    rope_parameters: RopeParameters | dict | None = None
+    partial_rotary_factor: float | None = None
+    attention_bias: bool = False
+    attention_dropout: float = 0.0
+
+    def validate_layer_type(self):
+        """V4 narrows the global ``ALLOWED_LAYER_TYPES`` to the two block types it actually
+        ships with, on top of the standard length / type-membership checks.
+        """
+        if self.layer_types is None or self.num_hidden_layers is None:
+            return
+        if len(self.layer_types) != self.num_hidden_layers:
+            raise ValueError(
+                f"`num_hidden_layers` ({self.num_hidden_layers}) must equal "
+                f"`len(layer_types)` ({len(self.layer_types)})."
+            )
+        bad = [layer_type for layer_type in self.layer_types if layer_type not in DEEPSEEK_V4_LAYER_TYPES]
+        if bad:
+            raise ValueError(
+                f"`layer_types` entries must be one of {DEEPSEEK_V4_LAYER_TYPES} for DeepSeek-V4; got {bad}."
+            )
+
+    def __post_init__(self, **kwargs):
+        compress_ratios = kwargs.pop("compress_ratios", None)
+        PreTrainedConfig.__post_init__(self, **kwargs)
+        n = self.num_hidden_layers
+        if self.layer_types is None and compress_ratios is not None:
+            # Translate the V4 checkpoint's per-layer integer ``compress_ratios`` into the
+            # named ``layer_types`` schedule (0 = sliding-only, 4 = CSA, 128 = HCA).
+            self.layer_types = [_COMPRESS_RATIO_TO_LAYER_TYPE[r] for r in compress_ratios]
+        if self.layer_types is None:
+            # V4-Pro default: two HCA bootstrap layers, then CSA / HCA interleaved.
+            interleave = [
+                "compressed_sparse_attention" if i % 2 else "heavily_compressed_attention"
+                for i in range(max(n - 2, 0))
+            ]
+            head = ["heavily_compressed_attention"] * min(n, 2)
+            self.layer_types = head + interleave
+        self.layer_types = list(self.layer_types[:n])
+        self.qk_nope_head_dim = self.head_dim - self.qk_rope_head_dim
+        if self.partial_rotary_factor is None:
+            self.partial_rotary_factor = self.qk_rope_head_dim / self.head_dim
+        # Normalize rope_parameters into a per-rope-type dict ``{"main": {...}, "compress": {...}}``
+        # (Gemma3 pattern, keys are *rope-type* labels — unrelated to ``layer_types``).
+        # Idempotent across save/load: round-tripping preserves structure.
+        #
+        # By the time we get here :class:`PreTrainedConfig` has already run
+        # :meth:`RotaryEmbeddingConfigMixin.convert_rope_params_to_dict`, which folds the
+        # checkpoint's legacy top-level ``rope_scaling`` block into ``self.rope_parameters``
+        # as a flat dict (``rope_type``, ``factor``, ``beta_fast``, ``beta_slow``,
+        # ``original_max_position_embeddings``, …). The block ships under
+        # ``rope_scaling`` in :attr:`config.json` and never appears as a top-level kwarg
+        # for us to intercept before the mixin runs — the mixin always wins. We just
+        # split that flat dict into the two rope-type buckets.
+        rp = self.rope_parameters or {}
+        if isinstance(rp.get("main"), dict) and isinstance(rp.get("compress"), dict):
+            self.rope_parameters = {"main": rp["main"], "compress": rp["compress"]}
+        else:
+            # Build the per-rope-type dict ``{"main", "compress"}``. The flat ``rp``
+            # already carries any YaRN params the checkpoint shipped under top-level
+            # ``rope_scaling`` (folded in by ``RotaryEmbeddingConfigMixin``). We propagate
+            # them into both buckets — the difference between the two is just the
+            # ``rope_theta`` base (the model's main attention uses ``rope_theta=10000``,
+            # the compressor / indexer uses ``compress_rope_theta=160000``).
+            base = {k: v for k, v in rp.items() if k not in ("main", "compress")}
+            base.setdefault("rope_theta", self.rope_theta)
+            base["partial_rotary_factor"] = self.partial_rotary_factor
+            base.setdefault("rope_type", "default")
+            main = dict(base)
+            compress = {**base, "rope_theta": self.compress_rope_theta}
+            self.rope_parameters = {"main": main, "compress": compress}
+
+
+class DeepseekV4RMSNorm(DeepseekV3RMSNorm):
+    pass
+
+
+class DeepseekV4RotaryEmbedding(Gemma3RotaryEmbedding):
+    """Multi-layer-type rotary embedding (Gemma3 pattern). Holds two ``inv_freq``
+    buffers — ``"main"`` for self-attention (``rope_theta``) and ``"compress"`` for
+    the Compressor / Indexer (``compress_rope_theta``). Both honour
+    ``partial_rotary_factor`` so cos/sin is sized to ``qk_rope_head_dim`` rather than
+    the full ``head_dim``. ``forward(x, position_ids, layer_type=...)`` (inherited
+    from :class:`Gemma3RotaryEmbedding`) picks one.
+
+    The ``layer_types`` here are the *rope* layer types (``"main"`` / ``"compress"``),
+    keys of ``config.rope_parameters``. They are unrelated to ``config.layer_types``,
+    which lists the per-decoder-block attention type.
+    """
+
+    layer_types = ("main", "compress")
+
+    def __init__(self, config: "DeepseekV4Config", device=None):
+        nn.Module.__init__(self)
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+        self.config = config
+        self.rope_type = {}
+        for layer_type in self.layer_types:
+            params = config.rope_parameters.get(layer_type)
+            if params is None:
+                continue
+            self.rope_type[layer_type] = params.get("rope_type", "default")
+            rope_init_fn: Callable = self.compute_default_rope_parameters
+            if self.rope_type[layer_type] != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type[layer_type]]
+            inv_freq, scaling = rope_init_fn(config, device, layer_type=layer_type)
+            self.register_buffer(f"{layer_type}_inv_freq", inv_freq, persistent=False)
+            self.register_buffer(f"{layer_type}_original_inv_freq", inv_freq.clone(), persistent=False)
+            setattr(self, f"{layer_type}_attention_scaling", scaling)
+
+    @staticmethod
+    def compute_default_rope_parameters(config, device=None, seq_len=None, layer_type=None):
+        # V4 honours ``partial_rotary_factor`` so cos/sin sizes to ``qk_rope_head_dim``.
+        params = config.rope_parameters[layer_type]
+        base = params["rope_theta"]
+        head_dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        factor = params.get("partial_rotary_factor", 1.0)
+        dim = int(head_dim * factor)
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, 1.0
+
+
+def _sliding_kv_update(
+    cache_layer: "DynamicSlidingWindowLayer", key_states: torch.Tensor, value_states: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Shared sliding-window K=V update body. V4 uses shared-KV MQA, so ``keys`` and
+    ``values`` point to the same storage on every layer; both V4 cache layer types
+    (HCA / CSA) call this from their ``update``."""
+    if not cache_layer.is_initialized:
+        cache_layer.lazy_initialization(key_states, value_states)
+        cache_layer.values = cache_layer.keys
+    cache_layer.cumulative_length += key_states.shape[-2]
+    full = torch.cat([cache_layer.keys, key_states], dim=-2)
+    cache_layer.keys = full[:, :, -cache_layer.sliding_window + 1 :, :]
+    cache_layer.values = cache_layer.keys
+    return full, full
+
+
+def _update_window_buffer(
+    buffer_kv: torch.Tensor | None,
+    buffer_gate: torch.Tensor | None,
+    kv: torch.Tensor,
+    gate: torch.Tensor,
+    compress_rate: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    """Merge a still-buffered tail with freshly projected ``(kv, gate)`` and split off
+    the longest window-aligned chunk. Used by both the compressor- and indexer-side
+    window buffers; tokens past the last full window stay in the buffer until the
+    next call rounds them out to a multiple of ``compress_rate``."""
+    if buffer_kv is not None and buffer_kv.shape[1]:
+        kv = torch.cat([buffer_kv, kv], dim=1)
+        gate = torch.cat([buffer_gate, gate], dim=1)
+    usable = (kv.shape[1] // compress_rate) * compress_rate
+    return kv[:, :usable], gate[:, :usable], kv[:, usable:], gate[:, usable:]
+
+
+def _append_to_pool(pool: torch.Tensor | None, new_pooled: torch.Tensor) -> torch.Tensor:
+    """Append freshly emitted compressed entries to a running pool, returning the
+    full pool (or an empty tensor if nothing has been pooled yet)."""
+    if new_pooled.shape[1] > 0:
+        return new_pooled if pool is None else torch.cat([pool, new_pooled], dim=1)
+    if pool is None:
+        return new_pooled.new_zeros((new_pooled.shape[0], 0, new_pooled.shape[-1]))
+    return pool
+
+
+class DeepseekV4HCACache(DynamicSlidingWindowLayer):
+    """Cache layer for HCA blocks (paper §2.3.2). Holds the long-range compressor's
+    buffer / pool / count on top of the sliding-window K=V branch. HCA uses
+    *non-overlapping* windows, so there is **no** overlap state, and HCA has **no**
+    indexer either.
+
+    Fields on top of :class:`DynamicSlidingWindowLayer`:
+
+      * ``compressor_pool`` — the running list of compressed KV entries emitted so
+        far (one per ``compress_rate_hca`` source tokens; the long-range KVs the
+        attention concatenates onto its sliding-window keys / values).
+      * ``compressor_buffer_kv`` / ``compressor_buffer_gate`` — source tokens that
+        arrived between two full windows; once the buffer hits ``compress_rate_hca``
+        tokens the compressor closes a window, emits one pooled entry, and drains
+        the buffer.
+      * ``compressor_pool_count`` — number of compressed entries emitted so far,
+        so ``compressor_pool_count * compress_rate_hca`` is the absolute position
+        of the *next* window's first source token.
+
+    The class-level ``layer_type`` auto-registers this class with
+    :data:`LAYER_TYPE_CACHE_MAPPING` so :class:`DynamicCache` builds it on its own
+    when ``config.layer_types[i] == "heavily_compressed_attention"``.
+    """
+
+    layer_type = "heavily_compressed_attention"
+
+    def __init__(self, config: "DeepseekV4Config"):
+        super().__init__(config)
+        self.compress_rate = config.compress_rate_hca
+        self.compressor_buffer_kv: torch.Tensor | None = None
+        self.compressor_buffer_gate: torch.Tensor | None = None
+        self.compressor_pool: torch.Tensor | None = None
+        self.compressor_pool_count = 0
+
+    def update(self, key_states: torch.Tensor, value_states: torch.Tensor, *args, **kwargs):
+        return _sliding_kv_update(self, key_states, value_states)
+
+    def update_compressor(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Merge the freshly projected ``(kv, gate)`` (paper §2.3.2 eqs. 20–21:
+        ``C = H·W^{KV}``, ``Z = H·W^Z``) with the buffered tail from prior calls and
+        return the longest window-aligned chunk that's ready to pool, plus the
+        absolute source-token position of that chunk's first window. The returned
+        chunk is softmax-pooled by the compressor with ``position_bias`` to emit one
+        compressed entry per window of ``compress_rate_hca`` tokens (eqs. 22–23)."""
+        first_pool_position = self.compressor_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.compressor_buffer_kv, self.compressor_buffer_gate = _update_window_buffer(
+            self.compressor_buffer_kv, self.compressor_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_compressor_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted compressed entries to ``compressor_pool``
+        (``C^{Comp}``, paper §2.3.2 eq. 23) and return the full pool. Bumps
+        ``compressor_pool_count`` so the next ``update_compressor`` call knows the
+        absolute source-token position of its first window."""
+        self.compressor_pool = _append_to_pool(self.compressor_pool, new_pooled)
+        self.compressor_pool_count += new_pooled.shape[1]
+        return self.compressor_pool
+
+
+class DeepseekV4CSACache(DynamicSlidingWindowLayer):
+    """Cache layer for CSA blocks (paper §2.3.1). Holds two parallel sets of
+    buffer / pool / count / overlap state on top of the sliding-window K=V branch:
+
+      * **compressor side** — the main-branch ``head_dim`` pool (the long-range KVs
+        the attention concatenates after top-k indexer selection).
+      * **indexer side** — the Lightning Indexer's smaller ``index_head_dim`` pool
+        (the keys ``K^{IComp}`` that queries score against to pick the top-k blocks,
+        eqs. 14–17). Kept separate from the compressor pool because the head dim
+        differs.
+
+    Both sides use **overlapping** windows of stride ``compress_rate_csa`` and width
+    ``2 * compress_rate_csa`` (paper §2.3.1), so each side also keeps an
+    ``*_overlap_kv`` / ``*_overlap_gate`` pair holding the last full window's
+    projected ``(kv, gate)`` so the next forward call's first window can stitch in
+    its low-channel slice as the prior contribution.
+
+    The class-level ``layer_type`` auto-registers this class with
+    :data:`LAYER_TYPE_CACHE_MAPPING` so :class:`DynamicCache` builds it on its own
+    when ``config.layer_types[i] == "compressed_sparse_attention"``.
+    """
+
+    layer_type = "compressed_sparse_attention"
+
+    def __init__(self, config: "DeepseekV4Config"):
+        super().__init__(config)
+        self.compress_rate = config.compress_rate_csa
+        # Compressor side
+        self.compressor_buffer_kv: torch.Tensor | None = None
+        self.compressor_buffer_gate: torch.Tensor | None = None
+        self.compressor_pool: torch.Tensor | None = None
+        self.compressor_pool_count = 0
+        self.compressor_overlap_kv: torch.Tensor | None = None
+        self.compressor_overlap_gate: torch.Tensor | None = None
+        # Indexer side (parallel state at ``index_head_dim``)
+        self.indexer_buffer_kv: torch.Tensor | None = None
+        self.indexer_buffer_gate: torch.Tensor | None = None
+        self.indexer_pool: torch.Tensor | None = None
+        self.indexer_pool_count = 0
+        self.indexer_overlap_kv: torch.Tensor | None = None
+        self.indexer_overlap_gate: torch.Tensor | None = None
+
+    def update(self, key_states: torch.Tensor, value_states: torch.Tensor, *args, **kwargs):
+        return _sliding_kv_update(self, key_states, value_states)
+
+    def update_compressor(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Compressor-side window buffer (paper §2.3.1 main-branch pool, eqs. 9–12).
+        Same window-aligned tail-buffering as HCA, but at the CSA cadence
+        (``compress_rate_csa``)."""
+        first_pool_position = self.compressor_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.compressor_buffer_kv, self.compressor_buffer_gate = _update_window_buffer(
+            self.compressor_buffer_kv, self.compressor_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_compressor_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted entries to the CSA compressor pool (the
+        ``C^{Comp}`` running list at ``head_dim``, eqs. 11–12)."""
+        self.compressor_pool = _append_to_pool(self.compressor_pool, new_pooled)
+        self.compressor_pool_count += new_pooled.shape[1]
+        return self.compressor_pool
+
+    def get_compressor_overlap(self) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        return self.compressor_overlap_kv, self.compressor_overlap_gate
+
+    def set_compressor_overlap(self, kv: torch.Tensor, gate: torch.Tensor) -> None:
+        self.compressor_overlap_kv = kv
+        self.compressor_overlap_gate = gate
+
+    def update_indexer(self, kv: torch.Tensor, gate: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, int]:
+        """Indexer-side mirror of :meth:`update_compressor` (paper §2.3.1, "Lightning
+        Indexer for Sparse Selection"). Same logic at the smaller ``index_head_dim``
+        — the small-head pool keys ``K^{IComp}`` (eq. 14's ``W^{IUQ}`` complement on
+        the key side) that the indexer scores queries against to pick the top-k
+        blocks (eqs. 15–17). Buffer / pool / count are kept separate from the
+        compressor's state because the head dim differs."""
+        first_pool_position = self.indexer_pool_count * self.compress_rate
+        chunk_kv, chunk_gate, self.indexer_buffer_kv, self.indexer_buffer_gate = _update_window_buffer(
+            self.indexer_buffer_kv, self.indexer_buffer_gate, kv, gate, self.compress_rate
+        )
+        return chunk_kv, chunk_gate, first_pool_position
+
+    def update_indexer_pool(self, new_pooled: torch.Tensor) -> torch.Tensor:
+        """Append freshly emitted entries to the indexer pool ``K^{IComp}`` (paper
+        §2.3.1 eq. 16: the keys against which the ``q^I_t`` queries score for top-k
+        selection). Same cadence as the compressor pool — one entry per
+        ``compress_rate_csa`` source tokens — but at ``index_head_dim``."""
+        self.indexer_pool = _append_to_pool(self.indexer_pool, new_pooled)
+        self.indexer_pool_count += new_pooled.shape[1]
+        return self.indexer_pool
+
+    def get_indexer_overlap(self) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        return self.indexer_overlap_kv, self.indexer_overlap_gate
+
+    def set_indexer_overlap(self, kv: torch.Tensor, gate: torch.Tensor) -> None:
+        self.indexer_overlap_kv = kv
+        self.indexer_overlap_gate = gate
+
+
+class DeepseekV4GroupedLinear(nn.Linear):
+    """Block-diagonal grouped linear used by the V4 grouped output projection
+    (paper §2.3.1, "Grouped Output Projection"; HCA reuses the same scheme,
+    §2.3.2). With ``num_attention_heads = n_h`` and per-head dim ``c``, the core
+    attention's stacked output is ``c·n_h``-dim, which is *very* large for V4
+    (V4-Flash: c=512, n_h=64 → 32768; V4-Pro: c=512, n_h=128 → 65536). A direct
+    ``c·n_h → hidden_size`` projection would dominate the per-token cost.
+
+    The paper sidesteps that by splitting the n_h heads into ``g`` groups, projecting
+    each ``c·n_h/g``-dim group independently to a ``d_g``-dim intermediate output
+    (with ``d_g < c·n_h/g``), and then mixing the resulting ``g·d_g`` vector to
+    ``hidden_size`` through a single follow-up linear (``self_attn.wo_b``). This
+    module owns the per-group block (``self_attn.wo_a``).
+
+    The ``weight`` parameter is shaped like a standard ``nn.Linear``
+    (``[out_features, in_features_per_group]``) so quantizers keyed on
+    ``nn.Linear.weight`` still pick it up; ``forward`` does the per-group ``bmm``.
+    """
+
+    def __init__(self, in_features_per_group: int, out_features: int, n_groups: int, bias: bool = False):
+        super().__init__(in_features_per_group, out_features, bias=bias)
+        self.n_groups = n_groups
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # x: [..., n_groups, in_features_per_group]
+        batch_shape = x.shape[:-2]
+        d_in = x.shape[-1]
+        out_per_group = self.out_features // self.n_groups
+        w = self.weight.view(self.n_groups, out_per_group, d_in)
+        x = x.reshape(-1, self.n_groups, d_in).permute(1, 0, 2)
+        y = torch.bmm(x, w.transpose(-1, -2)).permute(1, 0, 2)
+        return y.reshape(*batch_shape, self.n_groups, out_per_group)
+
+
+class DeepseekV4Indexer(nn.Module):
+    """Lightning Indexer (paper §2.3.1, eqs. 13–17). Used by Compressed Sparse
+    Attention (CSA) to pick the top-k compressed KV blocks per query. The indexer
+    runs its own scaled-down compressor at ``index_head_dim`` over the same windows
+    as the outer CSA compressor, then scores queries against the pooled keys with
+    ``∑_h w_{t,h} · ReLU(q_{t,h} · K^IComp_s)`` and keeps the top ``index_topk``
+    indices.
+
+    The indexer has its own rotary because it applies RoPE to two sets of tensors:
+
+      * **pool keys** at deterministic positions ``i * compress_rate + first_pool_position``,
+      * **queries** at the model's current ``position_ids`` (variable per forward).
+
+    Both must use the same theta as the outer compressor (``compress_rope_theta``) so
+    query/key inner products are translation-invariant in the standard rope sense — if
+    they used different thetas the score ``q · k`` would carry a residual position-
+    dependent skew. We can't precompute cos/sin once at init because the query
+    positions vary per call, so the indexer owns a rotary embedding and calls it with
+    ``layer_type="compress"`` twice per forward (once for pool keys, once for queries).
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_csa
+        self.n_heads = config.index_n_heads
+        self.head_dim = config.index_head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.index_topk = config.index_topk
+        self.softmax_scale = self.head_dim**-0.5
+        # The indexer always pools with the CSA cadence (``compress_rate=4``), so its
+        # inner pool runs the same overlapping-window scheme as :class:`DeepseekV4CSACompressor`
+        # (paper §2.3.1) — ``coff = 2`` everywhere on the pool branch.
+        self.coff = 2
+        self.wkv = nn.Linear(config.hidden_size, self.coff * self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, self.coff * self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, self.coff * self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.wq_b = nn.Linear(config.q_lora_rank, self.n_heads * self.head_dim, bias=False)
+        self.weights_proj = nn.Linear(config.hidden_size, self.n_heads, bias=False)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.LongTensor:
+        batch, seq_len, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+
+        # --- Pool side: same overlapping windows as the outer CSA compressor, at index_head_dim ---
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+            prior_kv, prior_gate = None, None
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_indexer(kv, gate)
+            prior_kv, prior_gate = cache_layer.get_indexer_overlap()
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, self.coff * self.head_dim)
+            chunk_gate = chunk_gate.view(
+                batch, n_windows, self.compress_rate, self.coff * self.head_dim
+            ) + self.position_bias.to(chunk_gate.dtype)
+            if cache_layer is not None:
+                cache_layer.set_indexer_overlap(chunk_kv[:, -1].clone(), chunk_gate[:, -1].clone())
+            chunk_kv, chunk_gate = _overlap_pool(chunk_kv, chunk_gate, prior_kv, prior_gate, self.head_dim)
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            cos, sin = self.rotary_emb(new_pooled, position_ids=positions, layer_type="compress")
+            # V4-Flash places the rotary slice at the *end* of each head (matches the
+            # reference's ``x[..., -rd:]`` indexing) — wkv weight is laid out [nope|rope]
+            # so the rotary half is the trailing ``rope_head_dim`` channels.
+            pool_nope, pool_rope = new_pooled[..., : -self.rope_head_dim], new_pooled[..., -self.rope_head_dim :]
+            pool_rope, _ = apply_rotary_pos_emb(
+                pool_rope.unsqueeze(1), torch.zeros_like(pool_rope.unsqueeze(1)), cos, sin
+            )
+            new_pooled = torch.cat([pool_nope, pool_rope.squeeze(1)], dim=-1)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        pooled_kv = new_pooled if cache_layer is None else cache_layer.update_indexer_pool(new_pooled)
+
+        # --- Query side ---
+        cos_q, sin_q = self.rotary_emb(hidden_states, position_ids=position_ids, layer_type="compress")
+        q = self.wq_b(q_residual).view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
+        q_nope, q_rope = q[..., : -self.rope_head_dim], q[..., -self.rope_head_dim :]
+        q_rope, _ = apply_rotary_pos_emb(q_rope, torch.zeros_like(q_rope), cos_q, sin_q)
+        q = torch.cat([q_nope, q_rope], dim=-1).transpose(1, 2)
+
+        # --- Score: ReLU(q·kᵀ) * weights, then top-k ---
+        scores = torch.matmul(q.float(), pooled_kv.transpose(-1, -2).float().unsqueeze(1))  # [B, S, H, T]
+        scores = F.relu(scores) * self.softmax_scale
+        weights = self.weights_proj(hidden_states).float() * (self.n_heads**-0.5)  # [B, S, H]
+        index_scores = (scores * weights.unsqueeze(-1)).sum(dim=2)  # [B, S, T]
+        topk = min(self.index_topk, pooled_kv.shape[1])
+        return index_scores.topk(topk, dim=-1).indices
+
+
+# -----------------------------------------------------------------------------
+# Compressors — :class:`DeepseekV4HCACompressor` and :class:`DeepseekV4CSACompressor`
+# are independent. They share the same softmax-gated window-pool primitive but differ
+# in three ways that we keep on each class explicitly: HCA pools non-overlapping
+# windows with ``coff = 1`` and has no indexer, CSA pools overlapping windows with
+# ``coff = 2`` and runs a Lightning Indexer on top of the pool.
+# -----------------------------------------------------------------------------
+
+
+def _overlap_pool(
+    chunk_kv: torch.Tensor,
+    chunk_gate: torch.Tensor,
+    prior_kv: torch.Tensor | None,
+    prior_gate: torch.Tensor | None,
+    head_dim: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    """Expand ``[B, n_win, ratio, 2*head_dim]`` chunks into ``[B, n_win, 2*ratio, head_dim]``
+    by stitching each window's *low-channel* slice onto the *high-channel* slice of the
+    prior window — matching the V4-Flash reference (``Compressor.overlap_transform``).
+
+    Each pooled output thus mixes ``ratio`` *current* source tokens (high half of the
+    learned 2d split) with ``ratio`` *previous* source tokens (low half), so windows
+    have width ``2*ratio`` but stride ``ratio`` (paper §2.3.1). For window 0, the prior
+    half is filled with zero (kv) / ``-inf`` (gate, so its softmax weight is exactly 0),
+    unless ``prior_kv`` / ``prior_gate`` carry the last full window from a previous
+    forward call — in which case its low-channel slice slots into row ``[0, :ratio]``.
+    """
+    batch, n_windows, ratio, _ = chunk_kv.shape
+    new_kv = chunk_kv.new_zeros((batch, n_windows, 2 * ratio, head_dim))
+    new_gate = chunk_gate.new_full((batch, n_windows, 2 * ratio, head_dim), float("-inf"))
+    new_kv[:, :, ratio:] = chunk_kv[..., head_dim:]
+    new_gate[:, :, ratio:] = chunk_gate[..., head_dim:]
+    if n_windows > 1:
+        new_kv[:, 1:, :ratio] = chunk_kv[:, :-1, :, :head_dim]
+        new_gate[:, 1:, :ratio] = chunk_gate[:, :-1, :, :head_dim]
+    if prior_kv is not None and prior_gate is not None:
+        new_kv[:, 0, :ratio] = prior_kv[..., :head_dim].to(new_kv.dtype)
+        new_gate[:, 0, :ratio] = prior_gate[..., :head_dim].to(new_gate.dtype)
+    return new_kv, new_gate
+
+
+def _rope_pool(
+    pooled: torch.Tensor, rotary_emb: nn.Module, positions: torch.Tensor, rope_head_dim: int
+) -> torch.Tensor:
+    """Apply RoPE to the trailing ``rope_head_dim`` slice of each pooled entry at its
+    deterministic absolute position. V4-Flash lays out each head as
+    ``[nope | rope]`` (matches the reference's ``x[..., -rd:]`` indexing) so the
+    rotary half is the trailing channels."""
+    cos, sin = rotary_emb(pooled, position_ids=positions, layer_type="compress")
+    pool_nope, pool_rope = pooled[..., :-rope_head_dim], pooled[..., -rope_head_dim:]
+    pool_rope, _ = apply_rotary_pos_emb(pool_rope.unsqueeze(1), torch.zeros_like(pool_rope.unsqueeze(1)), cos, sin)
+    return torch.cat([pool_nope, pool_rope.squeeze(1)], dim=-1)
+
+
+class DeepseekV4HCACompressor(nn.Module):
+    """Heavily Compressed Attention compressor (paper §2.3.2, eqs. 20–23). Pools
+    every ``compress_rate_hca`` (m'=128) source tokens into a single compressed KV
+    entry with **non-overlapping** windows — no overlap state, no indexer.
+
+    The three building blocks (paper notation in parentheses):
+
+      * **kv** = ``wkv(hidden_states)`` — head-dim KV projection ``C ∈ R^{n×c}``
+        (eq. 20). Doubles as both key and value (shared-KV MQA).
+      * **gate** = ``wgate(hidden_states)`` — head-dim compression weights
+        ``Z ∈ R^{n×c}`` (eq. 21). Combined with ``position_bias`` and softmaxed per
+        window to produce the convex combination that mixes ``compress_rate_hca``
+        source KVs into one pooled entry.
+      * **pool** — running list of compressed KV entries (``C^{Comp}``, eq. 23).
+        Lives on :class:`DeepseekV4HCACache`; the in-flight buffer of tokens that
+        haven't yet filled a window lives there too.
+
+    Each closed window of m' tokens produces one pooled entry:
+    ``C^{Comp}_i = Σ_{j∈window} softmax(Z_j + B)_j ⊙ C_j``. RoPE on the trailing
+    ``rope_head_dim`` slice is applied at the deterministic absolute position
+    ``i * compress_rate_hca + first_pool_position`` so cross-call concatenation stays
+    causality-correct. Returns the running pool ``[B, 1, T, head_dim]``.
+
+    When ``past_key_values is None`` (a checkpoint replay zeroes the cache to break
+    the grad-cache loop), runs in stateless single-shot mode: pool every complete
+    window from ``hidden_states`` and discard the remainder.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_hca
+        self.head_dim = config.head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        self.wkv = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.Tensor:
+        # ``q_residual`` / ``position_ids`` are unused — the uniform forward signature
+        # lets :class:`DeepseekV4Attention` call either compressor without branching.
+        batch, _, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_compressor(kv, gate)
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, self.head_dim)
+            chunk_gate = chunk_gate.view(batch, n_windows, self.compress_rate, self.head_dim) + self.position_bias.to(
+                chunk_gate.dtype
+            )
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            new_pooled = _rope_pool(new_pooled, self.rotary_emb, positions, self.rope_head_dim)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        if cache_layer is None:
+            return new_pooled.unsqueeze(1)
+        return cache_layer.update_compressor_pool(new_pooled).unsqueeze(1)
+
+
+class DeepseekV4CSACompressor(nn.Module):
+    """Compressed Sparse Attention compressor (paper §2.3.1, eqs. 9–17). Pools every
+    ``compress_rate_csa`` (m=4) source tokens with **overlapping** windows — stride
+    ``compress_rate_csa`` and effective width ``2 * compress_rate_csa`` — and runs a
+    Lightning Indexer on top of the pool that scores queries with
+    ``∑_h w_{t,h} · ReLU(q_{t,h} · K^{IComp}_s)`` to gather the top ``index_topk``
+    entries per query before they reach core attention.
+
+    Compared to :class:`DeepseekV4HCACompressor` the differences are explicit:
+
+      * ``wkv`` / ``wgate`` / ``position_bias`` project to **2 × head_dim** (the
+        learned channel split — high half pools into the current window, low half
+        pools into the next window's overlap with this one, see :func:`_overlap_pool`).
+      * The cache layer's ``compressor_overlap_*`` state carries the last full
+        window across forward calls.
+      * A :class:`DeepseekV4Indexer` sub-module gathers the top-``index_topk`` pool
+        entries per query (paper §2.3.1, "Lightning Indexer for Sparse Selection").
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.compress_rate = config.compress_rate_csa
+        self.head_dim = config.head_dim
+        self.rope_head_dim = config.qk_rope_head_dim
+        # ``2 * head_dim`` because windows overlap: each pooled entry is a softmax-gated
+        # convex combination of ``compress_rate_csa`` *current* tokens (high-channel half)
+        # mixed with ``compress_rate_csa`` *previous* tokens (low-channel half). The
+        # learned channel split happens in :func:`_overlap_pool`.
+        self.wkv = nn.Linear(config.hidden_size, 2 * self.head_dim, bias=False)
+        self.wgate = nn.Linear(config.hidden_size, 2 * self.head_dim, bias=False)
+        self.position_bias = nn.Parameter(torch.empty(self.compress_rate, 2 * self.head_dim))
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+        self.indexer = DeepseekV4Indexer(config)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        q_residual: torch.Tensor,
+        position_ids: torch.Tensor,
+        past_key_values: Cache | None,
+        layer_idx: int,
+    ) -> torch.Tensor:
+        batch, seq_len, _ = hidden_states.shape
+        cache_layer = past_key_values.layers[layer_idx] if past_key_values is not None else None
+        kv = self.wkv(hidden_states)
+        gate = self.wgate(hidden_states)
+        if cache_layer is None:
+            usable = (kv.shape[1] // self.compress_rate) * self.compress_rate
+            chunk_kv, chunk_gate, first_pool_position = kv[:, :usable], gate[:, :usable], 0
+            prior_kv, prior_gate = None, None
+        else:
+            chunk_kv, chunk_gate, first_pool_position = cache_layer.update_compressor(kv, gate)
+            prior_kv, prior_gate = cache_layer.get_compressor_overlap()
+        if chunk_kv.shape[1] > 0:
+            n_windows = chunk_kv.shape[1] // self.compress_rate
+            chunk_kv = chunk_kv.view(batch, n_windows, self.compress_rate, 2 * self.head_dim)
+            chunk_gate = chunk_gate.view(
+                batch, n_windows, self.compress_rate, 2 * self.head_dim
+            ) + self.position_bias.to(chunk_gate.dtype)
+            if cache_layer is not None:
+                # Persist the *raw* last full window (gate already biased) so the next
+                # forward call's first window can read its low-channel slice as prior.
+                cache_layer.set_compressor_overlap(chunk_kv[:, -1].clone(), chunk_gate[:, -1].clone())
+            chunk_kv, chunk_gate = _overlap_pool(chunk_kv, chunk_gate, prior_kv, prior_gate, self.head_dim)
+            new_pooled = self.kv_norm((chunk_kv * chunk_gate.softmax(dim=2)).sum(dim=2))
+            positions = (
+                (torch.arange(n_windows, device=new_pooled.device) * self.compress_rate + first_pool_position)
+                .unsqueeze(0)
+                .expand(batch, -1)
+            )
+            new_pooled = _rope_pool(new_pooled, self.rotary_emb, positions, self.rope_head_dim)
+        else:
+            new_pooled = chunk_kv.new_zeros((batch, 0, self.head_dim))
+        pooled = (
+            new_pooled.unsqueeze(1)
+            if cache_layer is None
+            else cache_layer.update_compressor_pool(new_pooled).unsqueeze(1)
+        )
+        # Lightning Indexer: gather top-``index_topk`` pool entries per query.
+        topk = self.indexer(hidden_states, q_residual, position_ids, past_key_values, layer_idx)  # [B, S, k]
+        expanded = pooled.unsqueeze(2).expand(-1, -1, seq_len, -1, -1)
+        idx = topk.unsqueeze(1).unsqueeze(-1).expand(-1, 1, -1, -1, self.head_dim)
+        return torch.gather(expanded, 3, idx).reshape(batch, 1, -1, self.head_dim)
+
+
+COMPRESSOR_CLASSES = {
+    "sliding_attention": None,
+    "compressed_sparse_attention": DeepseekV4CSACompressor,
+    "heavily_compressed_attention": DeepseekV4HCACompressor,
+}
+
+
+# -----------------------------------------------------------------------------
+# Attention with sink.
+# -----------------------------------------------------------------------------
+
+
+class DeepseekV4Attention(nn.Module):
+    """V4 attention block (paper §2.3). Single class for all three layer types — the
+    only thing that varies is the long-range branch (the ``compressor`` sub-module);
+    the surrounding QKV / RoPE / sink / sliding-window / output projection is
+    identical. The three layer types are dispatched by ``COMPRESSOR_CLASSES``:
+
+      * ``sliding_attention``: ``compressor = None``; only the local sliding-window
+        K=V branch ("Full Attention").
+      * ``compressed_sparse_attention``: :class:`DeepseekV4CSACompressor` —
+        low-compression overlapping-window pool plus a Lightning Indexer that keeps
+        the top-``index_topk`` pool entries per query (paper §2.3.1).
+      * ``heavily_compressed_attention``: :class:`DeepseekV4HCACompressor` —
+        high-compression non-overlapping-window pool, no indexer (paper §2.3.2).
+
+    Block components (paper §2.3.3):
+
+      * Shared-KV Multi-Query Attention: ``num_key_value_heads = 1``; ``wkv`` projects
+        directly to that single KV head and the same tensor is read as both key and
+        value.
+      * Partial RoPE on the first ``rope_head_dim`` of each head ("Partial Rotary
+        Positional Embedding"). RoPE is also applied with position ``-i`` to the
+        attention output's rope slice, so the contribution of each KV entry stays a
+        function of the *relative* distance to the query.
+      * RMSNorm on the queries (``q_norm``) and the compressed KV head (``kv_norm``)
+        right before the core attention, to keep logits bounded.
+      * Per-head learnable attention sink (eq. 27).
+      * Grouped low-rank output projection (§2.3.1, "Grouped Output Projection"):
+        ``g`` head-groups → ``d_g``-dim intermediate outputs through a block-diagonal
+        :class:`DeepseekV4GroupedLinear`, then mixed back to ``hidden_size`` by ``wo_b``.
+      * A supplementary uncompressed sliding-window KV branch of size
+        ``sliding_window`` ("Additional Branch of Sliding Window Attention") that
+        preserves local fine-grained dependencies, concatenated with the
+        long-range compressor's output before core attention.
+    """
+
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        # V4 doesn't reuse V3's MLA projections (q_a/q_b/kv_a_proj_with_mqa/kv_b_proj/
+        # o_proj) — every V4 block is shared-KV MQA with a single ``wkv`` and a grouped
+        # output projection — so inheriting from ``DeepseekV3Attention`` only to delete
+        # half of what its ``__init__`` builds is not worth it. We init from
+        # ``nn.Module`` directly and set up V4-specific projections inline.
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.layer_type = config.layer_types[layer_idx]
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_groups = config.num_attention_heads  # single KV head, broadcast to all
+        self.head_dim = config.head_dim
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.sliding_window = config.sliding_window
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.scaling = self.head_dim**-0.5
+
+        self.wq_a = nn.Linear(config.hidden_size, config.q_lora_rank, bias=False)
+        self.q_norm = DeepseekV4RMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
+        self.wq_b = nn.Linear(config.q_lora_rank, self.num_heads * self.head_dim, bias=False)
+        self.wkv = nn.Linear(config.hidden_size, self.head_dim, bias=False)
+        self.kv_norm = DeepseekV4RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.wo_a = DeepseekV4GroupedLinear(
+            self.num_heads * self.head_dim // config.o_groups, config.o_groups * config.o_lora_rank, config.o_groups
+        )
+        self.wo_b = nn.Linear(config.o_groups * config.o_lora_rank, config.hidden_size, bias=False)
+        self.sinks = nn.Parameter(torch.empty(self.num_heads))
+        # Long-range branch dispatched by ``layer_type`` (see ``COMPRESSOR_CLASSES``
+        # above). ``None`` means full-attention / sliding-only — no compressor is
+        # built and the layer keeps just the local sliding-window K=V branch.
+        compressor_cls = COMPRESSOR_CLASSES[self.layer_type]
+        self.compressor = compressor_cls(config) if compressor_cls is not None else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        position_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        batch, seq_len = hidden_states.shape[:2]
+        cos, sin = position_embeddings
+
+        # --- Q + KV projections + partial RoPE on the *trailing* qk_rope_head_dim of
+        # each head (matches the V4-Flash reference's ``[..., -rd:]`` indexing — wkv
+        # weights are laid out [nope|rope] in the checkpoint, so the trailing slice is
+        # what gets rotated).
+        q_residual = self.q_norm(self.wq_a(hidden_states))
+        q = self.wq_b(q_residual).view(batch, seq_len, self.num_heads, self.head_dim).transpose(1, 2)
+        # Per-head RMSNorm-style rescale (no learned weight) — the V4-Flash reference
+        # (``inference/model.py:498``) does ``q *= rsqrt(mean(q**2) + eps)`` on each
+        # head after wq_b, before RoPE. Skipping it leaves attention scores at the
+        # wrong scale and the model collapses to a single repeated token within a
+        # handful of layers.
+        q = q * torch.rsqrt(q.float().square().mean(-1, keepdim=True) + self.config.rms_norm_eps).to(q.dtype)
+        kv = self.kv_norm(self.wkv(hidden_states)).view(batch, seq_len, 1, self.head_dim).transpose(1, 2)
+        q_nope, q_rope = q[..., : -self.qk_rope_head_dim], q[..., -self.qk_rope_head_dim :]
+        kv_nope, kv_rope = kv[..., : -self.qk_rope_head_dim], kv[..., -self.qk_rope_head_dim :]
+        q_rope, kv_rope = apply_rotary_pos_emb(q_rope, kv_rope, cos, sin)
+        q = torch.cat([q_nope, q_rope], dim=-1)
+        kv = torch.cat([kv_nope, kv_rope], dim=-1)
+
+        # --- Sliding-window K=V branch goes through the standard cache update ---
+        if past_key_values is not None:
+            kv, _ = past_key_values.update(kv, kv, self.layer_idx)
+
+        # Sliding-only layers skip the long-range branch (no compressor was built).
+        # For HCA / CSA, ``DynamicCache(config=...)`` builds the right cache layer per
+        # ``config.layer_types[i]`` via ``LAYER_TYPE_CACHE_MAPPING``, so the compressor
+        # reads its layer state from ``past_key_values.layers[layer_idx]``.
+        # ``past_key_values`` is ``None`` only when ``GradientCheckpointingLayer`` zeroes
+        # it during a checkpoint replay — the compressor handles that as a single-shot
+        # window pool with no persistent state.
+        if self.compressor is None:
+            full_kv = kv
+        else:
+            compressed_kv = self.compressor(hidden_states, q_residual, position_ids, past_key_values, self.layer_idx)
+            full_kv = torch.cat([kv, compressed_kv], dim=2)
+
+        if attention_mask is not None and full_kv.shape[2] > attention_mask.shape[-1]:
+            attention_mask = F.pad(attention_mask, (0, full_kv.shape[2] - attention_mask.shape[-1]), value=0.0)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+        attn_output, attn_weights = attention_interface(
+            self,
+            q,
+            full_kv,
+            full_kv,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,
+            s_aux=self.sinks,
+            **kwargs,
+        )
+
+        # De-rotate the output's rope slice. V4 shares K and V (``wkv`` projects to a
+        # single tensor), so V's rope slice carries the same per-token rotation as K.
+        # Attention sums V-rotated values across attended positions, so the output's
+        # rope slice is a position-mixed content; conjugate rotation at the query
+        # position pulls it back into a position-independent frame before the output
+        # projection mixes heads.
+        out_nope, out_rope = attn_output[..., : -self.qk_rope_head_dim], attn_output[..., -self.qk_rope_head_dim :]
+        out_rope = out_rope.transpose(1, 2)
+        out_rope, _ = apply_rotary_pos_emb(out_rope, torch.zeros_like(out_rope), cos, -sin)
+        attn_output = torch.cat([out_nope, out_rope.transpose(1, 2)], dim=-1)
+
+        grouped = attn_output.reshape(batch, seq_len, -1).view(batch, seq_len, self.config.o_groups, -1)
+        return self.wo_b(self.wo_a(grouped).flatten(2)), attn_weights
+
+
+class DeepseekV4HyperConnection(nn.Module):
+    r"""
+    Manifold-Constrained Hyper-Connections
+    (mHC) (Xie et al., 2026) to strengthen the conventional residual connections between adjacent
+    Transformer blocks
+
+    Owns the learned (``fn``, ``base``, ``scale``)
+    parameters that turn the incoming ``hc_mult`` residual streams into collapse / expand
+    weights. The decoder layer instantiates two of these (one for the attention site,
+    one for the mlp site).
+
+    ASCII shape guide — ``B`` = batch, ``S`` = seq, ``H`` = hc_mult, ``D`` = hidden_size::
+
+              hidden_streams        flatten(2)        RMSNorm-rescale + F.linear(fn)
+         [B, S, H, D]  ──────────►  [B, S, H*D]  ─────────────────────────────────►
+                                                             mix-logits
+                                                             [B, S, (2+H)*H]
+                                                                    │
+                            ┌───────────────────────────────────────┴──────────────────────────────┐
+                            ▼                          ▼                                           ▼
+                        pre logits                post logits                               comb logits
+                        [B, S, H]                 [B, S, H]                                 [B, S, H, H]
+                        × scale[0]                × scale[1]                                × scale[2]
+                        + base[:H]                + base[H:2H]                              + base[2H:]
+                        σ() + eps                 σ() + eps                                 σ() + eps
+                        │                         │                                         │
+                        pre                        post                                     Sinkhorn(iters)
+                        (stream collapse weights)  (block-output placement)                 row/col normalise
+                                                                                            │
+                                                                                            comb
+                                                                                            (stream mixer)
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.hc_mult = config.hc_mult
+        self.hc_sinkhorn_iters = config.hc_sinkhorn_iters
+        self.hc_eps = config.hc_eps
+        self.norm_eps = config.rms_norm_eps
+        mix = (2 + self.hc_mult) * self.hc_mult
+        self.fn = nn.Parameter(torch.empty(mix, self.hc_mult * config.hidden_size))
+        self.base = nn.Parameter(torch.empty(mix))
+        self.scale = nn.Parameter(torch.empty(3))
+
+    def forward(self, hidden_streams: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        r"""
+        project it onto the manifold of doubly stochastic matrices M.
+        This is achieved by the Sinkhorn-Knopp algorithm, which first applies an exponential function
+        ˜
+        to
+        𝐵𝑙 to ensure positivity, getting 𝑀(0) = exp(˜
+        𝐵𝑙), and then iteratively performs column and row
+        normalization:
+        𝑀(𝑡) = T𝑟(T𝑐(𝑀(𝑡−1))), (8)
+        where T𝑟 and T𝑐 denote row and column normalization, respectively.
+        """
+        flat = hidden_streams.flatten(start_dim=2).float()
+        rsqrt = torch.rsqrt(flat.square().mean(-1, keepdim=True) + self.norm_eps)
+        mix = F.linear(flat, self.fn.float()) * rsqrt  # [B, S, (2+H)*H]
+        pre_scale, post_scale, comb_scale = self.scale.unbind(0)
+        hc = self.hc_mult
+        pre = torch.sigmoid(mix[..., :hc] * pre_scale + self.base[:hc]) + self.hc_eps
+        post = torch.sigmoid(mix[..., hc : 2 * hc] * post_scale + self.base[hc : 2 * hc]) + self.hc_eps
+        comb = (
+            torch.sigmoid(
+                mix[..., 2 * hc :].view(*mix.shape[:-1], hc, hc) * comb_scale + self.base[2 * hc :].view(hc, hc)
+            )
+            + self.hc_eps
+        )
+        for _ in range(self.hc_sinkhorn_iters):
+            comb = comb / (comb.sum(dim=-1, keepdim=True) + self.hc_eps)
+            comb = comb / (comb.sum(dim=-2, keepdim=True) + self.hc_eps)
+        return pre, post, comb
+
+
+class DeepseekV4HyperHead(nn.Module):
+    """Final HC-stream collapse; used by ``DeepseekV4Model`` before the shared RMSNorm."""
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__()
+        self.hc_mult = config.hc_mult
+        self.norm_eps = config.rms_norm_eps
+        self.eps = config.hc_eps
+        self.hc_fn = nn.Parameter(torch.empty(self.hc_mult, self.hc_mult * config.hidden_size))
+        self.hc_base = nn.Parameter(torch.empty(self.hc_mult))
+        self.hc_scale = nn.Parameter(torch.empty(1))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        flat = x.flatten(2).float()
+        rsqrt = torch.rsqrt(flat.square().mean(-1, keepdim=True) + self.norm_eps)
+        mixes = F.linear(flat, self.hc_fn.float()) * rsqrt
+        pre = torch.sigmoid(mixes * self.hc_scale.float() + self.hc_base.float()) + self.eps
+        return (pre.unsqueeze(-1) * x).sum(dim=2).to(x.dtype)
+
+
+class DeepseekV4MLP(Qwen2MoeMLP):
+    """Shared expert — plain SwiGLU MLP, ``moe_intermediate_size`` hidden."""
+
+    def __init__(self, config: DeepseekV4Config, intermediate_size: int | None = None):
+        super().__init__(config, intermediate_size or config.moe_intermediate_size)
+
+
+@use_experts_implementation
+class DeepseekV4Experts(GptOssExperts):
+    """Routed experts: per-expert iteration + ``_apply_gate`` hook from GPT-OSS, but
+    using the Mixtral weight layout (no biases, ``[num_experts, 2*intermediate, hidden]``
+    for ``gate_up_proj`` and ``[num_experts, hidden, intermediate]`` for ``down_proj``).
+    Activation is SiLU and gate/up are clamped to ``swiglu_limit`` before mixing.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        nn.Module.__init__(self)
+        self.num_experts = config.n_routed_experts
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.moe_intermediate_size
+        self.gate_up_proj = nn.Parameter(torch.empty(self.num_experts, 2 * self.intermediate_size, self.hidden_size))
+        self.down_proj = nn.Parameter(torch.empty(self.num_experts, self.hidden_size, self.intermediate_size))
+        self.limit = config.swiglu_limit
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def _apply_gate(self, gate_up: torch.Tensor) -> torch.Tensor:
+        gate, up = gate_up.chunk(2, dim=-1)
+        gate = gate.clamp(max=self.limit)
+        up = up.clamp(min=-self.limit, max=self.limit)
+        return self.act_fn(gate) * up
+
+    def forward(
+        self, hidden_states: torch.Tensor, top_k_index: torch.Tensor, top_k_weights: torch.Tensor
+    ) -> torch.Tensor:
+        final = torch.zeros_like(hidden_states)
+        with torch.no_grad():
+            mask = F.one_hot(top_k_index, num_classes=self.num_experts).permute(2, 1, 0)
+            hit = torch.greater(mask.sum(dim=(-1, -2)), 0).nonzero()
+        for expert_idx in hit:
+            expert_idx = expert_idx[0]
+            if expert_idx == self.num_experts:
+                continue
+            top_k_pos, token_idx = torch.where(mask[expert_idx])
+            gate_up = F.linear(hidden_states[token_idx], self.gate_up_proj[expert_idx])
+            current = self._apply_gate(gate_up)
+            current = F.linear(current, self.down_proj[expert_idx]) * top_k_weights[token_idx, top_k_pos, None]
+            final.index_add_(0, token_idx, current.to(final.dtype))
+        return final
+
+
+class DeepseekV4TopKRouter(MixtralTopKRouter):
+    """DeepSeekMoE top-k router (paper §2.1, "Mixture-of-Experts"). Two changes from
+    the V3 router:
+
+      * The expert affinity activation is ``Sqrt(Softplus(·))`` instead of the V3
+        Sigmoid (paper §2.1: "we change the activation function that computes the
+        affinity scores from Sigmoid(·) into Sqrt(Softplus(·))"). The ``scoring_func``
+        config field selects this for V4 checkpoints.
+      * The constraint on the number of routing target nodes used in V3 is dropped,
+        and the V3 ``n_group`` / ``topk_group`` machinery is removed entirely (paper
+        §2.1: "we remove the constraint on the number of routing target nodes").
+
+    The auxiliary-loss-free strategy is preserved via the per-expert ``bias`` buffer
+    that biases the top-k argmax without flowing gradients (same ``noaux_tc`` idea
+    as DeepSeek-V3).
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.score_fn = ACT2FN[config.scoring_func]
+        self.routed_scaling_factor = config.routed_scaling_factor
+        # The correction bias biases the argmax only — never gradient-carrying — so it's
+        # a buffer (same convention as DeepseekV3's ``e_score_correction_bias``).
+        self.register_buffer("bias", torch.zeros(self.num_experts), persistent=True)
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        flat = hidden_states.reshape(-1, self.hidden_dim)
+        logits = F.linear(flat.float(), self.weight.float())
+        scores = self.score_fn(logits)
+        indices = torch.topk(scores + self.bias, self.top_k, dim=-1, sorted=False).indices
+        weights = scores.gather(1, indices)
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-20)
+        return logits, weights * self.routed_scaling_factor, indices
+
+
+class DeepseekV4HashRouter(MixtralTopKRouter):
+    """Hash routing for the first ``num_hash_layers`` MoE layers (paper §2.1, "Mixture-
+    of-Experts"). The first three blocks of V4 replace the dense FFN of V3 with an MoE
+    where the expert selection is determined by a fixed hash of the input token id —
+    a frozen ``tid2eid`` (token id to expert id) lookup — instead of a learned gate.
+    The learned gate ``weight`` still produces the per-expert scoring values used to
+    weight the selected experts' activations; only the *which-experts* selection is
+    static.
+    """
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.score_fn = ACT2FN[config.scoring_func]
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.register_buffer(
+            "tid2eid",
+            torch.zeros(config.vocab_size, self.top_k, dtype=torch.long),
+            persistent=True,
+        )
+
+    def forward(
+        self, hidden_states: torch.Tensor, input_ids: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        flat = hidden_states.reshape(-1, self.hidden_dim)
+        logits = F.linear(flat.float(), self.weight.float())
+        scores = self.score_fn(logits)
+        indices = self.tid2eid[input_ids.reshape(-1)].long()
+        weights = scores.gather(1, indices)
+        weights = weights / (weights.sum(dim=-1, keepdim=True) + 1e-20)
+        return logits, weights * self.routed_scaling_factor, indices
+
+
+class DeepseekV4SparseMoeBlock(nn.Module):
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        super().__init__()
+        self.is_hash = layer_idx < config.num_hash_layers
+        self.gate = DeepseekV4HashRouter(config) if self.is_hash else DeepseekV4TopKRouter(config)
+        self.experts = DeepseekV4Experts(config)
+        self.shared_experts = DeepseekV4MLP(config)
+
+    def forward(self, hidden_states: torch.Tensor, input_ids: torch.Tensor | None = None, **_) -> torch.Tensor:
+        batch, seq_len, hidden_dim = hidden_states.shape
+        residual = hidden_states
+        flat = hidden_states.view(-1, hidden_dim)
+        if self.is_hash:
+            if input_ids is None:
+                raise ValueError(
+                    "DeepseekV4's hash-routing layers need `input_ids` to look up expert indices. "
+                    "The `inputs_embeds`-only inference path is not supported for models with "
+                    "`num_hash_layers > 0`."
+                )
+            _, weights, indices = self.gate(hidden_states, input_ids)
+        else:
+            _, weights, indices = self.gate(hidden_states)
+        routed = self.experts(flat, indices, weights).view(batch, seq_len, hidden_dim)
+        return routed + self.shared_experts(residual)
+
+
+class DeepseekV4DecoderLayer(GradientCheckpointingLayer):
+    r"""DeepSeek-V4 decoder block (paper §2). Differs from a classic residual block in
+    two places:
+
+      * The residual is a stack of ``hc_mult`` parallel streams kept in shape
+        ``[B, S, hc_mult, D]`` throughout the block, mixed in and out via two
+        :class:`DeepseekV4HyperConnection` modules (Manifold-Constrained Hyper-
+        Connections / mHC, paper §2.2; Xie et al., 2026). The mHC mappings constrain
+        the residual transform to the manifold of doubly-stochastic matrices via the
+        Sinkhorn-Knopp projection — making signal propagation non-expansive across
+        deep stacks.
+      * ``self_attn`` is :class:`DeepseekV4Attention` for every layer. Its compressor
+        sub-module is the only thing that varies by layer type
+        (:class:`DeepseekV4HCACompressor` for HCA layers,
+        :class:`DeepseekV4CSACompressor` for CSA, picked via
+        ``config.layer_types[layer_idx]``); the CSA compressor also owns the
+        Lightning Indexer at ``self_attn.compressor.indexer``.
+
+    Classic residual decoder layer::
+
+        h ──► norm ──► self_attn ──► + ──► norm ──► mlp ──► +
+        └──────── residual ────────┘   └─────── residual ───┘
+
+    Deepseek V4 decoder layer (``H = hc_mult`` parallel residual streams throughout)::
+
+                attention site                                    mlp site
+        ┌────────────────────────────────────────┐    ┌────────────────────────────────────────┐
+        │  hidden_streams [B, S, H, D]           │    │  hidden_streams [B, S, H, D]           │
+        │        │                               │    │        │                               │
+        │  attn_hc(streams) ─► (pre, post, comb) │    │  ffn_hc(streams) ─► (pre, post, comb)  │
+        │        │                               │    │        │                               │
+        │   Σ pre·streams  (collapse)            │    │   Σ pre·streams  (collapse)            │
+        │        │                               │    │        │                               │
+        │   input_layernorm                      │    │   post_attention_layernorm             │
+        │        │                               │    │        │                               │
+        │   self_attn                            │    │   mlp  (MoE routed + shared)           │
+        │        │                               │    │        │                               │
+        │   post·output + comb·streams  (expand) │    │   post·output + comb·streams  (expand) │
+        │        │                               │    │        │                               │
+        │        ▼                               │    │        ▼                               │
+        │  new hidden_streams  ──────────────────┘    │  new hidden_streams                    │
+        └────────────────────────────────────────┘    └────────────────────────────────────────┘
+
+
+    """
+
+    def __init__(self, config: DeepseekV4Config, layer_idx: int):
+        super().__init__()
+        self.layer_idx = layer_idx
+        self.self_attn = DeepseekV4Attention(config, layer_idx)
+        self.mlp = DeepseekV4SparseMoeBlock(config, layer_idx)
+        self.input_layernorm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attn_hc = DeepseekV4HyperConnection(config)
+        self.ffn_hc = DeepseekV4HyperConnection(config)
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> torch.Tensor:
+        # hidden_states throughout: [B, S, hc_mult, hidden].
+
+        # --- Attention site: collapse → norm → attn → expand ---
+        pre, post, comb = self.attn_hc(hidden_states)
+        collapsed = (pre.unsqueeze(-1) * hidden_states).sum(dim=2).to(hidden_states.dtype)
+        attn_output, _ = self.self_attn(self.input_layernorm(collapsed), **kwargs)
+        dtype = hidden_states.dtype
+        hidden_states = post.to(dtype).unsqueeze(-1) * attn_output.unsqueeze(-2) + torch.matmul(
+            comb.to(dtype), hidden_states
+        )
+
+        # --- MLP site: collapse → norm → mlp → expand ---
+        pre, post, comb = self.ffn_hc(hidden_states)
+        collapsed = (pre.unsqueeze(-1) * hidden_states).sum(dim=2).to(hidden_states.dtype)
+        mlp_output = self.mlp(self.post_attention_layernorm(collapsed), input_ids=kwargs.get("input_ids"))
+        dtype = hidden_states.dtype
+        return post.to(dtype).unsqueeze(-1) * mlp_output.unsqueeze(-2) + torch.matmul(comb.to(dtype), hidden_states)
+
+
+# -----------------------------------------------------------------------------
+# Pre-trained base + Model + ForCausalLM.
+# -----------------------------------------------------------------------------
+
+
+class DeepseekV4PreTrainedModel(MixtralPreTrainedModel):
+    config_class = DeepseekV4Config
+    base_model_prefix = "model"
+    _no_split_modules = ["DeepseekV4DecoderLayer"]
+    # V4 ships eager-only: the compressor / indexer paths weren't validated against
+    # SDPA / FlashAttention / FlexAttention kernels — leaving these ``False`` makes
+    # ``set_attn_implementation`` reject those backends instead of silently routing
+    # through them.
+    _supports_flash_attn = False
+    _supports_sdpa = False
+    _supports_flex_attn = False
+    # The compressor's rolling-window buffer / pool / overlap state lives on the
+    # per-layer cache (:class:`DeepseekV4HCACache` / :class:`DeepseekV4CSACache`)
+    # and isn't compatible with :class:`StaticCache` — that path would hand the
+    # compressor a :class:`StaticSlidingWindowLayer` with no ``update_compressor``
+    # method. Disabling fullgraph compile keeps generation tests on the dynamic
+    # cache build that does dispatch to V4's own cache layers.
+    _can_compile_fullgraph = False
+    _keep_in_fp32_modules_strict = ["attn_hc", "ffn_hc"]
+    _keys_to_ignore_on_load_unexpected = [r"model\.mtp\..*"]
+    # ``_is_stateful`` opts out of generation modes that need to roll the cache
+    # back across drafts (assisted generation, prompt lookup, contrastive search).
+    # The compressor's running-window state isn't rewindable, so ``generate``
+    # raises a clear error early instead of failing deep in the compressor with
+    # a missing-method ``AttributeError``.
+    _is_stateful = True
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(DeepseekV4TopKRouter, index=0),
+        "hidden_states": DeepseekV4DecoderLayer,
+        "attentions": DeepseekV4Attention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        PreTrainedModel._init_weights(self, module)
+        std = self.config.initializer_range
+        if isinstance(module, (DeepseekV4TopKRouter, DeepseekV4HashRouter)):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if isinstance(module, DeepseekV4TopKRouter):
+                init.zeros_(module.bias)  # buffer
+            if isinstance(module, DeepseekV4HashRouter):
+                init.zeros_(module.tid2eid)  # buffer; real values come from the checkpoint
+        elif isinstance(module, DeepseekV4Experts):
+            init.normal_(module.gate_up_proj, mean=0.0, std=std)
+            init.normal_(module.down_proj, mean=0.0, std=std)
+        elif isinstance(module, DeepseekV4Attention):
+            init.zeros_(module.sinks)
+        elif isinstance(module, DeepseekV4HyperConnection):
+            init.normal_(module.fn, mean=0.0, std=std)
+            init.zeros_(module.base)
+            init.ones_(module.scale)
+        elif isinstance(module, DeepseekV4HyperHead):
+            init.normal_(module.hc_fn, mean=0.0, std=std)
+            init.zeros_(module.hc_base)
+            init.ones_(module.hc_scale)
+        elif isinstance(module, (DeepseekV4HCACompressor, DeepseekV4CSACompressor, DeepseekV4Indexer)):
+            init.zeros_(module.position_bias)
+        elif isinstance(module, DeepseekV4RotaryEmbedding):
+            for layer_type in module.layer_types:
+                rope_init_fn = module.compute_default_rope_parameters
+                if module.rope_type[layer_type] != "default":
+                    rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type[layer_type]]
+                curr_inv_freq, _ = rope_init_fn(module.config, layer_type=layer_type)
+                init.copy_(getattr(module, f"{layer_type}_inv_freq"), curr_inv_freq)
+                init.copy_(getattr(module, f"{layer_type}_original_inv_freq"), curr_inv_freq)
+
+
+@auto_docstring
+class DeepseekV4Model(DeepseekV4PreTrainedModel):
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList(
+            [DeepseekV4DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = DeepseekV4RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.hc_head = DeepseekV4HyperHead(config)
+        self.rotary_emb = DeepseekV4RotaryEmbedding(config)
+        self.rotary_emb_compress = DeepseekV4RotaryEmbedding(config)
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+        # V4's compressor reads / writes per-layer buffer state on the cache, so we
+        # always build a ``DynamicCache(config=...)`` internally — even when
+        # ``use_cache=False`` we need a forward-scoped cache to thread the compressor's
+        # buffer through the window pooling. ``LAYER_TYPE_CACHE_MAPPING`` populates the
+        # right :class:`DeepseekV4HCACache` / :class:`DeepseekV4CSACache` per layer.
+        # When ``use_cache=False`` we still hand the layers a real cache; we just don't
+        # surface it back to the caller so the user-facing semantics match other models.
+        return_cache = past_key_values if use_cache else None
+        if past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+        if position_ids is None:
+            past_seen = past_key_values.get_seq_length()
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen
+            position_ids = position_ids.unsqueeze(0)
+        # ``generate()`` may pass a per-layer-type mask dict already built by
+        # ``create_masks_for_generate``; all V4 layer types use the same sliding-window
+        # mask, so use the prebuilt one directly. Otherwise build it here.
+        if isinstance(attention_mask, dict):
+            causal_mask = next(iter(attention_mask.values()))
+        else:
+            causal_mask = create_sliding_window_causal_mask(
+                config=self.config,
+                inputs_embeds=inputs_embeds,
+                attention_mask=attention_mask,
+                past_key_values=past_key_values,
+                position_ids=position_ids,
+            )
+        hidden_states = inputs_embeds.unsqueeze(2).expand(-1, -1, self.config.hc_mult, -1).contiguous()
+        cos_sin = self.rotary_emb(inputs_embeds, position_ids=position_ids, layer_type="main")
+
+        for layer in self.layers:
+            hidden_states = layer(
+                hidden_states,
+                position_embeddings=cos_sin,
+                position_ids=position_ids,
+                attention_mask=causal_mask,
+                input_ids=input_ids,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(self.hc_head(hidden_states))
+        return MoeModelOutputWithPast(last_hidden_state=hidden_states, past_key_values=return_cache)
+
+
+class DeepseekV4ForCausalLM(MixtralForCausalLM):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+
+    def __init__(self, config: DeepseekV4Config):
+        super().__init__(config)
+        self.model = DeepseekV4Model(config)
+
+
+__all__ = [
+    "DeepseekV4Config",
+    "DeepseekV4PreTrainedModel",
+    "DeepseekV4Model",
+    "DeepseekV4ForCausalLM",
+]
diff --git a/src/transformers/models/deepseek_vl/modeling_deepseek_vl.py b/src/transformers/models/deepseek_vl/modeling_deepseek_vl.py
index c58f56ddfac0..59cc5864105f 100644
--- a/src/transformers/models/deepseek_vl/modeling_deepseek_vl.py
+++ b/src/transformers/models/deepseek_vl/modeling_deepseek_vl.py
@@ -34,12 +34,12 @@
 from .configuration_deepseek_vl import DeepseekVLConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DeepseekVL model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class DeepseekVLBaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -67,12 +67,12 @@ class DeepseekVLBaseModelOutputWithPast(ModelOutput):
     image_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DeepseekVL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class DeepseekVLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -180,9 +180,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/deepseek_vl/processing_deepseek_vl.py b/src/transformers/models/deepseek_vl/processing_deepseek_vl.py
index 7057ff152a67..be55db718b82 100644
--- a/src/transformers/models/deepseek_vl/processing_deepseek_vl.py
+++ b/src/transformers/models/deepseek_vl/processing_deepseek_vl.py
@@ -24,9 +24,11 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
+from .image_processing_deepseek_vl import DeepseekVLImageProcessorKwargs
 
 
 class DeepseekVLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: DeepseekVLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {"padding": False},
         "common_kwargs": {"return_tensors": "pt"},
diff --git a/src/transformers/models/deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py b/src/transformers/models/deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py
index eb85a8d02a76..d99ac7ed03cf 100644
--- a/src/transformers/models/deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py
+++ b/src/transformers/models/deepseek_vl_hybrid/modeling_deepseek_vl_hybrid.py
@@ -34,8 +34,8 @@
 from .configuration_deepseek_vl_hybrid import DeepseekVLHybridConfig
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithHighResVisionEncodings(BaseModelOutputWithPooling):
     r"""
     high_res_vision_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -58,12 +58,12 @@ class BaseModelOutputWithHighResVisionEncodings(BaseModelOutputWithPooling):
     high_res_vision_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DeepseekVLHybrid model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class DeepseekVLHybridBaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -91,12 +91,12 @@ class DeepseekVLHybridBaseModelOutputWithPast(ModelOutput):
     image_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DeepseekVLHybrid causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class DeepseekVLHybridCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -331,9 +331,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -373,7 +373,7 @@ def forward(
             else:
                 image_attention_mask = input_ids == self.config.image_token_id
 
-            image_attention_mask = image_attention_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+            image_attention_mask = image_attention_mask.unsqueeze(-1).to(inputs_embeds.device)
             image_embeds = self.get_image_features(pixel_values, high_res_pixel_values, return_dict=True).pooler_output
             image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1])
             image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
diff --git a/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py b/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
index 99d24c163562..7f943bb34685 100644
--- a/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
+++ b/src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py
@@ -114,8 +114,8 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithHighResVisionEncodings(BaseModelOutputWithPooling):
     r"""
     high_res_vision_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -332,7 +332,7 @@ def forward(
             else:
                 image_attention_mask = input_ids == self.config.image_token_id
 
-            image_attention_mask = image_attention_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+            image_attention_mask = image_attention_mask.unsqueeze(-1).to(inputs_embeds.device)
             image_embeds = self.get_image_features(pixel_values, high_res_pixel_values, return_dict=True).pooler_output
             image_features = image_embeds.reshape(-1, inputs_embeds.shape[-1])
             image_features = image_features.to(inputs_embeds.device, inputs_embeds.dtype)
diff --git a/src/transformers/models/deepseek_vl_hybrid/processing_deepseek_vl_hybrid.py b/src/transformers/models/deepseek_vl_hybrid/processing_deepseek_vl_hybrid.py
index 7948b954b6d7..9c1f4f8c012d 100644
--- a/src/transformers/models/deepseek_vl_hybrid/processing_deepseek_vl_hybrid.py
+++ b/src/transformers/models/deepseek_vl_hybrid/processing_deepseek_vl_hybrid.py
@@ -23,9 +23,11 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
+from .image_processing_deepseek_vl_hybrid import DeepseekVLHybridImageProcessorKwargs
 
 
 class DeepseekVLHybridProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: DeepseekVLHybridImageProcessorKwargs
     _defaults = {
         "text_kwargs": {"padding": False},
         "common_kwargs": {"return_tensors": "pt"},
diff --git a/src/transformers/models/deformable_detr/modeling_deformable_detr.py b/src/transformers/models/deformable_detr/modeling_deformable_detr.py
index 3ee685a887c1..08fe520f9cbe 100755
--- a/src/transformers/models/deformable_detr/modeling_deformable_detr.py
+++ b/src/transformers/models/deformable_detr/modeling_deformable_detr.py
@@ -68,12 +68,12 @@ class DeformableDetrDecoderOutput(BaseModelOutputWithCrossAttentions):
     intermediate_reference_points: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the Deformable DETR encoder-decoder model.
     """
 )
+@dataclass
 class DeformableDetrModelOutput(ModelOutput):
     r"""
     init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
@@ -106,12 +106,12 @@ class DeformableDetrModelOutput(ModelOutput):
     enc_outputs_coord_logits: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`DeformableDetrForObjectDetection`].
     """
 )
+@dataclass
 class DeformableDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/deformable_detr/modular_deformable_detr.py b/src/transformers/models/deformable_detr/modular_deformable_detr.py
index a4a5b4acd95a..0e1ad6a8d923 100644
--- a/src/transformers/models/deformable_detr/modular_deformable_detr.py
+++ b/src/transformers/models/deformable_detr/modular_deformable_detr.py
@@ -218,12 +218,12 @@ class DeformableDetrDecoderOutput(DetrDecoderOutput):
     intermediate_reference_points: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the Deformable DETR encoder-decoder model.
     """
 )
+@dataclass
 class DeformableDetrModelOutput(ModelOutput):
     r"""
     init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
diff --git a/src/transformers/models/deit/modeling_deit.py b/src/transformers/models/deit/modeling_deit.py
index 3ec3b52ba1b6..670ebd7be27b 100644
--- a/src/transformers/models/deit/modeling_deit.py
+++ b/src/transformers/models/deit/modeling_deit.py
@@ -671,12 +671,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`DeiTForImageClassificationWithTeacher`].
     """
 )
+@dataclass
 class DeiTForImageClassificationWithTeacherOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
diff --git a/src/transformers/models/depth_anything/modeling_depth_anything.py b/src/transformers/models/depth_anything/modeling_depth_anything.py
index 3da9cef51e4a..c55e89715988 100644
--- a/src/transformers/models/depth_anything/modeling_depth_anything.py
+++ b/src/transformers/models/depth_anything/modeling_depth_anything.py
@@ -19,7 +19,7 @@
 from ...backbone_utils import load_backbone
 from ...modeling_outputs import DepthEstimatorOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
 from .configuration_depth_anything import DepthAnythingConfig
 
 
@@ -326,16 +326,14 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.FloatTensor,
         labels: torch.LongTensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple[torch.Tensor] | DepthEstimatorOutput:
+    ) -> DepthEstimatorOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
             Ground truth depth estimation maps for computing the loss.
@@ -378,15 +376,7 @@ def forward(
         if labels is not None:
             raise NotImplementedError("Training is not implemented yet")
 
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-
-        outputs = self.backbone.forward_with_filtered_kwargs(
-            pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
-        )
+        outputs = self.backbone.forward_with_filtered_kwargs(pixel_values, **kwargs)
         hidden_states = outputs.feature_maps
 
         _, _, height, width = pixel_values.shape
@@ -398,17 +388,10 @@ def forward(
 
         predicted_depth = self.head(hidden_states, patch_height, patch_width)
 
-        if not return_dict:
-            if output_hidden_states:
-                output = (predicted_depth,) + outputs[1:]
-            else:
-                output = (predicted_depth,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return DepthEstimatorOutput(
             loss=loss,
             predicted_depth=predicted_depth,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
 
diff --git a/src/transformers/models/depth_pro/modeling_depth_pro.py b/src/transformers/models/depth_pro/modeling_depth_pro.py
index f8ee3c84b716..702a41a9b22c 100644
--- a/src/transformers/models/depth_pro/modeling_depth_pro.py
+++ b/src/transformers/models/depth_pro/modeling_depth_pro.py
@@ -30,12 +30,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DepthPro's outputs.
     """
 )
+@dataclass
 class DepthProOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, n_patches_per_batch, sequence_length, hidden_size)`):
@@ -50,12 +50,12 @@ class DepthProOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for DepthProForDepthEstimation's output.
     """
 )
+@dataclass
 class DepthProDepthEstimatorOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/detr/modeling_detr.py b/src/transformers/models/detr/modeling_detr.py
index 384cc388cfd7..212b4286fb16 100644
--- a/src/transformers/models/detr/modeling_detr.py
+++ b/src/transformers/models/detr/modeling_detr.py
@@ -89,12 +89,12 @@ class DetrModelOutput(Seq2SeqModelOutput):
     intermediate_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`DetrForObjectDetection`].
     """
 )
+@dataclass
 class DetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
@@ -132,12 +132,12 @@ class DetrObjectDetectionOutput(ModelOutput):
     encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`DetrForSegmentation`].
     """
 )
+@dataclass
 class DetrSegmentationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/dia/modeling_dia.py b/src/transformers/models/dia/modeling_dia.py
index 629dfd4cdb35..cc649f4459b4 100644
--- a/src/transformers/models/dia/modeling_dia.py
+++ b/src/transformers/models/dia/modeling_dia.py
@@ -193,7 +193,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/diffllama/modeling_diffllama.py b/src/transformers/models/diffllama/modeling_diffllama.py
index d80ccd572dc3..6a6703bcc50f 100644
--- a/src/transformers/models/diffllama/modeling_diffllama.py
+++ b/src/transformers/models/diffllama/modeling_diffllama.py
@@ -126,7 +126,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/dinat/modeling_dinat.py b/src/transformers/models/dinat/modeling_dinat.py
index de71c29a4b65..2a2d218df32a 100644
--- a/src/transformers/models/dinat/modeling_dinat.py
+++ b/src/transformers/models/dinat/modeling_dinat.py
@@ -52,12 +52,12 @@ def natten2dav(*args, **kwargs):
 # drop_path and DinatDropPath are from the timm library.
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Dinat encoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class DinatEncoderOutput(ModelOutput):
     r"""
     reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -74,12 +74,12 @@ class DinatEncoderOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Dinat model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class DinatModelOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
@@ -99,12 +99,12 @@ class DinatModelOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Dinat outputs for image classification.
     """
 )
+@dataclass
 class DinatImageClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/dinov3_vit/modeling_dinov3_vit.py b/src/transformers/models/dinov3_vit/modeling_dinov3_vit.py
index 74f47fc43b7a..965d7821fdd0 100644
--- a/src/transformers/models/dinov3_vit/modeling_dinov3_vit.py
+++ b/src/transformers/models/dinov3_vit/modeling_dinov3_vit.py
@@ -30,7 +30,7 @@
 from ...activations import ACT2FN
 from ...backbone_utils import BackboneMixin, filter_output_hidden_states
 from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...pytorch_utils import compile_compatible_method_lru_cache
@@ -632,4 +632,62 @@ def forward(
         return output
 
 
-__all__ = ["DINOv3ViTModel", "DINOv3ViTPreTrainedModel", "DINOv3ViTBackbone"]
+@auto_docstring(
+    custom_intro="""
+    DINOv3ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state
+    of the [CLS] token) e.g. for ImageNet.
+    """
+)
+class DINOv3ViTForImageClassification(DINOv3ViTPreTrainedModel):
+    def __init__(self, config: DINOv3ViTConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.dinov3_vit = DINOv3ViTModel(config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.dinov3_vit.embeddings.patch_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ImageClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        outputs: BaseModelOutputWithPooling = self.dinov3_vit(pixel_values, **kwargs)
+
+        sequence_output = outputs.last_hidden_state  # batch_size, sequence_length, hidden_size
+        cls_token = sequence_output[:, 0]
+        patch_tokens = sequence_output[:, 1 + self.config.num_register_tokens :]
+        linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)
+        logits = self.classifier(linear_input)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(labels, logits, self.config, **kwargs)
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["DINOv3ViTModel", "DINOv3ViTPreTrainedModel", "DINOv3ViTBackbone", "DINOv3ViTForImageClassification"]
diff --git a/src/transformers/models/dinov3_vit/modular_dinov3_vit.py b/src/transformers/models/dinov3_vit/modular_dinov3_vit.py
index b35ea9767877..42033232cd37 100644
--- a/src/transformers/models/dinov3_vit/modular_dinov3_vit.py
+++ b/src/transformers/models/dinov3_vit/modular_dinov3_vit.py
@@ -34,7 +34,7 @@
 from ... import initialization as init
 from ...backbone_utils import BackboneMixin, filter_output_hidden_states
 from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
 from ...processing_utils import Unpack
 from ...pytorch_utils import compile_compatible_method_lru_cache
@@ -529,4 +529,62 @@ def forward(
         return output
 
 
-__all__ = ["DINOv3ViTModel", "DINOv3ViTPreTrainedModel", "DINOv3ViTBackbone"]
+@auto_docstring(
+    custom_intro="""
+    DINOv3ViT Model transformer with an image classification head on top (a linear layer on top of the final hidden state
+    of the [CLS] token) e.g. for ImageNet.
+    """
+)
+class DINOv3ViTForImageClassification(DINOv3ViTPreTrainedModel):
+    def __init__(self, config: DINOv3ViTConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.dinov3_vit = DINOv3ViTModel(config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.dinov3_vit.embeddings.patch_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ImageClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        outputs: BaseModelOutputWithPooling = self.dinov3_vit(pixel_values, **kwargs)
+
+        sequence_output = outputs.last_hidden_state  # batch_size, sequence_length, hidden_size
+        cls_token = sequence_output[:, 0]
+        patch_tokens = sequence_output[:, 1 + self.config.num_register_tokens :]
+        linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)
+        logits = self.classifier(linear_input)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(labels, logits, self.config, **kwargs)
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = ["DINOv3ViTModel", "DINOv3ViTPreTrainedModel", "DINOv3ViTBackbone", "DINOv3ViTForImageClassification"]
diff --git a/src/transformers/models/doge/modeling_doge.py b/src/transformers/models/doge/modeling_doge.py
index 4aad59b52a9a..ffe7beb05170 100644
--- a/src/transformers/models/doge/modeling_doge.py
+++ b/src/transformers/models/doge/modeling_doge.py
@@ -33,7 +33,7 @@
 from ...cache_utils import Cache, DynamicCache
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub
-from ...integrations.flex_attention import compile_friendly_flex_attention
+from ...integrations.flex_attention import compile_friendly_flex_attention, get_flex_attention_lse_kwargs
 from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
 from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
@@ -128,7 +128,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -241,9 +241,7 @@ def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
         block_mask=block_mask,
         enable_gqa=True,
         scale=scaling,
-        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
-        # For simplification, we thus always return it as no additional computations are introduced.
-        return_lse=True,
+        **get_flex_attention_lse_kwargs(True),
     )
     # lse is returned in float32
     attention_weights = attention_weights.to(value.dtype)
@@ -493,6 +491,8 @@ def forward(
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
+        if isinstance(hidden_states, tuple):
+            hidden_states, _ = hidden_states
         hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
         hidden_states = self.post_attention_residual * residual + hidden_states
 
@@ -524,6 +524,9 @@ def _init_weights(self, module):
         if isinstance(module, DogeAttention):
             if hasattr(module, "A"):
                 init.zeros_(module.A)
+        elif isinstance(module, DogeCDMoE):
+            if hasattr(module, "router_gate"):
+                init.zeros_(module.router_gate.weight)
         elif isinstance(module, DogeDecoderLayer):
             if hasattr(module, "input_residual"):
                 init.ones_(module.input_residual)
diff --git a/src/transformers/models/doge/modular_doge.py b/src/transformers/models/doge/modular_doge.py
index 8b78126c0a00..81ed7d028d9b 100644
--- a/src/transformers/models/doge/modular_doge.py
+++ b/src/transformers/models/doge/modular_doge.py
@@ -28,7 +28,7 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache
 from ...configuration_utils import PreTrainedConfig
-from ...integrations.flex_attention import compile_friendly_flex_attention
+from ...integrations.flex_attention import compile_friendly_flex_attention, get_flex_attention_lse_kwargs
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_rope_utils import RopeParameters
@@ -179,9 +179,7 @@ def score_mod(score, batch_idx, head_idx, q_idx, kv_idx):
         block_mask=block_mask,
         enable_gqa=True,
         scale=scaling,
-        # Last time checked on PyTorch == 2.5.1: Flex Attention always computes the lse regardless.
-        # For simplification, we thus always return it as no additional computations are introduced.
-        return_lse=True,
+        **get_flex_attention_lse_kwargs(True),
     )
     # lse is returned in float32
     attention_weights = attention_weights.to(value.dtype)
@@ -419,6 +417,8 @@ def forward(
         residual = hidden_states
         hidden_states = self.post_attention_layernorm(hidden_states)
         hidden_states = self.mlp(hidden_states)
+        if isinstance(hidden_states, tuple):
+            hidden_states, _ = hidden_states
         hidden_states = F.dropout(hidden_states, p=self.hidden_dropout, training=self.training)
         hidden_states = self.post_attention_residual * residual + hidden_states
 
@@ -441,6 +441,9 @@ def _init_weights(self, module):
         if isinstance(module, DogeAttention):
             if hasattr(module, "A"):
                 init.zeros_(module.A)
+        elif isinstance(module, DogeCDMoE):
+            if hasattr(module, "router_gate"):
+                init.zeros_(module.router_gate.weight)
         elif isinstance(module, DogeDecoderLayer):
             if hasattr(module, "input_residual"):
                 init.ones_(module.input_residual)
diff --git a/src/transformers/models/dots1/modeling_dots1.py b/src/transformers/models/dots1/modeling_dots1.py
index 95b21258ffd5..7e4df0cc9365 100644
--- a/src/transformers/models/dots1/modeling_dots1.py
+++ b/src/transformers/models/dots1/modeling_dots1.py
@@ -84,8 +84,8 @@ def __init__(self, config: Dots1Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -125,7 +125,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -461,7 +461,7 @@ class Dots1PreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_flex_attn = True
 
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": Dots1DecoderLayer,
diff --git a/src/transformers/models/dpr/modeling_dpr.py b/src/transformers/models/dpr/modeling_dpr.py
index 3ed9a1759db6..f5aaa12a4604 100644
--- a/src/transformers/models/dpr/modeling_dpr.py
+++ b/src/transformers/models/dpr/modeling_dpr.py
@@ -23,6 +23,7 @@
 from ...utils import (
     ModelOutput,
     auto_docstring,
+    can_return_tuple,
     logging,
 )
 from ..bert.modeling_bert import BertModel
@@ -37,12 +38,12 @@
 ##########
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`DPRQuestionEncoder`].
     """
 )
+@dataclass
 class DPRContextEncoderOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
@@ -56,12 +57,12 @@ class DPRContextEncoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`DPRQuestionEncoder`].
     """
 )
+@dataclass
 class DPRQuestionEncoderOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, embeddings_size)`):
@@ -75,12 +76,12 @@ class DPRQuestionEncoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`DPRQuestionEncoder`].
     """
 )
+@dataclass
 class DPRReaderOutput(ModelOutput):
     r"""
     start_logits (`torch.FloatTensor` of shape `(n_passages, sequence_length)`):
@@ -118,15 +119,13 @@ def __init__(self, config: DPRConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     def forward(
         self,
         input_ids: Tensor,
         attention_mask: Tensor | None = None,
         token_type_ids: Tensor | None = None,
         inputs_embeds: Tensor | None = None,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = False,
         **kwargs,
     ) -> BaseModelOutputWithPooling | tuple[Tensor, ...]:
         outputs = self.bert_model(
@@ -134,9 +133,7 @@ def forward(
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
         sequence_output = outputs[0]
         pooled_output = sequence_output[:, 0, :]
@@ -144,9 +141,6 @@ def forward(
         if self.projection_dim > 0:
             pooled_output = self.encode_proj(pooled_output)
 
-        if not return_dict:
-            return (sequence_output, pooled_output) + outputs[2:]
-
         return BaseModelOutputWithPooling(
             last_hidden_state=sequence_output,
             pooler_output=pooled_output,
@@ -172,14 +166,12 @@ def __init__(self, config: DPRConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     def forward(
         self,
         input_ids: Tensor,
         attention_mask: Tensor,
         inputs_embeds: Tensor | None = None,
-        output_attentions: bool = False,
-        output_hidden_states: bool = False,
-        return_dict: bool = False,
         **kwargs,
     ) -> DPRReaderOutput | tuple[Tensor, ...]:
         # notations: N - number of questions in a batch, M - number of passages per questions, L - sequence length
@@ -189,9 +181,7 @@ def forward(
             input_ids,
             attention_mask=attention_mask,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
         sequence_output = outputs[0]
 
@@ -207,9 +197,6 @@ def forward(
         end_logits = end_logits.view(n_passages, sequence_length)
         relevance_logits = relevance_logits.view(n_passages)
 
-        if not return_dict:
-            return (start_logits, end_logits, relevance_logits) + outputs[2:]
-
         return DPRReaderOutput(
             start_logits=start_logits,
             end_logits=end_logits,
@@ -272,6 +259,7 @@ def __init__(self, config: DPRConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -279,9 +267,6 @@ def forward(
         attention_mask: Tensor | None = None,
         token_type_ids: Tensor | None = None,
         inputs_embeds: Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> DPRContextEncoderOutput | tuple[Tensor, ...]:
         r"""
@@ -322,12 +307,6 @@ def forward(
         >>> embeddings = model(input_ids).pooler_output
         ```"""
 
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -353,13 +332,9 @@ def forward(
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        if not return_dict:
-            return outputs[1:]
         return DPRContextEncoderOutput(
             pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
@@ -378,6 +353,7 @@ def __init__(self, config: DPRConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -385,9 +361,6 @@ def forward(
         attention_mask: Tensor | None = None,
         token_type_ids: Tensor | None = None,
         inputs_embeds: Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> DPRQuestionEncoderOutput | tuple[Tensor, ...]:
         r"""
@@ -428,12 +401,6 @@ def forward(
         >>> embeddings = model(input_ids).pooler_output
         ```
         """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -460,13 +427,9 @@ def forward(
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        if not return_dict:
-            return outputs[1:]
         return DPRQuestionEncoderOutput(
             pooler_output=outputs.pooler_output, hidden_states=outputs.hidden_states, attentions=outputs.attentions
         )
@@ -485,15 +448,13 @@ def __init__(self, config: DPRConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         input_ids: Tensor | None = None,
         attention_mask: Tensor | None = None,
         inputs_embeds: Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> DPRReaderOutput | tuple[Tensor, ...]:
         r"""
@@ -534,12 +495,6 @@ def forward(
         >>> relevance_logits = outputs.relevance_logits
         ```
         """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -559,9 +514,7 @@ def forward(
             input_ids,
             attention_mask,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
 
diff --git a/src/transformers/models/dpt/image_processing_dpt.py b/src/transformers/models/dpt/image_processing_dpt.py
index 6d157f6385c0..7969cead3f21 100644
--- a/src/transformers/models/dpt/image_processing_dpt.py
+++ b/src/transformers/models/dpt/image_processing_dpt.py
@@ -192,9 +192,10 @@ def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
         """Reduce label values by 1, replacing 0 with 255."""
         for idx in range(len(labels)):
             label = labels[idx]
-            label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label)
-            label = label - 1
-            label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label)
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
             labels[idx] = label
         return labels
 
diff --git a/src/transformers/models/dpt/image_processing_pil_dpt.py b/src/transformers/models/dpt/image_processing_pil_dpt.py
index 6f770cac4e5f..07e711769829 100644
--- a/src/transformers/models/dpt/image_processing_pil_dpt.py
+++ b/src/transformers/models/dpt/image_processing_pil_dpt.py
@@ -180,9 +180,10 @@ def _preprocess_image_like_inputs(
 
     def reduce_label(self, image: np.ndarray) -> np.ndarray:
         """Reduce label values by 1, replacing 0 with 255."""
-        image[image == 0] = 255
-        image = image - 1
-        image[image == 254] = 255
+        image = image.copy()
+        ignore_mask = (image == 0) | (image == 255)
+        image[ignore_mask] = 255
+        image[~ignore_mask] = image[~ignore_mask] - 1
         return image
 
     def resize(
diff --git a/src/transformers/models/dpt/modeling_dpt.py b/src/transformers/models/dpt/modeling_dpt.py
index dfb8b911d4ae..6f6bf3ff9ddb 100755
--- a/src/transformers/models/dpt/modeling_dpt.py
+++ b/src/transformers/models/dpt/modeling_dpt.py
@@ -42,13 +42,13 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that also contains intermediate activations that can be used at later stages. Useful
     in the context of Vision models.:
     """
 )
+@dataclass
 class BaseModelOutputWithIntermediateActivations(ModelOutput):
     r"""
     last_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -61,13 +61,13 @@ class BaseModelOutputWithIntermediateActivations(ModelOutput):
     intermediate_activations: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that also contains a pooling of the last hidden states as well as intermediate
     activations that can be used by the model at later stages.
     """
 )
+@dataclass
 class BaseModelOutputWithPoolingAndIntermediateActivations(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
diff --git a/src/transformers/models/edgetam/modeling_edgetam.py b/src/transformers/models/edgetam/modeling_edgetam.py
index 099f9ca789c0..2d4e402a7fcf 100644
--- a/src/transformers/models/edgetam/modeling_edgetam.py
+++ b/src/transformers/models/edgetam/modeling_edgetam.py
@@ -473,8 +473,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the EdgeTam model's output.")
+@dataclass
 class EdgeTamImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
diff --git a/src/transformers/models/edgetam_video/modeling_edgetam_video.py b/src/transformers/models/edgetam_video/modeling_edgetam_video.py
index 89a72e6c88b5..c90bfe0f98ad 100644
--- a/src/transformers/models/edgetam_video/modeling_edgetam_video.py
+++ b/src/transformers/models/edgetam_video/modeling_edgetam_video.py
@@ -1535,8 +1535,8 @@ def _forward_2d(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.
         return latents_2d, positional_encoding_2d
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the EdgeTamVideo model's output.")
+@dataclass
 class EdgeTamVideoImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
@@ -1576,8 +1576,8 @@ class EdgeTamVideoImageSegmentationOutput(ModelOutput):
     object_pointer: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class EdgeTamVideoSegmentationOutput(ModelOutput):
     r"""
     object_ids (`list[int]`, *optional*):
diff --git a/src/transformers/models/efficientloftr/modeling_efficientloftr.py b/src/transformers/models/efficientloftr/modeling_efficientloftr.py
index e1b5f1fb40a8..9e3664c14a28 100644
--- a/src/transformers/models/efficientloftr/modeling_efficientloftr.py
+++ b/src/transformers/models/efficientloftr/modeling_efficientloftr.py
@@ -38,7 +38,6 @@
 from .configuration_efficientloftr import EfficientLoFTRConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of EfficientLoFTR keypoint matching models. Due to the nature of keypoint detection and matching, the number
@@ -46,6 +45,7 @@
     images, the maximum number of matches is set as the dimension of the matches and matching scores.
     """
 )
+@dataclass
 class EfficientLoFTRKeypointMatchingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
diff --git a/src/transformers/models/electra/modeling_electra.py b/src/transformers/models/electra/modeling_electra.py
index c428ff0a2aeb..1af436dc336f 100644
--- a/src/transformers/models/electra/modeling_electra.py
+++ b/src/transformers/models/electra/modeling_electra.py
@@ -525,12 +525,12 @@ def _init_weights(self, module):
             init.zeros_(module.token_type_ids)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ElectraForPreTraining`].
     """
 )
+@dataclass
 class ElectraForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/emu3/modeling_emu3.py b/src/transformers/models/emu3/modeling_emu3.py
index 2481decd7aeb..2a93bfe05909 100644
--- a/src/transformers/models/emu3/modeling_emu3.py
+++ b/src/transformers/models/emu3/modeling_emu3.py
@@ -46,8 +46,8 @@
 from .configuration_emu3 import Emu3Config, Emu3TextConfig, Emu3VQVAEConfig
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Emu3VQVAEModelOutput(BaseModelOutputWithPooling):
     r"""
     image_tokens (`torch.LongTensor` of shape `(batch_size, config.vocab_size`):
@@ -1188,7 +1188,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -1447,9 +1447,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/emu3/modular_emu3.py b/src/transformers/models/emu3/modular_emu3.py
index 598687892727..69b9d0b5ddb0 100644
--- a/src/transformers/models/emu3/modular_emu3.py
+++ b/src/transformers/models/emu3/modular_emu3.py
@@ -42,8 +42,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Emu3VQVAEModelOutput(BaseModelOutputWithPooling):
     r"""
     image_tokens (`torch.LongTensor` of shape `(batch_size, config.vocab_size`):
@@ -1016,9 +1016,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/encodec/modeling_encodec.py b/src/transformers/models/encodec/modeling_encodec.py
index 352a1e94006c..6af8e2d8c968 100644
--- a/src/transformers/models/encodec/modeling_encodec.py
+++ b/src/transformers/models/encodec/modeling_encodec.py
@@ -455,23 +455,12 @@ class EncodecPreTrainedModel(PreTrainedAudioTokenizerBase):
 
     @torch.no_grad()
     def _init_weights(self, module):
-        """Initialize the weights"""
-        if isinstance(module, nn.GroupNorm):
-            init.zeros_(module.bias)
-            init.ones_(module.weight)
-        elif isinstance(module, nn.Conv1d):
+        super()._init_weights(module)
+        if isinstance(module, nn.Conv1d):
             init.kaiming_normal_(module.weight)
             if module.bias is not None:
                 k = math.sqrt(module.groups / (module.in_channels * module.kernel_size[0]))
                 init.uniform_(module.bias, a=-k, b=k)
-        elif isinstance(module, nn.ConvTranspose1d):
-            module.reset_parameters()
-        elif isinstance(module, nn.LSTM):
-            for name, param in module.named_parameters():
-                if "weight" in name:
-                    init.xavier_uniform_(param)
-                elif "bias" in name:
-                    init.constant_(param, 0.0)
         elif isinstance(module, EncodecConv1d):
             kernel_size = module.conv.kernel_size[0]
             stride = torch.tensor(module.conv.stride[0], dtype=torch.int64)
diff --git a/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py b/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
index f634f89ab89f..9718ec588100 100644
--- a/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
+++ b/src/transformers/models/encoder_decoder/modeling_encoder_decoder.py
@@ -35,11 +35,16 @@
 logger = logging.get_logger(__name__)
 
 
-DEPRECATION_WARNING = (
+# Warning about deprecated practice of passing decoder_input_ids when labels are provided
+DEPRECATED_DECODER_INPUT_IDS_WARNING = (
+    "The decoder_input_ids are created based on the labels, no need to pass them yourself anymore."
+)
+
+# Warning about v4.12.0 loss computation change - always shown when training with labels
+V4_12_LOSS_COMPUTATION_WARNING = (
     "Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the"
     " encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if"
-    " fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the"
-    " labels, no need to pass them yourself anymore."
+    " fine-tuning a model trained with versions anterior to 4.12.0."
 )
 
 
@@ -423,6 +428,9 @@ def forward(
             )
             if decoder_attention_mask is None:
                 decoder_attention_mask = (decoder_input_ids != self.config.pad_token_id).to(decoder_input_ids.dtype)
+        elif (labels is not None) and (decoder_input_ids is not None):
+            # User provided both labels and decoder_input_ids - this is the deprecated path
+            warnings.warn(DEPRECATED_DECODER_INPUT_IDS_WARNING, FutureWarning)
 
         # Decode
         decoder_outputs = self.decoder(
@@ -440,7 +448,8 @@ def forward(
         # Compute loss independent from decoder (as some shift the logits inside them)
         loss = None
         if labels is not None:
-            warnings.warn(DEPRECATION_WARNING, FutureWarning)
+            # Always warn about v4.12.0 loss computation change
+            warnings.warn(V4_12_LOSS_COMPUTATION_WARNING, FutureWarning)
             logits = decoder_outputs.logits
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.view(-1))
diff --git a/src/transformers/models/eomt/modeling_eomt.py b/src/transformers/models/eomt/modeling_eomt.py
index 589b023d4db8..554b37da4b03 100644
--- a/src/transformers/models/eomt/modeling_eomt.py
+++ b/src/transformers/models/eomt/modeling_eomt.py
@@ -1109,6 +1109,14 @@ def forward(
             list of tuples indicating the image index and start and end positions of patches for semantic segmentation.
         """
 
+        if mask_labels is not None:
+            target_device = pixel_values.device
+            mask_labels = [mask.to(target_device) for mask in mask_labels]
+
+        if class_labels is not None:
+            target_device = pixel_values.device
+            class_labels = [label.to(target_device) for label in class_labels]
+
         masks_queries_logits_per_layer, class_queries_logits_per_layer = (), ()
         attention_mask = None
 
diff --git a/src/transformers/models/eomt/modular_eomt.py b/src/transformers/models/eomt/modular_eomt.py
index e4dafa024861..9cc2d228e24e 100644
--- a/src/transformers/models/eomt/modular_eomt.py
+++ b/src/transformers/models/eomt/modular_eomt.py
@@ -455,6 +455,14 @@ def forward(
             list of tuples indicating the image index and start and end positions of patches for semantic segmentation.
         """
 
+        if mask_labels is not None:
+            target_device = pixel_values.device
+            mask_labels = [mask.to(target_device) for mask in mask_labels]
+
+        if class_labels is not None:
+            target_device = pixel_values.device
+            class_labels = [label.to(target_device) for label in class_labels]
+
         masks_queries_logits_per_layer, class_queries_logits_per_layer = (), ()
         attention_mask = None
 
diff --git a/src/transformers/models/ernie/modeling_ernie.py b/src/transformers/models/ernie/modeling_ernie.py
index fb299b2f00a1..ac580af35add 100644
--- a/src/transformers/models/ernie/modeling_ernie.py
+++ b/src/transformers/models/ernie/modeling_ernie.py
@@ -693,12 +693,12 @@ def _create_attention_masks(
         return attention_mask, encoder_attention_mask
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ErnieForPreTraining`].
     """
 )
+@dataclass
 class ErnieForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/ernie4_5_moe/modeling_ernie4_5_moe.py b/src/transformers/models/ernie4_5_moe/modeling_ernie4_5_moe.py
index 9c106a90010d..bfa8c79b6bab 100644
--- a/src/transformers/models/ernie4_5_moe/modeling_ernie4_5_moe.py
+++ b/src/transformers/models/ernie4_5_moe/modeling_ernie4_5_moe.py
@@ -605,7 +605,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -613,13 +613,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -630,8 +634,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/ernie4_5_vl_moe/modeling_ernie4_5_vl_moe.py b/src/transformers/models/ernie4_5_vl_moe/modeling_ernie4_5_vl_moe.py
index f3d7bc590f5d..25293ad5fe5e 100644
--- a/src/transformers/models/ernie4_5_vl_moe/modeling_ernie4_5_vl_moe.py
+++ b/src/transformers/models/ernie4_5_vl_moe/modeling_ernie4_5_vl_moe.py
@@ -1334,18 +1334,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1517,7 +1517,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1525,7 +1525,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -1542,8 +1544,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/ernie4_5_vl_moe/processing_ernie4_5_vl_moe.py b/src/transformers/models/ernie4_5_vl_moe/processing_ernie4_5_vl_moe.py
index 8413907ef3c2..4091b6998128 100644
--- a/src/transformers/models/ernie4_5_vl_moe/processing_ernie4_5_vl_moe.py
+++ b/src/transformers/models/ernie4_5_vl_moe/processing_ernie4_5_vl_moe.py
@@ -22,9 +22,11 @@
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...video_utils import VideoInput
+from .image_processing_ernie4_5_vl_moe import Ernie4_5_VL_MoeImageProcessorKwargs
 
 
 class Ernie4_5_VLMoeProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Ernie4_5_VL_MoeImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
diff --git a/src/transformers/models/ernie4_5_vl_moe/video_processing_ernie4_5_vl_moe.py b/src/transformers/models/ernie4_5_vl_moe/video_processing_ernie4_5_vl_moe.py
index 28b7311b96a1..b29a78b2f103 100644
--- a/src/transformers/models/ernie4_5_vl_moe/video_processing_ernie4_5_vl_moe.py
+++ b/src/transformers/models/ernie4_5_vl_moe/video_processing_ernie4_5_vl_moe.py
@@ -21,8 +21,7 @@
 import torch
 from huggingface_hub import is_offline_mode
 from huggingface_hub.dataclasses import validate_typed_dict
-from PIL import ImageDraw, ImageFont
-from torchvision.transforms.functional import pil_to_tensor, to_pil_image
+from PIL import Image, ImageDraw, ImageFont
 
 from ...image_processing_utils import BatchFeature
 from ...image_utils import (
@@ -64,6 +63,72 @@
 logger = logging.get_logger(__name__)
 
 
+class _TimestampOverlayCache:
+    """Cache for timestamp overlays to avoid slow torch->PIL->torch conversion."""
+
+    def __init__(self, font_path: str, max_cache_size: int = 512):
+        self.font_path = font_path
+        self.max_cache_size = max_cache_size
+        self._font_cache: dict[int, ImageFont.FreeTypeFont] = {}
+        self._overlay_cache: dict[tuple, tuple[torch.Tensor, int, int]] = {}
+
+    def _get_font(self, font_size: int) -> ImageFont.FreeTypeFont:
+        if font_size not in self._font_cache:
+            self._font_cache[font_size] = ImageFont.truetype(self.font_path, font_size)
+        return self._font_cache[font_size]
+
+    def _render_overlay(self, timestamp: str, font_size: int, outline_size: int):
+        cache_key = (timestamp, font_size, outline_size)
+        if cache_key in self._overlay_cache:
+            return self._overlay_cache[cache_key]
+
+        font = self._get_font(font_size)
+        dummy_img = Image.new("RGBA", (1, 1), (0, 0, 0, 0))
+        dummy_draw = ImageDraw.Draw(dummy_img)
+        bbox = dummy_draw.textbbox((0, 0), timestamp, font=font, stroke_width=outline_size)
+
+        text_width = bbox[2] + outline_size + 2
+        text_height = bbox[3] + outline_size + 2
+
+        overlay = Image.new("RGBA", (text_width, text_height), (0, 0, 0, 0))
+        draw = ImageDraw.Draw(overlay)
+        draw.text(
+            (0, 0),
+            timestamp,
+            font=font,
+            fill=(0, 0, 0, 255),
+            stroke_width=outline_size,
+            stroke_fill=(255, 255, 255),
+        )
+
+        overlay_tensor = torch.from_numpy(np.array(overlay)).permute(2, 0, 1).contiguous()
+        result = (overlay_tensor, text_width, text_height)
+
+        if len(self._overlay_cache) >= self.max_cache_size:
+            oldest_key = next(iter(self._overlay_cache))
+            del self._overlay_cache[oldest_key]
+
+        self._overlay_cache[cache_key] = result
+        return result
+
+    def apply(self, image: torch.Tensor, timestamp: str, size_factor: float = 0.1) -> torch.Tensor:
+        _, height, width = image.shape
+        font_size = int(min(height, width) * size_factor)
+        outline_size = int(font_size * size_factor)
+
+        overlay, overlay_width, overlay_height = self._render_overlay(timestamp, font_size, outline_size)
+        paste_height, paste_width = min(overlay_height, height), min(overlay_width, width)
+
+        result = image.clone()
+        alpha = overlay[3:4, :paste_height, :paste_width].float() / 255.0
+        rgb_overlay = overlay[:3, :paste_height, :paste_width].float()
+        original_region = result[:, :paste_height, :paste_width].float()
+        blended = alpha * rgb_overlay + (1.0 - alpha) * original_region
+        result[:, :paste_height, :paste_width] = blended.to(result.dtype)
+
+        return result
+
+
 class Ernie4_5_VLMoeVideoProcessorInitKwargs(VideosKwargs, total=False):
     patch_size: int
     temporal_patch_size: int
@@ -359,33 +424,19 @@ def _convert_timestamp(self, time_stamp_in_seconds):
         time_stamp_in_seconds = time_stamp_in_seconds % 60
         return f"time: {int(hours):02d}:{int(mins):02d}:{time_stamp_in_seconds:05.02f}"
 
+    _timestamp_cache: _TimestampOverlayCache = None
+
+    @property
+    def timestamp_cache(self) -> _TimestampOverlayCache:
+        if self._timestamp_cache is None:
+            self._timestamp_cache = _TimestampOverlayCache(font_path=self.font)
+        return self._timestamp_cache
+
     def _render_image_with_timestamp(self, image: torch.Tensor, timestamp: str, size_factor: float = 0.1):
         """Draws a black timestamp with a white border on the corner of the frame"""
         if self.font is None:
             raise AttributeError("To draw on frames with Ernie 4.5 VL, you need an associated font; found nothing")
-
-        # FIXME: conversion `torch->PIL->torch` is inefficient ~6ms per frame
-        # Left for optimization if anyone want to pick it up
-        #
-        # This can take up to ~1s in preprocessing (if default sampling is used):
-        #   180 (frames) x 6ms = 1080ms = ~1,1s
-        image = to_pil_image(image)
-
-        font_size = int(min(*image.size) * size_factor)
-        outline_size = int(font_size * size_factor)
-        font = ImageFont.truetype(self.font, font_size)
-
-        # Draw a black text with a white border
-        draw = ImageDraw.Draw(image)
-        draw.text(
-            (0, 0),
-            timestamp,
-            font=font,
-            fill=(0, 0, 0),
-            stroke_width=outline_size,
-            stroke_fill=(255, 255, 255),
-        )
-        return pil_to_tensor(image)
+        return self.timestamp_cache.apply(image, timestamp, size_factor)
 
     def _prepare_input_videos(
         self,
diff --git a/src/transformers/models/esm/modeling_esmfold.py b/src/transformers/models/esm/modeling_esmfold.py
index 02ceaad0c955..6e19935c173c 100644
--- a/src/transformers/models/esm/modeling_esmfold.py
+++ b/src/transformers/models/esm/modeling_esmfold.py
@@ -51,12 +51,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`EsmForProteinFoldingOutput`].
     """
 )
+@dataclass
 class EsmForProteinFoldingOutput(ModelOutput):
     r"""
     frames (`torch.FloatTensor`):
diff --git a/src/transformers/models/evolla/modeling_evolla.py b/src/transformers/models/evolla/modeling_evolla.py
index 74fd137882d2..4efa653779ea 100644
--- a/src/transformers/models/evolla/modeling_evolla.py
+++ b/src/transformers/models/evolla/modeling_evolla.py
@@ -1086,7 +1086,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/exaone4/configuration_exaone4.py b/src/transformers/models/exaone4/configuration_exaone4.py
index f29cab8dd8ea..56e88dd364d0 100644
--- a/src/transformers/models/exaone4/configuration_exaone4.py
+++ b/src/transformers/models/exaone4/configuration_exaone4.py
@@ -98,15 +98,29 @@ class Exaone4Config(PreTrainedConfig):
     layer_types: list[str] | None = None
 
     def __post_init__(self, **kwargs):
-        if self.sliding_window is None:
-            self.sliding_window_pattern = 0
         if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention"
-                if ((i + 1) % (self.sliding_window_pattern) != 0 and i < self.num_hidden_layers)
-                else "full_attention"
-                for i in range(self.num_hidden_layers)
-            ]
+            if self.sliding_window in (None, 0):
+                self.layer_types = ["full_attention"] * self.num_hidden_layers
+            elif isinstance(self.sliding_window_pattern, str) and self.sliding_window_pattern:
+                layer_pattern = [
+                    "sliding_attention" if layer_type.upper() == "L" else "full_attention"
+                    for layer_type in self.sliding_window_pattern
+                ]
+                self.layer_types = [
+                    layer_pattern[i % len(layer_pattern)] for i in range(self.num_hidden_layers - 1)
+                ] + ["full_attention"]
+            else:
+                repeat_period = (
+                    self.sliding_window_pattern
+                    if isinstance(self.sliding_window_pattern, int) and self.sliding_window_pattern > 0
+                    else 1
+                )
+                self.layer_types = [
+                    "sliding_attention"
+                    if (i + 1) % repeat_period != 0 and i < self.num_hidden_layers - 1
+                    else "full_attention"
+                    for i in range(self.num_hidden_layers)
+                ]
 
         super().__post_init__(**kwargs)
 
diff --git a/src/transformers/models/exaone4/modeling_exaone4.py b/src/transformers/models/exaone4/modeling_exaone4.py
index fab10b9b6937..a7fb52808cd4 100644
--- a/src/transformers/models/exaone4/modeling_exaone4.py
+++ b/src/transformers/models/exaone4/modeling_exaone4.py
@@ -83,8 +83,8 @@ def __init__(self, config: Exaone4Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -124,7 +124,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/exaone4/modular_exaone4.py b/src/transformers/models/exaone4/modular_exaone4.py
index c6d9202170a0..a33c0ec049bc 100644
--- a/src/transformers/models/exaone4/modular_exaone4.py
+++ b/src/transformers/models/exaone4/modular_exaone4.py
@@ -127,15 +127,29 @@ class Exaone4Config(PreTrainedConfig):
     layer_types: list[str] | None = None
 
     def __post_init__(self, **kwargs):
-        if self.sliding_window is None:
-            self.sliding_window_pattern = 0
         if self.layer_types is None:
-            self.layer_types = [
-                "sliding_attention"
-                if ((i + 1) % (self.sliding_window_pattern) != 0 and i < self.num_hidden_layers)
-                else "full_attention"
-                for i in range(self.num_hidden_layers)
-            ]
+            if self.sliding_window in (None, 0):
+                self.layer_types = ["full_attention"] * self.num_hidden_layers
+            elif isinstance(self.sliding_window_pattern, str) and self.sliding_window_pattern:
+                layer_pattern = [
+                    "sliding_attention" if layer_type.upper() == "L" else "full_attention"
+                    for layer_type in self.sliding_window_pattern
+                ]
+                self.layer_types = [
+                    layer_pattern[i % len(layer_pattern)] for i in range(self.num_hidden_layers - 1)
+                ] + ["full_attention"]
+            else:
+                repeat_period = (
+                    self.sliding_window_pattern
+                    if isinstance(self.sliding_window_pattern, int) and self.sliding_window_pattern > 0
+                    else 1
+                )
+                self.layer_types = [
+                    "sliding_attention"
+                    if (i + 1) % repeat_period != 0 and i < self.num_hidden_layers - 1
+                    else "full_attention"
+                    for i in range(self.num_hidden_layers)
+                ]
 
         super().__post_init__(**kwargs)
 
diff --git a/src/transformers/models/exaone4_5/__init__.py b/src/transformers/models/exaone4_5/__init__.py
new file mode 100644
index 000000000000..486e91fa3db1
--- /dev/null
+++ b/src/transformers/models/exaone4_5/__init__.py
@@ -0,0 +1,18 @@
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_exaone4_5 import *
+    from .image_processing_exaone4_5 import *
+    from .image_processing_pil_exaone4_5 import *
+    from .modeling_exaone4_5 import *
+    from .processing_exaone4_5 import *
+    from .video_processing_exaone4_5 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/exaone4_5/configuration_exaone4_5.py b/src/transformers/models/exaone4_5/configuration_exaone4_5.py
new file mode 100644
index 000000000000..cdd81a050665
--- /dev/null
+++ b/src/transformers/models/exaone4_5/configuration_exaone4_5.py
@@ -0,0 +1,77 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/exaone4_5/modular_exaone4_5.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_exaone4_5.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+@strict
+class Exaone4_5_VisionConfig(PreTrainedConfig):
+    r"""
+    tokens_per_second (`int`, *optional*, defaults to 41):
+        Number of tokens to merge for each second of video.
+    window_size (`int`, *optional*, defaults to 11):
+        Size of windows.
+    out_hidden_size (`int`, *optional*, defaults to 3584):
+        The output hidden size of the vision model.
+    fullatt_block_indexes (`int`, *optional*, defaults to `[7, 15, 23, 31]`):
+        Indices of layers with full attention
+    """
+
+    model_type = "exaone4_5_vision"
+    base_config_key = "vision_config"
+
+    depth: int = 32
+    hidden_size: int = 3584
+    hidden_act: str = "silu"
+    intermediate_size: int = 3420
+    num_heads: int = 16
+    in_channels: int = 3
+    patch_size: int | list[int] | tuple[int, int] = 14
+    spatial_merge_size: int = 2
+    temporal_patch_size: int | list[int] | tuple[int, int] = 2
+    tokens_per_second: int = 4
+    window_size: int = 112
+    out_hidden_size: int = 3584
+    fullatt_block_indexes: list[int] | tuple[int, ...] = (7, 15, 23, 31)
+    initializer_range: float = 0.02
+    num_key_value_heads: int = 8
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+@strict
+class Exaone4_5_Config(PreTrainedConfig):
+    model_type = "exaone4_5"
+    sub_configs = {"vision_config": AutoConfig, "text_config": AutoConfig}
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+    image_token_id: int = 67
+    video_token_id: int = 68
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.vision_config, dict):
+            self.vision_config["model_type"] = self.vision_config.get("model_type", "exaone4_5_vision")
+            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)
+        elif self.vision_config is None:
+            self.vision_config = CONFIG_MAPPING["exaone4_5_vision"]()
+
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "exaone4")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["exaone4"]()
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["Exaone4_5_Config", "Exaone4_5_VisionConfig"]
diff --git a/src/transformers/models/exaone4_5/modeling_exaone4_5.py b/src/transformers/models/exaone4_5/modeling_exaone4_5.py
new file mode 100644
index 000000000000..c4f5623a53bd
--- /dev/null
+++ b/src/transformers/models/exaone4_5/modeling_exaone4_5.py
@@ -0,0 +1,1405 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/exaone4_5/modular_exaone4_5.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_exaone4_5.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+
+import itertools
+from collections.abc import Callable
+from typing import Any
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, CausalLMOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
+from ...utils.generic import is_flash_attention_requested, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
+from .configuration_exaone4_5 import Exaone4_5_Config, Exaone4_5_VisionConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Exaone4_5_RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        Exaone4_5_RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Exaone4_5_PatchEmbed(nn.Module):
+    def __init__(
+        self,
+        patch_size: int | list[int] | tuple[int, int] = 14,
+        temporal_patch_size: int | list[int] | tuple[int, int] = 2,
+        in_channels: int = 3,
+        embed_dim: int = 1152,
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.in_channels = in_channels
+        self.embed_dim = embed_dim
+
+        kernel_size = [temporal_patch_size, patch_size, patch_size]
+        self.proj = nn.Conv3d(in_channels, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        target_dtype = self.proj.weight.dtype
+        hidden_states = hidden_states.view(
+            -1, self.in_channels, self.temporal_patch_size, self.patch_size, self.patch_size
+        )
+        hidden_states = self.proj(hidden_states.to(dtype=target_dtype)).view(-1, self.embed_dim)
+        return hidden_states
+
+
+class Exaone4_5_VisionRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, dim: int, theta: float = 10000.0) -> None:
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    def forward(self, seqlen: int) -> torch.Tensor:
+        seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(seq, self.inv_freq)
+        return freqs
+
+
+class Exaone4_5_PatchMerger(nn.Module):
+    def __init__(self, dim: int, context_dim: int, spatial_merge_size: int = 2) -> None:
+        super().__init__()
+        self.hidden_size = context_dim * (spatial_merge_size**2)
+        self.ln_q = Exaone4_5_RMSNorm(context_dim, eps=1e-6)
+        self.mlp = nn.Sequential(
+            nn.Linear(self.hidden_size, self.hidden_size),
+            nn.GELU(),
+            nn.Linear(self.hidden_size, dim),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.mlp(self.ln_q(x).view(-1, self.hidden_size))
+        return x
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb_vision(
+    q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+    orig_q_dtype = q.dtype
+    orig_k_dtype = k.dtype
+    q, k = q.float(), k.float()
+    cos, sin = cos.unsqueeze(-2).float(), sin.unsqueeze(-2).float()
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    q_embed = q_embed.to(orig_q_dtype)
+    k_embed = k_embed.to(orig_k_dtype)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class Exaone4_5_VisionAttention(nn.Module):
+    def __init__(self, config: Exaone4_5_VisionConfig) -> None:
+        super().__init__()
+        self.num_key_value_heads = config.num_key_value_heads
+        self.dim = config.hidden_size
+        self.num_heads = config.num_heads
+        self.head_dim = self.dim // self.num_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.proj = nn.Linear(self.dim, self.dim)
+        self.scaling = self.head_dim**-0.5
+        self.config = config
+        self.attention_dropout = 0.0
+        self.is_causal = False
+        self.q_dim = self.num_heads * self.head_dim
+        self.kv_dim = self.num_key_value_heads * self.head_dim
+        self.qkv = nn.Linear(self.dim, self.q_dim + (self.kv_dim * 2), bias=True)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        query_states, key_states, value_states = self._split_qkv(hidden_states)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+        query_states = query_states.transpose(0, 1).unsqueeze(0)
+        key_states = key_states.transpose(0, 1).unsqueeze(0)
+        value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        if is_flash_attention_requested(self.config):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, _ = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+            ]
+            attn_outputs = [
+                attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )[0]
+                for q, k, v in zip(*splits)
+            ]
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(seq_length, -1).contiguous()
+        return self.proj(attn_output)
+
+    def _split_qkv(self, hidden_states: torch.Tensor):
+        seq_length = hidden_states.shape[0]
+        qkv = self.qkv(hidden_states)
+        q, kv = torch.split(qkv, [self.q_dim, 2 * self.kv_dim], dim=-1)
+        query_states = q.view(seq_length, self.num_heads, self.head_dim)
+        kv = kv.view(seq_length, 2, self.num_key_value_heads, self.head_dim)
+        key_states, value_states = kv[:, 0], kv[:, 1]
+        return query_states, key_states, value_states
+
+
+class Exaone4_5_MLP(nn.Module):
+    def __init__(self, config, bias: bool = False):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_state):
+        return self.down_proj(self.act_fn(self.gate_proj(hidden_state)) * self.up_proj(hidden_state))
+
+
+class Exaone4_5_VisionBlock(GradientCheckpointingLayer):
+    def __init__(self, config, attn_implementation: str = "sdpa") -> None:
+        super().__init__()
+        self.norm1 = Exaone4_5_RMSNorm(config.hidden_size, eps=1e-6)
+        self.norm2 = Exaone4_5_RMSNorm(config.hidden_size, eps=1e-6)
+        self.attn = Exaone4_5_VisionAttention(config=config)
+        self.mlp = Exaone4_5_MLP(config, bias=True)
+
+    @auto_docstring
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        r"""
+        cu_seqlens (`torch.Tensor`):
+            Cumulative sequence lengths used for packed variable-length attention in Flash Attention kernels.
+        rotary_pos_emb (`torch.Tensor`, *optional*):
+            Precomputed rotary positional embeddings applied to the vision attention query/key states.
+        """
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Exaone4_5_Attention(nn.Module):
+    def __init__(self, config: Exaone4_5_Config, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.num_attention_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.hidden_size = config.hidden_size
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+        self.scaling = self.head_dim**-0.5
+        self.sliding_window = config.sliding_window
+        self.sliding_window_pattern = config.sliding_window_pattern
+        layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+        self.is_sliding = layer_type == "sliding_attention"
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_attention_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_attention_heads * self.head_dim, self.hidden_size, bias=False)
+
+        self.q_norm = Exaone4_5_RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+        self.k_norm = Exaone4_5_RMSNorm(self.head_dim, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        # We use QK-norm
+        query_states = self.q_norm(query_states)
+        key_states = self.k_norm(key_states)
+
+        cos, sin = position_embeddings
+        # We use global NoPE for hybrid attention model
+        if self.sliding_window is None or self.is_sliding:
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window if self.is_sliding else None,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class Exaone4_5_DecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Exaone4_5_Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Exaone4_5_Attention(config=config, layer_idx=layer_idx)
+
+        self.mlp = Exaone4_5_MLP(config)
+        self.post_attention_layernorm = Exaone4_5_RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_feedforward_layernorm = Exaone4_5_RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class Exaone4_5_PreTrainedModel(PreTrainedModel):
+    config: Exaone4_5_Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Exaone4_5_VisionBlock", "Exaone4DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": Exaone4_5_DecoderLayer,
+        "attentions": Exaone4_5_Attention,
+    }
+    config_class = Exaone4_5_Config
+    _keys_to_ignore_on_load_unexpected = [r"mtp.*"]
+
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, Exaone4_5_VisionRotaryEmbedding):
+            inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim))
+            init.copy_(module.inv_freq, inv_freq)
+
+
+class Exaone4_5_VisionModel(Exaone4_5_PreTrainedModel):
+    config: Exaone4_5_VisionConfig
+    _no_split_modules = ["Exaone4_5_VisionBlock"]
+    _input_embed_layer = "patch_embed"
+    _can_record_outputs = {
+        "hidden_states": Exaone4_5_VisionBlock,
+        "attentions": Exaone4_5_VisionAttention,
+    }
+    config_class = Exaone4_5_VisionConfig
+
+    def __init__(self, config: Exaone4_5_VisionConfig, *inputs, **kwargs) -> None:
+        super().__init__(config, *inputs, **kwargs)
+        self.spatial_merge_size = config.spatial_merge_size
+        self.patch_size = config.patch_size
+        self.fullatt_block_indexes = config.fullatt_block_indexes
+        self.window_size = config.window_size
+        self.spatial_merge_unit = self.spatial_merge_size * self.spatial_merge_size
+        self.patch_embed = Exaone4_5_PatchEmbed(
+            patch_size=config.patch_size,
+            temporal_patch_size=config.temporal_patch_size,
+            in_channels=config.in_channels,
+            embed_dim=config.hidden_size,
+        )
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = Exaone4_5_VisionRotaryEmbedding(head_dim // 2)
+        self.blocks = nn.ModuleList([Exaone4_5_VisionBlock(config) for _ in range(config.depth)])
+        self.merger = Exaone4_5_PatchMerger(
+            dim=config.out_hidden_size,
+            context_dim=config.hidden_size,
+            spatial_merge_size=config.spatial_merge_size,
+        )
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    def rot_pos_emb(self, grid_thw):
+        pos_ids = []
+        for t, h, w in grid_thw.tolist():
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w)
+            hpos_ids = hpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1)
+            wpos_ids = wpos_ids.reshape(
+                h // self.spatial_merge_size,
+                self.spatial_merge_size,
+                w // self.spatial_merge_size,
+                self.spatial_merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+            pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+        pos_ids = torch.cat(pos_ids, dim=0)
+        max_grid_size = grid_thw[:, 1:].max()
+        rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size)
+        rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1)
+        return rotary_pos_emb
+
+    def get_window_index(self, grid_thw):
+        window_index: list = []
+        cu_window_seqlens: list = [0]
+        window_index_id = 0
+        vit_merger_window_size = self.window_size // self.spatial_merge_size // self.patch_size
+        grid_thw_list = grid_thw.tolist()
+
+        for grid_t, grid_h, grid_w in grid_thw_list:
+            llm_grid_h, llm_grid_w = (
+                grid_h // self.spatial_merge_size,
+                grid_w // self.spatial_merge_size,
+            )
+            index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape(grid_t, llm_grid_h, llm_grid_w)
+            pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size
+            pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size
+            num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size
+            num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size
+            index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100)
+            index_padded = index_padded.reshape(
+                grid_t,
+                num_windows_h,
+                vit_merger_window_size,
+                num_windows_w,
+                vit_merger_window_size,
+            )
+            index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape(
+                grid_t,
+                num_windows_h * num_windows_w,
+                vit_merger_window_size,
+                vit_merger_window_size,
+            )
+            seqlens = (index_padded != -100).sum([2, 3]).reshape(-1)
+            index_padded = index_padded.reshape(-1)
+            index_new = index_padded[index_padded != -100]
+            window_index.append(index_new + window_index_id)
+            cu_seqlens_tmp = seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1]
+            cu_window_seqlens.extend(cu_seqlens_tmp.tolist())
+            window_index_id += grid_t * llm_grid_h * llm_grid_w
+        window_index = torch.cat(window_index, dim=0)
+
+        return window_index, cu_window_seqlens
+
+    @merge_with_config_defaults
+    @capture_outputs
+    def forward(
+        self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        """
+        Args:
+            hidden_states (`torch.Tensor` of shape `(seq_len, hidden_size)`):
+                The final hidden states of the model.
+            grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+                The temporal, height and width of feature shape of each image in LLM.
+
+        Returns:
+            `torch.Tensor`: hidden_states.
+        """
+        hidden_states = self.patch_embed(hidden_states)
+        rotary_pos_emb = self.rot_pos_emb(grid_thw)
+        window_index, cu_window_seqlens = self.get_window_index(grid_thw)
+        cu_window_seqlens = torch.tensor(
+            cu_window_seqlens,
+            device=hidden_states.device,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens)
+
+        seq_len, _ = hidden_states.size()
+        hidden_states = hidden_states.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        hidden_states = hidden_states[window_index, :, :]
+        hidden_states = hidden_states.reshape(seq_len, -1)
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1)
+        rotary_pos_emb = rotary_pos_emb[window_index, :, :]
+        rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1)
+        emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1)
+        position_embeddings = (emb.cos(), emb.sin())
+
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0,
+            # Select dtype based on the following factors:
+            #  - FA2 requires that cu_seqlens_q must have dtype int32
+            #  - torch.onnx.export requires that cu_seqlens_q must have same dtype as grid_thw
+            # See https://github.com/huggingface/transformers/pull/34852 for more information
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        for layer_num, blk in enumerate(self.blocks):
+            if layer_num in self.fullatt_block_indexes:
+                cu_seqlens_now = cu_seqlens
+            else:
+                cu_seqlens_now = cu_window_seqlens
+
+            hidden_states = blk(
+                hidden_states,
+                cu_seqlens=cu_seqlens_now,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        merged_hidden_states = self.merger(hidden_states)
+        reverse_indices = torch.argsort(window_index)
+        merged_hidden_states = merged_hidden_states[reverse_indices, :]
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=hidden_states,
+            pooler_output=merged_hidden_states,
+        )
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+class Exaone4_5_Model(Exaone4_5_PreTrainedModel):
+    base_model_prefix = "model"
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+    config: Exaone4_5_Config
+    _no_split_modules = ["Exaone4_5_DecoderLayer", "Exaone4_5_VisionBlock"]
+
+    def __init__(self, config: Exaone4_5_Config):
+        super().__init__(config)
+        self.visual = Exaone4_5_VisionModel._from_config(config.vision_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.rope_deltas = None  # cache rope_deltas here
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def get_vision_position_ids(
+        self,
+        start_position: int,
+        grid_thw: list[int, int, int] | torch.Tensor,
+        temp_merge_size: int = 1,
+        spatial_merge_size: int = 1,
+        time_interval: int = 1,
+        device: str | torch.device | None = None,
+    ):
+        """
+        Compute 3D positional indices for vision tokens derived from a single image or video input.
+
+        The positions are generated from the input grid defined by temporal (T), height (H), and
+        width (W) dimensions. Temporal and spatial dimensions can be downscaled according to the
+        merge sizes used in the vision backbone. The resulting positions are offset by `start_position`.
+
+        Args:
+            start_position (`int`):
+                Offset added to all computed positional indices.
+            grid_thw (`Sequence[int]` or `torch.Tensor` of shape `(3,)`):
+                The (T, H, W) grid representing the feature layout of the current image or video after patch embedding.
+            temp_merge_size (`int`, *optional*):
+                Factor by which the temporal dimension is reduced in the backbone. The temporal grid size is divided
+                by this value. Defaults to 1.
+            spatial_merge_size (`int`, *optional*):
+                Factor by which the spatial dimensions (H and W) are reduced in the backbone. Both H and W are divided
+                by this value. Defaults to 1.
+            time_interval (`int`, *optional*):
+                Spacing factor applied between consecutive temporal position indices.Defaults to 1.
+            device (`str` or `torch.device`, *optional*):
+                Device on which the resulting tensor is allocated. If `None`, uses the current default device.
+
+        Returns:
+            torch.LongTensor of shape (3, sequence_length):
+                Positional indices for temporal, height, and width dimensions,
+                flattened into sequence form and offset by `start_position`.
+        """
+        llm_grid_t, llm_grid_h, llm_grid_w = (
+            grid_thw[0].item() // temp_merge_size,
+            grid_thw[1].item() // spatial_merge_size,
+            grid_thw[2].item() // spatial_merge_size,
+        )
+
+        # Add `start_position` after arange for compile
+        position_temporal = torch.arange(llm_grid_t, device=device) * time_interval
+        position_width = torch.arange(llm_grid_w, device=device) + start_position
+        position_height = torch.arange(llm_grid_h, device=device) + start_position
+
+        # Repeat the positions per each grid and per video frame. Repeat patterns are important
+        # do not modify without checking values!
+        position_width = position_width.repeat(llm_grid_h * llm_grid_t)
+        position_height = position_height.repeat_interleave(llm_grid_w).repeat(llm_grid_t)
+        # Important: add `start_positions` after applying `time_interval`, order matters
+        position_temporal = position_temporal.repeat_interleave(llm_grid_h * llm_grid_w) + start_position
+        vision_position_ids = torch.stack([position_temporal, position_height, position_width], dim=0)
+
+        return vision_position_ids
+
+    def get_rope_index(
+        self,
+        input_ids: torch.LongTensor,
+        mm_token_type_ids: torch.IntTensor,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Calculate the 3D rope index based on image and video's sizes. The utility expects a `vision + text`
+        sequence and will error out otherwise. For pure text sequence, please rely on model's auto-inferred
+        position ids. In a mixed vision + text sequence, vision tokens use 3D RoPE (temporal, height, width)
+        while text tokens use standard 1D RoPE.
+
+        Example:
+            Temporal patches: 3; Height patches: 2; Width patches: 2
+            Each vision input results in (temporal x height × width) positions. Here: 3 x 2 × 2 = 12 positions total.
+
+            Temporal position IDs are spaced by:
+                `interval = tokens_per_second * temporal_patch_size / fps`
+
+                If fps = 1; tokens_per_second = 25; temporal_patch_size = 2, temporal IDs increase by 50 for each temporal patch:
+                `[0, 0, 0, 0, 50, 50, 50, 50, 100, 100, 100, 100]`
+
+            Height IDs repeat per row: `[0, 0, 1, 1, ...]`
+            Width IDs alternate per column: `[0, 1, 0, 1, ...]`
+            Text tokens follow standard 1D RoPE and the position IDs grow consequently with a step of `1`
+
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+                it.
+            mm_token_type_ids (`torch.IntTensor` of shape `(batch_size, sequence_length)`):
+                Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2).
+            image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+                The temporal, height and width of feature shape of each image in LLM.
+            video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+                The temporal, height and width of feature shape of each video in LLM.
+            second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+                The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+        Returns:
+            position_ids (`torch.LongTensor` of shape `(3, batch_size, sequence_length)`)
+            mrope_position_deltas (`torch.Tensor` of shape `(batch_size)`)
+        """
+        spatial_merge_size = self.config.vision_config.spatial_merge_size
+        tokens_per_second = self.config.vision_config.tokens_per_second
+
+        mrope_position_deltas = []
+        position_ids = torch.zeros(
+            3,
+            input_ids.shape[0],
+            input_ids.shape[1],
+            dtype=input_ids.dtype,
+            device=input_ids.device,
+        )
+        grid_iters = {
+            1: iter(image_grid_thw) if image_grid_thw is not None else None,
+            2: iter(video_grid_thw) if video_grid_thw is not None else None,
+        }
+        second_per_grid_ts = (
+            iter(second_per_grid_ts) if second_per_grid_ts is not None else iter([1] * input_ids.shape[1])
+        )
+        for batch_idx, current_input_ids in enumerate(input_ids):
+            input_token_type = mm_token_type_ids[batch_idx]
+            if attention_mask is not None:
+                current_input_ids = current_input_ids[attention_mask[batch_idx].bool()]
+                input_token_type = input_token_type[attention_mask[batch_idx].bool()]
+
+            input_type_group = []
+            for key, group in itertools.groupby(enumerate(input_token_type.tolist()), lambda x: x[1]):
+                group = list(group)
+                start_index = group[0][0]
+                end_index = group[-1][0] + 1
+                input_type_group.append((key, start_index, end_index))
+
+            current_pos = 0
+            llm_pos_ids_list = []
+            for modality_type, start_idx, end_idx in input_type_group:
+                # text == 0
+                if modality_type == 0:
+                    text_len = end_idx - start_idx
+                    llm_pos_ids_list.append(
+                        torch.arange(text_len, device=input_ids.device).view(1, -1).expand(3, -1) + current_pos
+                    )
+                    current_pos += text_len
+                # image == 1, video == 2
+                else:
+                    grid_thw = next(grid_iters[modality_type])
+                    # Only apply temporal scaling for videos; still images have no
+                    # temporal dimension to space out (fixes #45325).
+                    if modality_type == 2:
+                        time_interval = tokens_per_second * int(next(second_per_grid_ts))
+                    else:
+                        time_interval = 1
+                    vision_position_ids = self.get_vision_position_ids(
+                        current_pos, grid_thw, 1, spatial_merge_size, time_interval, device=input_ids.device
+                    )
+                    llm_pos_ids_list.append(vision_position_ids)
+                    current_pos += max(grid_thw[1], grid_thw[2]) // spatial_merge_size
+            llm_positions = torch.cat(llm_pos_ids_list, dim=1).reshape(3, -1)
+            if attention_mask is not None:
+                position_ids[:, batch_idx, attention_mask[batch_idx].bool()] = llm_positions.to(position_ids.device)
+            else:
+                position_ids[:, batch_idx] = llm_positions.to(position_ids.device)
+            mrope_position_deltas.append(llm_positions.max() + 1 - len(current_input_ids))
+        mrope_position_deltas = torch.tensor(mrope_position_deltas, device=input_ids.device).unsqueeze(1)
+        return position_ids, mrope_position_deltas
+
+    @can_return_tuple
+    @auto_docstring
+    def get_video_features(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        video_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+            The tensors corresponding to the input videos.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        """
+        pixel_values_videos = pixel_values_videos.type(self.visual.dtype)
+        vision_outputs = self.visual(pixel_values_videos, grid_thw=video_grid_thw, **kwargs)
+        split_sizes = (video_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
+        video_embeds = torch.split(vision_outputs.pooler_output, split_sizes)
+        vision_outputs.pooler_output = video_embeds
+
+        return vision_outputs
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+            The tensors corresponding to the input images.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        pixel_values = pixel_values.type(self.visual.dtype)
+        vision_outputs = self.visual(pixel_values, grid_thw=image_grid_thw, **kwargs)
+        split_sizes = (image_grid_thw.prod(-1) // self.visual.spatial_merge_size**2).tolist()
+        image_embeds = torch.split(vision_outputs.pooler_output, split_sizes)
+        vision_outputs.pooler_output = image_embeds
+
+        return vision_outputs
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor | None = None,
+        video_features: torch.FloatTensor | None = None,
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+            special_video_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.video_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_video_mask = special_video_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+            special_video_mask = input_ids == self.config.video_token_id
+
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        if image_features is not None:
+            torch_compilable_check(
+                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
+            )
+
+        n_video_tokens = special_video_mask.sum()
+        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        if video_features is not None:
+            torch_compilable_check(
+                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
+            )
+        return special_image_mask, special_video_mask
+
+    def compute_3d_position_ids(
+        self,
+        input_ids: torch.Tensor | None,
+        image_grid_thw: torch.Tensor | None,
+        video_grid_thw: torch.Tensor | None,
+        inputs_embeds: torch.Tensor | None,
+        attention_mask: torch.Tensor | None,
+        past_key_values: torch.Tensor | None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        mm_token_type_ids: torch.IntTensor | None = None,
+    ) -> torch.Tensor | None:
+        past_key_values_length = 0 if past_key_values is None else past_key_values.get_seq_length()
+        can_compute_mrope = (
+            input_ids is not None
+            and mm_token_type_ids is not None
+            and (image_grid_thw is not None or video_grid_thw is not None)
+        )
+
+        if can_compute_mrope and (self.rope_deltas is None or past_key_values_length == 0):
+            position_ids, rope_deltas = self.get_rope_index(
+                input_ids,
+                image_grid_thw=image_grid_thw,
+                video_grid_thw=video_grid_thw,
+                attention_mask=attention_mask,
+                second_per_grid_ts=second_per_grid_ts,
+                mm_token_type_ids=mm_token_type_ids,
+            )
+            self.rope_deltas = rope_deltas
+        # Use pre-calculated rope-deltas to infer correct 3D position ids during incremental
+        # generation (past_key_values_length > 0) or when only inputs_embeds is provided (no input_ids
+        # to recompute from). Skip when input_ids is provided without past_key_values to avoid shape
+        # mismatches from stale rope_deltas (e.g., training forward pass after generation).
+        elif self.rope_deltas is not None and (past_key_values_length > 0 or input_ids is None):
+            batch_size, seq_length, _ = inputs_embeds.shape
+            if attention_mask is not None:
+                position_ids = attention_mask.long().cumsum(-1) - 1
+                position_ids = position_ids.masked_fill(attention_mask == 0, 0)
+                position_ids = position_ids.view(1, batch_size, -1).repeat(3, 1, 1).to(inputs_embeds.device)
+            else:
+                position_ids = torch.arange(past_key_values_length, past_key_values_length + seq_length)
+                position_ids = position_ids.view(1, 1, -1).expand(3, batch_size, -1).to(inputs_embeds.device)
+            delta = self.rope_deltas.repeat_interleave(batch_size // self.rope_deltas.shape[0], dim=0)
+            position_ids = position_ids + delta.to(device=position_ids.device)
+        else:
+            # Can't build correct 3D positions. Let the model infer it
+            position_ids = None
+        return position_ids
+
+    @auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+        """
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output
+            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            image_mask, _ = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        if pixel_values_videos is not None:
+            video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw).pooler_output
+            video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            _, video_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            ).unsqueeze(0)
+        elif position_ids.ndim > 2:
+            position_ids = position_ids[-1]
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+class Exaone4_5_ForConditionalGeneration(Exaone4_5_PreTrainedModel, GenerationMixin):
+    """
+    Main EXAONE 4.5 conditional generation class.
+
+    Note: Unlike Qwen2VL, the EXAONE 4.5 vision encoder uses 2D rotary positional embeddings (2D-RoPE)
+    and adopts a Grouped Query Attention (GQA) structure throughout the multimodal stack.
+    """
+
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+
+    def __init__(self, config: Exaone4_5_Config):
+        super().__init__(config)
+        self.model = Exaone4_5_Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    @auto_docstring
+    def get_video_features(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        video_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values_videos (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+            The tensors corresponding to the input videos.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        """
+        return self.model.get_video_features(
+            pixel_values_videos=pixel_values_videos, video_grid_thw=video_grid_thw, **kwargs
+        )
+
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
+            The tensors corresponding to the input images.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        return self.model.get_image_features(pixel_values=pixel_values, image_grid_thw=image_grid_thw, **kwargs)
+
+    @auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, Exaone4_5_ForConditionalGeneration
+        >>> import torch
+
+        >>> model = Exaone4_5_ForConditionalGeneration.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
+        >>> processor = AutoProcessor.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
+
+        >>> messages = [
+        ...     {
+        ...         "role": "user",
+        ...         "content": [
+        ...             {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+        ...             {"type": "text", "text": "Describe the image."},
+        ...         ],
+        ...     }
+        ... ]
+        >>> inputs = processor.apply_chat_template(
+        ...     messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
+        ... )
+        >>> inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=64)
+        ```
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        second_per_grid_ts=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            use_cache=use_cache,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+        model_inputs["position_ids"] = None
+        if not is_first_iteration and use_cache:
+            model_inputs["pixel_values"] = None
+            model_inputs["pixel_values_videos"] = None
+        return model_inputs
+
+    def _prepare_position_ids_for_generation(self, inputs_tensor, model_kwargs):
+        # Overwritten -- requires 3D position ids
+
+        text_positions = super()._prepare_position_ids_for_generation(inputs_tensor, model_kwargs)
+
+        # Early exit in case we are continuing generation from past kv
+        past_length = 0
+        if (cache := model_kwargs.get("past_key_values")) is not None:
+            past_length = cache.get_seq_length()
+        if past_length != 0 and self.model.rope_deltas is not None:
+            position_ids = text_positions[None, ...] + self.model.rope_deltas
+            return position_ids
+
+        # Otherwise compute 3d position ids for vision tokens and concat with text position ids
+        if "input_ids" in model_kwargs and model_kwargs["input_ids"].shape[1] > 0:
+            inputs_tensor = model_kwargs["input_ids"]
+
+        is_input_ids = len(inputs_tensor.shape) == 2 and inputs_tensor.dtype in [torch.int, torch.long]
+        if (
+            is_input_ids
+            and model_kwargs.get("mm_token_type_ids") is not None
+            and (model_kwargs.get("image_grid_thw") is not None or model_kwargs.get("video_grid_thw") is not None)
+        ):
+            model_kwargs = {k: v for k, v in model_kwargs.items() if k != "input_ids"}
+            vision_positions, rope_deltas = self.model.get_rope_index(inputs_tensor, **model_kwargs)
+            self.model.rope_deltas = rope_deltas
+        else:
+            vision_positions = text_positions.unsqueeze(0).expand(3, -1, -1)
+            self.model.rope_deltas = torch.zeros(
+                inputs_tensor.shape[0], 1, dtype=torch.long, device=inputs_tensor.device
+            )
+
+        # Concatenate "text + vision" positions into [4, bs, seq-len]
+        text_positions = text_positions[None, ...]
+        position_ids = torch.cat([text_positions, vision_positions], dim=0)
+
+        return position_ids
+
+    def _get_image_nums_and_video_nums(
+        self,
+        input_ids: torch.LongTensor | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Returns per-sample counts of image and video placeholder tokens.
+
+        If `inputs_embeds` are provided, placeholder positions are inferred by comparing against
+        the embedding vectors of `image_token_id` and `video_token_id`. Otherwise, counts are
+        computed directly from `input_ids`.
+        """
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+
+        if inputs_embeds is not None:
+            image_mask = (
+                inputs_embeds
+                == self.get_input_embeddings()(
+                    torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device)
+                )
+            )[..., 0]
+            video_mask = (
+                inputs_embeds
+                == self.get_input_embeddings()(
+                    torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device)
+                )
+            )[..., 0]
+        else:
+            image_mask = input_ids == image_token_id
+            video_mask = input_ids == video_token_id
+
+        image_nums = torch.sum(image_mask, dim=1)
+        video_nums = torch.sum(video_mask, dim=1)
+
+        return image_nums, video_nums
+
+    def _expand_inputs_for_generation(
+        self,
+        expand_size: int = 1,
+        is_encoder_decoder: bool = False,
+        input_ids: torch.LongTensor | None = None,
+        **model_kwargs,
+    ) -> tuple[torch.LongTensor, dict[str, Any]]:
+        # Overwritten -- Support for expanding tensors without a batch size dimension
+        # e.g., pixel_values, image_grid_thw, pixel_values_videos, video_grid_thw, second_per_grid_t
+        # pixel_values.shape[0] is sum(seqlen_images for samples)
+        # image_grid_thw.shape[0] is sum(num_images for samples)
+
+        if expand_size == 1:
+            return input_ids, model_kwargs
+
+        visual_keys = ["pixel_values", "image_grid_thw", "pixel_values_videos", "video_grid_thw", "second_per_grid_ts"]
+
+        def _expand_dict_for_generation_visual(dict_to_expand):
+            image_grid_thw = model_kwargs.get("image_grid_thw", None)
+            video_grid_thw = model_kwargs.get("video_grid_thw", None)
+            image_nums, video_nums = self._get_image_nums_and_video_nums(
+                input_ids, inputs_embeds=model_kwargs.get("inputs_embeds", None)
+            )
+
+            def _repeat_interleave_samples(x, lengths, repeat_times):
+                samples = torch.split(x, lengths)
+                repeat_args = [repeat_times] + [1] * (x.dim() - 1)
+                result = torch.cat([sample.repeat(*repeat_args) for sample in samples], dim=0)
+                return result
+
+            for key in dict_to_expand:
+                if key == "pixel_values":
+                    # split images into samples
+                    samples = torch.split(image_grid_thw, list(image_nums))
+                    # compute the sequence length of images for each sample
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "image_grid_thw":
+                    # get the num of images for each sample
+                    lengths = list(image_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "pixel_values_videos":
+                    samples = torch.split(video_grid_thw, list(video_nums))
+                    lengths = [torch.prod(sample, dim=1).sum() for sample in samples]
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "video_grid_thw":
+                    lengths = list(video_nums)
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=lengths, repeat_times=expand_size
+                    )
+                elif key == "second_per_grid_ts":
+                    dict_to_expand[key] = _repeat_interleave_samples(
+                        dict_to_expand[key], lengths=list(video_nums), repeat_times=expand_size
+                    )
+            return dict_to_expand
+
+        def _expand_dict_for_generation(dict_to_expand):
+            for key in dict_to_expand:
+                if key == "position_ids" and dict_to_expand[key].ndim == 3:
+                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=1)
+                elif (
+                    dict_to_expand[key] is not None
+                    and isinstance(dict_to_expand[key], torch.Tensor)
+                    and key not in visual_keys
+                ):
+                    dict_to_expand[key] = dict_to_expand[key].repeat_interleave(expand_size, dim=0)
+            return dict_to_expand
+
+        model_kwargs = _expand_dict_for_generation_visual(model_kwargs)
+
+        if input_ids is not None:
+            input_ids = input_ids.repeat_interleave(expand_size, dim=0)
+
+        model_kwargs = _expand_dict_for_generation(model_kwargs)
+
+        if is_encoder_decoder:
+            if model_kwargs.get("encoder_outputs") is None:
+                raise ValueError("If `is_encoder_decoder` is True, make sure that `encoder_outputs` is defined.")
+            model_kwargs["encoder_outputs"] = _expand_dict_for_generation(model_kwargs["encoder_outputs"])
+
+        return input_ids, model_kwargs
+
+
+__all__ = [
+    "Exaone4_5_ForConditionalGeneration",
+    "Exaone4_5_Model",
+    "Exaone4_5_PreTrainedModel",
+    "Exaone4_5_VisionModel",
+]
diff --git a/src/transformers/models/exaone4_5/modular_exaone4_5.py b/src/transformers/models/exaone4_5/modular_exaone4_5.py
new file mode 100644
index 000000000000..425afca79417
--- /dev/null
+++ b/src/transformers/models/exaone4_5/modular_exaone4_5.py
@@ -0,0 +1,501 @@
+"""PyTorch EXAONE 4.5 model."""
+
+from collections.abc import Callable
+
+import torch
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import ProcessingKwargs, Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import is_flash_attention_requested
+from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel
+from ..exaone4.modeling_exaone4 import Exaone4PreTrainedModel, Exaone4RMSNorm
+from ..qwen2_5_vl.configuration_qwen2_5_vl import Qwen2_5_VLVisionConfig
+from ..qwen2_5_vl.modeling_qwen2_5_vl import (
+    Qwen2_5_VisionPatchEmbed,
+    Qwen2_5_VisionRotaryEmbedding,
+    Qwen2_5_VisionTransformerPretrainedModel,
+    Qwen2_5_VLForConditionalGeneration,
+    Qwen2_5_VLMLP,
+    Qwen2_5_VLModel,
+    Qwen2_5_VLPatchMerger,
+    Qwen2_5_VLVisionAttention,
+    Qwen2_5_VLVisionBlock,
+)
+from ..qwen2_5_vl.processing_qwen2_5_vl import Qwen2_5_VLProcessor
+from ..qwen2_vl.modeling_qwen2_vl import (
+    apply_rotary_pos_emb_vision,
+    eager_attention_forward,
+)
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+@strict
+class Exaone4_5_VisionConfig(Qwen2_5_VLVisionConfig):
+    model_type = "exaone4_5_vision"
+    base_config_key = "vision_config"
+    num_key_value_heads: int = 8
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+@strict
+class Exaone4_5_Config(PreTrainedConfig):
+    model_type = "exaone4_5"
+    sub_configs = {"vision_config": AutoConfig, "text_config": AutoConfig}
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+    image_token_id: int = 67
+    video_token_id: int = 68
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.vision_config, dict):
+            self.vision_config["model_type"] = self.vision_config.get("model_type", "exaone4_5_vision")
+            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)
+        elif self.vision_config is None:
+            self.vision_config = CONFIG_MAPPING["exaone4_5_vision"]()
+
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "exaone4")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["exaone4"]()
+
+        super().__post_init__(**kwargs)
+
+
+class Exaone4_5_RMSNorm(Exaone4RMSNorm):
+    pass
+
+
+class Exaone4_5_PatchEmbed(Qwen2_5_VisionPatchEmbed):
+    pass
+
+
+class Exaone4_5_VisionRotaryEmbedding(Qwen2_5_VisionRotaryEmbedding):
+    pass
+
+
+class Exaone4_5_PatchMerger(Qwen2_5_VLPatchMerger):
+    pass
+
+
+class Exaone4_5_VisionAttention(Qwen2_5_VLVisionAttention):
+    def __init__(self, config: Exaone4_5_VisionConfig):
+        self.num_key_value_heads = config.num_key_value_heads
+        super().__init__(config)
+        del self.qkv
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.q_dim = self.num_heads * self.head_dim
+        self.kv_dim = self.num_key_value_heads * self.head_dim
+        self.qkv = nn.Linear(self.dim, self.q_dim + (self.kv_dim * 2), bias=True)
+
+    def _split_qkv(self, hidden_states: torch.Tensor):
+        seq_length = hidden_states.shape[0]
+        qkv = self.qkv(hidden_states)
+        q, kv = torch.split(qkv, [self.q_dim, 2 * self.kv_dim], dim=-1)
+        query_states = q.view(seq_length, self.num_heads, self.head_dim)
+        kv = kv.view(seq_length, 2, self.num_key_value_heads, self.head_dim)
+        key_states, value_states = kv[:, 0], kv[:, 1]
+        return query_states, key_states, value_states
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        query_states, key_states, value_states = self._split_qkv(hidden_states)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+        query_states = query_states.transpose(0, 1).unsqueeze(0)
+        key_states = key_states.transpose(0, 1).unsqueeze(0)
+        value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        if is_flash_attention_requested(self.config):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, _ = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+            ]
+            attn_outputs = [
+                attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )[0]
+                for q, k, v in zip(*splits)
+            ]
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(seq_length, -1).contiguous()
+        return self.proj(attn_output)
+
+
+class Exaone4_5_MLP(Qwen2_5_VLMLP):
+    pass
+
+
+class Exaone4_5_VisionBlock(Qwen2_5_VLVisionBlock):
+    pass
+
+
+class Exaone4_5_PreTrainedModel(Exaone4PreTrainedModel):
+    config_class = Exaone4_5_Config
+    base_model_prefix = "model"
+    _no_split_modules = ["Exaone4_5_VisionBlock", "Exaone4DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _keys_to_ignore_on_load_unexpected = [r"mtp.*"]
+
+    def _init_weights(self, module):
+        PreTrainedModel._init_weights(module)
+        if isinstance(module, Exaone4_5_VisionRotaryEmbedding):
+            inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim))
+            init.copy_(module.inv_freq, inv_freq)
+
+
+class Exaone4_5_VisionModel(Exaone4_5_PreTrainedModel, Qwen2_5_VisionTransformerPretrainedModel):
+    config_class = Exaone4_5_VisionConfig
+
+    def __init__(self, config: Exaone4_5_VisionConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.patch_embed = Exaone4_5_PatchEmbed(
+            patch_size=config.patch_size,
+            temporal_patch_size=config.temporal_patch_size,
+            in_channels=config.in_channels,
+            embed_dim=config.hidden_size,
+        )
+        head_dim = config.hidden_size // config.num_heads
+        self.rotary_pos_emb = Exaone4_5_VisionRotaryEmbedding(head_dim // 2)
+        self.blocks = nn.ModuleList([Exaone4_5_VisionBlock(config) for _ in range(config.depth)])
+        self.merger = Exaone4_5_PatchMerger(
+            dim=config.out_hidden_size,
+            context_dim=config.hidden_size,
+            spatial_merge_size=config.spatial_merge_size,
+        )
+        self.gradient_checkpointing = False
+        self.post_init()
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+class Exaone4_5_Model(Exaone4_5_PreTrainedModel, Qwen2_5_VLModel):
+    def __init__(self, config: Exaone4_5_Config):
+        super().__init__(config)
+        self.visual = Exaone4_5_VisionModel._from_config(config.vision_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.post_init()
+
+    @auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+        """
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output
+            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            image_mask, _ = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        if pixel_values_videos is not None:
+            video_embeds = self.get_video_features(pixel_values_videos, video_grid_thw).pooler_output
+            video_embeds = torch.cat(video_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            _, video_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, video_features=video_embeds
+            )
+            inputs_embeds = inputs_embeds.masked_scatter(video_mask, video_embeds)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            ).unsqueeze(0)
+        elif position_ids.ndim > 2:
+            position_ids = position_ids[-1]
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+class Exaone4_5_ForConditionalGeneration(Exaone4_5_PreTrainedModel, Qwen2_5_VLForConditionalGeneration):
+    """
+    Main EXAONE 4.5 conditional generation class.
+
+    Note: Unlike Qwen2VL, the EXAONE 4.5 vision encoder uses 2D rotary positional embeddings (2D-RoPE)
+    and adopts a Grouped Query Attention (GQA) structure throughout the multimodal stack.
+    """
+
+    def __init__(self, config: Exaone4_5_Config):
+        super().__init__(config)
+        self.model = Exaone4_5_Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def _get_image_nums_and_video_nums(
+        self,
+        input_ids: torch.LongTensor | None,
+        inputs_embeds: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Returns per-sample counts of image and video placeholder tokens.
+
+        If `inputs_embeds` are provided, placeholder positions are inferred by comparing against
+        the embedding vectors of `image_token_id` and `video_token_id`. Otherwise, counts are
+        computed directly from `input_ids`.
+        """
+        image_token_id = self.config.image_token_id
+        video_token_id = self.config.video_token_id
+
+        if inputs_embeds is not None:
+            image_mask = (
+                inputs_embeds
+                == self.get_input_embeddings()(
+                    torch.tensor(image_token_id, dtype=torch.long, device=inputs_embeds.device)
+                )
+            )[..., 0]
+            video_mask = (
+                inputs_embeds
+                == self.get_input_embeddings()(
+                    torch.tensor(video_token_id, dtype=torch.long, device=inputs_embeds.device)
+                )
+            )[..., 0]
+        else:
+            image_mask = input_ids == image_token_id
+            video_mask = input_ids == video_token_id
+
+        image_nums = torch.sum(image_mask, dim=1)
+        video_nums = torch.sum(video_mask, dim=1)
+
+        return image_nums, video_nums
+
+    @auto_docstring(checkpoint="LGAI-EXAONE/EXAONE-4.5-33B")
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        second_per_grid_ts: torch.Tensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        video_grid_thw (`torch.LongTensor` of shape `(num_videos, 3)`, *optional*):
+            The temporal, height and width of feature shape of each video in LLM.
+        second_per_grid_ts (`torch.Tensor` of shape `(num_videos)`, *optional*):
+            The time interval (in seconds) for each grid along the temporal dimension in the 3D position IDs.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, Exaone4_5_ForConditionalGeneration
+        >>> import torch
+
+        >>> model = Exaone4_5_ForConditionalGeneration.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
+        >>> processor = AutoProcessor.from_pretrained("LGAI-EXAONE/EXAONE-4.5-33B")
+
+        >>> messages = [
+        ...     {
+        ...         "role": "user",
+        ...         "content": [
+        ...             {"type": "image", "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"},
+        ...             {"type": "text", "text": "Describe the image."},
+        ...         ],
+        ...     }
+        ... ]
+        >>> inputs = processor.apply_chat_template(
+        ...     messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
+        ... )
+        >>> inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=64)
+        ```
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values=None,
+        pixel_values_videos=None,
+        image_grid_thw=None,
+        video_grid_thw=None,
+        second_per_grid_ts=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            second_per_grid_ts=second_per_grid_ts,
+            use_cache=use_cache,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+        model_inputs["position_ids"] = None
+        if not is_first_iteration and use_cache:
+            model_inputs["pixel_values"] = None
+            model_inputs["pixel_values_videos"] = None
+        return model_inputs
+
+
+class Exaone4_5_ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+        "videos_kwargs": {"return_metadata": True},
+    }
+
+
+class Exaone4_5_Processor(Qwen2_5_VLProcessor):
+    tokenizer_class = "AutoTokenizer"
+
+
+__all__ = [
+    "Exaone4_5_Config",
+    "Exaone4_5_ForConditionalGeneration",
+    "Exaone4_5_Model",
+    "Exaone4_5_PreTrainedModel",
+    "Exaone4_5_Processor",
+    "Exaone4_5_VisionModel",
+    "Exaone4_5_VisionConfig",
+]
diff --git a/src/transformers/models/exaone4_5/processing_exaone4_5.py b/src/transformers/models/exaone4_5/processing_exaone4_5.py
new file mode 100644
index 000000000000..06773e692e5a
--- /dev/null
+++ b/src/transformers/models/exaone4_5/processing_exaone4_5.py
@@ -0,0 +1,208 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/exaone4_5/modular_exaone4_5.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_exaone4_5.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import auto_docstring
+from ...video_utils import VideoInput
+
+
+class Exaone4_5_ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+        "videos_kwargs": {"return_metadata": True},
+    }
+
+
+@auto_docstring
+class Exaone4_5_Processor(ProcessorMixin):
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
+        )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.video_token)
+        )
+        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        videos: VideoInput | None = None,
+        **kwargs: Unpack[Exaone4_5_ProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Exaone4_5_ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = videos_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if videos is not None:
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+            # Get video metadata
+            if not kwargs.get("return_metadata"):
+                video_metadata = videos_inputs.pop("video_metadata")
+            else:
+                video_metadata = videos_inputs["video_metadata"]
+
+            fps = [metadata.sampled_fps for metadata in video_metadata]
+
+            if isinstance(fps, (int, float)):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+                second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
+            else:
+                raise ValueError(
+                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+                )
+            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+        if images is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if videos is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    num_video_tokens = video_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Exaone4_5_ProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Exaone4_5_ProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        video_processor_input_names = self.video_processor.model_input_names
+        names_from_processor = list(
+            dict.fromkeys(tokenizer_input_names + image_processor_input_names + video_processor_input_names)
+        )
+        return names_from_processor + ["second_per_grid_ts", "mm_token_type_ids"]
+
+
+__all__ = ["Exaone4_5_Processor"]
diff --git a/src/transformers/models/exaone_moe/modeling_exaone_moe.py b/src/transformers/models/exaone_moe/modeling_exaone_moe.py
index a7f80fc979c4..0058807def18 100644
--- a/src/transformers/models/exaone_moe/modeling_exaone_moe.py
+++ b/src/transformers/models/exaone_moe/modeling_exaone_moe.py
@@ -427,8 +427,8 @@ def __init__(self, config: ExaoneMoeConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
diff --git a/src/transformers/models/falcon/modeling_falcon.py b/src/transformers/models/falcon/modeling_falcon.py
index 016b3209b6b1..89141439e668 100644
--- a/src/transformers/models/falcon/modeling_falcon.py
+++ b/src/transformers/models/falcon/modeling_falcon.py
@@ -157,7 +157,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -280,15 +280,15 @@ def _split_heads(self, fused_qkv: torch.Tensor) -> tuple[torch.Tensor, torch.Ten
             return query, key, value
         elif not self.multi_query:
             batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
-            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
+            fused_qkv = fused_qkv.view(batch_size, seq_length, -1, 3, self.head_dim)
             return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
         else:
             batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
-            fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
+            fused_qkv = fused_qkv.view(batch_size, seq_length, -1, self.head_dim)
             return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
 
     # Copied from transformers.models.bloom.modeling_bloom.BloomAttention._merge_heads
-    def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
+    def _merge_heads(self, x: torch.Tensor, tp_aware_num_heads: int) -> torch.Tensor:
         """
         Merge heads together over the last dimension
 
@@ -301,17 +301,17 @@ def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
         # What we want to achieve is:
         # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
         batch_size_and_num_heads, seq_length, _ = x.shape
-        batch_size = batch_size_and_num_heads // self.num_heads
+        batch_size = batch_size_and_num_heads // tp_aware_num_heads
 
         # First view to decompose the batch size
         # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
-        x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
+        x = x.view(batch_size, tp_aware_num_heads, seq_length, self.head_dim)
 
         # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
         x = x.permute(0, 2, 1, 3)
 
         # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
-        return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
+        return x.reshape(batch_size, seq_length, tp_aware_num_heads * self.head_dim)
 
     def forward(
         self,
@@ -326,15 +326,20 @@ def forward(
         **kwargs,
     ):
         fused_qkv = self.query_key_value(hidden_states)  # [batch_size, seq_length, 3 x hidden_size]
-        num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
         # 3 x [batch_size, seq_length, num_heads, head_dim]
         (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
 
         batch_size, query_length, _, _ = query_layer.shape
 
-        query_layer = query_layer.transpose(1, 2).reshape(batch_size, self.num_heads, query_length, self.head_dim)
-        key_layer = key_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
-        value_layer = value_layer.transpose(1, 2).reshape(batch_size, num_kv_heads, query_length, self.head_dim)
+        tp_aware_num_heads = query_layer.shape[2]
+        tp_aware_key_heads = key_layer.shape[2]
+        tp_aware_value_heads = value_layer.shape[2]
+
+        query_layer = query_layer.transpose(1, 2).reshape(batch_size, tp_aware_num_heads, query_length, self.head_dim)
+        key_layer = key_layer.transpose(1, 2).reshape(batch_size, tp_aware_key_heads, query_length, self.head_dim)
+        value_layer = value_layer.transpose(1, 2).reshape(
+            batch_size, tp_aware_value_heads, query_length, self.head_dim
+        )
 
         if alibi is None:
             cos, sin = position_embeddings
@@ -369,9 +374,9 @@ def forward(
                 # It is unclear why dropout is not applied here (while it is with alibi).
                 attn_output = attention_scores @ value_layer
 
-            attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
+            attn_output = attn_output.view(batch_size, tp_aware_num_heads, query_length, self.head_dim)
             attn_output = attn_output.permute(0, 2, 1, 3)
-            attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
+            attn_output = attn_output.reshape(batch_size, query_length, tp_aware_num_heads * self.head_dim)
 
             attn_output = self.dense(attn_output)
 
@@ -392,14 +397,14 @@ def forward(
                 )
                 attention_probs = None
                 attn_output = attn_output.transpose(1, 2)
-                attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
+                attn_output = attn_output.reshape(batch_size, query_length, tp_aware_num_heads * self.head_dim)
 
                 attn_output = self.dense(attn_output)
             else:
                 matmul_result = query_layer @ key_layer.transpose(-1, -2)
 
                 # change view to [batch_size, num_heads, q_length, kv_length]
-                attention_scores = matmul_result.view(batch_size, self.num_heads, query_length, kv_length)
+                attention_scores = matmul_result.view(batch_size, tp_aware_num_heads, query_length, kv_length)
 
                 # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
                 input_dtype = attention_scores.dtype
@@ -407,20 +412,22 @@ def forward(
                 if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
                     attention_scores = attention_scores.to(torch.float32)
 
-                attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
+                attention_logits = attention_scores + alibi.view(batch_size, tp_aware_num_heads, 1, -1)
                 attention_logits *= self.inv_norm_factor
                 attention_probs = F.softmax(attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
                 # [batch_size, num_heads, q_length, kv_length]
                 attention_probs = self.attention_dropout(attention_probs)
 
                 # change view [batch_size, num_heads, q_length, kv_length]
-                attention_probs_reshaped = attention_probs.view(batch_size, self.num_heads, query_length, kv_length)
+                attention_probs_reshaped = attention_probs.view(
+                    batch_size, tp_aware_num_heads, query_length, kv_length
+                )
 
                 # matmul: [batch_size * num_heads, q_length, head_dim]
                 attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
 
                 # change view [batch_size, q_length, num_heads * head_dim]
-                attn_output = self._merge_heads(attn_output)
+                attn_output = self._merge_heads(attn_output, tp_aware_num_heads)
 
                 attn_output = self.dense(attn_output)
 
@@ -771,7 +778,7 @@ def forward(
             attention_mask=attention_mask,
             past_key_values=past_key_values,
             # Force mask creation for alibi
-            and_mask_function=lambda *args: torch.tensor(True, dtype=torch.bool),
+            and_mask_function=(lambda *args: torch.tensor(True, dtype=torch.bool)) if self.use_alibi else None,
         )
         if alibi is not None and causal_mask is not None and causal_mask.ndim == 4:
             min_dtype = torch.finfo(inputs_embeds.dtype).min
diff --git a/src/transformers/models/falcon_h1/modeling_falcon_h1.py b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
index 37b5da9df4b3..dd0d66b5dece 100644
--- a/src/transformers/models/falcon_h1/modeling_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modeling_falcon_h1.py
@@ -110,7 +110,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -258,11 +258,11 @@ def forward(
 
 
 class FalconH1RMSNormGated(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6, n_groups=1, norm_before_gate=True):
+    def __init__(self, hidden_size, group_size, eps=1e-6, norm_before_gate=True):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
-        self.n_groups = n_groups
+        self.group_size = group_size
         self.norm_before_gate = norm_before_gate
 
     def forward(self, hidden_states, gate=None):
@@ -278,12 +278,13 @@ def forward(self, hidden_states, gate=None):
             seq_len = 1
         hidden_states = hidden_states.to(torch.float32)
 
-        hidden_states = hidden_states.view(batch_size, seq_len, self.n_groups, int(dim // self.n_groups))
+        group_count = dim // self.group_size
+        hidden_states = hidden_states.view(batch_size, seq_len, group_count, self.group_size)
         variance = hidden_states.pow(2).mean(-1, keepdim=True)
 
         hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
 
-        hidden_states = self.weight.view(self.n_groups, int(dim // self.n_groups)) * hidden_states
+        hidden_states = self.weight.view(group_count, self.group_size) * hidden_states
         hidden_states = hidden_states.view(batch_size, seq_len, dim)
 
         if seq_len == 1:
@@ -426,8 +427,8 @@ def __init__(self, config: FalconH1Config, layer_idx: int):
         if self.mamba_rms_norm:
             self.norm = FalconH1RMSNormGated(
                 self.intermediate_size,
+                group_size=self.intermediate_size // self.n_groups,
                 eps=self.layer_norm_epsilon,
-                n_groups=self.n_groups,
                 norm_before_gate=config.mamba_norm_before_gate,
             )
         self.D = nn.Parameter(torch.ones(self.num_heads))
diff --git a/src/transformers/models/falcon_h1/modular_falcon_h1.py b/src/transformers/models/falcon_h1/modular_falcon_h1.py
index 75cbed28a646..ccecafd21936 100644
--- a/src/transformers/models/falcon_h1/modular_falcon_h1.py
+++ b/src/transformers/models/falcon_h1/modular_falcon_h1.py
@@ -111,11 +111,11 @@ def forward(
 
 
 class FalconH1RMSNormGated(MambaRMSNormGated):
-    def __init__(self, hidden_size, eps=1e-6, n_groups=1, norm_before_gate=True):
+    def __init__(self, hidden_size, group_size, eps=1e-6, norm_before_gate=True):
         super().__init__(hidden_size=hidden_size, eps=eps)
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
-        self.n_groups = n_groups
+        self.group_size = group_size
         self.norm_before_gate = norm_before_gate
 
     def forward(self, hidden_states, gate=None):
@@ -131,12 +131,13 @@ def forward(self, hidden_states, gate=None):
             seq_len = 1
         hidden_states = hidden_states.to(torch.float32)
 
-        hidden_states = hidden_states.view(batch_size, seq_len, self.n_groups, int(dim // self.n_groups))
+        group_count = dim // self.group_size
+        hidden_states = hidden_states.view(batch_size, seq_len, group_count, self.group_size)
         variance = hidden_states.pow(2).mean(-1, keepdim=True)
 
         hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
 
-        hidden_states = self.weight.view(self.n_groups, int(dim // self.n_groups)) * hidden_states
+        hidden_states = self.weight.view(group_count, self.group_size) * hidden_states
         hidden_states = hidden_states.view(batch_size, seq_len, dim)
 
         if seq_len == 1:
@@ -213,8 +214,8 @@ def __init__(self, config: FalconH1Config, layer_idx: int):
         if self.mamba_rms_norm:
             self.norm = FalconH1RMSNormGated(
                 self.intermediate_size,
+                group_size=self.intermediate_size // self.n_groups,
                 eps=self.layer_norm_epsilon,
-                n_groups=self.n_groups,
                 norm_before_gate=config.mamba_norm_before_gate,
             )
         self.D = nn.Parameter(torch.ones(self.num_heads))
diff --git a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
index 32fd1bf4a358..d11b32de6863 100644
--- a/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
+++ b/src/transformers/models/falcon_mamba/modeling_falcon_mamba.py
@@ -541,12 +541,12 @@ def _init_weights(self, module):
             init.normal_(module.weight, std=std)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for the FALCON_MAMBA model outputs.
     """
 )
+@dataclass
 class FalconMambaOutput(ModelOutput):
     r"""
     cache_params (`Cache`):
@@ -561,12 +561,12 @@ class FalconMambaOutput(ModelOutput):
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class FalconMambaCausalLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/fast_vlm/modeling_fast_vlm.py b/src/transformers/models/fast_vlm/modeling_fast_vlm.py
index 85c2eeb82b64..19ae7b2a11ec 100644
--- a/src/transformers/models/fast_vlm/modeling_fast_vlm.py
+++ b/src/transformers/models/fast_vlm/modeling_fast_vlm.py
@@ -162,9 +162,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -226,12 +226,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for FastVlm causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class FastVlmCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py b/src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
index de07be32a115..9755858f76d9 100644
--- a/src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
+++ b/src/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py
@@ -34,12 +34,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`FastSpeech2ConformerModel`].
     """
 )
+@dataclass
 class FastSpeech2ConformerModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/flava/modeling_flava.py b/src/transformers/models/flava/modeling_flava.py
index 4e21020026a7..96b5895baa8b 100644
--- a/src/transformers/models/flava/modeling_flava.py
+++ b/src/transformers/models/flava/modeling_flava.py
@@ -88,12 +88,12 @@ def to_tuple(self) -> tuple[Any]:
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class representing pretraining losses from FLAVA model
     """
 )
+@dataclass
 class FlavaLosses(ModelOutput):
     r"""
     mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.):
diff --git a/src/transformers/models/flex_olmo/modeling_flex_olmo.py b/src/transformers/models/flex_olmo/modeling_flex_olmo.py
index 100e6fa35554..d7a08bf43d96 100644
--- a/src/transformers/models/flex_olmo/modeling_flex_olmo.py
+++ b/src/transformers/models/flex_olmo/modeling_flex_olmo.py
@@ -300,8 +300,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -548,7 +548,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -556,7 +556,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -573,8 +575,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/florence2/modeling_florence2.py b/src/transformers/models/florence2/modeling_florence2.py
index fd941b85ce66..6abd07dddca2 100644
--- a/src/transformers/models/florence2/modeling_florence2.py
+++ b/src/transformers/models/florence2/modeling_florence2.py
@@ -716,9 +716,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/florence2/processing_florence2.py b/src/transformers/models/florence2/processing_florence2.py
index 94fb8bed3abc..3dc0c862a9b1 100644
--- a/src/transformers/models/florence2/processing_florence2.py
+++ b/src/transformers/models/florence2/processing_florence2.py
@@ -35,7 +35,7 @@
 
 class Florence2ProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
-        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
+        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False, "return_text_replacement_offsets": False},
     }
 
 
diff --git a/src/transformers/models/fnet/modeling_fnet.py b/src/transformers/models/fnet/modeling_fnet.py
index c5b08bd94050..103db7c13d67 100755
--- a/src/transformers/models/fnet/modeling_fnet.py
+++ b/src/transformers/models/fnet/modeling_fnet.py
@@ -379,12 +379,12 @@ def _init_weights(self, module):
             init.zeros_(module.token_type_ids)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`FNetForPreTraining`].
     """
 )
+@dataclass
 class FNetForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/focalnet/modeling_focalnet.py b/src/transformers/models/focalnet/modeling_focalnet.py
index b4e03c8884d5..d9a690441427 100644
--- a/src/transformers/models/focalnet/modeling_focalnet.py
+++ b/src/transformers/models/focalnet/modeling_focalnet.py
@@ -26,20 +26,20 @@
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import BackboneOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import ModelOutput, auto_docstring, logging
-from ...utils.generic import can_return_tuple
+from ...utils import ModelOutput, auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_focalnet import FocalNetConfig
 
 
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     FocalNet encoder's outputs, with potential hidden states.
     """
 )
+@dataclass
 class FocalNetEncoderOutput(ModelOutput):
     r"""
     reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -55,12 +55,12 @@ class FocalNetEncoderOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     FocalNet model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class FocalNetModelOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
@@ -79,12 +79,12 @@ class FocalNetModelOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     FocalNet masked image model outputs.
     """
 )
+@dataclass
 class FocalNetMaskedImageModelingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
@@ -105,12 +105,12 @@ class FocalNetMaskedImageModelingOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     FocalNet outputs for image classification.
     """
 )
+@dataclass
 class FocalNetImageClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -517,15 +517,14 @@ def __init__(self, config, grid_size):
 
         self.gradient_checkpointing = False
 
+    @can_return_tuple
     def forward(
         self,
         hidden_states: torch.Tensor,
         input_dimensions: tuple[int, int],
         output_hidden_states: bool | None = False,
         output_hidden_states_before_downsampling: bool | None = False,
-        return_dict: bool | None = True,
-    ) -> tuple | FocalNetEncoderOutput:
-        all_hidden_states = () if output_hidden_states else None
+    ) -> FocalNetEncoderOutput:
         all_reshaped_hidden_states = () if output_hidden_states else None
 
         if output_hidden_states:
@@ -533,12 +532,10 @@ def forward(
             # rearrange b (h w) c -> b c h w
             reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
             reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
-            all_hidden_states += (hidden_states,)
             all_reshaped_hidden_states += (reshaped_hidden_state,)
 
         for i, stage_module in enumerate(self.stages):
             stage_outputs = stage_module(hidden_states, input_dimensions)
-
             hidden_states = stage_outputs[0]
             hidden_states_before_downsampling = stage_outputs[1]
             output_dimensions = stage_outputs[2]
@@ -553,22 +550,16 @@ def forward(
                     batch_size, *(output_dimensions[0], output_dimensions[1]), hidden_size
                 )
                 reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
-                all_hidden_states += (hidden_states_before_downsampling,)
                 all_reshaped_hidden_states += (reshaped_hidden_state,)
             elif output_hidden_states and not output_hidden_states_before_downsampling:
                 batch_size, _, hidden_size = hidden_states.shape
                 # rearrange b (h w) c -> b c h w
                 reshaped_hidden_state = hidden_states.view(batch_size, *input_dimensions, hidden_size)
                 reshaped_hidden_state = reshaped_hidden_state.permute(0, 3, 1, 2)
-                all_hidden_states += (hidden_states,)
                 all_reshaped_hidden_states += (reshaped_hidden_state,)
 
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
-
         return FocalNetEncoderOutput(
             last_hidden_state=hidden_states,
-            hidden_states=all_hidden_states,
             reshaped_hidden_states=all_reshaped_hidden_states,
         )
 
@@ -580,6 +571,7 @@ class FocalNetPreTrainedModel(PreTrainedModel):
     main_input_name = "pixel_values"
     supports_gradient_checkpointing = True
     _no_split_modules = ["FocalNetStage"]
+    _can_record_outputs = {"hidden_states": FocalNetStage}
 
     @torch.no_grad()
     def _init_weights(self, module):
@@ -621,22 +613,17 @@ def get_input_embeddings(self):
         return self.embeddings.patch_embeddings
 
     @auto_docstring
+    @capture_outputs
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
         bool_masked_pos: torch.BoolTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | FocalNetModelOutput:
+    ) -> FocalNetModelOutput:
         r"""
         bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
             Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
         """
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
@@ -646,8 +633,7 @@ def forward(
         encoder_outputs = self.encoder(
             embedding_output,
             input_dimensions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            output_hidden_states=kwargs.get("output_hidden_states", self.config.output_hidden_states),
         )
 
         sequence_output = encoder_outputs[0]
@@ -658,15 +644,9 @@ def forward(
             pooled_output = self.pooler(sequence_output.transpose(1, 2))
             pooled_output = torch.flatten(pooled_output, 1)
 
-        if not return_dict:
-            output = (sequence_output, pooled_output) + encoder_outputs[1:]
-
-            return output
-
         return FocalNetModelOutput(
             last_hidden_state=sequence_output,
             pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
             reshaped_hidden_states=encoder_outputs.reshaped_hidden_states,
         )
 
@@ -704,14 +684,13 @@ def __init__(self, config):
         self.post_init()
 
     @auto_docstring
+    @can_return_tuple
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
         bool_masked_pos: torch.BoolTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | FocalNetMaskedImageModelingOutput:
+    ) -> FocalNetMaskedImageModelingOutput:
         r"""
         bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, num_patches)`):
             Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).
@@ -742,13 +721,11 @@ def forward(
         >>> list(reconstructed_pixel_values.shape)
         [1, 3, 192, 192]
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.focalnet(
             pixel_values,
             bool_masked_pos=bool_masked_pos,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         sequence_output = outputs[0]
@@ -774,10 +751,6 @@ def forward(
             reconstruction_loss = nn.functional.l1_loss(pixel_values, reconstructed_pixel_values, reduction="none")
             masked_im_loss = (reconstruction_loss * mask).sum() / (mask.sum() + 1e-5) / self.config.num_channels
 
-        if not return_dict:
-            output = (reconstructed_pixel_values,) + outputs[2:]
-            return ((masked_im_loss,) + output) if masked_im_loss is not None else output
-
         return FocalNetMaskedImageModelingOutput(
             loss=masked_im_loss,
             reconstruction=reconstructed_pixel_values,
@@ -809,12 +782,11 @@ def __init__(self, config):
         self.post_init()
 
     @auto_docstring
+    @can_return_tuple
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | FocalNetImageClassifierOutput:
         r"""
@@ -823,12 +795,10 @@ def forward(
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.focalnet(
             pixel_values,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         pooled_output = outputs[1]
@@ -839,10 +809,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return FocalNetImageClassifierOutput(
             loss=loss,
             logits=logits,
@@ -874,8 +840,6 @@ def __init__(self, config: FocalNetConfig):
     def forward(
         self,
         pixel_values: torch.Tensor,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> BackboneOutput:
         r"""
@@ -898,13 +862,8 @@ def forward(
         >>> inputs = processor(image, return_tensors="pt")
         >>> outputs = model(**inputs)
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
 
         outputs = self.focalnet(pixel_values, output_hidden_states=True, return_dict=True)
-
         hidden_states = outputs.reshaped_hidden_states
 
         feature_maps = ()
@@ -912,15 +871,9 @@ def forward(
             if stage in self.out_features:
                 feature_maps += (hidden_states[idx],)
 
-        if not return_dict:
-            output = (feature_maps,)
-            if output_hidden_states:
-                output += (outputs.hidden_states,)
-            return output
-
         return BackboneOutput(
             feature_maps=feature_maps,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            hidden_states=outputs.hidden_states if kwargs.get("output_hidden_states") else None,
             attentions=None,
         )
 
diff --git a/src/transformers/models/funnel/modeling_funnel.py b/src/transformers/models/funnel/modeling_funnel.py
index eb378ebf7b09..b5c6a32aed3e 100644
--- a/src/transformers/models/funnel/modeling_funnel.py
+++ b/src/transformers/models/funnel/modeling_funnel.py
@@ -711,12 +711,12 @@ def forward(self, hidden: torch.Tensor) -> torch.Tensor:
         return self.linear_out(hidden)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`FunnelForPreTraining`].
     """
 )
+@dataclass
 class FunnelForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/fuyu/modeling_fuyu.py b/src/transformers/models/fuyu/modeling_fuyu.py
index df57519032b9..e38b4a099ea8 100644
--- a/src/transformers/models/fuyu/modeling_fuyu.py
+++ b/src/transformers/models/fuyu/modeling_fuyu.py
@@ -141,9 +141,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/fuyu/processing_fuyu.py b/src/transformers/models/fuyu/processing_fuyu.py
index 76287ae3a5ea..02cefd3785b6 100644
--- a/src/transformers/models/fuyu/processing_fuyu.py
+++ b/src/transformers/models/fuyu/processing_fuyu.py
@@ -33,7 +33,7 @@
 
 
 if is_torch_available():
-    from .image_processing_fuyu import FuyuBatchFeature
+    from .image_processing_fuyu import FuyuBatchFeature, FuyuImagesKwargs
 
 
 logger = logging.get_logger(__name__)
@@ -56,6 +56,7 @@
 
 
 class FuyuProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: FuyuImagesKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
@@ -360,7 +361,10 @@ def __init__(self, image_processor, tokenizer, **kwargs):
         self.dummy_image_index = -1
         self.image_token_id = tokenizer.encode("|SPEAKER|", add_special_tokens=False)[1]
         self.image_newline_id = tokenizer.encode("|NEWLINE|", add_special_tokens=False)[1]
-        self.image_ids = [self.image_newline_id, self.image_token_id]
+
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_newline_id, self.image_token_id]
 
     def _left_pad_inputs_with_attention_mask(self, model_inputs: list[dict], return_attention_mask: bool):
         max_length_input_ids = max(entry["input_ids"].shape[1] for entry in model_inputs)
diff --git a/src/transformers/models/gemma/modeling_gemma.py b/src/transformers/models/gemma/modeling_gemma.py
index c6c5a55b8790..45e8774252e9 100644
--- a/src/transformers/models/gemma/modeling_gemma.py
+++ b/src/transformers/models/gemma/modeling_gemma.py
@@ -55,7 +55,7 @@ class GemmaTextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -154,7 +154,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/gemma/modular_gemma.py b/src/transformers/models/gemma/modular_gemma.py
index 25f436473fbe..091ff0b51239 100644
--- a/src/transformers/models/gemma/modular_gemma.py
+++ b/src/transformers/models/gemma/modular_gemma.py
@@ -110,7 +110,7 @@ class GemmaTextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
diff --git a/src/transformers/models/gemma2/modeling_gemma2.py b/src/transformers/models/gemma2/modeling_gemma2.py
index 20673571b2d2..8141d2549d30 100644
--- a/src/transformers/models/gemma2/modeling_gemma2.py
+++ b/src/transformers/models/gemma2/modeling_gemma2.py
@@ -98,8 +98,8 @@ def __init__(self, config: Gemma2Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -139,7 +139,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -354,7 +354,7 @@ class Gemma2TextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -386,6 +386,13 @@ def _init_weights(self, module):
             init.zeros_(module.weight)
         elif isinstance(module, Gemma2TextScaledWordEmbedding):
             init.constant_(module.embed_scale, module.scalar_embed_scale)
+        if isinstance(module, Gemma2RotaryEmbedding):
+            rope_init_fn = module.compute_default_rope_parameters
+            if module.rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type]
+            inv_freq, _ = rope_init_fn(module.config)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)
 
 
 @auto_docstring
diff --git a/src/transformers/models/gemma2/modular_gemma2.py b/src/transformers/models/gemma2/modular_gemma2.py
index 2edd9ef5f101..034e2c5bb062 100644
--- a/src/transformers/models/gemma2/modular_gemma2.py
+++ b/src/transformers/models/gemma2/modular_gemma2.py
@@ -18,6 +18,7 @@
 import torch.nn as nn
 from huggingface_hub.dataclasses import strict
 
+from ... import initialization as init
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
 from ...configuration_utils import PreTrainedConfig
@@ -159,8 +160,8 @@ def __init__(self, config: Gemma2Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @torch.no_grad()
     @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
@@ -170,7 +171,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -314,7 +315,16 @@ def forward(
 
 
 class Gemma2PreTrainedModel(GemmaPreTrainedModel):
-    pass
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, Gemma2RotaryEmbedding):
+            rope_init_fn = module.compute_default_rope_parameters
+            if module.rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type]
+            inv_freq, _ = rope_init_fn(module.config)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)
 
 
 class Gemma2Model(GemmaModel):
diff --git a/src/transformers/models/gemma3/modeling_gemma3.py b/src/transformers/models/gemma3/modeling_gemma3.py
index 38f50e95bb6d..2f90d5e33929 100644
--- a/src/transformers/models/gemma3/modeling_gemma3.py
+++ b/src/transformers/models/gemma3/modeling_gemma3.py
@@ -42,7 +42,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
 from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
@@ -50,9 +50,6 @@
 from .configuration_gemma3 import Gemma3Config, Gemma3TextConfig
 
 
-logger = logging.get_logger(__name__)
-
-
 @dataclass
 @auto_docstring(
     custom_intro="""
@@ -69,12 +66,12 @@ class Gemma3ModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Gemma3 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Gemma3CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -107,7 +104,7 @@ class Gemma3TextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -704,73 +701,6 @@ def forward(self, vision_outputs: torch.Tensor):
         return projected_vision_outputs.type_as(vision_outputs)
 
 
-def token_type_ids_mask_function(group_ids: torch.Tensor) -> Callable:
-    """
-    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
-    not start and end indices.
-    Args:
-        group_ids (`torch.Tensor`):
-            A tensor of shape `(bs, len)` assigning each token to a vision group. Tokens with the same group
-            come from the same input image. Text is denoted by `-1`.
-    """
-
-    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
-        seq_length = group_ids.shape[-1]
-
-        # clamp indices because with static cache they can go beyond `group_ids.shape[-1]`
-        q_idx_clamped = q_idx.clamp(max=seq_length - 1)
-        kv_idx_clamped = kv_idx.clamp(max=seq_length - 1)
-
-        # Unmask if the q and kv come from same group which is not -1 (i.e. non-text)
-        q_group = group_ids[batch_idx, q_idx_clamped]
-        kv_group = group_ids[batch_idx, kv_idx_clamped]
-        q_group = torch.where(q_idx < seq_length, q_group, -1)
-        kv_group = torch.where(kv_idx < seq_length, kv_group, -1)
-        return (q_group == kv_group) & (q_group >= 0)
-
-    return inner_mask
-
-
-@deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds")
-def create_causal_mask_mapping(
-    config: PreTrainedConfig,
-    inputs_embeds: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    past_key_values: Cache | None,
-    position_ids: torch.Tensor | None,
-    token_type_ids: torch.Tensor | None = None,
-    is_first_iteration: bool | None = None,
-    **kwargs,
-) -> dict:
-    """
-    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
-    for all kinds of forward passes. Gemma3 uses a bidirectional mask for images.
-
-    Uses `pixel_values` as an optional input to disambiguate edge cases.
-    """
-    mask_kwargs = {
-        "config": config.get_text_config(),
-        "inputs_embeds": inputs_embeds,
-        "attention_mask": attention_mask,
-        "past_key_values": past_key_values,
-        "position_ids": position_ids,
-    }
-    if token_type_ids is not None:
-        # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
-        # undo the causal masking)
-
-        # First find where a new image block starts: 1 if image and previous not image
-        # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
-        is_image = (token_type_ids == 1).to(inputs_embeds.device)
-        is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
-        new_image_start = is_image & ~is_previous_image
-        group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
-        group_ids = torch.where(is_image, group_ids, -1)
-        mask_kwargs["or_mask_function"] = token_type_ids_mask_function(group_ids)
-
-    return create_masks_for_generate(**mask_kwargs)
-
-
 @auto_docstring(
     custom_intro="""
     The Base Gemma3 model which consists of a vision backbone and a language model without language modeling head.,
@@ -824,9 +754,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -900,14 +830,30 @@ def forward(
 
         # It may already have been prepared by e.g. `generate`
         if not isinstance(causal_mask_mapping := attention_mask, dict):
-            causal_mask_mapping = create_causal_mask_mapping(
-                self.config,
-                inputs_embeds=inputs_embeds,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-                position_ids=position_ids,
-                token_type_ids=token_type_ids,
-            )
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            if token_type_ids is not None:
+                # First find where a new image block starts: 1 if image and previous not image
+                # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
+                is_image = (token_type_ids == 1).to(inputs_embeds.device)
+                is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
+                new_image_start = is_image & ~is_previous_image
+                group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
+                group_ids = torch.where(is_image, group_ids, -1)
+                mask_kwargs["block_sequence_ids"] = group_ids
+
+            # Create the masks
+            sliding_mask_kwargs = mask_kwargs.copy()
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+                "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
+            }
 
         outputs = self.language_model(
             attention_mask=causal_mask_mapping,
@@ -1113,37 +1059,38 @@ def create_masks_for_generate(
         is_first_iteration: bool | None = False,
         **kwargs,
     ) -> dict:
-        # Uses the overwritten `create_masks_for_generate` with `token_type_ids` masking
-        return create_causal_mask_mapping(
-            config,
-            inputs_embeds,
-            attention_mask,
-            past_key_values,
-            position_ids,
-            token_type_ids,
-            is_first_iteration=is_first_iteration,
-            **{k: v for k, v in kwargs.items() if k != "pixel_values"},
-        )
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+
+        if token_type_ids is not None:
+            # First find where a new image block starts: 1 if image and previous not image
+            # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
+            is_image = (token_type_ids == 1).to(inputs_embeds.device)
+            is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
+            new_image_start = is_image & ~is_previous_image
+            group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
+            group_ids = torch.where(is_image, group_ids, -1)
+            mask_kwargs["block_sequence_ids"] = group_ids
+
+        return create_masks_for_generate(**mask_kwargs)
 
 
-class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Gemma3Model(config)
-        self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
-
-        # Initialize weights and apply final processing
-        self.post_init()
-
-    def get_input_embeddings(self):
-        return self.model.get_input_embeddings()
+@auto_docstring(
+    custom_intro="""
+Gemma3TextForSequenceClassification is a text-only sequence classification model that works with Gemma3TextConfig.
+It uses the generic sequence classification implementation for efficiency and consistency."""
+)
+class Gemma3TextForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
+    config: Gemma3TextConfig
+    input_modalities = ("text",)
 
-    def set_input_embeddings(self, value):
-        self.model.set_input_embeddings(value)
 
-    @can_return_tuple
-    @auto_docstring
+class Gemma3ForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
     def forward(
         self,
         input_ids: torch.LongTensor | None = None,
@@ -1151,78 +1098,22 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
         past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
         token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> SequenceClassifierOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        transformer_outputs = self.model(
-            input_ids,
+        return super().forward(
+            input_ids=input_ids,
             attention_mask=attention_mask,
-            pixel_values=pixel_values,
             position_ids=position_ids,
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
             token_type_ids=token_type_ids,
-            use_cache=use_cache,
-            return_dict=True,
+            labels=labels,
             **kwargs,
         )
-        hidden_states = transformer_outputs.last_hidden_state
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.text_config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.text_config.pad_token_id is None:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
-            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-class Gemma3TextForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
-    """
-    Gemma3TextForSequenceClassification is a text-only sequence classification model that works with Gemma3TextConfig.
-    It uses the generic sequence classification implementation for efficiency and consistency.
-    """
-
-    config: Gemma3TextConfig
-    input_modalities = ("text",)
 
 
 __all__ = [
diff --git a/src/transformers/models/gemma3/modular_gemma3.py b/src/transformers/models/gemma3/modular_gemma3.py
index 9de1d8172513..96c75860c981 100644
--- a/src/transformers/models/gemma3/modular_gemma3.py
+++ b/src/transformers/models/gemma3/modular_gemma3.py
@@ -32,7 +32,6 @@
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
-from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import maybe_autocast
 from ..gemma2.configuration_gemma2 import Gemma2Config
 from ..gemma2.modeling_gemma2 import (
@@ -51,7 +50,6 @@
     PaliGemmaForConditionalGeneration,
     PaliGemmaModel,
     PaligemmaModelOutputWithPast,
-    token_type_ids_mask_function,
 )
 from ..siglip import SiglipVisionConfig
 
@@ -235,7 +233,7 @@ class Gemma3TextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -604,46 +602,6 @@ def forward(self, vision_outputs: torch.Tensor):
         return projected_vision_outputs.type_as(vision_outputs)
 
 
-@deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds")
-def create_causal_mask_mapping(
-    config: PreTrainedConfig,
-    inputs_embeds: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    past_key_values: Cache | None,
-    position_ids: torch.Tensor | None,
-    token_type_ids: torch.Tensor | None = None,
-    is_first_iteration: bool | None = None,
-    **kwargs,
-) -> dict:
-    """
-    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
-    for all kinds of forward passes. Gemma3 uses a bidirectional mask for images.
-
-    Uses `pixel_values` as an optional input to disambiguate edge cases.
-    """
-    mask_kwargs = {
-        "config": config.get_text_config(),
-        "inputs_embeds": inputs_embeds,
-        "attention_mask": attention_mask,
-        "past_key_values": past_key_values,
-        "position_ids": position_ids,
-    }
-    if token_type_ids is not None:
-        # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
-        # undo the causal masking)
-
-        # First find where a new image block starts: 1 if image and previous not image
-        # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
-        is_image = (token_type_ids == 1).to(inputs_embeds.device)
-        is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
-        new_image_start = is_image & ~is_previous_image
-        group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
-        group_ids = torch.where(is_image, group_ids, -1)
-        mask_kwargs["or_mask_function"] = token_type_ids_mask_function(group_ids)
-
-    return create_masks_for_generate(**mask_kwargs)
-
-
 class Gemma3Model(PaliGemmaModel):
     # we are filtering the logits/labels so we shouldn't divide the loss based on num_items_in_batch
     accepts_loss_kwargs = False
@@ -703,14 +661,30 @@ def forward(
 
         # It may already have been prepared by e.g. `generate`
         if not isinstance(causal_mask_mapping := attention_mask, dict):
-            causal_mask_mapping = create_causal_mask_mapping(
-                self.config,
-                inputs_embeds=inputs_embeds,
-                attention_mask=attention_mask,
-                past_key_values=past_key_values,
-                position_ids=position_ids,
-                token_type_ids=token_type_ids,
-            )
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            if token_type_ids is not None:
+                # First find where a new image block starts: 1 if image and previous not image
+                # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
+                is_image = (token_type_ids == 1).to(inputs_embeds.device)
+                is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
+                new_image_start = is_image & ~is_previous_image
+                group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
+                group_ids = torch.where(is_image, group_ids, -1)
+                mask_kwargs["block_sequence_ids"] = group_ids
+
+            # Create the masks
+            sliding_mask_kwargs = mask_kwargs.copy()
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+                "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
+            }
 
         outputs = self.language_model(
             attention_mask=causal_mask_mapping,
@@ -888,25 +862,48 @@ def prepare_inputs_for_generation(
 
         return model_inputs
 
+    def create_masks_for_generate(
+        config: PreTrainedConfig,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None,
+        position_ids: torch.Tensor | None,
+        token_type_ids: torch.Tensor | None = None,
+        is_first_iteration: bool | None = False,
+        **kwargs,
+    ) -> dict:
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
 
-class Gemma3ForSequenceClassification(Gemma3PreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
-        self.num_labels = config.num_labels
-        self.model = Gemma3Model(config)
-        self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
+        if token_type_ids is not None:
+            # First find where a new image block starts: 1 if image and previous not image
+            # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
+            is_image = (token_type_ids == 1).to(inputs_embeds.device)
+            is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
+            new_image_start = is_image & ~is_previous_image
+            group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
+            group_ids = torch.where(is_image, group_ids, -1)
+            mask_kwargs["block_sequence_ids"] = group_ids
 
-        # Initialize weights and apply final processing
-        self.post_init()
+        return create_masks_for_generate(**mask_kwargs)
 
-    def get_input_embeddings(self):
-        return self.model.get_input_embeddings()
 
-    def set_input_embeddings(self, value):
-        self.model.set_input_embeddings(value)
+@auto_docstring(
+    custom_intro="""
+Gemma3TextForSequenceClassification is a text-only sequence classification model that works with Gemma3TextConfig.
+It uses the generic sequence classification implementation for efficiency and consistency."""
+)
+class Gemma3TextForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
+    config: Gemma3TextConfig
+    input_modalities = ("text",)
 
-    @can_return_tuple
-    @auto_docstring
+
+class Gemma3ForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
     def forward(
         self,
         input_ids: torch.LongTensor | None = None,
@@ -914,78 +911,22 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         position_ids: torch.LongTensor | None = None,
         past_key_values: Cache | None = None,
-        inputs_embeds: torch.FloatTensor | None = None,
         token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
-        use_cache: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> SequenceClassifierOutputWithPast:
-        r"""
-        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
-            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
-            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
-            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
-        """
-
-        transformer_outputs = self.model(
-            input_ids,
+        return super().forward(
+            input_ids=input_ids,
             attention_mask=attention_mask,
-            pixel_values=pixel_values,
             position_ids=position_ids,
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
             token_type_ids=token_type_ids,
-            use_cache=use_cache,
-            return_dict=True,
+            labels=labels,
             **kwargs,
         )
-        hidden_states = transformer_outputs.last_hidden_state
-        logits = self.score(hidden_states)
-
-        if input_ids is not None:
-            batch_size = input_ids.shape[0]
-        else:
-            batch_size = inputs_embeds.shape[0]
-
-        if self.config.text_config.pad_token_id is None and batch_size != 1:
-            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
-        if self.config.text_config.pad_token_id is None:
-            last_non_pad_token = -1
-        elif input_ids is not None:
-            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
-            non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
-            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
-            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
-        else:
-            last_non_pad_token = -1
-            logger.warning_once(
-                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
-                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
-            )
-
-        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
-
-        loss = None
-        if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
-
-        return SequenceClassifierOutputWithPast(
-            loss=loss,
-            logits=pooled_logits,
-            past_key_values=transformer_outputs.past_key_values,
-            hidden_states=transformer_outputs.hidden_states,
-            attentions=transformer_outputs.attentions,
-        )
-
-
-class Gemma3TextForSequenceClassification(GenericForSequenceClassification, Gemma3PreTrainedModel):
-    """
-    Gemma3TextForSequenceClassification is a text-only sequence classification model that works with Gemma3TextConfig.
-    It uses the generic sequence classification implementation for efficiency and consistency.
-    """
-
-    config: Gemma3TextConfig
-    input_modalities = ("text",)
 
 
 __all__ = [
diff --git a/src/transformers/models/gemma3/processing_gemma3.py b/src/transformers/models/gemma3/processing_gemma3.py
index 048fe1adfa66..8342df195101 100644
--- a/src/transformers/models/gemma3/processing_gemma3.py
+++ b/src/transformers/models/gemma3/processing_gemma3.py
@@ -12,16 +12,17 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import re
 
 from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput, make_nested_list_of_images
+from ...image_utils import ImageInput, make_nested_list_of_images, valid_images
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import auto_docstring, to_py_obj
+from ...utils import auto_docstring
+from .image_processing_gemma3 import Gemma3ImageProcessorKwargs
 
 
 class Gemma3ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Gemma3ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -39,6 +40,8 @@ class Gemma3ProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Gemma3Processor(ProcessorMixin):
+    valid_processor_kwargs = Gemma3ProcessorKwargs
+
     def __init__(
         self,
         image_processor,
@@ -50,7 +53,7 @@ def __init__(
         self.image_seq_length = image_seq_length
         self.image_token_id = tokenizer.image_token_id
         self.boi_token = tokenizer.boi_token
-        self.image_token = tokenizer.image_token
+        self.image_token = tokenizer.boi_token
         image_tokens_expanded = "".join([tokenizer.image_token] * image_seq_length)
         self.full_image_sequence = f"\n\n{tokenizer.boi_token}{image_tokens_expanded}{tokenizer.eoi_token}\n\n"
 
@@ -64,71 +67,101 @@ def __init__(
     @auto_docstring
     def __call__(
         self,
-        images: ImageInput | None = None,
+        images: ImageInput | list[ImageInput] | None = None,
         text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
         **kwargs: Unpack[Gemma3ProcessorKwargs],
     ) -> BatchFeature:
-        if text is None and images is None:
-            raise ValueError("Provide at least one of `text` or `images`.")
+        model_inputs = super().__call__(images=images, text=text, **kwargs)
+        model_inputs["token_type_ids"] = model_inputs.pop("mm_token_type_ids", None)
+        return model_inputs
 
-        output_kwargs = self._merge_kwargs(
-            Gemma3ProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
+    def prepare_inputs_layout(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        videos=None,
+        audio=None,
+        **kwargs,
+    ):
+        images, text, videos, audio = super().prepare_inputs_layout(
+            images=images, text=text, videos=videos, audio=audio, **kwargs
         )
 
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        image_inputs = {}
+        # Model requires nested struct
         if images is not None:
-            images = self.image_processor.fetch_images(images)
-            batched_images = make_nested_list_of_images(images)
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+            images = make_nested_list_of_images(images)
 
-            # Create empty text to be replaced with placeholders
-            if not text:
-                text = [" ".join([self.boi_token] * len(images)) for images in batched_images]
+        # Create empty text to be replaced with placeholders
+        if images and not text:
+            text = [" ".join([self.boi_token] * len(image_list)) for image_list in images]
 
-            if len(batched_images) != len(text):
-                raise ValueError(
-                    f"Received inconsistently sized batches of images ({len(batched_images)}) and text ({len(text)})."
-                )
+        return images, text, videos, audio
+
+    def validate_inputs(
+        self,
+        images: ImageInput | list[ImageInput] | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        videos=None,
+        audio=None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(images=images, text=text, **kwargs)
 
-            # Replace image tokens by the full expanded sequence
-            num_crops = to_py_obj(image_inputs.pop("num_crops"))
-            batch_num_crops = [[num_crops.pop(0) for _ in range(len(images))] for images in batched_images]
-            for batch_idx, (prompt, images, num_crops) in enumerate(zip(text, batched_images, batch_num_crops)):
-                image_indexes = [m.start() for m in re.finditer(self.boi_token, prompt)]
+        if text is None and images is None:
+            raise ValueError("You must provide either `text` or `images`.")
 
-                if len(images) != len(image_indexes):
+        if text is not None:
+            n_images_in_text = [sample.count(self.boi_token) for sample in text]
+            if images is not None:
+                if len(images) != len(text):
                     raise ValueError(
-                        f"Prompt contained {len(image_indexes)} image tokens but received {len(images)} images."
+                        f"Received inconsistently sized batches of images ({len(images)}) and text ({len(text)})."
                     )
 
-                # Insert additional image tokens for Pan-and-Scan crops
-                for num, idx in reversed(list(zip(num_crops, image_indexes))):
-                    if num:
-                        formatted_image_text = (
-                            f"Here is the original image {self.boi_token} and here are some crops to help you see better "
-                            + " ".join([self.boi_token] * num)
-                        )
-                        prompt = prompt[:idx] + formatted_image_text + prompt[idx + len(self.boi_token) :]
-                        text[batch_idx] = prompt
-
-            # Expand placeholder image tokens to the full image token sequence
-            text = [prompt.replace(self.boi_token, self.full_image_sequence) for prompt in text]
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+                n_images_in_images = [len(sublist) for sublist in images]
+                if n_images_in_text != n_images_in_images:
+                    raise ValueError(
+                        f"The total number of {self.boi_token} tokens in the prompts should be the same as the number of images passed."
+                        f" Found {n_images_in_text} {self.boi_token} tokens and {n_images_in_images} images per sample."
+                    )
+            elif images is None and any(n_images_in_text):
+                raise ValueError(
+                    f"Found {sum(n_images_in_text)} {self.boi_token} tokens in the text but no images were passed."
+                )
 
-        if return_mm_token_type_ids:
-            text_inputs["token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid input images. Please provide a single image or a list of images or a list of list of images."
+            )
+
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        num_crops = image_inputs["num_crops"][image_idx]
+        if num_crops > 0:
+            formatted_image_text = (
+                f"Here is the original image {self.full_image_sequence} and here are some crops to help you see better "
+                + " ".join([self.full_image_sequence] * num_crops)
+            )
+            return formatted_image_text
+        else:
+            return self.full_image_sequence
+
+    def _check_special_mm_tokens(self, text: list[str], text_inputs: "BatchFeature", modalities: list[str]):
+        """
+        Checks that number of special tokens in text and processed text is same. The count can be different
+        if tokenized text was truncated, leading to issues in model code.
+        """
+        # Gemma3 uses BOI token instead of image token, which changed `self.attributes`
+        token_str = self.tokenizer.image_token
+        token_id = self.image_token_id
+        if token_str is not None and token_id is not None:
+            ids_count = [list(ids).count(token_id) for ids in text_inputs["input_ids"]]
+            text_count = [sample.count(token_str) for sample in text]
+
+            if ids_count != text_count:
+                raise ValueError(
+                    f"Mismatch in `image` token count between text and `input_ids`. Got ids={ids_count} and text={text_count}. "
+                    "Likely due to `truncation='max_length'`. Please disable truncation or increase `max_length`."
+                )
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
@@ -154,12 +187,12 @@ def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         return MultiModalData(**vision_data)
 
     @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names + ["token_type_ids"]
-        image_processor_input_names = self.image_processor.model_input_names
+    def model_input_names(self) -> list[str]:
+        return super().model_input_names + ["token_type_ids"]
 
-        image_processor_input_names = [name for name in image_processor_input_names if name != "num_crops"]
-        return list(tokenizer_input_names + image_processor_input_names)
+    @property
+    def unused_input_names(self) -> list[str]:
+        return ["num_crops"]
 
 
 __all__ = ["Gemma3Processor"]
diff --git a/src/transformers/models/gemma3n/configuration_gemma3n.py b/src/transformers/models/gemma3n/configuration_gemma3n.py
index e61c5f0038e7..60be54dacc18 100644
--- a/src/transformers/models/gemma3n/configuration_gemma3n.py
+++ b/src/transformers/models/gemma3n/configuration_gemma3n.py
@@ -394,7 +394,7 @@ def to_dict(self) -> dict[str, Any]:
 @strict
 class Gemma3nConfig(PreTrainedConfig):
     r"""
-    audio_soft_tokens_per_image (`int`, *optional*, defaults to 188):
+    audio_soft_tokens_per_audio (`int`, *optional*, defaults to 188):
         The number of soft tokens per audio clip.
     vision_soft_tokens_per_image (`int`, *optional*, defaults to 256):
         The number of soft tokens per image.
@@ -441,7 +441,7 @@ class Gemma3nConfig(PreTrainedConfig):
     text_config: Gemma3nTextConfig | dict[str, Any] | None = None
     vision_config: Gemma3nVisionConfig | dict[str, Any] | None = None
     audio_config: Gemma3nAudioConfig | dict[str, Any] | None = None
-    audio_soft_tokens_per_image: int | None = 188
+    audio_soft_tokens_per_audio: int | None = 188
     vision_soft_tokens_per_image: int | None = 256
     boi_token_id: int | None = 255_999
     eoi_token_id: int | None = 262_144
diff --git a/src/transformers/models/gemma3n/modeling_gemma3n.py b/src/transformers/models/gemma3n/modeling_gemma3n.py
index 8d1c5348d378..dafc678c2907 100644
--- a/src/transformers/models/gemma3n/modeling_gemma3n.py
+++ b/src/transformers/models/gemma3n/modeling_gemma3n.py
@@ -55,8 +55,8 @@
     from accelerate.hooks import add_hook_to_module
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Gemma3nAudioEncoderModelOutput(BaseModelOutputWithPooling):
     r"""
     audio_mel_mask (`torch.BoolTensor`, *optional*):
@@ -92,12 +92,12 @@ class Gemma3nModelOutputWithPast(BaseModelOutputWithPast):
     audio_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Gemma3n causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Gemma3nCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -134,7 +134,9 @@ def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True):
         self.with_scale = with_scale
 
         if self.with_scale:
-            self.weight = nn.Parameter(torch.ones(dim), requires_grad=True)
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.weight = nn.parameter.Buffer(torch.tensor(1.0), persistent=False)
 
     def _norm(self, hidden_states: torch.Tensor):
         mean_squared = hidden_states.pow(2).mean(-1, keepdim=True) + self.eps
@@ -169,8 +171,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         num_timescales = self.channels // 2
         log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(num_timescales - 1, 1)
         inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
-        self.register_buffer(
-            "inv_timescales",
+        self.inv_timescales = nn.parameter.Buffer(
             inv_timescales.float().unsqueeze(0).unsqueeze(0),
             persistent=False,
         )
@@ -345,13 +346,12 @@ def __init__(self, config: Gemma3nAudioConfig):
 
         q_scale = self.head_dim**-0.5
         r_softplus_0 = 1.0 / torch.nn.functional.softplus(torch.tensor(0.0))
-        self.register_buffer("q_scale", (q_scale * r_softplus_0).clone().detach(), persistent=False)
+        self.q_scale = nn.parameter.Buffer((q_scale * r_softplus_0).clone().detach(), persistent=False)
 
         local_causal_valid_mask = self.create_local_causal_valid_mask()
-        self.register_buffer("local_causal_valid_mask", local_causal_valid_mask, persistent=False)
+        self.local_causal_valid_mask = nn.parameter.Buffer(local_causal_valid_mask, persistent=False)
 
-        self.register_buffer(
-            "softcap",
+        self.softcap = nn.parameter.Buffer(
             torch.tensor(self.attention_logits_soft_cap).float(),
             persistent=False,
         )
@@ -805,7 +805,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         super().__init__()
         self.config = config
         self.post_in_features = self.config.hidden_size
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.pre_attn_norm = Gemma3nRMSNorm(self.config.hidden_size)
         self.attn = Gemma3nAudioAttention(config)
         self.post = nn.Linear(self.post_in_features, self.config.hidden_size, bias=False)
@@ -833,7 +833,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         super().__init__()
         self.config = config
 
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
 
         self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
         self.ffw_layer_1 = nn.Linear(self.config.hidden_size, self.config.hidden_size * 4, bias=False)
@@ -869,7 +869,7 @@ def __init__(self, config: Gemma3nAudioConfig):
             groups=self.config.hidden_size,  # Depthwise
             bias=False,
         )
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.conv_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
         self.linear_end = nn.Linear(self.config.hidden_size, self.config.hidden_size, bias=False)
 
@@ -905,7 +905,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         self.attention = Gemma3nAudioConformerAttention(self.config)
         self.lconv1d = Gemma3nAudioConformerLightConv1d(self.config)
         self.ffw_layer_end = Gemma3nAudioConformerFeedForward(self.config)
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.norm = Gemma3nRMSNorm(self.config.hidden_size)
 
     def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
@@ -931,7 +931,7 @@ class Gemma3nTextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -1013,7 +1013,7 @@ def __init__(self, config: Gemma3nTextConfig):
         self.prediction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs**2, bias=False)
         self.modality_router = nn.Linear(self.config.hidden_size, self.config.altup_num_inputs, bias=False)
         self.router_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
-        self.register_buffer("router_input_scale", torch.tensor(self.config.hidden_size**-1.0), persistent=False)
+        self.router_input_scale = nn.parameter.Buffer(torch.tensor(self.config.hidden_size**-1.0), persistent=False)
 
     def compute_router_modalities(self, x: torch.Tensor) -> torch.Tensor:
         router_inputs = self.router_norm(x) * self.router_input_scale
@@ -1401,6 +1401,8 @@ def _init_weights(self, module):
         elif isinstance(module, Gemma3nTextModel):
             init.constant_(module.per_layer_projection_scale, self.hidden_size**-0.5)
             init.constant_(module.per_layer_input_scale, 1 / math.sqrt(2.0))
+        elif isinstance(module, Gemma3nRMSNorm) and not module.with_scale:
+            init.constant_(module.weight, 1.0)
         elif isinstance(module, Gemma3nRotaryEmbedding):
             for layer_type in module.layer_types:
                 rope_init_fn = module.compute_default_rope_parameters
@@ -1485,7 +1487,7 @@ def forward(
 
         Returns:
             audio_encodings: a torch.Tensor of shape
-                `[batch_size, self.config.audio_soft_tokens_per_image,
+                `[batch_size, self.config.audio_soft_tokens_per_audio,
                 self.config.audio_config.hidden_size]`
             audio_mel_mask: a torch.BoolTensor of shape [batch, num_frames].
         """
@@ -1662,8 +1664,8 @@ def __init__(self, config: Gemma3nTextConfig):
             [nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
         )
 
-        self.register_buffer("per_layer_projection_scale", torch.tensor(self.hidden_size**-0.5), persistent=False)
-        self.register_buffer("per_layer_input_scale", torch.rsqrt(torch.tensor(2.0)), persistent=False)
+        self.per_layer_projection_scale = nn.parameter.Buffer(torch.tensor(self.hidden_size**-0.5), persistent=False)
+        self.per_layer_input_scale = nn.parameter.Buffer(torch.rsqrt(torch.tensor(2.0)), persistent=False)
 
         # Update `_keys_to_ignore_on_load_unexpected` to drop all k/v proj and norms for the shared layers
         self._keys_to_ignore_on_load_unexpected = []
@@ -2040,18 +2042,18 @@ def get_placeholder_mask(
             special_audio_mask = input_ids == self.config.audio_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0] * image_features.shape[1]}",
             )
 
         n_audio_tokens = special_audio_mask.sum()
-        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_audio_mask = special_audio_mask.unsqueeze(-1).to(inputs_embeds.device)
         if audio_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+                n_audio_tokens * inputs_embeds.shape[-1] == audio_features.numel(),
                 f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {audio_features.shape[0] * audio_features.shape[1]}",
             )
 
@@ -2124,7 +2126,7 @@ def forward(
             vision_input_ids = torch.where(vision_mask, input_ids, dummy_vision_token_id).to(inputs_embeds.device)
             vision_embeds = self.embed_vision(input_ids=vision_input_ids)
             vision_embeds = vision_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            expanded_vision_mask = vision_mask.unsqueeze(-1).expand_as(inputs_embeds)
+            expanded_vision_mask = vision_mask.unsqueeze(-1)
             inputs_embeds = torch.where(expanded_vision_mask, vision_embeds, inputs_embeds)
 
             # Handle audio tokens (>= embed_audio.vocab_offset)
@@ -2133,7 +2135,7 @@ def forward(
             audio_input_ids = torch.where(audio_mask, input_ids, dummy_audio_token_id).to(inputs_embeds.device)
             audio_embeds = self.embed_audio(input_ids=audio_input_ids)
             audio_embeds = audio_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            expanded_audio_mask = audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
+            expanded_audio_mask = audio_mask.unsqueeze(-1)
             inputs_embeds = torch.where(expanded_audio_mask, audio_embeds, inputs_embeds)
         else:
             per_layer_inputs = None
@@ -2163,7 +2165,7 @@ def forward(
             audio_features = torch.where(audio_mask.unsqueeze(-1), audio_padding_embs, audio_features)
 
             audio_batch_size, audio_seq_len, audio_embed_dim = audio_features.shape
-            extra_padding_tokens = self.config.audio_soft_tokens_per_image - audio_seq_len
+            extra_padding_tokens = self.config.audio_soft_tokens_per_audio - audio_seq_len
             extra_padding_features = audio_padding_embs.expand(audio_batch_size, extra_padding_tokens, audio_embed_dim)
 
             audio_features = torch.cat((audio_features, extra_padding_features), dim=1)
diff --git a/src/transformers/models/gemma3n/modular_gemma3n.py b/src/transformers/models/gemma3n/modular_gemma3n.py
index e97e1ef4c6d2..7f992955d969 100644
--- a/src/transformers/models/gemma3n/modular_gemma3n.py
+++ b/src/transformers/models/gemma3n/modular_gemma3n.py
@@ -357,7 +357,7 @@ class Gemma3nVisionConfig(TimmWrapperConfig):
 @strict
 class Gemma3nConfig(PreTrainedConfig):
     r"""
-    audio_soft_tokens_per_image (`int`, *optional*, defaults to 188):
+    audio_soft_tokens_per_audio (`int`, *optional*, defaults to 188):
         The number of soft tokens per audio clip.
     vision_soft_tokens_per_image (`int`, *optional*, defaults to 256):
         The number of soft tokens per image.
@@ -404,7 +404,7 @@ class Gemma3nConfig(PreTrainedConfig):
     text_config: Gemma3nTextConfig | dict[str, Any] | None = None
     vision_config: Gemma3nVisionConfig | dict[str, Any] | None = None
     audio_config: Gemma3nAudioConfig | dict[str, Any] | None = None
-    audio_soft_tokens_per_image: int | None = 188
+    audio_soft_tokens_per_audio: int | None = 188
     vision_soft_tokens_per_image: int | None = 256
     boi_token_id: int | None = 255_999
     eoi_token_id: int | None = 262_144
@@ -437,8 +437,8 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Gemma3nAudioEncoderModelOutput(BaseModelOutputWithPooling):
     r"""
     audio_mel_mask (`torch.BoolTensor`, *optional*):
@@ -495,7 +495,9 @@ def __init__(self, dim: int, eps: float = 1e-6, with_scale: bool = True):
         self.with_scale = with_scale
 
         if self.with_scale:
-            self.weight = nn.Parameter(torch.ones(dim), requires_grad=True)
+            self.weight = nn.Parameter(torch.ones(dim))
+        else:
+            self.weight = nn.parameter.Buffer(torch.tensor(1.0), persistent=False)
 
     def _norm(self, hidden_states: torch.Tensor):
         mean_squared = hidden_states.pow(2).mean(-1, keepdim=True) + self.eps
@@ -530,8 +532,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         num_timescales = self.channels // 2
         log_timescale_increment = math.log(float(max_timescale) / float(min_timescale)) / max(num_timescales - 1, 1)
         inv_timescales = min_timescale * torch.exp(torch.arange(num_timescales) * -log_timescale_increment)
-        self.register_buffer(
-            "inv_timescales",
+        self.inv_timescales = nn.parameter.Buffer(
             inv_timescales.float().unsqueeze(0).unsqueeze(0),
             persistent=False,
         )
@@ -706,13 +707,12 @@ def __init__(self, config: Gemma3nAudioConfig):
 
         q_scale = self.head_dim**-0.5
         r_softplus_0 = 1.0 / torch.nn.functional.softplus(torch.tensor(0.0))
-        self.register_buffer("q_scale", (q_scale * r_softplus_0).clone().detach(), persistent=False)
+        self.q_scale = nn.parameter.Buffer((q_scale * r_softplus_0).clone().detach(), persistent=False)
 
         local_causal_valid_mask = self.create_local_causal_valid_mask()
-        self.register_buffer("local_causal_valid_mask", local_causal_valid_mask, persistent=False)
+        self.local_causal_valid_mask = nn.parameter.Buffer(local_causal_valid_mask, persistent=False)
 
-        self.register_buffer(
-            "softcap",
+        self.softcap = nn.parameter.Buffer(
             torch.tensor(self.attention_logits_soft_cap).float(),
             persistent=False,
         )
@@ -1166,7 +1166,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         super().__init__()
         self.config = config
         self.post_in_features = self.config.hidden_size
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.pre_attn_norm = Gemma3nRMSNorm(self.config.hidden_size)
         self.attn = Gemma3nAudioAttention(config)
         self.post = nn.Linear(self.post_in_features, self.config.hidden_size, bias=False)
@@ -1194,7 +1194,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         super().__init__()
         self.config = config
 
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
 
         self.pre_layer_norm = Gemma3nRMSNorm(self.config.hidden_size)
         self.ffw_layer_1 = nn.Linear(self.config.hidden_size, self.config.hidden_size * 4, bias=False)
@@ -1230,7 +1230,7 @@ def __init__(self, config: Gemma3nAudioConfig):
             groups=self.config.hidden_size,  # Depthwise
             bias=False,
         )
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.conv_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
         self.linear_end = nn.Linear(self.config.hidden_size, self.config.hidden_size, bias=False)
 
@@ -1266,7 +1266,7 @@ def __init__(self, config: Gemma3nAudioConfig):
         self.attention = Gemma3nAudioConformerAttention(self.config)
         self.lconv1d = Gemma3nAudioConformerLightConv1d(self.config)
         self.ffw_layer_end = Gemma3nAudioConformerFeedForward(self.config)
-        self.register_buffer("gradient_clipping", torch.tensor(self.config.gradient_clipping), persistent=False)
+        self.gradient_clipping = nn.parameter.Buffer(torch.tensor(self.config.gradient_clipping), persistent=False)
         self.norm = Gemma3nRMSNorm(self.config.hidden_size)
 
     def forward(self, audio_encodings: torch.Tensor, audio_mel_mask: torch.BoolTensor) -> torch.Tensor:
@@ -1361,7 +1361,7 @@ def __init__(self, config: Gemma3nTextConfig):
         self.prediction_coefs = nn.Linear(self.config.altup_num_inputs, self.config.altup_num_inputs**2, bias=False)
         self.modality_router = nn.Linear(self.config.hidden_size, self.config.altup_num_inputs, bias=False)
         self.router_norm = Gemma3nRMSNorm(self.config.hidden_size, eps=self.config.rms_norm_eps)
-        self.register_buffer("router_input_scale", torch.tensor(self.config.hidden_size**-1.0), persistent=False)
+        self.router_input_scale = nn.parameter.Buffer(torch.tensor(self.config.hidden_size**-1.0), persistent=False)
 
     def compute_router_modalities(self, x: torch.Tensor) -> torch.Tensor:
         router_inputs = self.router_norm(x) * self.router_input_scale
@@ -1680,6 +1680,8 @@ def _init_weights(self, module):
         elif isinstance(module, Gemma3nTextModel):
             init.constant_(module.per_layer_projection_scale, self.hidden_size**-0.5)
             init.constant_(module.per_layer_input_scale, 1 / math.sqrt(2.0))
+        elif isinstance(module, Gemma3nRMSNorm) and not module.with_scale:
+            init.constant_(module.weight, 1.0)
         elif isinstance(module, Gemma3nRotaryEmbedding):
             for layer_type in module.layer_types:
                 rope_init_fn = module.compute_default_rope_parameters
@@ -1764,7 +1766,7 @@ def forward(
 
         Returns:
             audio_encodings: a torch.Tensor of shape
-                `[batch_size, self.config.audio_soft_tokens_per_image,
+                `[batch_size, self.config.audio_soft_tokens_per_audio,
                 self.config.audio_config.hidden_size]`
             audio_mel_mask: a torch.BoolTensor of shape [batch, num_frames].
         """
@@ -1854,8 +1856,8 @@ def __init__(self, config: Gemma3nTextConfig):
             [nn.Linear(self.hidden_size, self.hidden_size, bias=False) for _ in range(1, self.config.altup_num_inputs)]
         )
 
-        self.register_buffer("per_layer_projection_scale", torch.tensor(self.hidden_size**-0.5), persistent=False)
-        self.register_buffer("per_layer_input_scale", torch.rsqrt(torch.tensor(2.0)), persistent=False)
+        self.per_layer_projection_scale = nn.parameter.Buffer(torch.tensor(self.hidden_size**-0.5), persistent=False)
+        self.per_layer_input_scale = nn.parameter.Buffer(torch.rsqrt(torch.tensor(2.0)), persistent=False)
 
         # Update `_keys_to_ignore_on_load_unexpected` to drop all k/v proj and norms for the shared layers
         self._keys_to_ignore_on_load_unexpected = []
@@ -2149,18 +2151,18 @@ def get_placeholder_mask(
             special_audio_mask = input_ids == self.config.audio_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0] * image_features.shape[1]}",
             )
 
         n_audio_tokens = special_audio_mask.sum()
-        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_audio_mask = special_audio_mask.unsqueeze(-1).to(inputs_embeds.device)
         if audio_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+                n_audio_tokens * inputs_embeds.shape[-1] == audio_features.numel(),
                 f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {audio_features.shape[0] * audio_features.shape[1]}",
             )
 
@@ -2233,7 +2235,7 @@ def forward(
             vision_input_ids = torch.where(vision_mask, input_ids, dummy_vision_token_id).to(inputs_embeds.device)
             vision_embeds = self.embed_vision(input_ids=vision_input_ids)
             vision_embeds = vision_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            expanded_vision_mask = vision_mask.unsqueeze(-1).expand_as(inputs_embeds)
+            expanded_vision_mask = vision_mask.unsqueeze(-1)
             inputs_embeds = torch.where(expanded_vision_mask, vision_embeds, inputs_embeds)
 
             # Handle audio tokens (>= embed_audio.vocab_offset)
@@ -2242,7 +2244,7 @@ def forward(
             audio_input_ids = torch.where(audio_mask, input_ids, dummy_audio_token_id).to(inputs_embeds.device)
             audio_embeds = self.embed_audio(input_ids=audio_input_ids)
             audio_embeds = audio_embeds.to(inputs_embeds.device, inputs_embeds.dtype)
-            expanded_audio_mask = audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
+            expanded_audio_mask = audio_mask.unsqueeze(-1)
             inputs_embeds = torch.where(expanded_audio_mask, audio_embeds, inputs_embeds)
         else:
             per_layer_inputs = None
@@ -2272,7 +2274,7 @@ def forward(
             audio_features = torch.where(audio_mask.unsqueeze(-1), audio_padding_embs, audio_features)
 
             audio_batch_size, audio_seq_len, audio_embed_dim = audio_features.shape
-            extra_padding_tokens = self.config.audio_soft_tokens_per_image - audio_seq_len
+            extra_padding_tokens = self.config.audio_soft_tokens_per_audio - audio_seq_len
             extra_padding_features = audio_padding_embs.expand(audio_batch_size, extra_padding_tokens, audio_embed_dim)
 
             audio_features = torch.cat((audio_features, extra_padding_features), dim=1)
diff --git a/src/transformers/models/gemma4/modeling_gemma4.py b/src/transformers/models/gemma4/modeling_gemma4.py
index cdc4a6daeafc..6e361f832640 100644
--- a/src/transformers/models/gemma4/modeling_gemma4.py
+++ b/src/transformers/models/gemma4/modeling_gemma4.py
@@ -41,8 +41,13 @@
     create_sliding_window_causal_mask,
 )
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
-from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, CausalLMOutputWithPast
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
+from ...modeling_outputs import (
+    BaseModelOutputWithPast,
+    BaseModelOutputWithPooling,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutputWithPast,
+)
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
@@ -52,6 +57,7 @@
     auto_docstring,
     can_return_tuple,
     is_accelerate_available,
+    logging,
     torch_compilable_check,
 )
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
@@ -60,6 +66,8 @@
 from .configuration_gemma4 import Gemma4AudioConfig, Gemma4Config, Gemma4TextConfig, Gemma4VisionConfig
 
 
+logger = logging.get_logger(__name__)
+
 if is_accelerate_available():
     from accelerate.hooks import add_hook_to_module
 
@@ -90,12 +98,12 @@ class Gemma4ModelOutputWithPast(BaseModelOutputWithPast):
     audio_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Gemma4 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Gemma4CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -125,8 +133,8 @@ class Gemma4CausalLMOutputWithPast(ModelOutput):
     audio_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Gemma4AudioModelOutput(BaseModelOutputWithPooling):
     r"""
     attention_mask (`torch.BoolTensor`, *optional*):
@@ -1442,7 +1450,7 @@ class Gemma4PreTrainedModel(PreTrainedModel):
     supports_gradient_checkpointing = True
     _no_split_modules = ["Gemma4TextDecoderLayer", "Gemma4VisionEncoderLayer", "Gemma4AudioLayer"]
     _skip_keys_device_placement = ["past_key_values", "shared_kv_states"]
-    _supports_flash_attn = True
+    _supports_flash_attn = False  # released checkpoints use head_dim=512, which is not supported yet by FA kernels
     _supports_sdpa = True
     _supports_flex_attn = True
 
@@ -1775,7 +1783,6 @@ class Gemma4ForCausalLM(Gemma4PreTrainedModel, GenerationMixin):
     _tp_plan = {"lm_head": "colwise_gather_output"}
     _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
     config: Gemma4TextConfig
-    base_model_prefix = "model"
 
     def __init__(self, config: Gemma4TextConfig):
         super().__init__(config)
@@ -1941,7 +1948,8 @@ def forward(
                 (self.config.attention_context_left - 1, self.config.attention_context_right)
             ),
         )
-        attention_mask = self._convert_4d_mask_to_blocked_5d(attention_mask)
+        if attention_mask is not None:
+            attention_mask = self._convert_4d_mask_to_blocked_5d(attention_mask)
 
         for encoder_layer in self.layers[: self.config.num_hidden_layers]:
             hidden_states = encoder_layer(
@@ -2047,84 +2055,6 @@ def forward(self, inputs_embeds: torch.Tensor) -> torch.Tensor:
         return self.embedding_projection(embs_normed)
 
 
-# Identical as Gemma3 but modular can't resolve if we simply import. FIXME: @cyril
-def token_type_ids_mask_function(
-    token_type_ids: torch.Tensor | None,
-    image_group_ids: torch.Tensor | None,
-) -> Callable | None:
-    """
-    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
-    not start and end indices.
-    """
-    # Do not return an additional mask in this case
-    if token_type_ids is None:
-        return None
-
-    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
-        seq_length = image_group_ids.shape[-1]
-
-        # clamp indices because with static cache they can go beyond `image_group_ids.shape[-1]`
-        q_idx_clamped = q_idx.clamp(max=seq_length - 1)
-        kv_idx_clamped = kv_idx.clamp(max=seq_length - 1)
-
-        # Unmask if the q and kv come from same group which is not -1 (i.e. non-text)
-        q_group = image_group_ids[batch_idx, q_idx_clamped]
-        kv_group = image_group_ids[batch_idx, kv_idx_clamped]
-        q_group = torch.where(q_idx < seq_length, q_group, -1)
-        kv_group = torch.where(kv_idx < seq_length, kv_group, -1)
-        return (q_group == kv_group) & (q_group >= 0)
-
-    return inner_mask
-
-
-# Similar to Gemma3 but `sliding_mask_kwargs` and `mask_kwargs` are different and `token_type_ids->mm_token_type_ids`
-def create_causal_mask_mapping(
-    config: PreTrainedConfig,
-    inputs_embeds: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    past_key_values: Cache | None,
-    position_ids: torch.Tensor | None,
-    mm_token_type_ids: torch.Tensor | None = None,
-    is_first_iteration: bool | None = None,
-    **kwargs,
-) -> dict:
-    """
-    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
-    for all kinds of forward passes. Gemma4 uses a bidirectional mask for images.
-
-    Uses `pixel_values` as an optional input to disambiguate edge cases.
-    """
-    mask_kwargs = {
-        "config": config.get_text_config(),
-        "inputs_embeds": inputs_embeds,
-        "attention_mask": attention_mask,
-        "past_key_values": past_key_values,
-        "position_ids": position_ids,
-    }
-    sliding_mask_kwargs = mask_kwargs.copy()
-
-    if mm_token_type_ids is not None:
-        # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
-        # undo the causal masking)
-
-        # First find where a new vision block starts. Vision tokens cannot attend to
-        # future vision tokens, but can attend to all prev tokens and to itself bidirectionally
-        is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
-        is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
-        is_prev_vision[..., 0] = False
-        new_vision_starts = is_vision & ~is_prev_vision
-        vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
-        vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
-        sliding_mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
-            mm_token_type_ids.to(inputs_embeds.device), vision_group_ids
-        )
-
-    return {
-        "full_attention": create_causal_mask(**mask_kwargs),
-        "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
-    }
-
-
 @auto_docstring(
     custom_intro="""
     The base Gemma 4 model comprising a vision backbone, an audio backbone, and a language model without a
@@ -2350,25 +2280,30 @@ def forward(
             position_ids = position_ids.unsqueeze(0)
 
         if not isinstance(causal_mask_mapping := attention_mask, dict):
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
+            # Smaller Gemma models use a conventional casual attention mask
             if self.config.get_text_config().use_bidirectional_attention == "vision":
-                # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
-                causal_mask_mapping = create_causal_mask_mapping(
-                    self.config,
-                    inputs_embeds=inputs_embeds,
-                    attention_mask=attention_mask,
-                    past_key_values=past_key_values,
-                    position_ids=position_ids,
-                    mm_token_type_ids=mm_token_type_ids,
-                )
-            else:
-                # Smaller Gemma models use a conventional casual attention mask
-                causal_mask_mapping = create_masks_for_generate(
-                    self.config,
-                    inputs_embeds,
-                    attention_mask,
-                    past_key_values,
-                    position_ids,
-                )
+                vision_group_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
+                if mm_token_type_ids is not None:
+                    is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
+                    is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
+                    is_prev_vision[..., 0] = False
+                    new_vision_starts = is_vision & ~is_prev_vision
+                    vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
+                    vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
+
+                mask_kwargs["block_sequence_ids"] = vision_group_ids
+
+            # Create the masks
+            causal_mask_mapping = create_masks_for_generate(**mask_kwargs)
 
         outputs = self.language_model(
             per_layer_inputs=per_layer_inputs,
@@ -2455,7 +2390,6 @@ def get_video_features(
 class Gemma4ForConditionalGeneration(Gemma4PreTrainedModel, GenerationMixin):
     _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
     accepts_loss_kwargs = False
-    base_model_prefix = "model"
 
     def __init__(self, config: Gemma4Config):
         super().__init__(config)
@@ -2615,24 +2549,147 @@ def create_masks_for_generate(
         is_first_iteration: bool | None = False,
         **kwargs,
     ) -> dict:
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+
+        # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
+        # Smaller Gemma models use a conventional casual attention mask
         if getattr(config.get_text_config(), "use_bidirectional_attention", None) == "vision":
-            # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
-            return create_causal_mask_mapping(
-                config,
-                inputs_embeds,
-                attention_mask,
-                past_key_values,
-                position_ids,
-                mm_token_type_ids,
-                is_first_iteration=is_first_iteration,
-                **{k: v for k, v in kwargs.items() if k != "pixel_values"},
-            )
+            vision_group_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
+            if mm_token_type_ids is not None:
+                is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
+                is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
+                is_prev_vision[..., 0] = False
+                new_vision_starts = is_vision & ~is_prev_vision
+                vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
+                vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
+
+            mask_kwargs["block_sequence_ids"] = vision_group_ids
+
+        return create_masks_for_generate(**mask_kwargs)
+
+
+class Gemma4ForSequenceClassification(Gemma4PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Gemma4Model(config)
+        self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        mm_token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        image_position_ids: torch.LongTensor | None = None,
+        video_position_ids: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> SequenceClassifierOutputWithPast:
+        r"""
+        input_features_mask (`torch.FloatTensor` of shape `(num_images, seq_length)`):
+            The attention mask for the input audio.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
+            2D patch position coordinates from the image processor, with `(-1, -1)` indicating padding.
+            Passed through to the vision encoder for positional embedding computation.
+        video_position_ids (`torch.LongTensor` of shape `(num_videos, num_frames, max_patches, 2)`, *optional*):
+            2D patch position coordinates from the video processor, with `(-1, -1)` indicating padding.
+            Passed through to the vision encoder for positional embedding computation.
+        """
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            mm_token_type_ids=mm_token_type_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            image_position_ids=image_position_ids,
+            video_position_ids=video_position_ids,
+            return_dict=True,
+            **kwargs,
+        )
+        hidden_states = transformer_outputs.last_hidden_state
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
         else:
-            # Smaller Gemma models use a conventional casual attention mask
-            return create_masks_for_generate(
-                config, inputs_embeds, attention_mask, past_key_values, position_ids, **kwargs
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.text_config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.text_config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
             )
 
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+class Gemma4TextForSequenceClassification(GenericForSequenceClassification, Gemma4PreTrainedModel):
+    """
+    Gemma4TextForSequenceClassification is a text-only sequence classification model that works with Gemma4TextConfig.
+    It uses the generic sequence classification implementation for efficiency and consistency.
+    """
+
+    config: Gemma4TextConfig
+    input_modalities = ("text",)
+
 
 __all__ = [
     "Gemma4AudioModel",
@@ -2642,4 +2699,6 @@ def create_masks_for_generate(
     "Gemma4PreTrainedModel",
     "Gemma4TextModel",
     "Gemma4VisionModel",
+    "Gemma4ForSequenceClassification",
+    "Gemma4TextForSequenceClassification",
 ]
diff --git a/src/transformers/models/gemma4/modular_gemma4.py b/src/transformers/models/gemma4/modular_gemma4.py
index 739870f2a177..8fa27a0526b5 100644
--- a/src/transformers/models/gemma4/modular_gemma4.py
+++ b/src/transformers/models/gemma4/modular_gemma4.py
@@ -32,7 +32,8 @@
     create_sliding_window_causal_mask,
 )
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
-from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
+from ...modeling_layers import GenericForSequenceClassification
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, SequenceClassifierOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
@@ -88,8 +89,8 @@ class Gemma4CausalLMOutputWithPast(Gemma3nCausalLMOutputWithPast):
     pass
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Gemma4AudioModelOutput(BaseModelOutputWithPooling):
     r"""
     attention_mask (`torch.BoolTensor`, *optional*):
@@ -1159,6 +1160,7 @@ class Gemma4TextScaledWordEmbedding(Gemma3TextScaledWordEmbedding):
 class Gemma4PreTrainedModel(Gemma3nPreTrainedModel):
     _no_split_modules = ["Gemma4TextDecoderLayer", "Gemma4VisionEncoderLayer", "Gemma4AudioLayer"]
     input_modalities = ("image", "text", "video", "audio")
+    _supports_flash_attn = False  # released checkpoints use head_dim=512, which is not supported yet by FA kernels
     _can_record_outputs = None  # override
 
     @torch.no_grad()
@@ -1430,8 +1432,6 @@ def forward(
 
 @auto_docstring(custom_intro="The base Gemma 4 language model with a language modeling head.")
 class Gemma4ForCausalLM(Gemma3ForCausalLM):
-    base_model_prefix = "model"
-
     def __init__(self, config: Gemma4TextConfig):
         super().__init__(config)
         # Grab the ones from the child
@@ -1511,7 +1511,8 @@ def forward(
                 (self.config.attention_context_left - 1, self.config.attention_context_right)
             ),
         )
-        attention_mask = self._convert_4d_mask_to_blocked_5d(attention_mask)
+        if attention_mask is not None:
+            attention_mask = self._convert_4d_mask_to_blocked_5d(attention_mask)
 
         for encoder_layer in self.layers[: self.config.num_hidden_layers]:
             hidden_states = encoder_layer(
@@ -1650,54 +1651,6 @@ def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
     return inner_mask
 
 
-# Similar to Gemma3 but `sliding_mask_kwargs` and `mask_kwargs` are different and `token_type_ids->mm_token_type_ids`
-def create_causal_mask_mapping(
-    config: PreTrainedConfig,
-    inputs_embeds: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    past_key_values: Cache | None,
-    position_ids: torch.Tensor | None,
-    mm_token_type_ids: torch.Tensor | None = None,
-    is_first_iteration: bool | None = None,
-    **kwargs,
-) -> dict:
-    """
-    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
-    for all kinds of forward passes. Gemma4 uses a bidirectional mask for images.
-
-    Uses `pixel_values` as an optional input to disambiguate edge cases.
-    """
-    mask_kwargs = {
-        "config": config.get_text_config(),
-        "inputs_embeds": inputs_embeds,
-        "attention_mask": attention_mask,
-        "past_key_values": past_key_values,
-        "position_ids": position_ids,
-    }
-    sliding_mask_kwargs = mask_kwargs.copy()
-
-    if mm_token_type_ids is not None:
-        # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
-        # undo the causal masking)
-
-        # First find where a new vision block starts. Vision tokens cannot attend to
-        # future vision tokens, but can attend to all prev tokens and to itself bidirectionally
-        is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
-        is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
-        is_prev_vision[..., 0] = False
-        new_vision_starts = is_vision & ~is_prev_vision
-        vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
-        vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
-        sliding_mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
-            mm_token_type_ids.to(inputs_embeds.device), vision_group_ids
-        )
-
-    return {
-        "full_attention": create_causal_mask(**mask_kwargs),
-        "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
-    }
-
-
 @auto_docstring(
     custom_intro="""
     The base Gemma 4 model comprising a vision backbone, an audio backbone, and a language model without a
@@ -1933,25 +1886,30 @@ def forward(
             position_ids = position_ids.unsqueeze(0)
 
         if not isinstance(causal_mask_mapping := attention_mask, dict):
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
+            # Smaller Gemma models use a conventional casual attention mask
             if self.config.get_text_config().use_bidirectional_attention == "vision":
-                # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
-                causal_mask_mapping = create_causal_mask_mapping(
-                    self.config,
-                    inputs_embeds=inputs_embeds,
-                    attention_mask=attention_mask,
-                    past_key_values=past_key_values,
-                    position_ids=position_ids,
-                    mm_token_type_ids=mm_token_type_ids,
-                )
-            else:
-                # Smaller Gemma models use a conventional casual attention mask
-                causal_mask_mapping = create_masks_for_generate(
-                    self.config,
-                    inputs_embeds,
-                    attention_mask,
-                    past_key_values,
-                    position_ids,
-                )
+                vision_group_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
+                if mm_token_type_ids is not None:
+                    is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
+                    is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
+                    is_prev_vision[..., 0] = False
+                    new_vision_starts = is_vision & ~is_prev_vision
+                    vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
+                    vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
+
+                mask_kwargs["block_sequence_ids"] = vision_group_ids
+
+            # Create the masks
+            causal_mask_mapping = create_masks_for_generate(**mask_kwargs)
 
         outputs = self.language_model(
             per_layer_inputs=per_layer_inputs,
@@ -2008,6 +1966,13 @@ def get_audio_features(
 class Gemma4ForConditionalGeneration(Gemma3nForConditionalGeneration):
     base_model_prefix = "model"
 
+    def __init__(self, config: Gemma4Config):
+        super().__init__(config)
+        # Grab the ones from the child
+        self._keys_to_ignore_on_load_unexpected = [
+            f"model.{name}" for name in self.model._keys_to_ignore_on_load_unexpected
+        ]
+
     def get_per_layer_input_embeddings(self):
         return self.model.get_per_layer_input_embeddings()
 
@@ -2110,23 +2075,29 @@ def create_masks_for_generate(
         is_first_iteration: bool | None = False,
         **kwargs,
     ) -> dict:
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+
+        # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
+        # Smaller Gemma models use a conventional casual attention mask
         if getattr(config.get_text_config(), "use_bidirectional_attention", None) == "vision":
-            # Larger Gemma 4 models use Gemma 3's bidirectional attention mask for vision inputs
-            return create_causal_mask_mapping(
-                config,
-                inputs_embeds,
-                attention_mask,
-                past_key_values,
-                position_ids,
-                mm_token_type_ids,
-                is_first_iteration=is_first_iteration,
-                **{k: v for k, v in kwargs.items() if k != "pixel_values"},
-            )
-        else:
-            # Smaller Gemma models use a conventional casual attention mask
-            return create_masks_for_generate(
-                config, inputs_embeds, attention_mask, past_key_values, position_ids, **kwargs
-            )
+            vision_group_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
+            if mm_token_type_ids is not None:
+                is_vision = (mm_token_type_ids == 1) | (mm_token_type_ids == 2)
+                is_prev_vision = torch.roll(is_vision, shifts=1, dims=-1)
+                is_prev_vision[..., 0] = False
+                new_vision_starts = is_vision & ~is_prev_vision
+                vision_group_ids = torch.cumsum(new_vision_starts.int(), dim=1) - 1
+                vision_group_ids = torch.where(is_vision, vision_group_ids, -1)
+
+            mask_kwargs["block_sequence_ids"] = vision_group_ids
+
+        return create_masks_for_generate(**mask_kwargs)
 
     def prepare_inputs_for_generation(
         self,
@@ -2173,6 +2144,123 @@ def prepare_inputs_for_generation(
         return model_inputs
 
 
+class Gemma4ForSequenceClassification(Gemma4PreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = Gemma4Model(config)
+        self.score = nn.Linear(config.text_config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        mm_token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        image_position_ids: torch.LongTensor | None = None,
+        video_position_ids: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> SequenceClassifierOutputWithPast:
+        r"""
+        input_features_mask (`torch.FloatTensor` of shape `(num_images, seq_length)`):
+            The attention mask for the input audio.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        image_position_ids (`torch.LongTensor` of shape `(batch_size, max_patches, 2)`, *optional*):
+            2D patch position coordinates from the image processor, with `(-1, -1)` indicating padding.
+            Passed through to the vision encoder for positional embedding computation.
+        video_position_ids (`torch.LongTensor` of shape `(num_videos, num_frames, max_patches, 2)`, *optional*):
+            2D patch position coordinates from the video processor, with `(-1, -1)` indicating padding.
+            Passed through to the vision encoder for positional embedding computation.
+        """
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            mm_token_type_ids=mm_token_type_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            image_position_ids=image_position_ids,
+            video_position_ids=video_position_ids,
+            return_dict=True,
+            **kwargs,
+        )
+        hidden_states = transformer_outputs.last_hidden_state
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.text_config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.text_config.pad_token_id is None:
+            last_non_pad_token = -1
+        elif input_ids is not None:
+            # To handle both left- and right- padding, we take the rightmost token that is not equal to pad_token_id
+            non_pad_mask = (input_ids != self.config.text_config.pad_token_id).to(logits.device, torch.int32)
+            token_indices = torch.arange(input_ids.shape[-1], device=logits.device, dtype=torch.int32)
+            last_non_pad_token = (token_indices * non_pad_mask).argmax(-1)
+        else:
+            last_non_pad_token = -1
+            logger.warning_once(
+                f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
+                "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
+            )
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), last_non_pad_token]
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, pooled_logits=pooled_logits, config=self.config)
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+class Gemma4TextForSequenceClassification(GenericForSequenceClassification, Gemma4PreTrainedModel):
+    """
+    Gemma4TextForSequenceClassification is a text-only sequence classification model that works with Gemma4TextConfig.
+    It uses the generic sequence classification implementation for efficiency and consistency.
+    """
+
+    config: Gemma4TextConfig
+    input_modalities = ("text",)
+
+
 __all__ = [
     "Gemma4AudioModel",
     "Gemma4ForCausalLM",
@@ -2181,4 +2269,6 @@ def prepare_inputs_for_generation(
     "Gemma4PreTrainedModel",
     "Gemma4TextModel",
     "Gemma4VisionModel",
+    "Gemma4ForSequenceClassification",
+    "Gemma4TextForSequenceClassification",
 ]
diff --git a/src/transformers/models/gemma4/processing_gemma4.py b/src/transformers/models/gemma4/processing_gemma4.py
index d688250d0b36..58e13414670b 100644
--- a/src/transformers/models/gemma4/processing_gemma4.py
+++ b/src/transformers/models/gemma4/processing_gemma4.py
@@ -12,12 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
 
 import numpy as np
 
 from ...audio_utils import AudioInput
-from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput, make_nested_list_of_images
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
@@ -51,6 +49,8 @@ class Gemma4ProcessorKwargs(ProcessingKwargs, total=False):
 @auto_docstring
 @requires(backends=("vision",))
 class Gemma4Processor(ProcessorMixin):
+    valid_processor_kwargs = Gemma4ProcessorKwargs
+
     def __init__(
         self,
         feature_extractor,
@@ -107,189 +107,100 @@ def __init__(
             **kwargs,
         )
 
-    @auto_docstring
-    def __call__(
+    def prepare_inputs_layout(
         self,
         images: ImageInput | None = None,
         text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        audio: AudioInput | None = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Gemma4ProcessorKwargs],
-    ) -> BatchFeature:
-        if text is None and images is None and audio is None and videos is None:
-            raise ValueError("Provide at least one of `text`, `images`, `audio`, or `videos`.")
-
-        output_kwargs = self._merge_kwargs(
-            Gemma4ProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
+        videos: VideoInput = None,
+        audio: AudioInput = None,
+        **kwargs,
+    ):
+        images, text, videos, audio = super().prepare_inputs_layout(
+            images=images, text=text, videos=videos, audio=audio, **kwargs
         )
 
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        image_inputs = {}
+        # Model requires nested struct
         if images is not None:
-            images = self.image_processor.fetch_images(images)
-            batched_images = make_nested_list_of_images(images)
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+            images = make_nested_list_of_images(images)
 
-            num_soft_tokens = image_inputs.pop("num_soft_tokens_per_image")
+        # Create empty text to be replaced with placeholders
+        if images and not text:
+            text = [" ".join([self.boi_token] * len(image_list)) for image_list in images]
+        if audio and not text:
+            text = [self.audio_token] * len(audio)
 
-            # Create empty text to be replaced with placeholders
-            if not text:
-                text = [" ".join([self.image_token] * len(images)) for images in batched_images]
+        return images, text, videos, audio
 
-            if len(batched_images) != len(text):
-                raise ValueError(
-                    f"Received inconsistently sized batches of images ({len(batched_images)}) and text ({len(text)})."
-                )
+    def validate_inputs(
+        self,
+        images: ImageInput | list[ImageInput] | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        videos: VideoInput = None,
+        audio: AudioInput = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(images=images, text=text, **kwargs)
 
-            replacements = [f"{self.boi_token}{self.image_token * n}{self.eoi_token}" for n in num_soft_tokens]
-            replacements_iter = iter(replacements)
-
-            # Expand image_token placeholders to per-image soft token sequences.
-            # re.sub never re-scans replaced text, so it is safe
-            pattern = re.escape(self.image_token)
-            text = [re.sub(pattern, lambda _: next(replacements_iter), prompt) for prompt in text]
-
-        # Process video inputs in same way
-        video_inputs = {}
-        if videos is not None:
-            video_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            num_video_tokens = video_inputs.pop("num_soft_tokens_per_video")
-
-            # If user has not requested video metadata, pop it so it isn't returned
-            if not kwargs.get("return_metadata"):
-                video_metadata = video_inputs.pop("video_metadata")
-            else:
-                video_metadata = video_inputs["video_metadata"]
-
-            video_replacements = []
-            for metadata, n_tokens in zip(video_metadata, num_video_tokens):
-                if metadata.fps is None:
-                    logger.warning_once(
-                        "Gemma 4 requires frame timestamps to construct prompts, but the `fps` of the input video "
-                        "could not be inferred. Probably `video_metadata` was missing from inputs and you passed "
-                        "pre-sampled frames. Defaulting to `fps=24`. Please provide `video_metadata` for more "
-                        "accurate results."
-                    )
-                metadata.fps = 24 if metadata.fps is None else metadata.fps
-                # mm:ss format for timestamps
-                timestamp_str = [
-                    f"{int(seconds // 60):02d}:{int(seconds % 60):02d}" for seconds in metadata.timestamps
-                ]
-                video_replacements.append(
-                    " ".join(
-                        [f"{t} {self.boi_token}{self.video_token * n_tokens}{self.eoi_token}" for t in timestamp_str]
-                    )
-                )
+        if text is None and images is None:
+            raise ValueError("You must provide either `text` or `images`.")
 
-            video_replacements = iter(video_replacements)
-            pattern = re.escape(self.video_token)
-            text = [re.sub(pattern, lambda _: next(video_replacements), prompt) for prompt in text]
+        if audio is not None and self.audio_token is None or self.boa_token is None or self.eoa_token is None:
+            raise ValueError("Audio inputs were provided, but the tokenizer does not have an `audio_token` defined.")
 
-        # Process audio inputs
-        audio_inputs = {}
-        if audio is not None:
-            if self.audio_token is None or self.boa_token is None or self.eoa_token is None:
+        if text is not None:
+            n_images_in_text = [sample.count(self.image_token) for sample in text]
+            if images is not None:
+                if len(images) != len(text):
+                    raise ValueError(
+                        f"Received inconsistently sized batches of images ({len(images)}) and text ({len(text)})."
+                    )
+
+                n_images_in_images = [len(sublist) for sublist in images]
+                if n_images_in_text != n_images_in_images:
+                    raise ValueError(
+                        f"The total number of {self.image_token} tokens in the prompts should be the same as the number of images passed."
+                        f" Found {n_images_in_text} {self.image_token} tokens and {n_images_in_images} images per sample."
+                    )
+            elif images is None and any(n_images_in_text):
                 raise ValueError(
-                    "Audio inputs were provided, but the tokenizer does not have an `audio_token` defined."
+                    f"Found {sum(n_images_in_text)} {self.image_token} tokens in the text but no images were passed."
                 )
 
-            # Normalize audio input to list of waveforms
-            if isinstance(audio, np.ndarray) and audio.ndim == 1:
-                audio = [audio]
-
-            # TODO: Add tests for audio-only processor inputs.
-            if not text:
-                text = [self.audio_token] * len(audio)
-
-            # Dynamic audio token expansion wihtout padding:
-            #   * Extract audio features with feature extractor;
-            #   * Compute precise per-audio token counts from the waveform duration;
-            #   * Generate full audio token sequence for each computed audio length;
-            #   * Expand text prompts with full audio token sequences.
-            audio_kwargs = output_kwargs.get("audio_kwargs", {})
-            audio_inputs = self.feature_extractor(audio, **audio_kwargs)
-            sampling_rate = self.feature_extractor.sampling_rate
-            num_audio_tokens = [self._compute_audio_num_tokens(a, sampling_rate) for a in audio]
-            replacements = [f"{self.boa_token}{self.audio_token * n}{self.eoa_token}" for n in num_audio_tokens]
-            replacements_iter = iter(replacements)
-            audio_pattern = re.escape(self.audio_token)
-            text = [re.sub(audio_pattern, lambda _: next(replacements_iter), prompt) for prompt in text]
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
-
-        # Check special tokens for all active modalities
-        active_modalities = []
-        if images is not None:
-            active_modalities.append("image")
-        if videos is not None:
-            active_modalities.append("video")
-        if audio is not None:
-            active_modalities.append("audio")
-        if active_modalities:
-            self._check_special_mm_tokens(text, text_inputs, modalities=active_modalities)
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-
-        return BatchFeature(
-            data={**text_inputs, **image_inputs, **audio_inputs, **video_inputs},
-            tensor_type=return_tensors,
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        num_soft_tokens = image_inputs["num_soft_tokens_per_image"][image_idx]
+        return f"{self.boi_token}{self.image_token * num_soft_tokens}{self.eoi_token}"
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        num_soft_tokens = video_inputs["num_soft_tokens_per_video"][video_idx]
+        metadata = video_inputs["video_metadata"][video_idx]
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "Gemma4 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
+
+        # mm:ss format for timestamps
+        timestamp_str = [f"{int(seconds // 60):02d}:{int(seconds % 60):02d}" for seconds in metadata.timestamps]
+        video_replacement = " ".join(
+            [f"{t} {self.boi_token}{self.video_token * num_soft_tokens}{self.eoi_token}" for t in timestamp_str]
         )
+        return video_replacement
 
-    def _compute_audio_num_tokens(self, audio_waveform, sampling_rate: int) -> int:
-        """Compute the number of audio soft tokens for a single waveform.
-
-        Replicates the exact sequence-length arithmetic of the audio encoder
-        so that the processor inserts the correct number of placeholder tokens.
-        The computation mirrors:
-
-        1. Mel framing via ``_unfold`` in ``Gemma4AudioFeatureExtractor``
-        2. Two ``Conv2d`` subsampling layers in ``Gemma4AudioSubSampleConvProjection``
-           (each: kernel=3, stride=2, semicausal padding top=1, bottom=1)
-
-        The result is capped at ``self.audio_seq_length`` (the configured maximum).
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        # TODO: Add tests for audio-only processor inputs.
+        mask = audio_inputs["input_features_mask"][audio_idx]
 
-        Args:
-            audio_waveform: A 1-D numpy array or list containing the raw audio samples.
-            sampling_rate: The sampling rate of the audio waveform in Hz.
-
-        Returns:
-            The number of audio soft tokens to insert as placeholders.
-        """
-        num_samples = len(audio_waveform)
-
-        # Step 1: Mel frames (matches feature_extraction_gemma4.py _unfold)
-        frame_length = int(round(sampling_rate * 20.0 / 1000.0))  # 320 @ 16kHz
-        hop_length = int(round(sampling_rate * 10.0 / 1000.0))  # 160 @ 16kHz
-        frame_size_for_unfold = frame_length + 1  # 321
-
-        # The feature extractor prepends (frame_length // 2) zero samples as
-        # semicausal time-padding before the unfold.  We must include this to
-        # match the actual number of mel frames it produces.
-        pad_left = frame_length // 2  # 160 @ 16kHz
-        padded_samples = num_samples + pad_left
-        num_mel_frames = (padded_samples - frame_size_for_unfold) // hop_length + 1
-
-        if num_mel_frames <= 0:
-            return 0
-
-        # Step 2: Two SSCP conv layers (kernel=3, stride=2, semicausal pad top=1, bottom=1)
-        # Each layer: T_out = (T_in + pad_top + pad_bottom - kernel) // stride + 1
-        t = num_mel_frames
+        # Simulate two stride-2 conv blocks on the mask
+        t = len(mask)
         for _ in range(2):
-            t_padded = t + 2  # pad_top=1, pad_bottom=1
-            t = (t_padded - 3) // 2 + 1
+            t_out = (t + 2 - 3) // 2 + 1
+            mask = mask[::2][:t_out]
+            t = len(mask)
 
-        # Cap at the configured maximum
-        return min(t, self.audio_seq_length)
+        return f"{self.boa_token}{self.audio_token * int(mask.sum())}{self.eoa_token}"
 
     def _get_num_multimodal_tokens(self, image_sizes=None, audio_lengths=None, **kwargs):
         """
@@ -348,19 +259,11 @@ def _get_num_multimodal_tokens(self, image_sizes=None, audio_lengths=None, **kwa
 
     @property
     def model_input_names(self):
-        model_input_names = super().model_input_names
-        model_input_names = [
-            name
-            for name in model_input_names
-            if name not in ["num_soft_tokens_per_image", "num_soft_tokens_per_video"]
-        ]
-
-        # Include audio feature extractor input names if available
-        if self.feature_extractor is not None:
-            feature_extractor_input_names = self.feature_extractor.model_input_names
-            model_input_names.extend([name for name in feature_extractor_input_names if name not in model_input_names])
-
-        return model_input_names + ["mm_token_type_ids"]
+        return super().model_input_names + ["mm_token_type_ids"]
+
+    @property
+    def unused_input_names(self) -> list[str]:
+        return ["num_soft_tokens_per_image", "num_soft_tokens_per_video"]
 
 
 __all__ = ["Gemma4Processor"]
diff --git a/src/transformers/models/git/modeling_git.py b/src/transformers/models/git/modeling_git.py
index 9be97d01c425..051db01cf382 100644
--- a/src/transformers/models/git/modeling_git.py
+++ b/src/transformers/models/git/modeling_git.py
@@ -24,9 +24,8 @@
 from ... import initialization as init
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
-from ...configuration_utils import PreTrainedConfig
 from ...generation import GenerationMixin
-from ...masking_utils import create_masks_for_generate
+from ...masking_utils import create_causal_mask
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import (
     BaseModelOutput,
@@ -44,7 +43,6 @@
     logging,
     torch_int,
 )
-from ...utils.deprecation import deprecate_kwarg
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from .configuration_git import GitConfig, GitVisionConfig
@@ -100,47 +98,6 @@ def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
     return inner_mask
 
 
-@deprecate_kwarg("input_embeds", version="5.6.0", new_name="inputs_embeds")
-# Copied from transformers.models.gemma3.modeling_gemma3.create_causal_mask_mapping
-def create_causal_mask_mapping(
-    config: PreTrainedConfig,
-    inputs_embeds: torch.Tensor,
-    attention_mask: torch.Tensor | None,
-    past_key_values: Cache | None,
-    position_ids: torch.Tensor | None,
-    token_type_ids: torch.Tensor | None = None,
-    is_first_iteration: bool | None = None,
-    **kwargs,
-) -> dict:
-    """
-    Overwrites the base `create_masks_for_generate` with `token_type_ids` masking to create the causal mask mapping
-    for all kinds of forward passes. Gemma3 uses a bidirectional mask for images.
-
-    Uses `pixel_values` as an optional input to disambiguate edge cases.
-    """
-    mask_kwargs = {
-        "config": config.get_text_config(),
-        "inputs_embeds": inputs_embeds,
-        "attention_mask": attention_mask,
-        "past_key_values": past_key_values,
-        "position_ids": position_ids,
-    }
-    if token_type_ids is not None:
-        # We need to pass an additional mask function to account for token type ids, and it needs to be an `or` (to
-        # undo the causal masking)
-
-        # First find where a new image block starts: 1 if image and previous not image
-        # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
-        is_image = (token_type_ids == 1).to(inputs_embeds.device)
-        is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
-        new_image_start = is_image & ~is_previous_image
-        group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
-        group_ids = torch.where(is_image, group_ids, -1)
-        mask_kwargs["or_mask_function"] = token_type_ids_mask_function(group_ids)
-
-    return create_masks_for_generate(**mask_kwargs)
-
-
 class GitEmbeddings(nn.Module):
     """Construct the embeddings from word and position embeddings."""
 
@@ -679,7 +636,7 @@ def __init__(self, config: GitVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = GitVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = GitVisionEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
 
@@ -694,7 +651,7 @@ def forward(
             raise ValueError("You have to specify pixel_values")
 
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
@@ -933,14 +890,21 @@ def forward(
             attention_mask = torch.cat([extended_attention_mask, attention_mask], dim=-1)
 
         # Images attend each other bidirectionally while text remains causal
-        causal_mask = create_causal_mask_mapping(
-            self.config,
-            inputs_embeds=embedding_output,
-            attention_mask=attention_mask,
-            past_key_values=past_key_values,
-            position_ids=None,
-            token_type_ids=token_type_ids,
-        )
+        group_ids = torch.full([*embedding_output.size()[:-1]], -1, device=embedding_output.device)
+        if token_type_ids is not None:
+            # Can attend bidirectionally in images and causally in suffix
+            group_ids = torch.where(token_type_ids == 1, 0, -1)
+
+        mask_kwargs = {
+            "config": self.config.get_text_config(),
+            "inputs_embeds": embedding_output,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+            "block_sequence_ids": group_ids,
+        }
+
+        causal_mask = create_causal_mask(**mask_kwargs)
 
         hidden_states = embedding_output
 
diff --git a/src/transformers/models/glm/modeling_glm.py b/src/transformers/models/glm/modeling_glm.py
index 712202580943..186cbcc238e1 100644
--- a/src/transformers/models/glm/modeling_glm.py
+++ b/src/transformers/models/glm/modeling_glm.py
@@ -121,7 +121,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/glm4/modeling_glm4.py b/src/transformers/models/glm4/modeling_glm4.py
index e99930ae57f6..64c349c1a5bc 100644
--- a/src/transformers/models/glm4/modeling_glm4.py
+++ b/src/transformers/models/glm4/modeling_glm4.py
@@ -319,7 +319,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/glm46v/modeling_glm46v.py b/src/transformers/models/glm46v/modeling_glm46v.py
index 81207e4c8608..99be84a9798b 100644
--- a/src/transformers/models/glm46v/modeling_glm46v.py
+++ b/src/transformers/models/glm46v/modeling_glm46v.py
@@ -57,12 +57,12 @@ class Glm46VPreTrainedModel(PreTrainedModel):
     _can_record_outputs = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Glm46VModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -333,18 +333,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -467,12 +467,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Glm46V causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Glm46VCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/glm46v/modular_glm46v.py b/src/transformers/models/glm46v/modular_glm46v.py
index 0fdcef45136f..3d239161f738 100644
--- a/src/transformers/models/glm46v/modular_glm46v.py
+++ b/src/transformers/models/glm46v/modular_glm46v.py
@@ -105,8 +105,8 @@ class Glm46VForConditionalGeneration(Glm4vForConditionalGeneration):
 
 
 class Glm46VProcessor(Glm4vProcessor):
-    def replace_frame_token_id(self, timestamp_sec):
-        return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec:.1f} seconds"
+    def replace_frame_token_id(self, timestamp_sec, num_image_tokens: int = 1):
+        return f"<|begin_of_image|>{self.image_token * num_image_tokens}<|end_of_image|>{timestamp_sec:.1f} seconds"
 
 
 class Glm46VImageProcessorPil(Glm4vImageProcessorPil):
diff --git a/src/transformers/models/glm46v/processing_glm46v.py b/src/transformers/models/glm46v/processing_glm46v.py
index 9dcf7c4856e6..9570ab9ffbb9 100644
--- a/src/transformers/models/glm46v/processing_glm46v.py
+++ b/src/transformers/models/glm46v/processing_glm46v.py
@@ -21,18 +21,16 @@
 
 import numpy as np
 
-from ...image_processing_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from .image_processing_glm46v import Glm46VImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Glm46VProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Glm46VImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -45,6 +43,8 @@ class Glm46VProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Glm46VProcessor(ProcessorMixin):
+    valid_processor_kwargs = Glm46VProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -62,115 +62,41 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         self.video_start_id = tokenizer.convert_tokens_to_ids("<|begin_of_video|>")
         self.video_end_id = tokenizer.convert_tokens_to_ids("<|end_of_video|>")
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Glm46VProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Glm46VProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-        else:
-            image_inputs = {}
-            image_grid_thw = None
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            # If user has not requested video metadata, pop it
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-            video_grid_thw = videos_inputs["video_grid_thw"]
-        else:
-            videos_inputs = {}
-            video_grid_thw = None
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if image_grid_thw is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if video_grid_thw is not None:
-            merge_length = self.video_processor.merge_size**2
-            video_index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_frames = video_grid_thw[video_index][0]
-                    video_structure = ""
-
-                    metadata = video_metadata[video_index]
-                    if metadata.fps is None:
-                        logger.warning_once(
-                            "SmolVLM requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
-                        )
-                    metadata.fps = 24 if metadata.fps is None else metadata.fps
-                    timestamps = metadata.timestamps[::2]  # mrope
-
-                    unique_timestamps = []
-                    for idx in range(0, len(timestamps)):
-                        unique_timestamps.append(timestamps[idx])
-
-                    selected_timestamps = unique_timestamps[:num_frames]
-                    while len(selected_timestamps) < num_frames:
-                        selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
-
-                    for frame_idx in range(num_frames):
-                        timestamp_sec = selected_timestamps[frame_idx]
-                        frame_structure = self.replace_frame_token_id(timestamp_sec)
-                        video_structure += frame_structure
-
-                    text[i] = text[i].replace(self.video_token, video_structure, 1)
-                    num_image_tokens = (
-                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
-                    )
-                    for frame_idx in range(num_frames):
-                        if self.image_token in text[i]:
-                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-
-                    video_index += 1
-
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_frames = video_inputs["video_grid_thw"][video_idx][0]
+        num_image_tokens = video_inputs["video_grid_thw"][video_idx].prod() // merge_length // num_frames
+        metadata = video_inputs["video_metadata"][video_idx]
+        video_structure = ""
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "GLM46V requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
+        timestamps = metadata.timestamps[::2]  # mrope
+
+        unique_timestamps = []
+        for idx in range(0, len(timestamps)):
+            unique_timestamps.append(timestamps[idx])
+
+        selected_timestamps = unique_timestamps[:num_frames]
+        while len(selected_timestamps) < num_frames:
+            selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
+        for frame_idx in range(num_frames):
+            timestamp_sec = selected_timestamps[frame_idx]
+            frame_structure = self.replace_frame_token_id(timestamp_sec, num_image_tokens=num_image_tokens)
+            video_structure += frame_structure
+
+        return video_structure
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
@@ -239,9 +165,7 @@ def post_process_image_text_to_text(
 
     @property
     def model_input_names(self):
-        model_input_names = super().model_input_names
-        model_input_names.append("mm_token_type_ids")
-        return model_input_names
+        return super().model_input_names + ["mm_token_type_ids"]
 
     def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
         # We have to iterate for each list separately because inputs
@@ -264,8 +188,8 @@ def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
             mm_token_type_ids.append(mm_token_types.tolist())
         return mm_token_type_ids
 
-    def replace_frame_token_id(self, timestamp_sec):
-        return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec:.1f} seconds"
+    def replace_frame_token_id(self, timestamp_sec, num_image_tokens: int = 1):
+        return f"<|begin_of_image|>{self.image_token * num_image_tokens}<|end_of_image|>{timestamp_sec:.1f} seconds"
 
 
 __all__ = ["Glm46VProcessor"]
diff --git a/src/transformers/models/glm4_moe/configuration_glm4_moe.py b/src/transformers/models/glm4_moe/configuration_glm4_moe.py
index a18123e90b33..d6df747d024e 100644
--- a/src/transformers/models/glm4_moe/configuration_glm4_moe.py
+++ b/src/transformers/models/glm4_moe/configuration_glm4_moe.py
@@ -33,6 +33,11 @@ class Glm4MoeConfig(PreTrainedConfig):
     first_k_dense_replace (`int`, *optional*, defaults to 1):
         Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                         \--k dense layers--/
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
 
     Example:
 
@@ -101,6 +106,7 @@ class Glm4MoeConfig(PreTrainedConfig):
     topk_group: int = 1
     first_k_dense_replace: int = 1
     norm_topk_prob: bool = True
+    num_nextn_predict_layers: int = 0
     use_qk_norm: bool = False
     bos_token_id: int | None = None
     eos_token_id: int | list[int] | None = None
diff --git a/src/transformers/models/glm4_moe/modeling_glm4_moe.py b/src/transformers/models/glm4_moe/modeling_glm4_moe.py
index cc5a564ab86f..7e54e3abbea4 100644
--- a/src/transformers/models/glm4_moe/modeling_glm4_moe.py
+++ b/src/transformers/models/glm4_moe/modeling_glm4_moe.py
@@ -102,7 +102,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -487,6 +487,9 @@ class Glm4MoePreTrainedModel(PreTrainedModel):
         "attentions": Glm4MoeAttention,
     }
     _keep_in_fp32_modules_strict = ["e_score_correction_bias"]
+    # MTP weights live at `model.layers.{num_hidden_layers}.*` (layer 46 for GLM-4.5-Air,
+    # layer 92 for the larger GLM-4.5 variant). They are loaded into `MTPCandidateGenerator`
+    # and ignored here on the main model.
     _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]
 
     @torch.no_grad()
diff --git a/src/transformers/models/glm4_moe/modular_glm4_moe.py b/src/transformers/models/glm4_moe/modular_glm4_moe.py
index 868018d744b5..afe4d0b3a1fb 100644
--- a/src/transformers/models/glm4_moe/modular_glm4_moe.py
+++ b/src/transformers/models/glm4_moe/modular_glm4_moe.py
@@ -46,6 +46,11 @@ class Glm4MoeConfig(PreTrainedConfig):
     first_k_dense_replace (`int`, *optional*, defaults to 1):
         Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                         \--k dense layers--/
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
 
     Example:
 
@@ -114,6 +119,7 @@ class Glm4MoeConfig(PreTrainedConfig):
     topk_group: int = 1
     first_k_dense_replace: int = 1
     norm_topk_prob: bool = True
+    num_nextn_predict_layers: int = 0
     use_qk_norm: bool = False
     bos_token_id: int | None = None
     eos_token_id: int | list[int] | None = None
@@ -184,6 +190,9 @@ class Glm4MoeDecoderLayer(DeepseekV3DecoderLayer):
 
 
 class Glm4MoePreTrainedModel(DeepseekV3PreTrainedModel):
+    # MTP weights live at `model.layers.{num_hidden_layers}.*` (layer 46 for GLM-4.5-Air,
+    # layer 92 for the larger GLM-4.5 variant). They are loaded into `MTPCandidateGenerator`
+    # and ignored here on the main model.
     _keys_to_ignore_on_load_unexpected = [r"model\.layers\.92.*", r"model\.layers\.46.*"]
 
 
diff --git a/src/transformers/models/glm4_moe_lite/modeling_glm4_moe_lite.py b/src/transformers/models/glm4_moe_lite/modeling_glm4_moe_lite.py
index 0b8ccc865775..153bad424033 100644
--- a/src/transformers/models/glm4_moe_lite/modeling_glm4_moe_lite.py
+++ b/src/transformers/models/glm4_moe_lite/modeling_glm4_moe_lite.py
@@ -249,7 +249,7 @@ def __init__(self, config: Glm4MoeLiteConfig, layer_idx: int):
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = Glm4MoeLiteRMSNorm(config.q_lora_rank)
+            self.q_a_layernorm = Glm4MoeLiteRMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
@@ -257,7 +257,7 @@ def __init__(self, config: Glm4MoeLiteConfig, layer_idx: int):
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = Glm4MoeLiteRMSNorm(self.kv_lora_rank)
+        self.kv_a_layernorm = Glm4MoeLiteRMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
         self.kv_b_proj = nn.Linear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
diff --git a/src/transformers/models/glm4v/modeling_glm4v.py b/src/transformers/models/glm4v/modeling_glm4v.py
index 6121dc8d3fe8..184ded713698 100644
--- a/src/transformers/models/glm4v/modeling_glm4v.py
+++ b/src/transformers/models/glm4v/modeling_glm4v.py
@@ -645,12 +645,12 @@ def forward(
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Glm4vModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1176,18 +1176,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1310,12 +1310,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Glm4v causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Glm4vCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/glm4v/modular_glm4v.py b/src/transformers/models/glm4v/modular_glm4v.py
index d4a34a1952ad..a18df73232a1 100644
--- a/src/transformers/models/glm4v/modular_glm4v.py
+++ b/src/transformers/models/glm4v/modular_glm4v.py
@@ -24,8 +24,6 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
 from ...configuration_utils import PreTrainedConfig
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
 from ...masking_utils import create_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_layers import GradientCheckpointingLayer
@@ -33,7 +31,6 @@
 from ...modeling_rope_utils import RopeParameters
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import (
     TransformersKwargs,
     auto_docstring,
@@ -43,7 +40,6 @@
 )
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
-from ...video_utils import VideoInput
 from ..glm4.modeling_glm4 import Glm4MLP, Glm4RMSNorm, Glm4RotaryEmbedding, eager_attention_forward
 from ..qwen2_5_vl.modeling_qwen2_5_vl import (
     Qwen2_5_VisionPatchEmbed,
@@ -861,18 +857,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1193,114 +1189,36 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         self.video_start_id = tokenizer.convert_tokens_to_ids("<|begin_of_video|>")
         self.video_end_id = tokenizer.convert_tokens_to_ids("<|end_of_video|>")
 
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Glm4vProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Glm4vProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-        else:
-            image_inputs = {}
-            image_grid_thw = None
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            # If user has not requested video metadata, pop it
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-            video_grid_thw = videos_inputs["video_grid_thw"]
-        else:
-            videos_inputs = {}
-            video_grid_thw = None
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if image_grid_thw is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_frames = video_inputs["video_grid_thw"][video_idx][0]
+        num_image_tokens = video_inputs["video_grid_thw"][video_idx].prod() // merge_length // num_frames
+        metadata = video_inputs["video_metadata"][video_idx]
+        video_structure = ""
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "GLM4V requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
+        timestamps = metadata.timestamps[::2]  # mrope
 
-        if video_grid_thw is not None:
-            merge_length = self.video_processor.merge_size**2
-            video_index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_frames = video_grid_thw[video_index][0]
-                    video_structure = ""
-
-                    metadata = video_metadata[video_index]
-                    if metadata.fps is None:
-                        logger.warning_once(
-                            "SmolVLM requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
-                        )
-                    metadata.fps = 24 if metadata.fps is None else metadata.fps
-                    timestamps = metadata.timestamps[::2]  # mrope
-
-                    unique_timestamps = []
-                    for idx in range(0, len(timestamps)):
-                        unique_timestamps.append(timestamps[idx])
-
-                    selected_timestamps = unique_timestamps[:num_frames]
-                    while len(selected_timestamps) < num_frames:
-                        selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
-
-                    for frame_idx in range(num_frames):
-                        timestamp_sec = selected_timestamps[frame_idx]
-                        frame_structure = self.replace_frame_token_id(timestamp_sec)
-                        video_structure += frame_structure
-
-                    text[i] = text[i].replace(self.video_token, video_structure, 1)
-                    num_image_tokens = (
-                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
-                    )
-                    for frame_idx in range(num_frames):
-                        if self.image_token in text[i]:
-                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-
-                    video_index += 1
-
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+        unique_timestamps = []
+        for idx in range(0, len(timestamps)):
+            unique_timestamps.append(timestamps[idx])
+
+        selected_timestamps = unique_timestamps[:num_frames]
+        while len(selected_timestamps) < num_frames:
+            selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
+        for frame_idx in range(num_frames):
+            timestamp_sec = selected_timestamps[frame_idx]
+            frame_structure = self.replace_frame_token_id(timestamp_sec, num_image_tokens=num_image_tokens)
+            video_structure += frame_structure
+
+        return video_structure
 
     def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
         # We have to iterate for each list separately because inputs
@@ -1323,8 +1241,8 @@ def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
             mm_token_type_ids.append(mm_token_types.tolist())
         return mm_token_type_ids
 
-    def replace_frame_token_id(self, timestamp_sec):
-        return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{int(timestamp_sec)}"
+    def replace_frame_token_id(self, timestamp_sec, num_image_tokens: int = 1):
+        return f"<|begin_of_image|>{self.image_token * num_image_tokens}<|end_of_image|>{int(timestamp_sec)}"
 
 
 __all__ = [
diff --git a/src/transformers/models/glm4v/processing_glm4v.py b/src/transformers/models/glm4v/processing_glm4v.py
index 2d3e93aec9ed..634d427f0739 100644
--- a/src/transformers/models/glm4v/processing_glm4v.py
+++ b/src/transformers/models/glm4v/processing_glm4v.py
@@ -20,18 +20,16 @@
 
 import numpy as np
 
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from .image_processing_glm4v import Glm4vImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Glm4vProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Glm4vImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -44,6 +42,8 @@ class Glm4vProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Glm4vProcessor(ProcessorMixin):
+    valid_processor_kwargs = Glm4vProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -61,115 +61,41 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         self.video_start_id = tokenizer.convert_tokens_to_ids("<|begin_of_video|>")
         self.video_end_id = tokenizer.convert_tokens_to_ids("<|end_of_video|>")
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Glm4vProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Glm4vProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-        else:
-            image_inputs = {}
-            image_grid_thw = None
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            # If user has not requested video metadata, pop it
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-            video_grid_thw = videos_inputs["video_grid_thw"]
-        else:
-            videos_inputs = {}
-            video_grid_thw = None
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if image_grid_thw is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if video_grid_thw is not None:
-            merge_length = self.video_processor.merge_size**2
-            video_index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_frames = video_grid_thw[video_index][0]
-                    video_structure = ""
-
-                    metadata = video_metadata[video_index]
-                    if metadata.fps is None:
-                        logger.warning_once(
-                            "SmolVLM requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
-                        )
-                    metadata.fps = 24 if metadata.fps is None else metadata.fps
-                    timestamps = metadata.timestamps[::2]  # mrope
-
-                    unique_timestamps = []
-                    for idx in range(0, len(timestamps)):
-                        unique_timestamps.append(timestamps[idx])
-
-                    selected_timestamps = unique_timestamps[:num_frames]
-                    while len(selected_timestamps) < num_frames:
-                        selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
-
-                    for frame_idx in range(num_frames):
-                        timestamp_sec = selected_timestamps[frame_idx]
-                        frame_structure = self.replace_frame_token_id(timestamp_sec)
-                        video_structure += frame_structure
-
-                    text[i] = text[i].replace(self.video_token, video_structure, 1)
-                    num_image_tokens = (
-                        video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0]
-                    )
-                    for frame_idx in range(num_frames):
-                        if self.image_token in text[i]:
-                            text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-
-                    video_index += 1
-
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_frames = video_inputs["video_grid_thw"][video_idx][0]
+        num_image_tokens = video_inputs["video_grid_thw"][video_idx].prod() // merge_length // num_frames
+        metadata = video_inputs["video_metadata"][video_idx]
+        video_structure = ""
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "GLM4V requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
+        timestamps = metadata.timestamps[::2]  # mrope
+
+        unique_timestamps = []
+        for idx in range(0, len(timestamps)):
+            unique_timestamps.append(timestamps[idx])
+
+        selected_timestamps = unique_timestamps[:num_frames]
+        while len(selected_timestamps) < num_frames:
+            selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0)
+
+        for frame_idx in range(num_frames):
+            timestamp_sec = selected_timestamps[frame_idx]
+            frame_structure = self.replace_frame_token_id(timestamp_sec, num_image_tokens=num_image_tokens)
+            video_structure += frame_structure
+
+        return video_structure
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
@@ -238,9 +164,7 @@ def post_process_image_text_to_text(
 
     @property
     def model_input_names(self):
-        model_input_names = super().model_input_names
-        model_input_names.append("mm_token_type_ids")
-        return model_input_names
+        return super().model_input_names + ["mm_token_type_ids"]
 
     def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
         # We have to iterate for each list separately because inputs
@@ -263,8 +187,8 @@ def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
             mm_token_type_ids.append(mm_token_types.tolist())
         return mm_token_type_ids
 
-    def replace_frame_token_id(self, timestamp_sec):
-        return f"<|begin_of_image|>{self.image_token}<|end_of_image|>{int(timestamp_sec)}"
+    def replace_frame_token_id(self, timestamp_sec, num_image_tokens: int = 1):
+        return f"<|begin_of_image|>{self.image_token * num_image_tokens}<|end_of_image|>{int(timestamp_sec)}"
 
 
 __all__ = ["Glm4vProcessor"]
diff --git a/src/transformers/models/glm4v_moe/configuration_glm4v_moe.py b/src/transformers/models/glm4v_moe/configuration_glm4v_moe.py
index 0e4d6a9cb191..3d4be908c7ac 100644
--- a/src/transformers/models/glm4v_moe/configuration_glm4v_moe.py
+++ b/src/transformers/models/glm4v_moe/configuration_glm4v_moe.py
@@ -33,6 +33,11 @@ class Glm4vMoeTextConfig(PreTrainedConfig):
     first_k_dense_replace (`int`, *optional*, defaults to 1):
         Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                                 \--k dense layers--/
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
 
     Example:
 
diff --git a/src/transformers/models/glm4v_moe/modeling_glm4v_moe.py b/src/transformers/models/glm4v_moe/modeling_glm4v_moe.py
index 3bf3dc157d3f..c9de7e4f1b16 100644
--- a/src/transformers/models/glm4v_moe/modeling_glm4v_moe.py
+++ b/src/transformers/models/glm4v_moe/modeling_glm4v_moe.py
@@ -428,12 +428,12 @@ def _init_weights(self, module):
             init.copy_(module.inv_freq, inv_freq)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Glm4vMoe causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Glm4vMoeCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1068,12 +1068,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Glm4vMoeModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1345,18 +1345,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1515,7 +1515,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1523,7 +1523,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -1540,8 +1542,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/glm4v_moe/modular_glm4v_moe.py b/src/transformers/models/glm4v_moe/modular_glm4v_moe.py
index 0929f3797e22..015fcb614514 100644
--- a/src/transformers/models/glm4v_moe/modular_glm4v_moe.py
+++ b/src/transformers/models/glm4v_moe/modular_glm4v_moe.py
@@ -64,6 +64,11 @@ class Glm4vMoeTextConfig(Glm4MoeConfig):
     first_k_dense_replace (`int`, *optional*, defaults to 1):
         Number of dense layers in shallow layers(embed->dense->dense->...->dense->moe->moe...->lm_head).
                                                                 \--k dense layers--/
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
 
     Example:
 
diff --git a/src/transformers/models/glm_image/modeling_glm_image.py b/src/transformers/models/glm_image/modeling_glm_image.py
index 012da8513453..cd8a4c32db31 100644
--- a/src/transformers/models/glm_image/modeling_glm_image.py
+++ b/src/transformers/models/glm_image/modeling_glm_image.py
@@ -434,12 +434,12 @@ def _init_weights(self, module):
         super()._init_weights(module)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class GlmImageModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -510,8 +510,8 @@ def forward(self, hidden_state: torch.Tensor):
         return hidden_state_quant, loss, min_encoding_indices
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class GlmImageVQVAEModelOutput(BaseModelOutputWithPooling):
     r"""
     quantized_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
@@ -1410,12 +1410,12 @@ def get_image_tokens(
         return torch.cat(all_image_toks, dim=0)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for GlmImage causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class GlmImageCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/glm_ocr/modeling_glm_ocr.py b/src/transformers/models/glm_ocr/modeling_glm_ocr.py
index 828a99a705b5..b04243e5ee09 100644
--- a/src/transformers/models/glm_ocr/modeling_glm_ocr.py
+++ b/src/transformers/models/glm_ocr/modeling_glm_ocr.py
@@ -340,12 +340,12 @@ def _init_weights(self, module):
             init.copy_(module.inv_freq, inv_freq)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class GlmOcrModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1092,18 +1092,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1226,12 +1226,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for GlmOcr causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class GlmOcrCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/glmasr/modeling_glmasr.py b/src/transformers/models/glmasr/modeling_glmasr.py
index 9430e8a91018..f2c68e56df71 100644
--- a/src/transformers/models/glmasr/modeling_glmasr.py
+++ b/src/transformers/models/glmasr/modeling_glmasr.py
@@ -30,7 +30,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, is_torch_available
+from ...utils import TransformersKwargs, auto_docstring, is_torch_available, torch_compilable_check
 from ...utils.generic import can_return_tuple, maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..auto import AutoModel, AutoModelForCausalLM
@@ -426,6 +426,30 @@ def get_audio_features(
 
         return audio_outputs
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -478,10 +502,10 @@ def forward(
             audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: CausalLMOutputWithPast = self.language_model(
             inputs_embeds=inputs_embeds,
diff --git a/src/transformers/models/glmasr/modular_glmasr.py b/src/transformers/models/glmasr/modular_glmasr.py
index 2c6085eb3a18..f46c64224b15 100644
--- a/src/transformers/models/glmasr/modular_glmasr.py
+++ b/src/transformers/models/glmasr/modular_glmasr.py
@@ -49,31 +49,8 @@
 class GlmAsrProcessorKwargs(AudioFlamingo3ProcessorKwargs): ...
 
 
+@auto_docstring
 class GlmAsrProcessor(AudioFlamingo3Processor):
-    r"""
-    Constructs an GlmAsr processor which wraps an GlmAsr feature extractor and an GlmAsr
-    tokenizer into a single processor.
-
-    [`GlmAsrProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and
-    [`Qwen2TokenizerFast`]. See the [`~GlmAsrProcessor.__call__`] for more information.
-
-    Args:
-            feature_extractor ([`WhisperFeatureExtractor`]):
-                The feature extractor is a required input.
-            tokenizer ([`Qwen2TokenizerFast`]):
-                The tokenizer is a required input.
-            chat_template (`Optional[str]`, *optional*):
-                The Jinja template to use for formatting the conversation. If not provided, the tokenizer's default chat
-                template will be used.
-            audio_token (`Optional[str]`, *optional*, defaults to `"<|pad|>`"):
-                Special token used to represent audio inputs in the chat template.
-            default_transcription_prompt (`str`, *optional*, defaults to `"Please transcribe this audio into text"`):
-                Default prompt to use for transcription tasks when applying transcription requests.
-            max_audio_len (`int`, *optional*, defaults to 655):
-                Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
-                655 gives approximately 8192 tokens, corresponding to the maximum sequence length of the text model.
-    """
-
     def __init__(
         self,
         feature_extractor,
@@ -83,6 +60,15 @@ def __init__(
         default_transcription_prompt="Please transcribe this audio into text",
         max_audio_len=655,
     ):
+        r"""
+        audio_token (`Optional[str]`, *optional*, defaults to `"<|pad|>`"):
+            Special token used to represent audio inputs in the chat template.
+        default_transcription_prompt (`str`, *optional*, defaults to `"Please transcribe this audio into text"`):
+            Default prompt to use for transcription tasks when applying transcription requests.
+        max_audio_len (`int`, *optional*, defaults to 655):
+            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
+            655 gives approximately 8192 tokens, corresponding to the maximum sequence length of the text model.
+        """
         super().__init__(
             feature_extractor,
             tokenizer,
diff --git a/src/transformers/models/glmasr/processing_glmasr.py b/src/transformers/models/glmasr/processing_glmasr.py
index cfd38e423da2..51c78ec1649b 100644
--- a/src/transformers/models/glmasr/processing_glmasr.py
+++ b/src/transformers/models/glmasr/processing_glmasr.py
@@ -19,15 +19,13 @@
 # limitations under the License.
 
 
-import re
-
 import numpy as np
 
 from ...audio_utils import AudioInput, make_list_of_audio
 from ...feature_extraction_utils import BatchFeature
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import TextInput
-from ...utils import is_torch_available, logging
+from ...utils import auto_docstring, is_torch_available, logging
 
 
 if is_torch_available():
@@ -54,30 +52,9 @@ class GlmAsrProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
+@auto_docstring
 class GlmAsrProcessor(ProcessorMixin):
-    r"""
-    Constructs an GlmAsr processor which wraps an GlmAsr feature extractor and an GlmAsr
-    tokenizer into a single processor.
-
-    [`GlmAsrProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and
-    [`Qwen2TokenizerFast`]. See the [`~GlmAsrProcessor.__call__`] for more information.
-
-    Args:
-            feature_extractor ([`WhisperFeatureExtractor`]):
-                The feature extractor is a required input.
-            tokenizer ([`Qwen2TokenizerFast`]):
-                The tokenizer is a required input.
-            chat_template (`Optional[str]`, *optional*):
-                The Jinja template to use for formatting the conversation. If not provided, the tokenizer's default chat
-                template will be used.
-            audio_token (`Optional[str]`, *optional*, defaults to `"<|pad|>`"):
-                Special token used to represent audio inputs in the chat template.
-            default_transcription_prompt (`str`, *optional*, defaults to `"Please transcribe this audio into text"`):
-                Default prompt to use for transcription tasks when applying transcription requests.
-            max_audio_len (`int`, *optional*, defaults to 655):
-                Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
-                655 gives approximately 8192 tokens, corresponding to the maximum sequence length of the text model.
-    """
+    valid_processor_kwargs = GlmAsrProcessorKwargs
 
     def __init__(
         self,
@@ -88,31 +65,22 @@ def __init__(
         default_transcription_prompt="Please transcribe this audio into text",
         max_audio_len=655,
     ):
+        r"""
+        audio_token (`Optional[str]`, *optional*, defaults to `"<|pad|>`"):
+            Special token used to represent audio inputs in the chat template.
+        default_transcription_prompt (`str`, *optional*, defaults to `"Please transcribe this audio into text"`):
+            Default prompt to use for transcription tasks when applying transcription requests.
+        max_audio_len (`int`, *optional*, defaults to 655):
+            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
+            655 gives approximately 8192 tokens, corresponding to the maximum sequence length of the text model.
+        """
         self.audio_token = audio_token
         self.audio_token_id = tokenizer.convert_tokens_to_ids(audio_token)
         self.default_transcription_prompt = default_transcription_prompt
         self.max_audio_len = max_audio_len
         super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
 
-    def _get_audio_token_length(self, audio_lengths: "torch.Tensor") -> "torch.Tensor":
-        merge_factor = 4
-        for padding, kernel_size, stride in [(1, 3, 1), (1, 3, 2)]:
-            audio_lengths = (audio_lengths + 2 * padding - (kernel_size - 1) - 1) // stride + 1
-
-        num_tokens = (audio_lengths - merge_factor) // merge_factor + 1
-        return num_tokens
-
-    def _expand_audio_tokens(self, text, padding_mask, per_sample_windows):
-        audio_lengths = torch.stack([s.sum() for s in torch.split(padding_mask.sum(-1), per_sample_windows)])
-        audio_tokens_lengths = self._get_audio_token_length(audio_lengths)
-        audio_token_pattern = re.compile(re.escape(self.audio_token))
-        for i, audio_length in enumerate(audio_tokens_lengths):
-            text[i] = audio_token_pattern.sub(self.audio_token * audio_length, text[i])
-        return text
-
-    def _get_audio_tokens_mask(self, input_ids):
-        return input_ids == self.audio_token_id
-
+    @auto_docstring
     def __call__(
         self,
         text: TextInput | list[TextInput],
@@ -121,98 +89,99 @@ def __call__(
         **kwargs: Unpack[GlmAsrProcessorKwargs],
     ) -> BatchFeature:
         r"""
-        Main method to prepare one or several text sequence(s) and audio waveform(s) for the model. This
-        method expands `<sound>` placeholders in the text based on the post-pool frame counts of the
-        audio windows, then tokenizes the provided strings as-is, and extracts log-mel features
-        with [`WhisperFeatureExtractor`]. If `audio` is `None`, no audio processing is performed and
-        the text is tokenized as-is (LM-only behavior).
-
-        Args:
-            text (`str` or `list[str]`):
-                Input sequence or batch of sequences.
-            audio (`np.ndarray` or `list[np.ndarray]`):
-                Input audio or batch of audios as NumPy arrays. If provided, there must be as many `text` inputs as
-                `audio` inputs.
-            output_labels (bool, *optional*, default=False):
-                Whether to return labels for training.
+        output_labels (bool, *optional*, default=False):
+            Whether to return labels for training.
 
         Returns:
             [`BatchFeature`]: A dictionary with tokenized text (`input_ids`, `attention_mask`) and
             audio features (`input_features`, `input_features_mask`).
         """
+        # Force tensor outputs for AudioFlamingo, other types not supported
+        kwargs["return_tensors"] = "pt"
 
-        # Merge defaults with user kwargs
-        call_kwargs = self._merge_kwargs(
-            GlmAsrProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
+        if output_labels:
+            kwargs["return_mm_token_type_ids"] = True
+        model_inputs = super().__call__(audio=audio, text=text, **kwargs)
 
-        text_kwargs = call_kwargs["text_kwargs"]
-        audio_kwargs = call_kwargs["audio_kwargs"]
-        return_tensors = text_kwargs.get("return_tensors")
-        if return_tensors != "pt":
-            raise ValueError(f"{self.__class__.__name__} only supports `return_tensors='pt'`.")
-
-        if isinstance(text, str):
-            text = [text]
-        elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-
-        audio_inputs = {}
-        if audio is not None:
-            audio = make_list_of_audio(audio)
-            if len(text) != len(audio):
-                raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
-
-            # Determine number of chunks per sample, and flatten
-            window_size = int(audio_kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
-            max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
-
-            per_sample_windows: list[int] = []
-            flat_chunks: list[np.ndarray] = []
-
-            for audio_el in audio:
-                n_samples = int(audio_el.shape[0])
-                n_win = max(1, (n_samples + window_size - 1) // window_size)
-                if n_win > max_windows:
-                    logger.warning(
-                        f"Audio duration ({n_samples / audio_kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
-                    )
-                    n_win = max_windows
-                per_sample_windows.append(n_win)
-
-                time_cap = min(n_samples, n_win * window_size)
-                for i in range(n_win):
-                    start = i * window_size
-                    end = min((i + 1) * window_size, time_cap)
-                    flat_chunks.append(audio_el[start:end])
-
-            # Feature extraction
-            audio_inputs = self.feature_extractor(flat_chunks, **audio_kwargs)
-            padding_mask = audio_inputs.pop("attention_mask")
-            audio_inputs["input_features_mask"] = padding_mask
-
-            # Expand audio tokens in text
-            text = self._expand_audio_tokens(text, padding_mask, per_sample_windows)
-
-        # Tokenize
-        text_inputs = self.tokenizer(text, **text_kwargs)
-
-        data = {**text_inputs, **audio_inputs}
         if output_labels:
-            labels = data["input_ids"].clone()
-            labels[self._get_audio_tokens_mask(labels)] = -100
+            labels = model_inputs.pop("mm_token_type_ids")
             labels[labels == self.tokenizer.pad_token_id] = -100
-            data["labels"] = labels
+            model_inputs["labels"] = labels
+        return BatchFeature(data=model_inputs, tensor_type="pt")
+
+    def validate_inputs(
+        self,
+        audio: AudioInput | None = None,
+        text: TextInput | list[TextInput] | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(audio=audio, text=text, **kwargs)
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        if text is not None and audio is not None and len(text) != len(audio):
+            raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
+
+    def _get_audio_token_length(self, audio_lengths: "torch.Tensor") -> "torch.Tensor":
+        merge_factor = 4
+        for padding, kernel_size, stride in [(1, 3, 1), (1, 3, 2)]:
+            audio_lengths = (audio_lengths + 2 * padding - (kernel_size - 1) - 1) // stride + 1
+
+        num_tokens = (audio_lengths - merge_factor) // merge_factor + 1
+        return num_tokens
+
+    def _process_audio(self, audio: AudioInput, **kwargs):
+        # Determine number of chunks per sample, and flatten
+        window_size = int(kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
+        max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
+
+        per_sample_windows: list[int] = []
+        flat_chunks: list[np.ndarray] = []
+        for audio_el in audio:
+            n_samples = int(audio_el.shape[0])
+            n_win = max(1, (n_samples + window_size - 1) // window_size)
+            if n_win > max_windows:
+                logger.warning(
+                    f"Audio duration ({n_samples / kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
+                )
+                n_win = max_windows
+            per_sample_windows.append(n_win)
+
+            time_cap = min(n_samples, n_win * window_size)
+            for i in range(n_win):
+                start = i * window_size
+                end = min((i + 1) * window_size, time_cap)
+                flat_chunks.append(audio_el[start:end])
+
+        audio = self.feature_extractor.fetch_audio(audio)
+        audio_inputs = self.feature_extractor(flat_chunks, **kwargs)
+        audio_inputs["input_features_mask"] = audio_inputs.pop("attention_mask")
+
+        # AudioFlamingo doesn't have its own feature extractor and crops audio into
+        # chunks here. Save the number of tokens based on crops/padding in analogy
+        # with some vision processors
+        audio_lengths = torch.stack(
+            [s.sum() for s in torch.split(audio_inputs["input_features_mask"].sum(-1), per_sample_windows)]
+        )
+        audio_inputs["num_audio_tokens"] = self._get_audio_token_length(audio_lengths)
+
+        audio_replacements = []
+        for idx in range(len(audio)):
+            replacement_text = self.replace_audio_token(audio_inputs, audio_idx=idx)
+            audio_replacements.append(replacement_text)
+
+        return audio_inputs, audio_replacements
+
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        num_audio_tokens = audio_inputs["num_audio_tokens"][audio_idx]
+        return self.audio_token * num_audio_tokens
 
     @property
     def model_input_names(self) -> list[str]:
-        tok_names = self.tokenizer.model_input_names
-        fea_names = self.feature_extractor.model_input_names
-        return list(dict.fromkeys(tok_names + fea_names + ["input_features_mask"]))
+        return super().model_input_names + ["input_features_mask"]
+
+    @property
+    def unused_input_names(self) -> list[str]:
+        "Input names returned always by subprocessors but not used in model's `forward`"
+        return ["num_audio_tokens"]
 
     def apply_transcription_request(
         self,
diff --git a/src/transformers/models/got_ocr2/modeling_got_ocr2.py b/src/transformers/models/got_ocr2/modeling_got_ocr2.py
index ab072a8b1f5f..83099d6105ab 100644
--- a/src/transformers/models/got_ocr2/modeling_got_ocr2.py
+++ b/src/transformers/models/got_ocr2/modeling_got_ocr2.py
@@ -298,13 +298,13 @@ def _init_weights(self, module):
                 init.zeros_(module.pos_embed)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for got_ocr2 vision model's outputs that also contains image embeddings obtained by applying the projection
     layer to the pooler_output.
     """
 )
+@dataclass
 class GotOcr2VisionEncoderOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -476,12 +476,12 @@ def forward(self, vision_embeddings: torch.Tensor) -> torch.Tensor:
         return hidden_state
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for GotOcr2 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class GotOcr2CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -579,9 +579,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/gpt2/configuration_gpt2.py b/src/transformers/models/gpt2/configuration_gpt2.py
index 709e6ca86a48..13323ab3d83c 100644
--- a/src/transformers/models/gpt2/configuration_gpt2.py
+++ b/src/transformers/models/gpt2/configuration_gpt2.py
@@ -81,7 +81,7 @@ class GPT2Config(PreTrainedConfig):
     n_layer: int = 12
     n_head: int = 12
     n_inner: int | None = None
-    activation_function: str = "gelu_new"
+    activation_function: str = "gelu"
     resid_pdrop: float | int = 0.1
     embd_pdrop: float | int = 0.1
     attn_pdrop: float | int = 0.1
diff --git a/src/transformers/models/gpt2/modeling_gpt2.py b/src/transformers/models/gpt2/modeling_gpt2.py
index 7bb2a7cd74af..175aaa557b80 100644
--- a/src/transformers/models/gpt2/modeling_gpt2.py
+++ b/src/transformers/models/gpt2/modeling_gpt2.py
@@ -422,8 +422,8 @@ class GPT2PreTrainedModel(PreTrainedModel):
     _can_compile_fullgraph = True
     _can_record_outputs = {
         "hidden_states": GPT2Block,
-        "attentions": OutputRecorder(GPT2Attention, layer_name=".attn", index=1),
-        "cross_attentions": OutputRecorder(GPT2Attention, layer_name=".crossattention", index=1),
+        "attentions": OutputRecorder(GPT2Attention, layer_name=r"\.attn", index=1),
+        "cross_attentions": OutputRecorder(GPT2Attention, layer_name=r"\.crossattention", index=1),
     }
 
     # No longer used as we directly use our masks instead
@@ -458,12 +458,12 @@ def _init_weights(self, module):
                     init.normal_(p, mean=0.0, std=self.config.initializer_range / math.sqrt(2 * self.config.n_layer))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of models predicting if two sentences are consecutive or not.
     """
 )
+@dataclass
 class GPT2DoubleHeadsModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -703,7 +703,11 @@ def forward(
         hidden_states = transformer_outputs.last_hidden_state
 
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        # Fix for Python 3.13 numerical stability issue: clone weight to avoid NaN/Inf values
+        # caused by tied weights (lm_head.weight is tied to transformer.wte.weight)
+        # See: https://github.com/huggingface/transformers/issues/XXXXX
+        hidden_slice = hidden_states[:, slice_indices, :]
+        logits = nn.functional.linear(hidden_slice, self.lm_head.weight.clone())
 
         loss = None
         if labels is not None:
@@ -824,7 +828,10 @@ def forward(
 
         hidden_states = transformer_outputs.last_hidden_state
 
-        lm_logits = self.lm_head(hidden_states)
+        # Fix for Python 3.13 numerical stability issue: clone weight to avoid NaN/Inf values
+        # caused by tied weights (lm_head.weight is tied to transformer.wte.weight)
+        # See: https://github.com/huggingface/transformers/issues/XXXXX
+        lm_logits = nn.functional.linear(hidden_states, self.lm_head.weight.clone())
         mc_logits = self.multiple_choice_head(hidden_states, mc_token_ids).squeeze(-1)
 
         mc_loss = None
diff --git a/src/transformers/models/gpt_neox/modeling_gpt_neox.py b/src/transformers/models/gpt_neox/modeling_gpt_neox.py
index 10e4b5922add..d227d71120a8 100755
--- a/src/transformers/models/gpt_neox/modeling_gpt_neox.py
+++ b/src/transformers/models/gpt_neox/modeling_gpt_neox.py
@@ -108,7 +108,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py b/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
index e334ce023d67..d92020b0152b 100755
--- a/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
+++ b/src/transformers/models/gpt_neox_japanese/modeling_gpt_neox_japanese.py
@@ -111,7 +111,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/gpt_oss/configuration_gpt_oss.py b/src/transformers/models/gpt_oss/configuration_gpt_oss.py
index 47c029a5bca9..66c993a94fdf 100644
--- a/src/transformers/models/gpt_oss/configuration_gpt_oss.py
+++ b/src/transformers/models/gpt_oss/configuration_gpt_oss.py
@@ -23,9 +23,6 @@
 @strict
 class GptOssConfig(PreTrainedConfig):
     model_type = "gpt_oss"
-    attribute_map = {
-        "num_experts": "num_local_experts",
-    }
     default_theta = 150000.0
     base_model_pp_plan = {
         "embed_tokens": (["input_ids"], ["inputs_embeds"]),
diff --git a/src/transformers/models/gpt_oss/modeling_gpt_oss.py b/src/transformers/models/gpt_oss/modeling_gpt_oss.py
index 55381a7e3c21..81d83963e85d 100644
--- a/src/transformers/models/gpt_oss/modeling_gpt_oss.py
+++ b/src/transformers/models/gpt_oss/modeling_gpt_oss.py
@@ -162,8 +162,8 @@ def __init__(self, config: GptOssConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -475,6 +475,7 @@ def forward(
                 "inputs_embeds": inputs_embeds,
                 "attention_mask": attention_mask,
                 "past_key_values": past_key_values,
+                "position_ids": position_ids,
             }
             causal_mask_mapping = {
                 "full_attention": create_causal_mask(**mask_kwargs),
@@ -537,7 +538,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -545,13 +546,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -562,8 +567,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/granite/modeling_granite.py b/src/transformers/models/granite/modeling_granite.py
index 934345fe6723..cf0b132f1962 100644
--- a/src/transformers/models/granite/modeling_granite.py
+++ b/src/transformers/models/granite/modeling_granite.py
@@ -30,7 +30,7 @@
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...masking_utils import create_causal_mask
-from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@@ -356,7 +356,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -516,4 +516,8 @@ def forward(
         )
 
 
-__all__ = ["GraniteForCausalLM", "GraniteModel", "GranitePreTrainedModel"]
+class GraniteForSequenceClassification(GenericForSequenceClassification, GranitePreTrainedModel):
+    pass
+
+
+__all__ = ["GraniteForCausalLM", "GraniteForSequenceClassification", "GraniteModel", "GranitePreTrainedModel"]
diff --git a/src/transformers/models/granite/modular_granite.py b/src/transformers/models/granite/modular_granite.py
index 3b62306582ee..0acca52f6eb6 100644
--- a/src/transformers/models/granite/modular_granite.py
+++ b/src/transformers/models/granite/modular_granite.py
@@ -18,6 +18,7 @@
 
 from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
+from ...modeling_layers import GenericForSequenceClassification
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
@@ -220,4 +221,8 @@ def forward(
         )
 
 
-__all__ = ["GraniteForCausalLM", "GraniteModel", "GranitePreTrainedModel"]
+class GraniteForSequenceClassification(GenericForSequenceClassification, GranitePreTrainedModel):
+    pass
+
+
+__all__ = ["GraniteForCausalLM", "GraniteForSequenceClassification", "GraniteModel", "GranitePreTrainedModel"]
diff --git a/src/transformers/models/granite4_vision/__init__.py b/src/transformers/models/granite4_vision/__init__.py
new file mode 100644
index 000000000000..113694a1a26c
--- /dev/null
+++ b/src/transformers/models/granite4_vision/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_granite4_vision import *
+    from .modeling_granite4_vision import *
+    from .processing_granite4_vision import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/granite4_vision/configuration_granite4_vision.py b/src/transformers/models/granite4_vision/configuration_granite4_vision.py
new file mode 100644
index 000000000000..3e695ec2fe27
--- /dev/null
+++ b/src/transformers/models/granite4_vision/configuration_granite4_vision.py
@@ -0,0 +1,146 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/granite4_vision/modular_granite4_vision.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_granite4_vision.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Literal
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+# ── Config ──────────────────────────────────────────────────────────────────
+
+
+@strict
+class Granite4VisionTextConfig(PreTrainedConfig):
+    model_type = "granite4_vision_text"
+    base_config_key = "text_config"
+
+
+@auto_docstring(checkpoint="llava-hf/llava-v1.6-mistral-7b-hf")
+@strict
+class Granite4VisionConfig(PreTrainedConfig):
+    r"""
+    downsample_rate (`str`, *optional*):
+        Fractional downsample rate for the Window Q-Former projector, e.g. `"1/4"` or `"3/8"`.
+        The numerator is the query window side, the denominator is the key window side.
+    deepstack_layer_map (`list`, *optional*):
+        List of `[vision_layer_idx, llm_layer_idx]` pairs. Features from each vision encoder layer
+        are projected and injected at the corresponding LLM decoder layer during forward pass.
+    use_spatial_sampling (`bool`, *optional*, defaults to `False`):
+        Whether to enable spatial offset sampling, which creates 4 groups (TL, TR, BL, BR) from
+        a single vision layer, each injected at a different LLM layer.
+    spatial_vision_layer (`int`, *optional*, defaults to `-1`):
+        Index of the vision encoder layer used for spatial sampling.
+    spatial_target_layers (`list`, *optional*, defaults to `[12, 15, 18, 21]`):
+        Target LLM layers for the 4 spatial offset groups.
+    projector_dropout (`float`, *optional*, defaults to `0.1`):
+        Dropout probability in the Window Q-Former projector.
+    qformer_config (`dict` or `Blip2QFormerConfig`, *optional*):
+        Configuration for the Window Q-Former projector. If `None`, defaults are derived from
+        `vision_config.hidden_size`.
+    image_grid_pinpoints (`list`, *optional*):
+        A list of possible resolutions to use for processing high resolution images. Each item in the list should be a
+        tuple or list of the form `(height, width)`.
+    """
+
+    model_type = "granite4_vision"
+    attribute_map = {"image_token_id": "image_token_index"}
+    # LlavaNextConfig.sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig, "qformer_config": AutoConfig}
+
+    vision_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    image_token_index: int = 32000
+    projector_hidden_act: str = "gelu"
+    vision_feature_select_strategy: Literal["default", "full"] = "default"
+    vision_feature_layer: int | list[int] = -2
+    multimodal_projector_bias: bool = True
+    tie_word_embeddings: bool = False
+    image_grid_pinpoints: list | None = None
+    image_seq_length: int = 576
+
+    downsample_rate: str | None = None
+    deepstack_layer_map: list | None = None
+    use_spatial_sampling: bool = False
+    spatial_vision_layer: int = -1
+    spatial_target_layers: list | None = None
+    projector_dropout: float = 0.1
+    qformer_config: dict | PreTrainedConfig | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.deepstack_layer_map is not None:
+            self.deepstack_layer_map = [(int(v), int(l)) for v, l in self.deepstack_layer_map]
+
+        if self.spatial_target_layers is None:
+            self.spatial_target_layers = [12, 15, 18, 21]
+
+        # Must convert qformer_config before super().__post_init__() which triggers
+        # _attn_implementation.setter and expects sub_configs to be config objects, not dicts.
+        from ..blip_2.configuration_blip_2 import Blip2QFormerConfig
+
+        if self.qformer_config is None:
+            self.qformer_config = Blip2QFormerConfig(
+                num_hidden_layers=1,
+                intermediate_size=3072,
+                cross_attention_frequency=1,
+                max_position_embeddings=2048,
+                use_qformer_text_input=False,
+            )
+        elif isinstance(self.qformer_config, dict):
+            self.qformer_config = Blip2QFormerConfig(**self.qformer_config)
+        if isinstance(self.vision_config, dict):
+            self.vision_config["model_type"] = self.vision_config.get("model_type", "clip_vision_model")
+            self.vision_config = CONFIG_MAPPING[self.vision_config["model_type"]](**self.vision_config)
+        elif self.vision_config is None:
+            self.vision_config = CONFIG_MAPPING["clip_vision_model"](
+                intermediate_size=4096,
+                hidden_size=1024,
+                patch_size=14,
+                image_size=336,
+                num_hidden_layers=24,
+                num_attention_heads=16,
+                vocab_size=32000,
+                projection_dim=768,
+            )
+
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "llama")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["llama"]()
+
+        self.image_grid_pinpoints = (
+            self.image_grid_pinpoints
+            if self.image_grid_pinpoints is not None
+            else [[336, 672], [672, 336], [672, 672], [1008, 336], [336, 1008]]
+        )
+
+        super().__post_init__(**kwargs)
+
+        # Set vision-dependent QFormer fields from the resolved vision_config
+        self.qformer_config.hidden_size = self.vision_config.hidden_size
+        self.qformer_config.num_attention_heads = self.vision_config.hidden_size // 64
+        self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
+
+
+__all__ = ["Granite4VisionConfig", "Granite4VisionTextConfig"]
diff --git a/src/transformers/models/granite4_vision/modeling_granite4_vision.py b/src/transformers/models/granite4_vision/modeling_granite4_vision.py
new file mode 100644
index 000000000000..9a19c9e113d7
--- /dev/null
+++ b/src/transformers/models/granite4_vision/modeling_granite4_vision.py
@@ -0,0 +1,1339 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/granite4_vision/modular_granite4_vision.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_granite4_vision.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable
+from dataclasses import dataclass
+from fractions import Fraction
+from typing import Optional
+
+import numpy as np
+import torch
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...image_processing_utils import select_best_resolution
+from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
+from ...utils.generic import maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
+from .configuration_granite4_vision import Granite4VisionConfig, Granite4VisionTextConfig
+
+
+@dataclass
+class Granite4VisionModelOutputWithPast(BaseModelOutputWithPast):
+    """
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`, *optional*):
+            List of `(llm_layer_idx, packed_features)` pairs produced by the deepstack
+            and spatial projectors. Each entry targets one LLM decoder layer; `packed_features`
+            is a per-image list of tensors of shape `(num_image_tokens, hidden_size)`.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+    deepstack_features: list | None = None
+
+
+@dataclass
+class Granite4VisionCausalLMOutputWithPast(ModelOutput):
+    """
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`, *optional*):
+            List of `(llm_layer_idx, packed_features)` pairs. See `Granite4VisionModelOutputWithPast`.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+    deepstack_features: list | None = None
+
+
+@dataclass
+class Granite4VisionImageFeaturesOutput(ModelOutput):
+    """
+    Output of `Granite4VisionModel.get_image_features`.
+
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`):
+            List of `(llm_layer_idx, packed_features)` pairs. Each entry targets one LLM
+            decoder layer; `packed_features` is a per-image list of tensors of shape
+            `(num_image_tokens, hidden_size)`.
+    """
+
+    deepstack_features: list | None = None
+
+
+# ── Downsampling helpers ─────────────────────────────────────────────────────
+
+
+def interpolate_downsample(image_features: torch.Tensor, config) -> torch.Tensor:
+    """Spatial downsampling via area interpolation."""
+    orig_side = config.vision_config.image_size // config.vision_config.patch_size
+    new_side = int(orig_side * Fraction(config.downsample_rate))
+    B, _, C = image_features.size()
+    x = image_features.view(B, orig_side, orig_side, C).permute(0, 3, 1, 2)
+    x = torch.nn.functional.interpolate(x, size=(new_side, new_side), mode="area")
+    return x.permute(0, 2, 3, 1).flatten(1, 2)
+
+
+def spatial_offset_downsample(image_features: torch.Tensor, config, offset: int = 0) -> torch.Tensor:
+    """Sample one position from each 2x2 block; offset selects which corner (0=TL,1=TR,2=BL,3=BR)."""
+    offset_h, offset_w = [(0, 0), (0, 1), (1, 0), (1, 1)][offset]
+    orig_side = config.vision_config.image_size // config.vision_config.patch_size
+    new_side = orig_side // 2
+    B, _, C = image_features.shape
+    x = image_features.reshape(B, orig_side, orig_side, C)
+    x = x.reshape(B, new_side, 2, new_side, 2, C)
+    return x[:, :, offset_h, :, offset_w, :].reshape(B, -1, C)
+
+
+class WindowQFormerDownsampler(nn.Module):
+    """Window-based QFormer downsampler that processes image patches in windows."""
+
+    def __init__(self, config, spatial_offset=None):
+        super().__init__()
+        llm_hidden_size = config.text_config.hidden_size
+        vision_hidden_size = config.vision_config.hidden_size
+
+        from ..blip_2.modeling_blip_2 import Blip2QFormerModel  # trf-ignore: TRF009
+
+        self.dropout = nn.Dropout(config.projector_dropout)
+        self._spatial_offset = spatial_offset
+        self._model_config = config  # needed by downsampler functions
+
+        self.qformer = Blip2QFormerModel(config.qformer_config)
+
+        self.image_side = config.vision_config.image_size // config.vision_config.patch_size
+        q, w = config.downsample_rate.split("/")
+        self.query_side, self.window_side = int(q), int(w)
+        self.query_length = self.query_side**2
+        self.norm = nn.LayerNorm(vision_hidden_size, eps=1e-6)
+        self.query = nn.Parameter(torch.empty(1, self.query_length, vision_hidden_size))
+        self.image_positions = nn.Parameter(torch.empty(1, self.window_side**2, vision_hidden_size))
+        self.out_linear = nn.Linear(vision_hidden_size, llm_hidden_size, bias=True)
+
+    def _windowed_raster(self, x, side, window_size):
+        """(B, side*side, C) raster -> (B*num_win*num_win, window_size*window_size, C)"""
+        batch, _, channels = x.shape
+        num_win = side // window_size
+        return (
+            x.view(batch, side, side, channels)
+            .view(batch, num_win, window_size, num_win, window_size, channels)
+            .transpose(2, 3)
+            .flatten(0, 2)
+            .flatten(1, 2)
+        )
+
+    def _unwindowed_raster(self, x_win, num_win, window_size):
+        """(B*num_win*num_win, window_size*window_size, C) -> (B, (num_win*window_size)^2, C)"""
+        batch_win, _, channels = x_win.shape
+        assert batch_win % (num_win * num_win) == 0
+        batch = batch_win // (num_win * num_win)
+        side = num_win * window_size
+        return (
+            x_win.view(batch, num_win, num_win, window_size, window_size, channels)
+            .transpose(2, 3)
+            .contiguous()
+            .view(batch, side, side, channels)
+            .flatten(1, 2)
+        )
+
+    def forward(self, image_features):
+        B, HW, C = image_features.shape
+        assert self.image_side * self.image_side == HW
+        n = self.image_side // self.window_side
+        image_features = self.norm(image_features)
+        enc = self._windowed_raster(image_features, self.image_side, self.window_side)
+
+        if self._spatial_offset is not None:
+            downsampled = spatial_offset_downsample(image_features, self._model_config, self._spatial_offset)
+        else:
+            downsampled = interpolate_downsample(image_features, self._model_config)
+
+        new_side = n * self.query_side
+        downsampled_w = self._windowed_raster(downsampled, new_side, self.query_side)
+
+        query_embeds = self.query + downsampled_w
+        encoder_embeds = self.dropout(enc + self.image_positions)
+        out_w = self.qformer(
+            query_embeds=query_embeds,
+            encoder_hidden_states=encoder_embeds,
+            return_dict=True,
+        ).last_hidden_state
+
+        out = self._unwindowed_raster(out_w, num_win=n, window_size=self.query_side)
+        out = self.dropout(out)
+        return self.out_linear(out)
+
+
+class Granite4VisionTextRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: Granite4VisionTextConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Granite4VisionTextConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class Granite4VisionPreTrainedModel(PreTrainedModel):
+    config: Granite4VisionConfig
+    base_model_prefix = "model"
+    input_modalities = ("image", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["LlamaDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = True
+    _supports_flex_attn = True
+    _supports_attention_backend = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        std = getattr(self.config, "initializer_range", self.config.get_text_config().initializer_range)
+
+        if isinstance(module, nn.Linear):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Granite4VisionModel):
+            embed_std = 1 / math.sqrt(self.config.text_config.hidden_size)
+            init.normal_(module.image_newline, mean=0.0, std=embed_std)
+        if isinstance(module, Granite4VisionTextRotaryEmbedding):
+            # Non-persistent buffers (inv_freq, original_inv_freq) are replaced with
+            # torch.empty_like() garbage by _move_missing_keys_from_meta_to_device.
+            # Recompute them here so _initialize_missing_keys restores correct values.
+            rope_type = module.config.rope_parameters.get("rope_type", "default")
+            if rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
+            else:
+                rope_init_fn = module.compute_default_rope_parameters
+            inv_freq, attention_scaling = rope_init_fn(module.config, module.inv_freq.device)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)
+            module.attention_scaling = attention_scaling
+        if isinstance(module, WindowQFormerDownsampler):
+            embed_std = 1 / math.sqrt(module.query.shape[-1])
+            init.normal_(module.query, mean=0.0, std=embed_std)
+            init.normal_(module.image_positions, mean=0.0, std=embed_std)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class Granite4VisionTextAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: Granite4VisionTextConfig, layer_idx: int | None = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = config.attention_multiplier
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Granite4VisionTextRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        Granite4VisionTextRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class Granite4VisionTextMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class Granite4VisionTextDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Granite4VisionTextConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = Granite4VisionTextAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = Granite4VisionTextMLP(config)
+        self.input_layernorm = Granite4VisionTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = Granite4VisionTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.residual_multiplier = config.residual_multiplier
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_values (`Cache`, *optional*): cached past key and value projection states
+            position_embeddings (`tuple[torch.FloatTensor, torch.FloatTensor]`, *optional*):
+                Tuple containing the cosine and sine positional embeddings of shape `(batch_size, seq_len, head_dim)`,
+                with `head_dim` being the embedding dimension of each attention head.
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states * self.residual_multiplier
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * self.residual_multiplier
+
+        return hidden_states
+
+
+@auto_docstring
+class Granite4VisionTextModel(Granite4VisionPreTrainedModel):
+    """Granite LLM backbone with deepstack feature injection support."""
+
+    base_model_prefix = "model"
+    _no_split_modules = ["Granite4VisionTextDecoderLayer"]
+
+    def __init__(self, config: Granite4VisionTextConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Granite4VisionTextDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = Granite4VisionTextRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Granite4VisionTextRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.embedding_multiplier = config.embedding_multiplier
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        vision_mask: torch.BoolTensor | None = None,
+        deepstack_features: dict | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        r"""
+        vision_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask marking image token positions. Required when `deepstack_features` is provided.
+        deepstack_features (`dict[int, torch.Tensor]`, *optional*):
+            Mapping from LLM layer index to projected vision features of shape `(num_image_tokens, hidden_size)`.
+            Features are added into image-token positions of hidden states before the corresponding decoder layer.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        inputs_embeds = inputs_embeds * self.embedding_multiplier
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = (
+                torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            ).unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if deepstack_features is not None and layer_idx in deepstack_features:
+                hidden_states = self._deepstack_inject(hidden_states, vision_mask, deepstack_features[layer_idx])
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+            hidden_states = layer_outputs[0] if isinstance(layer_outputs, tuple) else layer_outputs
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return Granite4VisionModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _deepstack_inject(
+        self,
+        hidden_states: torch.Tensor,
+        vision_mask: torch.Tensor,
+        features: torch.Tensor,
+    ) -> torch.Tensor:
+        """Add projected vision features into the image-token positions of hidden_states."""
+        vision_mask = vision_mask.to(hidden_states.device)
+        features = features.to(hidden_states.device, hidden_states.dtype)
+        hidden_states = hidden_states.clone()
+        hidden_states[vision_mask] = hidden_states[vision_mask] + features
+        return hidden_states
+
+
+def get_anyres_image_grid_shape(image_size, grid_pinpoints, patch_size):
+    """
+    Calculate the shape of the image patch grid after the preprocessing for images of any resolution.
+
+    Args:
+        image_size (`tuple`):
+            The size of the input image in the format (width, height).
+        grid_pinpoints (`List`):
+            A list containing possible resolutions. Each item in the list should be a tuple or list
+            of the form `(height, width)`.
+        patch_size (`int`):
+            The size of each image patch.
+
+    Returns:
+        tuple: The shape of the image patch grid in the format (width, height).
+    """
+    if not isinstance(grid_pinpoints, list):
+        raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+    # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+    if not isinstance(image_size, (list, tuple)):
+        if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+            raise TypeError(
+                f"image_size invalid type: {type(image_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+            )
+        image_size = image_size.tolist()
+
+    height, width = select_best_resolution(image_size, grid_pinpoints)
+    return height // patch_size, width // patch_size
+
+
+def image_size_to_num_patches(image_size, grid_pinpoints, patch_size: int):
+    """
+    Calculate the number of patches after the preprocessing for images of any resolution.
+
+    Args:
+        image_size (`torch.LongTensor` or `np.ndarray` or `tuple[int, int]`):
+            The size of the input image in the format (height, width). ?
+        grid_pinpoints (`List`):
+            A list containing possible resolutions. Each item in the list should be a tuple or list
+            of the form `(height, width)`.
+        patch_size (`int`):
+            The size of each image patch.
+
+    Returns:
+        int: the number of patches
+    """
+    if not isinstance(grid_pinpoints, list):
+        raise TypeError("grid_pinpoints should be a list of tuples or lists")
+
+    # ! VERY IMPORTANT if image_size is tensor, must convert to into tuple, otherwise it will cause wrong calculate
+    if not isinstance(image_size, (list, tuple)):
+        if not isinstance(image_size, (torch.Tensor, np.ndarray)):
+            raise TypeError(f"image_size invalid type {type(image_size)} with value {image_size}")
+        image_size = image_size.tolist()
+
+    best_resolution = select_best_resolution(image_size, grid_pinpoints)
+    height, width = best_resolution
+    num_patches = 0
+    # consider change to ceil(height/patch_size)*ceil(width/patch_size) + 1
+    for i in range(0, height, patch_size):
+        for j in range(0, width, patch_size):
+            num_patches += 1
+    # add the base patch
+    num_patches += 1
+    return num_patches
+
+
+def unpad_image(tensor, original_size):
+    """
+    Unpads a PyTorch tensor of a padded and resized image.
+
+    Args:
+        tensor (`torch.Tensor`):
+            The image tensor, assumed to be of shape (num_channels, height, width).
+        original_size (`tuple`):
+            The original size of the image (height, width).
+
+    Returns:
+        `torch.Tensor`: The unpadded image tensor.
+    """
+    if not isinstance(original_size, (list, tuple)):
+        if not isinstance(original_size, (torch.Tensor, np.ndarray)):
+            raise TypeError(
+                f"image_size invalid type: {type(original_size)} not valid, should be either list, tuple, np.ndarray or tensor"
+            )
+        original_size = original_size.tolist()
+    original_height, original_width = original_size
+    current_height, current_width = tensor.shape[1:]
+
+    original_aspect_ratio = original_width / original_height
+    current_aspect_ratio = current_width / current_height
+
+    if original_aspect_ratio > current_aspect_ratio:
+        scale_factor = current_width / original_width
+        new_height = int(round(original_height * scale_factor, 7))
+        padding = (current_height - new_height) // 2
+        unpadded_tensor = tensor[:, padding : current_height - padding, :]
+    else:
+        scale_factor = current_height / original_height
+        new_width = int(round(original_width * scale_factor, 7))
+        padding = (current_width - new_width) // 2
+        unpadded_tensor = tensor[:, :, padding : current_width - padding]
+
+    return unpadded_tensor
+
+
+@auto_docstring(
+    custom_intro="""
+    The Llava-Next model which consists of a vision backbone and a language model without language modeling head.
+    """
+)
+class Granite4VisionModel(Granite4VisionPreTrainedModel):
+    base_model_prefix = "model"
+    config_class = Granite4VisionConfig
+
+    def __init__(self, config: Granite4VisionConfig):
+        super().__init__(config)
+        self.vision_tower = AutoModel.from_config(config.vision_config)
+        embed_std = 1 / math.sqrt(config.text_config.hidden_size)
+        self.image_newline = nn.Parameter(torch.randn(config.text_config.hidden_size, dtype=self.dtype) * embed_std)
+
+        self.vocab_size = config.text_config.vocab_size
+
+        # Replace the inherited LLM backbone with our deepstack-aware subclass
+        self.language_model = Granite4VisionTextModel(config.text_config)
+
+        self.spatial_projectors = None
+        self.downsample_rate = config.downsample_rate
+        self.projector_dropout = config.projector_dropout
+
+        # Deepstack projectors: one per (vision_layer, llm_layer) pair
+        self.layerwise_projectors = nn.ModuleList(
+            [WindowQFormerDownsampler(config) for _ in range(len(config.deepstack_layer_map))]
+        )
+
+        # Spatial sampling projectors: 4 offset groups (TL, TR, BL, BR)
+        if config.use_spatial_sampling:
+            self.spatial_projectors = nn.ModuleList(
+                [WindowQFormerDownsampler(config, spatial_offset=i) for i in range(4)]
+            )
+
+        self.pad_token_id = (
+            self.config.text_config.pad_token_id if self.config.text_config.pad_token_id is not None else -1
+        )
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+        """
+        Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+        Overrides the parent to apply downsample_rate to height/width calculations.
+        """
+        new_image_features = []
+        feature_lens = []
+        for image_idx, image_feature in enumerate(image_features):
+            if image_feature.shape[0] > 1:
+                base_image_feature = image_feature[0]
+                image_feature = image_feature[1:]
+                height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+
+                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                    image_sizes[image_idx],
+                    self.config.image_grid_pinpoints,
+                    self.config.vision_config.image_size,
+                )
+                if self.layerwise_projectors is not None:
+                    ds_rate = Fraction(self.downsample_rate)
+                    height = int(height * ds_rate)
+                    width = int(width * ds_rate)
+
+                if (
+                    np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
+                    and vision_feature_select_strategy == "default"
+                ):
+                    raise ValueError(
+                        "Image feature shape does not line up with the provided patch size. "
+                        "You may be using the `default` vision_feature_select_strategy with a "
+                        "visual encoder that does not have CLS token."
+                    )
+
+                image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+                image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+                image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                image_feature = unpad_image(image_feature, image_sizes[image_idx])
+                if image_newline is not None:
+                    image_feature = torch.cat(
+                        (
+                            image_feature,
+                            image_newline[:, None, None]
+                            .expand(*image_feature.shape[:-1], 1)
+                            .to(image_feature.device, image_feature.dtype),
+                        ),
+                        dim=-1,
+                    )
+                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+            else:
+                image_feature = image_feature[0]
+                if image_newline is not None:
+                    image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+            new_image_features.append(image_feature)
+            feature_lens.append(image_feature.size(0))
+        feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+        return new_image_features, feature_lens
+
+    @merge_with_config_defaults
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
+    )
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_sizes: torch.Tensor,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+    ) -> Granite4VisionImageFeaturesOutput:
+        """
+        Extract image features via deepstack (multi-layer) and spatial sampling projections.
+
+        Runs the vision tower once, then:
+        1. Deepstack: for each (vision_layer, llm_layer) in deepstack_layer_map,
+           extracts features from that vision layer, downsamples via interpolation + QFormer,
+           and pairs them with the target LLM layer.
+        2. Spatial: if enabled, extracts the spatial_vision_layer and creates 4 spatial
+           offset groups (TL, TR, BL, BR), each targeting a different LLM layer.
+        """
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        image_num_patches = [
+            image_size_to_num_patches(
+                image_size=imsize,
+                grid_pinpoints=self.config.image_grid_pinpoints,
+                patch_size=self.config.vision_config.image_size,
+            )
+            for imsize in image_sizes
+        ]
+
+        if pixel_values.dim() == 5:
+            _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+            pixel_values = torch.cat(_pixel_values_list, dim=0)
+        elif pixel_values.dim() != 4:
+            raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+        vision_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
+
+        # Deepstack features: extract from multiple vision layers, downsample via interpolation
+        all_features = []
+        for projection_idx, (vision_layer, llm_layer) in enumerate(self.config.deepstack_layer_map):
+            selected_feature = vision_outputs.hidden_states[vision_layer]
+
+            if vision_feature_select_strategy == "default":
+                selected_feature = selected_feature[:, 1:]
+
+            projected_features = self.layerwise_projectors[projection_idx](selected_feature)
+            projected_features = torch.split(projected_features, image_num_patches, dim=0)
+
+            packed_features, _ = self.pack_image_features(
+                projected_features,
+                image_sizes,
+                vision_feature_select_strategy=vision_feature_select_strategy,
+                image_newline=self.image_newline,
+            )
+
+            all_features.append((llm_layer, packed_features))
+
+        # Spatial features: extract 4 offset groups from a single vision layer
+        if self.config.use_spatial_sampling:
+            spatial_feature = vision_outputs.hidden_states[self.config.spatial_vision_layer]
+
+            if vision_feature_select_strategy == "default":
+                spatial_feature = spatial_feature[:, 1:]
+
+            for group_idx, llm_layer in enumerate(self.config.spatial_target_layers):
+                projected_group = self.spatial_projectors[group_idx](spatial_feature)
+                projected_group_split = torch.split(projected_group, image_num_patches, dim=0)
+
+                packed_group, _ = self.pack_image_features(
+                    projected_group_split,
+                    image_sizes,
+                    vision_feature_select_strategy=vision_feature_select_strategy,
+                    image_newline=self.image_newline,
+                )
+
+                all_features.append((llm_layer, packed_group))
+
+        return Granite4VisionImageFeaturesOutput(deepstack_features=all_features)
+
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, image_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
+        )
+        return special_image_mask
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_sizes: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+        use_cache: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Granite4VisionModelOutputWithPast:
+        r"""
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+            If `"full"`, the full vision features are used.
+        """
+        output_attentions = kwargs.pop("output_attentions", None) or self.config.output_attentions
+        output_hidden_states = kwargs.pop("output_hidden_states", None) or self.config.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        # Build deepstack injection map and scatter initial image embeddings
+        deepstack_features = None
+        vision_mask = None
+        image_features = None
+        if pixel_values is not None and pixel_values.size(0) > 0:
+            image_features = self.get_image_features(
+                pixel_values,
+                image_sizes,
+                vision_feature_layer=vision_feature_layer,
+                vision_feature_select_strategy=vision_feature_select_strategy,
+            )
+
+            deepstack_features = {}
+            for idx, (llm_layer_idx, packed_features) in enumerate(image_features.deepstack_features):
+                concat_features = torch.cat(packed_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+                if idx == 0:
+                    vision_mask_3d = self.get_placeholder_mask(
+                        input_ids, inputs_embeds=inputs_embeds, image_features=concat_features
+                    )
+                    vision_mask = vision_mask_3d[..., 0]
+                    inputs_embeds = inputs_embeds.masked_fill(vision_mask_3d, 0.0)
+                deepstack_features[llm_layer_idx] = concat_features
+
+        outputs = self.language_model(
+            input_ids=None,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            vision_mask=vision_mask,
+            deepstack_features=deepstack_features,
+            **kwargs,
+        )
+
+        return Granite4VisionModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            deepstack_features=image_features.deepstack_features if pixel_values is not None else None,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The LLAVA-NeXT model which consists of a vision backbone and a language model.
+    """
+)
+class Granite4VisionForConditionalGeneration(Granite4VisionPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+    config_class = Granite4VisionConfig
+
+    def __init__(self, config: Granite4VisionConfig):
+        super().__init__(config)
+        self.model = Granite4VisionModel(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+        return self.model.pack_image_features(
+            image_features=image_features,
+            image_sizes=image_sizes,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            image_newline=image_newline,
+        )
+
+    @merge_with_config_defaults
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_sizes: torch.Tensor,
+        vision_feature_layer: int | list[int] | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
+            The tensors corresponding to the input images.
+        image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
+            Actual image size of each images (H, W).
+        vision_feature_layer (`Union[int, list[int]]`, *optional*):
+            The index of the layer to select the vision feature. If multiple indices are provided,
+            the vision feature of the corresponding indices will be concatenated to form the
+            vision features.
+        vision_feature_select_strategy (`str`, *optional*):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_sizes: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Granite4VisionCausalLMOutputWithPast:
+        r"""
+        vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Can be one of `"default"` or `"full"`. If `"default"`, the CLS token is removed from the vision features.
+            If `"full"`, the full vision features are used.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Example:
+
+        ```python
+        >>> from PIL import Image
+        >>> import httpx
+        >>> from io import BytesIO
+        >>> from transformers import AutoProcessor, Granite4VisionForConditionalGeneration
+
+        >>> model = Granite4VisionForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+        >>> processor = AutoProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
+
+        >>> prompt = "[INST] <image>\nWhat is shown in this image? [/INST]"
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> with httpx.stream("GET", url) as response:
+        ...     image = Image.open(BytesIO(response.read()))
+
+        >>> inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(**inputs, max_length=30)
+        >>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "[INST]  \nWhat is shown in this image? [/INST] The image appears to be a radar chart, which is a type of multi-dimensional plot (...)"
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        outputs = self.model(
+            input_ids,
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+
+        loss = None
+        logits = self.lm_head(hidden_states)
+        logits = logits / self.config.text_config.logits_scaling
+        if labels is not None:
+            loss = self.loss_function(
+                logits,
+                labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        if isinstance(logits_to_keep, int) and logits_to_keep > 0:
+            logits = logits[:, -logits_to_keep:, :]
+
+        return Granite4VisionCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            deepstack_features=outputs.deepstack_features,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        image_sizes=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        **kwargs,
+    ):
+        is_first = kwargs.get("is_first_iteration", False)
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+        model_inputs = self._init_hybrid_cache(**model_inputs)
+        if is_first:
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["image_sizes"] = image_sizes
+
+        return model_inputs
+
+    def _init_hybrid_cache(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        """Handle HybridMambaAttentionDynamicCache for GraniteMoeHybrid language model."""
+        empty_past_kv = past_key_values is None or (
+            isinstance(past_key_values, DynamicCache) and past_key_values.get_seq_length() == 0
+        )
+
+        if use_cache and empty_past_kv:
+            past_key_values = DynamicCache(config=self.model.language_model.config)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if not empty_past_kv and input_ids is not None:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        if inputs_embeds is not None and (input_ids is None or empty_past_kv):
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+
+        for key, value in kwargs.items():
+            if key not in model_inputs:
+                model_inputs[key] = value
+
+        return model_inputs
+
+
+__all__ = [
+    "Granite4VisionPreTrainedModel",
+    "Granite4VisionTextModel",
+    "Granite4VisionModel",
+    "Granite4VisionForConditionalGeneration",
+]
diff --git a/src/transformers/models/granite4_vision/modular_granite4_vision.py b/src/transformers/models/granite4_vision/modular_granite4_vision.py
new file mode 100644
index 000000000000..8fb9174de84c
--- /dev/null
+++ b/src/transformers/models/granite4_vision/modular_granite4_vision.py
@@ -0,0 +1,895 @@
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+from fractions import Fraction
+
+import numpy as np
+import torch
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ... import initialization as init
+from ...cache_utils import Cache, DynamicCache
+from ...configuration_utils import PreTrainedConfig
+from ...image_processing_utils import select_best_resolution
+from ...masking_utils import create_causal_mask
+from ...modeling_outputs import ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, can_return_tuple
+from ..auto import AutoConfig
+from ..granite.modeling_granite import GraniteModel, GraniteRotaryEmbedding
+from ..llava_next.configuration_llava_next import LlavaNextConfig
+from ..llava_next.modeling_llava_next import (
+    LlavaNextCausalLMOutputWithPast,
+    LlavaNextForConditionalGeneration,
+    LlavaNextModel,
+    LlavaNextModelOutputWithPast,
+    LlavaNextPreTrainedModel,
+    get_anyres_image_grid_shape,
+    image_size_to_num_patches,
+    unpad_image,
+)
+from ..llava_next.processing_llava_next import LlavaNextProcessor
+
+
+# ── Output classes ──────────────────────────────────────────────────────────
+
+
+@dataclass
+class Granite4VisionModelOutputWithPast(LlavaNextModelOutputWithPast):
+    """
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`, *optional*):
+            List of `(llm_layer_idx, packed_features)` pairs produced by the deepstack
+            and spatial projectors. Each entry targets one LLM decoder layer; `packed_features`
+            is a per-image list of tensors of shape `(num_image_tokens, hidden_size)`.
+    """
+
+    deepstack_features: list | None = None
+
+
+@dataclass
+class Granite4VisionCausalLMOutputWithPast(LlavaNextCausalLMOutputWithPast):
+    """
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`, *optional*):
+            List of `(llm_layer_idx, packed_features)` pairs. See `Granite4VisionModelOutputWithPast`.
+    """
+
+    deepstack_features: list | None = None
+
+
+@dataclass
+class Granite4VisionImageFeaturesOutput(ModelOutput):
+    """
+    Output of `Granite4VisionModel.get_image_features`.
+
+    Args:
+        deepstack_features (`list[tuple[int, list[torch.Tensor]]]`):
+            List of `(llm_layer_idx, packed_features)` pairs. Each entry targets one LLM
+            decoder layer; `packed_features` is a per-image list of tensors of shape
+            `(num_image_tokens, hidden_size)`.
+    """
+
+    deepstack_features: list | None = None
+
+
+# ── Config ──────────────────────────────────────────────────────────────────
+
+
+@strict
+class Granite4VisionTextConfig(PreTrainedConfig):
+    model_type = "granite4_vision_text"
+    base_config_key = "text_config"
+
+
+class Granite4VisionConfig(LlavaNextConfig):
+    r"""
+    downsample_rate (`str`, *optional*):
+        Fractional downsample rate for the Window Q-Former projector, e.g. `"1/4"` or `"3/8"`.
+        The numerator is the query window side, the denominator is the key window side.
+    deepstack_layer_map (`list`, *optional*):
+        List of `[vision_layer_idx, llm_layer_idx]` pairs. Features from each vision encoder layer
+        are projected and injected at the corresponding LLM decoder layer during forward pass.
+    use_spatial_sampling (`bool`, *optional*, defaults to `False`):
+        Whether to enable spatial offset sampling, which creates 4 groups (TL, TR, BL, BR) from
+        a single vision layer, each injected at a different LLM layer.
+    spatial_vision_layer (`int`, *optional*, defaults to `-1`):
+        Index of the vision encoder layer used for spatial sampling.
+    spatial_target_layers (`list`, *optional*, defaults to `[12, 15, 18, 21]`):
+        Target LLM layers for the 4 spatial offset groups.
+    projector_dropout (`float`, *optional*, defaults to `0.1`):
+        Dropout probability in the Window Q-Former projector.
+    qformer_config (`dict` or `Blip2QFormerConfig`, *optional*):
+        Configuration for the Window Q-Former projector. If `None`, defaults are derived from
+        `vision_config.hidden_size`.
+    image_grid_pinpoints (`list`, *optional*):
+        A list of possible resolutions to use for processing high resolution images. Each item in the list should be a
+        tuple or list of the form `(height, width)`.
+    """
+
+    model_type = "granite4_vision"
+    # LlavaNextConfig.sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig}
+    sub_configs = {"text_config": AutoConfig, "vision_config": AutoConfig, "qformer_config": AutoConfig}
+
+    downsample_rate: str | None = None
+    deepstack_layer_map: list | None = None
+    use_spatial_sampling: bool = False
+    spatial_vision_layer: int = -1
+    spatial_target_layers: list | None = None
+    projector_dropout: float = 0.1
+    qformer_config: dict | PreTrainedConfig | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.deepstack_layer_map is not None:
+            self.deepstack_layer_map = [(int(v), int(l)) for v, l in self.deepstack_layer_map]
+
+        if self.spatial_target_layers is None:
+            self.spatial_target_layers = [12, 15, 18, 21]
+
+        # Must convert qformer_config before super().__post_init__() which triggers
+        # _attn_implementation.setter and expects sub_configs to be config objects, not dicts.
+        from ..blip_2.configuration_blip_2 import Blip2QFormerConfig
+
+        if self.qformer_config is None:
+            self.qformer_config = Blip2QFormerConfig(
+                num_hidden_layers=1,
+                intermediate_size=3072,
+                cross_attention_frequency=1,
+                max_position_embeddings=2048,
+                use_qformer_text_input=False,
+            )
+        elif isinstance(self.qformer_config, dict):
+            self.qformer_config = Blip2QFormerConfig(**self.qformer_config)
+
+        super().__post_init__(**kwargs)
+
+        # Set vision-dependent QFormer fields from the resolved vision_config
+        self.qformer_config.hidden_size = self.vision_config.hidden_size
+        self.qformer_config.num_attention_heads = self.vision_config.hidden_size // 64
+        self.qformer_config.encoder_hidden_size = self.vision_config.hidden_size
+
+
+# ── Processor ───────────────────────────────────────────────────────────────
+
+
+class Granite4VisionProcessor(LlavaNextProcessor):
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        patch_size=None,
+        vision_feature_select_strategy=None,
+        chat_template=None,
+        image_token="<image>",
+        num_additional_image_tokens=0,
+        downsample_rate=None,
+        **kwargs,
+    ):
+        r"""
+        patch_size (`int`, *optional*):
+            Patch size from the vision tower.
+        vision_feature_select_strategy (`str`, *optional*):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Should be same as in model's config.
+        image_token (`str`, *optional*, defaults to `"<image>"`):
+            Special token used to denote image location.
+        num_additional_image_tokens (`int`, *optional*, defaults to `0`):
+            Number of additional tokens added to the image embeddings, such as CLS (+1).
+        downsample_rate (`str`, *optional*):
+            Fractional downsample rate (e.g. `"1/4"`), used to adjust the number of image tokens
+            when computing token counts for padding/truncation.
+        """
+        super().__init__(
+            image_processor=image_processor,
+            tokenizer=tokenizer,
+            patch_size=patch_size,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            chat_template=chat_template,
+            image_token=image_token,
+            num_additional_image_tokens=num_additional_image_tokens,
+        )
+        self.downsample_rate = downsample_rate
+
+    def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
+        image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+        height_best_resolution, width_best_resolution = select_best_resolution(
+            [orig_height, orig_width], image_grid_pinpoints
+        )
+        scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
+
+        patches_height = height // self.patch_size
+        patches_width = width // self.patch_size
+        if self.downsample_rate is not None:
+            ds_rate = Fraction(self.downsample_rate)
+            patches_height = int(patches_height * ds_rate)
+            patches_width = int(patches_width * ds_rate)
+
+        unpadded_features, newline_features = self._get_unpadded_features(
+            orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
+        )
+        base_features = patches_height * patches_width + self.num_additional_image_tokens
+        num_image_tokens = unpadded_features + newline_features + base_features
+        return num_image_tokens
+
+
+# ── Downsampling helpers ─────────────────────────────────────────────────────
+
+
+def interpolate_downsample(image_features: torch.Tensor, config) -> torch.Tensor:
+    """Spatial downsampling via area interpolation."""
+    orig_side = config.vision_config.image_size // config.vision_config.patch_size
+    new_side = int(orig_side * Fraction(config.downsample_rate))
+    B, _, C = image_features.size()
+    x = image_features.view(B, orig_side, orig_side, C).permute(0, 3, 1, 2)
+    x = torch.nn.functional.interpolate(x, size=(new_side, new_side), mode="area")
+    return x.permute(0, 2, 3, 1).flatten(1, 2)
+
+
+def spatial_offset_downsample(image_features: torch.Tensor, config, offset: int = 0) -> torch.Tensor:
+    """Sample one position from each 2x2 block; offset selects which corner (0=TL,1=TR,2=BL,3=BR)."""
+    offset_h, offset_w = [(0, 0), (0, 1), (1, 0), (1, 1)][offset]
+    orig_side = config.vision_config.image_size // config.vision_config.patch_size
+    new_side = orig_side // 2
+    B, _, C = image_features.shape
+    x = image_features.reshape(B, orig_side, orig_side, C)
+    x = x.reshape(B, new_side, 2, new_side, 2, C)
+    return x[:, :, offset_h, :, offset_w, :].reshape(B, -1, C)
+
+
+class WindowQFormerDownsampler(nn.Module):
+    """Window-based QFormer downsampler that processes image patches in windows."""
+
+    def __init__(self, config, spatial_offset=None):
+        super().__init__()
+        llm_hidden_size = config.text_config.hidden_size
+        vision_hidden_size = config.vision_config.hidden_size
+
+        from ..blip_2.modeling_blip_2 import Blip2QFormerModel  # trf-ignore: TRF009
+
+        self.dropout = nn.Dropout(config.projector_dropout)
+        self._spatial_offset = spatial_offset
+        self._model_config = config  # needed by downsampler functions
+
+        self.qformer = Blip2QFormerModel(config.qformer_config)
+
+        self.image_side = config.vision_config.image_size // config.vision_config.patch_size
+        q, w = config.downsample_rate.split("/")
+        self.query_side, self.window_side = int(q), int(w)
+        self.query_length = self.query_side**2
+        self.norm = nn.LayerNorm(vision_hidden_size, eps=1e-6)
+        self.query = nn.Parameter(torch.empty(1, self.query_length, vision_hidden_size))
+        self.image_positions = nn.Parameter(torch.empty(1, self.window_side**2, vision_hidden_size))
+        self.out_linear = nn.Linear(vision_hidden_size, llm_hidden_size, bias=True)
+
+    def _windowed_raster(self, x, side, window_size):
+        """(B, side*side, C) raster -> (B*num_win*num_win, window_size*window_size, C)"""
+        batch, _, channels = x.shape
+        num_win = side // window_size
+        return (
+            x.view(batch, side, side, channels)
+            .view(batch, num_win, window_size, num_win, window_size, channels)
+            .transpose(2, 3)
+            .flatten(0, 2)
+            .flatten(1, 2)
+        )
+
+    def _unwindowed_raster(self, x_win, num_win, window_size):
+        """(B*num_win*num_win, window_size*window_size, C) -> (B, (num_win*window_size)^2, C)"""
+        batch_win, _, channels = x_win.shape
+        assert batch_win % (num_win * num_win) == 0
+        batch = batch_win // (num_win * num_win)
+        side = num_win * window_size
+        return (
+            x_win.view(batch, num_win, num_win, window_size, window_size, channels)
+            .transpose(2, 3)
+            .contiguous()
+            .view(batch, side, side, channels)
+            .flatten(1, 2)
+        )
+
+    def forward(self, image_features):
+        B, HW, C = image_features.shape
+        assert self.image_side * self.image_side == HW
+        n = self.image_side // self.window_side
+        image_features = self.norm(image_features)
+        enc = self._windowed_raster(image_features, self.image_side, self.window_side)
+
+        if self._spatial_offset is not None:
+            downsampled = spatial_offset_downsample(image_features, self._model_config, self._spatial_offset)
+        else:
+            downsampled = interpolate_downsample(image_features, self._model_config)
+
+        new_side = n * self.query_side
+        downsampled_w = self._windowed_raster(downsampled, new_side, self.query_side)
+
+        query_embeds = self.query + downsampled_w
+        encoder_embeds = self.dropout(enc + self.image_positions)
+        out_w = self.qformer(
+            query_embeds=query_embeds,
+            encoder_hidden_states=encoder_embeds,
+            return_dict=True,
+        ).last_hidden_state
+
+        out = self._unwindowed_raster(out_w, num_win=n, window_size=self.query_side)
+        out = self.dropout(out)
+        return self.out_linear(out)
+
+
+# ── Model ───────────────────────────────────────────────────────────────────
+
+
+class Granite4VisionTextRotaryEmbedding(GraniteRotaryEmbedding):
+    pass
+
+
+class Granite4VisionPreTrainedModel(LlavaNextPreTrainedModel):
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, Granite4VisionTextRotaryEmbedding):
+            # Non-persistent buffers (inv_freq, original_inv_freq) are replaced with
+            # torch.empty_like() garbage by _move_missing_keys_from_meta_to_device.
+            # Recompute them here so _initialize_missing_keys restores correct values.
+            rope_type = module.config.rope_parameters.get("rope_type", "default")
+            if rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[rope_type]
+            else:
+                rope_init_fn = module.compute_default_rope_parameters
+            inv_freq, attention_scaling = rope_init_fn(module.config, module.inv_freq.device)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)
+            module.attention_scaling = attention_scaling
+        if isinstance(module, WindowQFormerDownsampler):
+            embed_std = 1 / math.sqrt(module.query.shape[-1])
+            init.normal_(module.query, mean=0.0, std=embed_std)
+            init.normal_(module.image_positions, mean=0.0, std=embed_std)
+
+    pass
+
+
+class Granite4VisionTextModel(Granite4VisionPreTrainedModel, GraniteModel):
+    """Granite LLM backbone with deepstack feature injection support."""
+
+    base_model_prefix = "model"
+    _no_split_modules = ["Granite4VisionTextDecoderLayer"]
+
+    def __init__(self, config: Granite4VisionTextConfig):
+        super().__init__(config)
+
+    def _deepstack_inject(
+        self,
+        hidden_states: torch.Tensor,
+        vision_mask: torch.Tensor,
+        features: torch.Tensor,
+    ) -> torch.Tensor:
+        """Add projected vision features into the image-token positions of hidden_states."""
+        vision_mask = vision_mask.to(hidden_states.device)
+        features = features.to(hidden_states.device, hidden_states.dtype)
+        hidden_states = hidden_states.clone()
+        hidden_states[vision_mask] = hidden_states[vision_mask] + features
+        return hidden_states
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        vision_mask: torch.BoolTensor | None = None,
+        deepstack_features: dict | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ):
+        r"""
+        vision_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask marking image token positions. Required when `deepstack_features` is provided.
+        deepstack_features (`dict[int, torch.Tensor]`, *optional*):
+            Mapping from LLM layer index to projected vision features of shape `(num_image_tokens, hidden_size)`.
+            Features are added into image-token positions of hidden states before the corresponding decoder layer.
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        inputs_embeds = inputs_embeds * self.embedding_multiplier
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = (
+                torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            ).unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for layer_idx, decoder_layer in enumerate(self.layers[: self.config.num_hidden_layers]):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if deepstack_features is not None and layer_idx in deepstack_features:
+                hidden_states = self._deepstack_inject(hidden_states, vision_mask, deepstack_features[layer_idx])
+
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+            hidden_states = layer_outputs[0] if isinstance(layer_outputs, tuple) else layer_outputs
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return Granite4VisionModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Granite4VisionModel(LlavaNextModel):
+    config_class = Granite4VisionConfig
+
+    def __init__(self, config: Granite4VisionConfig):
+        super().__init__(config)
+
+        # Replace parent's single multi_modal_projector with layerwise_projectors
+        del self.multi_modal_projector
+
+        self.spatial_projectors = None
+        self.downsample_rate = config.downsample_rate
+        self.projector_dropout = config.projector_dropout
+
+        # Deepstack projectors: one per (vision_layer, llm_layer) pair
+        self.layerwise_projectors = nn.ModuleList(
+            [WindowQFormerDownsampler(config) for _ in range(len(config.deepstack_layer_map))]
+        )
+
+        # Spatial sampling projectors: 4 offset groups (TL, TR, BL, BR)
+        if config.use_spatial_sampling:
+            self.spatial_projectors = nn.ModuleList(
+                [WindowQFormerDownsampler(config, spatial_offset=i) for i in range(4)]
+            )
+
+        self.pad_token_id = (
+            self.config.text_config.pad_token_id if self.config.text_config.pad_token_id is not None else -1
+        )
+
+        # Replace the inherited LLM backbone with our deepstack-aware subclass
+        self.language_model = Granite4VisionTextModel(config.text_config)
+
+    def pack_image_features(self, image_features, image_sizes, vision_feature_select_strategy, image_newline=None):
+        """
+        Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
+
+        Overrides the parent to apply downsample_rate to height/width calculations.
+        """
+        new_image_features = []
+        feature_lens = []
+        for image_idx, image_feature in enumerate(image_features):
+            if image_feature.shape[0] > 1:
+                base_image_feature = image_feature[0]
+                image_feature = image_feature[1:]
+                height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
+
+                num_patch_height, num_patch_width = get_anyres_image_grid_shape(
+                    image_sizes[image_idx],
+                    self.config.image_grid_pinpoints,
+                    self.config.vision_config.image_size,
+                )
+                if self.layerwise_projectors is not None:
+                    ds_rate = Fraction(self.downsample_rate)
+                    height = int(height * ds_rate)
+                    width = int(width * ds_rate)
+
+                if (
+                    np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
+                    and vision_feature_select_strategy == "default"
+                ):
+                    raise ValueError(
+                        "Image feature shape does not line up with the provided patch size. "
+                        "You may be using the `default` vision_feature_select_strategy with a "
+                        "visual encoder that does not have CLS token."
+                    )
+
+                image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
+                image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
+                image_feature = image_feature.flatten(1, 2).flatten(2, 3)
+                image_feature = unpad_image(image_feature, image_sizes[image_idx])
+                if image_newline is not None:
+                    image_feature = torch.cat(
+                        (
+                            image_feature,
+                            image_newline[:, None, None]
+                            .expand(*image_feature.shape[:-1], 1)
+                            .to(image_feature.device, image_feature.dtype),
+                        ),
+                        dim=-1,
+                    )
+                image_feature = image_feature.flatten(1, 2).transpose(0, 1)
+                image_feature = torch.cat((base_image_feature, image_feature), dim=0)
+            else:
+                image_feature = image_feature[0]
+                if image_newline is not None:
+                    image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
+            new_image_features.append(image_feature)
+            feature_lens.append(image_feature.size(0))
+        feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features[0].device)
+        return new_image_features, feature_lens
+
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_sizes: torch.Tensor,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+    ) -> Granite4VisionImageFeaturesOutput:
+        """
+        Extract image features via deepstack (multi-layer) and spatial sampling projections.
+
+        Runs the vision tower once, then:
+        1. Deepstack: for each (vision_layer, llm_layer) in deepstack_layer_map,
+           extracts features from that vision layer, downsamples via interpolation + QFormer,
+           and pairs them with the target LLM layer.
+        2. Spatial: if enabled, extracts the spatial_vision_layer and creates 4 spatial
+           offset groups (TL, TR, BL, BR), each targeting a different LLM layer.
+        """
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        image_num_patches = [
+            image_size_to_num_patches(
+                image_size=imsize,
+                grid_pinpoints=self.config.image_grid_pinpoints,
+                patch_size=self.config.vision_config.image_size,
+            )
+            for imsize in image_sizes
+        ]
+
+        if pixel_values.dim() == 5:
+            _pixel_values_list = [pix_val[:num_patch] for pix_val, num_patch in zip(pixel_values, image_num_patches)]
+            pixel_values = torch.cat(_pixel_values_list, dim=0)
+        elif pixel_values.dim() != 4:
+            raise ValueError(f"pixel_values of shape {pixel_values.shape}, expect to be of 4 or 5 dimensions")
+
+        vision_outputs = self.vision_tower(pixel_values, output_hidden_states=True)
+
+        # Deepstack features: extract from multiple vision layers, downsample via interpolation
+        all_features = []
+        for projection_idx, (vision_layer, llm_layer) in enumerate(self.config.deepstack_layer_map):
+            selected_feature = vision_outputs.hidden_states[vision_layer]
+
+            if vision_feature_select_strategy == "default":
+                selected_feature = selected_feature[:, 1:]
+
+            projected_features = self.layerwise_projectors[projection_idx](selected_feature)
+            projected_features = torch.split(projected_features, image_num_patches, dim=0)
+
+            packed_features, _ = self.pack_image_features(
+                projected_features,
+                image_sizes,
+                vision_feature_select_strategy=vision_feature_select_strategy,
+                image_newline=self.image_newline,
+            )
+
+            all_features.append((llm_layer, packed_features))
+
+        # Spatial features: extract 4 offset groups from a single vision layer
+        if self.config.use_spatial_sampling:
+            spatial_feature = vision_outputs.hidden_states[self.config.spatial_vision_layer]
+
+            if vision_feature_select_strategy == "default":
+                spatial_feature = spatial_feature[:, 1:]
+
+            for group_idx, llm_layer in enumerate(self.config.spatial_target_layers):
+                projected_group = self.spatial_projectors[group_idx](spatial_feature)
+                projected_group_split = torch.split(projected_group, image_num_patches, dim=0)
+
+                packed_group, _ = self.pack_image_features(
+                    projected_group_split,
+                    image_sizes,
+                    vision_feature_select_strategy=vision_feature_select_strategy,
+                    image_newline=self.image_newline,
+                )
+
+                all_features.append((llm_layer, packed_group))
+
+        return Granite4VisionImageFeaturesOutput(deepstack_features=all_features)
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_sizes: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+        use_cache: bool | None = None,
+        return_dict: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Granite4VisionModelOutputWithPast:
+        output_attentions = kwargs.pop("output_attentions", None) or self.config.output_attentions
+        output_hidden_states = kwargs.pop("output_hidden_states", None) or self.config.output_hidden_states
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        # Build deepstack injection map and scatter initial image embeddings
+        deepstack_features = None
+        vision_mask = None
+        image_features = None
+        if pixel_values is not None and pixel_values.size(0) > 0:
+            image_features = self.get_image_features(
+                pixel_values,
+                image_sizes,
+                vision_feature_layer=vision_feature_layer,
+                vision_feature_select_strategy=vision_feature_select_strategy,
+            )
+
+            deepstack_features = {}
+            for idx, (llm_layer_idx, packed_features) in enumerate(image_features.deepstack_features):
+                concat_features = torch.cat(packed_features, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+                if idx == 0:
+                    vision_mask_3d = self.get_placeholder_mask(
+                        input_ids, inputs_embeds=inputs_embeds, image_features=concat_features
+                    )
+                    vision_mask = vision_mask_3d[..., 0]
+                    inputs_embeds = inputs_embeds.masked_fill(vision_mask_3d, 0.0)
+                deepstack_features[llm_layer_idx] = concat_features
+
+        outputs = self.language_model(
+            input_ids=None,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            vision_mask=vision_mask,
+            deepstack_features=deepstack_features,
+            **kwargs,
+        )
+
+        return Granite4VisionModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            deepstack_features=image_features.deepstack_features if pixel_values is not None else None,
+        )
+
+
+# ── ForConditionalGeneration ────────────────────────────────────────────────
+
+
+class Granite4VisionForConditionalGeneration(LlavaNextForConditionalGeneration):
+    config_class = Granite4VisionConfig
+
+    def __init__(self, config: Granite4VisionConfig):
+        super().__init__(config)
+        self.model = Granite4VisionModel(config)
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_sizes: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        vision_feature_layer: int | list[int] | None = None,
+        vision_feature_select_strategy: str | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Granite4VisionCausalLMOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        vision_feature_layer = (
+            vision_feature_layer if vision_feature_layer is not None else self.config.vision_feature_layer
+        )
+        vision_feature_select_strategy = (
+            vision_feature_select_strategy
+            if vision_feature_select_strategy is not None
+            else self.config.vision_feature_select_strategy
+        )
+
+        outputs = self.model(
+            input_ids,
+            pixel_values=pixel_values,
+            image_sizes=image_sizes,
+            vision_feature_layer=vision_feature_layer,
+            vision_feature_select_strategy=vision_feature_select_strategy,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+
+        loss = None
+        logits = self.lm_head(hidden_states)
+        logits = logits / self.config.text_config.logits_scaling
+        if labels is not None:
+            loss = self.loss_function(
+                logits,
+                labels,
+                vocab_size=self.config.text_config.vocab_size,
+                **kwargs,
+            )
+
+        if isinstance(logits_to_keep, int) and logits_to_keep > 0:
+            logits = logits[:, -logits_to_keep:, :]
+
+        return Granite4VisionCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            deepstack_features=outputs.deepstack_features,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        image_sizes=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        **kwargs,
+    ):
+        is_first = kwargs.get("is_first_iteration", False)
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            **kwargs,
+        )
+        model_inputs = self._init_hybrid_cache(**model_inputs)
+        if is_first:
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["image_sizes"] = image_sizes
+
+        return model_inputs
+
+    def _init_hybrid_cache(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        """Handle HybridMambaAttentionDynamicCache for GraniteMoeHybrid language model."""
+        empty_past_kv = past_key_values is None or (
+            isinstance(past_key_values, DynamicCache) and past_key_values.get_seq_length() == 0
+        )
+
+        if use_cache and empty_past_kv:
+            past_key_values = DynamicCache(config=self.model.language_model.config)
+
+        if attention_mask is not None and position_ids is None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if not empty_past_kv and input_ids is not None:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+        if inputs_embeds is not None and (input_ids is None or empty_past_kv):
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids.contiguous()}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+
+        for key, value in kwargs.items():
+            if key not in model_inputs:
+                model_inputs[key] = value
+
+        return model_inputs
+
+
+__all__ = [
+    "Granite4VisionConfig",
+    "Granite4VisionTextConfig",
+    "Granite4VisionProcessor",
+    "Granite4VisionPreTrainedModel",
+    "Granite4VisionTextModel",
+    "Granite4VisionModel",
+    "Granite4VisionForConditionalGeneration",
+]
diff --git a/src/transformers/models/granite4_vision/processing_granite4_vision.py b/src/transformers/models/granite4_vision/processing_granite4_vision.py
new file mode 100644
index 000000000000..572287d02215
--- /dev/null
+++ b/src/transformers/models/granite4_vision/processing_granite4_vision.py
@@ -0,0 +1,237 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/granite4_vision/modular_granite4_vision.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_granite4_vision.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from fractions import Fraction
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import select_best_resolution
+from ...image_utils import ImageInput, SizeDict, get_image_size, to_numpy_array
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import auto_docstring
+
+
+class Granite4VisionProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": False,
+        },
+        "images_kwargs": {
+            "do_pad": True,
+        },
+    }
+
+
+@auto_docstring
+class Granite4VisionProcessor(ProcessorMixin):
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        patch_size=None,
+        vision_feature_select_strategy=None,
+        chat_template=None,
+        image_token="<image>",
+        num_additional_image_tokens=0,
+        downsample_rate=None,
+        **kwargs,
+    ):
+        r"""
+        patch_size (`int`, *optional*):
+            Patch size from the vision tower.
+        vision_feature_select_strategy (`str`, *optional*):
+            The feature selection strategy used to select the vision feature from the vision backbone.
+            Should be same as in model's config.
+        image_token (`str`, *optional*, defaults to `"<image>"`):
+            Special token used to denote image location.
+        num_additional_image_tokens (`int`, *optional*, defaults to `0`):
+            Number of additional tokens added to the image embeddings, such as CLS (+1).
+        downsample_rate (`str`, *optional*):
+            Fractional downsample rate (e.g. `"1/4"`), used to adjust the number of image tokens
+            when computing token counts for padding/truncation.
+        """
+        self.patch_size = patch_size
+        self.num_additional_image_tokens = num_additional_image_tokens
+        self.vision_feature_select_strategy = vision_feature_select_strategy
+        self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
+        )
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.downsample_rate = downsample_rate
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[Granite4VisionProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+        if images is None and text is None:
+            raise ValueError("You have to specify at least images or text.")
+
+        output_kwargs = self._merge_kwargs(
+            Granite4VisionProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+        if images is not None:
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+        else:
+            image_inputs = {}
+
+        if isinstance(text, str):
+            text = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+        prompt_strings = text
+        if image_inputs:
+            image_sizes = iter(image_inputs["image_sizes"])
+            height, width = get_image_size(to_numpy_array(image_inputs["pixel_values"][0][0]))
+            prompt_strings = []
+            for sample in text:
+                while self.image_token in sample:
+                    image_size = next(image_sizes)
+                    if not isinstance(image_size, (list, tuple)):
+                        # cast to list to avoid numerical precision errors when calculating unpadding
+                        image_size = image_size.tolist()
+                    orig_height, orig_width = image_size
+                    num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
+                    if self.vision_feature_select_strategy == "default":
+                        num_image_tokens -= 1
+                    sample = sample.replace(self.image_token, "<placeholder>" * num_image_tokens, 1)
+                prompt_strings.append(sample)
+            prompt_strings = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+        if return_mm_token_type_ids:
+            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
+        image_grid_pinpoints = self.image_processor.image_grid_pinpoints
+
+        height_best_resolution, width_best_resolution = select_best_resolution(
+            [orig_height, orig_width], image_grid_pinpoints
+        )
+        scale_height, scale_width = height_best_resolution // height, width_best_resolution // width
+
+        patches_height = height // self.patch_size
+        patches_width = width // self.patch_size
+        if self.downsample_rate is not None:
+            ds_rate = Fraction(self.downsample_rate)
+            patches_height = int(patches_height * ds_rate)
+            patches_width = int(patches_width * ds_rate)
+
+        unpadded_features, newline_features = self._get_unpadded_features(
+            orig_height, orig_width, patches_height, patches_width, scale_height, scale_width
+        )
+        base_features = patches_height * patches_width + self.num_additional_image_tokens
+        num_image_tokens = unpadded_features + newline_features + base_features
+        return num_image_tokens
+
+    def _get_unpadded_features(self, height, width, patches_height, patches_width, scale_height, scale_width):
+        """
+        Get number of features for a given image with height/width. LLaVA-NeXT is different from LLaVA
+        because it divided each image into patches depending on its resolution. Therefore we need to calculate how many
+        patches an image is divided into and get the number of features from that.
+        """
+        current_height = patches_height * scale_height
+        current_width = patches_width * scale_width
+
+        original_aspect_ratio = width / height
+        current_aspect_ratio = current_width / current_height
+        if original_aspect_ratio > current_aspect_ratio:
+            new_height = int(round(height * (current_width / width), 7))
+            padding = (current_height - new_height) // 2
+            current_height -= padding * 2
+        else:
+            new_width = int(round(width * (current_height / height), 7))
+            padding = (current_width - new_width) // 2
+            current_width -= padding * 2
+
+        unpadded_features = current_height * current_width
+        newline_features = current_height
+        return (unpadded_features, newline_features)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (list[list[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Granite4VisionProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            size = images_kwargs.get("size", None) or self.image_processor.size
+            if isinstance(size, SizeDict):
+                size = (
+                    (size.shortest_edge, size.shortest_edge)
+                    if size.shortest_edge is not None
+                    else (min(size.height, size.width), min(size.height, size.width))
+                )
+            else:
+                size = (
+                    (size["shortest_edge"], size["shortest_edge"])
+                    if "shortest_edge" in size
+                    else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+                )
+            processed_height, processed_width = size
+
+            batch_num_image_tokens = []
+            num_image_patches = [1] * len(image_sizes)  # llava-next doesn't batch pixels as Idefics, thus `1` patch`
+            for image_size in image_sizes:
+                orig_height, orig_width = image_size
+                num_image_tokens = self._get_number_of_features(
+                    orig_height, orig_width, processed_height, processed_width
+                )
+                if self.vision_feature_select_strategy == "default":
+                    num_image_tokens -= 1
+                batch_num_image_tokens.append(num_image_tokens)
+            vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
+
+__all__ = ["Granite4VisionProcessor"]
diff --git a/src/transformers/models/granite_speech/configuration_granite_speech.py b/src/transformers/models/granite_speech/configuration_granite_speech.py
index d02ac9998696..7fe331573617 100644
--- a/src/transformers/models/granite_speech/configuration_granite_speech.py
+++ b/src/transformers/models/granite_speech/configuration_granite_speech.py
@@ -53,13 +53,19 @@ class GraniteSpeechEncoderConfig(PreTrainedConfig):
     ```"""
 
     model_type = "granite_speech_encoder"
+    attribute_map = {
+        "hidden_size": "hidden_dim",
+        "num_hidden_layers": "num_layers",
+        "num_attention_heads": "num_heads",
+        "num_mel_bins": "input_dim",
+    }
 
     input_dim: int = 160
     num_layers: int = 10
     hidden_dim: int = 1024
     feedforward_mult: int = 4
     num_heads: int = 8
-    dim_head: int = 128
+    dim_head: int | None = None
     output_dim: int = 42
     context_size: int = 200
     max_pos_emb: int = 512
@@ -67,6 +73,11 @@ class GraniteSpeechEncoderConfig(PreTrainedConfig):
     conv_kernel_size: int = 15
     conv_expansion_factor: int = 2
 
+    def __post_init__(self, **kwargs):
+        super().__post_init__(**kwargs)
+        if self.dim_head is None:
+            self.dim_head = self.hidden_dim // self.num_heads
+
 
 @auto_docstring(checkpoint="ibm-granite/granite-speech-3.3-2b")
 @strict
@@ -81,6 +92,8 @@ class GraniteSpeechConfig(PreTrainedConfig):
         Downsample rate for the audio feature extractor.
     window_size (`int`, *optional*, defaults to 15):
         Window size for the audio feature projector.
+    encoder_hidden_layers (`list[int]`, *optional*):
+        List of hidden layers from the encoder that are used by the projector.
 
     Example:
 
@@ -115,6 +128,7 @@ class GraniteSpeechConfig(PreTrainedConfig):
     has_lora_adapter: bool = True
     downsample_rate: int = 5
     window_size: int = 15
+    encoder_hidden_layers: list[int] | None = None
 
     def __post_init__(self, **kwargs):
         if isinstance(self.text_config, dict):
@@ -133,6 +147,22 @@ def __post_init__(self, **kwargs):
             self.encoder_config = {} if self.encoder_config is None else self.encoder_config
             self.encoder_config = GraniteSpeechEncoderConfig(**self.encoder_config)
 
+        if self.encoder_hidden_layers is not None:
+            # Verify that all the required hidden layers are in the encoder's range
+            for idx in self.encoder_hidden_layers:
+                if (idx < 0) or (idx >= self.encoder_config.num_layers):
+                    raise ValueError(
+                        f"Asking for hidden layer {idx} but number of layers is {self.encoder_config.num_layers}."
+                    )
+            # Verify that the encoder output size matches the projector input
+            num_layers_concat = len(self.encoder_hidden_layers) + 1  # +1 for final layer
+            if self.projector_config.encoder_hidden_size != self.encoder_config.hidden_dim * num_layers_concat:
+                raise ValueError(
+                    f"Mismatch in projector input dimension {self.projector_config.encoder_hidden_size}"
+                    " and number of layers * encoder dimension "
+                    f"{self.encoder_config.hidden_dim * num_layers_concat}."
+                )
+
         super().__post_init__(**kwargs)
 
 
diff --git a/src/transformers/models/granite_speech/feature_extraction_granite_speech.py b/src/transformers/models/granite_speech/feature_extraction_granite_speech.py
index cd32d0433bae..62c7d838bcde 100644
--- a/src/transformers/models/granite_speech/feature_extraction_granite_speech.py
+++ b/src/transformers/models/granite_speech/feature_extraction_granite_speech.py
@@ -28,10 +28,59 @@
 
 if is_torch_available():
     import torch
+    from torch import nn
 
 if is_torchaudio_available():
     import torchaudio
 
+    class _GraniteSpeechFeatureExtractorModule(nn.Module):
+        def __init__(self, feature_extractor: "GraniteSpeechFeatureExtractor"):
+            super().__init__()
+            self.melspec_kwargs = feature_extractor.melspec_kwargs
+            self.mel_filters = torchaudio.transforms.MelSpectrogram(**self.melspec_kwargs)
+            self.projector_window_size = feature_extractor.projector_window_size
+            self.projector_downsample_rate = feature_extractor.projector_downsample_rate
+
+        def _get_num_audio_features(self, audio_lengths: "torch.Tensor") -> "torch.Tensor":
+            """
+            Gets the (variable length) number of features (i.e., projector output) for the sequences
+            being considered.
+
+            Args:
+                audio_lengths (`torch.Tensor`):
+                    Sequence of one or more raw audio lengths.
+            """
+            hop_length = self.melspec_kwargs["hop_length"]
+            effective_window_size = self.projector_window_size // self.projector_downsample_rate
+
+            # mel sequence length computation
+            mel_length = audio_lengths // hop_length + 1
+            # encoder frame takes two mel features
+            encoder_length = mel_length // 2
+            nblocks = (encoder_length + self.projector_window_size - 1) // self.projector_window_size
+            # projector output length
+            projector_length = nblocks * effective_window_size
+            return projector_length
+
+        def forward(self, audio: "torch.Tensor"):
+            """
+            Compute the Mel features to be passed to the conformer encoder.
+            """
+            bsz = audio.shape[0]
+            # Compute mel features
+            mel = self.mel_filters(audio.float())
+            logmel = mel.transpose(-1, -2).clip_(min=1e-10).log10_()
+            mx = logmel.amax(dim=(-2, -1), keepdim=True)
+            logmel = torch.maximum(logmel, mx - 8.0).div_(4).add_(1)
+            # remove last frame if odd
+            if logmel.shape[1] % 2 == 1:
+                logmel = logmel[:, :-1]
+
+            # stacking and skipping by 2
+            audio = logmel.reshape(bsz, -1, 2 * logmel.shape[-1])
+
+            return audio
+
 
 class GraniteSpeechFeatureExtractor(FeatureExtractionMixin):
     model_input_names = ["input_features"]
@@ -57,10 +106,16 @@ def __init__(
             "n_mels": n_mels,
         }
         requires_backends(self, ["torchaudio"])
-        self.mel_filters = torchaudio.transforms.MelSpectrogram(**self.melspec_kwargs)
         self.projector_window_size = projector_window_size
         self.projector_downsample_rate = projector_downsample_rate
 
+    def to_exportable_module(self) -> "nn.Module":
+        """
+        Returns an exportable version of the feature extractor, which can be used with `torch.export`.
+        """
+        requires_backends(self, "torch")
+        return _GraniteSpeechFeatureExtractorModule(self)
+
     def __call__(
         self,
         audios: AudioInput,
@@ -94,27 +149,13 @@ def _extract_mel_spectrograms(self, audio: "torch.Tensor", device="cpu"):
         """
         Compute the Mel features to be passed to the conformer encoder.
         """
-        requires_backends(self, ["torchaudio"])
+        module = self.to_exportable_module()
         if device is not None:
-            melspec = self.mel_filters.to(device)
+            module = module.to(device)
             audio = audio.to(device)
-        else:
-            melspec = self.mel_filters
 
-        bsz = audio.shape[0]
         with torch.no_grad():
-            # Compute mel features
-            mel = melspec(audio.float())
-            logmel = mel.transpose(-1, -2).clip_(min=1e-10).log10_()
-            mx = logmel.amax(dim=(-2, -1), keepdim=True)
-            logmel = torch.maximum(logmel, mx - 8.0).div_(4).add_(1)
-            # remove last frame if odd
-            if logmel.shape[1] % 2 == 1:
-                logmel = logmel[:, :-1]
-
-            # stacking and skipping by 2
-            audio = logmel.reshape(bsz, -1, 2 * logmel.shape[-1])
-
+            audio = module(audio)
         return audio
 
     def _get_num_audio_features(self, audio_lengths: Sequence[int]) -> Sequence[int]:
@@ -126,6 +167,7 @@ def _get_num_audio_features(self, audio_lengths: Sequence[int]) -> Sequence[int]
             audio_lengths (`Sequence[int]`):
                 Sequence of one or more raw audio lengths.
         """
+        # TODO: make this torch based and exportable
         hop_length = self.melspec_kwargs["hop_length"]
         effective_window_size = self.projector_window_size // self.projector_downsample_rate
 
diff --git a/src/transformers/models/granite_speech/modeling_granite_speech.py b/src/transformers/models/granite_speech/modeling_granite_speech.py
index 03024afe8337..2170c98bbbe0 100644
--- a/src/transformers/models/granite_speech/modeling_granite_speech.py
+++ b/src/transformers/models/granite_speech/modeling_granite_speech.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import math
+from collections.abc import Container
 from dataclasses import dataclass
 
 import torch
@@ -42,12 +43,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for LlavaNext causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class GraniteSpeechCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -307,17 +308,28 @@ def __init__(self, config: GraniteSpeechEncoderConfig):
     @merge_with_config_defaults
     @capture_outputs
     def forward(
-        self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+        self,
+        hidden_states: torch.Tensor,
+        returned_hidden_states: Container[int] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
     ) -> tuple | BaseModelOutputWithPooling:
         hidden_states = self.input_linear(hidden_states)
+        exported_hidden_states = []
+        if returned_hidden_states is None:
+            returned_hidden_states = []
+        if 0 in returned_hidden_states:
+            exported_hidden_states.append(hidden_states)
         for idx, layer in enumerate(self.layers, start=1):
             hidden_states = layer(hidden_states, attention_dists=self.attention_dists)
+            if idx in returned_hidden_states:
+                exported_hidden_states.append(hidden_states)
 
             if idx == self.num_layers // 2:
                 hidden_states_mid = hidden_states.clone()
                 hidden_states_mid = self.out(hidden_states_mid)
                 hidden_states += self.out_mid(nn.Softmax(dim=-1)(hidden_states_mid))
-
+        if len(exported_hidden_states) > 0:
+            hidden_states = torch.cat(exported_hidden_states + [hidden_states], dim=-1)
         return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
 
 
@@ -373,8 +385,11 @@ def get_output_embeddings(self):
     def get_audio_features(
         self, input_features: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
     ) -> tuple | BaseModelOutputWithPooling:
-        audio_outputs = self.encoder(input_features, return_dict=True, **kwargs)
-        projected_embeds = self.projector(audio_outputs.last_hidden_state)
+        audio_outputs = self.encoder(
+            input_features, returned_hidden_states=self.config.encoder_hidden_layers, return_dict=True, **kwargs
+        )
+        encoder_embeds = audio_outputs.last_hidden_state
+        projected_embeds = self.projector(encoder_embeds)
         audio_outputs.pooler_output = projected_embeds
 
         return audio_outputs
@@ -516,6 +531,30 @@ def prepare_inputs_for_generation(
             model_inputs["input_features"] = input_features
         return model_inputs
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     def get_merged_audio_embeddings(
         self, input_ids: torch.Tensor, audio_features: torch.Tensor, input_features_mask: torch.Tensor | None = None
     ) -> torch.Tensor:
@@ -536,20 +575,14 @@ def get_merged_audio_embeddings(
         llm_input_ids = torch.where(is_audio_index, 0, input_ids)
         inputs_embeds = self.language_model.get_input_embeddings()(llm_input_ids)  # [bsz, # features, hidden size]
 
-        # Mask the audio features into the text embeddings
-        special_audio_mask = is_audio_index.unsqueeze(-1)
         audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
         if input_features_mask is not None:
-            torch_compilable_check(
-                not torch.all(is_audio_index.int().sum(dim=1) != input_features_mask.int().sum(dim=1)),
-                "Number of audio tokens does not match number of audio features",
-            )
             audio_features = audio_features[input_features_mask]
 
-        inputs_embeds = inputs_embeds.masked_scatter(
-            special_audio_mask,
-            audio_features,
+        special_audio_mask = self.get_placeholder_mask(
+            input_ids, inputs_embeds=inputs_embeds, audio_features=audio_features
         )
+        inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_features)
         return inputs_embeds
 
     def generate(self, *args, **kwargs) -> torch.LongTensor:
diff --git a/src/transformers/models/granite_speech_plus/__init__.py b/src/transformers/models/granite_speech_plus/__init__.py
new file mode 100644
index 000000000000..34e8db1b45e6
--- /dev/null
+++ b/src/transformers/models/granite_speech_plus/__init__.py
@@ -0,0 +1,27 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_granite_speech_plus import *
+    from .modeling_granite_speech_plus import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/granite_speech_plus/configuration_granite_speech_plus.py b/src/transformers/models/granite_speech_plus/configuration_granite_speech_plus.py
new file mode 100644
index 000000000000..735eea74f34f
--- /dev/null
+++ b/src/transformers/models/granite_speech_plus/configuration_granite_speech_plus.py
@@ -0,0 +1,170 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/granite_speech_plus/modular_granite_speech_plus.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_granite_speech_plus.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+@auto_docstring(checkpoint="ibm-granite/granite-speech-4.1-2b-plus")
+@strict
+class GraniteSpeechPlusEncoderConfig(PreTrainedConfig):
+    r"""
+    feedforward_mult (`int`, *optional*, defaults to 4):
+        Multiplier for the up/down projections in the encoder's feedforward layers;
+        The projections will have intermediate dim of size `hidden_dim * feedforward_mult`.
+    output_dim (`int`, *optional*, defaults to 42):
+        Intermediate dimension of the feedforward projections in the conformer
+        to be added to every other encoder block's output.
+    context_size (`int`, *optional*, defaults to 200):
+        Context size to be used in conformer attention.
+    max_pos_emb (`int`, *optional*, defaults to 512):
+        Max pos embeds to be used in attention (shaw's relative positional encoding).
+    conv_expansion_factor (`int`, *optional*, defaults to 2):
+        Intermediate dimension to be used in conformer convolutions.
+    cat_hidden_layers (`list[int]`, *optional*):
+        Indices of encoder conformer layers whose outputs are concatenated with the final encoder
+        output (along the feature dimension) before being passed to the projector. When set, the
+        projector's ``encoder_hidden_size`` must equal
+        ``encoder_config.hidden_dim * (len(cat_hidden_layers) + 1)``.
+
+    Example:
+
+    ```python
+    >>> from transformers import GraniteSpeechPlusEncoderConfig, GraniteSpeechPlusCTCEncoder
+
+    >>> # Initializing a GraniteSpeechPlusEncoderConfig
+    >>> configuration = GraniteSpeechPlusEncoderConfig()
+
+    >>> # Initializing a GraniteSpeechPlusCTCEncoder (with random weights)
+    >>> model = GraniteSpeechPlusCTCEncoder(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "granite_speech_plus_encoder"
+
+    input_dim: int = 160
+    num_layers: int = 10
+    hidden_dim: int = 1024
+    feedforward_mult: int = 4
+    num_heads: int = 8
+    dim_head: int = 128
+    output_dim: int = 42
+    context_size: int = 200
+    max_pos_emb: int = 512
+    dropout: float | int = 0.1
+    conv_kernel_size: int = 15
+    conv_expansion_factor: int = 2
+
+    cat_hidden_layers: list[int] | None = None
+
+
+@auto_docstring(checkpoint="ibm-granite/granite-speech-4.1-2b-plus")
+@strict
+class GraniteSpeechPlusConfig(PreTrainedConfig):
+    r"""
+    projector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Blip2QFormerConfig`):
+        The config object or dictionary of the audio projector.
+    has_lora_adapter (`bool`, *optional*, defaults to `True`):
+        Indicates whether or not the model has a lora adapter that should only
+        be activate when processing audio inputs.
+    downsample_rate (`int`, *optional*, defaults to 5):
+        Downsample rate for the audio feature extractor.
+    window_size (`int`, *optional*, defaults to 15):
+        Window size for the audio feature projector.
+    encoder_hidden_layers (`list[int]`, *optional*):
+        Indices of encoder conformer layers whose outputs are concatenated with the final encoder
+        output (along the feature dimension) before being passed to the projector. When set, the
+        projector's ``encoder_hidden_size`` must equal
+        ``encoder_config.hidden_dim * (len(encoder_hidden_layers) + 1)``.
+
+    Example:
+
+    ```python
+    >>> from transformers import GraniteSpeechPlusConfig, GraniteSpeechPlusForConditionalGeneration
+
+    >>> # Initializing a GraniteSpeechPlusConfig
+    >>> configuration = GraniteSpeechPlusConfig()
+
+    >>> # Initializing a GraniteSpeechPlusForConditionalGeneration (with random weights)
+    >>> model = GraniteSpeechPlusForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "granite_speech_plus"
+    attribute_map = {
+        "audio_token_id": "audio_token_index",
+    }
+    sub_configs = {
+        "text_config": AutoConfig,
+        "encoder_config": GraniteSpeechPlusEncoderConfig,
+        "projector_config": AutoConfig,
+    }
+
+    text_config: dict | PreTrainedConfig | None = None
+    encoder_config: dict | PreTrainedConfig | None = None
+    projector_config: dict | PreTrainedConfig | None = None
+    audio_token_index: int = 49155
+    initializer_range: float = 0.02
+    has_lora_adapter: bool = True
+    downsample_rate: int = 5
+    window_size: int = 15
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "granite")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["granite"]()
+
+        if isinstance(self.projector_config, dict):
+            self.projector_config["model_type"] = self.projector_config.get("model_type", "blip_2_qformer")
+            self.projector_config = CONFIG_MAPPING[self.projector_config["model_type"]](**self.projector_config)
+        elif self.projector_config is None:
+            self.projector_config = CONFIG_MAPPING["blip_2_qformer"]()
+
+        if not isinstance(self.encoder_config, GraniteSpeechPlusEncoderConfig):
+            self.encoder_config = {} if self.encoder_config is None else self.encoder_config
+            self.encoder_config = GraniteSpeechPlusEncoderConfig(**self.encoder_config)
+
+        super().__post_init__(**kwargs)
+
+        if self.encoder_config.cat_hidden_layers is not None:
+            for idx in self.encoder_config.cat_hidden_layers:
+                if idx < 0 or idx >= self.encoder_config.num_layers:
+                    raise ValueError(
+                        f"cat_hidden_layers index {idx} is out of range [0, {self.encoder_config.num_layers})."
+                    )
+        if self.encoder_config.cat_hidden_layers is not None:
+            num_concat = len(self.encoder_config.cat_hidden_layers) + 1
+            if self.projector_config.encoder_hidden_size != self.encoder_config.hidden_dim * num_concat:
+                raise ValueError(
+                    f"projector encoder_hidden_size {self.projector_config.encoder_hidden_size} "
+                    f"must equal encoder hidden_dim * {num_concat} = "
+                    f"{self.encoder_config.hidden_dim * num_concat}."
+                )
+
+
+__all__ = ["GraniteSpeechPlusConfig", "GraniteSpeechPlusEncoderConfig"]
diff --git a/src/transformers/models/granite_speech_plus/modeling_granite_speech_plus.py b/src/transformers/models/granite_speech_plus/modeling_granite_speech_plus.py
new file mode 100644
index 000000000000..bb8942bf0ff5
--- /dev/null
+++ b/src/transformers/models/granite_speech_plus/modeling_granite_speech_plus.py
@@ -0,0 +1,606 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/granite_speech_plus/modular_granite_speech_plus.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_granite_speech_plus.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...modeling_outputs import BaseModelOutputWithPooling, ModelOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    is_peft_available,
+    logging,
+    torch_compilable_check,
+)
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel, AutoModelForCausalLM
+from .configuration_granite_speech_plus import GraniteSpeechPlusConfig, GraniteSpeechPlusEncoderConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+### Projector
+class GraniteSpeechPlusEncoderProjector(nn.Module):
+    def __init__(self, config: GraniteSpeechPlusConfig):
+        super().__init__()
+        self.hidden_size = config.projector_config.hidden_size
+        self.downsample_rate = config.downsample_rate
+        self.window_size = config.window_size
+        self.num_queries = config.window_size // config.downsample_rate
+
+        self.query = nn.Parameter(torch.zeros(1, self.num_queries, config.projector_config.hidden_size))
+        self.query.data.normal_(mean=0.0, std=1.0)
+
+        # By default, this will be a blip_2_qformer config
+        self.qformer = AutoModel.from_config(config.projector_config)
+        self.linear = nn.Linear(config.projector_config.hidden_size, config.text_config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, seq_len, dim = hidden_states.size()
+        nblocks = math.ceil(seq_len / self.window_size)
+        pad = nblocks * self.window_size - seq_len
+        hidden_states = nn.functional.pad(hidden_states, (0, 0, 0, pad), "constant", 0)
+        hidden_states = hidden_states.view(batch_size * nblocks, self.window_size, dim)
+
+        query_output = self.qformer(
+            query_embeds=self.query,
+            encoder_hidden_states=hidden_states,
+            encoder_attention_mask=None,
+            return_dict=True,
+        )
+        query_proj = self.linear(
+            query_output.last_hidden_state.view(batch_size, nblocks * self.window_size // self.downsample_rate, -1)
+        )
+        return query_proj
+
+
+### Encoder - conformer is adapted from: https://github.com/lucidrains/conformer.git
+class GraniteSpeechPlusConformerFeedForward(nn.Module):
+    """Feedforward module for conformer encoder blocks."""
+
+    def __init__(self, config: GraniteSpeechPlusEncoderConfig):
+        super().__init__()
+        self.pre_norm = nn.LayerNorm(config.hidden_dim)
+        self.up_proj = nn.Linear(config.hidden_dim, config.hidden_dim * config.feedforward_mult)
+        self.silu = nn.SiLU()
+        self.dropout = nn.Dropout(config.dropout)
+        self.down_proj = nn.Linear(config.hidden_dim * config.feedforward_mult, config.hidden_dim)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(hidden_states)
+        hidden_states = self.up_proj(hidden_states)
+        hidden_states = self.dropout(self.silu(hidden_states))
+        hidden_states = self.down_proj(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class GraniteSpeechPlusConformerAttention(nn.Module):
+    """Attention for conformer blocks using Shaw's relative positional embeddings.
+    See the following [paper](https://huggingface.co/papers/1803.02155) for more details.
+    """
+
+    def __init__(self, config: GraniteSpeechPlusEncoderConfig):
+        super().__init__()
+
+        inner_dim = config.dim_head * config.num_heads
+        self.max_pos_emb = config.max_pos_emb
+        self.context_size = config.context_size
+        self.num_heads = config.num_heads
+        self.dim_head = config.dim_head
+        self.scale = self.dim_head**-0.5
+        self.pre_norm = nn.LayerNorm(config.hidden_dim)
+        self.to_q = nn.Linear(config.hidden_dim, inner_dim, bias=False)
+        self.to_kv = nn.Linear(config.hidden_dim, inner_dim * 2, bias=False)
+        self.to_out = nn.Linear(inner_dim, config.hidden_dim)
+        self.rel_pos_emb = nn.Embedding(2 * self.max_pos_emb + 1, self.dim_head)
+        self.dropout = nn.Dropout(config.dropout)
+
+        if self.context_size <= 0 or self.context_size > self.max_pos_emb:
+            raise ValueError("Context size is either less than 0 or exceeds the max_pos_emb")
+
+    def forward(self, hidden_states: torch.Tensor, attention_dists: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.pre_norm(hidden_states)
+        bsz, num_features, _ = hidden_states.shape
+
+        num_blocks = math.ceil(num_features / self.context_size)
+        remainder = num_features % self.context_size
+        if remainder > 0:
+            # right padding to reach block size
+            hidden_states = torch.nn.functional.pad(hidden_states, (0, 0, 0, self.context_size - remainder))
+
+        query_states = self.to_q(hidden_states)
+        key_states, value_states = self.to_kv(hidden_states).chunk(2, dim=-1)
+
+        query_states = query_states.reshape(bsz, num_blocks, self.context_size, self.num_heads, -1).transpose(2, 3)
+        key_states = key_states.reshape(bsz, num_blocks, self.context_size, self.num_heads, -1).transpose(2, 3)
+        value_states = value_states.reshape(bsz, num_blocks, self.context_size, self.num_heads, -1).transpose(2, 3)
+
+        # shaw's relative positional embedding
+        rel_pos_emb = self.rel_pos_emb(attention_dists)
+        # alternative computation of `pos_attn` - for readability
+        # rel_pos_emb_expanded = rel_pos_emb.view([1, 1, 1] + list(rel_pos_emb.shape))
+        # pos_attn = torch.sum(query_states.unsqueeze(-2) * rel_pos_emb_expanded, dim=-1) * self.scale
+        # einsum implementation of pos_attn - gives x30 speedup over the alternative
+        # TODO (@avihu111) find a fast alternative to einsum
+        pos_attn = torch.einsum("b m h c d, c r d -> b m h c r", query_states, rel_pos_emb) * self.scale
+
+        if remainder > 0:
+            # masked attention in the extended block
+            mask = torch.ones(self.context_size, self.context_size, dtype=bool, device=hidden_states.device)
+            mask[:remainder, :remainder] = 0
+            mask_value = -torch.finfo(pos_attn.dtype).max
+            pos_attn[:, -1, :].masked_fill_(mask, mask_value)
+
+        with torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
+            out = F.scaled_dot_product_attention(
+                query_states, key_states, value_states, attn_mask=pos_attn, scale=self.scale
+            )
+        out = out.transpose(2, 3).reshape(bsz, hidden_states.shape[1], -1)
+        out = self.to_out(out[:, :num_features, :])
+        return self.dropout(out)
+
+
+class GraniteSpeechPlusConformerDepthWiseConv1d(nn.Module):
+    """Wrapper for padded 1D pointwise convolution."""
+
+    def __init__(self, chan_in: int, chan_out: int, kernel_size: int):
+        super().__init__()
+        # Padding for the 1D conv is symmetric or close (i.e., offset by one).
+        pad = kernel_size // 2
+        pad_offset = (kernel_size + 1) % 2
+        self.padding = (pad, pad - pad_offset)
+
+        self.conv = nn.Conv1d(chan_in, chan_out, kernel_size, groups=chan_in, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = F.pad(hidden_states, self.padding)
+        return self.conv(hidden_states)
+
+
+class GraniteSpeechPlusConformerConvModule(nn.Module):
+    """Conformer conv module consisting of several 1D/depthwise 1D convolutional layers."""
+
+    def __init__(self, config: GraniteSpeechPlusEncoderConfig):
+        super().__init__()
+        inner_dim = config.hidden_dim * config.conv_expansion_factor
+
+        self.norm = nn.LayerNorm(config.hidden_dim)
+        self.up_conv = nn.Conv1d(config.hidden_dim, inner_dim * 2, 1)
+        self.glu = nn.GLU(dim=1)
+        self.depth_conv = GraniteSpeechPlusConformerDepthWiseConv1d(
+            inner_dim,
+            inner_dim,
+            kernel_size=config.conv_kernel_size,
+        )
+        self.silu = nn.SiLU()
+        self.batch_norm = nn.BatchNorm1d(inner_dim)
+        self.down_conv = nn.Conv1d(inner_dim, config.hidden_dim, 1)
+        self.dropout = nn.Dropout(config.dropout)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm(hidden_states)
+        hidden_states = self.up_conv(hidden_states.permute(0, 2, 1))
+        hidden_states = self.glu(hidden_states)
+        hidden_states = self.depth_conv(hidden_states)
+        hidden_states = self.silu(self.batch_norm(hidden_states))
+        hidden_states = self.down_conv(hidden_states).permute(0, 2, 1)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class GraniteSpeechPlusConformerBlock(nn.Module):
+    """Conformer block, consisting largely of linear layers, attention, and convolutional layers."""
+
+    def __init__(self, config: GraniteSpeechPlusEncoderConfig):
+        super().__init__()
+        self.ff1 = GraniteSpeechPlusConformerFeedForward(config)
+        self.attn = GraniteSpeechPlusConformerAttention(config)
+        self.conv = GraniteSpeechPlusConformerConvModule(config)
+        self.ff2 = GraniteSpeechPlusConformerFeedForward(config)
+        self.post_norm = nn.LayerNorm(config.hidden_dim)
+
+    def forward(self, hidden_states: torch.Tensor, attention_dists: torch.Tensor) -> torch.Tensor:
+        hidden_states = 0.5 * self.ff1(hidden_states) + hidden_states
+        hidden_states = self.attn(hidden_states, attention_dists=attention_dists) + hidden_states
+        hidden_states = self.conv(hidden_states) + hidden_states
+        hidden_states = 0.5 * self.ff2(hidden_states) + hidden_states
+        hidden_states = self.post_norm(hidden_states)
+        return hidden_states
+
+
+@auto_docstring
+class GraniteSpeechPlusPreTrainedModel(PreTrainedModel):
+    config: GraniteSpeechPlusConfig
+    input_modalities = ("audio", "text")
+
+    _supports_flash_attn = False  # `blip_2_qformer` dependency does not allow for this
+    _supports_sdpa = True
+
+    @torch.no_grad()
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights."""
+        super()._init_weights(module)
+        if isinstance(module, GraniteSpeechPlusEncoderProjector):
+            init.normal_(module.query)
+        elif isinstance(module, GraniteSpeechPlusCTCEncoder):
+            context_size = module.config.context_size
+            seq = torch.arange(context_size)
+            relpos_dist = seq.view(-1, 1) - seq.view(1, -1)
+            attention_dists = torch.clamp(relpos_dist, -context_size, context_size) + module.config.max_pos_emb
+            init.copy_(module.attention_dists, attention_dists)
+
+
+class GraniteSpeechPlusCTCEncoder(GraniteSpeechPlusPreTrainedModel):
+    config: GraniteSpeechPlusEncoderConfig
+    input_modalities = "audio"
+    _can_record_outputs = {
+        "hidden_states": GraniteSpeechPlusConformerBlock,
+        "attentions": GraniteSpeechPlusConformerAttention,
+    }
+
+    def __init__(self, config: GraniteSpeechPlusEncoderConfig):
+        super().__init__(config)
+
+        # Precompute clamped relative positional encoding distances
+        seq = torch.arange(config.context_size)
+        relpos_dist = seq.view(-1, 1) - seq.view(1, -1)
+        attention_dists = torch.clamp(relpos_dist, -config.context_size, config.context_size) + config.max_pos_emb
+        self.register_buffer("attention_dists", attention_dists, persistent=False)
+        self.input_linear = nn.Linear(config.input_dim, config.hidden_dim, bias=True)
+        self.layers = nn.ModuleList([GraniteSpeechPlusConformerBlock(config) for _ in range(config.num_layers)])
+
+        self.out = nn.Linear(config.hidden_dim, config.output_dim, bias=True)
+        self.out_mid = nn.Linear(config.output_dim, config.hidden_dim, bias=True)
+        self.num_layers = config.num_layers
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        hidden_states = self.input_linear(hidden_states)
+        cat_layers = set(self.config.cat_hidden_layers or [])
+        exported_hidden_states = []
+
+        if 0 in cat_layers:
+            exported_hidden_states.append(hidden_states)
+
+        for idx, layer in enumerate(self.layers, start=1):
+            hidden_states = layer(hidden_states, attention_dists=self.attention_dists)
+
+            if idx in cat_layers:
+                exported_hidden_states.append(hidden_states)
+
+            if idx == self.num_layers // 2:
+                hidden_states_mid = hidden_states.clone()
+                hidden_states_mid = self.out(hidden_states_mid)
+                hidden_states += self.out_mid(nn.Softmax(dim=-1)(hidden_states_mid))
+
+        if exported_hidden_states:
+            hidden_states = torch.cat([*exported_hidden_states, hidden_states], dim=-1)
+
+        return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for LlavaNext causal language model (or autoregressive) outputs.
+    """
+)
+class GraniteSpeechPlusCausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+
+
+@auto_docstring(
+    custom_intro="""
+    The Granite Speech Plus model, a Granite Speech variant whose projector consumes the concatenation of the
+    encoder's final hidden states with an arbitrary subset of its intermediate hidden states.
+    """
+)
+class GraniteSpeechPlusForConditionalGeneration(GraniteSpeechPlusPreTrainedModel, GenerationMixin):
+    _supports_attention_backend = True
+
+    def __init__(self, config: GraniteSpeechPlusConfig):
+        super().__init__(config)
+        # NOTE: It doesn't matter when we initialize from config, but we should be careful
+        # to make sure this does not pick up the adapter_config if in the future we use
+        # from_pretrained or something similar, since that should be set by the composite
+        # model; don't need to consider it twice
+        self.language_model = AutoModelForCausalLM.from_config(config.text_config)
+
+        self.encoder = GraniteSpeechPlusCTCEncoder(config.encoder_config)
+        self.projector = GraniteSpeechPlusEncoderProjector(config)
+
+        if config.has_lora_adapter and not is_peft_available():
+            logger.warning(
+                "Config indicates that a lora adapter should be present, but "
+                "peft is not installed; this will cause the model to perform "
+                "incorrectly when audio inputs are provided. Please install "
+                "peft and reload the model!"
+            )
+
+        self.post_init()
+
+    def set_decoder(self, decoder):
+        self.language_model.set_decoder(decoder)
+
+    def get_decoder(self):
+        return self.language_model.get_decoder()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    def set_output_embeddings(self, new_embeddings):
+        self.language_model.set_output_embeddings(new_embeddings)
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def get_output_embeddings(self):
+        return self.language_model.get_output_embeddings()
+
+    @can_return_tuple
+    @auto_docstring
+    def get_audio_features(
+        self, input_features: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        audio_outputs = self.encoder(input_features, return_dict=True, **kwargs)
+        projected_embeds = self.projector(audio_outputs.last_hidden_state)
+        audio_outputs.pooler_output = projected_embeds
+
+        return audio_outputs
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **lm_kwargs,
+    ) -> tuple[torch.Tensor] | GraniteSpeechPlusCausalLMOutputWithPast:
+        r"""
+        input_features_mask (`torch.Tensor`, *optional*):
+            Mask to be applied to audio features prior to scattering into the language embeddings.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        """
+        # TODO (@alex-jw-brooks) add an example to this docstring once models are released
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if input_features is not None and inputs_embeds is not None:
+            raise ValueError(
+                "You cannot specify both input_features and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if inputs_embeds is None:
+            # Get the base embeddings; set all audio tokens to 0 index
+            # to avoid out of vocabulary issues with the LLM embedding.
+            # Audio features will be masked into is_audio_idx indices later.
+            is_audio_idx = input_ids == self.config.audio_token_id
+            llm_input_ids = input_ids.clone()
+            llm_input_ids[is_audio_idx] = 0
+            inputs_embeds = self.get_input_embeddings()(llm_input_ids)
+
+        if input_features is not None:
+            if input_features.dtype != self.dtype:
+                input_features = input_features.to(self.dtype)
+            # Get the audio features from the encoder / projector
+            audio_embeds = self.get_audio_features(input_features, return_dict=True).pooler_output
+
+            # Merge the audio features into the LLM embeddings
+            inputs_embeds = self.get_merged_audio_embeddings(
+                input_ids=input_ids,
+                audio_features=audio_embeds,
+                input_features_mask=input_features_mask,
+            )
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            logits_to_keep=logits_to_keep,
+            **lm_kwargs,
+        )
+        logits = outputs[0]
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            if attention_mask is not None:
+                # we use the input attention mask to shift the logits and labels, because it is 2D.
+                # we also crop attn mask in case it is longer, which happens in PrefixTuning with peft
+                shift_attention_mask = attention_mask[:, -(logits.shape[1] - 1) :].to(logits.device)
+                shift_logits = logits[..., :-1, :][shift_attention_mask.to(logits.device) != 0].contiguous()
+                shift_labels = labels[..., 1:][shift_attention_mask.to(labels.device) != 0].contiguous()
+            else:
+                shift_logits = logits[..., :-1, :].contiguous()
+                shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = nn.CrossEntropyLoss()
+            loss = loss_fct(
+                shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1).to(shift_logits.device)
+            )
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return GraniteSpeechPlusCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        input_features=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward audio inputs to the model
+
+        model_inputs = self.language_model.prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        # If we're in cached decoding stage, input_features should be None because
+        # input ids do not contain special audio token anymore Otherwise we need
+        # input feature values to be passed to the model
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            model_inputs["input_features"] = input_features
+        return model_inputs
+
+    def get_merged_audio_embeddings(
+        self, input_ids: torch.Tensor, audio_features: torch.Tensor, input_features_mask: torch.Tensor | None = None
+    ) -> torch.Tensor:
+        """
+        Adds the audio token to the model's LLM vocabulary so that we can pass it
+        through the tokenizer; it's assumed that the embeddings corresponding to the
+        <|audio|> token will be clobbered with speech features.
+
+        Args:
+            input_ids (`torch.Tensor`):
+                Input IDs containing one or more audio tokens.
+            audio_features (`torch.Tensor`):
+                Audio features to be masked into the language embeddings to form multimodal embeddings.
+            input_features_mask (`torch.Tensor`, *optional*, defaults to `None`)
+                Mask to be applied to audio features prior to scattering into the language embeddings.
+        """
+        is_audio_index = input_ids == self.config.audio_token_id
+        llm_input_ids = torch.where(is_audio_index, 0, input_ids)
+        inputs_embeds = self.language_model.get_input_embeddings()(llm_input_ids)  # [bsz, # features, hidden size]
+
+        # Mask the audio features into the text embeddings
+        special_audio_mask = is_audio_index.unsqueeze(-1)
+        audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
+        if input_features_mask is not None:
+            torch_compilable_check(
+                not torch.all(is_audio_index.int().sum(dim=1) != input_features_mask.int().sum(dim=1)),
+                "Number of audio tokens does not match number of audio features",
+            )
+            audio_features = audio_features[input_features_mask]
+
+        inputs_embeds = inputs_embeds.masked_scatter(
+            special_audio_mask,
+            audio_features,
+        )
+        return inputs_embeds
+
+    def generate(self, *args, **kwargs) -> torch.LongTensor:
+        # This model is expected to have a lora adapter, which is only
+        # enabled when considering audio inputs. As such, we override generate
+        # to conditionally enable / disable the lora adapter based on whether
+        # or not any input features were provided.
+
+        input_features = kwargs.pop("input_features", None)
+        if is_peft_available and self._hf_peft_config_loaded:
+            if input_features is not None:
+                self.enable_adapters()
+            else:
+                self.disable_adapters()
+        return super().generate(*args, input_features=input_features, **kwargs)
+
+    def save_pretrained(self, save_directory, *args, **kwargs):
+        # overwrite save_pretrained to first save the adapter if we have one
+        if is_peft_available and self._hf_peft_config_loaded:
+            adapter_name = self._get_adapter_name()
+            self.peft_config[adapter_name].base_model_name_or_path = save_directory
+            super().save_pretrained(save_directory, *args, **kwargs)
+        # Then save the base model afterwards
+        prev_val = self._hf_peft_config_loaded
+        self._hf_peft_config_loaded = False
+        super().save_pretrained(save_directory, *args, **kwargs)
+        self._hf_peft_config_loaded = prev_val
+
+    def _get_adapter_name(self):
+        return list(self.peft_config.keys())[0]
+
+
+__all__ = ["GraniteSpeechPlusCTCEncoder", "GraniteSpeechPlusForConditionalGeneration"]
diff --git a/src/transformers/models/granite_speech_plus/modular_granite_speech_plus.py b/src/transformers/models/granite_speech_plus/modular_granite_speech_plus.py
new file mode 100644
index 000000000000..a16182e1124b
--- /dev/null
+++ b/src/transformers/models/granite_speech_plus/modular_granite_speech_plus.py
@@ -0,0 +1,172 @@
+# Copyright 2026 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Granite Speech Plus model, a Granite Speech variant whose projector consumes the concatenation of the
+encoder's final hidden states with an arbitrary subset of its intermediate hidden states."""
+
+import torch
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ...modeling_outputs import BaseModelOutputWithPooling
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..granite_speech.configuration_granite_speech import GraniteSpeechConfig, GraniteSpeechEncoderConfig
+from ..granite_speech.modeling_granite_speech import (
+    GraniteSpeechCTCEncoder,
+    GraniteSpeechForConditionalGeneration,
+)
+
+
+@auto_docstring(checkpoint="ibm-granite/granite-speech-4.1-2b-plus")
+@strict
+class GraniteSpeechPlusEncoderConfig(GraniteSpeechEncoderConfig):
+    r"""
+    feedforward_mult (`int`, *optional*, defaults to 4):
+        Multiplier for the up/down projections in the encoder's feedforward layers;
+        The projections will have intermediate dim of size `hidden_dim * feedforward_mult`.
+    output_dim (`int`, *optional*, defaults to 42):
+        Intermediate dimension of the feedforward projections in the conformer
+        to be added to every other encoder block's output.
+    context_size (`int`, *optional*, defaults to 200):
+        Context size to be used in conformer attention.
+    max_pos_emb (`int`, *optional*, defaults to 512):
+        Max pos embeds to be used in attention (shaw's relative positional encoding).
+    conv_expansion_factor (`int`, *optional*, defaults to 2):
+        Intermediate dimension to be used in conformer convolutions.
+    cat_hidden_layers (`list[int]`, *optional*):
+        Indices of encoder conformer layers whose outputs are concatenated with the final encoder
+        output (along the feature dimension) before being passed to the projector. When set, the
+        projector's ``encoder_hidden_size`` must equal
+        ``encoder_config.hidden_dim * (len(cat_hidden_layers) + 1)``.
+
+    Example:
+
+    ```python
+    >>> from transformers import GraniteSpeechPlusEncoderConfig, GraniteSpeechPlusCTCEncoder
+
+    >>> # Initializing a GraniteSpeechPlusEncoderConfig
+    >>> configuration = GraniteSpeechPlusEncoderConfig()
+
+    >>> # Initializing a GraniteSpeechPlusCTCEncoder (with random weights)
+    >>> model = GraniteSpeechPlusCTCEncoder(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    cat_hidden_layers: list[int] | None = None
+
+
+@auto_docstring(checkpoint="ibm-granite/granite-speech-4.1-2b-plus")
+@strict
+class GraniteSpeechPlusConfig(GraniteSpeechConfig):
+    r"""
+    projector_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Blip2QFormerConfig`):
+        The config object or dictionary of the audio projector.
+    has_lora_adapter (`bool`, *optional*, defaults to `True`):
+        Indicates whether or not the model has a lora adapter that should only
+        be activate when processing audio inputs.
+    downsample_rate (`int`, *optional*, defaults to 5):
+        Downsample rate for the audio feature extractor.
+    window_size (`int`, *optional*, defaults to 15):
+        Window size for the audio feature projector.
+    encoder_hidden_layers (`list[int]`, *optional*):
+        Indices of encoder conformer layers whose outputs are concatenated with the final encoder
+        output (along the feature dimension) before being passed to the projector. When set, the
+        projector's ``encoder_hidden_size`` must equal
+        ``encoder_config.hidden_dim * (len(encoder_hidden_layers) + 1)``.
+
+    Example:
+
+    ```python
+    >>> from transformers import GraniteSpeechPlusConfig, GraniteSpeechPlusForConditionalGeneration
+
+    >>> # Initializing a GraniteSpeechPlusConfig
+    >>> configuration = GraniteSpeechPlusConfig()
+
+    >>> # Initializing a GraniteSpeechPlusForConditionalGeneration (with random weights)
+    >>> model = GraniteSpeechPlusForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    def __post_init__(self, **kwargs):
+        super().__post_init__(**kwargs)
+
+        if self.encoder_config.cat_hidden_layers is not None:
+            for idx in self.encoder_config.cat_hidden_layers:
+                if idx < 0 or idx >= self.encoder_config.num_layers:
+                    raise ValueError(
+                        f"cat_hidden_layers index {idx} is out of range [0, {self.encoder_config.num_layers})."
+                    )
+        if self.encoder_config.cat_hidden_layers is not None:
+            num_concat = len(self.encoder_config.cat_hidden_layers) + 1
+            if self.projector_config.encoder_hidden_size != self.encoder_config.hidden_dim * num_concat:
+                raise ValueError(
+                    f"projector encoder_hidden_size {self.projector_config.encoder_hidden_size} "
+                    f"must equal encoder hidden_dim * {num_concat} = "
+                    f"{self.encoder_config.hidden_dim * num_concat}."
+                )
+
+
+class GraniteSpeechPlusCTCEncoder(GraniteSpeechCTCEncoder):
+    @merge_with_config_defaults
+    @capture_outputs
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPooling:
+        hidden_states = self.input_linear(hidden_states)
+        cat_layers = set(self.config.cat_hidden_layers or [])
+        exported_hidden_states = []
+
+        if 0 in cat_layers:
+            exported_hidden_states.append(hidden_states)
+
+        for idx, layer in enumerate(self.layers, start=1):
+            hidden_states = layer(hidden_states, attention_dists=self.attention_dists)
+
+            if idx in cat_layers:
+                exported_hidden_states.append(hidden_states)
+
+            if idx == self.num_layers // 2:
+                hidden_states_mid = hidden_states.clone()
+                hidden_states_mid = self.out(hidden_states_mid)
+                hidden_states += self.out_mid(nn.Softmax(dim=-1)(hidden_states_mid))
+
+        if exported_hidden_states:
+            hidden_states = torch.cat([*exported_hidden_states, hidden_states], dim=-1)
+
+        return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
+
+
+@auto_docstring(
+    custom_intro="""
+    The Granite Speech Plus model, a Granite Speech variant whose projector consumes the concatenation of the
+    encoder's final hidden states with an arbitrary subset of its intermediate hidden states.
+    """
+)
+class GraniteSpeechPlusForConditionalGeneration(GraniteSpeechForConditionalGeneration): ...
+
+
+__all__ = [
+    "GraniteSpeechPlusConfig",
+    "GraniteSpeechPlusEncoderConfig",
+    "GraniteSpeechPlusCTCEncoder",
+    "GraniteSpeechPlusForConditionalGeneration",
+]
diff --git a/src/transformers/models/granitemoe/modeling_granitemoe.py b/src/transformers/models/granitemoe/modeling_granitemoe.py
index 5fb53d6afe49..c5ab9281cea4 100644
--- a/src/transformers/models/granitemoe/modeling_granitemoe.py
+++ b/src/transformers/models/granitemoe/modeling_granitemoe.py
@@ -32,7 +32,7 @@
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...masking_utils import create_causal_mask
-from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@@ -121,7 +121,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -223,6 +223,7 @@ def forward(self, hidden_states):
         return index_sorted_experts, batch_index, batch_gates, expert_size, logits
 
 
+@use_kernel_forward_from_hub("ScatterMoEGatedMLP")
 class GraniteMoeMoE(nn.Module):
     """
     A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
@@ -577,7 +578,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -585,7 +586,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -602,8 +605,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -728,4 +733,13 @@ def forward(
         )
 
 
-__all__ = ["GraniteMoeForCausalLM", "GraniteMoeModel", "GraniteMoePreTrainedModel"]
+class GraniteMoeForSequenceClassification(GenericForSequenceClassification, GraniteMoePreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeForCausalLM",
+    "GraniteMoeForSequenceClassification",
+    "GraniteMoeModel",
+    "GraniteMoePreTrainedModel",
+]
diff --git a/src/transformers/models/granitemoe/modular_granitemoe.py b/src/transformers/models/granitemoe/modular_granitemoe.py
index 1ea37a919b49..e2fb82410ed8 100644
--- a/src/transformers/models/granitemoe/modular_granitemoe.py
+++ b/src/transformers/models/granitemoe/modular_granitemoe.py
@@ -19,7 +19,9 @@
 from ... import initialization as init
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
+from ...integrations import use_kernel_forward_from_hub
 from ...masking_utils import create_causal_mask
+from ...modeling_layers import GenericForSequenceClassification
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
@@ -49,6 +51,7 @@ class GraniteMoeTopKGating(JetMoeTopKGating):
     pass
 
 
+@use_kernel_forward_from_hub("ScatterMoEGatedMLP")
 class GraniteMoeMoE(nn.Module):
     """
     A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
@@ -313,4 +316,13 @@ def forward(
         )
 
 
-__all__ = ["GraniteMoeForCausalLM", "GraniteMoeModel", "GraniteMoePreTrainedModel"]
+class GraniteMoeForSequenceClassification(GenericForSequenceClassification, GraniteMoePreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeForCausalLM",
+    "GraniteMoeForSequenceClassification",
+    "GraniteMoeModel",
+    "GraniteMoePreTrainedModel",
+]
diff --git a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
index 2e0926f3e5d4..f6d5ceabae55 100644
--- a/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py
@@ -32,7 +32,7 @@
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...integrations.hub_kernels import lazy_load_kernel
 from ...masking_utils import create_causal_mask
-from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPast, MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@@ -317,7 +317,9 @@ def __init__(self, config: GraniteMoeHybridConfig, layer_idx: int):
         # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
         A = torch.arange(1, self.num_heads + 1)
         self.A_log = nn.Parameter(torch.log(A))
-        self.norm = GraniteMoeHybridRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
+        self.norm = GraniteMoeHybridRMSNormGated(
+            self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=self.layer_norm_epsilon
+        )
         self.D = nn.Parameter(torch.ones(self.num_heads))
 
         self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
@@ -733,10 +735,11 @@ def forward(
 
 
 class GraniteMoeHybridRMSNormGated(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
+    def __init__(self, hidden_size, group_size, eps=1e-6):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
+        self.group_size = group_size
 
     def forward(self, hidden_states, gate=None):
         input_dtype = hidden_states.dtype
@@ -744,8 +747,12 @@ def forward(self, hidden_states, gate=None):
 
         if gate is not None:
             hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        *prefix_dims, last_dim = hidden_states.shape
+        group_count = last_dim // self.group_size
+        hidden_states_group = hidden_states.view(*prefix_dims, group_count, self.group_size)
+        variance = hidden_states_group.pow(2).mean(-1, keepdim=True)
+        hidden_states_group = hidden_states_group * torch.rsqrt(variance + self.variance_epsilon)
+        hidden_states = hidden_states_group.view(*prefix_dims, group_count * self.group_size)
 
         return self.weight * hidden_states.to(input_dtype)
 
@@ -792,8 +799,8 @@ def __init__(self, config: GraniteMoeHybridConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -833,7 +840,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -935,6 +942,7 @@ def forward(self, hidden_states):
         return index_sorted_experts, batch_index, batch_gates, expert_size, logits
 
 
+@use_kernel_forward_from_hub("ScatterMoEGatedMLP")
 class GraniteMoeHybridMoE(nn.Module):
     """
     A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
@@ -1258,7 +1266,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1266,7 +1274,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -1283,8 +1293,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -1409,4 +1421,13 @@ def forward(
         )
 
 
-__all__ = ["GraniteMoeHybridForCausalLM", "GraniteMoeHybridModel", "GraniteMoeHybridPreTrainedModel"]
+class GraniteMoeHybridForSequenceClassification(GenericForSequenceClassification, GraniteMoeHybridPreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeHybridForCausalLM",
+    "GraniteMoeHybridForSequenceClassification",
+    "GraniteMoeHybridModel",
+    "GraniteMoeHybridPreTrainedModel",
+]
diff --git a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
index 741c58e005f8..47b1ce9b7b6e 100644
--- a/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
+++ b/src/transformers/models/granitemoehybrid/modular_granitemoehybrid.py
@@ -20,6 +20,7 @@
 from ... import initialization as init
 from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
+from ...modeling_layers import GenericForSequenceClassification
 from ...modeling_outputs import BaseModelOutputWithPast, MoeModelOutputWithPast
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
 from ...processing_utils import Unpack
@@ -99,8 +100,8 @@ def __init__(self, config: GraniteMoeHybridConfig, layer_idx: int):
 
 
 class GraniteMoeHybridRMSNormGated(BambaRMSNormGated):
-    def __init__(self, hidden_size, eps=1e-6):
-        super().__init__(hidden_size, eps)
+    def __init__(self, hidden_size, group_size, eps=1e-6):
+        super().__init__(hidden_size, group_size, eps)
 
 
 class GraniteMoeHybridMLP(GraniteMoeSharedMLP):
@@ -317,4 +318,13 @@ def forward(self, **super_kwargs):
         return super().forward(**super_kwargs)
 
 
-__all__ = ["GraniteMoeHybridForCausalLM", "GraniteMoeHybridModel", "GraniteMoeHybridPreTrainedModel"]
+class GraniteMoeHybridForSequenceClassification(GenericForSequenceClassification, GraniteMoeHybridPreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeHybridForCausalLM",
+    "GraniteMoeHybridForSequenceClassification",
+    "GraniteMoeHybridModel",
+    "GraniteMoeHybridPreTrainedModel",
+]
diff --git a/src/transformers/models/granitemoeshared/modeling_granitemoeshared.py b/src/transformers/models/granitemoeshared/modeling_granitemoeshared.py
index 71f8c6eaff7d..d9688378a003 100644
--- a/src/transformers/models/granitemoeshared/modeling_granitemoeshared.py
+++ b/src/transformers/models/granitemoeshared/modeling_granitemoeshared.py
@@ -31,7 +31,7 @@
 from ...generation import GenerationMixin
 from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
 from ...masking_utils import create_causal_mask
-from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
 from ...modeling_outputs import MoeCausalLMOutputWithPast, MoeModelOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
@@ -207,6 +207,7 @@ def forward(self, hidden_states):
         return index_sorted_experts, batch_index, batch_gates, expert_size, logits
 
 
+@use_kernel_forward_from_hub("ScatterMoEGatedMLP")
 class GraniteMoeSharedMoE(nn.Module):
     """
     A Sparsely gated mixture of experts layer with 1-layer Feed-Forward networks as experts.
@@ -524,7 +525,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -646,7 +647,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -654,7 +655,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -671,8 +674,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -797,4 +802,13 @@ def forward(
         )
 
 
-__all__ = ["GraniteMoeSharedForCausalLM", "GraniteMoeSharedModel", "GraniteMoeSharedPreTrainedModel"]
+class GraniteMoeSharedForSequenceClassification(GenericForSequenceClassification, GraniteMoeSharedPreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeSharedForCausalLM",
+    "GraniteMoeSharedForSequenceClassification",
+    "GraniteMoeSharedModel",
+    "GraniteMoeSharedPreTrainedModel",
+]
diff --git a/src/transformers/models/granitemoeshared/modular_granitemoeshared.py b/src/transformers/models/granitemoeshared/modular_granitemoeshared.py
index e51cd7712b9b..89f3498aa9a3 100644
--- a/src/transformers/models/granitemoeshared/modular_granitemoeshared.py
+++ b/src/transformers/models/granitemoeshared/modular_granitemoeshared.py
@@ -19,6 +19,7 @@
 
 from ...activations import ACT2FN
 from ...cache_utils import Cache
+from ...modeling_layers import GenericForSequenceClassification
 from ...processing_utils import Unpack
 from ...utils import logging
 from ..granitemoe.modeling_granitemoe import (
@@ -151,4 +152,13 @@ def __init__(self, config: GraniteMoeSharedConfig):
         self.post_init()
 
 
-__all__ = ["GraniteMoeSharedForCausalLM", "GraniteMoeSharedModel", "GraniteMoeSharedPreTrainedModel"]
+class GraniteMoeSharedForSequenceClassification(GenericForSequenceClassification, GraniteMoeSharedPreTrainedModel):
+    pass
+
+
+__all__ = [
+    "GraniteMoeSharedForCausalLM",
+    "GraniteMoeSharedForSequenceClassification",
+    "GraniteMoeSharedModel",
+    "GraniteMoeSharedPreTrainedModel",
+]
diff --git a/src/transformers/models/grounding_dino/modeling_grounding_dino.py b/src/transformers/models/grounding_dino/modeling_grounding_dino.py
index 953a9c7b0250..e926a6c291e7 100644
--- a/src/transformers/models/grounding_dino/modeling_grounding_dino.py
+++ b/src/transformers/models/grounding_dino/modeling_grounding_dino.py
@@ -91,7 +91,6 @@ def forward(
         return output.transpose(1, 2).contiguous()
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the GroundingDinoDecoder. This class adds two attributes to
@@ -100,6 +99,7 @@ def forward(
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class GroundingDinoDecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -115,7 +115,6 @@ class GroundingDinoDecoderOutput(ModelOutput):
     attentions: tuple[tuple[torch.FloatTensor]] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the GroundingDinoEncoder. This class extends BaseModelOutput, due to:
@@ -123,6 +122,7 @@ class GroundingDinoDecoderOutput(ModelOutput):
     - vision and text intermediate hidden states
     """
 )
+@dataclass
 class GroundingDinoEncoderOutput(ModelOutput):
     r"""
     last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -146,12 +146,12 @@ class GroundingDinoEncoderOutput(ModelOutput):
     attentions: tuple[tuple[torch.FloatTensor]] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the Grounding DINO encoder-decoder model.
     """
 )
+@dataclass
 class GroundingDinoModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -209,12 +209,12 @@ class GroundingDinoModelOutput(ModelOutput):
     encoder_pred_boxes: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`GroundingDinoForObjectDetection`].
     """
 )
+@dataclass
 class GroundingDinoObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/grounding_dino/processing_grounding_dino.py b/src/transformers/models/grounding_dino/processing_grounding_dino.py
index 7835885fd42d..4d6f0201cc7d 100644
--- a/src/transformers/models/grounding_dino/processing_grounding_dino.py
+++ b/src/transformers/models/grounding_dino/processing_grounding_dino.py
@@ -30,6 +30,7 @@
 
 if TYPE_CHECKING:
     from .modeling_grounding_dino import GroundingDinoObjectDetectionOutput
+from .image_processing_grounding_dino import GroundingDinoImageProcessorKwargs
 
 
 AnnotationType = dict[str, int | str | list[dict]]
@@ -98,6 +99,7 @@ def get(self, key, *args, **kwargs):
 
 
 class GroundingDinoProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: GroundingDinoImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/helium/modeling_helium.py b/src/transformers/models/helium/modeling_helium.py
index 8283fcb19e28..653867d7c5bd 100644
--- a/src/transformers/models/helium/modeling_helium.py
+++ b/src/transformers/models/helium/modeling_helium.py
@@ -119,7 +119,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/hiera/modeling_hiera.py b/src/transformers/models/hiera/modeling_hiera.py
index 59386c69b211..d4381c8fff07 100644
--- a/src/transformers/models/hiera/modeling_hiera.py
+++ b/src/transformers/models/hiera/modeling_hiera.py
@@ -39,12 +39,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Hiera encoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class HieraEncoderOutput(ModelOutput):
     r"""
     reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -61,12 +61,12 @@ class HieraEncoderOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Hiera model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class HieraModelOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
@@ -130,12 +130,12 @@ class HieraForImageClassificationOutput(ImageClassifierOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for HieraForPreTraining's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class HieraForPreTrainingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/higgs_audio_v2/modeling_higgs_audio_v2.py b/src/transformers/models/higgs_audio_v2/modeling_higgs_audio_v2.py
index a0f106167721..eec49fca3f07 100644
--- a/src/transformers/models/higgs_audio_v2/modeling_higgs_audio_v2.py
+++ b/src/transformers/models/higgs_audio_v2/modeling_higgs_audio_v2.py
@@ -524,7 +524,7 @@ def forward(
                 else audio_embeds
             )
             inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask[..., None].expand_as(inputs_embeds), audio_embeds.to(inputs_embeds.device)
+                audio_token_mask[..., None], audio_embeds.to(inputs_embeds.device)
             )
         elif audio_input_ids is not None:
             inputs_embeds = audio_embeds
diff --git a/src/transformers/models/higgs_audio_v2/modular_higgs_audio_v2.py b/src/transformers/models/higgs_audio_v2/modular_higgs_audio_v2.py
index 03d34b0e3444..8c48760a5a17 100644
--- a/src/transformers/models/higgs_audio_v2/modular_higgs_audio_v2.py
+++ b/src/transformers/models/higgs_audio_v2/modular_higgs_audio_v2.py
@@ -326,7 +326,7 @@ def forward(
                 else audio_embeds
             )
             inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask[..., None].expand_as(inputs_embeds), audio_embeds.to(inputs_embeds.device)
+                audio_token_mask[..., None], audio_embeds.to(inputs_embeds.device)
             )
         elif audio_input_ids is not None:
             inputs_embeds = audio_embeds
diff --git a/src/transformers/models/hunyuan_v1_dense/modeling_hunyuan_v1_dense.py b/src/transformers/models/hunyuan_v1_dense/modeling_hunyuan_v1_dense.py
index d1652d78cbbc..1812977963cf 100644
--- a/src/transformers/models/hunyuan_v1_dense/modeling_hunyuan_v1_dense.py
+++ b/src/transformers/models/hunyuan_v1_dense/modeling_hunyuan_v1_dense.py
@@ -376,7 +376,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/hunyuan_v1_moe/modeling_hunyuan_v1_moe.py b/src/transformers/models/hunyuan_v1_moe/modeling_hunyuan_v1_moe.py
index 19779da0528c..970daefaa2f3 100644
--- a/src/transformers/models/hunyuan_v1_moe/modeling_hunyuan_v1_moe.py
+++ b/src/transformers/models/hunyuan_v1_moe/modeling_hunyuan_v1_moe.py
@@ -465,7 +465,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/idefics/modeling_idefics.py b/src/transformers/models/idefics/modeling_idefics.py
index 8f4578e1d0f2..c07c34297623 100644
--- a/src/transformers/models/idefics/modeling_idefics.py
+++ b/src/transformers/models/idefics/modeling_idefics.py
@@ -46,12 +46,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class IdeficsBaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -79,12 +79,12 @@ class IdeficsBaseModelOutputWithPast(ModelOutput):
     image_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class IdeficsCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/idefics/processing_idefics.py b/src/transformers/models/idefics/processing_idefics.py
index b774d10b35c7..8d099c3bbcdd 100644
--- a/src/transformers/models/idefics/processing_idefics.py
+++ b/src/transformers/models/idefics/processing_idefics.py
@@ -31,6 +31,7 @@
 
 if is_torch_available():
     import torch
+from .image_processing_idefics import IdeficsImageProcessorKwargs
 
 
 IMAGE_TOKEN = "<image>"
@@ -52,6 +53,7 @@ class IdeficsTextKwargs(TextKwargs, total=False):
 
 
 class IdeficsProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: IdeficsImageProcessorKwargs
     text_kwargs: IdeficsTextKwargs
     _defaults = {
         "text_kwargs": {
diff --git a/src/transformers/models/idefics2/modeling_idefics2.py b/src/transformers/models/idefics2/modeling_idefics2.py
index 5d81439e27b6..5bac0813f41e 100644
--- a/src/transformers/models/idefics2/modeling_idefics2.py
+++ b/src/transformers/models/idefics2/modeling_idefics2.py
@@ -39,12 +39,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics2 model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class Idefics2BaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -815,7 +815,7 @@ def inputs_merger(
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         image_hidden_states = image_hidden_states.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_hidden_states)
         return inputs_embeds
diff --git a/src/transformers/models/idefics2/processing_idefics2.py b/src/transformers/models/idefics2/processing_idefics2.py
index dd87290838ff..95a1c41fea03 100644
--- a/src/transformers/models/idefics2/processing_idefics2.py
+++ b/src/transformers/models/idefics2/processing_idefics2.py
@@ -32,6 +32,7 @@
 
 if TYPE_CHECKING:
     from ...tokenization_utils_base import PreTokenizedInput
+from .image_processing_idefics2 import Idefics2ImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
@@ -46,6 +47,7 @@ def is_image_or_image_url(elem):
 
 
 class Idefics2ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Idefics2ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/idefics3/modeling_idefics3.py b/src/transformers/models/idefics3/modeling_idefics3.py
index 2c58aba032cd..2f08143a7983 100644
--- a/src/transformers/models/idefics3/modeling_idefics3.py
+++ b/src/transformers/models/idefics3/modeling_idefics3.py
@@ -38,12 +38,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics3 model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class Idefics3BaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -69,12 +69,12 @@ class Idefics3BaseModelOutputWithPast(ModelOutput):
     image_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Idefics3CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -559,7 +559,7 @@ def inputs_merger(
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         image_hidden_states = image_hidden_states.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_hidden_states)
         return inputs_embeds
diff --git a/src/transformers/models/idefics3/processing_idefics3.py b/src/transformers/models/idefics3/processing_idefics3.py
index f43ac76bf3ff..c61749bd54ca 100644
--- a/src/transformers/models/idefics3/processing_idefics3.py
+++ b/src/transformers/models/idefics3/processing_idefics3.py
@@ -22,7 +22,7 @@
 import numpy as np
 
 from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput, is_valid_image, load_image
+from ...image_utils import ImageInput, is_valid_image
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import AddedToken, BatchEncoding, TextInput
 from ...utils import auto_docstring, logging
@@ -30,63 +30,14 @@
 
 if TYPE_CHECKING:
     from ...tokenization_utils_base import PreTokenizedInput
-
-logger = logging.get_logger(__name__)
-
-
-def is_url(val) -> bool:
-    return isinstance(val, str) and val.startswith("http")
+from .image_processing_idefics3 import Idefics3ImageProcessorKwargs
 
 
-def is_image_or_image_url(elem):
-    return is_url(elem) or is_valid_image(elem)
-
-
-def _prompt_split_image(image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token):
-    """Prompt with expanded image tokens for when the image is split into patches."""
-    text_split_images = ""
-    for n_h in range(image_rows):
-        for n_w in range(image_cols):
-            text_split_images += (
-                f"{fake_token_around_image}" + f"<row_{n_h + 1}_col_{n_w + 1}>" + f"{image_token}" * image_seq_len
-            )
-        text_split_images += "\n"
-
-    text_split_images += (
-        f"\n{fake_token_around_image}"
-        + f"{global_img_token}"
-        + f"{image_token}" * image_seq_len
-        + f"{fake_token_around_image}"
-    )
-    return text_split_images
-
-
-def _prompt_single_image(image_seq_len, fake_token_around_image, image_token, global_img_token):
-    """Prompt with expanded image tokens for a single image."""
-    return (
-        f"{fake_token_around_image}"
-        + f"{global_img_token}"
-        + f"{image_token}" * image_seq_len
-        + f"{fake_token_around_image}"
-    )
-
-
-def get_image_prompt_string(
-    image_rows, image_cols, image_seq_len, fake_token_around_image, image_token, global_img_token
-):
-    if image_rows == 0 and image_cols == 0:
-        return _prompt_single_image(
-            image_seq_len,
-            fake_token_around_image=fake_token_around_image,
-            image_token=image_token,
-            global_img_token=global_img_token,
-        )
-    return _prompt_split_image(
-        image_seq_len, image_rows, image_cols, fake_token_around_image, image_token, global_img_token
-    )
+logger = logging.get_logger(__name__)
 
 
 class Idefics3ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Idefics3ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
@@ -102,6 +53,8 @@ class Idefics3ProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Idefics3Processor(ProcessorMixin):
+    valid_processor_kwargs = Idefics3ProcessorKwargs
+
     def __init__(
         self, image_processor, tokenizer=None, image_seq_len: int = 169, chat_template: str | None = None, **kwargs
     ):
@@ -139,18 +92,6 @@ def __init__(
 
         super().__init__(image_processor, tokenizer, chat_template=chat_template, **kwargs)
 
-    def _extract_images_from_prompts(self, prompts):
-        prompt_images = []
-        for prompt in prompts:
-            images = []
-            for elem in prompt:
-                if is_valid_image(elem):
-                    images.append(elem)
-                elif is_url(elem):
-                    images.append(load_image(elem))
-            prompt_images.append(images)
-        return prompt_images
-
     @auto_docstring
     def __call__(
         self,
@@ -164,8 +105,8 @@ def __call__(
             The length of the image sequence. If not provided, the default value of self.image_seq_len is used.
             image_seq_len should be equal to int(((image_size // patch_size) ** 2) / (scale_factor**2))
         """
-        if text is None and images is None:
-            raise ValueError("You must provide either `text` or `images`.")
+        images, text = self.prepare_inputs_layout(images=images, text=text, **kwargs)
+        self.validate_inputs(images=images, text=text, **kwargs)
 
         output_kwargs = self._merge_kwargs(
             Idefics3ProcessorKwargs,
@@ -174,113 +115,135 @@ def __call__(
         )
 
         image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
+        return_text_replacement_offsets = output_kwargs["text_kwargs"].pop("return_text_replacement_offsets", False)
         return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
 
-        n_images_in_text = []
-        n_images_in_images = []
-        inputs = {}
+        image_inputs = text_inputs = {}
+        if images is not None:
+            image_inputs, images_replacements = self._process_images(images, **output_kwargs["images_kwargs"])
+
+            # Pop inputs unused by the model
+            image_inputs.pop("rows", None)
+            image_inputs.pop("cols", None)
 
+            if text is not None:
+                text, text_replacement_offsets = self.get_text_with_replacements(
+                    text, images_replacements=images_replacements
+                )
+                text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+                if return_text_replacement_offsets:
+                    text_inputs["text_replacement_offsets"] = text_replacement_offsets
+
+                batch_image_seq_lengths = []
+                for batch_id, text_replacement_offset in enumerate(text_replacement_offsets):
+                    image_seq_lens = []
+                    for data in text_replacement_offset:
+                        start, end = data["new_span"]
+                        start_id_pos = text_inputs.char_to_token(batch_id, start)
+                        end_id_pos = text_inputs.char_to_token(batch_id, end - 1)
+                        # Add one to go from zero-indexing to actual length
+                        image_seq_lens.append(end_id_pos - start_id_pos + 1)
+                    batch_image_seq_lengths.append(image_seq_lens)
+
+                if return_mm_token_type_ids:
+                    text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(
+                        text_inputs["input_ids"], batch_image_seq_lengths
+                    )
+                self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+
+        elif text is not None:
+            text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def prepare_inputs_layout(
+        self,
+        images: ImageInput | None = None,
+        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
+        **kwargs: Unpack[Idefics3ProcessorKwargs],
+    ):
         if text is not None:
             if isinstance(text, str):
                 text = [text]
-            elif not isinstance(text, list) and not isinstance(text[0], str):
-                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-            n_images_in_text = [sample.count(self.image_token) for sample in text]
+            text = text.copy()
 
         if images is not None:
-            if is_image_or_image_url(images):
+            images = self.image_processor.fetch_images(images)
+            if is_valid_image(images):
                 images = [[images]]
-            elif isinstance(images, (list, tuple)) and is_image_or_image_url(images[0]):
+            elif isinstance(images, (list, tuple)) and is_valid_image(images[0]):
                 if text is not None:
-                    if sum(n_images_in_text) != len(images):
-                        raise ValueError(
-                            f"The total number of {self.image_token} tokens in the prompts should be the same as the number of images passed."
-                            f" Found {sum(n_images_in_text)} {self.image_token} tokens and {len(images)} images."
-                        )
                     # Reorganize the images to match the prompts
+                    n_images_in_text = [sample.count(self.image_token) for sample in text]
                     cumsum_images_in_text = [0] + list(accumulate(n_images_in_text))
-                    images = [
+                    split_images = [
                         images[cumsum_images_in_text[i] : cumsum_images_in_text[i + 1]]
                         for i in range(len(n_images_in_text))
                     ]
+                    # Append the rest if any, we will error out when validating if they don't match with text
+                    if len(images) > cumsum_images_in_text[-1]:
+                        images = split_images + [images[cumsum_images_in_text[-1] :]]
+                    else:
+                        images = split_images
                 else:
                     images = [images]
-            elif (
-                not isinstance(images, (list, tuple))
-                and not isinstance(images[0], (list, tuple))
-                and not is_image_or_image_url(images[0][0])
-            ):
-                raise ValueError(
-                    "Invalid input images. Please provide a single image or a list of images or a list of list of images."
-                )
-            n_images_in_images = [len(sample) for sample in images]
 
-            # Load images if they are URLs
-            images = [[load_image(im) if is_url(im) else im for im in sample] for sample in images]
+        return images, text
+
+    def validate_inputs(
+        self,
+        images: ImageInput | None = None,
+        text: Union[TextInput, "PreTokenizedInput", list[TextInput], list["PreTokenizedInput"]] = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(images, text, **kwargs)
 
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-            inputs.update(image_inputs)
+        if text is None and images is None:
+            raise ValueError("You must provide either `text` or `images`.")
 
-            if text is not None:
-                if n_images_in_images != n_images_in_text:
+        if text is not None:
+            n_images_in_text = [sample.count(self.image_token) for sample in text]
+            if images is not None:
+                n_images_in_images = [len(sublist) for sublist in images]
+                if n_images_in_text != n_images_in_images:
                     raise ValueError(
-                        f"The number of images in the text {n_images_in_text} and images {n_images_in_images} should be the same."
+                        f"The total number of {self.image_token} tokens in the prompts should be the same as the number of images passed."
+                        f" Found {n_images_in_text} {self.image_token} tokens and {n_images_in_images} images per sample."
                     )
-
-                image_rows = inputs.pop("rows", [[0] * n_images for n_images in n_images_in_text])
-                image_cols = inputs.pop("cols", [[0] * n_images for n_images in n_images_in_text])
-
-                fake_image_token = self.fake_image_token
-                image_token = self.image_token
-                global_img_token = self.global_image_tag
-
-                prompt_strings = []
-                batch_image_seq_lengths = []
-                for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
-                    # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
-                    image_prompt_strings = []
-                    image_seq_lengths = []
-                    for n_rows, n_cols in zip(sample_rows, sample_cols):
-                        image_prompt_string = get_image_prompt_string(
-                            n_rows,
-                            n_cols,
-                            image_seq_len,
-                            image_token=image_token,
-                            fake_token_around_image=fake_image_token,
-                            global_img_token=global_img_token,
-                        )
-                        # Add +2 and +3 for special BOI/EOI/fake_image_wrapper tokens
-                        row_length = (self.image_seq_len + 2) * n_cols + 1
-                        image_seq_lengths.append((self.image_seq_len + 3) + row_length * n_rows)
-                        image_prompt_strings.append(image_prompt_string)
-
-                    batch_image_seq_lengths.append(image_seq_lengths)
-                    split_sample = sample.split(image_token)
-                    if len(split_sample) == 0:
-                        raise ValueError("The image token should be present in the text.")
-
-                    # Place in the image prompt strings where the image tokens are
-                    sample = split_sample[0]
-                    for i, image_prompt_string in enumerate(image_prompt_strings):
-                        sample += image_prompt_string + split_sample[i + 1]
-                    prompt_strings.append(sample)
-
-                text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
-                self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-                inputs.update(text_inputs)
-
-        elif text is not None:
-            if any(n_images_in_text):
+            elif images is None and any(n_images_in_text):
                 raise ValueError(
                     f"Found {sum(n_images_in_text)} {self.image_token} tokens in the text but no images were passed."
                 )
-            text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
-            inputs.update(text_inputs)
 
-        if return_mm_token_type_ids:
-            inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(inputs["input_ids"], batch_image_seq_lengths)
-        return BatchFeature(data=inputs, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        image_rows = [row for row_list in image_inputs["rows"] for row in row_list][image_idx]
+        image_cols = [col for col_list in image_inputs["cols"] for col in col_list][image_idx]
+        if image_rows == 0 and image_cols == 0:
+            return (
+                f"{self.fake_image_token}"
+                + f"{self.global_image_tag}"
+                + f"{self.image_token}" * self.image_seq_len
+                + f"{self.fake_image_token}"
+            )
+        else:
+            text_split_images = ""
+            for n_h in range(image_rows):
+                for n_w in range(image_cols):
+                    text_split_images += (
+                        f"{self.fake_image_token}"
+                        + f"<row_{n_h + 1}_col_{n_w + 1}>"
+                        + f"{self.image_token}" * self.image_seq_len
+                    )
+                text_split_images += "\n"
+
+            text_split_images += (
+                f"\n{self.fake_image_token}"
+                + f"{self.global_image_tag}"
+                + f"{self.image_token}" * self.image_seq_len
+                + f"{self.fake_image_token}"
+            )
+            return text_split_images
 
     def create_mm_token_type_ids(self, input_ids: list, batch_image_seq_lengths: list[int]) -> list[list[int]]:
         # We have to iterate for each list separately because inputs
diff --git a/src/transformers/models/instructblip/modeling_instructblip.py b/src/transformers/models/instructblip/modeling_instructblip.py
index 29f32f17d6c4..3644c716ee35 100644
--- a/src/transformers/models/instructblip/modeling_instructblip.py
+++ b/src/transformers/models/instructblip/modeling_instructblip.py
@@ -47,8 +47,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithVisionQformerOutputs(BaseModelOutputWithPooling):
     r"""
     vision_outputs (`BaseModelOutputWithPooling`):
@@ -796,10 +796,10 @@ class InstructBlipQFormerModel(InstructBlipPreTrainedModel):
     _can_record_outputs = {
         "hidden_states": InstructBlipQFormerLayer,
         "attentions": [
-            OutputRecorder(InstructBlipQFormerMultiHeadAttention, index=1, layer_name=".attention"),
+            OutputRecorder(InstructBlipQFormerMultiHeadAttention, index=1, layer_name=r"\.attention"),
         ],
         "cross_attentions": [
-            OutputRecorder(InstructBlipQFormerMultiHeadAttention, index=1, layer_name=".crossattention"),
+            OutputRecorder(InstructBlipQFormerMultiHeadAttention, index=1, layer_name=r"\.crossattention"),
         ],
     }
 
@@ -998,7 +998,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
@@ -1257,7 +1257,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
diff --git a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
index 06d3d28b2c88..c7c52915f716 100644
--- a/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
+++ b/src/transformers/models/instructblipvideo/modeling_instructblipvideo.py
@@ -745,10 +745,10 @@ class InstructBlipVideoQFormerModel(InstructBlipVideoPreTrainedModel):
     _can_record_outputs = {
         "hidden_states": InstructBlipVideoQFormerLayer,
         "attentions": [
-            OutputRecorder(InstructBlipVideoQFormerMultiHeadAttention, index=1, layer_name=".attention"),
+            OutputRecorder(InstructBlipVideoQFormerMultiHeadAttention, index=1, layer_name=r"\.attention"),
         ],
         "cross_attentions": [
-            OutputRecorder(InstructBlipVideoQFormerMultiHeadAttention, index=1, layer_name=".crossattention"),
+            OutputRecorder(InstructBlipVideoQFormerMultiHeadAttention, index=1, layer_name=r"\.crossattention"),
         ],
     }
 
@@ -887,12 +887,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class defining the outputs of [`InstructBlipVideoForConditionalGeneration`].
     """
 )
+@dataclass
 class InstructBlipVideoForConditionalGenerationModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -982,7 +982,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.image_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
@@ -1074,7 +1074,7 @@ def forward(
             )
             special_image_mask = special_image_mask.all(-1)
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
 
@@ -1102,8 +1102,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithVisionQformerOutputs(BaseModelOutputWithPooling):
     r"""
     vision_outputs (`BaseModelOutputWithPooling`):
@@ -1205,7 +1205,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.video_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
diff --git a/src/transformers/models/instructblipvideo/modular_instructblipvideo.py b/src/transformers/models/instructblipvideo/modular_instructblipvideo.py
index d84f3fd13398..862a812fdeb5 100644
--- a/src/transformers/models/instructblipvideo/modular_instructblipvideo.py
+++ b/src/transformers/models/instructblipvideo/modular_instructblipvideo.py
@@ -209,7 +209,7 @@ def forward(
             )
             special_image_mask = special_image_mask.all(-1)
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         language_model_inputs = language_model_inputs.to(inputs_embeds.device, inputs_embeds.dtype)
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, language_model_inputs)
 
@@ -324,7 +324,7 @@ def get_placeholder_mask(self, input_ids: torch.LongTensor, inputs_embeds: torch
         else:
             special_image_mask = input_ids == self.config.video_token_id
 
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask
 
     @can_return_tuple
diff --git a/src/transformers/models/internvl/modeling_internvl.py b/src/transformers/models/internvl/modeling_internvl.py
index 284d97406e65..0629dc3dfbfb 100644
--- a/src/transformers/models/internvl/modeling_internvl.py
+++ b/src/transformers/models/internvl/modeling_internvl.py
@@ -609,9 +609,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -701,12 +701,12 @@ def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5
         return vision_features
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for InternVL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class InternVLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/internvl/processing_internvl.py b/src/transformers/models/internvl/processing_internvl.py
index 84c611115dcf..99ec1d96d491 100644
--- a/src/transformers/models/internvl/processing_internvl.py
+++ b/src/transformers/models/internvl/processing_internvl.py
@@ -21,9 +21,11 @@
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
 from ...video_utils import VideoInput
+from ..got_ocr2.image_processing_got_ocr2 import GotOcr2ImageProcessorKwargs
 
 
 class InternVLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: GotOcr2ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding_side": "left",
@@ -64,7 +66,10 @@ def __init__(
         self.image_token = tokenizer.context_image_token
         self.video_token = tokenizer.video_token
         self.image_token_id = tokenizer.context_image_token_id
-        self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
+
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
 
     def _insert_media_placeholders(
         self,
diff --git a/src/transformers/models/jais2/modeling_jais2.py b/src/transformers/models/jais2/modeling_jais2.py
index 5e6a37c0172d..714f0512e17b 100644
--- a/src/transformers/models/jais2/modeling_jais2.py
+++ b/src/transformers/models/jais2/modeling_jais2.py
@@ -312,7 +312,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/jamba/modeling_jamba.py b/src/transformers/models/jamba/modeling_jamba.py
index ae618fb4a2b3..67ca17549fac 100755
--- a/src/transformers/models/jamba/modeling_jamba.py
+++ b/src/transformers/models/jamba/modeling_jamba.py
@@ -794,7 +794,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -802,7 +802,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -819,8 +821,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/janus/modeling_janus.py b/src/transformers/models/janus/modeling_janus.py
index ff2292a9153e..00236ce7b7a1 100644
--- a/src/transformers/models/janus/modeling_janus.py
+++ b/src/transformers/models/janus/modeling_janus.py
@@ -63,12 +63,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Janus VQ-VAE mode model outputs.
     """
 )
+@dataclass
 class JanusVQVAEOutput(ModelOutput):
     r"""
     decoded_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
@@ -81,12 +81,12 @@ class JanusVQVAEOutput(ModelOutput):
     embedding_loss: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Janus model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class JanusBaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -114,12 +114,12 @@ class JanusBaseModelOutputWithPast(ModelOutput):
     image_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Janus causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class JanusCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -828,8 +828,8 @@ def forward(self, hidden_state: torch.FloatTensor) -> torch.FloatTensor:
         return hidden_state
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class JanusVQVAEModelOutput(BaseModelOutputWithPooling):
     r"""
     quantized_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
@@ -1018,9 +1018,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -1277,7 +1277,7 @@ def generate(
         input_ids, model_kwargs = self._expand_inputs_for_generation(
             input_ids=input_ids,
             attention_mask=attention_mask,
-            expand_size=generation_config.num_return_sequences,
+            expand_size=generation_config.num_return_sequences or 1,
             **model_kwargs,
         )
 
@@ -1290,6 +1290,17 @@ def generate(
         attention_mask = attention_mask.repeat(2, 1)
         model_kwargs["attention_mask"] = attention_mask
 
+        # Ensure generation_kwargs exists with boi_token_id
+        if not hasattr(generation_config, "generation_kwargs") or generation_config.generation_kwargs is None:
+            generation_config.generation_kwargs = {}
+        if "boi_token_id" not in generation_config.generation_kwargs:
+            # Default boi_token_id - usually the image_token_id from config
+            generation_config.generation_kwargs["boi_token_id"] = getattr(self.config, "image_token_id", 0)
+
+        # Ensure pad_token_id is set
+        if generation_config.pad_token_id is None:
+            generation_config.pad_token_id = getattr(self.config, "pad_token_id", 0)
+
         # Mask all the tokens that are neither BOS nor BOI with pad token in the unconditional logits.
         mask = (input_tokens[batch_size:, :] != generation_config.bos_token_id) & (
             input_tokens[batch_size:, :] != generation_config.generation_kwargs["boi_token_id"]
@@ -1300,12 +1311,18 @@ def generate(
 
         if model_kwargs.get("past_key_values", None) is None:
             # Prepare cache if not provided.
+            # Need enough space for: input sequence + num_image_tokens iterations + safety margin
+            # The loop runs num_image_tokens times, starting from seq_len position
+            max_length = generation_config.max_length
+            min_cache_len = seq_len + num_image_tokens + 100  # Ensure enough buffer
+            if max_length is None:
+                max_length = min_cache_len
             model_kwargs["past_key_values"] = self._prepare_static_cache(
                 cache_implementation=generation_config.cache_implementation or "static",
                 # batch_size should account for both conditional/unconditional input; hence multiplied by 2.
                 batch_size=batch_size * 2,
-                # we should have at least a cache len of seq_len + num_image_tokens.
-                max_cache_len=max(generation_config.max_length, num_image_tokens + seq_len),
+                # we should have at least a cache len of seq_len + num_image_tokens + buffer.
+                max_cache_len=max(max_length, min_cache_len),
                 model_kwargs=model_kwargs,
             )
 
diff --git a/src/transformers/models/janus/modular_janus.py b/src/transformers/models/janus/modular_janus.py
index b9c5ba79d934..77121f18b245 100644
--- a/src/transformers/models/janus/modular_janus.py
+++ b/src/transformers/models/janus/modular_janus.py
@@ -213,12 +213,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Janus VQ-VAE mode model outputs.
     """
 )
+@dataclass
 class JanusVQVAEOutput(ModelOutput):
     r"""
     decoded_pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
@@ -783,9 +783,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -1042,7 +1042,7 @@ def generate(
         input_ids, model_kwargs = self._expand_inputs_for_generation(
             input_ids=input_ids,
             attention_mask=attention_mask,
-            expand_size=generation_config.num_return_sequences,
+            expand_size=generation_config.num_return_sequences or 1,
             **model_kwargs,
         )
 
@@ -1055,6 +1055,17 @@ def generate(
         attention_mask = attention_mask.repeat(2, 1)
         model_kwargs["attention_mask"] = attention_mask
 
+        # Ensure generation_kwargs exists with boi_token_id
+        if not hasattr(generation_config, "generation_kwargs") or generation_config.generation_kwargs is None:
+            generation_config.generation_kwargs = {}
+        if "boi_token_id" not in generation_config.generation_kwargs:
+            # Default boi_token_id - usually the image_token_id from config
+            generation_config.generation_kwargs["boi_token_id"] = getattr(self.config, "image_token_id", 0)
+
+        # Ensure pad_token_id is set
+        if generation_config.pad_token_id is None:
+            generation_config.pad_token_id = getattr(self.config, "pad_token_id", 0)
+
         # Mask all the tokens that are neither BOS nor BOI with pad token in the unconditional logits.
         mask = (input_tokens[batch_size:, :] != generation_config.bos_token_id) & (
             input_tokens[batch_size:, :] != generation_config.generation_kwargs["boi_token_id"]
@@ -1065,12 +1076,18 @@ def generate(
 
         if model_kwargs.get("past_key_values", None) is None:
             # Prepare cache if not provided.
+            # Need enough space for: input sequence + num_image_tokens iterations + safety margin
+            # The loop runs num_image_tokens times, starting from seq_len position
+            max_length = generation_config.max_length
+            min_cache_len = seq_len + num_image_tokens + 100  # Ensure enough buffer
+            if max_length is None:
+                max_length = min_cache_len
             model_kwargs["past_key_values"] = self._prepare_static_cache(
                 cache_implementation=generation_config.cache_implementation or "static",
                 # batch_size should account for both conditional/unconditional input; hence multiplied by 2.
                 batch_size=batch_size * 2,
-                # we should have at least a cache len of seq_len + num_image_tokens.
-                max_cache_len=max(generation_config.max_length, num_image_tokens + seq_len),
+                # we should have at least a cache len of seq_len + num_image_tokens + buffer.
+                max_cache_len=max(max_length, min_cache_len),
                 model_kwargs=model_kwargs,
             )
 
diff --git a/src/transformers/models/janus/processing_janus.py b/src/transformers/models/janus/processing_janus.py
index bc0558b097b3..8efee13e3da8 100644
--- a/src/transformers/models/janus/processing_janus.py
+++ b/src/transformers/models/janus/processing_janus.py
@@ -20,6 +20,7 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_janus import JanusImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
@@ -43,6 +44,7 @@ class JanusTextKwargs(TextKwargs, total=False):
 
 
 class JanusProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: JanusImageProcessorKwargs
     text_kwargs: JanusTextKwargs
     _defaults = {
         "text_kwargs": {"padding": False, "padding_side": "left", "generation_mode": "text"},
@@ -52,6 +54,8 @@ class JanusProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class JanusProcessor(ProcessorMixin):
+    valid_processor_kwargs = JanusProcessorKwargs
+
     def __init__(self, image_processor, tokenizer, chat_template=None, use_default_system_prompt=False, **kwargs):
         r"""
         use_default_system_prompt (`bool`, *optional*, defaults to `False`):
@@ -87,37 +91,18 @@ def __call__(
             JanusProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs
         )
 
-        if text is None and images is None:
-            raise ValueError("You must specify either text or images.")
-
-        if text is not None:
-            if isinstance(text, str):
-                text = [text]
-            elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
-                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-
         generation_mode = output_kwargs["text_kwargs"].pop("generation_mode")
+        if self.use_default_system_prompt and generation_mode == "text":
+            text = [f"{DEFAULT_SYSTEM_PROMPT}{sample}" for sample in text]
+        elif generation_mode == "image":
+            text = [f"{sample}{self.image_start_token}" for sample in text]
+
+        model_inputs = super().__call__(images=images, text=text, **output_kwargs)
+        return model_inputs
 
-        # Replace the image token with expanded image tokens.
-        prompt_strings = []
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
         one_img_tokens = self.image_start_token + (self.image_token * self.num_image_tokens) + self.image_end_token
-        for prompt in text:
-            prompt = prompt.replace(self.image_token, one_img_tokens)
-            if self.use_default_system_prompt and generation_mode == "text":
-                prompt = DEFAULT_SYSTEM_PROMPT + prompt
-            if generation_mode == "image":
-                prompt += self.image_start_token
-            prompt_strings.append(prompt)
-
-        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
-
-        # Process images if pixel values are provided.
-        if images is not None and generation_mode != "image":
-            data["pixel_values"] = self.image_processor(images=images, **output_kwargs["images_kwargs"])[
-                "pixel_values"
-            ]
-
-        return BatchFeature(data=data)
+        return one_img_tokens
 
     def postprocess(self, images: ImageInput, **kwargs):
         """
diff --git a/src/transformers/models/jetmoe/modeling_jetmoe.py b/src/transformers/models/jetmoe/modeling_jetmoe.py
index d3ee0bb14875..20839b1ef3e5 100644
--- a/src/transformers/models/jetmoe/modeling_jetmoe.py
+++ b/src/transformers/models/jetmoe/modeling_jetmoe.py
@@ -123,7 +123,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -700,7 +700,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -708,7 +708,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -725,8 +727,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/kimi2_6/__init__.py b/src/transformers/models/kimi2_6/__init__.py
new file mode 100644
index 000000000000..f75926437f6e
--- /dev/null
+++ b/src/transformers/models/kimi2_6/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_kimi2_6 import *
+    from .image_processing_kimi2_6 import *
+    from .modeling_kimi2_6 import *
+    from .processing_kimi2_6 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/kimi2_6/configuration_kimi2_6.py b/src/transformers/models/kimi2_6/configuration_kimi2_6.py
new file mode 100644
index 000000000000..5f6ce0c11730
--- /dev/null
+++ b/src/transformers/models/kimi2_6/configuration_kimi2_6.py
@@ -0,0 +1,86 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/kimi2_6/modular_kimi2_6.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_kimi2_6.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...configuration_utils import PreTrainedConfig
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+class Kimi2_6VisionConfig(PreTrainedConfig):
+    r"""
+    pos_emb_height (`int`, *optional*):
+        Initial position embedding height.
+    pos_emb_width (`int`, *optional*):
+        Initial position embedding width.
+    pos_emb_time (`int`, *optional*):
+        Initial position embedding time dimension.
+    merge_kernel_size (`tuple[int] | list[int]`, *optional*):
+        Kernel size for patch merging.
+    """
+
+    model_type = "kimi2_6_vision"
+
+    patch_size: int = 14
+    pos_emb_height: int = 64
+    pos_emb_width: int = 64
+    pos_emb_time: int = 4
+    num_attention_heads: int = 16
+    num_hidden_layers: int = 27
+    hidden_size: int = 1152
+    intermediate_size: int = 4304
+    hidden_act: str = "gelu_pytorch_tanh"
+    merge_kernel_size: tuple[int, int] | list[int] = (2, 2)
+    rope_parameters: dict | None = None
+    max_position_embeddings: int | None = None
+
+
+class Kimi2_6Config(PreTrainedConfig):
+    r"""
+    projection_ln_eps (`float`, *optional*):
+        Layer norm epsilon for projector.
+    """
+
+    model_type = "kimi2_6"
+    sub_configs = {"text_config": AutoConfig, "vision_config": Kimi2_6VisionConfig}
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+    projection_hidden_size: int | None = 1152
+    projection_hidden_act: str = "gelu"
+    projection_ln_eps: float = 1e-5
+    image_token_id: int = 163605
+    video_token_id: int = 163606
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "deepseek_v3")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["deepseek_v3"]()
+
+        if isinstance(self.vision_config, dict):
+            self.vision_config = Kimi2_6VisionConfig(**self.vision_config)
+        elif self.vision_config is None:
+            self.vision_config = Kimi2_6VisionConfig()
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["Kimi2_6Config", "Kimi2_6VisionConfig"]
diff --git a/src/transformers/models/kimi2_6/image_processing_kimi2_6.py b/src/transformers/models/kimi2_6/image_processing_kimi2_6.py
new file mode 100644
index 000000000000..3f3389598d52
--- /dev/null
+++ b/src/transformers/models/kimi2_6/image_processing_kimi2_6.py
@@ -0,0 +1,190 @@
+# Copyright 2024 The Qwen team, Alibaba Group and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PIL Image processor class for Qwen2-VL."""
+
+import math
+
+import torch
+from torchvision.transforms.v2 import functional as tvF
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import group_images_by_shape, reorder_images
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+)
+from ...processing_utils import ImagesKwargs, Unpack
+from ...utils import TensorType, auto_docstring
+
+
+class Kimi2_6ImageProcessorKwargs(ImagesKwargs, total=False):
+    r"""
+    max_patches (`int`, *optional*, defaults to `16384`):
+        The max limit to resize resize the image.
+    patch_size (`int`, *optional*, defaults to 14):
+        The spatial patch size of the vision encoder.
+    merge_kernel_size (`int`, *optional*, defaults to 2):
+        The merge size of the vision encoder to llm encoder.
+    """
+
+    max_patches: int
+    patch_size: int
+    merge_size: int
+
+
+def navit_resize(
+    width: int,
+    height: int,
+    patch_size: int,
+    merge_kernel_size: int,
+    max_patches: int,
+    max_size_per_side: int,
+):
+    num_patches_w = max(1.0, width // patch_size)
+    num_patches_h = max(1.0, height // patch_size)
+    current_patch_count = num_patches_w * num_patches_h
+
+    # Scale to satisfy total patch budget (affects both dims, hence sqrt)
+    scale_for_total_patches = math.sqrt(max_patches / current_patch_count)
+
+    # Scale to satisfy per-side patch budget
+    scale_for_width_patches = (max_size_per_side * patch_size) / width
+    scale_for_height_patches = (max_size_per_side * patch_size) / height
+
+    # Use the most restrictive scale, never upscale
+    scale = min(1.0, scale_for_total_patches, scale_for_width_patches, scale_for_height_patches)
+
+    # Make sure the resized size doesn't go beyond predefined `max`
+    new_width, new_height = max(1, int(width * scale)), max(1, int(height * scale))
+    new_width = min(new_width, max_size_per_side * patch_size)
+    new_height = min(new_height, max_size_per_side * patch_size)
+
+    # Calculate the padding to make the height and width divisible by the merge kernel size and patch size.
+    factor = merge_kernel_size * patch_size
+    pad_height = (factor - new_height % factor) % factor
+    pad_width = (factor - new_width % factor) % factor
+
+    return (new_height, new_width), (pad_height, pad_width)
+
+
+@auto_docstring
+class Kimi2_6ImageProcessor(TorchvisionBackend):
+    do_resize = True
+    resample = PILImageResampling.BICUBIC
+    size = {"max_height": 512, "max_width": 512}
+    max_patches: 16384
+    default_to_square = False
+    do_rescale = True
+    do_normalize = True
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    do_convert_rgb = True
+    patch_size = 14
+    merge_size = 2
+    valid_kwargs = Kimi2_6ImageProcessorKwargs
+    model_input_names = ["pixel_values", "image_grid_thw"]
+
+    def __init__(self, **kwargs: Unpack[Kimi2_6ImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    def _validate_preprocess_kwargs(
+        self,
+        size: SizeDict | None = None,
+        **kwargs,
+    ) -> dict:
+        if size is not None and size.max_height is not None and (not size.max_height != size.max_width):
+            raise ValueError("size must contain 'max_height' and 'max_width' keys with identical values.")
+        super()._validate_preprocess_kwargs(size=size, **kwargs)
+
+    @auto_docstring
+    def preprocess(
+        self,
+        images: ImageInput,
+        **kwargs: Unpack[Kimi2_6ImageProcessorKwargs],
+    ) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        patch_size: int,
+        merge_size: int,
+        max_patches: int,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ) -> BatchFeature:
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            height, width = stacked_images.shape[-2:]
+            if do_resize:
+                (resized_height, resized_width), (pad_height, pad_width) = navit_resize(
+                    height,
+                    width,
+                    patch_size=patch_size,
+                    merge_kernel_size=merge_size,
+                    max_patches=max_patches,
+                    max_size_per_side=size.max_height,
+                )
+                stacked_images = self.resize(
+                    image=stacked_images,
+                    size=SizeDict(height=resized_height, width=resized_width),
+                    resample=resample,
+                )
+                stacked_images = self.pad(stacked_images, pad_size=SizeDict(height=pad_height, width=pad_width))
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+        processed_images_grouped = {}
+        processed_grids = {}
+        for shape, stacked_images in grouped_images.items():
+            resized_height, resized_width = stacked_images.shape[-2:]
+            stacked_images = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+
+            # Patchify in NaViT style, TODO maybe same as Siglip2 - needs to check with model
+            batch_size, channels, height, width = stacked_images.shape
+            grid_h, grid_w = height // patch_size, width // patch_size
+            patches = stacked_images.reshape(batch_size, channels, grid_h, patch_size, grid_w, patch_size)
+            patches = patches.transpose(0, 2, 4, 1, 3, 5)
+
+            processed_images_grouped[shape] = patches.reshape(-1, channels, patch_size, patch_size)
+            processed_grids[shape] = [[1, grid_h, grid_w]] * batch_size
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_grids_ordered = reorder_images(processed_grids, grouped_images_index)
+        pixel_values = torch.cat(processed_images, dim=0)
+        image_grid_thw = torch.tensor(processed_grids_ordered, dtype=torch.long)
+
+        return BatchFeature(
+            data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors
+        )
+
+
+__all__ = ["Kimi2_6ImageProcessor"]
diff --git a/src/transformers/models/kimi2_6/modeling_kimi2_6.py b/src/transformers/models/kimi2_6/modeling_kimi2_6.py
new file mode 100644
index 000000000000..52b23ae92e2a
--- /dev/null
+++ b/src/transformers/models/kimi2_6/modeling_kimi2_6.py
@@ -0,0 +1,842 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/kimi2_6/modular_kimi2_6.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_kimi2_6.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from collections.abc import Callable
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache
+from ...generation import GenerationMixin
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import (
+    ModelOutput,
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    torch_compilable_check,
+)
+from ...utils.generic import is_flash_attention_requested, maybe_autocast
+from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
+from .configuration_kimi2_6 import Kimi2_6Config, Kimi2_6VisionConfig
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for Kimi2_6 outputs, with hidden states and attentions.
+    """
+)
+class Kimi2_6ModelOutputWithPast(BaseModelOutputWithPast):
+    r"""
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for Kimi2_6 causal language model (or autoregressive) outputs.
+    """
+)
+class Kimi2_6CausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class Kimi2_6VisionPositionEmbeddings(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dim = config.hidden_size
+        self.num_frames = config.pos_emb_time
+
+        self.position_embeddings = nn.Parameter(
+            torch.empty(config.pos_emb_height, config.pos_emb_width, config.hidden_size)
+        )
+        time_position_embeddings = self.get_1d_sincos_pos_embed()
+        self.register_buffer("time_position_embeddings", time_position_embeddings, persistent=False)
+
+    # TODO: compute in torch
+    def get_1d_sincos_pos_embed(self):
+        grid_t = np.arange(self.num_frames, dtype=np.float32)
+        omega = np.arange(self.dim // 2, dtype=np.float32)
+        omega /= self.dim / 2.0
+        omega = 1.0 / 10000**omega  # (D/2,)
+
+        grid_t = grid_t.reshape(-1)  # (M,)
+        out = np.einsum("m,d->md", grid_t, omega)  # (M, D/2), outer product
+        emb_sin = np.sin(out)  # (M, D/2)
+        emb_cos = np.cos(out)  # (M, D/2)
+
+        pos_embed = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+        pos_embed = torch.tensor(pos_embed, dtype=torch.float).unsqueeze(1)
+        return pos_embed
+
+    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thw.tolist():
+            if t > self.num_frames:
+                raise ValueError(
+                    f"Got an input with {t} frames. Number of frames should be less than config.pos_emb_time=({self.num_frames})"
+                )
+
+            if (h, w) == self.position_embeddings.shape[:-1]:
+                position_embeddings = self.position_embeddings.flatten(0, 1)
+            else:
+                position_embeddings = self.position_embeddings.permute(2, 0, 1).unsqueeze(0)
+                position_embeddings = F.interpolate(
+                    position_embeddings,
+                    size=(h, w),
+                    mode="bicubic",
+                )
+                position_embeddings = position_embeddings.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+
+            position_embeddings = position_embeddings.unsqueeze(0).repeat(t, 1, 1)
+            if t > 1:
+                position_embeddings = position_embeddings + self.time_position_embeddings[0:t]
+
+            pos_embs.append(position_embeddings.flatten(0, 1))
+        hidden_states = hidden_states + torch.cat(pos_embs)
+        return hidden_states
+
+
+class Kimi2_6VisionPatchEmbed(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        patch_size = (
+            config.patch_size if not isinstance(config.patch_size, int) else (config.patch_size, config.patch_size)
+        )
+        self.proj = nn.Conv2d(3, config.hidden_size, kernel_size=patch_size, stride=patch_size)
+        self.pos_emb = Kimi2_6VisionPositionEmbeddings(config)
+
+    def forward(self, pixel_values: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.proj(pixel_values).view(pixel_values.size(0), -1)
+        hidden_states = self.pos_emb(hidden_states, grid_thw)
+        return hidden_states
+
+
+class Kimi2_6VisionRotaryEmbeddings(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: Kimi2_6VisionConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Kimi2_6VisionConfig | None = None,
+        device: torch.device | None = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        # The reference implementation computes RoPE frequencies INDEPENDENTLY
+        # for each spatial dimension using the partitioned head_dim (head_dim // ndim),
+        # so both x and y dimensions get identical frequency ranges.
+        # This is different from splitting the global inv_freq between dimensions.
+        spatial_dim = dim // 2
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+        inv_freq = 1.0 / (
+            base
+            ** (torch.arange(0, spatial_dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / spatial_dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+
+        # Multidimensional positions: [batch, num_patches, ndim]. Apply rotations to each spatial dim separately
+        all_cos, all_sin = [], []
+        for i in range(2):
+            dim_position_ids = position_ids[:, :, i]
+            dim_position_ids_expanded = dim_position_ids[:, None, :].float()
+
+            with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+                freqs = (inv_freq_expanded.float() @ dim_position_ids_expanded.float()).transpose(1, 2)
+                emb = torch.cat((freqs, freqs), dim=-1)
+                cos = emb.cos() * self.attention_scaling
+                sin = emb.sin() * self.attention_scaling
+            all_cos.append(cos)
+            all_sin.append(sin)
+
+        cos = torch.cat(all_cos, dim=-1).to(dtype=x.dtype)
+        sin = torch.cat(all_sin, dim=-1).to(dtype=x.dtype)
+        return cos, sin
+
+
+class Kimi2_6VisionMLP(nn.Module):
+    def __init__(self, dim: int, hidden_dim: int, hidden_act: str) -> None:
+        super().__init__()
+        self.fc1 = nn.Linear(dim, hidden_dim)
+        self.act = ACT2FN[hidden_act]
+        self.fc2 = nn.Linear(hidden_dim, dim)
+
+    def forward(self, x) -> torch.Tensor:
+        return self.fc2(self.act(self.fc1(x)))
+
+
+def rotate_half(x):
+    x1 = x[..., 0::2]
+    x2 = x[..., 1::2]
+    return torch.stack([-x2, x1], dim=-1).flatten(-2)
+
+
+def apply_rotary_pos_emb_vision(q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor to embed.
+        k (`torch.Tensor`): The key tensor to embed.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.squeeze(0).unsqueeze(1)  # (48, 1, 8) — broadcasts over heads
+    sin = sin.squeeze(0).unsqueeze(1)  # (48, 1, 8) — broadcasts over heads
+    q = (q * cos) + (rotate_half(q) * sin)
+    k = (k * cos) + (rotate_half(k) * sin)
+    return q, k
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class Kimi2_6VisionAttention(nn.Module):
+    def __init__(self, config: Kimi2_6VisionConfig) -> None:
+        super().__init__()
+        self.dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.dim // self.num_heads
+        self.num_key_value_groups = 1  # needed for eager attention
+        self.qkv = nn.Linear(self.dim, self.dim * 3, bias=True)
+        self.proj = nn.Linear(self.dim, self.dim)
+        self.scaling = self.head_dim**-0.5
+        self.config = config
+        self.attention_dropout = 0.0
+        self.is_causal = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        seq_length = hidden_states.shape[0]
+        query_states, key_states, value_states = (
+            self.qkv(hidden_states).reshape(seq_length, 3, self.num_heads, -1).permute(1, 0, 2, 3).unbind(0)
+        )
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb_vision(query_states, key_states, cos, sin)
+
+        query_states = query_states.transpose(0, 1).unsqueeze(0)
+        key_states = key_states.transpose(0, 1).unsqueeze(0)
+        value_states = value_states.transpose(0, 1).unsqueeze(0)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        if is_flash_attention_requested(self.config):
+            # Flash Attention: Use cu_seqlens for variable length attention
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, _ = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            # Other implementations: Process each chunk separately
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+            ]
+
+            attn_outputs = [
+                attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )[0]
+                for q, k, v in zip(*splits)
+            ]
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(seq_length, -1).contiguous()
+        attn_output = self.proj(attn_output)
+        return attn_output
+
+
+class Kimi2_6VisionEncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config) -> None:
+        super().__init__()
+        self.norm1 = nn.LayerNorm(config.intermediate_size, eps=1e-6)
+        self.norm2 = nn.LayerNorm(config.intermediate_size, eps=1e-6)
+
+        self.attn = Kimi2_6VisionAttention(config=config)
+        self.mlp = Kimi2_6VisionMLP(config.intermediate_size, config.hidden_size, config.hidden_act)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        rotary_pos_emb: torch.Tensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        hidden_states = hidden_states + self.attn(
+            self.norm1(hidden_states),
+            cu_seqlens=cu_seqlens,
+            rotary_pos_emb=rotary_pos_emb,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = hidden_states + self.mlp(self.norm2(hidden_states))
+        return hidden_states
+
+
+@auto_docstring
+class Kimi2_6PreTrainedModel(PreTrainedModel):
+    config: Kimi2_6Config
+    base_model_prefix = "model"
+    input_modalities = ("image", "video", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Kimi2_6VisionEncoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        super()._init_weights(module)
+
+
+class Kimi2_6VisionModel(Kimi2_6PreTrainedModel):
+    config: Kimi2_6VisionConfig
+    input_modalities = ("image", "video")
+    can_record_outputs = {
+        "hidden_states": Kimi2_6VisionEncoderLayer,
+        "attentions": Kimi2_6VisionAttention,
+    }
+
+    def __init__(self, config: Kimi2_6VisionConfig):
+        super().__init__(config)
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_embed = Kimi2_6VisionPatchEmbed(config)
+
+        self.rotary_emb = Kimi2_6VisionRotaryEmbeddings(config)
+        self.encoder_blocks = nn.ModuleList(
+            [Kimi2_6VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.final_layernorm = nn.LayerNorm(config.hidden_size)
+        self.post_init()
+
+    def temporal_patch_merger(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> list[torch.Tensor]:
+        hidden_dim = hidden_states.size(-1)
+        kernel_height, kernel_width = self.merge_kernel_size
+
+        outputs = []
+        pre_sum = 0
+        for t, h, w in grid_thw.tolist():
+            # Get the current sequence
+            seq = hidden_states[pre_sum : pre_sum + t * h * w]
+            # Reshape along self.merge_kernel_size and concat to the last dimension
+            new_height, new_width = h // kernel_height, w // kernel_width
+            reshaped_seq = seq.view(t, new_height, kernel_height, new_width, kernel_width, hidden_dim)
+            reshaped_seq = reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean(dim=0)  # temporal pooling
+            padded_seq = reshaped_seq.view(new_height * new_width, kernel_height * kernel_width, -1)
+            outputs.append(padded_seq)
+            pre_sum += t * h * w
+
+        return torch.cat(outputs, dim=0)
+
+    def get_position_ids(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        "Builds (h_pos, w_pos) grid for each sample, then cat across batch"
+        all_position_ids = []
+        for t, h, w in grid_thw.tolist():
+            h_ids = torch.arange(h, device=grid_thw.device)
+            w_ids = torch.arange(w, device=grid_thw.device)
+
+            # (h, w, 2) grid of (row, col) coordinates
+            grid = torch.stack(torch.meshgrid(h_ids, w_ids, indexing="ij"), dim=-1)
+
+            # (h*w, 2) -> repeat for each temporal frame -> (t*h*w, 2)
+            all_position_ids.append(grid.reshape(-1, 2).repeat(t, 1))
+
+        position_ids = torch.cat(all_position_ids, dim=0).unsqueeze(0)
+        return position_ids  # (1, total_patches, 2)
+
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        r"""
+        grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        hidden_states = self.patch_embed(pixel_values, grid_thw=grid_thw)
+        position_ids = self.get_position_ids(grid_thw=grid_thw)
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        lengths = torch.cat(
+            (
+                torch.zeros(1, dtype=grid_thw.dtype, device=grid_thw.device),
+                grid_thw[:, 0] * grid_thw[:, 1] * grid_thw[:, 2],
+            )
+        )
+
+        max_seqlen = lengths.max()
+        cu_seqlens = lengths.cumsum(dim=0, dtype=torch.int32)
+
+        for block in self.encoder_blocks:
+            hidden_states = block(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+                position_embeddings=position_embeddings,
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+        pooled_hidden_states = self.temporal_patch_merger(hidden_states, grid_thw)
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=hidden_states,
+            pooler_output=pooled_hidden_states,
+        )
+
+
+class Kimi2_6MultimodalProjection(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.vision_config.hidden_size * (
+            config.vision_config.merge_kernel_size[0] * config.vision_config.merge_kernel_size[1]
+        )
+        self.pre_norm = nn.LayerNorm(config.projection_hidden_size, eps=config.projection_ln_eps)
+
+        self.in_proj = nn.Linear(self.hidden_size, self.hidden_size)
+        self.act = nn.GELU()
+        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor):
+        batch_size = hidden_states.shape[0]
+        hidden_states = self.pre_norm(hidden_states).view(batch_size, -1, self.hidden_size)
+        hidden_states = self.in_proj(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+class Kimi2_6Model(Kimi2_6PreTrainedModel):
+    def __init__(self, config: Kimi2_6Config):
+        super().__init__(config)
+        self.vision_tower = Kimi2_6VisionModel._from_config(config.vision_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.mm_projector = Kimi2_6MultimodalProjection(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        vision_outputs = self.vision_tower(pixel_values, grid_thw=image_grid_thw, **kwargs)
+        image_embeds = self.mm_projector(vision_outputs.pooler_output)
+        vision_outputs.pooler_output = image_embeds
+        return vision_outputs
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor | None = None,
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        if image_features is not None:
+            torch_compilable_check(
+                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
+            )
+        return special_image_mask
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Kimi2_6ModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output
+            image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds)
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        return Kimi2_6ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    The KIMI2_6 model which consists of a vision backbone and a language model.
+    """
+)
+class Kimi2_6ForConditionalGeneration(Kimi2_6PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: Kimi2_6Config):
+        super().__init__(config)
+        self.model = Kimi2_6Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Kimi2_6CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, Kimi2_6ForConditionalGeneration
+
+        >>> model = Qwen2VLForConditionalGeneration.from_pretrained("TODO")
+        >>> processor = AutoProcessor.from_pretrained("TODO")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                    },
+                    {"type": "text", "text": "Describe the image."},
+                ],
+            }
+        ]
+
+        >>> inputs = processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            return_tensors="pt"
+        )
+
+        >>> # Generate
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=1024)
+        >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+        >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        >>> print(output_text)
+        ```
+        """
+
+        outputs: Kimi2_6ModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return Kimi2_6CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        # Overwritten -- in specific circumstances we don't want to forward image inputs to the model
+
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            # Pixel values are used only in the first iteration if available
+            # In subsequent iterations, they are already merged with text and cached
+            # NOTE: first iteration doesn't have to be prefill, it can be the first
+            # iteration with a question and cached system prompt (continue generate from cache)
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+
+__all__ = ["Kimi2_6ForConditionalGeneration", "Kimi2_6Model", "Kimi2_6PreTrainedModel", "Kimi2_6VisionModel"]
diff --git a/src/transformers/models/kimi2_6/modular_kimi2_6.py b/src/transformers/models/kimi2_6/modular_kimi2_6.py
new file mode 100644
index 000000000000..9eb98b428d7d
--- /dev/null
+++ b/src/transformers/models/kimi2_6/modular_kimi2_6.py
@@ -0,0 +1,571 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_outputs import BaseModelOutputWithPooling
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import ProcessorMixin, Unpack
+from ...utils import (
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    torch_compilable_check,
+)
+from ...utils.output_capturing import capture_outputs
+from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel
+from ..gemma4.modeling_gemma4 import Gemma4VisionRotaryEmbedding
+from ..llava.modeling_llava import LlavaCausalLMOutputWithPast, LlavaForConditionalGeneration, LlavaModelOutputWithPast
+from ..qwen2_vl.modeling_qwen2_vl import (
+    Qwen2VLPreTrainedModel,
+    Qwen2VLVisionBlock,
+    VisionAttention,
+    VisionMlp,
+)
+from ..qwen2_vl.processing_qwen2_vl import Qwen2VLProcessor
+
+
+class Kimi2_6VisionConfig(PreTrainedConfig):
+    r"""
+    pos_emb_height (`int`, *optional*):
+        Initial position embedding height.
+    pos_emb_width (`int`, *optional*):
+        Initial position embedding width.
+    pos_emb_time (`int`, *optional*):
+        Initial position embedding time dimension.
+    merge_kernel_size (`tuple[int] | list[int]`, *optional*):
+        Kernel size for patch merging.
+    """
+
+    model_type = "kimi2_6_vision"
+
+    patch_size: int = 14
+    pos_emb_height: int = 64
+    pos_emb_width: int = 64
+    pos_emb_time: int = 4
+    num_attention_heads: int = 16
+    num_hidden_layers: int = 27
+    hidden_size: int = 1152
+    intermediate_size: int = 4304
+    hidden_act: str = "gelu_pytorch_tanh"
+    merge_kernel_size: tuple[int, int] | list[int] = (2, 2)
+    rope_parameters: dict | None = None
+    max_position_embeddings: int | None = None
+
+
+class Kimi2_6Config(PreTrainedConfig):
+    r"""
+    projection_ln_eps (`float`, *optional*):
+        Layer norm epsilon for projector.
+    """
+
+    model_type = "kimi2_6"
+    sub_configs = {"text_config": AutoConfig, "vision_config": Kimi2_6VisionConfig}
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+    projection_hidden_size: int | None = 1152
+    projection_hidden_act: str = "gelu"
+    projection_ln_eps: float = 1e-5
+    image_token_id: int = 163605
+    video_token_id: int = 163606
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "deepseek_v3")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["deepseek_v3"]()
+
+        if isinstance(self.vision_config, dict):
+            self.vision_config = Kimi2_6VisionConfig(**self.vision_config)
+        elif self.vision_config is None:
+            self.vision_config = Kimi2_6VisionConfig()
+        super().__post_init__(**kwargs)
+
+
+class Kimi2_6ModelOutputWithPast(LlavaModelOutputWithPast):
+    pass
+
+
+class Kimi2_6CausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
+    pass
+
+
+class Kimi2_6VisionPositionEmbeddings(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.dim = config.hidden_size
+        self.num_frames = config.pos_emb_time
+
+        self.position_embeddings = nn.Parameter(
+            torch.empty(config.pos_emb_height, config.pos_emb_width, config.hidden_size)
+        )
+        time_position_embeddings = self.get_1d_sincos_pos_embed()
+        self.register_buffer("time_position_embeddings", time_position_embeddings, persistent=False)
+
+    # TODO: compute in torch
+    def get_1d_sincos_pos_embed(self):
+        grid_t = np.arange(self.num_frames, dtype=np.float32)
+        omega = np.arange(self.dim // 2, dtype=np.float32)
+        omega /= self.dim / 2.0
+        omega = 1.0 / 10000**omega  # (D/2,)
+
+        grid_t = grid_t.reshape(-1)  # (M,)
+        out = np.einsum("m,d->md", grid_t, omega)  # (M, D/2), outer product
+        emb_sin = np.sin(out)  # (M, D/2)
+        emb_cos = np.cos(out)  # (M, D/2)
+
+        pos_embed = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+        pos_embed = torch.tensor(pos_embed, dtype=torch.float).unsqueeze(1)
+        return pos_embed
+
+    def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        pos_embs = []
+        for t, h, w in grid_thw.tolist():
+            if t > self.num_frames:
+                raise ValueError(
+                    f"Got an input with {t} frames. Number of frames should be less than config.pos_emb_time=({self.num_frames})"
+                )
+
+            if (h, w) == self.position_embeddings.shape[:-1]:
+                position_embeddings = self.position_embeddings.flatten(0, 1)
+            else:
+                position_embeddings = self.position_embeddings.permute(2, 0, 1).unsqueeze(0)
+                position_embeddings = F.interpolate(
+                    position_embeddings,
+                    size=(h, w),
+                    mode="bicubic",
+                )
+                position_embeddings = position_embeddings.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+
+            position_embeddings = position_embeddings.unsqueeze(0).repeat(t, 1, 1)
+            if t > 1:
+                position_embeddings = position_embeddings + self.time_position_embeddings[0:t]
+
+            pos_embs.append(position_embeddings.flatten(0, 1))
+        hidden_states = hidden_states + torch.cat(pos_embs)
+        return hidden_states
+
+
+class Kimi2_6VisionPatchEmbed(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        patch_size = (
+            config.patch_size if not isinstance(config.patch_size, int) else (config.patch_size, config.patch_size)
+        )
+        self.proj = nn.Conv2d(3, config.hidden_size, kernel_size=patch_size, stride=patch_size)
+        self.pos_emb = Kimi2_6VisionPositionEmbeddings(config)
+
+    def forward(self, pixel_values: torch.Tensor, grid_thw: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.proj(pixel_values).view(pixel_values.size(0), -1)
+        hidden_states = self.pos_emb(hidden_states, grid_thw)
+        return hidden_states
+
+
+class Kimi2_6VisionRotaryEmbeddings(Gemma4VisionRotaryEmbedding):
+    pass
+
+
+class Kimi2_6VisionMLP(VisionMlp):
+    pass
+
+
+class Kimi2_6VisionAttention(VisionAttention):
+    def __init__(self, config: Kimi2_6VisionConfig) -> None:
+        super().__init__()
+        self.dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+
+
+class Kimi2_6VisionEncoderLayer(Qwen2VLVisionBlock):
+    def __init__(self, config):
+        super().__init__()
+        self.norm1 = nn.LayerNorm(config.intermediate_size, eps=1e-6)
+        self.norm2 = nn.LayerNorm(config.intermediate_size, eps=1e-6)
+
+        self.attn = Kimi2_6VisionAttention(config=config)
+        self.mlp = Kimi2_6VisionMLP(config.intermediate_size, config.hidden_size, config.hidden_act)
+
+
+class Kimi2_6PreTrainedModel(Qwen2VLPreTrainedModel):
+    _no_split_modules = ["Kimi2_6VisionEncoderLayer"]
+
+    def _init_weights(self, module):
+        PreTrainedModel._init_weights(module)
+
+
+class Kimi2_6VisionModel(Kimi2_6PreTrainedModel):
+    config: Kimi2_6VisionConfig
+    input_modalities = ("image", "video")
+    can_record_outputs = {
+        "hidden_states": Kimi2_6VisionEncoderLayer,
+        "attentions": Kimi2_6VisionAttention,
+    }
+
+    def __init__(self, config: Kimi2_6VisionConfig):
+        super().__init__(config)
+        self.merge_kernel_size = config.merge_kernel_size
+        self.patch_embed = Kimi2_6VisionPatchEmbed(config)
+
+        self.rotary_emb = Kimi2_6VisionRotaryEmbeddings(config)
+        self.encoder_blocks = nn.ModuleList(
+            [Kimi2_6VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+        self.final_layernorm = nn.LayerNorm(config.hidden_size)
+        self.post_init()
+
+    def temporal_patch_merger(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> list[torch.Tensor]:
+        hidden_dim = hidden_states.size(-1)
+        kernel_height, kernel_width = self.merge_kernel_size
+
+        outputs = []
+        pre_sum = 0
+        for t, h, w in grid_thw.tolist():
+            # Get the current sequence
+            seq = hidden_states[pre_sum : pre_sum + t * h * w]
+            # Reshape along self.merge_kernel_size and concat to the last dimension
+            new_height, new_width = h // kernel_height, w // kernel_width
+            reshaped_seq = seq.view(t, new_height, kernel_height, new_width, kernel_width, hidden_dim)
+            reshaped_seq = reshaped_seq.permute(0, 1, 3, 2, 4, 5).contiguous().mean(dim=0)  # temporal pooling
+            padded_seq = reshaped_seq.view(new_height * new_width, kernel_height * kernel_width, -1)
+            outputs.append(padded_seq)
+            pre_sum += t * h * w
+
+        return torch.cat(outputs, dim=0)
+
+    def get_position_ids(self, grid_thw: torch.Tensor) -> torch.Tensor:
+        "Builds (h_pos, w_pos) grid for each sample, then cat across batch"
+        all_position_ids = []
+        for t, h, w in grid_thw.tolist():
+            h_ids = torch.arange(h, device=grid_thw.device)
+            w_ids = torch.arange(w, device=grid_thw.device)
+
+            # (h, w, 2) grid of (row, col) coordinates
+            grid = torch.stack(torch.meshgrid(h_ids, w_ids, indexing="ij"), dim=-1)
+
+            # (h*w, 2) -> repeat for each temporal frame -> (t*h*w, 2)
+            all_position_ids.append(grid.reshape(-1, 2).repeat(t, 1))
+
+        position_ids = torch.cat(all_position_ids, dim=0).unsqueeze(0)
+        return position_ids  # (1, total_patches, 2)
+
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: torch.Tensor,
+    ) -> torch.Tensor:
+        r"""
+        grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        hidden_states = self.patch_embed(pixel_values, grid_thw=grid_thw)
+        position_ids = self.get_position_ids(grid_thw=grid_thw)
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        lengths = torch.cat(
+            (
+                torch.zeros(1, dtype=grid_thw.dtype, device=grid_thw.device),
+                grid_thw[:, 0] * grid_thw[:, 1] * grid_thw[:, 2],
+            )
+        )
+
+        max_seqlen = lengths.max()
+        cu_seqlens = lengths.cumsum(dim=0, dtype=torch.int32)
+
+        for block in self.encoder_blocks:
+            hidden_states = block(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                max_seqlen=max_seqlen,
+                position_embeddings=position_embeddings,
+            )
+
+        hidden_states = self.final_layernorm(hidden_states)
+        pooled_hidden_states = self.temporal_patch_merger(hidden_states, grid_thw)
+
+        return BaseModelOutputWithPooling(
+            last_hidden_state=hidden_states,
+            pooler_output=pooled_hidden_states,
+        )
+
+
+class Kimi2_6MultimodalProjection(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.hidden_size = config.vision_config.hidden_size * (
+            config.vision_config.merge_kernel_size[0] * config.vision_config.merge_kernel_size[1]
+        )
+        self.pre_norm = nn.LayerNorm(config.projection_hidden_size, eps=config.projection_ln_eps)
+
+        self.in_proj = nn.Linear(self.hidden_size, self.hidden_size)
+        self.act = nn.GELU()
+        self.out_proj = nn.Linear(self.hidden_size, self.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor):
+        batch_size = hidden_states.shape[0]
+        hidden_states = self.pre_norm(hidden_states).view(batch_size, -1, self.hidden_size)
+        hidden_states = self.in_proj(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.out_proj(hidden_states)
+        return hidden_states
+
+
+class Kimi2_6Model(Kimi2_6PreTrainedModel):
+    def __init__(self, config: Kimi2_6Config):
+        super().__init__(config)
+        self.vision_tower = Kimi2_6VisionModel._from_config(config.vision_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.mm_projector = Kimi2_6MultimodalProjection(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        vision_outputs = self.vision_tower(pixel_values, grid_thw=image_grid_thw, **kwargs)
+        image_embeds = self.mm_projector(vision_outputs.pooler_output)
+        vision_outputs.pooler_output = image_embeds
+        return vision_outputs
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor | None = None,
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        n_image_tokens = special_image_mask.sum()
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        if image_features is not None:
+            torch_compilable_check(
+                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
+            )
+        return special_image_mask
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Kimi2_6ModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(pixel_values, image_grid_thw).pooler_output
+            image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds)
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        return Kimi2_6ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class Kimi2_6ForConditionalGeneration(LlavaForConditionalGeneration):
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            The temporal, height and width of feature shape of each image in LLM.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            **kwargs,
+        )
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Kimi2_6CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            The temporal, height and width of feature shape of each image in LLM.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, Kimi2_6ForConditionalGeneration
+
+        >>> model = Qwen2VLForConditionalGeneration.from_pretrained("TODO")
+        >>> processor = AutoProcessor.from_pretrained("TODO")
+
+        >>> messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                    },
+                    {"type": "text", "text": "Describe the image."},
+                ],
+            }
+        ]
+
+        >>> inputs = processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            return_tensors="pt"
+        )
+
+        >>> # Generate
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=1024)
+        >>> generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
+        >>> output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        >>> print(output_text)
+        ```
+        """
+
+        outputs: Kimi2_6ModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return Kimi2_6CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class Kimi2_6Processor(Qwen2VLProcessor):
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        ProcessorMixin.__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.image_token = tokenizer.image_token
+        self.image_token_id = tokenizer.image_token_id
+        self.video_token = tokenizer.video_token
+        self.video_token_id = tokenizer.video_token_id
+
+
+__all__ = [
+    "Kimi2_6Config",
+    "Kimi2_6VisionConfig",
+    "Kimi2_6ForConditionalGeneration",
+    "Kimi2_6Model",
+    "Kimi2_6PreTrainedModel",
+    "Kimi2_6VisionModel",
+    "Kimi2_6Processor",
+]
diff --git a/src/transformers/models/kimi2_6/processing_kimi2_6.py b/src/transformers/models/kimi2_6/processing_kimi2_6.py
new file mode 100644
index 000000000000..94333fd62b14
--- /dev/null
+++ b/src/transformers/models/kimi2_6/processing_kimi2_6.py
@@ -0,0 +1,197 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/kimi2_6/modular_kimi2_6.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_kimi2_6.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import auto_docstring
+from ...video_utils import VideoInput
+
+
+class Kimi26ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": True,
+        },
+    }
+
+
+@auto_docstring
+class Kimi2_6Processor(ProcessorMixin):
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+        self.image_token = tokenizer.image_token
+        self.image_token_id = tokenizer.image_token_id
+        self.video_token = tokenizer.video_token
+        self.video_token_id = tokenizer.video_token_id
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        videos: VideoInput | None = None,
+        **kwargs: Unpack[Kimi26ProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
+            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
+            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            Kimi26ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = videos_inputs = {}
+        if images is not None:
+            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+            image_grid_thw = image_inputs["image_grid_thw"]
+
+        if videos is not None:
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grid_thw = videos_inputs["video_grid_thw"]
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+
+        if images is not None:
+            merge_length = self.image_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.image_token in text[i]:
+                    num_image_tokens = image_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        if videos is not None:
+            merge_length = self.video_processor.merge_size**2
+            index = 0
+            for i in range(len(text)):
+                while self.video_token in text[i]:
+                    num_video_tokens = video_grid_thw[index].prod() // merge_length
+                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
+                    index += 1
+                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
+
+        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`list[list[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Kimi26ProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Kimi26ProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
+    def post_process_image_text_to_text(
+        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
+    ):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        return self.tokenizer.batch_decode(
+            generated_outputs,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def model_input_names(self):
+        model_input_names = super().model_input_names
+        model_input_names.append("mm_token_type_ids")
+        return model_input_names
+
+
+__all__ = ["Kimi2_6Processor"]
diff --git a/src/transformers/models/kimi2_6/video_processing_kimi2_6.py b/src/transformers/models/kimi2_6/video_processing_kimi2_6.py
new file mode 100644
index 000000000000..9a0c2b14fb2a
--- /dev/null
+++ b/src/transformers/models/kimi2_6/video_processing_kimi2_6.py
@@ -0,0 +1,161 @@
+# Copyright 2026 the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""video processor class for Qwen2-VL."""
+
+import torch
+import torchvision.transforms.v2.functional as tvF
+
+from ...image_processing_utils import BatchFeature
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+)
+from ...processing_utils import Unpack, VideosKwargs
+from ...utils import TensorType
+from ...video_processing_utils import BaseVideoProcessor
+from ...video_utils import group_videos_by_shape, reorder_videos
+from .image_processing_kimi2_6 import navit_resize
+
+
+class Kimi2_6VideoProcessorInitKwargs(VideosKwargs, total=False):
+    r"""
+    max_patches (`int`, *optional*, defaults to `16384`):
+        The max limit to resize resize the video.
+    patch_size (`int`, *optional*, defaults to 14):
+        The spatial patch size of the vision encoder.
+    merge_kernel_size (`int`, *optional*, defaults to 2):
+        The merge size of the vision encoder to llm encoder.
+    TODO:  temporal_patch_size
+    """
+
+    max_patches: int
+    patch_size: int
+    merge_size: int
+    temporal_patch_size: int
+
+
+class Kimi2_6VideoProcessor(BaseVideoProcessor):
+    resample = PILImageResampling.BICUBIC
+    size = {"max_height": 512, "max_width": 512}
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    patch_size = 14
+    temporal_patch_size = 4
+    merge_size = 2
+    max_patches: 16384
+    do_sample_frames = True
+    valid_kwargs = Kimi2_6VideoProcessorInitKwargs
+    model_input_names = ["pixel_values_videos", "video_grid_thw"]
+
+    def __init__(self, **kwargs: Unpack[Kimi2_6VideoProcessorInitKwargs]):
+        super().__init__(**kwargs)
+
+    def _validate_preprocess_kwargs(
+        self,
+        size: SizeDict | None = None,
+        **kwargs,
+    ) -> dict:
+        if size is not None and size.max_height is not None and (not size.max_height != size.max_width):
+            raise ValueError("size must contain 'max_height' and 'max_width' keys with identical values.")
+        super()._validate_preprocess_kwargs(size=size, **kwargs)
+
+    def _preprocess(
+        self,
+        videos: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        patch_size: int,
+        temporal_patch_size: int,
+        merge_size: int,
+        max_patches: int,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ):
+        # Group videos by size for batched resizing
+        grouped_videos, grouped_videos_index = group_videos_by_shape(videos)
+        resized_videos_grouped = {}
+        for shape, stacked_videos in grouped_videos.items():
+            height, width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST)
+            resized_height, resized_width = height, width
+            if do_resize:
+                (resized_height, resized_width), (pad_height, pad_width) = navit_resize(
+                    height,
+                    width,
+                    patch_size=patch_size,
+                    merge_kernel_size=merge_size,
+                    max_patches=max_patches,
+                    max_size_per_side=size.max_height,
+                )
+                stacked_videos = self.resize(
+                    image=stacked_videos,
+                    size=SizeDict(height=resized_height, width=resized_width),
+                    resample=resample,
+                )
+                stacked_videos = self.pad(stacked_videos, pad_size=SizeDict(height=pad_height, width=pad_width))
+            resized_videos_grouped[shape] = stacked_videos
+        resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index)
+
+        # Group videos by size for further processing
+        # Needed in case do_resize is False, or resize returns videos with different sizes
+        grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos)
+        processed_videos_grouped = {}
+        processed_grids = {}
+        for shape, stacked_videos in grouped_videos.items():
+            resized_height, resized_width = get_image_size(stacked_videos[0], channel_dim=ChannelDimension.FIRST)
+
+            # Fused rescale and normalize
+            stacked_videos = self.rescale_and_normalize(
+                stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+
+            # Patchify in NaViT style, TODO maybe same as Siglip2 - needs to check with model
+            batch_size, time, channels, height, width = stacked_videos.shape
+            grid_h, grid_w = height // patch_size, width // patch_size
+            patches = stacked_videos.reshape(batch_size, time, channels, grid_h, patch_size, grid_w, patch_size)
+            patches = patches.transpose(0, 1, 3, 5, 2, 4, 6)
+
+            processed_videos_grouped[shape] = patches.reshape(-1, channels, patch_size, patch_size)
+            processed_grids[shape] = [[time, grid_h, grid_w]] * batch_size
+
+        processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index)
+        processed_grids = reorder_videos(processed_grids, grouped_videos_index)
+        pixel_values_videos = torch.cat(processed_videos, dim=0)
+        video_grid_thw = torch.tensor(processed_grids)
+
+        return BatchFeature(
+            data={"pixel_values_videos": pixel_values_videos, "video_grid_thw": video_grid_thw},
+            tensor_type=return_tensors,
+        )
+
+
+__all__ = ["Kimi2_6VideoProcessor"]
diff --git a/src/transformers/models/kosmos2/modeling_kosmos2.py b/src/transformers/models/kosmos2/modeling_kosmos2.py
index 63e4aed591fb..f8acdc9b0e71 100644
--- a/src/transformers/models/kosmos2/modeling_kosmos2.py
+++ b/src/transformers/models/kosmos2/modeling_kosmos2.py
@@ -146,8 +146,8 @@ def _make_causal_mask(
     return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithProjectionAttentions(BaseModelOutputWithPooling):
     r"""
     projection_attentions (`tuple(torch.FloatTensor)`):
@@ -161,12 +161,12 @@ class BaseModelOutputWithProjectionAttentions(BaseModelOutputWithPooling):
     projection_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for text model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class Kosmos2ModelOutput(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -202,12 +202,12 @@ def to_tuple(self) -> tuple[Any]:
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Model output class for `Kosmos2ForConditionalGeneration`.
     """
 )
+@dataclass
 class Kosmos2ForConditionalGenerationModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -528,7 +528,7 @@ def __init__(self, config: Kosmos2VisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = Kosmos2VisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = Kosmos2VisionEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
 
@@ -547,7 +547,7 @@ def forward(
             raise ValueError("You have to specify pixel_values")
 
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
@@ -722,6 +722,7 @@ def forward(
         encoder_hidden_states: torch.Tensor | None = None,
         past_key_values: Cache | None = None,
         attention_mask: torch.Tensor | None = None,
+        is_causal: bool | None = None,
         **kwargs,
     ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]:
         """Input shape: Batch x Time x Channel"""
@@ -776,6 +777,7 @@ def forward(
             attention_mask,
             dropout=0.0 if not self.training else self.dropout,
             scaling=self.scaling,
+            is_causal=is_causal,
             **kwargs,
         )
 
@@ -1345,6 +1347,7 @@ def forward(self, features):
             hidden_states=latent_query,
             encoder_hidden_states=key_value_states,
             past_key_values=None,
+            is_causal=False,
             attention_mask=None,
             output_attentions=None,
         )
diff --git a/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py b/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py
index b16274332baf..bd00139ec51b 100644
--- a/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py
+++ b/src/transformers/models/kyutai_speech_to_text/modeling_kyutai_speech_to_text.py
@@ -326,7 +326,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/lasr/configuration_lasr.py b/src/transformers/models/lasr/configuration_lasr.py
index d55ea6449d37..db9b187b2ea5 100644
--- a/src/transformers/models/lasr/configuration_lasr.py
+++ b/src/transformers/models/lasr/configuration_lasr.py
@@ -48,18 +48,18 @@ class LasrEncoderConfig(PreTrainedConfig):
         The momentum for the batch normalization layers
 
     Example:
-        ```python
-        >>> from transformers import LasrEncoderModel, LasrEncoderConfig
+    ```python
+    >>> from transformers import LasrEncoderModel, LasrEncoderConfig
 
-        >>> # Initializing a `LasrEncoder` configuration
-        >>> configuration = LasrEncoderConfig()
+    >>> # Initializing a `LasrEncoder` configuration
+    >>> configuration = LasrEncoderConfig()
 
-        >>> # Initializing a model from the configuration
-        >>> model = LasrEncoderModel(configuration)
+    >>> # Initializing a model from the configuration
+    >>> model = LasrEncoderModel(configuration)
 
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-        ```
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
 
     This configuration class is based on the LasrEncoder architecture from Google Health AI. You can find more details
     and pre-trained models at [google/medasr](https://huggingface.co/google/medasr).
@@ -111,15 +111,15 @@ class LasrCTCConfig(PreTrainedConfig):
         of [`LasrForCTC`].
 
     Example:
-        ```python
-        >>> from transformers import LasrForCTC, LasrCTCConfig
-        >>> # Initializing a Lasr configuration
-        >>> configuration = LasrCTCConfig()
-        >>> # Initializing a model from the configuration
-        >>> model = LasrForCTC(configuration)
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-        ```
+    ```python
+    >>> from transformers import LasrForCTC, LasrCTCConfig
+    >>> # Initializing a Lasr configuration
+    >>> configuration = LasrCTCConfig()
+    >>> # Initializing a model from the configuration
+    >>> model = LasrForCTC(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
     This configuration class is based on the Lasr CTC architecture from Google Health AI. You can find more details
     and pre-trained models at [google/medasr](https://huggingface.co/google/medasr).
     """
diff --git a/src/transformers/models/lasr/feature_extraction_lasr.py b/src/transformers/models/lasr/feature_extraction_lasr.py
index 7cf1822ee40d..26cacd39b09a 100644
--- a/src/transformers/models/lasr/feature_extraction_lasr.py
+++ b/src/transformers/models/lasr/feature_extraction_lasr.py
@@ -232,17 +232,17 @@ def __call__(
                 f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                 "We will take the mean of the channels to convert to mono."
             )
-            raw_speech = raw_speech.mean(-1)
+            raw_speech = raw_speech.mean(1)
 
         is_batched_sequence = isinstance(raw_speech, (list, tuple))
         if is_batched_sequence:
-            for speech in raw_speech:
+            for index, speech in enumerate(raw_speech):
                 if len(speech.shape) > 1:
                     logger.warning(
                         f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                         "We will take the mean of the channels to convert to mono."
                     )
-                    speech = speech.mean(-1)
+                    raw_speech[index] = speech.mean(0)
 
         if is_batched_torch or is_batched_sequence:
             raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech]
diff --git a/src/transformers/models/lasr/modeling_lasr.py b/src/transformers/models/lasr/modeling_lasr.py
index 7ecea9099410..28f3d57ea08f 100644
--- a/src/transformers/models/lasr/modeling_lasr.py
+++ b/src/transformers/models/lasr/modeling_lasr.py
@@ -26,16 +26,18 @@
 from torch import nn
 
 from ...activations import ACT2FN
+from ...generation import CompileConfig, GenerationMixin
 from ...integrations import use_kernel_func_from_hub, use_kernelized_func
 from ...masking_utils import create_bidirectional_mask
 from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput
+from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutput
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
 from .configuration_lasr import LasrCTCConfig, LasrEncoderConfig
 
 
@@ -124,7 +126,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -458,6 +460,17 @@ def _get_output_attention_mask(self, attention_mask: torch.Tensor, target_length
         return attention_mask
 
 
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Extends [~modeling_outputs.BaseModelOutputWithPooling] to include the output attention mask since sequence length
+    is not preserved in the model's forward.
+    """
+)
+class LasrEncoderModelOutput(BaseModelOutputWithPooling):
+    attention_mask: torch.Tensor | None = None
+
+
 @auto_docstring(
     custom_intro="""
     The LasrEncoder model, based on the Conformer architecture](https://arxiv.org/abs/2005.08100).
@@ -492,16 +505,20 @@ def forward(
         self,
         input_features: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
+        output_attention_mask: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> BaseModelOutput:
+    ) -> LasrEncoderModelOutput:
         r"""
+        output_attention_mask (`bool`, *optional*):
+            Whether to return the output attention mask.
+
         Example:
 
         ```python
         >>> from transformers import AutoProcessor, LasrEncoder
         >>> from datasets import load_dataset, Audio
 
-        >>> model_id = TODO
+        >>> model_id = "google/medasr"
         >>> processor = AutoProcessor.from_pretrained(model_id)
         >>> encoder = ParakeetEncoder.from_pretrained(model_id)
 
@@ -524,8 +541,10 @@ def forward(
         cos = nn.functional.dropout(cos, p=self.dropout_positions, training=self.training)
         sin = nn.functional.dropout(sin, p=self.dropout_positions, training=self.training)
 
+        output_mask = None
         if attention_mask is not None:
-            attention_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
+            output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
+            attention_mask = output_mask
 
         attention_mask = create_bidirectional_mask(
             config=self.config,
@@ -551,13 +570,16 @@ def forward(
 
         hidden_states = self.out_norm(hidden_states)
 
-        return BaseModelOutput(last_hidden_state=hidden_states)
+        return LasrEncoderModelOutput(
+            last_hidden_state=hidden_states,
+            attention_mask=output_mask.int() if output_attention_mask and output_mask is not None else None,
+        )
 
 
 @dataclass
-class LasrGenerateOutput(ModelOutput):
+class LasrCTCGenerateOutput(ModelOutput):
     """
-    Outputs of Lasr models.
+    Outputs of Lasr CTC model generation.
 
     Args:
         sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -586,12 +608,12 @@ class LasrGenerateOutput(ModelOutput):
     Lasr Encoder with a Connectionist Temporal Classification (CTC) head.
     """
 )
-class LasrForCTC(LasrPreTrainedModel):
+class LasrForCTC(LasrPreTrainedModel, GenerationMixin):
     config: LasrCTCConfig
 
     def __init__(self, config: LasrCTCConfig):
         super().__init__(config)
-        self.encoder = LasrEncoder(config.encoder_config)
+        self.encoder = AutoModel.from_config(config.encoder_config)
         # Conv rather than linear to be consistent with NeMO decoding layer
         self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)
 
@@ -626,6 +648,8 @@ def forward(
         >>> print(outputs.loss)
         ```"""
 
+        if labels is not None:
+            kwargs.setdefault("output_attention_mask", True)
         encoder_outputs = self.encoder(
             input_features=input_features,
             attention_mask=attention_mask,
@@ -637,14 +661,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            # retrieve loss input_lengths from attention_mask
-            attention_mask = (
-                attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
-            )
-            input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
+            encoder_lengths = encoder_outputs.attention_mask.sum(-1)
 
-            # assuming that padded tokens are filled with -100
-            # when not being attended to
+            # assuming that padded tokens are filled with pad_token_id when not being attended to
             labels_mask = labels != self.config.pad_token_id
             target_lengths = labels_mask.sum(-1)
             flattened_targets = labels.masked_select(labels_mask)
@@ -656,7 +675,7 @@ def forward(
                 loss = nn.functional.ctc_loss(
                     log_probs,
                     flattened_targets,
-                    input_lengths,
+                    encoder_lengths,
                     target_lengths,
                     blank=self.config.pad_token_id,
                     reduction=self.config.ctc_loss_reduction,
@@ -676,8 +695,9 @@ def generate(
         input_features: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         return_dict_in_generate: bool = False,
+        compile_config: CompileConfig | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> LasrGenerateOutput | torch.LongTensor:
+    ) -> LasrCTCGenerateOutput | torch.LongTensor:
         r"""
         Example:
 
@@ -685,7 +705,7 @@ def generate(
         >>> from transformers import AutoProcessor, LasrForCTC
         >>> from datasets import load_dataset, Audio
 
-        >>> model_id = TODO
+        >>> model_id = "google/medasr"
         >>> processor = AutoProcessor.from_pretrained(model_id)
         >>> model = LasrForCTC.from_pretrained(model_id)
 
@@ -699,8 +719,10 @@ def generate(
         >>> print(transcription)
         ```
         """
+        model_forward = self.get_compiled_call(compile_config) if compile_config is not None else self.__call__
+
         kwargs["return_dict"] = True
-        outputs: CausalLMOutput = self.forward(
+        outputs: CausalLMOutput = model_forward(
             input_features=input_features,
             attention_mask=attention_mask,
             **kwargs,
@@ -715,7 +737,7 @@ def generate(
             sequences[~attention_mask] = self.config.pad_token_id
 
         if return_dict_in_generate:
-            return LasrGenerateOutput(
+            return LasrCTCGenerateOutput(
                 sequences=sequences,
                 logits=outputs.logits,
                 attentions=outputs.attentions,
diff --git a/src/transformers/models/lasr/modular_lasr.py b/src/transformers/models/lasr/modular_lasr.py
index f016f16cff45..1329c5c0a2af 100644
--- a/src/transformers/models/lasr/modular_lasr.py
+++ b/src/transformers/models/lasr/modular_lasr.py
@@ -21,12 +21,13 @@
 from tokenizers.models import Unigram
 from torch import nn
 
+from ...audio_utils import AudioInput, make_list_of_audio
 from ...masking_utils import create_bidirectional_mask
-from ...modeling_outputs import BaseModelOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from ...processing_utils import Unpack
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...tokenization_utils_tokenizers import TokenizersBackend
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..llama.modeling_llama import LlamaAttention, LlamaRotaryEmbedding, apply_rotary_pos_emb, eager_attention_forward
@@ -34,13 +35,16 @@
 from ..parakeet.modeling_parakeet import (
     ParakeetEncoderBlock,
     ParakeetEncoderConvolutionModule,
+    ParakeetEncoderModelOutput,
     ParakeetForCTC,
     ParakeetPreTrainedModel,
 )
-from ..parakeet.processing_parakeet import ParakeetProcessor
 from ..t5.tokenization_t5 import T5Tokenizer
 
 
+logger = logging.get_logger(__name__)
+
+
 class LasrTokenizer(T5Tokenizer, TokenizersBackend):
     def __init__(
         self,
@@ -144,8 +148,74 @@ def _decode(
         )
 
 
-class LasrProcessor(ParakeetProcessor):
-    pass
+class LasrProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "audio_kwargs": {
+            "sampling_rate": 16000,
+            "padding": "longest",
+            "return_attention_mask": True,
+        },
+        "text_kwargs": {
+            "padding": True,
+            "padding_side": "right",
+            "add_special_tokens": False,
+        },
+        "common_kwargs": {"return_tensors": "pt"},
+    }
+
+
+@auto_docstring
+class LasrProcessor(ProcessorMixin):
+    def __init__(self, feature_extractor, tokenizer):
+        super().__init__(feature_extractor, tokenizer)
+
+    @auto_docstring
+    def __call__(
+        self,
+        audio: AudioInput,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        sampling_rate: int | None = None,
+        **kwargs: Unpack[LasrProcessorKwargs],
+    ):
+        r"""
+        sampling_rate (`int`, *optional*):
+            The sampling rate of the input audio in Hz. This should match the sampling rate expected by the feature
+            extractor (defaults to 16000 Hz). If provided, it will be validated against the processor's expected
+            sampling rate, and an error will be raised if they don't match. If not provided, a warning will be
+            issued and the default sampling rate will be assumed.
+        """
+        audio = make_list_of_audio(audio)
+
+        output_kwargs = self._merge_kwargs(
+            LasrProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if sampling_rate is None:
+            logger.warning_once(
+                f"You've provided audio without specifying the sampling rate. It will be assumed to be {output_kwargs['audio_kwargs']['sampling_rate']}, which can result in silent errors."
+            )
+        elif sampling_rate != output_kwargs["audio_kwargs"]["sampling_rate"]:
+            raise ValueError(
+                f"The sampling rate of the audio ({sampling_rate}) does not match the sampling rate of the processor ({output_kwargs['audio_kwargs']['sampling_rate']}). Please provide resampled the audio to the expected sampling rate."
+            )
+
+        if audio is not None:
+            inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
+        if text is not None:
+            encodings = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        if text is None:
+            return inputs
+        else:
+            inputs["labels"] = encodings["input_ids"]
+            return inputs
+
+    @property
+    def model_input_names(self):
+        feature_extractor_input_names = self.feature_extractor.model_input_names
+        return feature_extractor_input_names + ["labels"]
 
 
 @auto_docstring(checkpoint="google/medasr")
@@ -172,18 +242,18 @@ class LasrEncoderConfig(ParakeetEncoderConfig):
         The momentum for the batch normalization layers
 
     Example:
-        ```python
-        >>> from transformers import LasrEncoderModel, LasrEncoderConfig
+    ```python
+    >>> from transformers import LasrEncoderModel, LasrEncoderConfig
 
-        >>> # Initializing a `LasrEncoder` configuration
-        >>> configuration = LasrEncoderConfig()
+    >>> # Initializing a `LasrEncoder` configuration
+    >>> configuration = LasrEncoderConfig()
 
-        >>> # Initializing a model from the configuration
-        >>> model = LasrEncoderModel(configuration)
+    >>> # Initializing a model from the configuration
+    >>> model = LasrEncoderModel(configuration)
 
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-        ```
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
 
     This configuration class is based on the LasrEncoder architecture from Google Health AI. You can find more details
     and pre-trained models at [google/medasr](https://huggingface.co/google/medasr).
@@ -221,15 +291,15 @@ class LasrCTCConfig(ParakeetCTCConfig):
         of [`LasrForCTC`].
 
     Example:
-        ```python
-        >>> from transformers import LasrForCTC, LasrCTCConfig
-        >>> # Initializing a Lasr configuration
-        >>> configuration = LasrCTCConfig()
-        >>> # Initializing a model from the configuration
-        >>> model = LasrForCTC(configuration)
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-        ```
+    ```python
+    >>> from transformers import LasrForCTC, LasrCTCConfig
+    >>> # Initializing a Lasr configuration
+    >>> configuration = LasrCTCConfig()
+    >>> # Initializing a model from the configuration
+    >>> model = LasrForCTC(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
     This configuration class is based on the Lasr CTC architecture from Google Health AI. You can find more details
     and pre-trained models at [google/medasr](https://huggingface.co/google/medasr).
     """
@@ -390,6 +460,10 @@ def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
         return input_lengths
 
 
+class LasrEncoderModelOutput(ParakeetEncoderModelOutput):
+    pass
+
+
 @auto_docstring(
     custom_intro="""
     The LasrEncoder model, based on the Conformer architecture](https://arxiv.org/abs/2005.08100).
@@ -424,16 +498,20 @@ def forward(
         self,
         input_features: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
+        output_attention_mask: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> BaseModelOutput:
+    ) -> LasrEncoderModelOutput:
         r"""
+        output_attention_mask (`bool`, *optional*):
+            Whether to return the output attention mask.
+
         Example:
 
         ```python
         >>> from transformers import AutoProcessor, LasrEncoder
         >>> from datasets import load_dataset, Audio
 
-        >>> model_id = TODO
+        >>> model_id = "google/medasr"
         >>> processor = AutoProcessor.from_pretrained(model_id)
         >>> encoder = ParakeetEncoder.from_pretrained(model_id)
 
@@ -456,8 +534,10 @@ def forward(
         cos = nn.functional.dropout(cos, p=self.dropout_positions, training=self.training)
         sin = nn.functional.dropout(sin, p=self.dropout_positions, training=self.training)
 
+        output_mask = None
         if attention_mask is not None:
-            attention_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
+            output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
+            attention_mask = output_mask
 
         attention_mask = create_bidirectional_mask(
             config=self.config,
@@ -483,7 +563,10 @@ def forward(
 
         hidden_states = self.out_norm(hidden_states)
 
-        return BaseModelOutput(last_hidden_state=hidden_states)
+        return LasrEncoderModelOutput(
+            last_hidden_state=hidden_states,
+            attention_mask=output_mask.int() if output_attention_mask and output_mask is not None else None,
+        )
 
 
 class LasrForCTC(ParakeetForCTC):
@@ -495,7 +578,7 @@ def generate(**super_kwargs):
         >>> from transformers import AutoProcessor, LasrForCTC
         >>> from datasets import load_dataset, Audio
 
-        >>> model_id = TODO
+        >>> model_id = "google/medasr"
         >>> processor = AutoProcessor.from_pretrained(model_id)
         >>> model = LasrForCTC.from_pretrained(model_id)
 
diff --git a/src/transformers/models/lasr/processing_lasr.py b/src/transformers/models/lasr/processing_lasr.py
index c1acaebaae07..9eb093a49c7a 100644
--- a/src/transformers/models/lasr/processing_lasr.py
+++ b/src/transformers/models/lasr/processing_lasr.py
@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+
 from ...audio_utils import AudioInput, make_list_of_audio
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
diff --git a/src/transformers/models/led/modeling_led.py b/src/transformers/models/led/modeling_led.py
index 6fb1baea2bf5..d8e061a38614 100755
--- a/src/transformers/models/led/modeling_led.py
+++ b/src/transformers/models/led/modeling_led.py
@@ -1112,13 +1112,13 @@ class LEDEncoderBaseModelOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
     decoding.
     """
 )
+@dataclass
 class LEDSeq2SeqModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -1151,12 +1151,12 @@ class LEDSeq2SeqModelOutput(ModelOutput):
     encoder_global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for sequence-to-sequence language models outputs.
     """
 )
+@dataclass
 class LEDSeq2SeqLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1189,12 +1189,12 @@ class LEDSeq2SeqLMOutput(ModelOutput):
     encoder_global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of sequence-to-sequence sentence classification models.
     """
 )
+@dataclass
 class LEDSeq2SeqSequenceClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
@@ -1227,12 +1227,12 @@ class LEDSeq2SeqSequenceClassifierOutput(ModelOutput):
     encoder_global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of sequence-to-sequence question answering models.
     """
 )
+@dataclass
 class LEDSeq2SeqQuestionAnsweringModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/levit/modeling_levit.py b/src/transformers/models/levit/modeling_levit.py
index 2d334afe0cb7..68c3af9ac5e7 100644
--- a/src/transformers/models/levit/modeling_levit.py
+++ b/src/transformers/models/levit/modeling_levit.py
@@ -34,12 +34,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`LevitForImageClassificationWithTeacher`].
     """
 )
+@dataclass
 class LevitForImageClassificationWithTeacherOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, config.num_labels)`):
diff --git a/src/transformers/models/lfm2/modeling_lfm2.py b/src/transformers/models/lfm2/modeling_lfm2.py
index ef753e3b2893..4fd2232abb6d 100644
--- a/src/transformers/models/lfm2/modeling_lfm2.py
+++ b/src/transformers/models/lfm2/modeling_lfm2.py
@@ -83,8 +83,8 @@ def __init__(self, config: Lfm2Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -124,7 +124,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py b/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
index 0369ae31b8ae..a6bcc9448bf6 100644
--- a/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
+++ b/src/transformers/models/lfm2_moe/modeling_lfm2_moe.py
@@ -90,8 +90,8 @@ def __init__(self, config: Lfm2MoeConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -131,7 +131,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/lfm2_vl/modeling_lfm2_vl.py b/src/transformers/models/lfm2_vl/modeling_lfm2_vl.py
index b66ba44bef3f..cc6ad86922f1 100755
--- a/src/transformers/models/lfm2_vl/modeling_lfm2_vl.py
+++ b/src/transformers/models/lfm2_vl/modeling_lfm2_vl.py
@@ -88,12 +88,12 @@ class Lfm2VlPreTrainedModel(PreTrainedModel):
     _supports_attention_backend = True
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Lfm2Vl causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Lfm2VlCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -225,10 +225,10 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/lfm2_vl/modular_lfm2_vl.py b/src/transformers/models/lfm2_vl/modular_lfm2_vl.py
index 4cf94132367c..efd966886e64 100644
--- a/src/transformers/models/lfm2_vl/modular_lfm2_vl.py
+++ b/src/transformers/models/lfm2_vl/modular_lfm2_vl.py
@@ -156,10 +156,10 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/lfm2_vl/processing_lfm2_vl.py b/src/transformers/models/lfm2_vl/processing_lfm2_vl.py
index bf654310d0d3..baf2744d7210 100755
--- a/src/transformers/models/lfm2_vl/processing_lfm2_vl.py
+++ b/src/transformers/models/lfm2_vl/processing_lfm2_vl.py
@@ -23,6 +23,7 @@
 )
 from ...tokenization_utils_base import BatchEncoding, TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_lfm2_vl_fast import Lfm2VlImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
@@ -40,6 +41,7 @@ class Lfm2VlTextKwargs(TextKwargs, total=False):
 
 
 class Lfm2VlProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Lfm2VlImageProcessorKwargs
     text_kwargs: Lfm2VlTextKwargs
     _defaults = {
         "images_kwargs": {
diff --git a/src/transformers/models/lightglue/modeling_lightglue.py b/src/transformers/models/lightglue/modeling_lightglue.py
index 1c5b90e0f4fd..4591e1be90ca 100644
--- a/src/transformers/models/lightglue/modeling_lightglue.py
+++ b/src/transformers/models/lightglue/modeling_lightglue.py
@@ -35,7 +35,6 @@
 from .configuration_lightglue import LightGlueConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of LightGlue keypoint matching models. Due to the nature of keypoint detection and matching,
@@ -45,6 +44,7 @@
     matching information.
     """
 )
+@dataclass
 class LightGlueKeypointMatchingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
diff --git a/src/transformers/models/lightglue/modular_lightglue.py b/src/transformers/models/lightglue/modular_lightglue.py
index afc8a3efec25..4b0ce57b0273 100644
--- a/src/transformers/models/lightglue/modular_lightglue.py
+++ b/src/transformers/models/lightglue/modular_lightglue.py
@@ -109,7 +109,6 @@ def validate_architecture(self):
             raise ValueError("descriptor_dim % num_heads is different from zero")
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of LightGlue keypoint matching models. Due to the nature of keypoint detection and matching,
@@ -119,6 +118,7 @@ def validate_architecture(self):
     matching information.
     """
 )
+@dataclass
 class LightGlueKeypointMatchingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
diff --git a/src/transformers/models/lighton_ocr/modeling_lighton_ocr.py b/src/transformers/models/lighton_ocr/modeling_lighton_ocr.py
index 946971c02119..b807b89c3a41 100644
--- a/src/transformers/models/lighton_ocr/modeling_lighton_ocr.py
+++ b/src/transformers/models/lighton_ocr/modeling_lighton_ocr.py
@@ -205,9 +205,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -260,12 +260,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for LightOnOcr causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class LightOnOcrCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/lighton_ocr/modular_lighton_ocr.py b/src/transformers/models/lighton_ocr/modular_lighton_ocr.py
index 8237620195ee..a428216e254a 100644
--- a/src/transformers/models/lighton_ocr/modular_lighton_ocr.py
+++ b/src/transformers/models/lighton_ocr/modular_lighton_ocr.py
@@ -153,7 +153,9 @@ def __init__(
         self.image_break_token_id = tokenizer.image_break_token_id
         self.image_end_token_id = tokenizer.image_end_token_id
 
-        self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
 
     def __call__(
         self,
diff --git a/src/transformers/models/lighton_ocr/processing_lighton_ocr.py b/src/transformers/models/lighton_ocr/processing_lighton_ocr.py
index f7c189c3d849..846fed02e5d4 100644
--- a/src/transformers/models/lighton_ocr/processing_lighton_ocr.py
+++ b/src/transformers/models/lighton_ocr/processing_lighton_ocr.py
@@ -26,9 +26,11 @@
 from ...image_utils import ChannelDimension, ImageInput, get_image_size
 from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ..pixtral.image_processing_pixtral import PixtralImageProcessorKwargs
 
 
 class LightOnOcrProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: PixtralImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -125,7 +127,9 @@ def __init__(
         self.image_break_token_id = tokenizer.image_break_token_id
         self.image_end_token_id = tokenizer.image_end_token_id
 
-        self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
 
     def __call__(
         self,
diff --git a/src/transformers/models/llama/modeling_llama.py b/src/transformers/models/llama/modeling_llama.py
index 9d659c7c6f08..0f750673e208 100644
--- a/src/transformers/models/llama/modeling_llama.py
+++ b/src/transformers/models/llama/modeling_llama.py
@@ -127,7 +127,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -235,15 +235,8 @@ def __init__(self, config: LlamaConfig, layer_idx: int):
         self.attention_dropout = config.attention_dropout
         self.is_causal = True
 
-        self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.k_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
-        self.v_proj = nn.Linear(
-            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
-        )
+        op_size = config.num_attention_heads * self.head_dim + 2 * (config.num_key_value_heads * self.head_dim)
+        self.qkv_proj = nn.Linear(config.hidden_size, op_size, bias=config.attention_bias)
         self.o_proj = nn.Linear(
             config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
         )
@@ -259,9 +252,15 @@ def forward(
         input_shape = hidden_states.shape[:-1]
         hidden_shape = (*input_shape, -1, self.head_dim)
 
-        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        key_states = self.k_proj(hidden_states).view(hidden_shape).transpose(1, 2)
-        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+        qkv = self.qkv_proj(hidden_states)
+        query_pos = self.config.num_attention_heads * self.head_dim
+        query_states = qkv[..., :query_pos]
+        key_states = qkv[..., query_pos : query_pos + self.config.num_key_value_heads * self.head_dim]
+        value_states = qkv[..., query_pos + self.config.num_key_value_heads * self.head_dim :]
+
+        query_states = query_states.view(hidden_shape).transpose(1, 2)
+        key_states = key_states.view(hidden_shape).transpose(1, 2)
+        value_states = value_states.view(hidden_shape).transpose(1, 2)
 
         cos, sin = position_embeddings
         query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
@@ -482,13 +481,24 @@ def forward(
         )
 
         hidden_states = outputs.last_hidden_state
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
-        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
+        logits = None
         if labels is not None:
-            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+            loss = self.loss_function(
+                logits=None,
+                labels=labels,
+                vocab_size=self.config.vocab_size,
+                hidden_states=hidden_states,
+                lm_head_weight=self.lm_head.weight,
+                logits_to_keep=logits_to_keep,
+                **kwargs,
+            )
+        else:
+            # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+            slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+            logits = self.lm_head(hidden_states[:, slice_indices, :])
+
 
         return CausalLMOutputWithPast(
             loss=loss,
diff --git a/src/transformers/models/llama/tokenization_llama.py b/src/transformers/models/llama/tokenization_llama.py
index 366e50d74ec2..10caed8de8fa 100644
--- a/src/transformers/models/llama/tokenization_llama.py
+++ b/src/transformers/models/llama/tokenization_llama.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from tokenizers import Tokenizer, decoders, pre_tokenizers
+from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
 from tokenizers.models import BPE
 
 from ...tokenization_utils_base import _get_prepend_scheme
@@ -116,10 +116,16 @@ def __init__(
         self._tokenizer = Tokenizer(
             BPE(vocab=self._vocab, merges=self._merges, fuse_unk=True, byte_fallback=True, dropout=None)
         )
-        self._tokenizer.normalizer = None
-        self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(
-            replacement="▁", prepend_scheme=_get_prepend_scheme(self.add_prefix_space, self), split=False
-        )
+        if not self.legacy:
+            self._tokenizer.normalizer = None
+            self._tokenizer.pre_tokenizer = pre_tokenizers.Metaspace(
+                replacement="▁", prepend_scheme=_get_prepend_scheme(self.add_prefix_space, self), split=False
+            )
+        else:
+            self._tokenizer.pre_tokenizer = None
+            self._tokenizer.normalizer = normalizers.Sequence(
+                [normalizers.Prepend(prepend="▁"), normalizers.Replace(pattern=" ", content="▁")]
+            )
 
         sequence = [
             decoders.Replace("▁", " "),
diff --git a/src/transformers/models/llama4/convert_llama4_weights_to_hf.py b/src/transformers/models/llama4/convert_llama4_weights_to_hf.py
index 4ceab6067b4c..5983215e7708 100644
--- a/src/transformers/models/llama4/convert_llama4_weights_to_hf.py
+++ b/src/transformers/models/llama4/convert_llama4_weights_to_hf.py
@@ -265,7 +265,7 @@ def write_model(
 
     num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
 
-    if params.get("moe_args", False):
+    if "moe_args" in params and params["moe_args"] is not None:
         num_experts = params["moe_args"]["num_experts"]
         interleave_moe_layer_step = params["moe_args"].get("interleave_moe_layer_step", 1)
     else:
diff --git a/src/transformers/models/llama4/modeling_llama4.py b/src/transformers/models/llama4/modeling_llama4.py
index 08d50bd63f72..90dcfddb9b89 100644
--- a/src/transformers/models/llama4/modeling_llama4.py
+++ b/src/transformers/models/llama4/modeling_llama4.py
@@ -665,12 +665,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Llama4CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1240,9 +1240,9 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
         )
         return special_image_mask
diff --git a/src/transformers/models/llama4/processing_llama4.py b/src/transformers/models/llama4/processing_llama4.py
index f67e37a1e80a..51f0fe318e1e 100644
--- a/src/transformers/models/llama4/processing_llama4.py
+++ b/src/transformers/models/llama4/processing_llama4.py
@@ -19,9 +19,11 @@
 from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput, make_flat_list_of_images
 from ...utils import auto_docstring
+from .image_processing_llama4_fast import Llama4ImageProcessorKwargs
 
 
 class Llama4ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Llama4ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding_side": "left",
diff --git a/src/transformers/models/llava/modeling_llava.py b/src/transformers/models/llava/modeling_llava.py
index f17041dca72b..1ecf3937cfce 100644
--- a/src/transformers/models/llava/modeling_llava.py
+++ b/src/transformers/models/llava/modeling_llava.py
@@ -54,12 +54,12 @@ class LlavaModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class LlavaCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -211,9 +211,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/llava/processing_llava.py b/src/transformers/models/llava/processing_llava.py
index f219e5208caa..c1f518513e8f 100644
--- a/src/transformers/models/llava/processing_llava.py
+++ b/src/transformers/models/llava/processing_llava.py
@@ -15,15 +15,12 @@
 Processor class for Llava.
 """
 
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput, get_image_size, to_numpy_array
+from ...image_utils import get_image_size, to_numpy_array
 from ...processing_utils import (
     MultiModalData,
     ProcessingKwargs,
     ProcessorMixin,
-    Unpack,
 )
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
 
 
@@ -32,12 +29,14 @@
 
 class LlavaProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
-        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
+        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False, "return_text_replacement_offsets": False},
     }
 
 
 @auto_docstring
 class LlavaProcessor(ProcessorMixin):
+    valid_processor_kwargs = LlavaProcessorKwargs
+
     def __init__(
         self,
         image_processor=None,
@@ -68,66 +67,13 @@ def __init__(
         self.image_token_id = tokenizer.encode(self.image_token, add_special_tokens=False)[0]
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        **kwargs: Unpack[LlavaProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-        """
-        if images is None and text is None:
-            raise ValueError("You have to specify at least one of `images` or `text`.")
-
-        output_kwargs = self._merge_kwargs(
-            LlavaProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-        else:
-            image_inputs = {}
-
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        # try to expand inputs in processing if we have the necessary parts
-        prompt_strings = text
-        if image_inputs.get("pixel_values") is not None:
-            # Replace the image token with the expanded image token sequence
-            pixel_values = image_inputs["pixel_values"]
-            height, width = get_image_size(to_numpy_array(pixel_values[0]))
-            num_image_tokens = (height // self.patch_size) * (
-                width // self.patch_size
-            ) + self.num_additional_image_tokens
-            if self.vision_feature_select_strategy == "default":
-                num_image_tokens -= 1
-
-            prompt_strings = []
-            for sample in text:
-                sample = sample.replace(self.image_token, self.image_token * num_image_tokens)
-                prompt_strings.append(sample)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        pixel_values = image_inputs["pixel_values"][image_idx]
+        height, width = get_image_size(to_numpy_array(pixel_values))
+        num_image_tokens = (height // self.patch_size) * (width // self.patch_size) + self.num_additional_image_tokens
+        if self.vision_feature_select_strategy == "default":
+            num_image_tokens -= 1
+        return self.image_token * num_image_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
         """
diff --git a/src/transformers/models/llava_next/modeling_llava_next.py b/src/transformers/models/llava_next/modeling_llava_next.py
index 2443669f109b..81d96951611f 100644
--- a/src/transformers/models/llava_next/modeling_llava_next.py
+++ b/src/transformers/models/llava_next/modeling_llava_next.py
@@ -166,12 +166,12 @@ class LlavaNextModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for LlavaNext causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class LlavaNextCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -432,9 +432,9 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
         )
         return special_image_mask
@@ -634,7 +634,7 @@ def forward(
         hidden_states = outputs[0]
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
-        logits = self.lm_head(hidden_states[:, slice_indices, :])
+        logits = self.lm_head(hidden_states[:, slice_indices, :].to(self.lm_head.weight.dtype))
 
         loss = None
         if labels is not None:
diff --git a/src/transformers/models/llava_next/processing_llava_next.py b/src/transformers/models/llava_next/processing_llava_next.py
index 5208ae2713ee..2db703e5c25b 100644
--- a/src/transformers/models/llava_next/processing_llava_next.py
+++ b/src/transformers/models/llava_next/processing_llava_next.py
@@ -26,12 +26,14 @@
 )
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_llava_next import LlavaNextImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class LlavaNextProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: LlavaNextImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
diff --git a/src/transformers/models/llava_next_video/modeling_llava_next_video.py b/src/transformers/models/llava_next_video/modeling_llava_next_video.py
index 5e20ab888db7..056137397e1e 100644
--- a/src/transformers/models/llava_next_video/modeling_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/modeling_llava_next_video.py
@@ -69,12 +69,12 @@ class LlavaNextVideoModelOutputWithPast(BaseModelOutputWithPast):
     video_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for LlavaNextVideo causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class LlavaNextVideoCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -495,18 +495,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/llava_next_video/modular_llava_next_video.py b/src/transformers/models/llava_next_video/modular_llava_next_video.py
index c5d521e5034b..b8b706f6f26c 100644
--- a/src/transformers/models/llava_next_video/modular_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/modular_llava_next_video.py
@@ -375,18 +375,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/llava_next_video/processing_llava_next_video.py b/src/transformers/models/llava_next_video/processing_llava_next_video.py
index 543898f29fd1..f2280fb940fe 100644
--- a/src/transformers/models/llava_next_video/processing_llava_next_video.py
+++ b/src/transformers/models/llava_next_video/processing_llava_next_video.py
@@ -17,19 +17,18 @@
 
 import numpy as np
 
-from ...feature_extraction_utils import BatchFeature
 from ...image_processing_utils import select_best_resolution
-from ...image_utils import ImageInput, get_image_size, to_numpy_array
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...image_utils import get_image_size, to_numpy_array
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from ..llava_next.image_processing_llava_next import LlavaNextImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class LlavaNextVideoProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: LlavaNextImageProcessorKwargs
     # see processing_utils.ProcessingKwargs documentation for usage.
     _defaults = {
         "text_kwargs": {
@@ -43,8 +42,8 @@ class LlavaNextVideoProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class LlavaNextVideoProcessor(ProcessorMixin):
-    # video and image processor share same args, but have different processing logic
-    # only image processor config is saved in the hub
+    valid_processor_kwargs = LlavaNextVideoProcessorKwargs
+
     def __init__(
         self,
         video_processor=None,
@@ -89,87 +88,32 @@ def __init__(
         )
         super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[LlavaNextVideoProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-        """
-
-        output_kwargs = self._merge_kwargs(
-            LlavaNextVideoProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
-        else:
-            image_inputs = {}
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"])
+    def replace_image_token(self, image_inputs: dict | None = None, image_idx: int = 0) -> str:
+        image_size = image_inputs["image_sizes"][image_idx]
+        pixel_values = [pixel_values for sub_list in image_inputs["pixel_values"] for pixel_values in sub_list]
+        height, width = get_image_size(to_numpy_array(pixel_values[image_idx]))
+        if not isinstance(image_size, (list, tuple)):
+            # cast to list to avoid numerical precision errors when calculating unpadding
+            image_size = image_size.tolist()
+        orig_height, orig_width = image_size
+        num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
+        if self.vision_feature_select_strategy == "default":
+            num_image_tokens -= 1
+        return self.image_token * num_image_tokens
+
+    def replace_video_token(self, video_inputs: dict | None = None, video_idx: int = 0) -> str:
+        processed_video = video_inputs["pixel_values_videos"][video_idx]
+        if isinstance(processed_video, (list, tuple)):
+            processed_video = np.array(processed_video)
         else:
-            videos_inputs = {}
-
-        if isinstance(text, str):
-            text = [text]
-        elif not isinstance(text, list) and not isinstance(text[0], str):
-            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
-
-        if image_inputs:
-            image_sizes = iter(image_inputs["image_sizes"])
-            height, width = get_image_size(to_numpy_array(image_inputs["pixel_values"][0][0]))
-            prompt_strings = []
-            for sample in text:
-                while self.image_token in sample:
-                    image_size = next(image_sizes)
-                    if not isinstance(image_size, (list, tuple)):
-                        # cast to list to avoid numerical precision errors when calculating unpadding
-                        image_size = image_size.tolist()
-                    orig_height, orig_width = image_size
-                    num_image_tokens = self._get_number_of_features(orig_height, orig_width, height, width)
-                    if self.vision_feature_select_strategy == "default":
-                        num_image_tokens -= 1
-                    sample = sample.replace(self.image_token, "<placeholder>" * num_image_tokens, 1)
-                prompt_strings.append(sample)
-            text = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
-
-        # videos are easier, simply get frames and multiply
-        if videos_inputs:
-            one_video = videos_inputs.get("pixel_values_videos")[0]
-            if isinstance(one_video, (list, tuple)):
-                one_video = np.array(one_video)
-            else:
-                one_video = to_numpy_array(one_video)
-            height, width = get_image_size(one_video[0])
-            num_frames = one_video.shape[0]  # frame dim is always after batch dim
-
-            # no `self.num_additional_image_tokens` added because video always has a default feature selection strategy
-            num_image_tokens = (height // self.patch_size) * (width // self.patch_size)
-            num_video_tokens = num_image_tokens // 4 * num_frames  # divide by 4 needed for avg pooling layer
-            prompt_strings = []
-            for sample in text:
-                sample = sample.replace(self.video_token, self.video_token * num_video_tokens)
-                prompt_strings.append(sample)
-            text = prompt_strings
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+            processed_video = to_numpy_array(processed_video)
+        height, width = get_image_size(processed_video[0])
+        num_frames = processed_video.shape[0]  # frame dim is always after batch dim
+
+        # no `self.num_additional_image_tokens` added because video always has a default feature selection strategy
+        num_image_tokens = (height // self.patch_size) * (width // self.patch_size)
+        num_video_tokens = num_image_tokens // 4 * num_frames  # divide by 4 needed for avg pooling layer
+        return self.video_token * num_video_tokens
 
     # Copied from transformers.models.llava_next.processing_llava_next.LlavaNextProcessor._get_number_of_features
     def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
diff --git a/src/transformers/models/llava_onevision/modeling_llava_onevision.py b/src/transformers/models/llava_onevision/modeling_llava_onevision.py
index a6ef4131de48..141f50829c08 100644
--- a/src/transformers/models/llava_onevision/modeling_llava_onevision.py
+++ b/src/transformers/models/llava_onevision/modeling_llava_onevision.py
@@ -66,12 +66,12 @@ class LlavaOnevisionModelOutputWithPast(BaseModelOutputWithPast):
     video_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for LlavaOnevision causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class LlavaOnevisionCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -456,18 +456,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/llava_onevision/processing_llava_onevision.py b/src/transformers/models/llava_onevision/processing_llava_onevision.py
index ca5f6e3a5bd1..9ae9b6c73986 100644
--- a/src/transformers/models/llava_onevision/processing_llava_onevision.py
+++ b/src/transformers/models/llava_onevision/processing_llava_onevision.py
@@ -27,12 +27,14 @@
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
 from ...video_utils import VideoInput
+from .image_processing_llava_onevision import LlavaOnevisionImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: LlavaOnevisionImageProcessorKwargs
     # see processing_utils.ProcessingKwargs documentation for usage.
     _defaults = {
         "text_kwargs": {
diff --git a/src/transformers/models/longcat_flash/modeling_longcat_flash.py b/src/transformers/models/longcat_flash/modeling_longcat_flash.py
index d5ac6e237742..6fe7d0711f8d 100644
--- a/src/transformers/models/longcat_flash/modeling_longcat_flash.py
+++ b/src/transformers/models/longcat_flash/modeling_longcat_flash.py
@@ -122,7 +122,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -356,7 +356,7 @@ def __init__(self, config, layer_idx: int):
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = LongcatFlashRMSNorm(config.q_lora_rank)
+            self.q_a_layernorm = LongcatFlashRMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
@@ -364,7 +364,7 @@ def __init__(self, config, layer_idx: int):
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = LongcatFlashRMSNorm(self.kv_lora_rank)
+        self.kv_a_layernorm = LongcatFlashRMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
         self.kv_b_proj = nn.Linear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
diff --git a/src/transformers/models/longformer/modeling_longformer.py b/src/transformers/models/longformer/modeling_longformer.py
index 4078b87bbdb9..c7f532371753 100755
--- a/src/transformers/models/longformer/modeling_longformer.py
+++ b/src/transformers/models/longformer/modeling_longformer.py
@@ -31,12 +31,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Longformer's outputs, with potential hidden states, local and global attentions.
     """
 )
+@dataclass
 class LongformerBaseModelOutput(ModelOutput):
     r"""
     attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
@@ -69,12 +69,12 @@ class LongformerBaseModelOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Longformer's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class LongformerBaseModelOutputWithPooling(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
@@ -112,12 +112,12 @@ class LongformerBaseModelOutputWithPooling(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for masked language models outputs.
     """
 )
+@dataclass
 class LongformerMaskedLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -155,12 +155,12 @@ class LongformerMaskedLMOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of question answering Longformer models.
     """
 )
+@dataclass
 class LongformerQuestionAnsweringModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -197,12 +197,12 @@ class LongformerQuestionAnsweringModelOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of sentence classification models.
     """
 )
+@dataclass
 class LongformerSequenceClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -240,12 +240,12 @@ class LongformerSequenceClassifierOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of multiple choice Longformer models.
     """
 )
+@dataclass
 class LongformerMultipleChoiceModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
@@ -285,12 +285,12 @@ class LongformerMultipleChoiceModelOutput(ModelOutput):
     global_attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of token classification models.
     """
 )
+@dataclass
 class LongformerTokenClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/luke/modeling_luke.py b/src/transformers/models/luke/modeling_luke.py
index baa1b9b2c3ea..67b5c5166e91 100644
--- a/src/transformers/models/luke/modeling_luke.py
+++ b/src/transformers/models/luke/modeling_luke.py
@@ -76,12 +76,12 @@ class BaseLukeModelOutput(BaseModelOutput):
     entity_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class LukeMaskedLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -110,12 +110,12 @@ class LukeMaskedLMOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of entity classification models.
     """
 )
+@dataclass
 class EntityClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -135,12 +135,12 @@ class EntityClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of entity pair classification models.
     """
 )
+@dataclass
 class EntityPairClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -160,12 +160,12 @@ class EntityPairClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of entity span classification models.
     """
 )
+@dataclass
 class EntitySpanClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -185,12 +185,12 @@ class EntitySpanClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of sentence classification models.
     """
 )
+@dataclass
 class LukeSequenceClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -210,12 +210,12 @@ class LukeSequenceClassifierOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of token classification models.
     """
 )
+@dataclass
 class LukeTokenClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -235,12 +235,12 @@ class LukeTokenClassifierOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of question answering models.
     """
 )
+@dataclass
 class LukeQuestionAnsweringModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -259,12 +259,12 @@ class LukeQuestionAnsweringModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of multiple choice models.
     """
 )
+@dataclass
 class LukeMultipleChoiceModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/lw_detr/modeling_lw_detr.py b/src/transformers/models/lw_detr/modeling_lw_detr.py
index 129cd91078ef..653af95041c3 100644
--- a/src/transformers/models/lw_detr/modeling_lw_detr.py
+++ b/src/transformers/models/lw_detr/modeling_lw_detr.py
@@ -1172,12 +1172,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the LwDetr backbone-decoder model.
     """
 )
+@dataclass
 class LwDetrModelOutput(ModelOutput):
     r"""
     init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
@@ -1495,12 +1495,12 @@ def forward(self, x):
         return x
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`LwDetrForObjectDetection`].
     """
 )
+@dataclass
 class LwDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/lw_detr/modular_lw_detr.py b/src/transformers/models/lw_detr/modular_lw_detr.py
index 0ecf405a009b..849b7ad37aeb 100644
--- a/src/transformers/models/lw_detr/modular_lw_detr.py
+++ b/src/transformers/models/lw_detr/modular_lw_detr.py
@@ -974,12 +974,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the LwDetr backbone-decoder model.
     """
 )
+@dataclass
 class LwDetrModelOutput(ModelOutput):
     r"""
     init_reference_points (`torch.FloatTensor` of shape  `(batch_size, num_queries, 4)`):
@@ -1258,12 +1258,12 @@ class LwDetrMLPPredictionHead(DeformableDetrMLPPredictionHead):
     pass
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`LwDetrForObjectDetection`].
     """
 )
+@dataclass
 class LwDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/lxmert/modeling_lxmert.py b/src/transformers/models/lxmert/modeling_lxmert.py
index 908c9761dd60..f23644e07cdb 100644
--- a/src/transformers/models/lxmert/modeling_lxmert.py
+++ b/src/transformers/models/lxmert/modeling_lxmert.py
@@ -38,7 +38,6 @@ def forward(self, x):
         return gelu(x)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Lxmert's outputs that contain the last hidden states, pooled outputs, and attention probabilities for the language,
@@ -46,6 +45,7 @@ def forward(self, x):
     encoder")
     """
 )
+@dataclass
 class LxmertModelOutput(ModelOutput):
     r"""
     language_output (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -85,12 +85,12 @@ class LxmertModelOutput(ModelOutput):
     cross_encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`LxmertForQuestionAnswering`].
     """
 )
+@dataclass
 class LxmertForQuestionAnsweringOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -127,12 +127,12 @@ class LxmertForQuestionAnsweringOutput(ModelOutput):
     cross_encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`LxmertForPreTraining`].
     """
 )
+@dataclass
 class LxmertForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/mamba/modeling_mamba.py b/src/transformers/models/mamba/modeling_mamba.py
index 2e5695c1d4fa..87987e3e6646 100644
--- a/src/transformers/models/mamba/modeling_mamba.py
+++ b/src/transformers/models/mamba/modeling_mamba.py
@@ -470,12 +470,12 @@ def _init_weights(self, module):
             init.normal_(module.weight, std=std)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for the MAMBA model outputs.
     """
 )
+@dataclass
 class MambaOutput(ModelOutput):
     r"""
     cache_params (`Cache`):
@@ -490,12 +490,12 @@ class MambaOutput(ModelOutput):
     hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class MambaCausalLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/mamba2/modeling_mamba2.py b/src/transformers/models/mamba2/modeling_mamba2.py
index d0a47ef9dc63..1bcff42b8fe9 100644
--- a/src/transformers/models/mamba2/modeling_mamba2.py
+++ b/src/transformers/models/mamba2/modeling_mamba2.py
@@ -101,10 +101,11 @@ def apply_mask_to_padding_states(hidden_states, attention_mask):
 
 
 class MambaRMSNormGated(torch.nn.Module):
-    def __init__(self, hidden_size, eps=1e-6):
+    def __init__(self, hidden_size, group_size, eps=1e-6):
         super().__init__()
         self.weight = nn.Parameter(torch.ones(hidden_size))
         self.variance_epsilon = eps
+        self.group_size = group_size
 
     def forward(self, hidden_states, gate=None):
         input_dtype = hidden_states.dtype
@@ -112,8 +113,12 @@ def forward(self, hidden_states, gate=None):
 
         if gate is not None:
             hidden_states = hidden_states * nn.functional.silu(gate.to(torch.float32))
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        *prefix_dims, last_dim = hidden_states.shape
+        group_count = last_dim // self.group_size
+        hidden_states_group = hidden_states.view(*prefix_dims, group_count, self.group_size)
+        variance = hidden_states_group.pow(2).mean(-1, keepdim=True)
+        hidden_states_group = hidden_states_group * torch.rsqrt(variance + self.variance_epsilon)
+        hidden_states = hidden_states_group.view(*prefix_dims, group_count * self.group_size)
 
         return self.weight * hidden_states.to(input_dtype)
 
@@ -176,7 +181,9 @@ def __init__(self, config: Mamba2Config, layer_idx: int, initialize_mixer_weight
         # S4D real initialization. These are not discretized!
         # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
         self.A_log = nn.Parameter(torch.empty(self.num_heads))
-        self.norm = MambaRMSNormGated(self.intermediate_size, eps=self.layer_norm_epsilon)
+        self.norm = MambaRMSNormGated(
+            self.intermediate_size, group_size=self.intermediate_size // self.n_groups, eps=self.layer_norm_epsilon
+        )
         self.D = nn.Parameter(torch.empty(self.num_heads))
         if initialize_mixer_weights and self.dt_bias.device.type != "meta":
             self.init_mamba2_weights()
@@ -527,39 +534,25 @@ def torch_forward(
             # 1. Compute the output for each intra-chunk (diagonal blocks)
             # This is the analog of a causal mask
             L = torch.exp(segment_sum(A))
-
-            # Contraction of C and B to get G (attention-weights like)
-            G_intermediate = C[:, :, :, None, :, :] * B[:, :, None, :, :, :]  # shape: (b, c, l, s, h, n)
-            G = G_intermediate.sum(dim=-1)  # shape: (b, c, l, s, h)
-
-            # Compute M, equivalent to applying attention mask to weights
-            M_intermediate = G[..., None] * L.permute(0, 2, 3, 4, 1)[..., None]
-            M = M_intermediate.sum(dim=-1)
-
-            # Compute Y_diag (apply to values)
-            Y_diag = (M[..., None] * hidden_states[:, :, None]).sum(dim=3)
+            Y_diag = torch.einsum("bclhn, bcshn, bhcls, bcshp -> bclhp", C, B, L, hidden_states)
 
             # 2. Compute the state for each intra-chunk
             # (right term of low-rank factorization of off-diagonal blocks; B terms)
             decay_states = torch.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
-            B_decay = B * decay_states.permute(0, -2, -1, 1)[..., None]
-            states = (B_decay[..., None, :] * hidden_states[..., None]).sum(dim=2)
+            states = torch.einsum("bclhn, bhcl, bclhp -> bchpn", B, decay_states, hidden_states)
 
             # 3. Compute the inter-chunk SSM recurrence; produces correct SSM states at chunk boundaries
             # (middle term of factorization of off-diag blocks; A terms)
             previous_states = torch.zeros_like(states[:, :1])
             states = torch.cat([previous_states, states], dim=1)
             decay_chunk = torch.exp(segment_sum(nn.functional.pad(A_cumsum[:, :, :, -1], (1, 0))))
-            decay_chunk = decay_chunk.transpose(1, 3)
-            new_states = (decay_chunk[..., None, None] * states[:, :, None, ...]).sum(dim=1)
+            new_states = torch.einsum("bhzc, bchpn -> bzhpn", decay_chunk, states)
             states, ssm_state = new_states[:, :-1], new_states[:, -1]
 
             # 4. Compute state -> output conversion per chunk
             # (left term of low-rank factorization of off-diagonal blocks; C terms)
             state_decay_out = torch.exp(A_cumsum)
-            C_times_states = (C[..., None, :] * states[:, :, None, ...])
-            state_decay_out_permuted = state_decay_out.permute(0, 2, 3, 1)
-            Y_off = (C_times_states.sum(-1) * state_decay_out_permuted[..., None])
+            Y_off = torch.einsum('bclhn, bchpn, bhcl -> bclhp', C, states, state_decay_out)
 
             # Add output of intra-chunk and inter-chunk terms (diagonal and off-diagonal blocks)
             y = Y_diag + Y_off
diff --git a/src/transformers/models/mask2former/modeling_mask2former.py b/src/transformers/models/mask2former/modeling_mask2former.py
index b20646bc2cb4..4a7126d761aa 100644
--- a/src/transformers/models/mask2former/modeling_mask2former.py
+++ b/src/transformers/models/mask2former/modeling_mask2former.py
@@ -43,13 +43,13 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Mask2Former's pixel decoder module output, practically a Multi-Scale Deformable Attention based decoder. It returns
     the mask features and the multiscale features.
     """
 )
+@dataclass
 class Mask2FormerPixelDecoderOutput(ModelOutput):
     r"""
     multi_scale_features (`tuple(torch.FloatTensor)`):
@@ -134,12 +134,12 @@ class Mask2FormerPixelLevelModuleOutput(ModelOutput):
     decoder_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`Mask2FormerModel`]. This class returns all the needed hidden states to compute the logits.
     """
 )
+@dataclass
 class Mask2FormerModelOutput(ModelOutput):
     r"""
     encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`, *optional*):
@@ -2433,6 +2433,15 @@ def forward(
         torch.Size([338, 676])
         ```
         """
+
+        if mask_labels is not None:
+            target_device = pixel_values.device
+            mask_labels = [mask.to(target_device) for mask in mask_labels]
+
+        if class_labels is not None:
+            target_device = pixel_values.device
+            class_labels = [label.to(target_device) for label in class_labels]
+
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
diff --git a/src/transformers/models/maskformer/modeling_maskformer.py b/src/transformers/models/maskformer/modeling_maskformer.py
index 788775a52fcb..26380fa7fbab 100644
--- a/src/transformers/models/maskformer/modeling_maskformer.py
+++ b/src/transformers/models/maskformer/modeling_maskformer.py
@@ -113,13 +113,13 @@ class MaskFormerPixelLevelModuleOutput(ModelOutput):
     decoder_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state
     and (optionally) the hidden states.
     """
 )
+@dataclass
 class MaskFormerPixelDecoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
@@ -131,12 +131,12 @@ class MaskFormerPixelDecoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.
     """
 )
+@dataclass
 class MaskFormerModelOutput(ModelOutput):
     r"""
     encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
@@ -2003,6 +2003,13 @@ def forward(
         [480, 640]
         ```
         """
+        if mask_labels is not None:
+            target_device = pixel_values.device
+            mask_labels = [mask.to(target_device) for mask in mask_labels]
+
+        if class_labels is not None:
+            target_device = pixel_values.device
+            class_labels = [label.to(target_device) for label in class_labels]
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
diff --git a/src/transformers/models/maskformer/modeling_maskformer_swin.py b/src/transformers/models/maskformer/modeling_maskformer_swin.py
index fc30dd865dc0..f07488cc8cdd 100644
--- a/src/transformers/models/maskformer/modeling_maskformer_swin.py
+++ b/src/transformers/models/maskformer/modeling_maskformer_swin.py
@@ -34,12 +34,12 @@
 from .configuration_maskformer_swin import MaskFormerSwinConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for MaskFormerSwinModel's outputs that also contains the spatial dimensions of the hidden states.
     """
 )
+@dataclass
 class MaskFormerSwinModelOutputWithPooling(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`):
@@ -57,12 +57,12 @@ class MaskFormerSwinModelOutputWithPooling(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for SwinEncoder's outputs.
     """
 )
+@dataclass
 class MaskFormerSwinBaseModelOutput(ModelOutput):
     r"""
     hidden_states_spatial_dimensions (`tuple(tuple(int, int))`, *optional*):
diff --git a/src/transformers/models/maskformer/modular_maskformer.py b/src/transformers/models/maskformer/modular_maskformer.py
index 06705906c891..fad08cf77036 100644
--- a/src/transformers/models/maskformer/modular_maskformer.py
+++ b/src/transformers/models/maskformer/modular_maskformer.py
@@ -200,13 +200,13 @@ class MaskFormerPixelLevelModuleOutput(ModelOutput):
     decoder_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     MaskFormer's pixel decoder module output, practically a Feature Pyramid Network. It returns the last hidden state
     and (optionally) the hidden states.
     """
 )
+@dataclass
 class MaskFormerPixelDecoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
@@ -218,12 +218,12 @@ class MaskFormerPixelDecoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`MaskFormerModel`]. This class returns all the needed hidden states to compute the logits.
     """
 )
+@dataclass
 class MaskFormerModelOutput(ModelOutput):
     r"""
     encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
diff --git a/src/transformers/models/metaclip_2/modeling_metaclip_2.py b/src/transformers/models/metaclip_2/modeling_metaclip_2.py
index abfd4de8c24a..c6d3169fd91b 100644
--- a/src/transformers/models/metaclip_2/modeling_metaclip_2.py
+++ b/src/transformers/models/metaclip_2/modeling_metaclip_2.py
@@ -517,12 +517,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for text model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class MetaClip2TextModelOutput(ModelOutput):
     r"""
     text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -949,7 +949,7 @@ def __init__(self, config: MetaClip2VisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = MetaClip2VisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = MetaClip2Encoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.post_init()
@@ -986,7 +986,7 @@ def forward(
         >>> pooled_output = outputs.pooler_output  # pooled CLS states
         ```"""
         hidden_states = self.embeddings(pixel_values, interpolate_pos_encoding=interpolate_pos_encoding)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs: BaseModelOutput = self.encoder(
             inputs_embeds=hidden_states,
@@ -1003,12 +1003,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
     """
 )
+@dataclass
 class MetaClip2VisionModelOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
diff --git a/src/transformers/models/mgp_str/modeling_mgp_str.py b/src/transformers/models/mgp_str/modeling_mgp_str.py
index aac41fcc8cef..23bcda2312d0 100644
--- a/src/transformers/models/mgp_str/modeling_mgp_str.py
+++ b/src/transformers/models/mgp_str/modeling_mgp_str.py
@@ -61,12 +61,12 @@ def extra_repr(self) -> str:
         return f"p={self.drop_prob}"
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
     """
 )
+@dataclass
 class MgpstrModelOutput(ModelOutput):
     r"""
     logits (`tuple(torch.FloatTensor)` of shape `(batch_size, config.num_character_labels)`):
diff --git a/src/transformers/models/mimi/modeling_mimi.py b/src/transformers/models/mimi/modeling_mimi.py
index 30480d1d1c03..f4a72a7d6dd7 100644
--- a/src/transformers/models/mimi/modeling_mimi.py
+++ b/src/transformers/models/mimi/modeling_mimi.py
@@ -487,12 +487,21 @@ def __init__(self, config: MimiConfig):
             conv_layer = self.get_submodule(layername)
             setattr(conv_layer, "layer_idx", layer_idx)
 
-    def forward(self, hidden_states, padding_cache=None):
+    def forward(self, hidden_states, padding_cache=None, output_lengths=None):
         for layer in self.layers:
             if isinstance(layer, (MimiConv1d, MimiResnetBlock)):
                 hidden_states = layer(hidden_states, padding_cache=padding_cache)
             else:
                 hidden_states = layer(hidden_states)
+            # zero out positions after valid lengths so that garbage from conv bias
+            # does not leak into boundary positions at later strided convolutions.
+            if output_lengths is not None:
+                if isinstance(layer, MimiConv1d):
+                    output_lengths = layer._get_output_length(output_lengths)
+                time_mask = torch.arange(
+                    hidden_states.shape[-1], device=hidden_states.device
+                ) < output_lengths.unsqueeze(1)
+                hidden_states = hidden_states * time_mask.unsqueeze(1)
         return hidden_states
 
 
@@ -569,7 +578,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -1466,12 +1475,26 @@ def _encode_frame(
         Encodes the given input using the underlying VQVAE. The padding mask is required to compute the correct scale.
         """
 
-        # TODO: @eustlb, let's make the encoder support padding_mask so that batched inputs are supported.
-        embeddings = self.encoder(input_values, padding_cache=padding_cache)
+        input_lengths = None
+        if padding_mask is not None and padding_cache is None:
+            padding_mask_2d = padding_mask.any(dim=1) if padding_mask.dim() == 3 else padding_mask
+            input_lengths = padding_mask_2d.sum(dim=-1)
+        embeddings = self.encoder(input_values, padding_cache=padding_cache, output_lengths=input_lengths)
+        attention_mask = None
+        encoder_output_lengths = None
+        if input_lengths is not None:
+            encoder_output_lengths = input_lengths
+            for layer_name in self.encoder._mimiconv1d_layer_names:
+                encoder_output_lengths = self.encoder.get_submodule(layer_name)._get_output_length(
+                    encoder_output_lengths
+                )
+            attention_mask = torch.arange(embeddings.shape[-1], device=embeddings.device).unsqueeze(
+                0
+            ) < encoder_output_lengths.unsqueeze(1)
 
-        # TODO: @eustlb, convert the padding mask to attention mask.
         encoder_outputs = self.encoder_transformer(
             embeddings.transpose(1, 2),
+            attention_mask=attention_mask,
             past_key_values=past_key_values,
             use_cache=use_streaming,
             return_dict=return_dict,
@@ -1481,10 +1504,18 @@ def _encode_frame(
         elif len(encoder_outputs) > 1:
             past_key_values = encoder_outputs[1]
         embeddings = encoder_outputs[0].transpose(1, 2)
-        embeddings = self.downsample(embeddings, padding_cache=padding_cache)
 
+        if encoder_output_lengths is not None:
+            last_valid_idx = (encoder_output_lengths - 1).clamp(min=0)
+            last_valid_emb = embeddings.gather(2, last_valid_idx.view(-1, 1, 1).expand(-1, embeddings.shape[1], 1))
+            garbage_mask = torch.arange(embeddings.shape[-1], device=embeddings.device).unsqueeze(
+                0
+            ) >= encoder_output_lengths.unsqueeze(1)
+            embeddings = torch.where(garbage_mask.unsqueeze(1), last_valid_emb, embeddings)
+        embeddings = self.downsample(embeddings, padding_cache=padding_cache)
         codes = self.quantizer.encode(embeddings, num_quantizers)
         codes = codes.transpose(0, 1)
+
         return codes, past_key_values, padding_cache
 
     def get_encoded_length(self, input_length: torch.LongTensor) -> torch.LongTensor:
diff --git a/src/transformers/models/minicpm3/__init__.py b/src/transformers/models/minicpm3/__init__.py
new file mode 100644
index 000000000000..405741de6116
--- /dev/null
+++ b/src/transformers/models/minicpm3/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_minicpm3 import *
+    from .modeling_minicpm3 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/minicpm3/configuration_minicpm3.py b/src/transformers/models/minicpm3/configuration_minicpm3.py
new file mode 100644
index 000000000000..ad4645318054
--- /dev/null
+++ b/src/transformers/models/minicpm3/configuration_minicpm3.py
@@ -0,0 +1,126 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/minicpm3/modular_minicpm3.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_minicpm3.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="openbmb/MiniCPM3-4B")
+@strict
+class MiniCPM3Config(PreTrainedConfig):
+    r"""
+    kv_lora_rank (`int`, *optional*, defaults to 256):
+        Rank of the low-rank KV projection in multi-head latent attention.
+    q_lora_rank (`int`, *optional*, defaults to 768):
+        Rank of the low-rank query projection in multi-head latent attention.
+    qk_nope_head_dim (`int`, *optional*, defaults to 64):
+        Dimension of the non-RoPE part of each query/key head.
+    qk_rope_head_dim (`int`, *optional*, defaults to 32):
+        Dimension of the RoPE part of each query/key head.
+    v_head_dim (`int`, *optional*, defaults to 128):
+        Dimension of each value head.
+    scale_emb (`int`, *optional*, defaults to 1):
+        Scaling factor applied to input embeddings.
+    scale_depth (`float`, *optional*, defaults to 1.0):
+        Scaling factor for residual connections, applied as `scale_depth / sqrt(num_hidden_layers)`.
+    dim_model_base (`int`, *optional*, defaults to 1):
+        Base model dimension used to scale logits before the language model head.
+
+    Example:
+
+    ```python
+    >>> from transformers import MiniCPM3Model, MiniCPM3Config
+    >>> configuration = MiniCPM3Config()
+    >>> model = MiniCPM3Model(configuration)
+    >>> print(model.config)
+    ```
+    """
+
+    model_type = "minicpm3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.q_b_proj": "colwise",
+        "layers.*.self_attn.kv_a_proj_with_mqa": "mla_kv_a_proj",
+        "layers.*.self_attn.kv_b_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+
+    vocab_size: int = 73448
+    hidden_size: int = 2560
+    intermediate_size: int = 6400
+    num_hidden_layers: int = 62
+    num_attention_heads: int = 40
+    num_key_value_heads: int | None = 40
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 32768
+    initializer_range: float = 0.1
+    rms_norm_eps: float = 1e-5
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 2
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: float | None = 0.0
+    mlp_bias: bool = False
+    head_dim: int | None = None
+    kv_lora_rank: int = 256
+    q_lora_rank: int | None = 768
+    qk_nope_head_dim: int = 64
+    qk_rope_head_dim: int = 32
+    v_head_dim: int = 128
+    scale_emb: int = 1
+    scale_depth: float = 1.0
+    dim_model_base: int = 1
+
+    def __post_init__(self, **kwargs):
+        self.head_dim = self.qk_rope_head_dim
+        if self.head_dim is None:
+            self.head_dim = self.hidden_size // self.num_attention_heads
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        super().__post_init__(**kwargs)
+
+    def validate_architecture(self):
+        """Part of `@strict`-powered validation. Validates the architecture of the config."""
+        if self.hidden_size % self.num_attention_heads != 0:
+            raise ValueError(
+                f"The hidden size ({self.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({self.num_attention_heads})."
+            )
+
+
+__all__ = ["MiniCPM3Config"]
diff --git a/src/transformers/models/minicpm3/modeling_minicpm3.py b/src/transformers/models/minicpm3/modeling_minicpm3.py
new file mode 100644
index 000000000000..850140c782ee
--- /dev/null
+++ b/src/transformers/models/minicpm3/modeling_minicpm3.py
@@ -0,0 +1,522 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/minicpm3/modular_minicpm3.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_minicpm3.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_minicpm3 import MiniCPM3Config
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class MiniCPM3RMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        MiniCPM3RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class MiniCPM3RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: MiniCPM3Config, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: MiniCPM3Config | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.to(x.device) @ position_ids_expanded).transpose(1, 2)
+            freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # Convert to complex representation
+            freqs_cis = freqs_cis * self.attention_scaling
+
+        return freqs_cis
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def apply_rotary_emb(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cis: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+
+    # Broadcast to [1, 1, seq_len, dim // 2]
+    freqs_cis = freqs_cis.unsqueeze(1).to(xq_.device)
+
+    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+    return xq_out, xk_out
+
+
+class MiniCPM3Attention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: MiniCPM3Config, layer_idx: int | None = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = config.head_dim
+        self.max_position_embeddings = config.max_position_embeddings
+
+        self.q_lora_rank = config.q_lora_rank
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.kv_lora_rank = config.kv_lora_rank
+        self.v_head_dim = config.v_head_dim
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.qk_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+
+        self.is_causal = True
+
+        if self.q_lora_rank is None:
+            self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
+        else:
+            self.q_a_proj = nn.Linear(self.hidden_size, config.q_lora_rank, bias=config.attention_bias)
+            self.q_a_layernorm = MiniCPM3RMSNorm(config.q_lora_rank)
+            self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
+
+        self.kv_a_proj_with_mqa = nn.Linear(
+            self.hidden_size,
+            config.kv_lora_rank + config.qk_rope_head_dim,
+            bias=config.attention_bias,
+        )
+        self.kv_a_layernorm = MiniCPM3RMSNorm(config.kv_lora_rank)
+        self.kv_b_proj = nn.Linear(
+            config.kv_lora_rank,
+            self.num_heads * (self.qk_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+            bias=False,
+        )
+
+        self.o_proj = nn.Linear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=config.attention_bias,
+        )
+
+        self.scaling = self.qk_head_dim ** (-0.5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        batch_size, seq_length = hidden_states.shape[:-1]
+        query_shape = (batch_size, seq_length, -1, self.qk_head_dim)
+        key_shape = (batch_size, seq_length, -1, self.qk_nope_head_dim + self.v_head_dim)
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(hidden_states)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(hidden_states)))
+        q = q.view(query_shape).transpose(1, 2)
+        q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+        compressed_kv = self.kv_a_proj_with_mqa(hidden_states)
+        k_nope, k_pe = torch.split(compressed_kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        k_nope = self.kv_b_proj(self.kv_a_layernorm(k_nope)).view(key_shape).transpose(1, 2)
+        k_nope, value_states = torch.split(k_nope, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+
+        k_pe = k_pe.view(batch_size, 1, seq_length, self.qk_rope_head_dim)
+
+        q_pe, k_pe = apply_rotary_emb(q_pe, k_pe, position_embeddings.to(q_pe.device))
+
+        k_pe = k_pe.expand(*k_nope.shape[:-1], -1)
+        query_states = torch.cat((q_nope, q_pe), dim=-1)
+        key_states = torch.cat((k_nope, k_pe), dim=-1)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
+            value_states = F.pad(value_states, [0, self.qk_head_dim - self.v_head_dim])
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        if is_flash_attention_requested(self.config) and self.qk_head_dim != self.v_head_dim:
+            attn_output = attn_output[:, :, :, : self.v_head_dim]
+
+        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class MiniCPM3MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class MiniCPM3DecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: MiniCPM3Config, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = MiniCPM3Attention(config=config, layer_idx=layer_idx)
+        self.mlp = MiniCPM3MLP(config)
+        self.input_layernorm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.scale_depth = config.scale_depth
+        self.num_hidden_layers = config.num_hidden_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+        return hidden_states
+
+
+@auto_docstring
+class MiniCPM3PreTrainedModel(PreTrainedModel):
+    config: MiniCPM3Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniCPM3DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": MiniCPM3DecoderLayer,
+        "attentions": MiniCPM3Attention,
+    }
+
+
+@auto_docstring
+class MiniCPM3Model(MiniCPM3PreTrainedModel):
+    def __init__(self, config: MiniCPM3Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MiniCPM3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = MiniCPM3RotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) * self.config.scale_emb
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class MiniCPM3ForCausalLM(MiniCPM3PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tp_plan = {"lm_head": "colwise_gather_output"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniCPM3Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniCPM3ForCausalLM
+
+        >>> model = MiniCPM3ForCausalLM.from_pretrained("openbmb/MiniCPM3-4B")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM3-4B")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(
+            hidden_states[:, slice_indices, :] / (self.config.hidden_size / self.config.dim_model_base)
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class MiniCPM3ForSequenceClassification(GenericForSequenceClassification, MiniCPM3PreTrainedModel):
+    pass
+
+
+__all__ = ["MiniCPM3PreTrainedModel", "MiniCPM3Model", "MiniCPM3ForCausalLM", "MiniCPM3ForSequenceClassification"]
diff --git a/src/transformers/models/minicpm3/modular_minicpm3.py b/src/transformers/models/minicpm3/modular_minicpm3.py
new file mode 100644
index 000000000000..8b551853b132
--- /dev/null
+++ b/src/transformers/models/minicpm3/modular_minicpm3.py
@@ -0,0 +1,342 @@
+# Copyright 2025 HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import torch
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ...cache_utils import Cache, DynamicCache
+from ...masking_utils import create_causal_mask
+from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from ...modeling_rope_utils import RopeParameters
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..deepseek_v2.modeling_deepseek_v2 import (
+    DeepseekV2Attention,
+    DeepseekV2RotaryEmbedding,
+)
+from ..llama.configuration_llama import LlamaConfig
+from ..llama.modeling_llama import (
+    LlamaDecoderLayer,
+    LlamaForCausalLM,
+    LlamaForSequenceClassification,
+    LlamaMLP,
+    LlamaModel,
+    LlamaPreTrainedModel,
+    LlamaRMSNorm,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="openbmb/MiniCPM3-4B")
+@strict
+class MiniCPM3Config(LlamaConfig):
+    r"""
+    kv_lora_rank (`int`, *optional*, defaults to 256):
+        Rank of the low-rank KV projection in multi-head latent attention.
+    q_lora_rank (`int`, *optional*, defaults to 768):
+        Rank of the low-rank query projection in multi-head latent attention.
+    qk_nope_head_dim (`int`, *optional*, defaults to 64):
+        Dimension of the non-RoPE part of each query/key head.
+    qk_rope_head_dim (`int`, *optional*, defaults to 32):
+        Dimension of the RoPE part of each query/key head.
+    v_head_dim (`int`, *optional*, defaults to 128):
+        Dimension of each value head.
+    scale_emb (`int`, *optional*, defaults to 1):
+        Scaling factor applied to input embeddings.
+    scale_depth (`float`, *optional*, defaults to 1.0):
+        Scaling factor for residual connections, applied as `scale_depth / sqrt(num_hidden_layers)`.
+    dim_model_base (`int`, *optional*, defaults to 1):
+        Base model dimension used to scale logits before the language model head.
+
+    Example:
+
+    ```python
+    >>> from transformers import MiniCPM3Model, MiniCPM3Config
+    >>> configuration = MiniCPM3Config()
+    >>> model = MiniCPM3Model(configuration)
+    >>> print(model.config)
+    ```
+    """
+
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.q_b_proj": "colwise",
+        "layers.*.self_attn.kv_a_proj_with_mqa": "mla_kv_a_proj",
+        "layers.*.self_attn.kv_b_proj": "colwise",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+
+    model_type = "minicpm3"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    vocab_size: int = 73448
+    hidden_size: int = 2560
+    intermediate_size: int = 6400
+    num_hidden_layers: int = 62
+    num_attention_heads: int = 40
+    num_key_value_heads: int | None = 40
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 32768
+    initializer_range: float = 0.1
+    rms_norm_eps: float = 1e-5
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 1
+    eos_token_id: int | list[int] | None = 2
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    attention_bias: bool = False
+    attention_dropout: float | None = 0.0
+    mlp_bias: bool = False
+    kv_lora_rank: int = 256
+    q_lora_rank: int | None = 768
+    qk_nope_head_dim: int = 64
+    qk_rope_head_dim: int = 32
+    v_head_dim: int = 128
+    scale_emb: int = 1
+    scale_depth: float = 1.0
+    dim_model_base: int = 1
+
+    def __post_init__(self, **kwargs):
+        self.head_dim = self.qk_rope_head_dim
+        super().__post_init__(**kwargs)
+
+
+class MiniCPM3RMSNorm(LlamaRMSNorm):
+    pass
+
+
+class MiniCPM3RotaryEmbedding(DeepseekV2RotaryEmbedding):
+    pass
+
+
+class MiniCPM3Attention(DeepseekV2Attention):
+    pass
+
+
+class MiniCPM3MLP(LlamaMLP):
+    pass
+
+
+class MiniCPM3DecoderLayer(LlamaDecoderLayer):
+    def __init__(self, config: MiniCPM3Config, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.self_attn = MiniCPM3Attention(config=config, layer_idx=layer_idx)
+        self.mlp = MiniCPM3MLP(config)
+        self.input_layernorm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.scale_depth = config.scale_depth
+        self.num_hidden_layers = config.num_hidden_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+        return hidden_states
+
+
+class MiniCPM3PreTrainedModel(LlamaPreTrainedModel):
+    pass
+
+
+@auto_docstring
+class MiniCPM3Model(LlamaModel):
+    def __init__(self, config: MiniCPM3Config):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MiniCPM3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = MiniCPM3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = MiniCPM3RotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.embed_tokens(input_ids) * self.config.scale_emb
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask,
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+@auto_docstring
+class MiniCPM3ForCausalLM(LlamaForCausalLM):
+    _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
+    _tp_plan = {"lm_head": "colwise_gather_output"}
+    _pp_plan = {"lm_head": (["hidden_states"], ["logits"])}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniCPM3Model(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniCPM3ForCausalLM
+
+        >>> model = MiniCPM3ForCausalLM.from_pretrained("openbmb/MiniCPM3-4B")
+        >>> tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM3-4B")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(
+            hidden_states[:, slice_indices, :] / (self.config.hidden_size / self.config.dim_model_base)
+        )
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class MiniCPM3ForSequenceClassification(LlamaForSequenceClassification):
+    pass
+
+
+__all__ = [
+    "MiniCPM3PreTrainedModel",
+    "MiniCPM3Model",
+    "MiniCPM3ForCausalLM",
+    "MiniCPM3ForSequenceClassification",
+    "MiniCPM3Config",
+]
diff --git a/src/transformers/models/minimax/modeling_minimax.py b/src/transformers/models/minimax/modeling_minimax.py
index 69497f83cad8..f7a9cbfb0f47 100644
--- a/src/transformers/models/minimax/modeling_minimax.py
+++ b/src/transformers/models/minimax/modeling_minimax.py
@@ -274,8 +274,8 @@ def __init__(self, config: MiniMaxConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -315,7 +315,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -596,7 +596,7 @@ class MiniMaxPreTrainedModel(PreTrainedModel):
     _can_compile_fullgraph = False  # uses a non-compilable custom cache class MiniMaxCache
     _supports_attention_backend = True
     _can_record_outputs = {
-        "router_logits": OutputRecorder(MiniMaxTopKRouter, layer_name="mlp.gate", index=0),
+        "router_logits": OutputRecorder(MiniMaxTopKRouter, layer_name=r"mlp\.gate", index=0),
         "hidden_states": MiniMaxDecoderLayer,
         "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
     }
@@ -740,7 +740,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -748,13 +748,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -765,8 +769,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/minimax/modular_minimax.py b/src/transformers/models/minimax/modular_minimax.py
index 0bd400458129..63cded4ccf75 100644
--- a/src/transformers/models/minimax/modular_minimax.py
+++ b/src/transformers/models/minimax/modular_minimax.py
@@ -403,7 +403,7 @@ def forward(
 class MiniMaxPreTrainedModel(MixtralPreTrainedModel):
     _can_compile_fullgraph = False  # uses a non-compilable custom cache class MiniMaxCache
     _can_record_outputs = {
-        "router_logits": OutputRecorder(MiniMaxTopKRouter, layer_name="mlp.gate", index=0),
+        "router_logits": OutputRecorder(MiniMaxTopKRouter, layer_name=r"mlp\.gate", index=0),
         "hidden_states": MiniMaxDecoderLayer,
         "attentions": [MiniMaxAttention, MiniMaxLightningAttention],
     }
diff --git a/src/transformers/models/minimax_m2/modeling_minimax_m2.py b/src/transformers/models/minimax_m2/modeling_minimax_m2.py
index d19274262810..2abf1aeb20fb 100644
--- a/src/transformers/models/minimax_m2/modeling_minimax_m2.py
+++ b/src/transformers/models/minimax_m2/modeling_minimax_m2.py
@@ -539,7 +539,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -547,7 +547,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -564,8 +566,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/ministral/modeling_ministral.py b/src/transformers/models/ministral/modeling_ministral.py
index af4f7fbeae59..90a855ad3511 100644
--- a/src/transformers/models/ministral/modeling_ministral.py
+++ b/src/transformers/models/ministral/modeling_ministral.py
@@ -295,8 +295,8 @@ def __init__(self, config: MinistralConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -336,7 +336,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/ministral3/modeling_ministral3.py b/src/transformers/models/ministral3/modeling_ministral3.py
index 6aacf4c8ce3a..866bfa3a9dfc 100644
--- a/src/transformers/models/ministral3/modeling_ministral3.py
+++ b/src/transformers/models/ministral3/modeling_ministral3.py
@@ -327,7 +327,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/mistral/modeling_mistral.py b/src/transformers/models/mistral/modeling_mistral.py
index b79dea36c9e9..117683868d8e 100644
--- a/src/transformers/models/mistral/modeling_mistral.py
+++ b/src/transformers/models/mistral/modeling_mistral.py
@@ -316,7 +316,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/mistral3/modeling_mistral3.py b/src/transformers/models/mistral3/modeling_mistral3.py
index 03ad4e247770..b04023edb7ed 100644
--- a/src/transformers/models/mistral3/modeling_mistral3.py
+++ b/src/transformers/models/mistral3/modeling_mistral3.py
@@ -124,12 +124,12 @@ def forward(self, image_features: torch.Tensor, image_sizes: torch.Tensor):
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Mistral3 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Mistral3CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -268,9 +268,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/mistral4/configuration_mistral4.py b/src/transformers/models/mistral4/configuration_mistral4.py
index 0e16e0a14f45..04a3ef34e6dc 100644
--- a/src/transformers/models/mistral4/configuration_mistral4.py
+++ b/src/transformers/models/mistral4/configuration_mistral4.py
@@ -103,11 +103,12 @@ class Mistral4Config(PreTrainedConfig):
 
     def __post_init__(self, **kwargs):
         if self.rope_parameters is None:
+            default_rope_factor = 128.0
             self.rope_parameters = {
                 "type": "yarn",
                 "rope_theta": 10000.0,
-                "factor": 128.0,
-                "original_max_position_embeddings": 8192,
+                "factor": default_rope_factor,
+                "original_max_position_embeddings": max(1, int(self.max_position_embeddings / default_rope_factor)),
                 "max_position_embeddings": self.max_position_embeddings,
                 "beta_fast": 32.0,
                 "beta_slow": 1.0,
diff --git a/src/transformers/models/mistral4/modeling_mistral4.py b/src/transformers/models/mistral4/modeling_mistral4.py
index 006ddad187bf..8f89e0c6a029 100644
--- a/src/transformers/models/mistral4/modeling_mistral4.py
+++ b/src/transformers/models/mistral4/modeling_mistral4.py
@@ -17,8 +17,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import math
 from collections.abc import Callable
-from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -89,9 +89,9 @@ def __init__(self, config: Mistral4Config, device=None):
     @staticmethod
     def compute_default_rope_parameters(
         config: Mistral4Config | None = None,
-        device: Optional["torch.device"] = None,
+        device=None,
         seq_len: int | None = None,
-    ) -> tuple["torch.Tensor", float]:
+    ) -> tuple[torch.Tensor, float]:
         """
         Computes the inverse frequencies according to the original RoPE implementation
         Args:
@@ -106,11 +106,10 @@ def compute_default_rope_parameters(
             post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
         """
         base = config.rope_parameters["rope_theta"]
+        partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
         dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
-
+        dim = int(dim * partial_rotary_factor)  # Mixtral4 doesn't apply ROPE to the full attention head
         attention_factor = 1.0  # Unused in this type of RoPE
-
-        # Compute the inverse frequencies
         inv_freq = 1.0 / (
             base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
         )
@@ -363,6 +362,12 @@ def apply_rotary_pos_emb_interleave(q, k, cos, sin, position_ids=None, unsqueeze
     return q_embed, k_embed
 
 
+def yarn_get_mscale(scale=1, mscale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
 def get_llama_4_attn_scale(positions_ids: torch.Tensor, beta: float, max_position_embeddings: int) -> torch.Tensor:
     scaling = 1 + beta * torch.log(1 + torch.floor(positions_ids / max_position_embeddings))
     return scaling[:, None, :, None]
@@ -413,6 +418,12 @@ def __init__(self, config: Mistral4Config, layer_idx: int):
         )
 
         self.scaling = self.qk_head_dim ** (-0.5)
+        if self.config.rope_parameters.get("rope_type", "default") == "yarn":
+            mscale_all_dim = self.config.rope_parameters.get("mscale_all_dim", 0)
+            scaling_factor = self.config.rope_parameters["factor"]
+            if mscale_all_dim:
+                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+                self.scaling = self.scaling * mscale * mscale
 
     def forward(
         self,
@@ -544,7 +555,7 @@ class Mistral4PreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_flex_attn = True
 
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": Mistral4DecoderLayer,
diff --git a/src/transformers/models/mistral4/modular_mistral4.py b/src/transformers/models/mistral4/modular_mistral4.py
index acd9f1f60191..931686859b6f 100644
--- a/src/transformers/models/mistral4/modular_mistral4.py
+++ b/src/transformers/models/mistral4/modular_mistral4.py
@@ -31,6 +31,7 @@
     DeepseekV3MoE,
     DeepseekV3NaiveMoe,
     apply_rotary_pos_emb_interleave,
+    yarn_get_mscale,
 )
 from ..llama.modeling_llama import (
     LlamaForCausalLM,
@@ -53,7 +54,21 @@ class Mistral4RMSNorm(LlamaRMSNorm):
 
 
 class Mistral4RotaryEmbedding(LlamaRotaryEmbedding):
-    pass
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Mistral4Config | None = None,
+        device=None,
+        seq_len: int | None = None,
+    ) -> tuple[torch.Tensor, float]:
+        base = config.rope_parameters["rope_theta"]
+        partial_rotary_factor = config.rope_parameters.get("partial_rotary_factor", 1.0)
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        dim = int(dim * partial_rotary_factor)  # Mixtral4 doesn't apply ROPE to the full attention head
+        attention_factor = 1.0  # Unused in this type of RoPE
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
 
 
 class Mistral4MLP(Qwen2MoeMLP):
@@ -145,6 +160,12 @@ def __init__(self, config: Mistral4Config, layer_idx: int):
         )
 
         self.scaling = self.qk_head_dim ** (-0.5)
+        if self.config.rope_parameters.get("rope_type", "default") == "yarn":
+            mscale_all_dim = self.config.rope_parameters.get("mscale_all_dim", 0)
+            scaling_factor = self.config.rope_parameters["factor"]
+            if mscale_all_dim:
+                mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+                self.scaling = self.scaling * mscale * mscale
 
     def forward(
         self,
@@ -245,7 +266,7 @@ class Mistral4PreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_flex_attn = True
 
-    _can_compile_fullgraph = True
+    _can_compile_fullgraph = False
     _supports_attention_backend = True
     _can_record_outputs = {
         "hidden_states": Mistral4DecoderLayer,
diff --git a/src/transformers/models/mixtral/modeling_mixtral.py b/src/transformers/models/mixtral/modeling_mixtral.py
index 991851dbadd3..cf2465b04b26 100644
--- a/src/transformers/models/mixtral/modeling_mixtral.py
+++ b/src/transformers/models/mixtral/modeling_mixtral.py
@@ -81,12 +81,12 @@ def forward(
         with torch.no_grad():
             expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
             expert_mask = expert_mask.permute(2, 1, 0)
-            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() if self.training else None
 
-        for expert_idx in expert_hit:
-            expert_idx = expert_idx[0]
-            if expert_idx == self.num_experts:
-                continue
+        expert_indices = expert_hit if self.training else range(self.num_experts)
+        for expert_idx in expert_indices:
+            if self.training:
+                expert_idx = expert_idx[0]
             top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
             current_state = hidden_states[token_idx]
             gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
@@ -213,7 +213,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -531,7 +531,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -539,13 +539,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -556,8 +560,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/mixtral/modular_mixtral.py b/src/transformers/models/mixtral/modular_mixtral.py
index 139e580fbca7..d042a6de206a 100644
--- a/src/transformers/models/mixtral/modular_mixtral.py
+++ b/src/transformers/models/mixtral/modular_mixtral.py
@@ -86,7 +86,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -94,13 +94,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -111,8 +115,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -155,12 +161,12 @@ def forward(
         with torch.no_grad():
             expert_mask = torch.nn.functional.one_hot(top_k_index, num_classes=self.num_experts)
             expert_mask = expert_mask.permute(2, 1, 0)
-            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero()
+            expert_hit = torch.greater(expert_mask.sum(dim=(-1, -2)), 0).nonzero() if self.training else None
 
-        for expert_idx in expert_hit:
-            expert_idx = expert_idx[0]
-            if expert_idx == self.num_experts:
-                continue
+        expert_indices = expert_hit if self.training else range(self.num_experts)
+        for expert_idx in expert_indices:
+            if self.training:
+                expert_idx = expert_idx[0]
             top_k_pos, token_idx = torch.where(expert_mask[expert_idx])
             current_state = hidden_states[token_idx]
             gate, up = nn.functional.linear(current_state, self.gate_up_proj[expert_idx]).chunk(2, dim=-1)
diff --git a/src/transformers/models/mlcd/modeling_mlcd.py b/src/transformers/models/mlcd/modeling_mlcd.py
index ea7d87224faf..a764fdc7e289 100644
--- a/src/transformers/models/mlcd/modeling_mlcd.py
+++ b/src/transformers/models/mlcd/modeling_mlcd.py
@@ -465,7 +465,7 @@ def __init__(self, config: MLCDVisionConfig):
         embed_dim = config.hidden_size
 
         self.embeddings = MLCDVisionEmbeddings(config)
-        self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
+        self.pre_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.encoder = MLCDEncoder(config)
         self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps)
         self.vision_rotary_embedding = MLCDRotaryEmbedding(config.hidden_size // config.num_attention_heads // 2)
@@ -516,7 +516,7 @@ def forward(
         position_embeddings = (emb.cos(), emb.sin())
 
         hidden_states = self.embeddings(pixel_values)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/mlcd/modular_mlcd.py b/src/transformers/models/mlcd/modular_mlcd.py
index 0ffbf80f01b4..dcec4d3a934a 100644
--- a/src/transformers/models/mlcd/modular_mlcd.py
+++ b/src/transformers/models/mlcd/modular_mlcd.py
@@ -385,7 +385,7 @@ def forward(
         position_embeddings = (emb.cos(), emb.sin())
 
         hidden_states = self.embeddings(pixel_values)
-        hidden_states = self.pre_layrnorm(hidden_states)
+        hidden_states = self.pre_layernorm(hidden_states)
 
         encoder_outputs = self.encoder(
             inputs_embeds=hidden_states,
diff --git a/src/transformers/models/mllama/modeling_mllama.py b/src/transformers/models/mllama/modeling_mllama.py
index 3b9d12b9a225..4b86d5bd6bc9 100644
--- a/src/transformers/models/mllama/modeling_mllama.py
+++ b/src/transformers/models/mllama/modeling_mllama.py
@@ -351,12 +351,15 @@ def forward(
 
         """
         encoder_states = ()
+
         for encoder_layer in self.layers:
+            encoder_states = encoder_states + (hidden_states,)
             hidden_states = encoder_layer(
                 hidden_state=hidden_states,
                 attention_mask=attention_mask,
             )
-            encoder_states = encoder_states + (hidden_states,)
+
+        encoder_states = encoder_states + (hidden_states,)
 
         return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=encoder_states)
 
diff --git a/src/transformers/models/mllama/processing_mllama.py b/src/transformers/models/mllama/processing_mllama.py
index 2a604b4cf0b0..eebb98efad59 100644
--- a/src/transformers/models/mllama/processing_mllama.py
+++ b/src/transformers/models/mllama/processing_mllama.py
@@ -21,9 +21,11 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
+from .image_processing_mllama import MllamaImageProcessorKwargs
 
 
 class MllamaProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: MllamaImageProcessorKwargs
     _defaults = {
         "image_kwargs": {
             "max_image_tiles": 4,
@@ -166,6 +168,8 @@ def build_string_from_input(prompt: str, bos_token: str, image_token: str) -> st
 
 @auto_docstring
 class MllamaProcessor(ProcessorMixin):
+    valid_processor_kwargs = MllamaProcessorKwargs
+
     def __init__(self, image_processor, tokenizer, chat_template=None):
         if not hasattr(tokenizer, "image_token"):
             self.image_token = "<|image|>"
@@ -197,8 +201,8 @@ def __call__(
             - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
             TODO: add aspect_ratio_ids and aspect_ratio_mask and cross_attention_mask
         """
-        if text is None and images is None:
-            raise ValueError("You must specify either text or images.")
+        images, text = self.prepare_inputs_layout(images=images, text=text)
+        self.validate_inputs(images=images, text=text, **kwargs)
 
         output_kwargs = self._merge_kwargs(
             MllamaProcessorKwargs,
@@ -207,68 +211,82 @@ def __call__(
         )
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
 
-        data = {}
+        text_inputs = {}
         if text is not None:
-            if isinstance(text, str):
-                text = [text]
-            elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
-                raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-            n_images_in_text = [t.count(self.image_token) for t in text]
-            text = [build_string_from_input(text_item, self.bos_token, self.image_token) for text_item in text]
-            encoding = self.tokenizer(text, **output_kwargs["text_kwargs"])
-            self._check_special_mm_tokens(text, encoding, modalities=["image"])
-            n_images_in_ids = [token_ids.count(self.image_token_id) for token_ids in encoding["input_ids"]]
-            data.update(encoding)
+            text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+            self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
 
-        n_images_in_images = [0]
+        image_inputs = {}
         if images is not None:
-            images = self.image_processor.fetch_images(images)
-            images = make_nested_list_of_images(images)
-            n_images_in_images = [len(sample) for sample in images]
-
-        if text is not None:
-            if any(batch_img == 0 for batch_img in n_images_in_text) and not all(
-                batch_img == 0 for batch_img in n_images_in_text
-            ):
-                raise ValueError(
-                    "If a batch of text is provided, there should be either no images or at least one image per sample"
-                )
-            if sum(n_images_in_text) > 0 and (
-                n_images_in_images != n_images_in_text or n_images_in_ids != n_images_in_images
-            ):
-                if images is None:
-                    raise ValueError("No image were provided, but there are image tokens in the prompt")
-                else:
-                    add_message = ""
-                    if sum(n_images_in_images) == sum(n_images_in_text) and n_images_in_images != n_images_in_text:
-                        add_message = "Make sure to pass your images as a nested list, where each sub-list holds images per batch"
-                    elif n_images_in_ids != n_images_in_images:
-                        add_message = "If you activated truncation with `max_length`, increase the `max_length` so image tokens aren't cropped."
-
-                    raise ValueError(
-                        f"The number of image tokens in each text ({n_images_in_text}) should be the same as the "
-                        f"number of provided images per batch ({n_images_in_images}). {add_message}"
-                    )
-
-        if images is not None:
-            image_features = self.image_processor(images, **output_kwargs["images_kwargs"])
-            num_tiles = image_features.pop("num_tiles")
-            data.update(image_features)
+            image_inputs, _ = self._process_images(images, **output_kwargs["images_kwargs"])
+            num_tiles = image_inputs.pop("num_tiles")
 
         # Create cross attention mask
         if images is not None and text is not None:
             cross_attention_token_mask = [
-                get_cross_attention_token_mask(token_ids, self.image_token_id) for token_ids in encoding["input_ids"]
+                get_cross_attention_token_mask(token_ids, self.image_token_id)
+                for token_ids in text_inputs["input_ids"]
             ]
             cross_attention_mask = convert_sparse_cross_attention_mask_to_dense(
                 cross_attention_token_mask,
                 num_tiles=num_tiles,
                 max_num_tiles=self.image_processor.max_image_tiles,
-                length=max(len(input_ids) for input_ids in encoding["input_ids"]),
+                length=max(len(input_ids) for input_ids in text_inputs["input_ids"]),
             )
-            data["cross_attention_mask"] = cross_attention_mask
+            text_inputs["cross_attention_mask"] = cross_attention_mask
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def prepare_inputs_layout(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        **kwargs,
+    ):
+        images, text, *_ = super().prepare_inputs_layout(images=images, text=text, **kwargs)
+
+        # Model requires nested struct
+        if images is not None:
+            images = make_nested_list_of_images(images)
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        if text is not None:
+            text = [build_string_from_input(text_item, self.bos_token, self.image_token) for text_item in text]
+
+        return images, text
+
+    def validate_inputs(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(images, text, **kwargs)
+
+        if text is not None:
+            n_images_in_text = [t.count(self.image_token) for t in text]
+
+            if sum(n_images_in_text) > 0 and images is None:
+                raise ValueError("No image were provided, but there are image tokens in the prompt")
+            elif images is not None:
+                images = make_nested_list_of_images(images)
+                n_images_in_images = [len(sample) for sample in images]
+
+                if any(batch_img == 0 for batch_img in n_images_in_text) and not all(
+                    batch_img == 0 for batch_img in n_images_in_text
+                ):
+                    raise ValueError(
+                        "If a batch of text is provided, there should be either no images or at least one image per sample"
+                    )
+
+                if n_images_in_images != n_images_in_text:
+                    add_message = ""
+                    if sum(n_images_in_images) == sum(n_images_in_text) and n_images_in_images != n_images_in_text:
+                        add_message = "Make sure to pass your images as a nested list, where each sub-list holds images per batch"
+
+                    raise ValueError(
+                        f"The number of image tokens in each text ({n_images_in_text}) should be the same as the "
+                        f"number of provided images per batch ({n_images_in_images}). {add_message}"
+                    )
 
     def post_process_image_text_to_text(
         self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
diff --git a/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py b/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py
index efeeabe5cc23..8f589f8321a8 100644
--- a/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py
+++ b/src/transformers/models/mm_grounding_dino/convert_mm_grounding_dino_to_hf.py
@@ -376,7 +376,7 @@ def preprocess_old_state(state_dict: dict, config: MMGroundingDinoConfig) -> dic
         if (
             k == "dn_query_generator.label_embedding.weight"
             or k == "language_model.language_backbone.body.model.embeddings.position_ids"
-            or k == "image_seperate.weight"
+            or k == "image_separate.weight"
             or k.startswith("lmm")
             or k.startswith("connector")
             or k.startswith("region_connector")
diff --git a/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py b/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py
index e037ce850fe8..f3efdc15b2cf 100644
--- a/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py
+++ b/src/transformers/models/mm_grounding_dino/modeling_mm_grounding_dino.py
@@ -694,7 +694,6 @@ def forward(self, pixel_values, pixel_mask):
         return out, pos
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the MMGroundingDinoEncoder. This class extends BaseModelOutput, due to:
@@ -702,6 +701,7 @@ def forward(self, pixel_values, pixel_mask):
     - vision and text intermediate hidden states
     """
 )
+@dataclass
 class MMGroundingDinoEncoderOutput(ModelOutput):
     r"""
     last_hidden_state_vision (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -1253,7 +1253,6 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the MMGroundingDinoDecoder. This class adds two attributes to
@@ -1262,6 +1261,7 @@ def forward(
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class MMGroundingDinoDecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -1635,12 +1635,12 @@ def custom_forward(*inputs):
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the Grounding DINO encoder-decoder model.
     """
 )
+@dataclass
 class MMGroundingDinoModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -2213,12 +2213,12 @@ def forward(self, x):
         return x
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`MMGroundingDinoForObjectDetection`].
     """
 )
+@dataclass
 class MMGroundingDinoObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/mobilebert/modeling_mobilebert.py b/src/transformers/models/mobilebert/modeling_mobilebert.py
index 6ac582508eeb..db74822fc032 100644
--- a/src/transformers/models/mobilebert/modeling_mobilebert.py
+++ b/src/transformers/models/mobilebert/modeling_mobilebert.py
@@ -558,12 +558,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`MobileBertForPreTraining`].
     """
 )
+@dataclass
 class MobileBertForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/mobilellm/__init__.py b/src/transformers/models/mobilellm/__init__.py
new file mode 100644
index 000000000000..db3f95109db3
--- /dev/null
+++ b/src/transformers/models/mobilellm/__init__.py
@@ -0,0 +1,64 @@
+# Copyright 2024 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from transformers.utils import (
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    is_tokenizers_available,
+    is_torch_available,
+)
+
+
+_import_structure = {
+    "configuration_mobilellm": ["MobileLLMConfig"],
+}
+
+try:
+    if not is_torch_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_mobilellm"] = [
+        "MobileLLMForCausalLM",
+        "MobileLLMModel",
+        "MobileLLMPreTrainedModel",
+        "MobileLLMForSequenceClassification",
+        "MobileLLMForQuestionAnswering",
+        "MobileLLMForTokenClassification",
+    ]
+
+if TYPE_CHECKING:
+    from .configuration_mobilellm import MobileLLMConfig
+
+    try:
+        if not is_torch_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_mobilellm import (
+            MobileLLMForCausalLM,
+            MobileLLMForQuestionAnswering,
+            MobileLLMForSequenceClassification,
+            MobileLLMForTokenClassification,
+            MobileLLMModel,
+            MobileLLMPreTrainedModel,
+        )
+
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
diff --git a/src/transformers/models/mobilellm/configuration_mobilellm.py b/src/transformers/models/mobilellm/configuration_mobilellm.py
new file mode 100644
index 000000000000..fc8b521f8de1
--- /dev/null
+++ b/src/transformers/models/mobilellm/configuration_mobilellm.py
@@ -0,0 +1,146 @@
+# Copyright 2024 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""MobileLLM configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MobileLLMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MobileLLMModel`]. It is used to instantiate a
+    MobileLLM model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the MobileLLM-125M.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MobileLLM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MobileLLMModel`]
+        hidden_size (`int`, *optional*, defaults to 576):
+            Dimension of the hidden representations (also called embedding dimension).
+        intermediate_size (`int`, *optional*, defaults to 1536):
+            Dimension of the MLP representations (feed-forward network hidden size).
+        num_hidden_layers (`int`, *optional*, defaults to 30):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 9):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*, defaults to 3):
+            Number of key-value heads for Grouped Query Attention. Should be a divisor of `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder. Will default to `"silu"`.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-05):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 0):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie weight embeddings (input and output embeddings share the same weights).
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently does not support
+            MobileLLM as RoPE is not used in the original implementation.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        mlp_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the MLP layers.
+        share_embedding (`bool`, *optional*, defaults to `True`):
+            Whether input and output embeddings should share the same parameters (embedding sharing).
+
+    Example:
+    ```python
+    >>> from transformers import MobileLLMModel, MobileLLMConfig
+
+    >>> # Initializing a MobileLLM 125M style configuration
+    >>> configuration = MobileLLMConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = MobileLLMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "mobilellm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=576,
+        intermediate_size=1536,
+        num_hidden_layers=30,
+        num_attention_heads=9,
+        num_key_value_heads=3,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        mlp_bias=False,
+        share_embedding=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.mlp_bias = mlp_bias
+        self.share_embedding = share_embedding
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
diff --git a/src/transformers/models/mobilellm/modeling_mobilellm.py b/src/transformers/models/mobilellm/modeling_mobilellm.py
new file mode 100644
index 000000000000..188806b28e60
--- /dev/null
+++ b/src/transformers/models/mobilellm/modeling_mobilellm.py
@@ -0,0 +1,1479 @@
+# Copyright 2024 Meta Platforms, Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch MobileLLM model."""
+
+import math
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache, StaticCache
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+
+from .configuration_mobilellm import MobileLLMConfig
+
+
+if is_flash_attn_2_available():
+    from transformers.modeling_flash_attention_utils import _flash_attention_forward
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MobileLLMConfig"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+class MobileLLMRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MobileLLMRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+ALL_LAYERNORM_LAYERS.append(MobileLLMRMSNorm)
+
+
+class MobileLLMRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        super().__init__()
+        self.scaling_factor = scaling_factor
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        # For BC we register cos and sin cached
+        self.max_seq_len_cached = max_position_embeddings
+
+    @torch.no_grad()
+    def forward(self, x, position_ids):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        position_ids (`torch.Tensor`, *optional*):
+            Deprecated and unused.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class MobileLLMMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=config.mlp_bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=config.mlp_bias)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        # SwiGLU activation: gate_proj provides gating, up_proj provides the main path
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class MobileLLMAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: MobileLLMConfig, layer_idx: int | None = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+                "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = MobileLLMRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            raise ValueError("MobileLLM does not currently support rope_scaling")
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+        if attention_mask is not None:  # no matter the length, we just slice it
+            causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+            attn_weights = attn_weights + causal_mask
+
+        # upcast attention to fp32
+        attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+        attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+        attn_output = torch.matmul(attn_weights, value_states)
+
+        if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+                f" {attn_output.size()}"
+            )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MobileLLMFlashAttention2(MobileLLMAttention):
+    """
+    MobileLLM flash attention module. This module inherits from `MobileLLMAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.LongTensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        output_attentions = False
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+        # therefore the input hidden states gets silently casted in float32. Hence, we need
+        # cast them back in the correct dtype just to be sure everything works as expected.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (MobileLLMRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            if torch.is_autocast_enabled():
+                target_dtype = torch.get_autocast_gpu_dtype()
+            # Handle the case where the model is quantized
+            elif hasattr(self.config, "_pre_quantization_dtype"):
+                target_dtype = self.config._pre_quantization_dtype
+            else:
+                target_dtype = self.q_proj.weight.dtype
+
+            logger.warning_once(
+                f"The input hidden states seems to be silently casted in float32, this might be related to"
+                f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+                f" {target_dtype}."
+            )
+
+            query_states = query_states.to(target_dtype)
+            key_states = key_states.to(target_dtype)
+            value_states = value_states.to(target_dtype)
+
+        attn_output = _flash_attention_forward(
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            q_len,
+            position_ids=position_ids,
+            dropout=dropout_rate,
+            sliding_window=getattr(self, "sliding_window", None),
+            use_top_left_mask=self._flash_attn_uses_top_left_mask,
+            is_causal=self.is_causal,
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MobileLLMSdpaAttention(MobileLLMAttention):
+    """
+    MobileLLM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MobileLLMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MobileLLMAttention.forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MobileLLMModel is using MobileLLMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+                'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+            )
+            return super().forward(
+                hidden_states=hidden_states,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                past_key_value=past_key_value,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = self.rotary_emb(value_states, position_ids)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_value is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        key_states = repeat_kv(key_states, self.num_key_value_groups)
+        value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+        causal_mask = attention_mask
+        if attention_mask is not None:
+            causal_mask = causal_mask[:, :, :, : key_states.shape[-2]]
+
+        # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+        # Reference: https://github.com/pytorch/pytorch/issues/112577.
+        if query_states.device.type == "cuda" and causal_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment
+        # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling.
+        is_causal = bool(causal_mask is None and q_len > 1)
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=causal_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            is_causal=is_causal,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+MOBILELLM_ATTENTION_CLASSES = {
+    "eager": MobileLLMAttention,
+    "flash_attention_2": MobileLLMFlashAttention2,
+    "sdpa": MobileLLMSdpaAttention,
+}
+
+
+class MobileLLMDecoderLayer(nn.Module):
+    def __init__(self, config: MobileLLMConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = MOBILELLM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = MobileLLMMLP(config)
+        self.input_layernorm = MobileLLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MobileLLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_value: Cache | None = None,
+        output_attentions: bool | None = False,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            use_cache (`bool`, *optional*):
+                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+                (see `past_key_values`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+            cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+                Indices depicting the position of the input sequence tokens in the sequence
+            kwargs (`dict`, *optional*):
+                Arbitrary kwargs to be ignored, used for FSDP and other methods that injects code
+                into the model
+        """
+        residual = hidden_states
+
+        hidden_states = self.input_layernorm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_value=past_key_value,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+MOBILELLM_START_DOCSTRING = r"""
+    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+    and behavior.
+
+    Parameters:
+        config ([`MobileLLMConfig`]):
+            Model configuration class with all the parameters of the model. Initializing with a config file does not
+            load the weights associated with the model, only the configuration. Check out the
+            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+    "The bare MobileLLM Model outputting raw hidden-states without any specific head on top.",
+    MOBILELLM_START_DOCSTRING,
+)
+class MobileLLMPreTrainedModel(PreTrainedModel):
+    config_class = MobileLLMConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MobileLLMDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+    _supports_quantized_cache = True
+    _supports_static_cache = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MOBILELLM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
+            returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance, see our
+            [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache);
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+        cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*):
+            Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`,
+            this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
+            the complete sequence length.
+"""
+
+
+@add_start_docstrings(
+    "The bare MobileLLM Model outputting raw hidden-states without any specific head on top.",
+    MOBILELLM_START_DOCSTRING,
+)
+class MobileLLMModel(MobileLLMPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MobileLLMDecoderLayer`]
+
+    Args:
+        config: MobileLLMConfig
+    """
+
+    def __init__(self, config: MobileLLMConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MobileLLMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = MobileLLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MOBILELLM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+    ) -> tuple | BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # kept for BC (non `Cache` `past_key_values` inputs)
+        return_legacy_cache = False
+        if use_cache and not isinstance(past_key_values, Cache):
+            return_legacy_cache = True
+            if past_key_values is None:
+                past_key_values = DynamicCache()
+            else:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+                logger.warning_once(
+                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
+                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
+                    "(https://huggingface.co/docs/transformers/kv_cache#legacy-cache-format)"
+                )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = self._update_causal_mask(
+            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
+        )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    causal_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                    cache_position,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=causal_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = next_decoder_cache if use_cache else None
+        if return_legacy_cache:
+            next_cache = next_cache.to_legacy_cache()
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+    def _update_causal_mask(
+        self,
+        attention_mask: torch.Tensor,
+        input_tensor: torch.Tensor,
+        cache_position: torch.Tensor,
+        past_key_values: Cache,
+        output_attentions: bool,
+    ):
+        if self.config._attn_implementation == "flash_attention_2":
+            if attention_mask is not None and 0.0 in attention_mask:
+                return attention_mask
+            return None
+
+        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
+        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
+        # to infer the attention mask.
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        using_static_cache = isinstance(past_key_values, StaticCache)
+
+        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
+        if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions:
+            if AttentionMaskConverter._ignore_causal_mask_sdpa(
+                attention_mask,
+                inputs_embeds=input_tensor,
+                past_key_values_length=past_seen_tokens,
+                is_training=self.training,
+            ):
+                return None
+
+        dtype, device = input_tensor.dtype, input_tensor.device
+        min_dtype = torch.finfo(dtype).min
+        sequence_length = input_tensor.shape[1]
+        if using_static_cache:
+            target_length = past_key_values.get_max_length()
+        else:
+            target_length = (
+                attention_mask.shape[-1]
+                if isinstance(attention_mask, torch.Tensor)
+                else past_seen_tokens + sequence_length + 1
+            )
+
+        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
+        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
+            attention_mask,
+            sequence_length=sequence_length,
+            target_length=target_length,
+            dtype=dtype,
+            device=device,
+            min_dtype=min_dtype,
+            cache_position=cache_position,
+            batch_size=input_tensor.shape[0],
+        )
+
+        if (
+            self.config._attn_implementation == "sdpa"
+            and attention_mask is not None
+            and attention_mask.device.type == "cuda"
+            and not output_attentions
+        ):
+            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
+            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
+            # Details: https://github.com/pytorch/pytorch/issues/110213
+            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)
+
+        return causal_mask
+
+    @staticmethod
+    def _prepare_4d_causal_attention_mask_with_cache_position(
+        attention_mask: torch.Tensor,
+        sequence_length: int,
+        target_length: int,
+        dtype: torch.dtype,
+        device: torch.device,
+        min_dtype: float,
+        cache_position: torch.Tensor,
+        batch_size: int,
+    ):
+        """
+        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
+        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.
+
+        Args:
+            attention_mask (`torch.Tensor`):
+                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape
+                `(batch_size, 1, query_length, key_value_length)`.
+            sequence_length (`int`):
+                The sequence length being processed.
+            target_length (`int`):
+                The target length: when generating with static cache, the mask should be as long as the static cache,
+                to account for the 0 padding, the part of the cache that is not filled yet.
+            dtype (`torch.dtype`):
+                The dtype to use for the 4D attention mask.
+            device (`torch.device`):
+                The device to plcae the 4D attention mask on.
+            min_dtype (`float`):
+                The minimum value representable with the dtype `dtype`.
+            cache_position (`torch.Tensor`):
+                Indices depicting the position of the input sequence tokens in the sequence.
+            batch_size (`torch.Tensor`):
+                Batch size.
+        """
+        if attention_mask is not None and attention_mask.dim() == 4:
+            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
+            causal_mask = attention_mask
+        else:
+            causal_mask = torch.full(
+                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
+            )
+            if sequence_length != 1:
+                causal_mask = torch.triu(causal_mask, diagonal=1)
+            causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
+            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
+            if attention_mask is not None:
+                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
+                mask_length = attention_mask.shape[-1]
+                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
+                padding_mask = padding_mask == 0
+                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
+                    padding_mask, min_dtype
+                )
+
+        return causal_mask
+
+
+class MobileLLMForCausalLM(MobileLLMPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MobileLLMModel(config)
+        self.vocab_size = config.vocab_size
+
+        # Conditionally create lm_head based on share_embedding
+        if not config.share_embedding:
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        else:
+            self.lm_head = None
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        if self.lm_head is not None:
+            return self.lm_head
+        return self.model.embed_tokens
+
+    def set_output_embeddings(self, new_embeddings):
+        if self.lm_head is not None:
+            self.lm_head = new_embeddings
+        else:
+            self.model.embed_tokens = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MOBILELLM_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+    ) -> tuple | CausalLMOutputWithPast:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MobileLLMForCausalLM
+
+        >>> model = MobileLLMForCausalLM.from_pretrained("facebook/MobileLLM-125M")
+        >>> tokenizer = AutoTokenizer.from_pretrained("facebook/MobileLLM-125M")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+
+        # Use shared embeddings or separate lm_head
+        if self.lm_head is not None:
+            logits = self.lm_head(hidden_states)
+        else:
+            # Share weights with input embeddings
+            logits = F.linear(hidden_states, self.model.embed_tokens.weight)
+
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        **kwargs,
+    ):
+        # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
+        # Exception 1: when passing input_embeds, input_ids may be missing entries
+        # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
+        if past_key_values is not None:
+            if inputs_embeds is not None:  # Exception 1
+                input_ids = input_ids[:, -cache_position.shape[0] :]
+            elif input_ids.shape[1] != cache_position.shape[0]:  # Default case (the "else", a no op, is Exception 2)
+                input_ids = input_ids[:, cache_position]
+
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -input_ids.shape[1] :]
+
+                # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s  `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture.
+                position_ids = position_ids.clone(memory_format=torch.contiguous_format)
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and cache_position[0] == 0:
+            model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None}
+        else:
+            # The clone here is for the same reason as for `position_ids`.
+            model_inputs = {"input_ids": input_ids.clone(memory_format=torch.contiguous_format), "inputs_embeds": None}
+
+        if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2:
+            if model_inputs["inputs_embeds"] is not None:
+                batch_size, sequence_length, _ = model_inputs["inputs_embeds"].shape
+                device = model_inputs["inputs_embeds"].device
+            else:
+                batch_size, sequence_length = model_inputs["input_ids"].shape
+                device = model_inputs["input_ids"].device
+
+            dtype = self.lm_head.weight.dtype if self.lm_head is not None else self.model.embed_tokens.weight.dtype
+            min_dtype = torch.finfo(dtype).min
+
+            attention_mask = self.model._prepare_4d_causal_attention_mask_with_cache_position(
+                attention_mask,
+                sequence_length=sequence_length,
+                target_length=past_key_values.get_max_length(),
+                dtype=dtype,
+                device=device,
+                min_dtype=min_dtype,
+                cache_position=cache_position,
+                batch_size=batch_size,
+            )
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "use_cache": use_cache,
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+
+@add_start_docstrings(
+    """
+    The MobileLLM Model transformer with a sequence classification head on top (linear layer).
+
+    [`MobileLLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) do.
+
+    Since it does classification on the last token, it requires to know the position of the last token. If a
+    `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+    no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+    padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+    each row of the batch).
+    """,
+    MOBILELLM_START_DOCSTRING,
+)
+class MobileLLMForSequenceClassification(MobileLLMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MobileLLMModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MOBILELLM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | SequenceClassifierOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        transformer_outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        hidden_states = transformer_outputs[0]
+        logits = self.score(hidden_states)
+
+        if input_ids is not None:
+            batch_size = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        if self.config.pad_token_id is None and batch_size != 1:
+            raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+        if self.config.pad_token_id is None:
+            sequence_lengths = -1
+        else:
+            if input_ids is not None:
+                # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+                sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+                sequence_lengths = sequence_lengths % input_ids.shape[-1]
+                sequence_lengths = sequence_lengths.to(logits.device)
+            else:
+                sequence_lengths = -1
+
+        pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(pooled_logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(pooled_logits, labels)
+        if not return_dict:
+            output = (pooled_logits,) + transformer_outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutputWithPast(
+            loss=loss,
+            logits=pooled_logits,
+            past_key_values=transformer_outputs.past_key_values,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The MobileLLM Model transformer with a token classification head on top (a linear layer on top of the hidden-states
+    output) e.g. for Named-Entity-Recognition (NER) tasks.
+    """,
+    MOBILELLM_START_DOCSTRING,
+)
+class MobileLLMForTokenClassification(MobileLLMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MobileLLMModel(config)
+        if getattr(config, "classifier_dropout", None) is not None:
+            classifier_dropout = config.classifier_dropout
+        elif getattr(config, "hidden_dropout", None) is not None:
+            classifier_dropout = config.hidden_dropout
+        else:
+            classifier_dropout = 0.1
+        self.dropout = nn.Dropout(classifier_dropout)
+        self.score = nn.Linear(config.hidden_size, config.num_labels)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MOBILELLM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | TokenClassifierOutput:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = outputs[0]
+        sequence_output = self.dropout(sequence_output)
+        logits = self.score(sequence_output)
+
+        loss = None
+        if labels is not None:
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+@add_start_docstrings(
+    """
+    The MobileLLM Model transformer with a span classification head on top for extractive question-answering tasks like
+    SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
+    """,
+    MOBILELLM_START_DOCSTRING,
+)
+class MobileLLMForQuestionAnswering(MobileLLMPreTrainedModel):
+    base_model_prefix = "transformer"
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MobileLLMModel(config)
+        self.qa_outputs = nn.Linear(config.hidden_size, 2)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MOBILELLM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.FloatTensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        start_positions: torch.LongTensor | None = None,
+        end_positions: torch.LongTensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | QuestionAnsweringModelOutput:
+        r"""
+        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the start of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for position (index) of the end of the labelled span for computing the token classification loss.
+            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
+            are not taken into account for computing the loss.
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.model(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        logits = self.qa_outputs(sequence_output)
+        start_logits, end_logits = logits.split(1, dim=-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
+        end_logits = end_logits.squeeze(-1).contiguous()
+
+        total_loss = None
+        if start_positions is not None and end_positions is not None:
+            # If we are on multi-GPU, split add a dimension
+            if len(start_positions.size()) > 1:
+                start_positions = start_positions.squeeze(-1).to(start_logits.device)
+            if len(end_positions.size()) > 1:
+                end_positions = end_positions.squeeze(-1).to(end_logits.device)
+            # sometimes the start/end positions are outside our model inputs, we ignore these terms
+            ignored_index = start_logits.size(1)
+            start_positions = start_positions.clamp(0, ignored_index)
+            end_positions = end_positions.clamp(0, ignored_index)
+
+            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
+            start_loss = loss_fct(start_logits, start_positions)
+            end_loss = loss_fct(end_logits, end_positions)
+            total_loss = (start_loss + end_loss) / 2
+
+        if not return_dict:
+            output = (start_logits, end_logits) + outputs[2:]
+            return ((total_loss,) + output) if total_loss is not None else output
+
+        return QuestionAnsweringModelOutput(
+            loss=total_loss,
+            start_logits=start_logits,
+            end_logits=end_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
diff --git a/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py b/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
index 8524248f7796..6688d1db9a32 100755
--- a/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
+++ b/src/transformers/models/mobilenet_v1/modeling_mobilenet_v1.py
@@ -19,7 +19,8 @@
 from ...activations import ACT2FN
 from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_mobilenet_v1 import MobileNetV1Config
 
 
@@ -128,6 +129,7 @@ class MobileNetV1PreTrainedModel(PreTrainedModel):
     input_modalities = ("image",)
     supports_gradient_checkpointing = False
     _no_split_modules = []
+    _can_record_outputs = {"hidden_states": MobileNetV1ConvLayer}
 
 
 @auto_docstring
@@ -186,32 +188,21 @@ def __init__(self, config: MobileNetV1Config, add_pooling_layer: bool = True):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | BaseModelOutputWithPoolingAndNoAttention:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
+    ) -> BaseModelOutputWithPoolingAndNoAttention:
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
 
         hidden_states = self.conv_stem(pixel_values)
 
-        all_hidden_states = () if output_hidden_states else None
-
         for i, layer_module in enumerate(self.layer):
             hidden_states = layer_module(hidden_states)
 
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
         last_hidden_state = hidden_states
 
         if self.pooler is not None:
@@ -219,13 +210,9 @@ def forward(
         else:
             pooled_output = None
 
-        if not return_dict:
-            return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
-
         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
-            hidden_states=all_hidden_states,
         )
 
 
@@ -251,26 +238,23 @@ def __init__(self, config: MobileNetV1Config) -> None:
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
         labels: torch.Tensor | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | ImageClassifierOutputWithNoAttention:
+    ) -> ImageClassifierOutputWithNoAttention:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
-        outputs = self.mobilenet_v1(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+        outputs = self.mobilenet_v1(pixel_values, **kwargs)
 
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+        pooled_output = outputs.pooler_output
 
         logits = self.classifier(self.dropout(pooled_output))
 
@@ -278,10 +262,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return ImageClassifierOutputWithNoAttention(
             loss=loss,
             logits=logits,
diff --git a/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py b/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
index a9b8d92cb589..fd465e9c2de2 100755
--- a/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
+++ b/src/transformers/models/mobilenet_v2/modeling_mobilenet_v2.py
@@ -25,6 +25,7 @@
 )
 from ...modeling_utils import PreTrainedModel
 from ...utils import auto_docstring, logging
+from ...utils.output_manager import can_return_tuple, capture_outputs
 from .configuration_mobilenet_v2 import MobileNetV2Config
 
 
@@ -254,6 +255,7 @@ class MobileNetV2PreTrainedModel(PreTrainedModel):
     input_modalities = ("image",)
     supports_gradient_checkpointing = False
     _no_split_modules = []
+    _can_record_outputs = {"hidden_states": MobileNetV2InvertedResidual}
 
 
 @auto_docstring
@@ -323,31 +325,20 @@ def __init__(self, config: MobileNetV2Config, add_pooling_layer: bool = True):
         self.post_init()
 
     @auto_docstring
+    @capture_outputs
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | BaseModelOutputWithPoolingAndNoAttention:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if pixel_values is None:
             raise ValueError("You have to specify pixel_values")
 
         hidden_states = self.conv_stem(pixel_values)
 
-        all_hidden_states = () if output_hidden_states else None
-
         for i, layer_module in enumerate(self.layer):
             hidden_states = layer_module(hidden_states)
 
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
         last_hidden_state = self.conv_1x1(hidden_states)
 
         if self.pooler is not None:
@@ -355,13 +346,9 @@ def forward(
         else:
             pooled_output = None
 
-        if not return_dict:
-            return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
-
         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
-            hidden_states=all_hidden_states,
         )
 
 
@@ -388,12 +375,11 @@ def __init__(self, config: MobileNetV2Config) -> None:
         self.post_init()
 
     @auto_docstring
+    @can_return_tuple
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
         labels: torch.Tensor | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | ImageClassifierOutputWithNoAttention:
         r"""
@@ -402,11 +388,9 @@ def forward(
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
-        outputs = self.mobilenet_v2(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+        outputs = self.mobilenet_v2(pixel_values, **kwargs)
 
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+        pooled_output = outputs.pooler_output
 
         logits = self.classifier(self.dropout(pooled_output))
 
@@ -414,10 +398,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return ImageClassifierOutputWithNoAttention(
             loss=loss,
             logits=logits,
@@ -517,12 +497,11 @@ def __init__(self, config: MobileNetV2Config) -> None:
         self.post_init()
 
     @auto_docstring
+    @can_return_tuple
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | SemanticSegmenterOutput:
         r"""
@@ -553,21 +532,16 @@ def forward(
         >>> # logits are of shape (batch_size, num_labels, height, width)
         >>> logits = outputs.logits
         ```"""
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         if labels is not None and self.config.num_labels == 1:
             raise ValueError("The number of labels should be greater than one")
 
         outputs = self.mobilenet_v2(
             pixel_values,
             output_hidden_states=True,  # we need the intermediate hidden states
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        encoder_hidden_states = outputs.hidden_states if return_dict else outputs[1]
+        encoder_hidden_states = outputs.hidden_states
 
         logits = self.segmentation_head(encoder_hidden_states[-1])
 
@@ -580,17 +554,10 @@ def forward(
             loss_fct = CrossEntropyLoss(ignore_index=self.config.semantic_loss_ignore_index)
             loss = loss_fct(upsampled_logits, labels)
 
-        if not return_dict:
-            if output_hidden_states:
-                output = (logits,) + outputs[1:]
-            else:
-                output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return SemanticSegmenterOutput(
             loss=loss,
             logits=logits,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            hidden_states=outputs.hidden_states,
             attentions=None,
         )
 
diff --git a/src/transformers/models/mobilevit/image_processing_mobilevit.py b/src/transformers/models/mobilevit/image_processing_mobilevit.py
index d94c1912fbd9..2efd86398b2f 100644
--- a/src/transformers/models/mobilevit/image_processing_mobilevit.py
+++ b/src/transformers/models/mobilevit/image_processing_mobilevit.py
@@ -144,9 +144,10 @@ def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
         """Reduce label values by 1, replacing 0 with 255."""
         for idx in range(len(labels)):
             label = labels[idx]
-            label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label)
-            label = label - 1
-            label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label)
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
             labels[idx] = label
         return labels
 
diff --git a/src/transformers/models/mobilevit/image_processing_pil_mobilevit.py b/src/transformers/models/mobilevit/image_processing_pil_mobilevit.py
index 893e27fe4ccf..f6031a740eae 100644
--- a/src/transformers/models/mobilevit/image_processing_pil_mobilevit.py
+++ b/src/transformers/models/mobilevit/image_processing_pil_mobilevit.py
@@ -142,9 +142,10 @@ def _preprocess_image_like_inputs(
 
     def reduce_label(self, image: np.ndarray) -> np.ndarray:
         """Reduce label values by 1, replacing 0 with 255."""
-        image[image == 0] = 255
-        image = image - 1
-        image[image == 254] = 255
+        image = image.copy()
+        ignore_mask = (image == 0) | (image == 255)
+        image[ignore_mask] = 255
+        image[~ignore_mask] = image[~ignore_mask] - 1
         return image
 
     def flip_channel_order(self, image: np.ndarray) -> np.ndarray:
diff --git a/src/transformers/models/molmo2/__init__.py b/src/transformers/models/molmo2/__init__.py
new file mode 100644
index 000000000000..88d36605b134
--- /dev/null
+++ b/src/transformers/models/molmo2/__init__.py
@@ -0,0 +1,42 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_molmo2 import (
+        Molmo2AdapterConfig,
+        Molmo2Config,
+        Molmo2TextConfig,
+        Molmo2VitConfig,
+    )
+    from .image_processing_molmo2 import Molmo2ImageProcessor
+    from .modeling_molmo2 import (
+        Molmo2ForConditionalGeneration,
+        Molmo2Model,
+        Molmo2PreTrainedModel,
+        Molmo2TextModel,
+        Molmo2VisionBackbone,
+        Molmo2VisionModel,
+    )
+    from .processing_molmo2 import Molmo2Processor
+    from .video_processing_molmo2 import Molmo2VideoProcessor
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/molmo2/configuration_molmo2.py b/src/transformers/models/molmo2/configuration_molmo2.py
new file mode 100644
index 000000000000..baa90c271374
--- /dev/null
+++ b/src/transformers/models/molmo2/configuration_molmo2.py
@@ -0,0 +1,250 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Molmo2 configuration
+"""
+
+from dataclasses import field
+from typing import Any
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import logging
+from ...utils.auto_docstring import auto_docstring
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="allenai/Molmo2-8B")
+@strict
+class Molmo2VitConfig(PreTrainedConfig):
+    r"""
+    image_default_input_size (`list[int]`, *optional*, defaults to `[378, 378]`):
+        Default input image size (height, width).
+    image_patch_size (`int`, *optional*, defaults to 14):
+        Size of each image patch.
+    image_num_pos (`int`, *optional*, defaults to 577):
+        Number of positional embeddings for the image.
+    """
+
+    model_type = "molmo2"
+    base_config_key = "vit_config"
+
+    hidden_size: int = 1152
+    intermediate_size: int = 4304
+    num_hidden_layers: int = 27
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 16
+    head_dim: int = 72
+    hidden_act: str = "gelu_pytorch_tanh"
+    layer_norm_eps: float = 1e-6
+    image_default_input_size: list[int] = field(default_factory=lambda: [378, 378])
+    image_patch_size: int = 14
+    image_num_pos: int = 577
+    attention_dropout: float = 0.0
+    residual_dropout: float = 0.0
+    initializer_range: float = 0.02
+
+    @property
+    def image_num_patch(self):
+        h, w = self.image_default_input_size
+        return h // self.image_patch_size, w // self.image_patch_size
+
+
+@auto_docstring(checkpoint="allenai/Molmo2-8B")
+@strict
+class Molmo2AdapterConfig(PreTrainedConfig):
+    r"""
+    vit_layers (`list[int]`, *optional*, defaults to `[-3, -9]`):
+        Indices of ViT layers to extract features from.
+    pooling_attention_mask (`bool`, *optional*, defaults to `False`):
+        Whether to use attention mask during pooling.
+    text_hidden_size (`int`, *optional*, defaults to 3584):
+        Hidden size of the text model (used for projection).
+    image_feature_dropout (`float`, *optional*, defaults to 0.0):
+        Dropout rate for image features.
+    """
+
+    model_type = "molmo2"
+    base_config_key = "adapter_config"
+
+    vit_layers: list[int] = field(default_factory=lambda: [-3, -9])
+    pooling_attention_mask: bool = False
+    hidden_size: int = 1152
+    num_attention_heads: int = 16
+    num_key_value_heads: int = 16
+    head_dim: int = 72
+    attention_dropout: float = 0.0
+    residual_dropout: float = 0.0
+    hidden_act: str = "silu"
+    intermediate_size: int = 18944
+    text_hidden_size: int = 3584
+    image_feature_dropout: float = 0.0
+    initializer_range: float = 0.02
+
+
+@auto_docstring(checkpoint="allenai/Molmo2-8B")
+@strict
+class Molmo2TextConfig(PreTrainedConfig):
+    r"""
+    additional_vocab_size (`int`, *optional*, defaults to 128):
+        Number of additional vocabulary tokens beyond the base vocabulary.
+    qkv_bias (`bool`, *optional*, defaults to `True`):
+        Whether to use bias in query, key, and value projections.
+    embedding_dropout (`float`, *optional*, defaults to 0.0):
+        The dropout ratio for the embedding layer.
+    residual_dropout (`float`, *optional*, defaults to 0.0):
+        The dropout ratio applied after residual connections.
+    rope_theta (`float`, *optional*, defaults to 1000000.0):
+        The base period of the RoPE embeddings.
+    rope_scaling (`dict[str, Any]`, *optional*):
+        Dictionary containing the scaling configuration for the RoPE embeddings.
+    rope_scaling_layers (`list[int]`, *optional*):
+        List of layer indices where rope scaling is applied.
+    use_qk_norm (`bool`, *optional*, defaults to `False`):
+        Whether to apply query-key normalization.
+    qk_norm_type (`str`, *optional*, defaults to `"olmo"`):
+        The type of query-key normalization to use.
+    norm_after (`bool`, *optional*, defaults to `False`):
+        Whether to apply layer normalization after the attention/FFN blocks instead of before.
+    """
+
+    model_type = "molmo2_text"
+    base_config_key = "text_config"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "blocks.*.self_attn.att_proj": "colwise",
+        "blocks.*.self_attn.attn_out": "rowwise",
+        "blocks.*.mlp.ff_proj": "colwise",
+        "blocks.*.mlp.ff_out": "rowwise",
+    }
+    base_model_pp_plan = {
+        "wte": (["input_ids"], ["inputs_embeds"]),
+        "blocks": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "ln_f": (["hidden_states"], ["hidden_states"]),
+    }
+
+    hidden_size: int = 3584
+    num_attention_heads: int = 28
+    num_key_value_heads: int | None = 4
+    head_dim: int = 128
+    vocab_size: int = 152064
+    additional_vocab_size: int = 128
+    qkv_bias: bool = True
+    num_hidden_layers: int = 48
+    intermediate_size: int = 18944
+    hidden_act: str = "silu"
+    embedding_dropout: float = 0.0
+    attention_dropout: float = 0.0
+    residual_dropout: float = 0.0
+    max_position_embeddings: int = 4096
+    rope_theta: float = 1000000.0
+    rope_scaling: dict[str, Any] | None = None
+    rope_scaling_layers: list[int] | None = None
+    use_qk_norm: bool = False
+    qk_norm_type: str = "olmo"
+    layer_norm_eps: float = 1e-6
+    norm_after: bool = False
+    initializer_range: float = 0.02
+    use_cache: bool = True
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="allenai/Molmo2-8B")
+@strict
+class Molmo2Config(PreTrainedConfig):
+    r"""
+    vit_config (`Molmo2VitConfig`, *optional*):
+        Configuration for the vision transformer backbone.
+    adapter_config (`Molmo2AdapterConfig`, *optional*):
+        Configuration for the vision-to-language adapter.
+    image_start_token_id (`int`, *optional*):
+        Token ID marking the start of an image region.
+    low_res_image_start_token_id (`int`, *optional*):
+        Token ID marking the start of a low-resolution image crop.
+    image_end_token_id (`int`, *optional*):
+        Token ID marking the end of an image region.
+    image_low_res_id (`int`, *optional*):
+        Token ID for low-resolution image patches.
+    image_patch_id (`int`, *optional*):
+        Token ID for image patches.
+    image_col_id (`int`, *optional*):
+        Token ID for column separators in image patch sequences.
+    frame_start_token_id (`int`, *optional*):
+        Token ID marking the start of a video frame.
+    frame_end_token_id (`int`, *optional*):
+        Token ID marking the end of a video frame.
+    use_frame_special_tokens (`bool`, *optional*, defaults to `True`):
+        Whether to use special tokens to delineate video frames.
+    tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+        Whether the model's input and output word embeddings should be tied.
+    """
+
+    model_type = "molmo2"
+    sub_configs = {
+        "text_config": Molmo2TextConfig,
+        "vit_config": Molmo2VitConfig,
+        "adapter_config": Molmo2AdapterConfig,
+    }
+
+    vit_config: dict | PreTrainedConfig | None = None
+    adapter_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    image_start_token_id: int | None = None
+    low_res_image_start_token_id: int | None = None
+    image_end_token_id: int | None = None
+    image_low_res_id: int | None = None
+    image_patch_id: int | None = None
+    image_col_id: int | None = None
+    frame_start_token_id: int | None = None
+    frame_end_token_id: int | None = None
+    use_frame_special_tokens: bool = True
+    initializer_range: float = 0.02
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.vit_config, dict):
+            self.vit_config = self.sub_configs["vit_config"](**self.vit_config)
+        elif self.vit_config is None:
+            self.vit_config = self.sub_configs["vit_config"]()
+
+        if isinstance(self.adapter_config, dict):
+            self.adapter_config = self.sub_configs["adapter_config"](**self.adapter_config)
+        elif self.adapter_config is None:
+            self.adapter_config = self.sub_configs["adapter_config"]()
+
+        if isinstance(self.text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        self.image_high_res_id = self.image_patch_id
+        self.use_cache = self.text_config.use_cache
+        super().__post_init__(**kwargs)
+
+
+__all__ = [
+    "Molmo2AdapterConfig",
+    "Molmo2Config",
+    "Molmo2TextConfig",
+    "Molmo2VitConfig",
+]
diff --git a/src/transformers/models/molmo2/image_processing_molmo2.py b/src/transformers/models/molmo2/image_processing_molmo2.py
new file mode 100644
index 000000000000..8fc7a0cdad9e
--- /dev/null
+++ b/src/transformers/models/molmo2/image_processing_molmo2.py
@@ -0,0 +1,500 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Image processor class for Molmo2"""
+
+import numpy as np
+import torch
+import torchvision.transforms
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_processing_utils import BatchFeature, get_size_dict
+from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    infer_channel_dimension_format,
+    make_flat_list_of_images,
+    to_numpy_array,
+    valid_images,
+)
+from ...processing_utils import ImagesKwargs
+from ...utils import TensorType, auto_docstring, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+def resize_image(
+    image: np.ndarray,
+    desired_output_size: list[int],
+    resample: PILImageResampling,
+) -> np.ndarray:
+    """Resize an image and rescale to [0, 1] float32."""
+    image = torch.permute(torch.from_numpy(image), [2, 0, 1])
+    resized = torchvision.transforms.Resize(
+        desired_output_size,
+        resample,
+        antialias=False,
+    )(image)
+    resized = torch.clip(resized, 0, 255).to(torch.uint8)
+    resized = resized.to(torch.float32) / 255.0
+    resized = torch.permute(resized, [1, 2, 0]).numpy()
+    return resized
+
+
+# Copied from transformers.models.cohere2_vision.image_processing_cohere2_vision.get_all_supported_aspect_ratios
+def get_all_supported_aspect_ratios(max_image_tiles: int) -> list[tuple[int, int]]:
+    """
+    Computes all allowed aspect ratios for a given maximum number of input tiles.
+
+    This function calculates all possible arrangements of tiles that can be formed
+    within the constraint of the maximum number of tiles. Each arrangement is
+    represented by its aspect ratio (width/height) and the corresponding tile configuration.
+
+    Args:
+        max_image_tiles (`int`):
+            The maximum number of tiles allowed.
+
+    Returns:
+        `list[tuple[int, int]]`: A list of tuples, each tuple representing a valid (width, height)
+        configuration in terms of number of tiles.
+
+    Example:
+        >>> get_all_supported_aspect_ratios(4)
+        [(1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (3, 1), (4, 1)]
+
+    """
+    aspect_ratios = []
+    for width in range(1, max_image_tiles + 1):
+        for height in range(1, max_image_tiles + 1):
+            if width * height <= max_image_tiles:
+                aspect_ratios.append((width, height))
+    return aspect_ratios
+
+
+# Copied from transformers.models.cohere2_vision.image_processing_cohere2_vision.get_optimal_tiled_canvas
+def get_optimal_tiled_canvas(
+    original_image_size: tuple[int, int],
+    target_tile_size: tuple[int, int],
+    min_image_tiles: int,
+    max_image_tiles: int,
+) -> tuple[int, int]:
+    possible_resolutions = get_all_supported_aspect_ratios(max_image_tiles)
+    possible_resolutions = sorted(possible_resolutions, key=lambda x: x[0] * x[1])
+    image_height, image_width = original_image_size
+    patch_size_height, patch_size_width = target_tile_size  # (height == width)
+
+    candidate_resolutions = np.array(possible_resolutions) * patch_size_height
+    # tiles following (width, height) order to align with aspect ratio convention
+    tile_size = np.stack([image_width, image_height])
+    required_scales = candidate_resolutions / tile_size
+    required_scale = np.min(required_scales, axis=-1, keepdims=True)  # [n_resolutions, 1]
+    if np.all(required_scale < 1):
+        # We are forced to downscale, so try to minimize the amount of downscaling
+        best_grid = possible_resolutions[np.argmax(required_scale)]
+    else:
+        # Pick the resolution that required the least upscaling so that it most closely fits the image
+        required_scale = np.where(required_scale < 1.0, 10e9, required_scale)
+        best_grid = possible_resolutions[np.argmin(required_scale)]
+    return best_grid  # (width, height)
+
+
+def build_resized_image(
+    image: np.ndarray,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    resized = resize_image(
+        image,
+        base_image_input_size,
+        resample,
+    )
+    resized = normalize(resized, image_mean, image_std, input_data_format=ChannelDimension.LAST)
+    if len(resized.shape) == 3:
+        resized = np.expand_dims(resized, 0)
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    resize_idx = np.arange(crop_patch_w * crop_patch_h).reshape([crop_patch_h, crop_patch_w])
+    return resized, resize_idx
+
+
+def build_overlapping_crops(
+    image: np.ndarray,
+    max_crops: int,
+    overlap_margins: list[int],
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Decompose an image into a set of overlapping crops
+
+    :return crop_arr: [n_crops, h, w, 3] The crops
+    :return patch_idx: [overlap_patch_h, overlap_patch_w] For each patch in the resized image
+                        the crops were extracted from, what patch in `crop_arr` it corresponds to
+    """
+    original_image_h, original_image_w = image.shape[:2]
+    crop_size = base_image_input_size[0]
+    if base_image_input_size[0] != base_image_input_size[1]:
+        raise ValueError(f"Expected square base_image_input_size, got {base_image_input_size}")
+
+    left_margin, right_margin = overlap_margins
+    total_margin_pixels = image_patch_size * (right_margin + left_margin)  # pixels removed per dim
+    crop_patches = base_image_input_size[0] // image_patch_size  # patches per crop dim
+    crop_window_patches = crop_patches - (right_margin + left_margin)  # usable patches
+    crop_window_size = crop_window_patches * image_patch_size
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    original_image_h, original_image_w = image.shape[:2]
+    crop_size = base_image_input_size[0]
+
+    # Decide how to tile the image, to account for the overlap margins we compute the tiling
+    # as if we had an image without the margins and were using a crop size without the margins
+    effective_image_size = (original_image_h - total_margin_pixels, original_image_w - total_margin_pixels)
+    tiling_w, tiling_h = get_optimal_tiled_canvas(
+        original_image_size=effective_image_size,
+        target_tile_size=(crop_window_size, crop_window_size),
+        min_image_tiles=1,
+        max_image_tiles=max_crops,
+    )
+
+    src = resize_image(
+        image,
+        [tiling_h * crop_window_size + total_margin_pixels, tiling_w * crop_window_size + total_margin_pixels],
+        resample,
+    )
+    src = normalize(src, image_mean, image_std, input_data_format=ChannelDimension.LAST)
+
+    # Now we have to split the image into crops, and track what patches came from
+    # where in `patch_idx_arr`
+    n_crops = tiling_h * tiling_w
+    crop_arr = np.zeros([n_crops, crop_size, crop_size, 3], dtype=src.dtype)
+    patch_idx_arr = np.zeros([n_crops, crop_patch_h, crop_patch_w], dtype=np.int32)
+    on_crop = 0
+    for i in range(tiling_h):
+        # Slide over `src` by `crop_window_size` steps, but extract crops of size `crops_size`
+        # which results in overlapping crop windows
+        y0 = i * crop_window_size
+        for j in range(tiling_w):
+            x0 = j * crop_window_size
+            crop_arr[on_crop] = src[y0 : y0 + crop_size, x0 : x0 + crop_size]
+            patch_idx = np.arange(crop_patch_w * crop_patch_h).reshape(crop_patch_h, crop_patch_w)
+            patch_idx += on_crop * crop_patch_h * crop_patch_w
+
+            # Mask out idx that are in the overlap region
+            if i != 0:
+                patch_idx[:left_margin, :] = -1
+            if j != 0:
+                patch_idx[:, :left_margin] = -1
+            if i != tiling_h - 1:
+                patch_idx[-right_margin:, :] = -1
+            if j != tiling_w - 1:
+                patch_idx[:, -right_margin:] = -1
+            patch_idx_arr[on_crop] = patch_idx
+            on_crop += 1
+
+    # `patch_idx_arr` is ordered crop-by-crop, here we transpose `patch_idx_arr`
+    # so it is ordered left-to-right order
+    patch_idx_arr = np.reshape(patch_idx_arr, [tiling_h, tiling_w, crop_patch_h, crop_patch_w])
+    patch_idx_arr = np.transpose(patch_idx_arr, [0, 2, 1, 3])
+    patch_idx_arr = np.reshape(patch_idx_arr, [-1])
+
+    # Now get the parts not in the overlap region, so it should map each patch in `src`
+    # to the correct patch it should come from in `crop_arr`
+    patch_idx_arr = patch_idx_arr[patch_idx_arr >= 0].reshape(
+        src.shape[0] // image_patch_size,
+        src.shape[1] // image_patch_size,
+    )
+    return crop_arr, patch_idx_arr
+
+
+def batch_pixels_to_patches(array: np.ndarray, patch_size: int) -> np.ndarray:
+    """Reshape images of [n_images, h, w, 3] -> [n_images, n_patches, pixels_per_patch]"""
+    if len(array.shape) == 3:
+        n_crops, h, w = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size])
+        array = np.transpose(array, [0, 1, 3, 2, 4])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size])
+        return array
+    else:
+        n_crops, h, w, c = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size, c])
+        array = np.transpose(array, [0, 1, 3, 2, 4, 5])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size * c])
+        return array
+
+
+def arange_for_pooling(
+    idx_arr: np.ndarray,
+    pool_h: int,
+    pool_w: int,
+) -> np.ndarray:
+    h_pad = pool_h * ((idx_arr.shape[0] + pool_h - 1) // pool_h) - idx_arr.shape[0]
+    w_pad = pool_w * ((idx_arr.shape[1] + pool_w - 1) // pool_w) - idx_arr.shape[1]
+    idx_arr = np.pad(
+        idx_arr, [[h_pad // 2, (h_pad + 1) // 2], [w_pad // 2, (w_pad + 1) // 2]], mode="constant", constant_values=-1
+    )
+    h, w = idx_arr.shape[0] // pool_h, idx_arr.shape[1] // pool_w
+    return idx_arr.reshape(h, pool_h, w, pool_w).transpose(0, 2, 1, 3).reshape(h, w, pool_h * pool_w)
+
+
+def image_to_patches_and_grids(
+    image: np.ndarray,
+    max_crops: int,
+    overlap_margins: list[int],
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+    image_pooling_w: int,
+    image_pooling_h: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    :return image_grids, the shape of each (low-res, high-res) image after pooling
+    :return crops, the image crops to processes with the ViT
+    :return pooled_patch_idx, for each patch_id tokens in `image_tokens`, the indices of the
+                                patches in `crops` to pool for that token, masked with -1
+    """
+    if isinstance(base_image_input_size, int):
+        base_image_input_size = (base_image_input_size, base_image_input_size)
+
+    base_image_input_d = image_patch_size
+    pooling_w = image_pooling_w
+    pooling_h = image_pooling_h
+    crop_patch_w = base_image_input_size[1] // base_image_input_d
+    crop_patch_h = base_image_input_size[0] // base_image_input_d
+
+    crop_arr, patch_idx_arr = build_overlapping_crops(
+        image,
+        max_crops,
+        overlap_margins,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    pooling_idx = arange_for_pooling(patch_idx_arr, pooling_h, pooling_w)
+    h, w = pooling_idx.shape[:2]
+    pooling_idx = pooling_idx.reshape([-1, pooling_h * pooling_w])
+
+    # Finally do the same for the global image
+    resized, resize_idx = build_resized_image(
+        image,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    crop_arr = np.concatenate([resized, crop_arr], 0)
+
+    resize_idx = arange_for_pooling(resize_idx, pooling_h, pooling_w)
+    resized_h, resized_w = resize_idx.shape[:2]
+    resize_idx = resize_idx.reshape([-1, pooling_h * pooling_w])
+
+    # Global image goes first, so the order of patches in previous crops gets increased
+    pooling_idx = np.where(pooling_idx >= 0, pooling_idx + crop_patch_h * crop_patch_w, -1)
+    pooling_idx = np.concatenate([resize_idx, pooling_idx])
+    image_grid = [np.array([resized_h, resized_w, h, w])]
+
+    return (np.stack(image_grid, 0), batch_pixels_to_patches(crop_arr, image_patch_size), pooling_idx)
+
+
+class Molmo2ImagesKwargs(ImagesKwargs, total=False):
+    """
+    max_crops (`int`, *optional*, defaults to 8):
+        Maximum number of crops to use per image.
+    overlap_margins (`list[int]`, *optional*, defaults to `[4, 4]`):
+        Overlap margins (in patches) for overlapping crop extraction.
+    patch_size (`int`, *optional*, defaults to 14):
+        The spatial patch size of the vision encoder.
+    pooling_size (`list[int]`, *optional*, defaults to `[2, 2]`):
+        The pooling size of the vision adapter.
+    """
+
+    max_crops: int | None
+    overlap_margins: list[int] | None
+    patch_size: int | None
+    pooling_size: list[int] | None
+
+
+@auto_docstring
+class Molmo2ImageProcessor(TorchvisionBackend):
+    valid_kwargs = Molmo2ImagesKwargs
+    model_input_names = ["pixel_values", "image_token_pooling", "image_grids", "image_num_crops"]
+    resample = PILImageResampling.BILINEAR
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    size = {"height": 378, "width": 378}
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    max_crops = 8
+    overlap_margins = [4, 4]
+    patch_size = 14
+    pooling_size = [2, 2]
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool | None = None,
+        max_crops: int | None = None,
+        overlap_margins: list[int] | None = None,
+        patch_size: int | None = None,
+        pooling_size: list[int] | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Args:
+            images (`ImageInput`):
+                Image to preprocess.
+            size (`dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use when resizing the image. This can be one of the enum `PILImageResampling`. Only
+                has an effect if `do_resize` is set to `True`.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
+                `True`.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            max_crops (`int`, *optional*, defaults to `self.max_crops`):
+                Maximum number of crops to use per image.
+            overlap_margins (`list[int]`, *optional*, defaults to `self.overlap_margins`):
+                Overlap margins to use.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            pooling_size (`list[int]`, *optional*, defaults to `self.pooling_size`):
+                The pooling size of the vision adapter.
+            return_tensors (`str` or `TensorType`, *optional*):
+                The type of tensors to return. Can be one of:
+                - Unset: Return a list of `np.ndarray`.
+                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
+                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
+                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
+                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
+
+        Returns:
+            A `BatchFeature` containing the following keys:
+                - `pixel_values`: The preprocessed images.
+                - `image_token_pooling`: The indices of the patches in `crops` to pool for each token in `image_tokens`.
+                - `image_grids`: The image grids.
+                - `image_num_crops`: The number of crops for each image.
+        """
+        if size is not None:
+            size = get_size_dict(size, default_to_square=True)
+        else:
+            size = self.size if isinstance(self.size, dict) else {"height": self.size.height, "width": self.size.width}
+
+        base_image_input_size = [size["height"], size["width"]]
+
+        resample = resample or self.resample
+        image_mean = image_mean or self.image_mean
+        image_std = image_std or self.image_std
+        do_convert_rgb = do_convert_rgb or self.do_convert_rgb
+
+        max_crops = max_crops or self.max_crops
+        overlap_margins = overlap_margins or self.overlap_margins
+        patch_size = patch_size or self.patch_size
+        pooling_size = pooling_size or self.pooling_size
+
+        image_pooling_h, image_pooling_w = pooling_size
+
+        if images is not None:
+            images = self.fetch_images(images)
+            images = make_flat_list_of_images(images)
+
+        if images is not None and not valid_images(images):
+            raise ValueError(
+                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
+                "torch.Tensor, tf.Tensor or jax.ndarray."
+            )
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays in HWC format.
+        images = [to_numpy_array(image) for image in images]
+        # Ensure HWC layout; torch tensors and some numpy arrays arrive as CHW.
+        images = [
+            to_channel_dimension_format(image, ChannelDimension.LAST, infer_channel_dimension_format(image))
+            for image in images
+        ]
+
+        data = {}
+        if images is not None:
+            batch_grids = []
+            batch_crops = []
+            batch_pooled_patches_idx = []
+            batch_num_crops = []
+
+            for image in images:
+                image_grid, crops, pooled_idx = image_to_patches_and_grids(
+                    image,
+                    max_crops,
+                    overlap_margins,
+                    base_image_input_size,
+                    resample,
+                    image_mean,
+                    image_std,
+                    patch_size,
+                    image_pooling_w,
+                    image_pooling_h,
+                )
+                batch_grids.append(image_grid)
+                batch_crops.append(crops)
+                batch_pooled_patches_idx.append(pooled_idx)
+                batch_num_crops.append(crops.shape[0])
+
+            pixel_values = np.concatenate(batch_crops, 0)
+            image_token_pooling = np.concatenate(batch_pooled_patches_idx, 0)
+            image_grids = np.concatenate(batch_grids, 0)
+            image_num_crops = np.array(batch_num_crops)
+
+            data.update(
+                pixel_values=pixel_values,
+                image_token_pooling=image_token_pooling,
+                image_grids=image_grids,
+                image_num_crops=image_num_crops,
+            )
+
+        return BatchFeature(data, tensor_type=return_tensors)
+
+
+__all__ = ["Molmo2ImageProcessor"]
diff --git a/src/transformers/models/molmo2/modeling_molmo2.py b/src/transformers/models/molmo2/modeling_molmo2.py
new file mode 100644
index 000000000000..04708225b099
--- /dev/null
+++ b/src/transformers/models/molmo2/modeling_molmo2.py
@@ -0,0 +1,1826 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/molmo2/modular_molmo2.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_molmo2.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import math
+from collections.abc import Callable
+from copy import deepcopy
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub
+from ...masking_utils import create_causal_mask, create_masks_for_generate
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.generic import maybe_autocast
+from .configuration_molmo2 import Molmo2AdapterConfig, Molmo2Config, Molmo2TextConfig, Molmo2VitConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for Molmo2 causal language model (or autoregressive) outputs.
+    """
+)
+class Molmo2CausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for Molmo2 outputs, with hidden states and attentions.
+    """
+)
+class Molmo2ModelOutputWithPast(BaseModelOutputWithPast):
+    r"""
+    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
+        `past_key_values` input) to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`.
+        image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state.
+    """
+
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class Molmo2VisionMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.activation_fn = ACT2FN[config.hidden_act]
+        self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.fc1(hidden_states)
+        hidden_states = self.activation_fn(hidden_states)
+        hidden_states = self.fc2(hidden_states)
+        return hidden_states
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class Molmo2VisionAttention(nn.Module):
+    """Vision attention with GQA support."""
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim)
+        self.k_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim)
+        self.out_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Input shape: Batch x Time x Channel"""
+        batch_size, seq_length, _ = hidden_states.shape
+
+        queries = self.q_proj(hidden_states)
+        keys = self.k_proj(hidden_states)
+        values = self.v_proj(hidden_states)
+
+        queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        keys = keys.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        values = values.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface("sdpa", eager_attention_forward)
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            queries,
+            keys,
+            values,
+            attention_mask,
+            is_causal=self.is_causal,
+            scaling=self.scale,
+            dropout=0.0 if not self.training else self.dropout,
+        )
+
+        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Molmo2VisionEncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__()
+        self.embed_dim = config.hidden_size
+        self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.self_attn = Molmo2VisionAttention(config)
+        self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps)
+        self.mlp = Molmo2VisionMLP(config)
+
+    @auto_docstring
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        hidden_states = self.layer_norm1(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.layer_norm2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class Molmo2PoolingAttention(nn.Module):
+    """Cross-attention module used for image feature pooling in the vision adapter."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_key_value_heads: int,
+        head_dim: int,
+        input_dim: int | None = None,
+        attention_dropout: float = 0.0,
+        attn_implementation: str = "eager",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.num_key_value_heads = num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.scale = self.head_dim**-0.5
+        self.attn_implementation = attn_implementation
+        self.is_causal = False
+
+        input_dim = input_dim or hidden_size
+
+        self.q_proj = nn.Linear(input_dim, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(input_dim, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(input_dim, self.num_key_value_heads * self.head_dim, bias=True)
+        self.out_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size)
+        self.attention_dropout = attention_dropout
+
+    def forward(
+        self,
+        inputs_q: torch.Tensor,
+        inputs_kv: torch.Tensor | None = None,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if inputs_kv is not None:
+            inputs_k = inputs_kv
+            inputs_v = inputs_kv
+        else:
+            inputs_k = inputs_q
+            inputs_v = inputs_q
+
+        batch_size = inputs_q.shape[0]
+        queries = self.q_proj(inputs_q)
+        keys = self.k_proj(inputs_k)
+        values = self.v_proj(inputs_v)
+
+        queries = queries.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        keys = keys.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        values = values.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface("sdpa", eager_attention_forward)
+
+        attn_output, _ = attention_interface(
+            self,
+            queries,
+            keys,
+            values,
+            attn_mask,
+            is_causal=self.is_causal,
+            scaling=self.scale,
+            dropout=0.0 if not self.training else self.attention_dropout,
+        )
+
+        attn_output = attn_output.reshape(batch_size, -1, self.num_heads * self.head_dim).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class Molmo2VisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`Molmo2VisionEncoderLayer`].
+
+    Args:
+        config: Molmo2VitConfig
+    """
+
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([Molmo2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> list[torch.Tensor]:
+        """Returns a list of hidden states, one per encoder layer."""
+        hidden_states = inputs_embeds
+        all_hidden_states = []
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states, attention_mask=attention_mask, **kwargs)
+            all_hidden_states.append(hidden_states)
+        return all_hidden_states
+
+
+class Molmo2VisionModel(PreTrainedModel):
+    config_class = Molmo2VitConfig
+    _no_split_modules = ["Molmo2VisionEncoderLayer"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            init.ones_(module.weight)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2VisionModel):
+            init.normal_(module.positional_embedding, mean=0.0, std=std)
+
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__(config)
+        self.config = config
+        self.image_default_input_size = config.image_default_input_size
+
+        # positional embeddings
+        self.scale = config.hidden_size**-0.5
+        self.num_prefix_tokens: int = 0  # no class embeddings
+        self.positional_embedding = nn.Parameter(
+            torch.zeros(config.image_num_pos, config.hidden_size),
+        )
+
+        image_patch_size = config.image_patch_size
+        self.patch_embedding = nn.Linear(
+            image_patch_size * image_patch_size * 3,
+            config.hidden_size,
+            bias=True,
+        )
+
+        self.encoder = Molmo2VisionEncoder(config)
+
+        self.post_init()
+
+    def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
+        pos_emb = self.positional_embedding
+
+        pos_emb = pos_emb.reshape(
+            (int(math.sqrt(pos_emb.shape[0])), int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1])
+        )
+
+        (patch_num_0, patch_num_1) = patch_num
+
+        if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
+            # Dervied from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+            # antialias: default True in jax.image.resize
+            pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
+            pos_emb = F.interpolate(
+                pos_emb,
+                size=(patch_num_0, patch_num_1),
+                mode="bicubic",
+                align_corners=False,
+                antialias=True,
+            )
+            pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)
+
+        pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
+        x = x + pos_emb[None, :, :].to(x.dtype)
+        return x
+
+    def forward(self, x: torch.Tensor, patch_num: int | None = None, **kwargs) -> list[torch.Tensor]:
+        """
+        : param x: (batch_size, num_patch, n_pixels)
+        """
+        if patch_num is None:
+            patch_num = self.config.image_num_patch
+
+        B, N, D = x.shape
+
+        x = self.patch_embedding(x)
+
+        # class embeddings and positional embeddings
+        x = self.add_pos_emb(x, patch_num)
+
+        hidden_states = self.encoder(x)
+        return hidden_states
+
+
+# ===================== Vision Backbone / Adapter =====================
+
+
+class Molmo2ImageProjectorMLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        hidden_act: str,
+        device: str | torch.device = None,
+    ):
+        super().__init__()
+        self.w1 = nn.Linear(input_dim, hidden_dim, bias=False, device=device)
+        self.w2 = nn.Linear(hidden_dim, output_dim, bias=False, device=device)
+        self.w3 = nn.Linear(input_dim, hidden_dim, bias=False, device=device)
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(self.act(self.w1(x)) * self.w3(x))
+
+
+class Molmo2VisionBackbone(nn.Module):
+    def __init__(self, vit_config: Molmo2VitConfig, adapter_config: Molmo2AdapterConfig):
+        super().__init__()
+        self.adapter_config = adapter_config
+
+        self.vit_layers: list[int] = []
+        for layer in adapter_config.vit_layers:
+            if layer >= 0:
+                self.vit_layers.append(layer)
+            else:
+                self.vit_layers.append(layer + vit_config.num_hidden_layers)
+
+        last_layer_needed = max(self.vit_layers) + 1
+        if last_layer_needed < vit_config.num_hidden_layers:
+            new_vit_config = deepcopy(vit_config)
+            new_vit_config.num_hidden_layers = last_layer_needed
+            self.image_vit = Molmo2VisionModel(new_vit_config)
+        else:
+            self.image_vit = Molmo2VisionModel(vit_config)
+
+        self.num_prefix_tokens: int = self.image_vit.num_prefix_tokens
+
+        pool_dim = vit_config.hidden_size * len(adapter_config.vit_layers)
+        self.image_pooling_2d = Molmo2PoolingAttention(
+            hidden_size=adapter_config.hidden_size,
+            num_heads=adapter_config.num_attention_heads,
+            num_key_value_heads=adapter_config.num_key_value_heads,
+            head_dim=adapter_config.head_dim,
+            input_dim=pool_dim,
+            attention_dropout=adapter_config.attention_dropout,
+            attn_implementation=adapter_config._attn_implementation or "eager",
+        )
+        self.image_projector = Molmo2ImageProjectorMLP(
+            adapter_config.hidden_size,
+            adapter_config.intermediate_size,
+            adapter_config.text_hidden_size,
+            adapter_config.hidden_act,
+        )
+        self.image_feature_dropout = nn.Dropout(adapter_config.image_feature_dropout)
+
+    def encode_image(self, images: torch.Tensor) -> torch.Tensor:
+        """
+        : param images: (batch_size, num_crops, num_patch, n_pixels)
+        """
+        B, T, N, D = images.shape
+        images = images.view(B * T, N, D)
+        image_features = self.image_vit(images)
+
+        features = []
+        for layer in self.vit_layers:
+            features.append(image_features[layer])
+        image_features = torch.cat(features, dim=-1)
+
+        if self.num_prefix_tokens > 0:
+            image_features = image_features[:, 1:]
+        image_features = image_features.view(B, T, N, -1)
+        return image_features
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.image_vit.patch_embedding.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.image_vit.patch_embedding.weight.device
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        pooled_patches_idx: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim)
+        batch_size, num_image = images.shape[:2]
+        images = images.to(device=self.device, dtype=self.dtype)
+        image_features = self.encode_image(images)
+
+        image_features = self.image_feature_dropout(image_features)
+        dim = image_features.shape[-1]
+        valid = pooled_patches_idx >= 0
+        valid_token = torch.any(valid, -1)
+
+        # Use `pooled_patches_idx` to arange the features for image pooling
+        batch_idx = torch.arange(pooled_patches_idx.shape[0], dtype=torch.long, device=pooled_patches_idx.device)
+        batch_idx = torch.tile(
+            batch_idx.view(batch_size, 1, 1), [1, pooled_patches_idx.shape[1], pooled_patches_idx.shape[2]]
+        )
+
+        # Now [batch, num_high_res_features, pool_dim, dim]
+        to_pool = image_features.reshape(batch_size, -1, dim)[batch_idx, torch.clip(pooled_patches_idx, 0)]
+        to_pool = to_pool * valid.to(self.dtype)[:, :, :, None]
+        to_pool = to_pool.reshape([-1, pooled_patches_idx.shape[-1], dim])
+        if self.adapter_config.pooling_attention_mask:
+            attn_mask = valid.reshape([-1, 1, 1, valid.shape[-1]])
+            denom = valid.view(-1, to_pool.shape[-2]).float().sum(-1)
+            denom = torch.where(denom == 0, 1, denom)
+            query = to_pool.sum(-2, keepdim=True) / denom[:, None, None].to(to_pool.dtype)
+        else:
+            attn_mask = None
+            query = to_pool.mean(-2, keepdim=True)
+        pooled_features = self.image_pooling_2d(query, to_pool, attn_mask=attn_mask)
+        pooled_features = pooled_features.reshape([batch_size, -1, pooled_features.shape[-1]])
+
+        # MLP layer to map the feature.
+        pooled_features = self.image_projector(pooled_features)
+        return pooled_features.view(-1, pooled_features.shape[-1])[valid_token.flatten()]
+
+
+class Molmo2RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(
+        self,
+        config: Molmo2TextConfig,
+        device: str | torch.device = None,
+        rope_type: str | None = None,
+    ):
+        # Molmo2 has custom rope_type handling (not using config.rope_parameters)
+        if rope_type is not None:
+            self.rope_type = rope_type
+        elif hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            # BC: "rope_type" was originally "type"
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Molmo2TextConfig | None = None,
+        device: torch.device | None = None,
+        seq_len: int | None = None,
+    ) -> tuple[torch.Tensor, float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_theta
+        head_dim = config.head_dim or config.hidden_size // config.num_attention_heads
+        dim = int(head_dim)
+        attention_factor = 1.0
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class Molmo2RMSNorm(nn.Module):
+    def __init__(self, size: int, eps: float = 1e-6, device: str | torch.device = None) -> None:
+        """
+        Molmo2RMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        # Re-init weight with device support
+        self.weight = nn.Parameter(torch.ones(size, device=device))
+        self.variance_epsilon = eps
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        with torch.autocast(enabled=False, device_type=x.device.type):
+            og_dtype = x.dtype
+            x = x.to(torch.float32)
+            variance = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            x = x.to(og_dtype)
+
+        return self.weight * x
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Molmo2Attention(nn.Module):
+    """Molmo2 attention with fused QKV, optional QK norm, and custom weight names."""
+
+    def __init__(self, config: Molmo2TextConfig, layer_idx: int) -> None:
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.is_causal = True
+
+        self.fused_dims = (
+            config.num_attention_heads * config.head_dim,
+            config.head_dim * config.num_key_value_heads,
+            config.head_dim * config.num_key_value_heads,
+        )
+        self.att_proj = nn.Linear(
+            config.hidden_size,
+            sum(self.fused_dims),
+            bias=config.qkv_bias,
+        )
+
+        # Layer norms.
+        self.k_norm: Molmo2RMSNorm | None = None
+        self.q_norm: Molmo2RMSNorm | None = None
+        self.qk_norm_type: str | None = None
+        if config.use_qk_norm:
+            k_norm_size = (
+                config.head_dim if config.qk_norm_type == "qwen3" else config.num_key_value_heads * config.head_dim
+            )
+            self.k_norm = Molmo2RMSNorm(k_norm_size, eps=config.layer_norm_eps)
+            q_norm_size = (
+                config.head_dim if config.qk_norm_type == "qwen3" else config.num_attention_heads * config.head_dim
+            )
+            self.q_norm = Molmo2RMSNorm(q_norm_size, eps=config.layer_norm_eps)
+            self.qk_norm_type = config.qk_norm_type
+
+        self.attention_dropout = config.attention_dropout
+
+        self.attn_out = nn.Linear(
+            config.head_dim * config.num_attention_heads,
+            config.hidden_size,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        input_shape = hidden_states.shape[:-1]
+        q_shape = (*input_shape, self.num_heads, self.head_dim)
+        kv_shape = (*input_shape, self.num_key_value_heads, self.head_dim)
+
+        qkv = self.att_proj(hidden_states)
+        query_states, key_states, value_states = qkv.split(self.fused_dims, dim=-1)
+        value_states = value_states.view(kv_shape)
+
+        # Optionally apply layer norm to keys and queries.
+        if self.q_norm is not None and self.k_norm is not None and self.qk_norm_type != "qwen3":
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        query_states = query_states.view(q_shape)
+        key_states = key_states.view(kv_shape)
+        if self.q_norm is not None and self.k_norm is not None and self.qk_norm_type == "qwen3":
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if (self.config._attn_implementation or "eager") != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.attn_out(attn_output)
+        return attn_output, attn_weights
+
+
+class Molmo2MLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        intermediate_size: int,
+        hidden_act: str,
+        device: str | torch.device = None,
+    ):
+        super().__init__()
+        self.ff_proj = nn.Linear(input_dim, intermediate_size * 2, bias=False, device=device)
+        self.ff_out = nn.Linear(intermediate_size, input_dim, bias=False, device=device)
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ff_proj(x)
+        x, gate = x.chunk(2, dim=-1)
+        x = self.act(gate) * x
+        x = self.ff_out(x)
+        return x
+
+
+class Molmo2DecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Molmo2TextConfig, layer_idx: int | None = None, device: str | torch.device = None):
+        super().__init__()
+        self.config = config
+
+        self.self_attn = Molmo2Attention(config, layer_idx)
+        self.attn_norm = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps, device=device)
+        self.dropout = nn.Dropout(config.residual_dropout)
+        self.mlp = Molmo2MLP(config.hidden_size, config.intermediate_size, config.hidden_act, device=device)
+        self.ff_norm = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps, device=device)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool | None = False,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
+        residual = hidden_states
+        hidden_states = self.attn_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ff_norm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class Molmo2PostNormDecoderLayer(Molmo2DecoderLayer):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool | None = False,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
+        residual = hidden_states
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = self.attn_norm(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.ff_norm(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class Molmo2Embedding(nn.Module):
+    def __init__(
+        self,
+        num_embeddings: int,
+        num_new_embeddings: int,
+        features: int,
+        device: str | torch.device = None,
+    ):
+        super().__init__()
+        self.embedding = nn.Parameter(
+            torch.zeros(num_embeddings, features, device=device),
+        )
+        self.new_embedding = nn.Parameter(
+            torch.zeros(num_new_embeddings, features, device=device),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return F.embedding(x, torch.cat([self.embedding, self.new_embedding], dim=0))
+
+
+@auto_docstring
+class Molmo2PreTrainedModel(PreTrainedModel):
+    config: Molmo2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "Molmo2DecoderLayer",
+        "Molmo2PostNormDecoderLayer",
+        "Molmo2VisionEncoderLayer",
+        "Molmo2VisionAttention",
+    ]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": Molmo2DecoderLayer,
+        "attentions": Molmo2Attention,
+    }
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear,)):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2Embedding):
+            init.normal_(module.embedding, mean=0.0, std=std)
+            init.normal_(module.new_embedding, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.padding_idx is not None:
+                init.zeros_(module.weight[module.padding_idx])
+        elif isinstance(module, Molmo2RMSNorm):
+            init.ones_(module.weight)
+        elif isinstance(module, nn.LayerNorm):
+            init.ones_(module.weight)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2VisionModel):
+            init.normal_(module.positional_embedding, mean=0.0, std=std)
+        elif isinstance(module, Molmo2RotaryEmbedding):
+            rope_fn = (
+                ROPE_INIT_FUNCTIONS[module.rope_type]
+                if module.rope_type != "default"
+                else module.compute_default_rope_parameters
+            )
+            buffer_value, _ = rope_fn(module.config)
+            init.copy_(module.inv_freq, buffer_value)
+            init.copy_(module.original_inv_freq, buffer_value)
+
+
+class Molmo2TextModel(Molmo2PreTrainedModel):
+    config: Molmo2TextConfig
+    _input_embed_layer = "wte"
+
+    def __init__(self, config: Molmo2TextConfig):
+        super().__init__(config)
+        if config.additional_vocab_size is not None:
+            self.wte = Molmo2Embedding(
+                config.vocab_size,
+                config.additional_vocab_size,
+                config.hidden_size,
+            )
+        else:
+            self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.emb_drop = nn.Dropout(config.embedding_dropout)
+        decoder_layer = Molmo2PostNormDecoderLayer if config.norm_after else Molmo2DecoderLayer
+        self.blocks = nn.ModuleList(
+            [decoder_layer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.ln_f = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        if config.rope_scaling_layers is not None:
+            self.rotary_embs = nn.ModuleDict(
+                {
+                    "default": Molmo2RotaryEmbedding(config, rope_type="default"),
+                    "scaling": Molmo2RotaryEmbedding(config),
+                }
+            )
+        else:
+            self.rotary_emb = Molmo2RotaryEmbedding(config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            input_ids = input_ids * (input_ids != -1).to(input_ids.dtype)
+            inputs_embeds = self.wte(input_ids)
+
+        # torch.jit.trace() doesn't support cache objects in the output
+        if use_cache and past_key_values is None and not torch.jit.is_tracing():
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # Create the mask
+            causal_mask_mapping = create_causal_mask(**mask_kwargs)
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        if self.config.rope_scaling_layers is not None:
+            position_embeddings_mapping = {
+                "default": self.rotary_embs["default"](hidden_states, position_ids),
+                "scaling": self.rotary_embs["scaling"](hidden_states, position_ids),
+            }
+        else:
+            position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for layer_idx, decoder_block in enumerate(self.blocks[: self.config.num_hidden_layers]):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.config.rope_scaling_layers is not None:
+                position_embeddings_i = (
+                    position_embeddings_mapping["scaling"]
+                    if layer_idx in self.config.rope_scaling_layers
+                    else position_embeddings_mapping["default"]
+                )
+            else:
+                position_embeddings_i = position_embeddings
+
+            layer_outputs = decoder_block(
+                hidden_states,
+                attention_mask=causal_mask_mapping,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings_i,
+                **kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Adapted from ...models.gemma3.modeling_gemma3
+def token_type_ids_mask_function(
+    token_type_ids: torch.Tensor | None = None,
+) -> Callable | None:
+    """
+    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
+    not start and end indices.
+    """
+    # Do not return an additional mask in this case
+    if token_type_ids is None:
+        return None
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        # If it's 1 for both query and key/value, we are in an image block
+        # NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
+        # Since vmap doesn't support `if statement` we workaround it with `torch.where`
+        safe_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
+        token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_idx]
+        token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)
+
+        is_image_block = (token_type_ids[batch_idx, q_idx] == 1) & (token_type_ids_at_kv_idx == 1)
+
+        # This is bidirectional attention whenever we are dealing with image tokens
+        return is_image_block & is_image_block
+
+    return inner_mask
+
+
+class Molmo2Model(Molmo2PreTrainedModel):
+    base_model_prefix = "model"
+    _checkpoint_conversion_mapping = {}
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+    config: Molmo2Config
+
+    def __init__(self, config: Molmo2Config):
+        super().__init__(config)
+        self.language_model: Molmo2TextModel = Molmo2TextModel(config.text_config)
+        self.image_col_id = config.image_col_id
+        self.image_low_res_id = config.image_low_res_id
+        self.vision_backbone: Molmo2VisionBackbone | None = None
+        if config.vit_config is not None and config.adapter_config is not None:
+            self.vision_backbone = Molmo2VisionBackbone(config.vit_config, config.adapter_config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> torch.nn.Module:
+        return self.language_model.wte
+
+    def set_input_embeddings(self, value: torch.nn.Module) -> None:
+        self.language_model.wte = value
+
+    def build_batched_images(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: torch.Tensor,
+        image_token_pooling: torch.Tensor,
+        image_grids: torch.Tensor,
+        image_num_crops: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Normalize inputs to flattened image/crop layout expected by the model.
+        if pixel_values.dim() == 4:
+            batch_size, num_crops, n_patches, pixels_per_patch = pixel_values.shape
+            pixel_values = pixel_values.reshape(batch_size * num_crops, n_patches, pixels_per_patch)
+            if image_num_crops is None:
+                image_num_crops = torch.full(
+                    (batch_size,),
+                    num_crops,
+                    device=pixel_values.device,
+                    dtype=torch.long,
+                )
+        if image_num_crops is None:
+            image_num_crops = torch.ones(
+                image_grids.size(0),
+                device=image_grids.device,
+                dtype=torch.long,
+            )
+        if image_token_pooling.dim() == 3:
+            image_token_pooling = image_token_pooling.reshape(-1, image_token_pooling.size(-1))
+
+        # 1) Count the number of images in each example
+        raw_counts = (input_ids == self.config.image_end_token_id).sum(1)  # [N]
+        # Each image is represented by global view and high-res view
+        # so we divide by 2 to get the number of images
+        counts = raw_counts // 2
+        N = counts.size(0)
+        device = input_ids.device
+
+        # Total number of images in the batch
+        num_images = int(counts.sum().item())
+        if image_grids is not None and image_grids.size(0) == N and num_images != image_grids.size(0):
+            counts = torch.ones_like(counts)
+            num_images = int(counts.sum().item())
+
+        # Sanity check
+        assert image_grids.size(0) == num_images, f"Expected {num_images} image grids, but got {image_grids.size(0)}"
+        assert image_num_crops.size(0) == num_images, (
+            f"Expected {num_images} image num crops, but got {image_num_crops.size(0)}"
+        )
+
+        # 1-1) Compute per-image pooled patch count from image grids
+        with torch.no_grad():
+            first_prod = image_grids[:, :2].prod(dim=1)  # [num_images]
+            second_prod = image_grids[:, 2:].prod(dim=1)  # [num_images]
+            num_pooled_patches_per_image = (first_prod + second_prod).to(image_num_crops.dtype)  # [num_images]
+
+        # pixel_values: [n_crops, n_patches, pixels_per_patch]
+        n_crops, n_patches, pixels_per_patch = pixel_values.shape
+
+        # 2) Map each image index → example index
+        # Example: if counts = [2, 1, 3], then this becomes [0,0,1,2,2,2]
+        example_ids_for_image = torch.arange(N, device=device).repeat_interleave(counts)  # [num_images]
+        assert example_ids_for_image.numel() == num_images
+
+        # 2-1) Compute crops_per_example by summing per-image crop counts
+        crops_per_example = torch.zeros(N, dtype=image_num_crops.dtype, device=image_num_crops.device)
+        crops_per_example.index_add_(0, example_ids_for_image, image_num_crops)  # [N]
+
+        # 2-2) Per-image number of patches = (crops per image) * n_patches
+        patches_per_image = image_num_crops * n_patches  # [num_images]
+
+        # 2-3) Compute per-example per-image patch offsets
+        counts_list = counts.tolist()
+        index_offset_per_example_list = []
+        offset_img = 0
+        for c in counts_list:
+            per_img_patches = patches_per_image[offset_img : offset_img + c]  # [c]
+            # Offsets: [0, img0_total_patches, img0+img1_total_patches, ...]
+            index_offset = [0] + per_img_patches.cumsum(0).tolist()[:-1]
+            index_offset_per_example_list.append(index_offset)
+            offset_img += c
+
+        # 2-4) Compute num_pooled_patches_per_example
+        num_pooled_patches_per_example = torch.zeros(
+            N, dtype=num_pooled_patches_per_image.dtype, device=num_pooled_patches_per_image.device
+        )
+        num_pooled_patches_per_example.index_add_(0, example_ids_for_image, num_pooled_patches_per_image)
+
+        # Sanity checks
+        total_crops = int(crops_per_example.sum().item())
+        assert total_crops == n_crops, f"Expected {total_crops} crops, but got {n_crops}"
+
+        total_num_pooled_patches = int(num_pooled_patches_per_example.sum().item())
+        assert total_num_pooled_patches == image_token_pooling.size(0), (
+            f"Expected {total_num_pooled_patches} pooled patches, but got {image_token_pooling.size(0)}"
+        )
+
+        # 3) Build images tensor filled with -1
+        M = int(crops_per_example.max().item())
+        images = torch.full(
+            (N, M, n_patches, pixels_per_patch),
+            fill_value=-1,
+            dtype=pixel_values.dtype,
+            device=pixel_values.device,
+        )
+
+        # 4) Fill images with per-example slices from pixel_values
+        offset_crop = 0
+        for i in range(N):
+            num = int(crops_per_example[i].item())
+            cur = pixel_values[offset_crop : offset_crop + num]  # [num, n_patches, pixels_per_patch]
+            images[i, :num] = cur
+            offset_crop += num
+
+        # Sanity check
+        assert offset_crop == n_crops
+
+        # 5) Build new_token_pooling tensor filled with -1
+        P = int(num_pooled_patches_per_example.max().item())
+        _, dim = image_token_pooling.shape
+        new_token_pooling = torch.full(
+            (N, P, dim),
+            fill_value=-1,
+            dtype=image_token_pooling.dtype,
+            device=image_token_pooling.device,
+        )
+
+        # 6) Fill token_pooling with per-example slices, adding per-image patch offsets
+        patch_offset = 0
+        img_offset = 0
+
+        for i, c in enumerate(counts_list):
+            num_patches = int(num_pooled_patches_per_example[i].item())
+
+            # Subsequence of pooled tokens belonging to this example
+            cur = image_token_pooling[patch_offset : patch_offset + num_patches].clone()  # [num_patches, dim]
+
+            index_offset_per_example = index_offset_per_example_list[i]  # length = c
+            per_img_pooled = num_pooled_patches_per_image[img_offset : img_offset + c]  # [c]
+
+            assert len(index_offset_per_example) == per_img_pooled.numel()
+
+            # Apply per-image offsets to the (ragged) subsequence
+            offset = 0
+            for j in range(c):
+                index_offset = int(index_offset_per_example[j])
+                n = int(per_img_pooled[j].item())
+                cur_slice = cur[offset : offset + n]
+
+                # Apply offset across all columns
+                cur[offset : offset + n] = torch.where(
+                    cur_slice >= 0,
+                    cur_slice + index_offset,
+                    cur_slice,
+                )
+                offset += n
+
+            new_token_pooling[i, :num_patches] = cur
+
+            patch_offset += num_patches
+            img_offset += c
+
+        # Final sanity checks
+        assert patch_offset == total_num_pooled_patches
+        assert img_offset == num_images
+
+        return images, new_token_pooling
+
+    def build_batched_videos(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values_videos: torch.Tensor,
+        video_token_pooling: torch.Tensor,
+        video_grids: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # 1) Count the number of videos in each example
+        if self.config.use_frame_special_tokens:
+            end_token_id = self.config.frame_end_token_id
+        else:
+            end_token_id = self.config.image_end_token_id
+        counts = (input_ids == end_token_id).any(dim=1).long()  # [N]
+        N = counts.size(0)
+        device = input_ids.device
+
+        # Total number of videos in the batch
+        num_videos = int(counts.sum().item())
+
+        # Sanity check
+        assert video_grids.size(0) == num_videos, f"Expected {num_videos} videos, but got {video_grids.size(0)}"
+
+        video_num_frames = video_grids[:, 0]  # [num_videos]
+        num_pooled_patches_per_video = video_grids.prod(dim=1)  # [num_videos]
+
+        # pixel_values_videos: [n_frames, n_patches, pixels_per_patch]
+        n_frames, n_patches, pixels_per_patch = pixel_values_videos.shape
+
+        # 2) Map each video index -> example index
+        # Example: if counts = [2, 1, 3], then this becomes [0,0,1,2,2,2]
+        example_ids_for_video = torch.arange(N, device=device).repeat_interleave(counts)  # [num_videos]
+        assert example_ids_for_video.numel() == num_videos
+
+        # 2-1) Compute frames_per_example by summing per-video frame counts
+        frames_per_example = torch.zeros(
+            N,
+            dtype=video_num_frames.dtype,
+            device=device,
+        )
+        frames_per_example.index_add_(0, example_ids_for_video, video_num_frames)  # [N]
+
+        # 2-2) Compute num_pooled_patches_per_example
+        num_pooled_patches_per_example = torch.zeros(
+            N,
+            dtype=num_pooled_patches_per_video.dtype,
+            device=num_pooled_patches_per_video.device,
+        )
+        num_pooled_patches_per_example.index_add_(
+            0,
+            example_ids_for_video,
+            num_pooled_patches_per_video,
+        )
+
+        # Sanity checks
+        total_frames = int(frames_per_example.sum().item())
+        assert total_frames == n_frames, f"Expected {total_frames} frames, but got {n_frames}"
+
+        total_num_pooled_patches = int(num_pooled_patches_per_example.sum().item())
+        assert total_num_pooled_patches == video_token_pooling.size(0), (
+            f"Expected {total_num_pooled_patches} pooled patches, but got {video_token_pooling.size(0)}"
+        )
+
+        # 3) Build videos tensor filled with -1
+        M = int(frames_per_example.max().item())
+        videos = torch.full(
+            (N, M, n_patches, pixels_per_patch),
+            fill_value=-1,
+            dtype=pixel_values_videos.dtype,
+            device=device,
+        )
+
+        # 4) Fill videos with per-examples slices from pixel_values_videos
+        offset_frame = 0
+        for i in range(N):
+            num = int(frames_per_example[i].item())
+            cur = pixel_values_videos[offset_frame : offset_frame + num]  # [num, n_patches, pixels_per_patch]
+            videos[i, :num] = cur
+            offset_frame += num
+
+        # Sanity check
+        assert offset_frame == n_frames
+
+        # 5) Build new token_pooling tensor filled with -1
+        P = int(num_pooled_patches_per_example.max().item())
+        _, dim = video_token_pooling.shape
+        new_token_pooling = torch.full(
+            (N, P, dim),
+            fill_value=-1,
+            dtype=video_token_pooling.dtype,
+            device=video_token_pooling.device,
+        )
+
+        # 6) Fill new token_pooling with per-examples slices from video_token_pooling
+        patch_offset = 0
+        for i in range(N):
+            num_patches = int(num_pooled_patches_per_example[i].item())
+            cur = video_token_pooling[patch_offset : patch_offset + num_patches]  # [num_patches, dim]
+            new_token_pooling[i, :num_patches] = cur
+            patch_offset += num_patches
+
+        # Final sanity checks
+        assert patch_offset == total_num_pooled_patches
+
+        return videos, new_token_pooling
+
+    def merge_visual_inputs(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        if pixel_values is not None and pixel_values_videos is not None:
+            raise ValueError("pixel_values and pixel_values_videos are provided at the same time")
+        elif pixel_values is not None:
+            if input_ids is None:
+                return None, None
+            images, token_pooling = self.build_batched_images(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                image_token_pooling=image_token_pooling,
+                image_grids=image_grids,
+                image_num_crops=image_num_crops,
+            )
+        elif pixel_values_videos is not None:
+            if input_ids is None:
+                return None, None
+            images, token_pooling = self.build_batched_videos(
+                input_ids=input_ids,
+                pixel_values_videos=pixel_values_videos,
+                video_token_pooling=video_token_pooling,
+                video_grids=video_grids,
+            )
+        else:
+            images, token_pooling = None, None
+        return images, token_pooling
+
+    def build_input_embeddings(
+        self,
+        input_ids: torch.LongTensor,
+        images: torch.FloatTensor | None = None,  # image inputs
+        token_pooling: torch.LongTensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        # Get embeddings of input.
+        # shape: (batch_size, seq_len, d_model)
+        input_ids = input_ids * (input_ids != -1).to(input_ids.dtype)
+        x = self.language_model.wte(input_ids)
+
+        image_features: torch.FloatTensor | None = None
+        if images is not None:
+            image_features = self.vision_backbone(images, token_pooling).to(x.device)
+            is_image_patch = input_ids.view(-1) == self.config.image_patch_id
+            assert is_image_patch.sum() == len(image_features)
+            x.view(-1, x.shape[-1])[is_image_patch] += image_features
+
+        # shape: (batch_size, seq_len, d_model)
+        x = self.language_model.emb_drop(x)  # type: ignore
+
+        return x, image_features
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Molmo2ModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        images, token_pooling = self.merge_visual_inputs(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_token_pooling=image_token_pooling,
+            image_grids=image_grids,
+            image_num_crops=image_num_crops,
+            pixel_values_videos=pixel_values_videos,
+            video_token_pooling=video_token_pooling,
+            video_grids=video_grids,
+        )
+
+        if images is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both images and inputs_embeds at the same time.")
+
+        if inputs_embeds is None:
+            inputs_embeds, image_features = self.build_input_embeddings(
+                input_ids,
+                images,
+                token_pooling,
+            )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        # Adapted from ...models.gemma3.modeling_gemma3
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # NOTE: this `is_prefill` logic is not flawless, it fails when we're using a cache eagerly initialized
+            # (e.g. compiled prefill) AND `images` are not provided. Determining prefill in that case requires
+            # checking data values, which is not compile-compatible.
+            is_prefill = (
+                not use_cache or past_key_values is None or not past_key_values.is_initialized or images is not None
+            )
+            if token_type_ids is not None and is_prefill:
+                # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
+                mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
+                    token_type_ids.to(cache_position.device)
+                )
+
+            # Create the mask
+            causal_mask_mapping = create_causal_mask(**mask_kwargs)
+
+        outputs = self.language_model(
+            attention_mask=causal_mask_mapping,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return Molmo2ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features if images is not None else None,
+        )
+
+
+class Molmo2ForConditionalGeneration(Molmo2PreTrainedModel, GenerationMixin):
+    _checkpoint_conversion_mapping = {}
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.wte.weight"}
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+    config: Molmo2Config
+
+    def __init__(self, config: Molmo2Config):
+        super().__init__(config)
+
+        self.model = Molmo2Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.vocab_size = config.text_config.vocab_size
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.Tensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Molmo2CausalLMOutputWithPast:
+        r"""
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from ... import AutoProcessor, Molmo2ForConditionalGeneration
+
+        >>> model = Molmo2ForConditionalGeneration.from_pretrained("...")
+        >>> processor = AutoProcessor.from_pretrained("...")
+
+        >>> prompt = "What's the content of the image?"
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> messages = [{"role": "user", "content": [{"type": "text", "text": prompt}, {"type": "image", "image": image}]}]
+
+        >>> inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
+
+        >>> # Generate
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=15)
+        >>> generated_tokens = generated_ids[:, inputs['input_ids'].size(1):]
+        >>> processor.post_process_image_text_to_text(generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a bustling street scene in what appears to be a Chinatown area. There's ..."
+        ```"""
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_token_pooling=image_token_pooling,
+            image_grids=image_grids,
+            image_num_crops=image_num_crops,
+            pixel_values_videos=pixel_values_videos,
+            video_token_pooling=video_token_pooling,
+            video_grids=video_grids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size)
+
+        return Molmo2CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor | None = None,
+        is_first_iteration: bool = False,
+        use_cache: bool = True,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            token_type_ids=token_type_ids,
+            is_first_iteration=is_first_iteration,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        if is_first_iteration or not use_cache:
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["image_token_pooling"] = image_token_pooling
+            model_inputs["image_grids"] = image_grids
+            model_inputs["image_num_crops"] = image_num_crops
+            model_inputs["pixel_values_videos"] = pixel_values_videos
+            model_inputs["video_token_pooling"] = video_token_pooling
+            model_inputs["video_grids"] = video_grids
+
+        return model_inputs
+
+    # Adapted from ...models.gemma3.modeling_gemma3
+    @staticmethod
+    def create_masks_for_generate(
+        config: PreTrainedConfig,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        cache_position: torch.Tensor,
+        past_key_values: Cache | None,
+        position_ids: torch.Tensor | None,
+        token_type_ids: torch.Tensor | None = None,
+        **kwargs,
+    ) -> dict:
+        # Prepare mask arguments
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "cache_position": cache_position,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+        # Add the token type ids mask for generate as well
+        if token_type_ids is not None and inputs_embeds.shape[1] != 1:
+            # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
+            mask_kwargs["or_mask_function"] = token_type_ids_mask_function(token_type_ids.to(cache_position.device))
+
+        return create_masks_for_generate(**mask_kwargs)
+
+
+__all__ = [
+    "Molmo2ForConditionalGeneration",
+    "Molmo2Model",
+    "Molmo2PreTrainedModel",
+    "Molmo2TextModel",
+    "Molmo2VisionBackbone",
+    "Molmo2VisionModel",
+]
diff --git a/src/transformers/models/molmo2/modular_molmo2.py b/src/transformers/models/molmo2/modular_molmo2.py
new file mode 100644
index 000000000000..dac9558a750d
--- /dev/null
+++ b/src/transformers/models/molmo2/modular_molmo2.py
@@ -0,0 +1,1660 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""PyTorch Molmo2 model."""
+
+import math
+from collections.abc import Callable
+from copy import deepcopy
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...masking_utils import create_causal_mask, create_masks_for_generate
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPast
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, can_return_tuple, logging
+from ..llama.modeling_llama import (
+    LlamaPreTrainedModel,
+    LlamaRMSNorm,
+    LlamaRotaryEmbedding,
+    apply_rotary_pos_emb,
+    eager_attention_forward,
+)
+from ..llava.modeling_llava import (
+    LlavaCausalLMOutputWithPast,
+    LlavaModelOutputWithPast,
+)
+from ..phi3.modeling_phi3 import (
+    Phi3Attention,
+    Phi3DecoderLayer,
+    Phi3MLP,
+)
+from ..siglip2.modeling_siglip2 import (
+    Siglip2Attention,
+    Siglip2EncoderLayer,
+    Siglip2MLP,
+)
+from .configuration_molmo2 import Molmo2AdapterConfig, Molmo2Config, Molmo2TextConfig, Molmo2VitConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+# Output dataclasses - same structure as LLaVA
+class Molmo2CausalLMOutputWithPast(LlavaCausalLMOutputWithPast):
+    pass
+
+
+class Molmo2ModelOutputWithPast(LlavaModelOutputWithPast):
+    pass
+
+
+# ===================== Vision Components (from Siglip2) =====================
+
+
+class Molmo2VisionMLP(Siglip2MLP):
+    pass
+
+
+class Molmo2VisionAttention(Siglip2Attention):
+    """Vision attention with GQA support."""
+
+    def __init__(self, config):
+        nn.Module.__init__(self)
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.scale = self.head_dim**-0.5
+        self.dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim)
+        self.k_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim)
+        self.v_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim)
+        self.out_proj = nn.Linear(self.num_heads * self.head_dim, self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        batch_size, seq_length, _ = hidden_states.shape
+
+        queries = self.q_proj(hidden_states)
+        keys = self.k_proj(hidden_states)
+        values = self.v_proj(hidden_states)
+
+        queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+        keys = keys.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        values = values.view(batch_size, seq_length, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface("sdpa", eager_attention_forward)
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            queries,
+            keys,
+            values,
+            attention_mask,
+            is_causal=self.is_causal,
+            scaling=self.scale,
+            dropout=0.0 if not self.training else self.dropout,
+        )
+
+        attn_output = attn_output.reshape(batch_size, seq_length, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Molmo2VisionEncoderLayer(Siglip2EncoderLayer):
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__(config)
+        self.self_attn = Molmo2VisionAttention(config)
+        self.mlp = Molmo2VisionMLP(config)
+
+
+class Molmo2PoolingAttention(nn.Module):
+    """Cross-attention module used for image feature pooling in the vision adapter."""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_key_value_heads: int,
+        head_dim: int,
+        input_dim: int | None = None,
+        attention_dropout: float = 0.0,
+        attn_implementation: str = "eager",
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.num_heads = num_heads
+        self.head_dim = head_dim
+        self.num_key_value_heads = num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.scale = self.head_dim**-0.5
+        self.attn_implementation = attn_implementation
+        self.is_causal = False
+
+        input_dim = input_dim or hidden_size
+
+        self.q_proj = nn.Linear(input_dim, self.num_heads * self.head_dim, bias=True)
+        self.k_proj = nn.Linear(input_dim, self.num_key_value_heads * self.head_dim, bias=True)
+        self.v_proj = nn.Linear(input_dim, self.num_key_value_heads * self.head_dim, bias=True)
+        self.out_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size)
+        self.attention_dropout = attention_dropout
+
+    def forward(
+        self,
+        inputs_q: torch.Tensor,
+        inputs_kv: torch.Tensor | None = None,
+        attn_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if inputs_kv is not None:
+            inputs_k = inputs_kv
+            inputs_v = inputs_kv
+        else:
+            inputs_k = inputs_q
+            inputs_v = inputs_q
+
+        batch_size = inputs_q.shape[0]
+        queries = self.q_proj(inputs_q)
+        keys = self.k_proj(inputs_k)
+        values = self.v_proj(inputs_v)
+
+        queries = queries.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
+        keys = keys.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        values = values.view(batch_size, -1, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface("sdpa", eager_attention_forward)
+
+        attn_output, _ = attention_interface(
+            self,
+            queries,
+            keys,
+            values,
+            attn_mask,
+            is_causal=self.is_causal,
+            scaling=self.scale,
+            dropout=0.0 if not self.training else self.attention_dropout,
+        )
+
+        attn_output = attn_output.reshape(batch_size, -1, self.num_heads * self.head_dim).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class Molmo2VisionEncoder(nn.Module):
+    """
+    Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
+    [`Molmo2VisionEncoderLayer`].
+
+    Args:
+        config: Molmo2VitConfig
+    """
+
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__()
+        self.config = config
+        self.layers = nn.ModuleList([Molmo2VisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])
+
+    def forward(
+        self,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> list[torch.Tensor]:
+        """Returns a list of hidden states, one per encoder layer."""
+        hidden_states = inputs_embeds
+        all_hidden_states = []
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states, attention_mask=attention_mask, **kwargs)
+            all_hidden_states.append(hidden_states)
+        return all_hidden_states
+
+
+class Molmo2VisionModel(PreTrainedModel):
+    config_class = Molmo2VitConfig
+    _no_split_modules = ["Molmo2VisionEncoderLayer"]
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, nn.LayerNorm):
+            init.ones_(module.weight)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2VisionModel):
+            init.normal_(module.positional_embedding, mean=0.0, std=std)
+
+    def __init__(self, config: Molmo2VitConfig):
+        super().__init__(config)
+        self.config = config
+        self.image_default_input_size = config.image_default_input_size
+
+        # positional embeddings
+        self.scale = config.hidden_size**-0.5
+        self.num_prefix_tokens: int = 0  # no class embeddings
+        self.positional_embedding = nn.Parameter(
+            torch.zeros(config.image_num_pos, config.hidden_size),
+        )
+
+        image_patch_size = config.image_patch_size
+        self.patch_embedding = nn.Linear(
+            image_patch_size * image_patch_size * 3,
+            config.hidden_size,
+            bias=True,
+        )
+
+        self.encoder = Molmo2VisionEncoder(config)
+
+        self.post_init()
+
+    def add_pos_emb(self, x: torch.Tensor, patch_num: int) -> torch.Tensor:
+        pos_emb = self.positional_embedding
+
+        pos_emb = pos_emb.reshape(
+            (int(math.sqrt(pos_emb.shape[0])), int(math.sqrt(pos_emb.shape[0])), pos_emb.shape[1])
+        )
+
+        (patch_num_0, patch_num_1) = patch_num
+
+        if pos_emb.shape[0] != patch_num_0 or pos_emb.shape[1] != patch_num_1:
+            # Dervied from https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+            # antialias: default True in jax.image.resize
+            pos_emb = pos_emb.unsqueeze(0).permute(0, 3, 1, 2)
+            pos_emb = F.interpolate(
+                pos_emb,
+                size=(patch_num_0, patch_num_1),
+                mode="bicubic",
+                align_corners=False,
+                antialias=True,
+            )
+            pos_emb = pos_emb.permute(0, 2, 3, 1).squeeze(0)
+
+        pos_emb = pos_emb.reshape(-1, pos_emb.shape[-1])
+        x = x + pos_emb[None, :, :].to(x.dtype)
+        return x
+
+    def forward(self, x: torch.Tensor, patch_num: int | None = None, **kwargs) -> list[torch.Tensor]:
+        """
+        : param x: (batch_size, num_patch, n_pixels)
+        """
+        if patch_num is None:
+            patch_num = self.config.image_num_patch
+
+        B, N, D = x.shape
+
+        x = self.patch_embedding(x)
+
+        # class embeddings and positional embeddings
+        x = self.add_pos_emb(x, patch_num)
+
+        hidden_states = self.encoder(x)
+        return hidden_states
+
+
+# ===================== Vision Backbone / Adapter =====================
+
+
+class Molmo2ImageProjectorMLP(nn.Module):
+    def __init__(
+        self,
+        input_dim: int,
+        hidden_dim: int,
+        output_dim: int,
+        hidden_act: str,
+        device: str | torch.device = None,
+    ):
+        super().__init__()
+        self.w1 = nn.Linear(input_dim, hidden_dim, bias=False, device=device)
+        self.w2 = nn.Linear(hidden_dim, output_dim, bias=False, device=device)
+        self.w3 = nn.Linear(input_dim, hidden_dim, bias=False, device=device)
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.w2(self.act(self.w1(x)) * self.w3(x))
+
+
+class Molmo2VisionBackbone(nn.Module):
+    def __init__(self, vit_config: Molmo2VitConfig, adapter_config: Molmo2AdapterConfig):
+        super().__init__()
+        self.adapter_config = adapter_config
+
+        self.vit_layers: list[int] = []
+        for layer in adapter_config.vit_layers:
+            if layer >= 0:
+                self.vit_layers.append(layer)
+            else:
+                self.vit_layers.append(layer + vit_config.num_hidden_layers)
+
+        last_layer_needed = max(self.vit_layers) + 1
+        if last_layer_needed < vit_config.num_hidden_layers:
+            new_vit_config = deepcopy(vit_config)
+            new_vit_config.num_hidden_layers = last_layer_needed
+            self.image_vit = Molmo2VisionModel(new_vit_config)
+        else:
+            self.image_vit = Molmo2VisionModel(vit_config)
+
+        self.num_prefix_tokens: int = self.image_vit.num_prefix_tokens
+
+        pool_dim = vit_config.hidden_size * len(adapter_config.vit_layers)
+        self.image_pooling_2d = Molmo2PoolingAttention(
+            hidden_size=adapter_config.hidden_size,
+            num_heads=adapter_config.num_attention_heads,
+            num_key_value_heads=adapter_config.num_key_value_heads,
+            head_dim=adapter_config.head_dim,
+            input_dim=pool_dim,
+            attention_dropout=adapter_config.attention_dropout,
+            attn_implementation=adapter_config._attn_implementation or "eager",
+        )
+        self.image_projector = Molmo2ImageProjectorMLP(
+            adapter_config.hidden_size,
+            adapter_config.intermediate_size,
+            adapter_config.text_hidden_size,
+            adapter_config.hidden_act,
+        )
+        self.image_feature_dropout = nn.Dropout(adapter_config.image_feature_dropout)
+
+    def encode_image(self, images: torch.Tensor) -> torch.Tensor:
+        """
+        : param images: (batch_size, num_crops, num_patch, n_pixels)
+        """
+        B, T, N, D = images.shape
+        images = images.view(B * T, N, D)
+        image_features = self.image_vit(images)
+
+        features = []
+        for layer in self.vit_layers:
+            features.append(image_features[layer])
+        image_features = torch.cat(features, dim=-1)
+
+        if self.num_prefix_tokens > 0:
+            image_features = image_features[:, 1:]
+        image_features = image_features.view(B, T, N, -1)
+        return image_features
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self.image_vit.patch_embedding.weight.dtype
+
+    @property
+    def device(self) -> torch.device:
+        return self.image_vit.patch_embedding.weight.device
+
+    def forward(
+        self,
+        images: torch.Tensor,
+        pooled_patches_idx: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        # image_features: (batch_size, num_crops(=num_image), num_patch, nximage_emb_dim)
+        batch_size, num_image = images.shape[:2]
+        images = images.to(device=self.device, dtype=self.dtype)
+        image_features = self.encode_image(images)
+
+        image_features = self.image_feature_dropout(image_features)
+        dim = image_features.shape[-1]
+        valid = pooled_patches_idx >= 0
+        valid_token = torch.any(valid, -1)
+
+        # Use `pooled_patches_idx` to arange the features for image pooling
+        batch_idx = torch.arange(pooled_patches_idx.shape[0], dtype=torch.long, device=pooled_patches_idx.device)
+        batch_idx = torch.tile(
+            batch_idx.view(batch_size, 1, 1), [1, pooled_patches_idx.shape[1], pooled_patches_idx.shape[2]]
+        )
+
+        # Now [batch, num_high_res_features, pool_dim, dim]
+        to_pool = image_features.reshape(batch_size, -1, dim)[batch_idx, torch.clip(pooled_patches_idx, 0)]
+        to_pool = to_pool * valid.to(self.dtype)[:, :, :, None]
+        to_pool = to_pool.reshape([-1, pooled_patches_idx.shape[-1], dim])
+        if self.adapter_config.pooling_attention_mask:
+            attn_mask = valid.reshape([-1, 1, 1, valid.shape[-1]])
+            denom = valid.view(-1, to_pool.shape[-2]).float().sum(-1)
+            denom = torch.where(denom == 0, 1, denom)
+            query = to_pool.sum(-2, keepdim=True) / denom[:, None, None].to(to_pool.dtype)
+        else:
+            attn_mask = None
+            query = to_pool.mean(-2, keepdim=True)
+        pooled_features = self.image_pooling_2d(query, to_pool, attn_mask=attn_mask)
+        pooled_features = pooled_features.reshape([batch_size, -1, pooled_features.shape[-1]])
+
+        # MLP layer to map the feature.
+        pooled_features = self.image_projector(pooled_features)
+        return pooled_features.view(-1, pooled_features.shape[-1])[valid_token.flatten()]
+
+
+# ===================== Text Components (from Phi3/Llama) =====================
+
+
+class Molmo2RotaryEmbedding(LlamaRotaryEmbedding):
+    def __init__(
+        self,
+        config: Molmo2TextConfig,
+        device: str | torch.device = None,
+        rope_type: str | None = None,
+    ):
+        # Molmo2 has custom rope_type handling (not using config.rope_parameters)
+        if rope_type is not None:
+            self.rope_type = rope_type
+        elif hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            # BC: "rope_type" was originally "type"
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            self.rope_type = "default"
+
+        nn.Module.__init__(self)
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Molmo2TextConfig | None = None,
+        device: torch.device | None = None,
+        seq_len: int | None = None,
+    ) -> tuple[torch.Tensor, float]:
+        base = config.rope_theta
+        head_dim = config.head_dim or config.hidden_size // config.num_attention_heads
+        dim = int(head_dim)
+        attention_factor = 1.0
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+
+class Molmo2RMSNorm(LlamaRMSNorm):
+    def __init__(self, size: int, eps: float = 1e-6, device: str | torch.device = None):
+        super().__init__(size, eps=eps)
+        # Re-init weight with device support
+        self.weight = nn.Parameter(torch.ones(size, device=device))
+        self.eps = eps
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        with torch.autocast(enabled=False, device_type=x.device.type):
+            og_dtype = x.dtype
+            x = x.to(torch.float32)
+            variance = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(variance + self.eps)
+            x = x.to(og_dtype)
+
+        return self.weight * x
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.eps}"
+
+
+class Molmo2Attention(Phi3Attention):
+    """Molmo2 attention with fused QKV, optional QK norm, and custom weight names."""
+
+    def __init__(self, config: Molmo2TextConfig, layer_idx: int) -> None:
+        nn.Module.__init__(self)
+        self.config = config
+        self.layer_idx = layer_idx
+        self.num_heads = config.num_attention_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.head_dim = config.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.is_causal = True
+
+        self.fused_dims = (
+            config.num_attention_heads * config.head_dim,
+            config.head_dim * config.num_key_value_heads,
+            config.head_dim * config.num_key_value_heads,
+        )
+        self.att_proj = nn.Linear(
+            config.hidden_size,
+            sum(self.fused_dims),
+            bias=config.qkv_bias,
+        )
+
+        # Layer norms.
+        self.k_norm: Molmo2RMSNorm | None = None
+        self.q_norm: Molmo2RMSNorm | None = None
+        self.qk_norm_type: str | None = None
+        if config.use_qk_norm:
+            k_norm_size = (
+                config.head_dim if config.qk_norm_type == "qwen3" else config.num_key_value_heads * config.head_dim
+            )
+            self.k_norm = Molmo2RMSNorm(k_norm_size, eps=config.layer_norm_eps)
+            q_norm_size = (
+                config.head_dim if config.qk_norm_type == "qwen3" else config.num_attention_heads * config.head_dim
+            )
+            self.q_norm = Molmo2RMSNorm(q_norm_size, eps=config.layer_norm_eps)
+            self.qk_norm_type = config.qk_norm_type
+
+        self.attention_dropout = config.attention_dropout
+
+        self.attn_out = nn.Linear(
+            config.head_dim * config.num_attention_heads,
+            config.hidden_size,
+            bias=False,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        input_shape = hidden_states.shape[:-1]
+        q_shape = (*input_shape, self.num_heads, self.head_dim)
+        kv_shape = (*input_shape, self.num_key_value_heads, self.head_dim)
+
+        qkv = self.att_proj(hidden_states)
+        query_states, key_states, value_states = qkv.split(self.fused_dims, dim=-1)
+        value_states = value_states.view(kv_shape)
+
+        # Optionally apply layer norm to keys and queries.
+        if self.q_norm is not None and self.k_norm is not None and self.qk_norm_type != "qwen3":
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+
+        query_states = query_states.view(q_shape)
+        key_states = key_states.view(kv_shape)
+        if self.q_norm is not None and self.k_norm is not None and self.qk_norm_type == "qwen3":
+            query_states = self.q_norm(query_states)
+            key_states = self.k_norm(key_states)
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if (self.config._attn_implementation or "eager") != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.attn_out(attn_output)
+        return attn_output, attn_weights
+
+
+class Molmo2MLP(Phi3MLP):
+    def __init__(
+        self,
+        input_dim: int,
+        intermediate_size: int,
+        hidden_act: str,
+        device: str | torch.device = None,
+    ):
+        nn.Module.__init__(self)
+        self.ff_proj = nn.Linear(input_dim, intermediate_size * 2, bias=False, device=device)
+        self.ff_out = nn.Linear(intermediate_size, input_dim, bias=False, device=device)
+        self.act = ACT2FN[hidden_act]
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.ff_proj(x)
+        x, gate = x.chunk(2, dim=-1)
+        x = self.act(gate) * x
+        x = self.ff_out(x)
+        return x
+
+
+class Molmo2DecoderLayer(Phi3DecoderLayer):
+    def __init__(self, config: Molmo2TextConfig, layer_idx: int | None = None, device: str | torch.device = None):
+        GradientCheckpointingLayer.__init__(self)
+        self.config = config
+
+        self.self_attn = Molmo2Attention(config, layer_idx)
+        self.attn_norm = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps, device=device)
+        self.dropout = nn.Dropout(config.residual_dropout)
+        self.mlp = Molmo2MLP(config.hidden_size, config.intermediate_size, config.hidden_act, device=device)
+        self.ff_norm = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps, device=device)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool | None = False,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
+        residual = hidden_states
+        hidden_states = self.attn_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ff_norm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class Molmo2PostNormDecoderLayer(Molmo2DecoderLayer):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool | None = False,
+        use_cache: bool | None = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.FloatTensor, tuple[torch.FloatTensor, torch.FloatTensor] | None]:
+        residual = hidden_states
+
+        # Self Attention
+        hidden_states, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+        )
+        hidden_states = self.attn_norm(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = self.ff_norm(hidden_states)
+
+        hidden_states = residual + self.dropout(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        return outputs
+
+
+class Molmo2Embedding(nn.Module):
+    def __init__(
+        self,
+        num_embeddings: int,
+        num_new_embeddings: int,
+        features: int,
+        device: str | torch.device = None,
+    ):
+        super().__init__()
+        self.embedding = nn.Parameter(
+            torch.zeros(num_embeddings, features, device=device),
+        )
+        self.new_embedding = nn.Parameter(
+            torch.zeros(num_new_embeddings, features, device=device),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return F.embedding(x, torch.cat([self.embedding, self.new_embedding], dim=0))
+
+
+# ===================== PreTrainedModel =====================
+
+
+class Molmo2PreTrainedModel(LlamaPreTrainedModel):
+    config: Molmo2Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "Molmo2DecoderLayer",
+        "Molmo2PostNormDecoderLayer",
+        "Molmo2VisionEncoderLayer",
+        "Molmo2VisionAttention",
+    ]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+
+    _can_compile_fullgraph = False
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": Molmo2DecoderLayer,
+        "attentions": Molmo2Attention,
+    }
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, (nn.Linear,)):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2Embedding):
+            init.normal_(module.embedding, mean=0.0, std=std)
+            init.normal_(module.new_embedding, mean=0.0, std=std)
+        elif isinstance(module, nn.Embedding):
+            init.normal_(module.weight, mean=0.0, std=std)
+            if module.padding_idx is not None:
+                init.zeros_(module.weight[module.padding_idx])
+        elif isinstance(module, Molmo2RMSNorm):
+            init.ones_(module.weight)
+        elif isinstance(module, nn.LayerNorm):
+            init.ones_(module.weight)
+            if module.bias is not None:
+                init.zeros_(module.bias)
+        elif isinstance(module, Molmo2VisionModel):
+            init.normal_(module.positional_embedding, mean=0.0, std=std)
+        elif isinstance(module, Molmo2RotaryEmbedding):
+            rope_fn = (
+                ROPE_INIT_FUNCTIONS[module.rope_type]
+                if module.rope_type != "default"
+                else module.compute_default_rope_parameters
+            )
+            buffer_value, _ = rope_fn(module.config)
+            init.copy_(module.inv_freq, buffer_value)
+            init.copy_(module.original_inv_freq, buffer_value)
+
+
+class Molmo2TextModel(Molmo2PreTrainedModel):
+    config: Molmo2TextConfig
+    _input_embed_layer = "wte"
+
+    def __init__(self, config: Molmo2TextConfig):
+        super().__init__(config)
+        if config.additional_vocab_size is not None:
+            self.wte = Molmo2Embedding(
+                config.vocab_size,
+                config.additional_vocab_size,
+                config.hidden_size,
+            )
+        else:
+            self.wte = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.emb_drop = nn.Dropout(config.embedding_dropout)
+        decoder_layer = Molmo2PostNormDecoderLayer if config.norm_after else Molmo2DecoderLayer
+        self.blocks = nn.ModuleList(
+            [decoder_layer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.ln_f = Molmo2RMSNorm(config.hidden_size, eps=config.layer_norm_eps)
+        if config.rope_scaling_layers is not None:
+            self.rotary_embs = nn.ModuleDict(
+                {
+                    "default": Molmo2RotaryEmbedding(config, rope_type="default"),
+                    "scaling": Molmo2RotaryEmbedding(config),
+                }
+            )
+        else:
+            self.rotary_emb = Molmo2RotaryEmbedding(config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if self.gradient_checkpointing and self.training and use_cache:
+            logger.warning_once(
+                "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
+            )
+            use_cache = False
+
+        if inputs_embeds is None:
+            input_ids = input_ids * (input_ids != -1).to(input_ids.dtype)
+            inputs_embeds = self.wte(input_ids)
+
+        # torch.jit.trace() doesn't support cache objects in the output
+        if use_cache and past_key_values is None and not torch.jit.is_tracing():
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # Create the mask
+            causal_mask_mapping = create_causal_mask(**mask_kwargs)
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        if self.config.rope_scaling_layers is not None:
+            position_embeddings_mapping = {
+                "default": self.rotary_embs["default"](hidden_states, position_ids),
+                "scaling": self.rotary_embs["scaling"](hidden_states, position_ids),
+            }
+        else:
+            position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        for layer_idx, decoder_block in enumerate(self.blocks[: self.config.num_hidden_layers]):
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.config.rope_scaling_layers is not None:
+                position_embeddings_i = (
+                    position_embeddings_mapping["scaling"]
+                    if layer_idx in self.config.rope_scaling_layers
+                    else position_embeddings_mapping["default"]
+                )
+            else:
+                position_embeddings_i = position_embeddings
+
+            layer_outputs = decoder_block(
+                hidden_states,
+                attention_mask=causal_mask_mapping,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                output_attentions=output_attentions,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                position_embeddings=position_embeddings_i,
+                **kwargs,
+            )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.ln_f(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+# Adapted from ...models.gemma3.modeling_gemma3
+def token_type_ids_mask_function(
+    token_type_ids: torch.Tensor | None = None,
+) -> Callable | None:
+    """
+    This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
+    not start and end indices.
+    """
+    # Do not return an additional mask in this case
+    if token_type_ids is None:
+        return None
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        # If it's 1 for both query and key/value, we are in an image block
+        # NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
+        # Since vmap doesn't support `if statement` we workaround it with `torch.where`
+        safe_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
+        token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_idx]
+        token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)
+
+        is_image_block = (token_type_ids[batch_idx, q_idx] == 1) & (token_type_ids_at_kv_idx == 1)
+
+        # This is bidirectional attention whenever we are dealing with image tokens
+        return is_image_block & is_image_block
+
+    return inner_mask
+
+
+class Molmo2Model(Molmo2PreTrainedModel):
+    base_model_prefix = "model"
+    _checkpoint_conversion_mapping = {}
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+    config: Molmo2Config
+
+    def __init__(self, config: Molmo2Config):
+        super().__init__(config)
+        self.language_model: Molmo2TextModel = Molmo2TextModel(config.text_config)
+        self.image_col_id = config.image_col_id
+        self.image_low_res_id = config.image_low_res_id
+        self.vision_backbone: Molmo2VisionBackbone | None = None
+        if config.vit_config is not None and config.adapter_config is not None:
+            self.vision_backbone = Molmo2VisionBackbone(config.vit_config, config.adapter_config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self) -> torch.nn.Module:
+        return self.language_model.wte
+
+    def set_input_embeddings(self, value: torch.nn.Module) -> None:
+        self.language_model.wte = value
+
+    def build_batched_images(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values: torch.Tensor,
+        image_token_pooling: torch.Tensor,
+        image_grids: torch.Tensor,
+        image_num_crops: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Normalize inputs to flattened image/crop layout expected by the model.
+        if pixel_values.dim() == 4:
+            batch_size, num_crops, n_patches, pixels_per_patch = pixel_values.shape
+            pixel_values = pixel_values.reshape(batch_size * num_crops, n_patches, pixels_per_patch)
+            if image_num_crops is None:
+                image_num_crops = torch.full(
+                    (batch_size,),
+                    num_crops,
+                    device=pixel_values.device,
+                    dtype=torch.long,
+                )
+        if image_num_crops is None:
+            image_num_crops = torch.ones(
+                image_grids.size(0),
+                device=image_grids.device,
+                dtype=torch.long,
+            )
+        if image_token_pooling.dim() == 3:
+            image_token_pooling = image_token_pooling.reshape(-1, image_token_pooling.size(-1))
+
+        # 1) Count the number of images in each example
+        raw_counts = (input_ids == self.config.image_end_token_id).sum(1)  # [N]
+        # Each image is represented by global view and high-res view
+        # so we divide by 2 to get the number of images
+        counts = raw_counts // 2
+        N = counts.size(0)
+        device = input_ids.device
+
+        # Total number of images in the batch
+        num_images = int(counts.sum().item())
+        if image_grids is not None and image_grids.size(0) == N and num_images != image_grids.size(0):
+            counts = torch.ones_like(counts)
+            num_images = int(counts.sum().item())
+
+        # Sanity check
+        assert image_grids.size(0) == num_images, f"Expected {num_images} image grids, but got {image_grids.size(0)}"
+        assert image_num_crops.size(0) == num_images, (
+            f"Expected {num_images} image num crops, but got {image_num_crops.size(0)}"
+        )
+
+        # 1-1) Compute per-image pooled patch count from image grids
+        with torch.no_grad():
+            first_prod = image_grids[:, :2].prod(dim=1)  # [num_images]
+            second_prod = image_grids[:, 2:].prod(dim=1)  # [num_images]
+            num_pooled_patches_per_image = (first_prod + second_prod).to(image_num_crops.dtype)  # [num_images]
+
+        # pixel_values: [n_crops, n_patches, pixels_per_patch]
+        n_crops, n_patches, pixels_per_patch = pixel_values.shape
+
+        # 2) Map each image index → example index
+        # Example: if counts = [2, 1, 3], then this becomes [0,0,1,2,2,2]
+        example_ids_for_image = torch.arange(N, device=device).repeat_interleave(counts)  # [num_images]
+        assert example_ids_for_image.numel() == num_images
+
+        # 2-1) Compute crops_per_example by summing per-image crop counts
+        crops_per_example = torch.zeros(N, dtype=image_num_crops.dtype, device=image_num_crops.device)
+        crops_per_example.index_add_(0, example_ids_for_image, image_num_crops)  # [N]
+
+        # 2-2) Per-image number of patches = (crops per image) * n_patches
+        patches_per_image = image_num_crops * n_patches  # [num_images]
+
+        # 2-3) Compute per-example per-image patch offsets
+        counts_list = counts.tolist()
+        index_offset_per_example_list = []
+        offset_img = 0
+        for c in counts_list:
+            per_img_patches = patches_per_image[offset_img : offset_img + c]  # [c]
+            # Offsets: [0, img0_total_patches, img0+img1_total_patches, ...]
+            index_offset = [0] + per_img_patches.cumsum(0).tolist()[:-1]
+            index_offset_per_example_list.append(index_offset)
+            offset_img += c
+
+        # 2-4) Compute num_pooled_patches_per_example
+        num_pooled_patches_per_example = torch.zeros(
+            N, dtype=num_pooled_patches_per_image.dtype, device=num_pooled_patches_per_image.device
+        )
+        num_pooled_patches_per_example.index_add_(0, example_ids_for_image, num_pooled_patches_per_image)
+
+        # Sanity checks
+        total_crops = int(crops_per_example.sum().item())
+        assert total_crops == n_crops, f"Expected {total_crops} crops, but got {n_crops}"
+
+        total_num_pooled_patches = int(num_pooled_patches_per_example.sum().item())
+        assert total_num_pooled_patches == image_token_pooling.size(0), (
+            f"Expected {total_num_pooled_patches} pooled patches, but got {image_token_pooling.size(0)}"
+        )
+
+        # 3) Build images tensor filled with -1
+        M = int(crops_per_example.max().item())
+        images = torch.full(
+            (N, M, n_patches, pixels_per_patch),
+            fill_value=-1,
+            dtype=pixel_values.dtype,
+            device=pixel_values.device,
+        )
+
+        # 4) Fill images with per-example slices from pixel_values
+        offset_crop = 0
+        for i in range(N):
+            num = int(crops_per_example[i].item())
+            cur = pixel_values[offset_crop : offset_crop + num]  # [num, n_patches, pixels_per_patch]
+            images[i, :num] = cur
+            offset_crop += num
+
+        # Sanity check
+        assert offset_crop == n_crops
+
+        # 5) Build new_token_pooling tensor filled with -1
+        P = int(num_pooled_patches_per_example.max().item())
+        _, dim = image_token_pooling.shape
+        new_token_pooling = torch.full(
+            (N, P, dim),
+            fill_value=-1,
+            dtype=image_token_pooling.dtype,
+            device=image_token_pooling.device,
+        )
+
+        # 6) Fill token_pooling with per-example slices, adding per-image patch offsets
+        patch_offset = 0
+        img_offset = 0
+
+        for i, c in enumerate(counts_list):
+            num_patches = int(num_pooled_patches_per_example[i].item())
+
+            # Subsequence of pooled tokens belonging to this example
+            cur = image_token_pooling[patch_offset : patch_offset + num_patches].clone()  # [num_patches, dim]
+
+            index_offset_per_example = index_offset_per_example_list[i]  # length = c
+            per_img_pooled = num_pooled_patches_per_image[img_offset : img_offset + c]  # [c]
+
+            assert len(index_offset_per_example) == per_img_pooled.numel()
+
+            # Apply per-image offsets to the (ragged) subsequence
+            offset = 0
+            for j in range(c):
+                index_offset = int(index_offset_per_example[j])
+                n = int(per_img_pooled[j].item())
+                cur_slice = cur[offset : offset + n]
+
+                # Apply offset across all columns
+                cur[offset : offset + n] = torch.where(
+                    cur_slice >= 0,
+                    cur_slice + index_offset,
+                    cur_slice,
+                )
+                offset += n
+
+            new_token_pooling[i, :num_patches] = cur
+
+            patch_offset += num_patches
+            img_offset += c
+
+        # Final sanity checks
+        assert patch_offset == total_num_pooled_patches
+        assert img_offset == num_images
+
+        return images, new_token_pooling
+
+    def build_batched_videos(
+        self,
+        input_ids: torch.LongTensor,
+        pixel_values_videos: torch.Tensor,
+        video_token_pooling: torch.Tensor,
+        video_grids: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # 1) Count the number of videos in each example
+        if self.config.use_frame_special_tokens:
+            end_token_id = self.config.frame_end_token_id
+        else:
+            end_token_id = self.config.image_end_token_id
+        counts = (input_ids == end_token_id).any(dim=1).long()  # [N]
+        N = counts.size(0)
+        device = input_ids.device
+
+        # Total number of videos in the batch
+        num_videos = int(counts.sum().item())
+
+        # Sanity check
+        assert video_grids.size(0) == num_videos, f"Expected {num_videos} videos, but got {video_grids.size(0)}"
+
+        video_num_frames = video_grids[:, 0]  # [num_videos]
+        num_pooled_patches_per_video = video_grids.prod(dim=1)  # [num_videos]
+
+        # pixel_values_videos: [n_frames, n_patches, pixels_per_patch]
+        n_frames, n_patches, pixels_per_patch = pixel_values_videos.shape
+
+        # 2) Map each video index -> example index
+        # Example: if counts = [2, 1, 3], then this becomes [0,0,1,2,2,2]
+        example_ids_for_video = torch.arange(N, device=device).repeat_interleave(counts)  # [num_videos]
+        assert example_ids_for_video.numel() == num_videos
+
+        # 2-1) Compute frames_per_example by summing per-video frame counts
+        frames_per_example = torch.zeros(
+            N,
+            dtype=video_num_frames.dtype,
+            device=device,
+        )
+        frames_per_example.index_add_(0, example_ids_for_video, video_num_frames)  # [N]
+
+        # 2-2) Compute num_pooled_patches_per_example
+        num_pooled_patches_per_example = torch.zeros(
+            N,
+            dtype=num_pooled_patches_per_video.dtype,
+            device=num_pooled_patches_per_video.device,
+        )
+        num_pooled_patches_per_example.index_add_(
+            0,
+            example_ids_for_video,
+            num_pooled_patches_per_video,
+        )
+
+        # Sanity checks
+        total_frames = int(frames_per_example.sum().item())
+        assert total_frames == n_frames, f"Expected {total_frames} frames, but got {n_frames}"
+
+        total_num_pooled_patches = int(num_pooled_patches_per_example.sum().item())
+        assert total_num_pooled_patches == video_token_pooling.size(0), (
+            f"Expected {total_num_pooled_patches} pooled patches, but got {video_token_pooling.size(0)}"
+        )
+
+        # 3) Build videos tensor filled with -1
+        M = int(frames_per_example.max().item())
+        videos = torch.full(
+            (N, M, n_patches, pixels_per_patch),
+            fill_value=-1,
+            dtype=pixel_values_videos.dtype,
+            device=device,
+        )
+
+        # 4) Fill videos with per-examples slices from pixel_values_videos
+        offset_frame = 0
+        for i in range(N):
+            num = int(frames_per_example[i].item())
+            cur = pixel_values_videos[offset_frame : offset_frame + num]  # [num, n_patches, pixels_per_patch]
+            videos[i, :num] = cur
+            offset_frame += num
+
+        # Sanity check
+        assert offset_frame == n_frames
+
+        # 5) Build new token_pooling tensor filled with -1
+        P = int(num_pooled_patches_per_example.max().item())
+        _, dim = video_token_pooling.shape
+        new_token_pooling = torch.full(
+            (N, P, dim),
+            fill_value=-1,
+            dtype=video_token_pooling.dtype,
+            device=video_token_pooling.device,
+        )
+
+        # 6) Fill new token_pooling with per-examples slices from video_token_pooling
+        patch_offset = 0
+        for i in range(N):
+            num_patches = int(num_pooled_patches_per_example[i].item())
+            cur = video_token_pooling[patch_offset : patch_offset + num_patches]  # [num_patches, dim]
+            new_token_pooling[i, :num_patches] = cur
+            patch_offset += num_patches
+
+        # Final sanity checks
+        assert patch_offset == total_num_pooled_patches
+
+        return videos, new_token_pooling
+
+    def merge_visual_inputs(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        if pixel_values is not None and pixel_values_videos is not None:
+            raise ValueError("pixel_values and pixel_values_videos are provided at the same time")
+        elif pixel_values is not None:
+            if input_ids is None:
+                return None, None
+            images, token_pooling = self.build_batched_images(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                image_token_pooling=image_token_pooling,
+                image_grids=image_grids,
+                image_num_crops=image_num_crops,
+            )
+        elif pixel_values_videos is not None:
+            if input_ids is None:
+                return None, None
+            images, token_pooling = self.build_batched_videos(
+                input_ids=input_ids,
+                pixel_values_videos=pixel_values_videos,
+                video_token_pooling=video_token_pooling,
+                video_grids=video_grids,
+            )
+        else:
+            images, token_pooling = None, None
+        return images, token_pooling
+
+    def build_input_embeddings(
+        self,
+        input_ids: torch.LongTensor,
+        images: torch.FloatTensor | None = None,  # image inputs
+        token_pooling: torch.LongTensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        # Get embeddings of input.
+        # shape: (batch_size, seq_len, d_model)
+        input_ids = input_ids * (input_ids != -1).to(input_ids.dtype)
+        x = self.language_model.wte(input_ids)
+
+        image_features: torch.FloatTensor | None = None
+        if images is not None:
+            image_features = self.vision_backbone(images, token_pooling).to(x.device)
+            is_image_patch = input_ids.view(-1) == self.config.image_patch_id
+            assert is_image_patch.sum() == len(image_features)
+            x.view(-1, x.shape[-1])[is_image_patch] += image_features
+
+        # shape: (batch_size, seq_len, d_model)
+        x = self.language_model.emb_drop(x)  # type: ignore
+
+        return x, image_features
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Molmo2ModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        images, token_pooling = self.merge_visual_inputs(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_token_pooling=image_token_pooling,
+            image_grids=image_grids,
+            image_num_crops=image_num_crops,
+            pixel_values_videos=pixel_values_videos,
+            video_token_pooling=video_token_pooling,
+            video_grids=video_grids,
+        )
+
+        if images is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both images and inputs_embeds at the same time.")
+
+        if inputs_embeds is None:
+            inputs_embeds, image_features = self.build_input_embeddings(
+                input_ids,
+                images,
+                token_pooling,
+            )
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        # Adapted from ...models.gemma3.modeling_gemma3
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config.get_text_config(),
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "cache_position": cache_position,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+
+            # NOTE: this `is_prefill` logic is not flawless, it fails when we're using a cache eagerly initialized
+            # (e.g. compiled prefill) AND `images` are not provided. Determining prefill in that case requires
+            # checking data values, which is not compile-compatible.
+            is_prefill = (
+                not use_cache or past_key_values is None or not past_key_values.is_initialized or images is not None
+            )
+            if token_type_ids is not None and is_prefill:
+                # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
+                mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
+                    token_type_ids.to(cache_position.device)
+                )
+
+            # Create the mask
+            causal_mask_mapping = create_causal_mask(**mask_kwargs)
+
+        outputs = self.language_model(
+            attention_mask=causal_mask_mapping,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return Molmo2ModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_features if images is not None else None,
+        )
+
+
+class Molmo2ForConditionalGeneration(Molmo2PreTrainedModel, GenerationMixin):
+    _checkpoint_conversion_mapping = {}
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.wte.weight"}
+    # Reference: fix gemma3 grad acc #37208
+    accepts_loss_kwargs = False
+    config: Molmo2Config
+
+    def __init__(self, config: Molmo2Config):
+        super().__init__(config)
+
+        self.model = Molmo2Model(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.vocab_size = config.text_config.vocab_size
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.Tensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Molmo2CausalLMOutputWithPast:
+        r"""
+        ```python
+        >>> from PIL import Image
+        >>> import requests
+        >>> from ... import AutoProcessor, Molmo2ForConditionalGeneration
+
+        >>> model = Molmo2ForConditionalGeneration.from_pretrained("...")
+        >>> processor = AutoProcessor.from_pretrained("...")
+
+        >>> prompt = "What's the content of the image?"
+        >>> url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> messages = [{"role": "user", "content": [{"type": "text", "text": prompt}, {"type": "image", "image": image}]}]
+
+        >>> inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
+
+        >>> # Generate
+        >>> generated_ids = model.generate(**inputs, max_new_tokens=15)
+        >>> generated_tokens = generated_ids[:, inputs['input_ids'].size(1):]
+        >>> processor.post_process_image_text_to_text(generated_tokens, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "The image shows a bustling street scene in what appears to be a Chinatown area. There's ..."
+        ```"""
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_token_pooling=image_token_pooling,
+            image_grids=image_grids,
+            image_num_crops=image_num_crops,
+            pixel_values_videos=pixel_values_videos,
+            video_token_pooling=video_token_pooling,
+            video_grids=video_grids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size)
+
+        return Molmo2CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: list[torch.FloatTensor] | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        pixel_values: torch.FloatTensor | None = None,
+        image_token_pooling: torch.Tensor | None = None,
+        image_grids: torch.Tensor | None = None,
+        image_num_crops: torch.Tensor | None = None,
+        pixel_values_videos: torch.Tensor | None = None,
+        video_token_pooling: torch.Tensor | None = None,
+        video_grids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        token_type_ids: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor | None = None,
+        is_first_iteration: bool = False,
+        use_cache: bool = True,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            token_type_ids=token_type_ids,
+            is_first_iteration=is_first_iteration,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        if is_first_iteration or not use_cache:
+            model_inputs["pixel_values"] = pixel_values
+            model_inputs["image_token_pooling"] = image_token_pooling
+            model_inputs["image_grids"] = image_grids
+            model_inputs["image_num_crops"] = image_num_crops
+            model_inputs["pixel_values_videos"] = pixel_values_videos
+            model_inputs["video_token_pooling"] = video_token_pooling
+            model_inputs["video_grids"] = video_grids
+
+        return model_inputs
+
+    # Adapted from ...models.gemma3.modeling_gemma3
+    @staticmethod
+    def create_masks_for_generate(
+        config: PreTrainedConfig,
+        inputs_embeds: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        cache_position: torch.Tensor,
+        past_key_values: Cache | None,
+        position_ids: torch.Tensor | None,
+        token_type_ids: torch.Tensor | None = None,
+        **kwargs,
+    ) -> dict:
+        # Prepare mask arguments
+        mask_kwargs = {
+            "config": config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "cache_position": cache_position,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+        # Add the token type ids mask for generate as well
+        if token_type_ids is not None and inputs_embeds.shape[1] != 1:
+            # We need to pass an additional mask function to account for token type ids, and it needs to be an `or`
+            mask_kwargs["or_mask_function"] = token_type_ids_mask_function(token_type_ids.to(cache_position.device))
+
+        return create_masks_for_generate(**mask_kwargs)
+
+
+__all__ = [
+    "Molmo2ForConditionalGeneration",
+    "Molmo2Model",
+    "Molmo2PreTrainedModel",
+    "Molmo2TextModel",
+    "Molmo2VisionBackbone",
+    "Molmo2VisionModel",
+]
diff --git a/src/transformers/models/molmo2/processing_molmo2.py b/src/transformers/models/molmo2/processing_molmo2.py
new file mode 100644
index 000000000000..832cf0d7acdc
--- /dev/null
+++ b/src/transformers/models/molmo2/processing_molmo2.py
@@ -0,0 +1,392 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Processor class for Molmo2.
+"""
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import (
+    ImagesKwargs,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    VideosKwargs,
+)
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import auto_docstring, logging
+from ...video_utils import VideoInput
+
+
+logger = logging.get_logger(__name__)
+
+
+# Special tokens, these should be present in any tokenizer we use since the preprocessor uses them
+IMAGE_PATCH_TOKEN = "<im_patch>"  # Where to insert high-res tokens
+IMAGE_LOW_RES_TOKEN = "<im_low>"  # Where to insert low-res tokens
+IM_START_TOKEN = "<im_start>"
+LOW_RES_IMAGE_START_TOKEN = "<low_res_im_start>"
+FRAME_START_TOKEN = "<frame_start>"
+IM_END_TOKEN = "<im_end>"
+FRAME_END_TOKEN = "<frame_end>"
+IM_COL_TOKEN = "<im_col>"
+IMAGE_PROMPT = "<|image|>"
+VIDEO_PROMPT = "<|video|>"
+
+IMAGE_TOKENS = [
+    IMAGE_PATCH_TOKEN,
+    IM_COL_TOKEN,
+    IM_START_TOKEN,
+    LOW_RES_IMAGE_START_TOKEN,
+    FRAME_START_TOKEN,
+    IM_END_TOKEN,
+    FRAME_END_TOKEN,
+    IMAGE_LOW_RES_TOKEN,
+]
+
+
+class Molmo2ImagesKwargs(ImagesKwargs, total=False):
+    """
+    max_crops (`int`, *optional*):
+        Maximum number of image crops produced by the image processor.
+    overlap_margins (`list[int]`, *optional*):
+        Pixel margins `[left_right, top_bottom]` to overlap between neighboring crops.
+    patch_size (`int`, *optional*):
+        Side length in pixels of each ViT patch.
+    pooling_size (`list[int]`, *optional*):
+        `[pool_h, pool_w]` pooling window applied to patch features in the vision adapter.
+    """
+
+    max_crops: int | None
+    overlap_margins: list[int] | None
+    patch_size: int | None
+    pooling_size: list[int] | None
+
+
+class Molmo2VideosKwargs(VideosKwargs, total=False):
+    """
+    patch_size (`int`, *optional*):
+        Side length in pixels of each ViT patch for video frames.
+    pooling_size (`list[int]`, *optional*):
+        `[pool_h, pool_w]` pooling window applied to video patch features.
+    max_fps (`int`, *optional*):
+        Maximum sampling rate in frames per second for short videos.
+    """
+
+    patch_size: int | None
+    pooling_size: list[int] | None
+    max_fps: int | None
+
+
+class Molmo2ProcessorKwargs(ProcessingKwargs, total=False):
+    """Molmo2 processor kwargs"""
+
+    images_kwargs: Molmo2ImagesKwargs
+    videos_kwargs: Molmo2VideosKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+            "return_mm_token_type_ids": True,
+        },
+        "videos_kwargs": {"return_metadata": True},
+    }
+
+
+@auto_docstring
+class Molmo2Processor(ProcessorMixin):
+    @property
+    def model_input_names(self):
+        return super().model_input_names + ["token_type_ids"]
+
+    def __init__(
+        self,
+        image_processor=None,
+        video_processor=None,
+        tokenizer=None,
+        chat_template: str | None = None,
+        image_use_col_tokens: bool | None = True,
+        use_single_crop_col_tokens: bool | None = None,
+        use_single_crop_start_token: bool | None = True,
+        video_use_col_tokens: bool | None = False,
+        use_frame_special_tokens: bool | None = True,
+        **kwargs,
+    ) -> None:
+        r"""
+        image_use_col_tokens (`bool`, *optional*, defaults to `True`):
+            Whether to append column-separator tokens (`<im_col>`) after each patch row of the high-resolution image
+            view.
+        use_single_crop_col_tokens (`bool`, *optional*):
+            Whether to append column-separator tokens after each patch row of the low-resolution (single-crop) image
+            view. If `None`, falls back to `image_use_col_tokens`.
+        use_single_crop_start_token (`bool`, *optional*, defaults to `True`):
+            Whether to start the low-resolution image view with `<low_res_im_start>` instead of the regular
+            `<im_start>`.
+        video_use_col_tokens (`bool`, *optional*, defaults to `False`):
+            Whether to append column-separator tokens after each patch row of video frames.
+        use_frame_special_tokens (`bool`, *optional*, defaults to `True`):
+            Whether to wrap each video frame with `<frame_start>` / `<frame_end>` tokens. If `False`, falls back to
+            `<im_start>` / `<im_end>`.
+        """
+        super().__init__(image_processor, video_processor, tokenizer, chat_template=chat_template)
+
+        self.image_placeholder_token = IMAGE_PROMPT
+        self.video_placeholder_token = VIDEO_PROMPT
+        self.image_token_ids = [tokenizer.convert_tokens_to_ids(token) for token in IMAGE_TOKENS]
+        self.image_ids = self.image_token_ids
+        self.image_use_col_tokens = image_use_col_tokens
+        self.use_single_crop_col_tokens = use_single_crop_col_tokens
+        self.use_single_crop_start_token = use_single_crop_start_token
+        self.video_use_col_tokens = video_use_col_tokens
+        self.use_frame_special_tokens = use_frame_special_tokens
+
+    def get_image_tokens(self, image_grid: np.ndarray):
+        resized_h, resized_w, height, width = image_grid
+        per_row = np.full(width, IMAGE_PATCH_TOKEN)
+        if self.image_use_col_tokens:
+            per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+        joint = [
+            [IM_START_TOKEN],
+            np.tile(per_row, [height]),
+            [IM_END_TOKEN],
+        ]
+        per_row = np.full(resized_w, IMAGE_PATCH_TOKEN)
+        use_single_crop_col_tokens = (
+            self.image_use_col_tokens if self.use_single_crop_col_tokens is None else self.use_single_crop_col_tokens
+        )
+        image_start_token = LOW_RES_IMAGE_START_TOKEN if self.use_single_crop_start_token else IM_START_TOKEN
+        if use_single_crop_col_tokens:
+            per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+        joint = [
+            [image_start_token],
+            np.tile(per_row, [resized_h]),
+            [IM_END_TOKEN],
+        ] + joint
+
+        return np.concatenate(joint)
+
+    def get_video_string(
+        self,
+        video_grid: np.ndarray,
+        timestamps: np.ndarray,
+    ):
+        if self.use_frame_special_tokens:
+            start_token_id = FRAME_START_TOKEN
+            end_token_id = FRAME_END_TOKEN
+        else:
+            start_token_id = IM_START_TOKEN
+            end_token_id = IM_END_TOKEN
+
+        num_frames, h, w = video_grid
+        video_string: str = ""
+        for frame_idx, frame_time in enumerate(timestamps):
+            # `per-frame-compact` time mode
+            prev_space = " " if frame_idx > 0 else ""
+            frame_prefix = prev_space + f"{frame_time:.1f} "  # explicit whitespace before/after image tokens
+
+            video_string += frame_prefix
+            per_row = np.full(w, IMAGE_PATCH_TOKEN)
+            if self.video_use_col_tokens:
+                per_row = np.concatenate([per_row, [IM_COL_TOKEN]], 0)
+            extra_tokens = np.tile(per_row, [h])
+            video_tokens = [
+                [start_token_id],
+                extra_tokens,
+                [end_token_id],
+            ]
+            video_string += "".join(np.concatenate(video_tokens, 0))
+
+        return video_string
+
+    def insert_bos(
+        self,
+        input_ids: np.ndarray,
+        attention_mask: np.ndarray,
+        bos_token_id: int,
+        pad_token_id: int,
+    ):
+        """
+        Args:
+            input_ids: [B, S] array with left padding
+            attention_mask: [B, S] array (0 for pad, 1 for valid)
+            bos_token_id: int
+            pad_token_id: int
+        Returns:
+            input_ids_out: [B, S] or [B, S+1] array with bos inserted if needed
+            attention_mask_out: same shape as input_ids_out
+        """
+
+        need_to_expand = len(input_ids.shape) == 1
+        if need_to_expand:
+            input_ids = input_ids[None, :]
+            attention_mask = attention_mask[None, :]
+
+        B, S = input_ids.shape
+
+        # Handle zero-length sequence
+        if S == 0:
+            new_input_ids = np.full((B, 1), bos_token_id, dtype=input_ids.dtype)
+            new_attention_mask = np.ones((B, 1), dtype=attention_mask.dtype)
+            if need_to_expand:
+                new_input_ids = new_input_ids[0]
+                new_attention_mask = new_attention_mask[0]
+            return new_input_ids, new_attention_mask
+
+        first_valid_index = (attention_mask == 1).argmax(axis=-1)  # [B]
+        bos_already_present = np.all(input_ids[np.arange(B), first_valid_index] == bos_token_id)
+
+        if bos_already_present:
+            if need_to_expand:
+                input_ids = input_ids[0]
+                attention_mask = attention_mask[0]
+            return input_ids, attention_mask
+        else:
+            new_input_ids = np.full((B, S + 1), pad_token_id, dtype=input_ids.dtype)
+            new_attention_mask = np.zeros((B, S + 1), dtype=attention_mask.dtype)
+
+            src_idx = np.tile(np.arange(S), (B, 1))  # [B, S]
+            valid_mask = src_idx >= first_valid_index[:, None]  # [B, S]
+            tgt_idx = src_idx + 1  # shit right
+            batch_idx = np.tile(np.arange(B)[:, None], (1, S))  # [B, S]
+
+            # flatten valid_positions
+            flat_vals = input_ids[valid_mask]
+            flat_batch = batch_idx[valid_mask]
+            flat_tgt = tgt_idx[valid_mask]
+
+            new_input_ids[flat_batch, flat_tgt] = flat_vals
+            new_attention_mask[flat_batch, flat_tgt] = 1
+
+            insert_pos = first_valid_index
+            new_input_ids[np.arange(B), insert_pos] = bos_token_id
+            new_attention_mask[np.arange(B), insert_pos] = 1
+
+            if need_to_expand:
+                new_input_ids = new_input_ids[0]
+                new_attention_mask = new_attention_mask[0]
+
+            return new_input_ids, new_attention_mask
+
+    @auto_docstring
+    def __call__(
+        self,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        images: ImageInput = None,
+        videos: VideoInput = None,
+        **kwargs: Unpack[Molmo2ProcessorKwargs],
+    ) -> BatchFeature:
+        r"""
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+              `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+            - **image_token_pooling** -- Indices of the patches in `image_grids` to pool for each token.
+              Returned when `images` is not `None`.
+            - **image_grids** -- Grids of images. Returned when `images` is not `None`.
+            - **image_num_crops** -- Number of crops for each image. Returned when `images` is not `None`.
+            - **pixel_values_videos** -- Pixel values of videos. Returned when `videos` is not `None`.
+            - **video_token_pooling** -- Indices of the patches in `video_grids` to pool for each token.
+              Returned when `videos` is not `None`.
+            - **video_grids** -- Grids of videos. Returned when `videos` is not `None`.
+        """
+
+        output_kwargs = self._merge_kwargs(
+            Molmo2ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if images is not None:
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+            image_grids = image_inputs["image_grids"]
+        else:
+            image_inputs = {}
+            image_grids = None
+
+        if videos is not None:
+            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
+            video_grids = videos_inputs["video_grids"]
+            # If user has not requested video metadata, pop it
+            if "return_metadata" not in kwargs:
+                video_metadata = videos_inputs.pop("video_metadata")
+            else:
+                video_metadata = videos_inputs["video_metadata"]
+        else:
+            videos_inputs = {}
+            video_grids = None
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()  # below lines change text in-place
+
+        if image_grids is not None:
+            index = 0
+            for i in range(len(text)):
+                num_images = text[i].count(self.image_placeholder_token)
+                image_grids_i = image_grids[index : index + num_images]
+                for image_grid in image_grids_i:
+                    image_tokens = self.get_image_tokens(image_grid)
+                    image_string = "".join(image_tokens)
+                    text[i] = text[i].replace(self.image_placeholder_token, image_string, 1)
+                index += num_images
+
+        if video_grids is not None:
+            index = 0
+            for i in range(len(text)):
+                num_videos = text[i].count(self.video_placeholder_token)
+                if num_videos > 1:
+                    raise ValueError("At most one video is supported per sample.")
+                video_grids_i = video_grids[index : index + num_videos]
+                metadata_i = video_metadata[index : index + num_videos]
+                for video_grid, metadata in zip(video_grids_i, metadata_i):
+                    video_string = self.get_video_string(
+                        video_grid,
+                        metadata.timestamps,
+                    )
+                    text[i] = text[i].replace(self.video_placeholder_token, video_string, 1)
+                index += num_videos
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+
+        input_ids = text_inputs["input_ids"]
+        attention_mask = text_inputs["attention_mask"]
+
+        input_ids = np.array(input_ids)
+        attention_mask = np.array(attention_mask)
+
+        bos = self.tokenizer.bos_token_id or self.tokenizer.eos_token_id
+        input_ids, attention_mask = self.insert_bos(input_ids, attention_mask, bos, self.tokenizer.pad_token_id)
+
+        if return_mm_token_type_ids:
+            text_inputs["token_type_ids"] = self.create_mm_token_type_ids(input_ids.tolist())
+
+        text_inputs["input_ids"] = input_ids.tolist()
+        text_inputs["attention_mask"] = attention_mask.tolist()
+
+        return BatchFeature(
+            data={**text_inputs, **image_inputs, **videos_inputs},
+            tensor_type=return_tensors,
+        )
+
+
+__all__ = ["Molmo2Processor"]
diff --git a/src/transformers/models/molmo2/video_processing_molmo2.py b/src/transformers/models/molmo2/video_processing_molmo2.py
new file mode 100644
index 000000000000..d96bf5f67d0f
--- /dev/null
+++ b/src/transformers/models/molmo2/video_processing_molmo2.py
@@ -0,0 +1,324 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Video processor class for Molmo2"""
+
+import numpy as np
+import torch
+import torchvision.transforms
+
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import normalize
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    PILImageResampling,
+    SizeDict,
+)
+from ...processing_utils import Unpack, VideosKwargs
+from ...utils import TensorType, auto_docstring, logging
+from ...video_processing_utils import BaseVideoProcessor
+from ...video_utils import VideoMetadata
+
+
+logger = logging.get_logger(__name__)
+
+
+def resize_image(
+    image: np.ndarray,
+    desired_output_size: list[int],
+    resample: PILImageResampling,
+) -> np.ndarray:
+    """Resize an image or video and rescale to [0, 1] float32."""
+    if len(image.shape) == 3:
+        is_video = False
+        image = torch.permute(torch.from_numpy(image), [2, 0, 1])
+    else:
+        is_video = True
+        image = torch.permute(torch.from_numpy(image), [0, 3, 1, 2])
+
+    resized = torchvision.transforms.Resize(desired_output_size, resample, antialias=False)(image)
+    resized = torch.clip(resized, 0, 255).to(torch.uint8)
+    resized = resized.to(torch.float32) / 255.0
+
+    if is_video:
+        resized = torch.permute(resized, [0, 2, 3, 1]).numpy()
+    else:
+        resized = torch.permute(resized, [1, 2, 0]).numpy()
+
+    return resized
+
+
+def build_resized_image(
+    image: np.ndarray,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    resized = resize_image(
+        image,
+        base_image_input_size,
+        resample,
+    )
+    resized = normalize(resized, image_mean, image_std, input_data_format=ChannelDimension.LAST)
+    if len(resized.shape) == 3:
+        resized = np.expand_dims(resized, 0)
+    crop_patch_w = base_image_input_size[1] // image_patch_size
+    crop_patch_h = base_image_input_size[0] // image_patch_size
+    resize_idx = np.arange(crop_patch_w * crop_patch_h).reshape([crop_patch_h, crop_patch_w])
+    return resized, resize_idx
+
+
+def batch_pixels_to_patches(array: np.ndarray, patch_size: int) -> np.ndarray:
+    """Reshape images of [n_images, h, w, 3] -> [n_images, n_patches, pixels_per_patch]"""
+    if len(array.shape) == 3:
+        n_crops, h, w = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size])
+        array = np.transpose(array, [0, 1, 3, 2, 4])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size])
+        return array
+    else:
+        n_crops, h, w, c = array.shape
+        h_patches = h // patch_size
+        w_patches = w // patch_size
+        array = np.reshape(array, [n_crops, h_patches, patch_size, w_patches, patch_size, c])
+        array = np.transpose(array, [0, 1, 3, 2, 4, 5])
+        array = np.reshape(array, [n_crops, h_patches * w_patches, patch_size * patch_size * c])
+        return array
+
+
+def arange_for_pooling(
+    idx_arr: np.ndarray,
+    pool_h: int,
+    pool_w: int,
+) -> np.ndarray:
+    h_pad = pool_h * ((idx_arr.shape[0] + pool_h - 1) // pool_h) - idx_arr.shape[0]
+    w_pad = pool_w * ((idx_arr.shape[1] + pool_w - 1) // pool_w) - idx_arr.shape[1]
+    idx_arr = np.pad(
+        idx_arr, [[h_pad // 2, (h_pad + 1) // 2], [w_pad // 2, (w_pad + 1) // 2]], mode="constant", constant_values=-1
+    )
+    h, w = idx_arr.shape[0] // pool_h, idx_arr.shape[1] // pool_w
+    return idx_arr.reshape(h, pool_h, w, pool_w).transpose(0, 2, 1, 3).reshape(h, w, pool_h * pool_w)
+
+
+def image_to_patches_and_grids(
+    image: np.ndarray,
+    base_image_input_size: list[int],
+    resample: PILImageResampling,
+    image_mean: list[float],
+    image_std: list[float],
+    image_patch_size: int,
+    image_pooling_w: int,
+    image_pooling_h: int,
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+    """
+    :return image_grids, the shape of each image after pooling
+    :return crops, the image crops to processes with the ViT
+    :return pooled_patch_idx, for each patch_id tokens in `image_tokens`, the indices of the
+                                patches in `crops` to pool for that token, masked with -1
+    """
+    if isinstance(base_image_input_size, int):
+        base_image_input_size = (base_image_input_size, base_image_input_size)
+
+    pooling_w = image_pooling_w
+    pooling_h = image_pooling_h
+
+    resized, resize_idx = build_resized_image(
+        image,
+        base_image_input_size,
+        resample,
+        image_mean,
+        image_std,
+        image_patch_size,
+    )
+    pooling_idx = arange_for_pooling(resize_idx, pooling_h, pooling_w)
+    h, w = pooling_idx.shape[:2]
+    pooling_idx = pooling_idx.reshape([-1, pooling_h * pooling_w])
+    image_grid = [h, w]
+    return (
+        image_grid,
+        batch_pixels_to_patches(resized, image_patch_size),
+        pooling_idx,
+    )
+
+
+class Molmo2VideoProcessorKwargs(VideosKwargs, total=False):
+    """
+    patch_size (`int`, *optional*):
+        Side length in pixels of each ViT patch for video frames.
+    pooling_size (`list[int]`, *optional*):
+        `[pool_h, pool_w]` pooling window applied to video patch features.
+    max_fps (`int`, *optional*):
+        Maximum sampling rate in frames per second for short videos.
+    """
+
+    patch_size: int | None
+    pooling_size: list[int] | None
+    max_fps: int | None
+
+
+@auto_docstring
+class Molmo2VideoProcessor(BaseVideoProcessor):
+    resample = PILImageResampling.BILINEAR
+    size = {"height": 378, "width": 378}
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    do_resize = True
+    do_rescale = True
+    do_normalize = True
+    do_convert_rgb = True
+    patch_size = 14
+    pooling_size = [3, 3]
+    num_frames = 64
+    do_sample_frames = True
+    max_fps = 2
+    valid_kwargs = Molmo2VideoProcessorKwargs
+    model_input_names = ["pixel_values_videos", "video_token_pooling", "video_grids"]
+
+    def __init__(self, **kwargs: Unpack[Molmo2VideoProcessorKwargs]):
+        super().__init__(**kwargs)
+        if self.size is not None and (self.size.get("height", None) is None or self.size.get("width", None) is None):
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+    def _standardize_kwargs(
+        self,
+        size: SizeDict | None = None,
+        **kwargs,
+    ) -> dict:
+        if size is not None and ("height" not in size or "width" not in size):
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+        return super()._standardize_kwargs(size=size, **kwargs)
+
+    def sample_frames(
+        self,
+        metadata: VideoMetadata,
+        num_frames: int | None = None,
+        max_fps: int | None = None,
+        **kwargs,
+    ) -> np.ndarray:
+        """
+        Uniform sampling that always includes the last frame. When `max_fps` is set,
+        samples at that rate if the video is short enough; otherwise falls back to
+        uniform sampling of `num_frames` frames.
+
+        Args:
+            metadata (`VideoMetadata`):
+                Metadata of the video containing information about total duration, fps and total number of frames.
+            num_frames (`int`, *optional*):
+                Maximum number of frames to sample. Defaults to `self.num_frames`.
+            max_fps (`int`, *optional*):
+                Maximum frames per second to sample. Defaults to `self.max_fps`.
+        """
+        num_frames = num_frames if num_frames is not None else self.num_frames
+        max_fps = max_fps if max_fps is not None else self.max_fps
+        total_num_frames = metadata.total_num_frames
+
+        if total_num_frames <= 2:
+            return np.arange(total_num_frames).astype(int)
+
+        if max_fps is not None and metadata.fps is not None:
+            duration = total_num_frames / metadata.fps
+            if duration <= (num_frames - 1) / max_fps:
+                # Short video: sample at max_fps and include last frame
+                float_indices = np.arange(0.0, stop=total_num_frames - 1, step=float(metadata.fps / max_fps))
+                if np.round(float_indices[-1]) != total_num_frames - 1:
+                    float_indices = np.concatenate([float_indices, [total_num_frames - 1]], axis=0)
+                indices = np.round(float_indices).astype(int)
+                if len(indices) > num_frames:
+                    raise ValueError(f"Sampled {len(indices)} frames but max is {num_frames}.")
+                return indices
+
+        # Uniform fallback: evenly spaced including last frame
+        indices = np.linspace(
+            0,
+            total_num_frames - 1,
+            num=min(num_frames, total_num_frames),
+            endpoint=True,
+        ).astype(int)
+        return indices
+
+    def _preprocess(
+        self,
+        videos: list["torch.Tensor"],
+        size: SizeDict | None = None,
+        resample: PILImageResampling | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        patch_size: int | None = None,
+        pooling_size: list[int] | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if size.height is None or size.width is None:
+            raise ValueError("size must contain 'height' and 'width' keys.")
+
+        base_image_input_size = [size.height, size.width]
+        image_pooling_h, image_pooling_w = pooling_size
+
+        batch_grids = []
+        batch_crops = []
+        batch_pooled_patches_idx = []
+
+        for video in videos:
+            # Convert from torch (T, C, H, W) to numpy (T, H, W, C)
+            if isinstance(video, torch.Tensor):
+                video = video.permute(0, 2, 3, 1).numpy()
+
+            all_crops = []
+            pooled_patches_idx = []
+
+            for frame in video:
+                image_grid, crops, pooled_idx = image_to_patches_and_grids(
+                    frame,
+                    base_image_input_size,
+                    resample,
+                    image_mean,
+                    image_std,
+                    patch_size,
+                    image_pooling_w,
+                    image_pooling_h,
+                )
+                offset = sum(np.prod(x.shape[:2]) for x in all_crops)
+                pooled_idx_with_offset = np.where(pooled_idx >= 0, pooled_idx + offset, pooled_idx)
+                pooled_patches_idx.append(pooled_idx_with_offset)
+                all_crops.append(crops)
+
+            video_grid = np.array([len(video), image_grid[0], image_grid[1]])
+            all_crops = np.concatenate(all_crops, 0)
+            pooled_patches_idx = np.concatenate(pooled_patches_idx, 0)
+
+            batch_grids.append(video_grid)
+            batch_crops.append(all_crops)
+            batch_pooled_patches_idx.append(pooled_patches_idx)
+
+        video_grids = np.stack(batch_grids, 0)
+        pixel_values_videos = np.concatenate(batch_crops, 0)
+        video_token_pooling = np.concatenate(batch_pooled_patches_idx, 0)
+
+        data = {
+            "pixel_values_videos": pixel_values_videos,
+            "video_token_pooling": video_token_pooling,
+            "video_grids": video_grids,
+        }
+
+        return BatchFeature(data, tensor_type=return_tensors)
+
+
+__all__ = ["Molmo2VideoProcessor"]
diff --git a/src/transformers/models/moondream3/configuration_moondream3.py b/src/transformers/models/moondream3/configuration_moondream3.py
new file mode 100644
index 000000000000..6383333a960b
--- /dev/null
+++ b/src/transformers/models/moondream3/configuration_moondream3.py
@@ -0,0 +1,324 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.modeling_rope_utils import RopeParameters, rope_config_validation, standardize_rope_params
+
+
+class Moondream3TextConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Moondream3TextModel`]. It is used to instantiate a
+    Moondream3 model according to the specified arguments, defining the model architecture.
+
+    Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PreTrainedConfig`] for more information.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 51200):
+            Vocabulary size of the Moondream3 model.
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 8192):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 24):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 32):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        num_experts (`int`, *optional*, defaults to 64):
+            Number of experts for MoE layers.
+        num_experts_per_tok (`int`, *optional*, defaults to 8):
+            Number of selected experts per token.
+        moe_intermediate_size (`int`, *optional*, defaults to 1024):
+            Intermediate size of the routed expert.
+        moe_start_layer (`int`, *optional*, defaults to 4):
+            The layer index where MoE layers start.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            The id of the beginning-of-sequence token.
+        eos_token_id (`int`, *optional*, defaults to 0):
+            The id of the end-of-sequence token.
+        coord_token_id (`int`, *optional*, defaults to 5):
+            The id of the coordinate token used for region detection.
+        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function.
+        moe_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function used inside MoE experts.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-5):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether the model's input and output word embeddings should be tied.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers.
+        rope_parameters (`dict`, *optional*):
+            The dictionary containing parameters for RoPE (Rotary Positional Embeddings), such as `rope_theta` and `rope_type`.
+        head_dim (`int`, *optional*):
+            The dimension of the head. If not specified, will default to `hidden_size // num_attention_heads`.
+    """
+
+    model_type = "moondream3_text"
+    base_config_key = "text_config"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size: int = 51200,
+        hidden_size: int = 2048,
+        intermediate_size: int = 8192,
+        num_hidden_layers: int = 24,
+        num_attention_heads: int = 32,
+        num_key_value_heads: int = 32,
+        max_position_embeddings: int = 4096,
+        num_experts: int = 64,
+        num_experts_per_tok: int = 8,
+        moe_intermediate_size: int = 1024,
+        moe_start_layer: int = 4,
+        bos_token_id: int = 0,
+        eos_token_id: int = 0,
+        coord_token_id: int = 5,
+        hidden_act: str = "gelu_pytorch_tanh",
+        moe_hidden_act: str = "gelu",
+        initializer_range: float = 0.02,
+        rms_norm_eps: float = 1e-5,
+        use_cache: bool = True,
+        tie_word_embeddings: bool = False,
+        attention_bias: bool = True,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        head_dim: int | None = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.head_dim = head_dim or hidden_size // num_attention_heads
+        self.bos_token_id = bos_token_id
+        self.coord_token_id = coord_token_id
+        self.eos_token_id = eos_token_id
+
+        # MoE parameters (merged from TextMoeConfig)
+        self.num_experts = num_experts
+        self.num_experts_per_tok = num_experts_per_tok
+        self.moe_intermediate_size = moe_intermediate_size
+        self.moe_start_layer = moe_start_layer
+        self.moe_hidden_act = moe_hidden_act
+
+        # Try to set `rope_scaling` if available, otherwise use `rope_parameters`
+        rope_scaling = kwargs.pop("rope_scaling", None)
+        self.rope_parameters = rope_scaling or rope_parameters
+
+        # Validate the correctness of rotary position embeddings parameters
+        rope_theta = kwargs.get("rope_theta", 1500000.0)
+        standardize_rope_params(self, rope_theta=rope_theta)
+        rope_config_validation(self)
+
+        # HF compatibility attributes
+        self.output_router_logits = False
+        self.output_attentions = False
+        self.output_hidden_states = False
+        self.attention_dropout = 0.0
+
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
+
+
+class Moondream3VisionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of the Moondream3 vision encoder.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 1152):
+            Dimension of the encoder's hidden states.
+        intermediate_size (`int`, *optional*, defaults to 4304):
+            Dimension of the encoder's MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 27):
+            Number of hidden layers in the vision encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads in the vision encoder.
+        patch_size (`int`, *optional*, defaults to 14):
+            The size of each patch in the vision encoder.
+        in_channels (`int`, *optional*, defaults to 3):
+            Number of input channels.
+        proj_out_dim (`int`, *optional*, defaults to 2048):
+            Output dimension of the projection layer.
+        crop_size (`int`, *optional*, defaults to 378):
+            Size of image crops.
+        max_crops (`int`, *optional*, defaults to 12):
+            Maximum number of crops.
+        overlap_margin (`int`, *optional*, defaults to 4):
+            Overlap margin for crops.
+        proj_inner_dim (`int`, *optional*, defaults to 8192):
+            Inner dimension of the projection MLP.
+        prefix_len (`int`, *optional*, defaults to 730):
+            The number of tokens used to represent the visual input (prefix length).
+        hidden_act (`str`, *optional*, defaults to `"gelu_pytorch_tanh"`):
+            The non-linear activation function.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer.
+        attention_bias (`bool`, *optional*, defaults to `True`):
+            Whether to use a bias in the query, key, value and output projection layers.
+    """
+
+    model_type = "moondream3_vision"
+    base_config_key = "vision_config"
+
+    def __init__(
+        self,
+        hidden_size: int = 1152,
+        intermediate_size: int = 4304,
+        num_hidden_layers: int = 27,
+        num_attention_heads: int = 16,
+        patch_size: int = 14,
+        in_channels: int = 3,
+        proj_out_dim: int = 2048,
+        crop_size: int = 378,
+        max_crops: int = 12,
+        overlap_margin: int = 4,
+        proj_inner_dim: int = 8192,
+        prefix_len: int = 730,
+        hidden_act: str = "gelu_pytorch_tanh",
+        initializer_range: float = 0.02,
+        attention_bias: bool = True,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.patch_size = patch_size
+        self.in_channels = in_channels
+        self.proj_out_dim = proj_out_dim
+        self.crop_size = crop_size
+        self.max_crops = max_crops
+        self.prefix_len = prefix_len
+        self.overlap_margin = overlap_margin
+        self.proj_inner_dim = proj_inner_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.attention_dropout = 0.0
+        self.attention_bias = attention_bias
+
+        super().__init__(**kwargs)
+
+
+class Moondream3RegionConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of the Moondream3 region encoder for object detection and grounding.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 2048):
+            Dimension of the hidden representations for region features.
+        coord_feat_dim (`int`, *optional*, defaults to 256):
+            Dimension of coordinate feature embeddings.
+        coord_out_dim (`int`, *optional*, defaults to 1024):
+            Output dimension for coordinate features.
+        size_feat_dim (`int`, *optional*, defaults to 512):
+            Dimension of size feature embeddings.
+        size_out_dim (`int`, *optional*, defaults to 2048):
+            Output dimension for size features.
+    """
+
+    model_type = "moondream3_region"
+    base_config_key = "region_config"
+
+    def __init__(
+        self,
+        hidden_size: int = 2048,
+        coord_feat_dim: int = 256,
+        coord_out_dim: int = 1024,
+        size_feat_dim: int = 512,
+        size_out_dim: int = 2048,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.hidden_size = hidden_size
+        self.coord_feat_dim = coord_feat_dim
+        self.coord_out_dim = coord_out_dim
+        self.size_feat_dim = size_feat_dim
+        self.size_out_dim = size_out_dim
+
+
+class Moondream3Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`Moondream3Model`].
+
+    Args:
+        text_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Moondream3TextConfig`):
+            The config object or dictionary of the text backbone.
+        vision_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Moondream3VisionConfig`):
+            The config object or dictionary of the vision backbone.
+        region_config (`Union[PreTrainedConfig, dict]`, *optional*, defaults to `Moondream3RegionConfig`):
+            The config object or dictionary of the region backbone for object detection and grounding.
+        bos_token_id (`int`, *optional*, defaults to 0):
+            The id of the beginning-of-sequence token.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie the word embeddings.
+    """
+
+    model_type = "moondream3"
+    sub_configs = {
+        "vision_config": Moondream3VisionConfig,
+        "text_config": Moondream3TextConfig,
+        "region_config": Moondream3RegionConfig,
+    }
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        text_config=None,
+        vision_config=None,
+        region_config=None,
+        bos_token_id=0,
+        tie_word_embeddings: bool = False,
+        **kwargs,
+    ):
+        if isinstance(vision_config, dict):
+            self.vision_config = self.sub_configs["vision_config"](**vision_config)
+        elif vision_config is None:
+            self.vision_config = self.sub_configs["vision_config"]()
+
+        if isinstance(text_config, dict):
+            self.text_config = self.sub_configs["text_config"](**text_config)
+        elif text_config is None:
+            self.text_config = self.sub_configs["text_config"]()
+
+        if isinstance(region_config, dict):
+            self.region_config = self.sub_configs["region_config"](**region_config)
+        elif region_config is None:
+            self.region_config = self.sub_configs["region_config"]()
+
+        super().__init__(**kwargs, tie_word_embeddings=tie_word_embeddings)
+
+
+__all__ = [
+    "Moondream3Config",
+    "Moondream3TextConfig",
+    "Moondream3VisionConfig",
+    "Moondream3RegionConfig",
+]
diff --git a/src/transformers/models/moondream3/convert_moondream_weights_to_hf.py b/src/transformers/models/moondream3/convert_moondream_weights_to_hf.py
new file mode 100644
index 000000000000..1a11fefa19f3
--- /dev/null
+++ b/src/transformers/models/moondream3/convert_moondream_weights_to_hf.py
@@ -0,0 +1,303 @@
+#!/usr/bin/env python3
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import argparse
+import json
+import re
+from pathlib import Path
+
+from safetensors.torch import load_file, save_file
+
+
+# Key mapping from original Moondream to HF Moondream3
+OLD_KEY_TO_NEW_KEY_MAPPING = [
+    # Text model
+    (r"model\.text\.wte", "model.text_model.embed_tokens.weight"),
+    (r"model\.text\.post_ln\.(weight|bias)", r"model.text_model.norm.\1"),
+    (r"model\.text\.lm_head\.(weight|bias)", r"lm_head.\1"),
+    (
+        r"model\.text\.blocks\.(\d+)\.attn\.qkv\.(weight|bias)",
+        r"model.text_model.layers.\1.self_attn.qkv.\2",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.attn\.proj\.(weight|bias)",
+        r"model.text_model.layers.\1.self_attn.o_proj.\2",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.attn\.tau\.wq",
+        r"model.text_model.layers.\1.self_attn.tau_wq.weight",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.attn\.tau\.wv",
+        r"model.text_model.layers.\1.self_attn.tau_wv.weight",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.attn\.tau\.alpha",
+        r"model.text_model.layers.\1.self_attn.tau_alpha",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.ln\.(weight|bias)",
+        r"model.text_model.layers.\1.input_layernorm.\2",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.mlp\.fc1\.(weight|bias)",
+        r"model.text_model.layers.\1.mlp.up_proj.\2",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.mlp\.fc2\.(weight|bias)",
+        r"model.text_model.layers.\1.mlp.down_proj.\2",
+    ),
+    (
+        r"model\.text\.blocks\.(\d+)\.mlp\.router\.(weight|bias)",
+        r"model.text_model.layers.\1.mlp.gate.\2",
+    ),
+    # Vision model
+    (
+        r"model\.vision\.patch_emb\.(weight|bias)",
+        r"model.vision_model.embeddings.projection.\1",
+    ),
+    (r"model\.vision\.pos_emb", "model.vision_model.embeddings.position_embeddings"),
+    (r"model\.vision\.post_ln\.(weight|bias)", r"model.vision_model.post_layernorm.\1"),
+    (
+        r"model\.vision\.blocks\.(\d+)\.attn\.qkv\.(weight|bias)",
+        r"model.vision_model.layers.\1.self_attn.qkv.\2",
+    ),
+    (
+        r"model\.vision\.blocks\.(\d+)\.attn\.proj\.(weight|bias)",
+        r"model.vision_model.layers.\1.self_attn.o_proj.\2",
+    ),
+    (
+        r"model\.vision\.blocks\.(\d+)\.ln1\.(weight|bias)",
+        r"model.vision_model.layers.\1.input_layernorm.\2",
+    ),
+    (
+        r"model\.vision\.blocks\.(\d+)\.ln2\.(weight|bias)",
+        r"model.vision_model.layers.\1.post_attention_layernorm.\2",
+    ),
+    (
+        r"model\.vision\.blocks\.(\d+)\.mlp\.fc1\.(weight|bias)",
+        r"model.vision_model.layers.\1.mlp.up_proj.\2",
+    ),
+    (
+        r"model\.vision\.blocks\.(\d+)\.mlp\.fc2\.(weight|bias)",
+        r"model.vision_model.layers.\1.mlp.down_proj.\2",
+    ),
+    # Vision projection
+    (
+        r"model\.vision\.proj_mlp\.fc1\.(weight|bias)",
+        r"model.vision_model.vision_projection.up_proj.\1",
+    ),
+    (
+        r"model\.vision\.proj_mlp\.fc2\.(weight|bias)",
+        r"model.vision_model.vision_projection.down_proj.\1",
+    ),
+    # Region model
+    (
+        r"model\.region\.coord_encoder\.(weight|bias)",
+        r"model.region_encoder.coord_encoder.\1",
+    ),
+    (
+        r"model\.region\.coord_decoder\.(weight|bias)",
+        r"model.region_decoder.coord_decoder.\1",
+    ),
+    (
+        r"model\.region\.size_encoder\.(weight|bias)",
+        r"model.region_encoder.size_encoder.\1",
+    ),
+    (
+        r"model\.region\.size_decoder\.(weight|bias)",
+        r"model.region_decoder.size_decoder.\1",
+    ),
+    (r"model\.region\.coord_features", "model.region_encoder.coord_freq"),
+    (r"model\.region\.size_features", "model.region_encoder.size_freq"),
+]
+
+
+def rename_key(old_key: str) -> str:
+    """Convert original key name to HF key name."""
+    for pattern, new_key in OLD_KEY_TO_NEW_KEY_MAPPING:
+        if re.match(pattern, old_key):
+            return re.sub(pattern, new_key, old_key)
+    return old_key
+
+
+def convert_state_dict(original_state_dict: dict) -> dict:
+    """Convert original state dict to HF format."""
+    converted_state_dict = {}
+    converted_keys = []
+    for old_key, tensor in original_state_dict.items():
+        new_key = rename_key(old_key)
+
+        # Handle QKV weight splitting for attention
+        if "attn.qkv.weight" in old_key or "attn.qkv.bias" in old_key:
+            # Split QKV into separate Q, K, V matrices
+            layer_match = re.search(r"blocks\.(\d+)", old_key)
+            if layer_match:
+                layer_idx = int(layer_match.group(1))
+
+                # Determine if this is text or vision model
+                if "model.text.blocks" in old_key:
+                    n_heads = 32
+                    n_kv_heads = 32
+                    head_dim = 64  # 2048 / 32
+                    base_key = f"model.text_model.layers.{layer_idx}.self_attn"
+                else:  # vision
+                    n_heads = 16
+                    n_kv_heads = 16
+                    head_dim = 72  # 1152 / 16
+                    base_key = f"model.vision_model.layers.{layer_idx}.self_attn"
+
+                # Split tensor
+                q_dim = n_heads * head_dim
+                kv_dim = n_kv_heads * head_dim
+
+                if "weight" in old_key:
+                    q_weight = tensor[:q_dim]
+                    k_weight = tensor[q_dim : q_dim + kv_dim]
+                    v_weight = tensor[q_dim + kv_dim :]
+
+                    converted_state_dict[f"{base_key}.q_proj.weight"] = q_weight
+                    converted_state_dict[f"{base_key}.k_proj.weight"] = k_weight
+                    converted_state_dict[f"{base_key}.v_proj.weight"] = v_weight
+                    converted_keys.append(old_key)
+                else:  # bias
+                    q_bias = tensor[:q_dim]
+                    k_bias = tensor[q_dim : q_dim + kv_dim]
+                    v_bias = tensor[q_dim + kv_dim :]
+
+                    converted_state_dict[f"{base_key}.q_proj.bias"] = q_bias
+                    converted_state_dict[f"{base_key}.k_proj.bias"] = k_bias
+                    converted_state_dict[f"{base_key}.v_proj.bias"] = v_bias
+                    converted_keys.append(old_key)
+        # Handle MoE expert weight splitting
+        elif ("mlp.fc1.weight" in old_key or "mlp.fc2.weight" in old_key) and "proj_mlp" not in old_key:
+            layer_match = re.search(r"blocks\.(\d+)", old_key)
+            if layer_match:
+                layer_idx = int(layer_match.group(1))
+                # Only process MoE layers (4+ in this model)
+                if layer_idx >= 4 and "model.text." in old_key:
+                    n_experts = 64  # From config
+
+                    if "fc1.weight" in old_key:
+                        # Shape: (n_experts, 2 * d_ffn, d_model) → split into individual experts
+                        for expert_idx in range(n_experts):
+                            expert_weight = tensor[expert_idx]  # Shape: (2 * d_ffn, d_model)
+                            # For GeGLU, split into gate and up projections
+                            up_weight = expert_weight[: expert_weight.shape[0] // 2]  # First half
+                            gate_weight = expert_weight[expert_weight.shape[0] // 2 :]  # Second half
+
+                            converted_state_dict[
+                                f"model.text_model.layers.{layer_idx}.mlp.experts.{expert_idx}.gate_proj.weight"
+                            ] = gate_weight
+                            converted_state_dict[
+                                f"model.text_model.layers.{layer_idx}.mlp.experts.{expert_idx}.up_proj.weight"
+                            ] = up_weight
+                    elif "fc2.weight" in old_key:
+                        # Shape: (n_experts, d_model, d_ffn) → split into individual experts
+                        for expert_idx in range(n_experts):
+                            expert_weight = tensor[expert_idx]  # Shape: (d_model, d_ffn)
+                            converted_state_dict[
+                                f"model.text_model.layers.{layer_idx}.mlp.experts.{expert_idx}.down_proj.weight"
+                            ] = expert_weight
+                else:
+                    # Dense MLP for layers < 4
+                    converted_state_dict[new_key] = tensor
+        else:
+            converted_state_dict[new_key] = tensor
+    return converted_state_dict
+
+
+def convert_moondream_weights_to_hf(
+    original_model_path: str,
+    output_file: str,
+):
+    """Convert Moondream weights to HuggingFace format."""
+
+    # Load original state dict
+    print(f"Loading original model from {original_model_path}")
+
+    # Find safetensors files
+    model_path = Path(original_model_path)
+    if model_path.is_file() and model_path.suffix == ".safetensors":
+        # Single file
+        original_state_dict = load_file(str(model_path))
+    elif model_path.is_dir():
+        # Directory - look for index file or single model file
+        index_path = model_path / "model.safetensors.index.json"
+        single_file_path = model_path / "model.safetensors"
+
+        if index_path.exists():
+            with open(index_path) as f:
+                index = json.load(f)
+
+            original_state_dict = {}
+            for filename in set(index["weight_map"].values()):
+                file_path = model_path / filename
+                if file_path.exists():
+                    state_dict = load_file(str(file_path))
+                    for k, v in state_dict.items():
+                        original_state_dict[k] = v
+                else:
+                    print(f"Warning: {file_path} not found")
+        elif single_file_path.exists():
+            original_state_dict = load_file(str(single_file_path))
+        else:
+            raise FileNotFoundError(f"Could not find model files in {original_model_path}")
+    else:
+        raise FileNotFoundError(f"Could not find model files in {original_model_path}")
+
+    print(f"Loaded {len(original_state_dict)} tensors")
+
+    # Convert state dict
+    print("Converting state dict...")
+    converted_state_dict = convert_state_dict(original_state_dict)
+
+    print(f"Converted {len(converted_state_dict)} tensors")
+
+    # Save converted weights
+    output_path = Path(output_file)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    print(f"Saving converted weights to {output_path}")
+    save_file(converted_state_dict, str(output_path))
+
+    print(f"Converted weights saved to {output_path}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert Moondream weights to HuggingFace format")
+    parser.add_argument(
+        "--input_path",
+        type=str,
+        required=True,
+        help="Path to original Moondream model directory or safetensors file",
+    )
+    parser.add_argument(
+        "--output_file",
+        type=str,
+        required=True,
+        help="Path to save converted HuggingFace safetensors file",
+    )
+
+    args = parser.parse_args()
+
+    convert_moondream_weights_to_hf(
+        args.input_path,
+        args.output_file,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/moondream3/image_processing_moondream3.py b/src/transformers/models/moondream3/image_processing_moondream3.py
new file mode 100644
index 000000000000..4db788c8829b
--- /dev/null
+++ b/src/transformers/models/moondream3/image_processing_moondream3.py
@@ -0,0 +1,256 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Image processor class for Moondream3."""
+
+import math
+
+import numpy as np
+import PIL
+import torch
+
+from transformers.image_processing_utils import (
+    BaseImageProcessor,
+    BatchFeature,
+)
+from transformers.image_utils import (
+    ImageInput,
+    make_flat_list_of_images,
+    valid_images,
+    validate_kwargs,
+)
+from transformers.processing_utils import ImagesKwargs
+from transformers.utils import TensorType, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Moondream3ImageProcessorKwargs(ImagesKwargs, total=False):
+    """
+    patch_size (`Union[dict[str, int], int]` *optional*, defaults to `{"height": 16, "width": 16}`):
+        Size of the patches in the model, used to calculate the output image size. Can be overridden by `patch_size` in the `preprocess` method.
+    """
+
+    pass
+
+
+def select_tiling(height: int, width: int, crop_size: int, max_crops: int) -> tuple[int, int]:
+    """
+    Determine the optimal number of tiles to cover an image with overlapping crops.
+    """
+    if height <= crop_size or width <= crop_size:
+        return (1, 1)
+
+    # Minimum required tiles in each dimension
+    min_h = math.ceil(height / crop_size)
+    min_w = math.ceil(width / crop_size)
+
+    # If minimum required tiles exceed max_crops, return proportional distribution
+    if min_h * min_w > max_crops:
+        ratio = math.sqrt(max_crops / (min_h * min_w))
+        return (max(1, math.floor(min_h * ratio)), max(1, math.floor(min_w * ratio)))
+
+    # Perfect aspect-ratio tiles that satisfy max_crops
+    h_tiles = math.floor(math.sqrt(max_crops * height / width))
+    w_tiles = math.floor(math.sqrt(max_crops * width / height))
+
+    # Ensure we meet minimum tile requirements
+    h_tiles = max(h_tiles, min_h)
+    w_tiles = max(w_tiles, min_w)
+
+    # If we exceeded max_crops, scale down the larger dimension
+    if h_tiles * w_tiles > max_crops:
+        if w_tiles > h_tiles:
+            w_tiles = math.floor(max_crops / h_tiles)
+        else:
+            h_tiles = math.floor(max_crops / w_tiles)
+
+    return (max(1, h_tiles), max(1, w_tiles))
+
+
+def overlap_crop_image(
+    image: np.ndarray,
+    overlap_margin: int,
+    max_crops: int,
+    base_size: tuple[int, int] = (378, 378),
+    patch_size: int = 14,
+):
+    """
+    Process an image using an overlap-and-resize cropping strategy with margin handling.
+
+    This function takes an input image and creates multiple overlapping crops with
+    consistent margins. It produces:
+    1. A single global crop resized to base_size
+    2. Multiple overlapping local crops that maintain high resolution details
+    3. A patch ordering matrix that tracks correspondence between crops
+
+    The overlap strategy ensures:
+    - Smooth transitions between adjacent crops
+    - No loss of information at crop boundaries
+    - Proper handling of features that cross crop boundaries
+    - Consistent patch indexing across the full image
+
+    Args:
+        image (np.ndarray): Input image as numpy array with shape (H,W,C)
+        base_size (tuple[int,int]): Target size for crops, default (378,378)
+        patch_size (int): Size of patches in pixels, default 14
+        overlap_margin (int): Margin size in patch units, default 4
+        max_crops (int): Maximum number of crops allowed, default 12
+
+    Returns:
+        OverlapCropOutput: Dictionary containing:
+            - crops: A numpy array containing the global crop of the full image (index 0)
+                followed by the overlapping cropped regions (indices 1+)
+            - tiling: Tuple of (height,width) tile counts
+    """
+    original_h, original_w = image.shape[:2]
+
+    # Convert margin from patch units to pixels
+    margin_pixels = patch_size * overlap_margin
+    total_margin_pixels = margin_pixels * 2  # Both sides
+
+    # Calculate crop parameters
+    crop_patches = base_size[0] // patch_size  # patches per crop dimension
+    crop_window_patches = crop_patches - (2 * overlap_margin)  # usable patches
+    crop_window_size = crop_window_patches * patch_size  # usable size in pixels
+
+    # Determine tiling
+    tiling = select_tiling(
+        original_h - total_margin_pixels,
+        original_w - total_margin_pixels,
+        crop_window_size,
+        max_crops,
+    )
+
+    # Pre-allocate crops.
+    n_crops = tiling[0] * tiling[1] + 1  # 1 = global crop
+    crops = np.zeros((n_crops, base_size[0], base_size[1], image.shape[2]), dtype=np.uint8)
+
+    # Resize image to fit tiling
+    target_size = (
+        tiling[0] * crop_window_size + total_margin_pixels,
+        tiling[1] * crop_window_size + total_margin_pixels,
+    )
+
+    pil_img = PIL.Image.fromarray(image)
+    resized = pil_img.resize(
+        (int(target_size[1]), int(target_size[0])),
+        resample=PIL.Image.Resampling.LANCZOS,
+    )
+    image = np.asarray(resized)
+
+    # Create global crop
+    global_pil = pil_img.resize((int(base_size[1]), int(base_size[0])), resample=PIL.Image.Resampling.LANCZOS)
+    crops[0] = np.asarray(global_pil)
+
+    for i in range(tiling[0]):
+        for j in range(tiling[1]):
+            # Calculate crop coordinates
+            y0 = i * crop_window_size
+            x0 = j * crop_window_size
+
+            # Extract crop with padding if needed
+            y_end = min(y0 + base_size[0], image.shape[0])
+            x_end = min(x0 + base_size[1], image.shape[1])
+
+            crop_region = image[y0:y_end, x0:x_end]
+            crops[1 + i * tiling[1] + j, : crop_region.shape[0], : crop_region.shape[1]] = crop_region
+
+    return {"crops": crops, "tiling": tiling}
+
+
+def prepare_crops(image, max_crops=12, overlap_margin=4):
+    if isinstance(image, PIL.Image.Image):
+        np_image = np.array(image.convert("RGB"))
+    elif isinstance(image, torch.Tensor):
+        np_image = image.cpu().detach().numpy()
+    else:
+        np_image = image
+    overlap_crops = overlap_crop_image(np_image, max_crops=max_crops, overlap_margin=overlap_margin)
+    all_crops = overlap_crops["crops"]
+    all_crops = np.transpose(all_crops, (0, 3, 1, 2))
+    all_crops = all_crops = (
+        torch.from_numpy(all_crops).to(device="cpu", dtype=torch.bfloat16).div_(255.0).sub_(0.5).div_(0.5)
+    )
+    return all_crops.tolist(), overlap_crops["tiling"]
+
+
+class Moondream3ImageProcessor(BaseImageProcessor):
+    r"""
+    Constructs a Moondream3 image processor.
+    """
+
+    model_input_names = ["pixel_values", "image_sizes"]
+    valid_kwargs = Moondream3ImageProcessorKwargs
+
+    def __init__(
+        self,
+        max_crops: int = 12,
+        overlap_margin: int = 4,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.max_crops = max_crops
+        self.overlap_margin = overlap_margin
+        self._valid_processor_keys = [
+            "max_crops",
+            "overlap_margin",
+        ]
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        max_crops: int | None = None,
+        overlap_margin: int | None = None,
+        return_tensors: str | TensorType | None = None,
+        **kwargs,
+    ) -> PIL.Image.Image:
+        """
+        Preprocess an image or batch of images.
+
+        Args:
+            images (`ImageInput`):
+                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
+                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
+            max_crops (`bool`, *optional*, defaults to `self.max_crops`):
+            overlap_margin (`dict[str, int]`, *optional*, defaults to `self.overlap_margin`):
+        """
+        overlap_margin = overlap_margin if overlap_margin is not None else self.overlap_margin
+        max_crops = max_crops if max_crops is not None else self.max_crops
+
+        validate_kwargs(
+            captured_kwargs=kwargs.keys(),
+            valid_processor_keys=self._valid_processor_keys,
+        )
+
+        images = self.fetch_images(images)
+        images = make_flat_list_of_images(images)
+
+        if not valid_images(images[0]):
+            raise ValueError("Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, or torch.Tensor")
+
+        batch_images = []
+        batch_tiling = []
+        for image in images:
+            pixel_values, tiling = prepare_crops(image, max_crops=max_crops, overlap_margin=overlap_margin)
+            batch_images.append(pixel_values)
+            batch_tiling.append(tiling)
+
+        return BatchFeature(
+            data={"pixel_values": batch_images, "tiling": batch_tiling},
+            tensor_type=return_tensors,
+        )
+
+
+__all__ = ["Moondream3ImageProcessor"]
diff --git a/src/transformers/models/moondream3/modeling_moondream3.py b/src/transformers/models/moondream3/modeling_moondream3.py
new file mode 100644
index 000000000000..23f89b2afe45
--- /dev/null
+++ b/src/transformers/models/moondream3/modeling_moondream3.py
@@ -0,0 +1,1304 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.generation.utils import GenerateDecoderOnlyOutput
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, logging
+
+from .configuration_moondream3 import (
+    Moondream3Config,
+    Moondream3RegionConfig,
+    Moondream3TextConfig,
+    Moondream3VisionConfig,
+)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "Moondream3Config"
+
+
+def apply_rotary_pos_emb(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+    rot_dim: int = 32,
+):
+    """
+    Apply rotary position embeddings to query and key tensors.
+
+    Args:
+        q: Query tensor [batch, num_heads, seq_len, head_dim]
+        k: Key tensor [batch, num_heads, seq_len, head_dim]
+        cos: Cosine frequencies [batch, seq_len, rot_dim]
+        sin: Sine frequencies [batch, seq_len, rot_dim]
+        rot_dim: Number of dimensions to apply rotation to (default: 32)
+
+    Returns:
+        Tuple of (rotated_q, rotated_k)
+    """
+
+    def apply_rope(x):
+        x = x.to(torch.float64)
+        x_rot, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+
+        d_q = x_rot.shape[-1] // 2
+        xq_r, xq_i = x_rot[..., :d_q], x_rot[..., d_q:]
+
+        xq_out_r = xq_r * cos - xq_i * sin
+        xq_out_i = xq_r * sin + xq_i * cos
+
+        xq_out = torch.stack((xq_out_r, xq_out_i), dim=-1).flatten(-2)
+
+        return torch.cat([xq_out, x_pass], dim=-1)
+
+    return apply_rope(q), apply_rope(k)
+
+
+class Moondream3RotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor
+
+    def __init__(self, config: Moondream3Config, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = inv_freq
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: Moondream3Config | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+        dim //= 2
+
+        attention_factor = 1.0
+
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32)[: (dim // 2)] / dim))
+        if device is not None:
+            inv_freq = inv_freq.to(device=device)
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update
+    def forward(self, x, position_ids):
+        inv_freq_expanded = (
+            self.inv_freq[None, :, None].to(torch.float32).expand(position_ids.shape[0], -1, 1).to(x.device)
+        )
+        position_ids_expanded = position_ids[:, None, :].to(torch.float32)
+
+        freqs = (inv_freq_expanded.to(torch.float32) @ position_ids_expanded.to(torch.float32)).transpose(1, 2)
+        cfreqs = torch.exp(1j * freqs).unsqueeze(1).expand(-1, self.config.num_attention_heads, -1, -1)
+
+        return cfreqs.real, cfreqs.imag
+
+
+class Moondream3Attention(nn.Module):
+    def __init__(
+        self,
+        config: Moondream3TextConfig | Moondream3VisionConfig,
+        layer_idx: int | None = None,
+        use_tau: bool = True,
+    ):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = getattr(config, "head_dim", self.hidden_size // self.num_heads)
+        self.num_key_value_heads = getattr(config, "num_key_value_heads", self.num_heads)
+        attention_bias = config.attention_bias
+        self.attention_dropout = config.attention_dropout
+
+        if isinstance(config, Moondream3TextConfig):
+            self.is_causal = True
+        elif isinstance(config, Moondream3VisionConfig):
+            self.is_causal = False
+        else:
+            raise TypeError(f"Unsupported config type: {type(config)}")
+
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        self.use_tau = use_tau
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            self.hidden_size,
+            self.num_key_value_heads * self.head_dim,
+            bias=attention_bias,
+        )
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=attention_bias)
+
+        if self.use_tau:
+            # In original, tau weights are (n_heads, qkv_dim) where qkv_dim is the combined QKV dimension
+            qkv_dim = self.num_heads * self.head_dim + 2 * self.num_key_value_heads * self.head_dim
+            self.tau_wq = nn.Linear(qkv_dim, self.num_heads, bias=False)
+            self.tau_wv = nn.Linear(qkv_dim, self.num_heads, bias=False)
+            self.tau_alpha = nn.Parameter(torch.empty(self.num_heads))
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None, Cache | None]:
+        input_shape = hidden_states.shape[:-1]
+
+        bsz, q_len, _ = hidden_states.size()
+
+        query_states = self.q_proj(hidden_states)
+        key_states = self.k_proj(hidden_states)
+        value_states = self.v_proj(hidden_states)
+        if self.use_tau:
+            qkv_out = torch.cat([query_states, key_states, value_states], dim=-1)
+            tok_feat = F.gelu(qkv_out)
+            tok_q = torch.tanh(self.tau_wq(tok_feat)).permute(0, 2, 1)
+            tok_v = torch.tanh(self.tau_wv(tok_feat)).permute(0, 2, 1)
+
+            pos = position_ids.to(tok_q.dtype) + 1
+            alpha = self.tau_alpha.to(tok_q.dtype)
+            tau_pos = 1 + (torch.sigmoid(alpha[None, :, None] * pos[:, None, :].log()) - 0.5)
+            tau_q = (tok_q + tau_pos).unsqueeze(-1)
+            tau_v = (tok_v + tau_pos).unsqueeze(-1)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+        if self.use_tau:
+            query_states = query_states * tau_q
+
+            if self.num_key_value_groups > 1:
+                tau_v_repeated = tau_v.repeat(1, self.num_key_value_groups, 1, 1)[:, : self.num_key_value_heads, :, :]
+            else:
+                tau_v_repeated = tau_v
+            value_states = value_states * tau_v_repeated
+
+        cos, sin = None, None
+        if position_embeddings is not None:
+            cos, sin = position_embeddings
+
+            query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        query_states, key_states = (
+            query_states.to(value_states.dtype),
+            key_states.to(value_states.dtype),
+        )
+
+        if past_key_values is not None:
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        query_states = query_states.contiguous()
+        key_states = key_states.contiguous()
+        value_states = value_states.contiguous()
+
+        attn_output, attn_weights = ALL_ATTENTION_FUNCTIONS["sdpa"](
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Moondream3MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str = "gelu_pytorch_tanh",
+        out_size: int | None = None,
+        gated: bool = False,
+        bias: bool = True,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.out_size = self.hidden_size if out_size is None else out_size
+        self.hidden_act = hidden_act
+        self.gated = gated
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.down_proj = nn.Linear(self.intermediate_size, self.out_size, bias=bias)
+        self.gate_proj = None
+        if self.gated:
+            self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=bias)
+        self.act_fn = ACT2FN[self.hidden_act]
+
+    def forward(self, x) -> torch.Tensor:
+        if self.gated:
+            h = self.up_proj(x)
+            g = self.gate_proj(x)
+            x = self.act_fn(h) * (g + 1)
+        else:
+            x = self.act_fn(self.up_proj(x))
+        return self.down_proj(x)
+
+
+class Moondream3SparseMoeBlock(nn.Module):
+    def __init__(self, config: Moondream3TextConfig, layer_idx=None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.moe_intermediate_size = config.moe_intermediate_size
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+
+        self.gate = nn.Linear(self.hidden_size, self.num_experts, bias=True)
+        self.experts = nn.ModuleList(
+            [
+                Moondream3MLP(
+                    hidden_size=self.hidden_size,
+                    intermediate_size=self.moe_intermediate_size,
+                    # hidden_act=self.config.moe_hidden_act,
+                    gated=True,
+                    bias=False,
+                    hidden_act="gelu",
+                )
+                for _ in range(self.num_experts)
+            ]
+        )
+
+    def forward(self, hidden_states: torch.Tensor, cache_position=None) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        router_logits: torch.Tensor = self.gate(hidden_states)
+        routing_weights, selected_experts = torch.topk(router_logits, self.top_k, dim=-1)
+        routing_weights = F.softmax(routing_weights, dim=-1, dtype=torch.float32)
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            top_x, idx = (selected_experts == expert_idx).nonzero(as_tuple=True)
+
+            if top_x.shape[0] == 0:
+                continue
+
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class Moondream3DecoderLayer(nn.Module):
+    def __init__(self, config: Moondream3TextConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.self_attn = Moondream3Attention(config, layer_idx, use_tau=True)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.is_moe_layer = layer_idx >= config.moe_start_layer
+        if self.is_moe_layer:
+            self.mlp = Moondream3SparseMoeBlock(config, layer_idx=layer_idx)
+        else:
+            self.mlp = Moondream3MLP(
+                self.hidden_size,
+                self.intermediate_size,
+                # hidden_act=self.config.hidden_act,
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        output_attentions: bool = False,
+        output_router_logits: bool = False,
+        use_cache: bool = False,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> tuple:
+        hidden_states_ln = self.input_layernorm(hidden_states)
+
+        hidden_states_attn, self_attn_weights = self.self_attn(
+            hidden_states=hidden_states_ln,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            output_attentions=output_attentions,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+
+        if self.is_moe_layer:
+            hidden_states_mlp, router_logits = self.mlp(hidden_states_ln, cache_position=cache_position)
+        else:
+            hidden_states_mlp = self.mlp(hidden_states_ln)
+            router_logits = None
+
+        # Add both attention and MLP to residual like original
+        hidden_states = hidden_states + hidden_states_attn + hidden_states_mlp
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if output_router_logits:
+            outputs += (router_logits,)
+
+        return outputs
+
+
+class Moondream3PreTrainedModel(PreTrainedModel):
+    config_class = Moondream3Config
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Moondream3DecoderLayer", "Moondream3SparseMoeBlock"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": Moondream3DecoderLayer,
+        "attentions": Moondream3Attention,
+    }
+
+
+class Moondream3TextModel(Moondream3PreTrainedModel):
+    config_class = Moondream3TextConfig
+
+    def __init__(self, config: Moondream3TextConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id if hasattr(config, "pad_token_id") else 0
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [Moondream3DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = nn.LayerNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Moondream3RotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        self.post_init()
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        output_router_logits: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+    ) -> tuple | BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError(
+                "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache()
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        position_embeddings = self.rotary_emb(hidden_states, position_ids=position_ids)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_router_logits = () if output_router_logits else None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    output_router_logits,
+                    use_cache,
+                    cache_position,
+                    position_embeddings,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_values=past_key_values,
+                    output_attentions=output_attentions,
+                    output_router_logits=output_router_logits,
+                    use_cache=use_cache,
+                    cache_position=cache_position,
+                    position_embeddings=position_embeddings,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+            if output_router_logits and layer_outputs[-1] is not None:
+                all_router_logits += (layer_outputs[-1],)
+
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = past_key_values
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    hidden_states,
+                    next_cache,
+                    all_hidden_states,
+                    all_self_attns,
+                    all_router_logits,
+                ]
+                if v is not None
+            )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=next_cache,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class Moondream3VisionPatchEmbeddings(nn.Module):
+    def __init__(self, config: Moondream3VisionConfig):
+        super().__init__()
+        self.patch_size = config.patch_size
+        self.num_channels = config.in_channels
+        self.hidden_size = config.hidden_size
+        self.crop_size = config.crop_size
+        self.patch_size = config.patch_size
+        self.grid_size = self.crop_size // self.patch_size
+        self.num_patches = self.grid_size * self.grid_size
+
+        self.projection = nn.Linear(
+            self.patch_size * self.patch_size * self.num_channels,
+            self.hidden_size,
+            bias=True,
+        )
+        self.position_embeddings = nn.Parameter(torch.zeros(1, self.num_patches, config.hidden_size))
+
+    def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
+        B, C, H, W = pixel_values.shape
+        P1 = P2 = self.patch_size
+
+        x = pixel_values.reshape(B, C, H // P1, P1, W // P2, P2)
+
+        x = x.permute(0, 2, 4, 1, 3, 5)
+
+        x = x.reshape(B, (H // P1) * (W // P2), C * P1 * P2)
+
+        x = self.projection(x)
+        return x + self.position_embeddings
+
+
+class Moondream3VisionEncoderLayer(nn.Module):
+    def __init__(self, config: Moondream3VisionConfig, layer_idx: int):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.layer_idx = layer_idx
+
+        self.self_attn = Moondream3Attention(config, layer_idx=self.layer_idx, use_tau=False)
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=1e-5)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=1e-5)
+        self.mlp = Moondream3MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            # hidden_act=self.config.hidden_act,
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(hidden_states=hidden_states)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+class Moondream3VisionModel(Moondream3PreTrainedModel):
+    config_class = Moondream3VisionConfig
+    main_input_name = "pixel_values"
+    _no_split_modules = ["Moondream3VisionEncoderLayer"]
+
+    def __init__(self, config: Moondream3VisionConfig):
+        super().__init__(config)
+        self.config = config
+        self.hidden_size = self.config.hidden_size
+        self.num_hidden_layers = self.config.num_hidden_layers
+        self.proj_inner_dim = self.config.proj_inner_dim
+        self.proj_out_dim = self.config.proj_out_dim
+
+        self.embeddings = Moondream3VisionPatchEmbeddings(config)
+        self.layers = nn.ModuleList(
+            [Moondream3VisionEncoderLayer(config, layer_idx) for layer_idx in range(self.num_hidden_layers)]
+        )
+        self.post_layernorm = nn.LayerNorm(self.hidden_size, eps=1e-5)
+        self.vision_projection = Moondream3MLP(
+            hidden_size=self.hidden_size * 2,
+            intermediate_size=self.proj_inner_dim,
+            out_size=self.proj_out_dim,
+        )
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def _reconstruct_from_crops(
+        self,
+        crops: torch.Tensor,
+        tiling: tuple[int, int],
+        overlap_margin: int = 4,
+        patch_size: int = 14,
+    ) -> torch.Tensor:
+        """
+        Reconstruct the original image from overlapping crops into a single seamless image.
+
+        Takes a list of overlapping image crops along with their positional metadata and
+        reconstructs them into a single coherent image by carefully stitching together
+        non-overlapping regions. Handles both numpy arrays and PyTorch tensors.
+
+        Args:
+            crops: List of image crops as numpy arrays or PyTorch tensors with shape
+                (H,W,C)
+            tiling: Tuple of (height,width) indicating crop grid layout
+            patch_size: Size in pixels of each patch, default 14
+            overlap_margin: Number of overlapping patches on each edge, default 4
+
+        Returns:
+            Reconstructed image as numpy array or PyTorch tensor matching input type,
+            with shape (H,W,C) where H,W are the original image dimensions
+        """
+        if isinstance(tiling, torch.Tensor):
+            tiling_h, tiling_w = tiling[0].item(), tiling[1].item()
+        else:
+            tiling_h, tiling_w = tiling
+        tiling_h, tiling_w = int(tiling_h), int(tiling_w)
+        crop_height, crop_width = crops[0].shape[:2]
+        margin_pixels = overlap_margin * patch_size
+
+        output_h = (crop_height - 2 * margin_pixels) * tiling_h + 2 * margin_pixels
+        output_w = (crop_width - 2 * margin_pixels) * tiling_w + 2 * margin_pixels
+        reconstructed = torch.zeros(
+            (output_h, output_w, crops[0].shape[2]),
+            device=crops[0].device,
+            dtype=crops[0].dtype,
+        )
+
+        for i, crop in enumerate(crops):
+            tile_y = i // tiling_w
+            tile_x = i % tiling_w
+
+            x_start = 0 if tile_x == 0 else margin_pixels
+            x_end = crop_width if tile_x == tiling_w - 1 else crop_width - margin_pixels
+            y_start = 0 if tile_y == 0 else margin_pixels
+            y_end = crop_height if tile_y == tiling_h - 1 else crop_height - margin_pixels
+
+            out_x = tile_x * (crop_width - 2 * margin_pixels)
+            out_y = tile_y * (crop_height - 2 * margin_pixels)
+
+            reconstructed[out_y + y_start : out_y + y_end, out_x + x_start : out_x + x_end] = crop[
+                y_start:y_end, x_start:x_end
+            ]
+
+        return reconstructed
+
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor,
+        tiling: tuple[int, int],
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple | BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        batch_size, num_crops = pixel_values.shape[:2]
+        # flatten batch_size and num_crops into same dim
+        pixel_values = pixel_values.view(-1, *pixel_values.shape[2:])
+        hidden_states: torch.Tensor = self.embeddings(pixel_values)
+
+        all_hidden_states = () if output_hidden_states else None
+        all_attentions = () if output_attentions else None
+
+        for encoder_layer in self.layers:
+            if output_hidden_states and all_hidden_states is not None:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(encoder_layer.__call__, hidden_states)
+            else:
+                layer_outputs = encoder_layer(hidden_states)
+
+            hidden_states = layer_outputs
+
+        hidden_states = self.post_layernorm(hidden_states)
+
+        hidden_states = hidden_states.view(batch_size, num_crops, *hidden_states.shape[1:])
+        outputs = []
+        for b in range(batch_size):
+            hs = hidden_states[b]
+            t = tiling[b]
+
+            global_features = hs[0]
+            local_features = hs[1:].view(
+                -1,
+                self.num_hidden_layers,
+                self.num_hidden_layers,
+                self.hidden_size,
+            )
+
+            reconstructed = self._reconstruct_from_crops(
+                local_features,
+                t,
+                patch_size=1,
+                overlap_margin=self.config.overlap_margin,
+            )
+
+            reconstructed = reconstructed.permute(2, 0, 1)
+            reconstructed = F.adaptive_avg_pool2d(
+                reconstructed,
+                output_size=(self.num_hidden_layers, self.num_hidden_layers),
+            )
+            reconstructed = reconstructed.permute(1, 2, 0).view(
+                self.num_hidden_layers * self.num_hidden_layers, self.hidden_size
+            )
+            final_features = torch.cat([global_features, reconstructed], dim=-1)
+            outputs.append(final_features)
+        output = torch.stack(outputs, 0)
+
+        hidden_states = self.vision_projection(output)
+
+        if output_hidden_states and all_hidden_states is not None:
+            all_hidden_states += (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+class Moondream3RegionEncoder(nn.Module):
+    def __init__(self, config: Moondream3RegionConfig):
+        super().__init__()
+        self.coord_encoder = nn.Linear(config.coord_feat_dim, config.hidden_size)
+        self.size_encoder = nn.Linear(config.size_feat_dim, config.hidden_size)
+
+        coord_freq = torch.randn(config.coord_feat_dim // 2, 1) * 10.0
+        size_freq = torch.randn(config.size_feat_dim // 2, 2) * 10.0
+        self.register_buffer("coord_freq", coord_freq.T)
+        self.register_buffer("size_freq", size_freq.T)
+
+    def fourier_features(self, x: torch.Tensor, w: torch.Tensor) -> torch.Tensor:
+        x_proj = 2 * torch.pi * x @ w
+        return torch.cat([x_proj.cos(), x_proj.sin()], dim=-1)
+
+    def encode_coordinate(self, coord: torch.Tensor) -> torch.Tensor:
+        fourier_features = self.fourier_features(coord, self.coord_freq)
+        return self.coord_encoder(fourier_features)
+
+    def encode_size(self, size: torch.Tensor) -> torch.Tensor:
+        fourier_features = self.fourier_features(size, self.size_freq)
+        return self.size_encoder(fourier_features)
+
+
+class Moondream3RegionDecoder(nn.Module):
+    def __init__(self, config: Moondream3RegionConfig):
+        super().__init__()
+        self.coord_decoder = nn.Linear(config.hidden_size, config.coord_out_dim)
+        self.size_decoder = nn.Linear(config.hidden_size, config.size_out_dim)
+
+    def decode_coordinate(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        return self.coord_decoder(hidden_state)
+
+    def decode_size(self, hidden_state: torch.Tensor) -> torch.Tensor:
+        return self.size_decoder(hidden_state).view(hidden_state.shape[0], 2, -1)
+
+
+class Moondream3Model(Moondream3PreTrainedModel):
+    def __init__(self, config: Moondream3Config):
+        super().__init__(config)
+        self.config = config
+        self.text_model = Moondream3TextModel(config.text_config)
+        self.vision_model = Moondream3VisionModel(config.vision_config)
+        self.vocab_size = config.text_config.vocab_size
+
+        self.region_encoder = Moondream3RegionEncoder(config.region_config)
+        self.region_decoder = Moondream3RegionDecoder(config.region_config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.text_model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.text_model.embed_tokens = value
+
+    def set_decoder(self, decoder):
+        self.text_model = decoder
+
+    def get_decoder(self):
+        return self.text_model
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        tiling: tuple[int, int] | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int = 0,
+    ) -> tuple | BaseModelOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if (input_ids is not None) == (inputs_embeds is not None):
+            raise ValueError("Provide exactly one of input_ids or inputs_embeds.")
+
+        if not ((pixel_values is not None) ^ (tiling is None)):
+            raise ValueError("You must specify both pixel_values and tiling")
+
+        if inputs_embeds is not None and (pixel_values is not None or tiling is not None):
+            raise ValueError(
+                "When inputs_embeds is provided, do not pass pixel_values/tiling; "
+                "inputs_embeds must already include BOS+image(+text)."
+            )
+
+        if inputs_embeds is None:
+            inputs_embeds: torch.Tensor = self.text_model.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position: torch.Tensor = torch.arange(
+                past_seen_tokens, past_seen_tokens, device=inputs_embeds.device
+            )
+
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        if pixel_values is not None:
+            pixel_values = pixel_values.to(dtype=self.vision_model.embeddings.projection.weight.dtype)
+            image_embeds = self.vision_model(pixel_values, tiling=tiling)["last_hidden_state"]
+            prefix = self.text_model.embed_tokens(
+                torch.full(
+                    (input_ids.shape[0], 1),
+                    # self.config.text_config.bos_token_id is None, unsure, so for now just use 0 directly.
+                    0,
+                    dtype=input_ids.dtype,
+                    device=input_ids.device,
+                )
+            )
+            embeds = torch.cat([prefix, image_embeds], dim=1)
+            cache_pos = torch.arange(embeds.shape[-2], device=embeds.device)
+            pos = cache_pos.unsqueeze(0).expand(embeds.shape[0], -1)
+            attn_mask = torch.full(
+                (embeds.shape[0], 1, embeds.shape[-2], pos.shape[-1]),
+                True,
+                dtype=torch.bool,
+                device=embeds.device,
+            )
+
+            outputs = self.text_model(
+                input_ids=None,
+                attention_mask=attn_mask,
+                position_ids=pos,
+                past_key_values=past_key_values,
+                inputs_embeds=embeds,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=True,
+                cache_position=cache_pos,
+            )
+
+        attn_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=torch.cat(
+                [
+                    torch.ones(
+                        attention_mask.shape[0],
+                        cache_position[-1] + 1 - attention_mask.shape[-1],
+                        device=attention_mask.device,
+                        dtype=attention_mask.dtype,
+                    ),
+                    attention_mask,
+                ],
+                dim=-1,
+            ),
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        outputs = self.text_model(
+            input_ids=None,
+            attention_mask=attn_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+        )
+
+        if not return_dict:
+            return tuple(
+                v
+                for v in [
+                    outputs.last_hidden_state,
+                    getattr(outputs, "past_key_values", None),
+                    getattr(outputs, "hidden_states", None),
+                    getattr(outputs, "attentions", None),
+                ]
+                if v is not None
+            )
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=getattr(outputs, "past_key_values", None),
+            hidden_states=getattr(outputs, "hidden_states", None),
+            attentions=getattr(outputs, "attentions", None),
+        )
+
+
+@dataclass
+class Moondream3GenerateOutput(GenerateDecoderOnlyOutput):
+    objects: list[dict[str, float]] | None = None
+
+
+class Moondream3ForConditionalGeneration(Moondream3PreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config: Moondream3Config):
+        super().__init__(config)
+        self.objects = None
+        self.model = Moondream3Model(config)
+        self.vocab_size = config.text_config.vocab_size
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=True)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.text_model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.text_model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model.text_model = decoder
+
+    def get_decoder(self):
+        return self.model.text_model
+
+    def _prepare_generated_length(
+        self,
+        generation_config,
+        **kwargs,
+    ):
+        generation_config = super()._prepare_generated_length(generation_config, **kwargs)
+        generation_config.max_length += self.config.vision_config.prefix_len
+        return generation_config
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        pixel_values: torch.FloatTensor = None,
+        tiling: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | CausalLMOutputWithPast:
+        if pixel_values is not None and inputs_embeds is None:
+            position_ids += self.config.vision_config.prefix_len
+            cache_position += self.config.vision_config.prefix_len
+
+        model_outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            tiling=tiling,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            labels=None,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=True,
+            cache_position=cache_position,
+            logits_to_keep=logits_to_keep,
+        )
+        hidden_states = model_outputs.last_hidden_state
+
+        if isinstance(logits_to_keep, int) and logits_to_keep > 0:
+            hs = hidden_states[:, -logits_to_keep:, :]
+        elif isinstance(logits_to_keep, slice):
+            hs = hidden_states[:, logits_to_keep, :]
+        else:
+            hs = hidden_states
+
+        hs = self.model.text_model.norm(hs)
+        logits = self.lm_head(hs)
+
+        pred = torch.argmax(logits, dim=-1)
+        print(pred)
+
+        pos_ids = position_ids[:, -1:] + 1
+        cache_pos = cache_position[-1:] + 1
+        mask = torch.ones(hidden_states.shape[0], 1, device=self.device, dtype=torch.long)
+        is_processing_point = torch.any(pred == 5)
+        while is_processing_point:
+            batch_mask = pred[:, -1] == 5
+            hidden_states = hidden_states[:, -1:, :]
+            x_logits = self.model.region_decoder.decode_coordinate(hidden_states)
+            x_center = torch.argmax(x_logits, dim=-1) / x_logits.size(-1)
+            next_embeds = self.model.region_encoder.encode_coordinate(x_center.to(x_logits.dtype)).unsqueeze(1)
+            model_outputs = self.model(
+                input_ids=None,
+                pixel_values=None,
+                tiling=None,
+                attention_mask=mask,
+                position_ids=pos_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=next_embeds,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=True,
+                cache_position=cache_pos,
+                logits_to_keep=logits_to_keep,
+            )
+            hidden_states = model_outputs.last_hidden_state
+            y_logits = self.model.region_decoder.decode_coordinate(hidden_states)
+            y_center = torch.argmax(y_logits, dim=-1) / y_logits.size(-1)
+            next_embeds = self.model.region_encoder.encode_coordinate(y_center.to(y_logits.dtype)).unsqueeze(1)
+            coords = torch.cat([x_center, y_center], dim=1)
+            coords = coords * (batch_mask).unsqueeze(1)
+            pos_ids += 1
+            cache_pos = cache_pos + 1
+            bbox = None
+            if input_ids.shape[-1] > 1 and input_ids[0, 1] == 7235:
+                model_outputs = self.model(
+                    input_ids=None,
+                    pixel_values=None,
+                    tiling=None,
+                    attention_mask=mask,
+                    position_ids=pos_ids,
+                    past_key_values=past_key_values,
+                    inputs_embeds=next_embeds,
+                    labels=None,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                    output_hidden_states=output_hidden_states,
+                    return_dict=True,
+                    cache_position=cache_pos,
+                    logits_to_keep=logits_to_keep,
+                )
+                hidden_states = model_outputs.last_hidden_state
+                size_logits = self.model.region_decoder.decode_size(hidden_states)
+                bins = torch.argmax(size_logits, dim=-1)
+                w_bin = bins[:, 0]
+                h_bin = bins[:, 1]
+
+                w = torch.pow(2.0, (w_bin.float() / 1023.0) * 10.0 - 10.0)
+                h = torch.pow(2.0, (h_bin.float() / 1023.0) * 10.0 - 10.0)
+
+                next_embeds = (
+                    self.model.region_encoder.encode_size(torch.stack([w, h], dim=-1).to(size_logits.dtype))
+                ).unsqueeze(1)
+                bbox = [
+                    x_center.item() - w.item() / 2,
+                    y_center.item() - h.item() / 2,
+                    x_center.item() + w.item() / 2,
+                    y_center.item() + h.item() / 2,
+                ]
+                bbox = bbox * (batch_mask).unsqueeze(1)
+                pos_ids += 1
+                cache_pos = cache_pos + 1
+
+            new = coords.unsqueeze(1) if bbox is None else bbox.unsqueeze(1)
+            if self.objects is None:
+                self.objects = new
+            else:
+                self.objects = torch.cat([self.objects, new], dim=1)
+            model_outputs = self.model(
+                input_ids=None,
+                pixel_values=None,
+                tiling=None,
+                attention_mask=mask,
+                position_ids=pos_ids,
+                past_key_values=past_key_values,
+                inputs_embeds=next_embeds,
+                labels=None,
+                use_cache=use_cache,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+                return_dict=True,
+                cache_position=cache_pos,
+                logits_to_keep=logits_to_keep,
+            )
+            pos_ids += 1
+            cache_pos = cache_pos + 1
+            hidden_states = model_outputs.last_hidden_state
+
+            indices = torch.tensor(
+                [
+                    self.config.text_config.coord_token_id,
+                    0,  # self.config.text_config.eos_token_id,
+                ],
+                device=self.device,
+            )
+
+            hidden_states = self.model.text_model.norm(hidden_states)
+            logits = hidden_states @ self.lm_head.weight[indices].T + self.lm_head.bias[indices]
+
+            logits_full = torch.full(
+                (logits.shape[0], logits.shape[1], self.config.text_config.vocab_size),
+                float("-inf"),
+                device=logits.device,
+                dtype=logits.dtype,
+            )
+            logits_full[:, :, torch.tensor([5, 0])] = logits
+            logits = logits_full
+            pred[batch_mask] = torch.argmax(logits, dim=-1)[batch_mask]
+            print(pred)
+            is_processing_point = torch.any(pred == 5)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.vocab_size)
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=getattr(model_outputs, "past_key_values", None),
+            hidden_states=getattr(model_outputs, "hidden_states", None),
+            attentions=getattr(model_outputs, "attentions", None),
+        )
+
+    def generate(self, **kwargs) -> Moondream3GenerateOutput | torch.LongTensor:
+        outputs = super().generate(**kwargs)
+        if self.objects is not None and len(self.objects) > 0:
+            if isinstance(outputs, torch.Tensor):
+                outputs = self.objects
+                self.objects = []
+            else:
+                outputs = Moondream3GenerateOutput(**outputs, objects=self.objects)
+                self.objects = []
+        return outputs
+
+    def prepare_inputs_for_generation(self, input_ids, **model_kwargs):
+        model_inputs = super().prepare_inputs_for_generation(input_ids, **model_kwargs)
+        model_inputs["position_ids"] += model_inputs["cache_position"].unsqueeze(0) - model_inputs["position_ids"]
+        return model_inputs
+
+    def _update_model_kwargs_for_generation(
+        self,
+        outputs,
+        model_kwargs,
+        is_encoder_decoder,
+        num_new_tokens: int = 1,
+    ):
+        model_kwargs = super()._update_model_kwargs_for_generation(
+            outputs,
+            model_kwargs,
+            is_encoder_decoder=is_encoder_decoder,
+            num_new_tokens=num_new_tokens,
+        )
+        if model_kwargs["use_cache"]:
+            model_kwargs["pixel_values"] = None
+            model_kwargs["tiling"] = None
+        return model_kwargs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+
+__all__ = [
+    "Moondream3Config",
+    "Moondream3TextConfig",
+    "Moondream3VisionConfig",
+    "Moondream3RegionConfig",
+    "Moondream3PreTrainedModel",
+    "Moondream3Model",
+    "Moondream3TextModel",
+    "Moondream3VisionModel",
+    "Moondream3ForConditionalGeneration",
+]
diff --git a/src/transformers/models/moondream3/processing_moondream3.py b/src/transformers/models/moondream3/processing_moondream3.py
new file mode 100644
index 000000000000..7602f4b585fd
--- /dev/null
+++ b/src/transformers/models/moondream3/processing_moondream3.py
@@ -0,0 +1,158 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Processor class for Moondream3.
+"""
+
+from transformers.feature_extraction_utils import BatchFeature
+from transformers.image_utils import ImageInput, is_valid_image
+from transformers.processing_utils import (
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+)
+from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class Moondream3ProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {"padding": False, "return_token_type_ids": False},
+        "common_kwargs": {
+            "return_tensors": "pt",
+        },
+    }
+
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_url
+def is_url(val) -> bool:
+    return isinstance(val, str) and val.startswith("http")
+
+
+# Copied from transformers.models.idefics2.processing_idefics2.is_image_or_image_url
+def is_image_or_image_url(elem):
+    return is_url(elem) or is_valid_image(elem)
+
+
+class Moondream3Processor(ProcessorMixin):
+    r"""
+    Constructs a Moondream3 processor which wraps a Moondream3 image processor and a Moondream3 tokenizer into a single processor.
+
+    [`Moondream3Processor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
+    [`~Moondream3Processor.__call__`] and [`~Moondream3Processor.decode`] for more information.
+
+    Args:
+        image_processor ([`Moondream3ImageProcessor`], *optional*):
+            The image processor is a required input.
+        tokenizer ([`LlamaTokenizerFast`], *optional*):
+            The tokenizer is a required input.
+        patch_size (`int`, *optional*, defaults to 16):
+            Patch size from the vision tower.
+        spatial_merge_size (`int`, *optional*, defaults to 1):
+            The downsampling factor for the spatial merge operation.
+        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
+            in a chat into a tokenizable string.
+        image_token (`str`, *optional*, defaults to `"[IMG]"`):
+            Special token used to denote image location.
+        image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`):
+            Special token used to denote the end of a line of pixels in an image.
+        image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`):
+            Special token used to denote the end of an image input.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "AutoImageProcessor"
+    tokenizer_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        chat_template=None,
+        **kwargs,
+    ):
+        super().__init__(image_processor, tokenizer, chat_template=chat_template)
+
+    def __call__(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        **kwargs: Unpack[Moondream3ProcessorKwargs],
+    ) -> BatchFeature:
+        """
+        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
+        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
+        the text. To prepare the image(s), this method forwards the `images` and `kwargs` arguments to
+        CLIPImageProcessor's [`~CLIPImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring
+        of the above two methods for more information.
+
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+            text (`str`, `list[str]`, `list[list[str]]`):
+                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
+                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
+                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                If set, will return tensors of a particular framework. Acceptable values are:
+
+                - `'pt'`: Return PyTorch `torch.Tensor` objects.
+                - `'np'`: Return NumPy `np.ndarray` objects.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
+            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
+              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
+            `None`).
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+
+        output_kwargs = self._merge_kwargs(
+            Moondream3ProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        if images is not None:
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
+        else:
+            image_inputs = {}
+
+        if isinstance(text, str):
+            text = [text]
+        elif not isinstance(text, list) and not isinstance(text[0], str):
+            raise TypeError("Invalid input text. Please provide a string, or a list of strings")
+
+        # try to expand inputs in processing if we have the necessary parts
+        prompt_strings = text
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return tokenizer_input_names + image_processor_input_names + ["image_sizes"]
+
+
+__all__ = ["Moondream3Processor"]
diff --git a/src/transformers/models/moonshine/modeling_moonshine.py b/src/transformers/models/moonshine/modeling_moonshine.py
index 2900898a7991..a1d454db252c 100644
--- a/src/transformers/models/moonshine/modeling_moonshine.py
+++ b/src/transformers/models/moonshine/modeling_moonshine.py
@@ -148,7 +148,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/moshi/modeling_moshi.py b/src/transformers/models/moshi/modeling_moshi.py
index a967445c18ec..4be90715792e 100644
--- a/src/transformers/models/moshi/modeling_moshi.py
+++ b/src/transformers/models/moshi/modeling_moshi.py
@@ -45,12 +45,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Outputs of [`MoshiForConditionalConditionalGeneration.generate`].
     """
 )
+@dataclass
 class MoshiConditionalGenerationGenerateOutput(ModelOutput):
     r"""
     audio_sequences (`torch.LongTensor` of shape `(batch_size*num_return_sequences, 1, sequence_length)`, *optional*):
@@ -97,12 +97,12 @@ class MoshiConditionalGenerationGenerateOutput(ModelOutput):
     audio_codes: torch.LongTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     `MoshiForCausalLM` outputs.
     """
 )
+@dataclass
 class MoshiCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -124,12 +124,12 @@ class MoshiCausalLMOutputWithPast(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     `MoshiForConditionalGeneration` outputs.
     """
 )
+@dataclass
 class MoshiConditionalGenerationOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `text_labels` is provided):
@@ -323,7 +323,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/mpnet/modeling_mpnet.py b/src/transformers/models/mpnet/modeling_mpnet.py
index 7fb872fc3699..6696e43fccc5 100644
--- a/src/transformers/models/mpnet/modeling_mpnet.py
+++ b/src/transformers/models/mpnet/modeling_mpnet.py
@@ -32,7 +32,8 @@
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_mpnet import MPNetConfig
 
 
@@ -44,6 +45,8 @@ class MPNetPreTrainedModel(PreTrainedModel):
     config: MPNetConfig
     base_model_prefix = "mpnet"
 
+    _can_record_outputs = None
+
     @torch.no_grad()
     def _init_weights(self, module):
         """Initialize the weights"""
@@ -262,6 +265,12 @@ def forward(
         return outputs
 
 
+MPNetPreTrainedModel._can_record_outputs = {
+    "hidden_states": MPNetLayer,
+    "attentions": MPNetAttention,
+}
+
+
 class MPNetEncoder(nn.Module):
     def __init__(self, config):
         super().__init__()
@@ -276,13 +285,15 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         output_attentions: bool = False,
         output_hidden_states: bool = False,
-        return_dict: bool = False,
+        return_dict: bool = True,
         **kwargs,
     ):
         position_bias = self.compute_position_bias(hidden_states)
+
         all_hidden_states = () if output_hidden_states else None
         all_attentions = () if output_attentions else None
-        for i, layer_module in enumerate(self.layer):
+
+        for layer_module in self.layer:
             if output_hidden_states:
                 all_hidden_states = all_hidden_states + (hidden_states,)
 
@@ -291,19 +302,16 @@ def forward(
                 attention_mask,
                 position_bias,
                 output_attentions=output_attentions,
-                **kwargs,
             )
+
             hidden_states = layer_outputs[0]
 
             if output_attentions:
                 all_attentions = all_attentions + (layer_outputs[1],)
 
-        # Add last layer
         if output_hidden_states:
             all_hidden_states = all_hidden_states + (hidden_states,)
 
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
         return BaseModelOutput(
             last_hidden_state=hidden_states,
             hidden_states=all_hidden_states,
@@ -389,6 +397,7 @@ def set_input_embeddings(self, value):
         self.embeddings.word_embeddings = value
 
     @auto_docstring
+    @capture_outputs
     def forward(
         self,
         input_ids: torch.LongTensor | None = None,
@@ -400,42 +409,63 @@ def forward(
         return_dict: bool | None = None,
         **kwargs,
     ) -> tuple[torch.Tensor] | BaseModelOutputWithPooling:
+        # Resolve flags from config
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+
         output_hidden_states = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
         )
         return_dict = return_dict if return_dict is not None else self.config.return_dict
 
+        # Validate inputs
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
             self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
             input_shape = input_ids.size()
+            device = input_ids.device
         elif inputs_embeds is not None:
             input_shape = inputs_embeds.size()[:-1]
+            device = inputs_embeds.device
         else:
             raise ValueError("You have to specify either input_ids or inputs_embeds")
 
-        device = input_ids.device if input_ids is not None else inputs_embeds.device
-
+        # Default attention mask
         if attention_mask is None:
             attention_mask = torch.ones(input_shape, device=device)
-        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
 
-        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
+        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
+
+        # Embeddings
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            inputs_embeds=inputs_embeds,
+        )
+
+        # Encoder
         encoder_outputs = self.encoder(
             embedding_output,
             attention_mask=extended_attention_mask,
             output_attentions=output_attentions,
             output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            return_dict=True,
         )
-        sequence_output = encoder_outputs[0]
+
+        sequence_output = encoder_outputs.last_hidden_state
+
         pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
 
+        # tuple return support
         if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
+            return (
+                sequence_output,
+                pooled_output,
+                encoder_outputs.hidden_states,
+                encoder_outputs.attentions,
+            )
 
+        # Correct structured return
         return BaseModelOutputWithPooling(
             last_hidden_state=sequence_output,
             pooler_output=pooled_output,
@@ -466,6 +496,7 @@ def set_output_embeddings(self, new_embeddings):
         self.lm_head.decoder = new_embeddings
         self.lm_head.bias = new_embeddings.bias
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -556,6 +587,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -636,6 +668,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -729,6 +762,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -812,6 +846,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
diff --git a/src/transformers/models/mt5/modeling_mt5.py b/src/transformers/models/mt5/modeling_mt5.py
index a1ea0c8e51f5..dc65aa703615 100644
--- a/src/transformers/models/mt5/modeling_mt5.py
+++ b/src/transformers/models/mt5/modeling_mt5.py
@@ -33,6 +33,7 @@
     Seq2SeqModelOutput,
     Seq2SeqQuestionAnsweringModelOutput,
     Seq2SeqSequenceClassifierOutput,
+    SequenceClassifierOutput,
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
@@ -1671,6 +1672,99 @@ def forward(
         )
 
 
+@auto_docstring
+class MT5EncoderForSequenceClassification(MT5PreTrainedModel):
+    keys_to_ignore_on_load_unexpected = [r"decoder"]
+
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderForSequenceClassification.__init__ with T5->MT5
+    def __init__(self, config: MT5Config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+
+        self.transformer = MT5EncoderModel(config)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = MT5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.model_parallel = False
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple[torch.Tensor] | SequenceClassifierOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Mask to avoid performing attention on padding token indices.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]  #  outputs.last_hidden_state
+        hidden_states = self.dropout(hidden_states)
+
+        sentence_representation = (hidden_states * attention_mask.unsqueeze(-1)).sum(dim=1)
+        sentence_representation /= attention_mask.sum(dim=1).unsqueeze(-1)
+
+        logits = self.classifier(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            if self.config.num_labels > 0 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                self.config.problem_type = "single_label_classification"
+            else:
+                self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                batch_size, _ = input_ids.shape
+                loss = loss_fct(logits.view(batch_size, self.num_labels), labels.view(batch_size, self.num_labels))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
 __all__ = [
     "MT5EncoderModel",
     "MT5ForConditionalGeneration",
@@ -1679,4 +1773,5 @@ def forward(
     "MT5ForTokenClassification",
     "MT5Model",
     "MT5PreTrainedModel",
+    "MT5EncoderForSequenceClassification",
 ]
diff --git a/src/transformers/models/musicflamingo/modeling_musicflamingo.py b/src/transformers/models/musicflamingo/modeling_musicflamingo.py
index 4ec4215a2989..a9e05470662d 100644
--- a/src/transformers/models/musicflamingo/modeling_musicflamingo.py
+++ b/src/transformers/models/musicflamingo/modeling_musicflamingo.py
@@ -33,7 +33,7 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available, torch_compilable_check
 from ..auto import AutoModel, AutoModelForCausalLM
 from .configuration_musicflamingo import MusicFlamingoConfig
 
@@ -269,6 +269,30 @@ def get_audio_features(
 
         return audio_output
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -345,10 +369,10 @@ def forward(
             ).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: CausalLMOutputWithPast = self.language_model(
             inputs_embeds=inputs_embeds,
@@ -388,6 +412,13 @@ def _build_audio_timestamps(
         _, ends = torch.where(diff == -1)
         sample_lengths = (ends - starts).to(torch.long)
 
+        n_audio_tokens = audio_token_mask.sum()
+        n_audio_features = post_lengths.sum()
+        torch_compilable_check(
+            n_audio_tokens == n_audio_features,
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+
         # Account for 4x downsampling in audio encoder (conv2 and avg pooling)
         audio_embed_frame_step = self.config.audio_frame_step * 4
         frame_offsets = (
diff --git a/src/transformers/models/musicflamingo/modular_musicflamingo.py b/src/transformers/models/musicflamingo/modular_musicflamingo.py
index 7d98d0ffdeab..11da108e08c6 100644
--- a/src/transformers/models/musicflamingo/modular_musicflamingo.py
+++ b/src/transformers/models/musicflamingo/modular_musicflamingo.py
@@ -13,7 +13,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
 from math import pi
 
 from huggingface_hub.dataclasses import strict
@@ -25,7 +24,7 @@
 from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torch_available, torch_compilable_check
 from ..audioflamingo3.configuration_audioflamingo3 import AudioFlamingo3Config
 from ..audioflamingo3.modeling_audioflamingo3 import (
     AudioFlamingo3ForConditionalGeneration,
@@ -99,32 +98,8 @@ def __post_init__(self, **kwargs):
         PreTrainedConfig.__post_init__(**kwargs)
 
 
+@auto_docstring
 class MusicFlamingoProcessor(AudioFlamingo3Processor):
-    r"""
-    Constructs an MusicFlamingo processor which wraps an MusicFlamingo feature extractor and an MusicFlamingo
-    tokenizer into a single processor.
-
-    [`MusicFlamingoProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and
-    [`Qwen2TokenizerFast`]. See the [`~MusicFlamingoProcessor.__call__`] for more information.
-
-    Args:
-        feature_extractor ([`WhisperFeatureExtractor`]):
-            The feature extractor is a required input.
-        tokenizer ([`Qwen2TokenizerFast`]):
-            The tokenizer is a required input.
-        chat_template (`Optional[str]`, *optional*):
-            The Jinja template to use for formatting the conversation. If not provided, the tokenizer's default chat
-            template will be used.
-        audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
-            Special token used to represent audio inputs in the chat template.
-        audio_bos_token (`Optional[str]`, *optional*, defaults to `"<|sound_bos|>"`):
-            Special token used to represent the beginning of audio.
-        audio_eos_token (`Optional[str]`, *optional*, defaults to `"<|sound_eos|>"`):
-            Special token used to represent the end of audio.
-        max_audio_len (`int`, *optional*, defaults to 1200):
-            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
-    """
-
     def __init__(
         self,
         feature_extractor,
@@ -135,6 +110,16 @@ def __init__(
         audio_eos_token="<|sound_eos|>",
         max_audio_len=1200,
     ):
+        r"""
+        audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
+            Special token used to represent audio inputs in the chat template.
+        audio_bos_token (`Optional[str]`, *optional*, defaults to `"<|sound_bos|>"`):
+            Special token used to represent the beginning of audio.
+        audio_eos_token (`Optional[str]`, *optional*, defaults to `"<|sound_eos|>"`):
+            Special token used to represent the end of audio.
+        max_audio_len (`int`, *optional*, defaults to 1200):
+            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
+        """
         super().__init__(
             feature_extractor,
             tokenizer,
@@ -148,23 +133,13 @@ def __init__(
         self.audio_bos_token_id = tokenizer.convert_tokens_to_ids(audio_bos_token)
         self.audio_eos_token_id = tokenizer.convert_tokens_to_ids(audio_eos_token)
 
-    def _expand_audio_tokens(self, text, padding_mask, per_sample_windows):
-        audio_lengths = torch.stack([s.sum() for s in torch.split(padding_mask.sum(-1), per_sample_windows)])
-        audio_tokens_lengths = self._get_audio_token_length(audio_lengths)
-        audio_token_pattern = re.compile(re.escape(self.audio_token))
-        for i, audio_length in enumerate(audio_tokens_lengths):
-            text[i] = audio_token_pattern.sub(
-                self.audio_bos_token + self.audio_token * audio_length + self.audio_eos_token,
-                text[i],
-            )
-        return text
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        num_audio_tokens = audio_inputs["num_audio_tokens"][audio_idx]
+        return self.audio_bos_token + self.audio_token * num_audio_tokens + self.audio_eos_token
 
-    def _get_audio_tokens_mask(self, input_ids):
-        return (
-            (input_ids == self.audio_token_id)
-            | (input_ids == self.audio_bos_token_id)
-            | (input_ids == self.audio_eos_token_id)
-        )
+    @property
+    def audio_ids(self):
+        return [self.audio_token_id, self.audio_bos_token_id, self.audio_eos_token_id]
 
     def apply_transcription_request(self, *args, **kwargs):
         raise NotImplementedError("This method is not supported for MusicFlamingo.")
@@ -274,6 +249,13 @@ def _build_audio_timestamps(
         _, ends = torch.where(diff == -1)
         sample_lengths = (ends - starts).to(torch.long)
 
+        n_audio_tokens = audio_token_mask.sum()
+        n_audio_features = post_lengths.sum()
+        torch_compilable_check(
+            n_audio_tokens == n_audio_features,
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+
         # Account for 4x downsampling in audio encoder (conv2 and avg pooling)
         audio_embed_frame_step = self.config.audio_frame_step * 4
         frame_offsets = (
@@ -408,10 +390,10 @@ def forward(
             ).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: CausalLMOutputWithPast = self.language_model(
             inputs_embeds=inputs_embeds,
diff --git a/src/transformers/models/musicflamingo/processing_musicflamingo.py b/src/transformers/models/musicflamingo/processing_musicflamingo.py
index 8e8fe5e5b438..79a40ae047e1 100644
--- a/src/transformers/models/musicflamingo/processing_musicflamingo.py
+++ b/src/transformers/models/musicflamingo/processing_musicflamingo.py
@@ -19,15 +19,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
-
 import numpy as np
 
-from ...audio_utils import AudioInput, make_list_of_audio
+from ...audio_utils import AudioInput
 from ...feature_extraction_utils import BatchFeature
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import TextInput
-from ...utils import is_torch_available, logging
+from ...utils import auto_docstring, is_torch_available, logging
 
 
 if is_torch_available():
@@ -54,31 +52,9 @@ class MusicFlamingoProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
+@auto_docstring
 class MusicFlamingoProcessor(ProcessorMixin):
-    r"""
-    Constructs an MusicFlamingo processor which wraps an MusicFlamingo feature extractor and an MusicFlamingo
-    tokenizer into a single processor.
-
-    [`MusicFlamingoProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`] and
-    [`Qwen2TokenizerFast`]. See the [`~MusicFlamingoProcessor.__call__`] for more information.
-
-    Args:
-        feature_extractor ([`WhisperFeatureExtractor`]):
-            The feature extractor is a required input.
-        tokenizer ([`Qwen2TokenizerFast`]):
-            The tokenizer is a required input.
-        chat_template (`Optional[str]`, *optional*):
-            The Jinja template to use for formatting the conversation. If not provided, the tokenizer's default chat
-            template will be used.
-        audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
-            Special token used to represent audio inputs in the chat template.
-        audio_bos_token (`Optional[str]`, *optional*, defaults to `"<|sound_bos|>"`):
-            Special token used to represent the beginning of audio.
-        audio_eos_token (`Optional[str]`, *optional*, defaults to `"<|sound_eos|>"`):
-            Special token used to represent the end of audio.
-        max_audio_len (`int`, *optional*, defaults to 1200):
-            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
-    """
+    valid_processor_kwargs = MusicFlamingoProcessorKwargs
 
     def __init__(
         self,
@@ -90,6 +66,16 @@ def __init__(
         audio_eos_token="<|sound_eos|>",
         max_audio_len=1200,
     ):
+        r"""
+        audio_token (`Optional[str]`, *optional*, defaults to `"<sound>"`):
+            Special token used to represent audio inputs in the chat template.
+        audio_bos_token (`Optional[str]`, *optional*, defaults to `"<|sound_bos|>"`):
+            Special token used to represent the beginning of audio.
+        audio_eos_token (`Optional[str]`, *optional*, defaults to `"<|sound_eos|>"`):
+            Special token used to represent the end of audio.
+        max_audio_len (`int`, *optional*, defaults to 1200):
+            Maximum length of audio sequences in seconds. Audio longer than this will be truncated.
+        """
         self.audio_token = audio_token
         self.audio_token_id = tokenizer.convert_tokens_to_ids(audio_token)
         self.max_audio_len = max_audio_len
@@ -99,29 +85,7 @@ def __init__(
         self.audio_bos_token_id = tokenizer.convert_tokens_to_ids(audio_bos_token)
         self.audio_eos_token_id = tokenizer.convert_tokens_to_ids(audio_eos_token)
 
-    def _get_audio_token_length(self, audio_lengths):
-        conv_output_lengths = (audio_lengths - 1) // 2 + 1  # After conv2 downsampling
-        audio_tokens_lengths = (conv_output_lengths - 2) // 2 + 1  # After avg pooling
-        return audio_tokens_lengths
-
-    def _expand_audio_tokens(self, text, padding_mask, per_sample_windows):
-        audio_lengths = torch.stack([s.sum() for s in torch.split(padding_mask.sum(-1), per_sample_windows)])
-        audio_tokens_lengths = self._get_audio_token_length(audio_lengths)
-        audio_token_pattern = re.compile(re.escape(self.audio_token))
-        for i, audio_length in enumerate(audio_tokens_lengths):
-            text[i] = audio_token_pattern.sub(
-                self.audio_bos_token + self.audio_token * audio_length + self.audio_eos_token,
-                text[i],
-            )
-        return text
-
-    def _get_audio_tokens_mask(self, input_ids):
-        return (
-            (input_ids == self.audio_token_id)
-            | (input_ids == self.audio_bos_token_id)
-            | (input_ids == self.audio_eos_token_id)
-        )
-
+    @auto_docstring
     def __call__(
         self,
         text: TextInput | list[TextInput],
@@ -130,98 +94,100 @@ def __call__(
         **kwargs: Unpack[MusicFlamingoProcessorKwargs],
     ) -> BatchFeature:
         r"""
-        Main method to prepare one or several text sequence(s) and audio waveform(s) for the model. This
-        method expands `<sound>` placeholders in the text based on the post-pool frame counts of the
-        audio windows, then tokenizes the provided strings as-is, and extracts log-mel features
-        with [`WhisperFeatureExtractor`]. If `audio` is `None`, no audio processing is performed and
-        the text is tokenized as-is (LM-only behavior).
-
-        Args:
-            text (`str` or `list[str]`):
-                Input sequence or batch of sequences.
-            audio (`np.ndarray` or `list[np.ndarray]`):
-                Input audio or batch of audios as NumPy arrays. If provided, there must be as many `text` inputs as
-                `audio` inputs.
-            output_labels (bool, *optional*, default=False):
-                Whether to return labels for training.
+        output_labels (bool, *optional*, default=False):
+            Whether to return labels for training.
 
         Returns:
             [`BatchFeature`]: A dictionary with tokenized text (`input_ids`, `attention_mask`) and
             audio features (`input_features`, `input_features_mask`).
         """
+        # Force tensor outputs for AudioFlamingo, other types not supported
+        kwargs["return_tensors"] = "pt"
 
-        # Merge defaults with user kwargs
-        call_kwargs = self._merge_kwargs(
-            MusicFlamingoProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
+        if output_labels:
+            kwargs["return_mm_token_type_ids"] = True
+        model_inputs = super().__call__(audio=audio, text=text, **kwargs)
 
-        text_kwargs = call_kwargs["text_kwargs"]
-        audio_kwargs = call_kwargs["audio_kwargs"]
-        return_tensors = text_kwargs.get("return_tensors")
-        if return_tensors != "pt":
-            raise ValueError(f"{self.__class__.__name__} only supports `return_tensors='pt'`.")
-
-        if isinstance(text, str):
-            text = [text]
-        elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)):
-            raise ValueError("Invalid input text. Please provide a string, or a list of strings")
-
-        audio_inputs = {}
-        if audio is not None:
-            audio = make_list_of_audio(audio)
-            if len(text) != len(audio):
-                raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
-
-            # Determine number of chunks per sample, and flatten
-            window_size = int(audio_kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
-            max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
-
-            per_sample_windows: list[int] = []
-            flat_chunks: list[np.ndarray] = []
-
-            for audio_el in audio:
-                n_samples = int(audio_el.shape[0])
-                n_win = max(1, (n_samples + window_size - 1) // window_size)
-                if n_win > max_windows:
-                    logger.warning(
-                        f"Audio duration ({n_samples / audio_kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
-                    )
-                    n_win = max_windows
-                per_sample_windows.append(n_win)
-
-                time_cap = min(n_samples, n_win * window_size)
-                for i in range(n_win):
-                    start = i * window_size
-                    end = min((i + 1) * window_size, time_cap)
-                    flat_chunks.append(audio_el[start:end])
-
-            # Feature extraction
-            audio_inputs = self.feature_extractor(flat_chunks, **audio_kwargs)
-            padding_mask = audio_inputs.pop("attention_mask")
-            audio_inputs["input_features_mask"] = padding_mask
-
-            # Expand audio tokens in text
-            text = self._expand_audio_tokens(text, padding_mask, per_sample_windows)
-
-        # Tokenize
-        text_inputs = self.tokenizer(text, **text_kwargs)
-
-        data = {**text_inputs, **audio_inputs}
         if output_labels:
-            labels = data["input_ids"].clone()
-            labels[self._get_audio_tokens_mask(labels)] = -100
+            labels = model_inputs.pop("mm_token_type_ids")
             labels[labels == self.tokenizer.pad_token_id] = -100
-            data["labels"] = labels
+            model_inputs["labels"] = labels
+        return BatchFeature(data=model_inputs, tensor_type="pt")
+
+    def validate_inputs(
+        self,
+        audio: AudioInput | None = None,
+        text: TextInput | list[TextInput] | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        super().validate_inputs(audio=audio, text=text, **kwargs)
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        if text is not None and audio is not None and len(text) != len(audio):
+            raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
+
+    def _get_audio_token_length(self, audio_lengths):
+        conv_output_lengths = (audio_lengths - 1) // 2 + 1  # After conv2 downsampling
+        audio_tokens_lengths = (conv_output_lengths - 2) // 2 + 1  # After avg pooling
+        return audio_tokens_lengths
+
+    def _process_audio(self, audio: AudioInput, **kwargs):
+        # Determine number of chunks per sample, and flatten
+        window_size = int(kwargs["sampling_rate"] * self.feature_extractor.chunk_length)
+        max_windows = int(self.max_audio_len // self.feature_extractor.chunk_length)
+
+        per_sample_windows: list[int] = []
+        flat_chunks: list[np.ndarray] = []
+        for audio_el in audio:
+            n_samples = int(audio_el.shape[0])
+            n_win = max(1, (n_samples + window_size - 1) // window_size)
+            if n_win > max_windows:
+                logger.warning(
+                    f"Audio duration ({n_samples / kwargs['sampling_rate']:.1f}s) exceeds {self.max_audio_len}s; truncating to first {self.max_audio_len}s."
+                )
+                n_win = max_windows
+            per_sample_windows.append(n_win)
+
+            time_cap = min(n_samples, n_win * window_size)
+            for i in range(n_win):
+                start = i * window_size
+                end = min((i + 1) * window_size, time_cap)
+                flat_chunks.append(audio_el[start:end])
+
+        audio = self.feature_extractor.fetch_audio(audio)
+        audio_inputs = self.feature_extractor(flat_chunks, **kwargs)
+        audio_inputs["input_features_mask"] = audio_inputs.pop("attention_mask")
+
+        # AudioFlamingo doesn't have its own feature extractor and crops audio into
+        # chunks here. Save the number of tokens based on crops/padding in analogy
+        # with some vision processors
+        audio_lengths = torch.stack(
+            [s.sum() for s in torch.split(audio_inputs["input_features_mask"].sum(-1), per_sample_windows)]
+        )
+        audio_inputs["num_audio_tokens"] = self._get_audio_token_length(audio_lengths)
+
+        audio_replacements = []
+        for idx in range(len(audio)):
+            replacement_text = self.replace_audio_token(audio_inputs, audio_idx=idx)
+            audio_replacements.append(replacement_text)
+
+        return audio_inputs, audio_replacements
+
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        num_audio_tokens = audio_inputs["num_audio_tokens"][audio_idx]
+        return self.audio_bos_token + self.audio_token * num_audio_tokens + self.audio_eos_token
 
     @property
     def model_input_names(self) -> list[str]:
-        tok_names = self.tokenizer.model_input_names
-        fea_names = self.feature_extractor.model_input_names
-        return list(dict.fromkeys(tok_names + fea_names + ["input_features_mask"]))
+        return super().model_input_names + ["input_features_mask"]
+
+    @property
+    def unused_input_names(self) -> list[str]:
+        "Input names returned always by subprocessors but not used in model's `forward`"
+        return ["num_audio_tokens"]
+
+    @property
+    def audio_ids(self):
+        return [self.audio_token_id, self.audio_bos_token_id, self.audio_eos_token_id]
 
 
 __all__ = ["MusicFlamingoProcessor"]
diff --git a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
index e1d7919b41bf..a3e3f5c3251c 100644
--- a/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
+++ b/src/transformers/models/musicgen_melody/modeling_musicgen_melody.py
@@ -57,12 +57,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Musicgen Melody autoregressive outputs.
     """
 )
+@dataclass
 class MusicgenMelodyOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/nanochat/modeling_nanochat.py b/src/transformers/models/nanochat/modeling_nanochat.py
index 9205b89cd360..11a60c7562fa 100644
--- a/src/transformers/models/nanochat/modeling_nanochat.py
+++ b/src/transformers/models/nanochat/modeling_nanochat.py
@@ -114,7 +114,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/nemotron/modeling_nemotron.py b/src/transformers/models/nemotron/modeling_nemotron.py
index b91b45ffd183..adedd4eec7cd 100644
--- a/src/transformers/models/nemotron/modeling_nemotron.py
+++ b/src/transformers/models/nemotron/modeling_nemotron.py
@@ -143,7 +143,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/nemotron_h/modeling_nemotron_h.py b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
index 93bd47f2c3f4..0c59c411af88 100644
--- a/src/transformers/models/nemotron_h/modeling_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modeling_nemotron_h.py
@@ -974,22 +974,27 @@ def _init_weights(self, module):
         """Initialize the weights."""
         super()._init_weights(module)
         if isinstance(module, NemotronHMamba2Mixer):
-            # Initialize A_log and D parameters
-            A = torch.arange(1, self.config.mamba_num_heads + 1)
-            init.copy_(module.A_log, torch.log(A))
-            init.ones_(module.D)
-
-            dt = torch.exp(
-                torch.rand(self.config.mamba_num_heads)
-                * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
-                + math.log(self.config.time_step_min)
-            ).clamp(min=self.config.time_step_floor)
-
-            # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
-            inv_dt = dt + torch.log(-torch.expm1(-dt))
-            with torch.no_grad():
-                init.copy_(module.dt_bias, inv_dt)
-            module.dt_bias._no_reinit = True
+            # Only re-initialise params that were NOT loaded from a checkpoint.
+            # `_is_hf_initialized` is set by `from_pretrained` on each loaded
+            # parameter; without this guard a post-load safety pass of
+            # `_init_weights` would overwrite checkpoint values of
+            # A_log / D / dt_bias with fresh random draws.
+            if not getattr(module.A_log, "_is_hf_initialized", False):
+                A = torch.arange(1, self.config.mamba_num_heads + 1)
+                init.copy_(module.A_log, torch.log(A))
+            if not getattr(module.D, "_is_hf_initialized", False):
+                init.ones_(module.D)
+            if not getattr(module.dt_bias, "_is_hf_initialized", False):
+                dt = torch.exp(
+                    torch.rand(self.config.mamba_num_heads)
+                    * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+                    + math.log(self.config.time_step_min)
+                ).clamp(min=self.config.time_step_floor)
+
+                # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+                inv_dt = dt + torch.log(-torch.expm1(-dt))
+                with torch.no_grad():
+                    init.copy_(module.dt_bias, inv_dt)
         elif isinstance(module, NemotronHTopkRouter):
             init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
             init.zeros_(module.e_score_correction_bias)
@@ -1014,10 +1019,12 @@ def _init_weights(self, module):
             # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
             for name, p in module.named_parameters():
                 if name == "out_proj.weight":
+                    # Skip checkpoint-loaded weights so a post-load safety
+                    # pass of `_init_weights` doesn't silently overwrite them.
+                    if getattr(p, "_is_hf_initialized", False):
+                        continue
                     # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
                     # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
-                    # We need to reinit p since this code could be called multiple times
-                    # Having just p *= scale would repeatedly scale it down
                     init.kaiming_uniform_(p, a=math.sqrt(5))
                     with torch.no_grad():
                         p_new = p / math.sqrt(self.config.num_hidden_layers)
diff --git a/src/transformers/models/nemotron_h/modular_nemotron_h.py b/src/transformers/models/nemotron_h/modular_nemotron_h.py
index 803e5c638239..3cf46e97d097 100644
--- a/src/transformers/models/nemotron_h/modular_nemotron_h.py
+++ b/src/transformers/models/nemotron_h/modular_nemotron_h.py
@@ -327,22 +327,27 @@ def _init_weights(self, module):
         """Initialize the weights."""
         super()._init_weights(module)
         if isinstance(module, NemotronHMamba2Mixer):
-            # Initialize A_log and D parameters
-            A = torch.arange(1, self.config.mamba_num_heads + 1)
-            init.copy_(module.A_log, torch.log(A))
-            init.ones_(module.D)
-
-            dt = torch.exp(
-                torch.rand(self.config.mamba_num_heads)
-                * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
-                + math.log(self.config.time_step_min)
-            ).clamp(min=self.config.time_step_floor)
-
-            # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
-            inv_dt = dt + torch.log(-torch.expm1(-dt))
-            with torch.no_grad():
-                init.copy_(module.dt_bias, inv_dt)
-            module.dt_bias._no_reinit = True
+            # Only re-initialise params that were NOT loaded from a checkpoint.
+            # `_is_hf_initialized` is set by `from_pretrained` on each loaded
+            # parameter; without this guard a post-load safety pass of
+            # `_init_weights` would overwrite checkpoint values of
+            # A_log / D / dt_bias with fresh random draws.
+            if not getattr(module.A_log, "_is_hf_initialized", False):
+                A = torch.arange(1, self.config.mamba_num_heads + 1)
+                init.copy_(module.A_log, torch.log(A))
+            if not getattr(module.D, "_is_hf_initialized", False):
+                init.ones_(module.D)
+            if not getattr(module.dt_bias, "_is_hf_initialized", False):
+                dt = torch.exp(
+                    torch.rand(self.config.mamba_num_heads)
+                    * (math.log(self.config.time_step_max) - math.log(self.config.time_step_min))
+                    + math.log(self.config.time_step_min)
+                ).clamp(min=self.config.time_step_floor)
+
+                # # Inverse of softplus: https://github.com/pytorch/pytorch/issues/72759
+                inv_dt = dt + torch.log(-torch.expm1(-dt))
+                with torch.no_grad():
+                    init.copy_(module.dt_bias, inv_dt)
         elif isinstance(module, NemotronHTopkRouter):
             init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
             init.zeros_(module.e_score_correction_bias)
@@ -367,10 +372,12 @@ def _init_weights(self, module):
             # Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
             for name, p in module.named_parameters():
                 if name == "out_proj.weight":
+                    # Skip checkpoint-loaded weights so a post-load safety
+                    # pass of `_init_weights` doesn't silently overwrite them.
+                    if getattr(p, "_is_hf_initialized", False):
+                        continue
                     # Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
                     # Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
-                    # We need to reinit p since this code could be called multiple times
-                    # Having just p *= scale would repeatedly scale it down
                     init.kaiming_uniform_(p, a=math.sqrt(5))
                     with torch.no_grad():
                         p_new = p / math.sqrt(self.config.num_hidden_layers)
diff --git a/src/transformers/models/olmo3/modeling_olmo3.py b/src/transformers/models/olmo3/modeling_olmo3.py
index 78ade3570f97..a0a3ec133bfb 100644
--- a/src/transformers/models/olmo3/modeling_olmo3.py
+++ b/src/transformers/models/olmo3/modeling_olmo3.py
@@ -281,8 +281,8 @@ def __init__(self, config: Olmo3Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -322,7 +322,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
index 5c76f0a8ca22..6cee964946ca 100644
--- a/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
+++ b/src/transformers/models/olmo_hybrid/modeling_olmo_hybrid.py
@@ -431,8 +431,8 @@ def __init__(self, config: OlmoHybridConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -628,6 +628,24 @@ def torch_recurrent_gated_delta_rule(
 )
 
 
+def _cu_seqlens_from_packed_mask(attention_mask: torch.Tensor) -> torch.Tensor:
+    """Derive ``cu_seqlens`` from a packed attention mask with unique sequence IDs.
+
+    For a mask like ``[1, 1, 1, 2, 2, 0, 0]``, returns ``cu_seqlens = [0, 3, 5]``
+    (ignoring padding).  For a standard ``0/1`` mask, returns ``[0, num_ones]``.
+    """
+    flat = attention_mask.flatten()
+    non_pad = flat > 0
+    non_pad_ids = flat[non_pad]
+    if len(non_pad_ids) == 0:
+        return torch.tensor([0], dtype=torch.int32, device=attention_mask.device)
+    boundaries = torch.where(non_pad_ids[1:] != non_pad_ids[:-1])[0] + 1
+    cu_seqlens = torch.zeros(len(boundaries) + 2, dtype=torch.int32, device=attention_mask.device)
+    cu_seqlens[1:-1] = boundaries
+    cu_seqlens[-1] = len(non_pad_ids)
+    return cu_seqlens
+
+
 class OlmoHybridGatedDeltaNet(nn.Module):
     """
     GatedDeltaNet linear attention for OLMo Hybrid.
@@ -726,9 +744,6 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.Tensor:
-        # Requires LEFT padding to work correctly
-        hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
-
         batch_size, seq_len, _ = hidden_states.shape
 
         use_cache = cache_params is not None
@@ -737,6 +752,21 @@ def forward(
         # below, each of which gates on `seq_len == 1` locally.
         use_precomputed = use_cache and cache_params.has_previous_state()
 
+        # For packed sequences (attention_mask with unique sequence IDs > 1), derive
+        # cu_seqlens and unpad so recurrent state doesn't leak across sequence boundaries.
+        # Requires the FLA fast path; torch fallbacks don't support cu_seqlens.
+        cu_seqlens = None
+        unpad_indices = None
+        if attention_mask is not None and not use_precomputed and is_fast_path_available and attention_mask.max() > 1:
+            cu_seqlens = _cu_seqlens_from_packed_mask(attention_mask)
+            unpad_indices = attention_mask.flatten() > 0
+            hidden_states = hidden_states[:, unpad_indices, :]
+        else:
+            # Requires LEFT padding to work correctly
+            hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
+
+        effective_batch, effective_len, _ = hidden_states.shape
+
         conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None
         conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None
         conv_state_v = cache_params.conv_states_v[self.layer_idx] if cache_params else None
@@ -747,13 +777,13 @@ def forward(
         v = self.v_proj(hidden_states)
 
         q, new_conv_state_q = self.q_conv1d(
-            q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache
+            q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
         k, new_conv_state_k = self.k_conv1d(
-            k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache
+            k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
         v, new_conv_state_v = self.v_conv1d(
-            v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache
+            v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
 
         if cache_params is not None:
@@ -761,9 +791,9 @@ def forward(
             cache_params.conv_states_k[self.layer_idx] = new_conv_state_k
             cache_params.conv_states_v[self.layer_idx] = new_conv_state_v
 
-        q = q.view(batch_size, seq_len, -1, self.head_k_dim)
-        k = k.view(batch_size, seq_len, -1, self.head_k_dim)
-        v = v.view(batch_size, seq_len, -1, self.head_v_dim)
+        q = q.view(effective_batch, effective_len, -1, self.head_k_dim)
+        k = k.view(effective_batch, effective_len, -1, self.head_k_dim)
+        v = v.view(effective_batch, effective_len, -1, self.head_v_dim)
 
         if self.num_v_heads > self.num_k_heads:
             expand_ratio = self.num_v_heads // self.num_k_heads
@@ -788,6 +818,7 @@ def forward(
                 use_qk_l2norm_in_kernel=True,
             )
         else:
+            chunk_extra_kwargs = {"cu_seqlens": cu_seqlens} if cu_seqlens is not None else {}
             output, new_recurrent_state = self.chunk_gated_delta_rule(
                 q,
                 k,
@@ -797,6 +828,7 @@ def forward(
                 initial_state=recurrent_state if use_precomputed else None,
                 output_final_state=use_cache,
                 use_qk_l2norm_in_kernel=True,
+                **chunk_extra_kwargs,
             )
 
         if cache_params is not None:
@@ -806,10 +838,16 @@ def forward(
         output = output.reshape(-1, self.head_v_dim)
         gate = gate.reshape(-1, self.head_v_dim)
         output = self.o_norm(output, gate)
-        output = output.reshape(batch_size, seq_len, -1)
+        output = output.reshape(effective_batch, effective_len, -1)
 
         output = self.o_proj(output)
 
+        # Re-pad output to original shape for packed sequences
+        if unpad_indices is not None:
+            output_padded = output.new_zeros(batch_size, seq_len, output.shape[-1])
+            output_padded[:, unpad_indices, :] = output
+            output = output_padded
+
         return output
 
 
diff --git a/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py b/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
index 7e40d6d61f5d..967f54ab79eb 100644
--- a/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
+++ b/src/transformers/models/olmo_hybrid/modular_olmo_hybrid.py
@@ -444,6 +444,24 @@ def forward(self, x, position_ids):
         return cos, sin
 
 
+def _cu_seqlens_from_packed_mask(attention_mask: torch.Tensor) -> torch.Tensor:
+    """Derive ``cu_seqlens`` from a packed attention mask with unique sequence IDs.
+
+    For a mask like ``[1, 1, 1, 2, 2, 0, 0]``, returns ``cu_seqlens = [0, 3, 5]``
+    (ignoring padding).  For a standard ``0/1`` mask, returns ``[0, num_ones]``.
+    """
+    flat = attention_mask.flatten()
+    non_pad = flat > 0
+    non_pad_ids = flat[non_pad]
+    if len(non_pad_ids) == 0:
+        return torch.tensor([0], dtype=torch.int32, device=attention_mask.device)
+    boundaries = torch.where(non_pad_ids[1:] != non_pad_ids[:-1])[0] + 1
+    cu_seqlens = torch.zeros(len(boundaries) + 2, dtype=torch.int32, device=attention_mask.device)
+    cu_seqlens[1:-1] = boundaries
+    cu_seqlens[-1] = len(non_pad_ids)
+    return cu_seqlens
+
+
 class OlmoHybridGatedDeltaNet(nn.Module):
     """
     GatedDeltaNet linear attention for OLMo Hybrid.
@@ -542,9 +560,6 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> torch.Tensor:
-        # Requires LEFT padding to work correctly
-        hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
-
         batch_size, seq_len, _ = hidden_states.shape
 
         use_cache = cache_params is not None
@@ -553,6 +568,21 @@ def forward(
         # below, each of which gates on `seq_len == 1` locally.
         use_precomputed = use_cache and cache_params.has_previous_state()
 
+        # For packed sequences (attention_mask with unique sequence IDs > 1), derive
+        # cu_seqlens and unpad so recurrent state doesn't leak across sequence boundaries.
+        # Requires the FLA fast path; torch fallbacks don't support cu_seqlens.
+        cu_seqlens = None
+        unpad_indices = None
+        if attention_mask is not None and not use_precomputed and is_fast_path_available and attention_mask.max() > 1:
+            cu_seqlens = _cu_seqlens_from_packed_mask(attention_mask)
+            unpad_indices = attention_mask.flatten() > 0
+            hidden_states = hidden_states[:, unpad_indices, :]
+        else:
+            # Requires LEFT padding to work correctly
+            hidden_states = apply_mask_to_padding_states(hidden_states, attention_mask)
+
+        effective_batch, effective_len, _ = hidden_states.shape
+
         conv_state_q = cache_params.conv_states_q[self.layer_idx] if cache_params else None
         conv_state_k = cache_params.conv_states_k[self.layer_idx] if cache_params else None
         conv_state_v = cache_params.conv_states_v[self.layer_idx] if cache_params else None
@@ -563,13 +593,13 @@ def forward(
         v = self.v_proj(hidden_states)
 
         q, new_conv_state_q = self.q_conv1d(
-            q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache
+            q, cache=conv_state_q, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
         k, new_conv_state_k = self.k_conv1d(
-            k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache
+            k, cache=conv_state_k, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
         v, new_conv_state_v = self.v_conv1d(
-            v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache
+            v, cache=conv_state_v, use_precomputed=use_precomputed, output_final_state=use_cache, cu_seqlens=cu_seqlens
         )
 
         if cache_params is not None:
@@ -577,9 +607,9 @@ def forward(
             cache_params.conv_states_k[self.layer_idx] = new_conv_state_k
             cache_params.conv_states_v[self.layer_idx] = new_conv_state_v
 
-        q = q.view(batch_size, seq_len, -1, self.head_k_dim)
-        k = k.view(batch_size, seq_len, -1, self.head_k_dim)
-        v = v.view(batch_size, seq_len, -1, self.head_v_dim)
+        q = q.view(effective_batch, effective_len, -1, self.head_k_dim)
+        k = k.view(effective_batch, effective_len, -1, self.head_k_dim)
+        v = v.view(effective_batch, effective_len, -1, self.head_v_dim)
 
         if self.num_v_heads > self.num_k_heads:
             expand_ratio = self.num_v_heads // self.num_k_heads
@@ -604,6 +634,7 @@ def forward(
                 use_qk_l2norm_in_kernel=True,
             )
         else:
+            chunk_extra_kwargs = {"cu_seqlens": cu_seqlens} if cu_seqlens is not None else {}
             output, new_recurrent_state = self.chunk_gated_delta_rule(
                 q,
                 k,
@@ -613,6 +644,7 @@ def forward(
                 initial_state=recurrent_state if use_precomputed else None,
                 output_final_state=use_cache,
                 use_qk_l2norm_in_kernel=True,
+                **chunk_extra_kwargs,
             )
 
         if cache_params is not None:
@@ -622,10 +654,16 @@ def forward(
         output = output.reshape(-1, self.head_v_dim)
         gate = gate.reshape(-1, self.head_v_dim)
         output = self.o_norm(output, gate)
-        output = output.reshape(batch_size, seq_len, -1)
+        output = output.reshape(effective_batch, effective_len, -1)
 
         output = self.o_proj(output)
 
+        # Re-pad output to original shape for packed sequences
+        if unpad_indices is not None:
+            output_padded = output.new_zeros(batch_size, seq_len, output.shape[-1])
+            output_padded[:, unpad_indices, :] = output
+            output = output_padded
+
         return output
 
 
diff --git a/src/transformers/models/olmoe/modeling_olmoe.py b/src/transformers/models/olmoe/modeling_olmoe.py
index 5d89ec741529..641654b58e0c 100644
--- a/src/transformers/models/olmoe/modeling_olmoe.py
+++ b/src/transformers/models/olmoe/modeling_olmoe.py
@@ -123,7 +123,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -350,8 +350,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -555,7 +555,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -563,7 +563,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -580,8 +582,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py b/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py
index d06b94edcc9f..a00c0ab17c77 100644
--- a/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py
+++ b/src/transformers/models/omdet_turbo/modeling_omdet_turbo.py
@@ -45,12 +45,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the OmDetTurboHybridEncoder.
     """
 )
+@dataclass
 class OmDetTurboEncoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor`):
@@ -65,12 +65,12 @@ class OmDetTurboEncoderOutput(ModelOutput):
     extracted_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the OmDetTurboDecoder.
     """
 )
+@dataclass
 class OmDetTurboDecoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -100,12 +100,12 @@ class OmDetTurboDecoderOutput(ModelOutput):
     intermediate_reference_points: tuple[tuple[torch.FloatTensor]] = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`OmDetTurboObjectDetectionOutput`].
     """
 )
+@dataclass
 class OmDetTurboObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor`):
diff --git a/src/transformers/models/omdet_turbo/processing_omdet_turbo.py b/src/transformers/models/omdet_turbo/processing_omdet_turbo.py
index 6c154978cedb..915d77033e3c 100644
--- a/src/transformers/models/omdet_turbo/processing_omdet_turbo.py
+++ b/src/transformers/models/omdet_turbo/processing_omdet_turbo.py
@@ -33,6 +33,7 @@
 
 if TYPE_CHECKING:
     from .modeling_omdet_turbo import OmDetTurboObjectDetectionOutput
+from ..detr.image_processing_detr import DetrImageProcessorKwargs
 
 
 class OmDetTurboTextKwargs(TextKwargs, total=False):
@@ -55,6 +56,7 @@ class OmDetTurboTextKwargs(TextKwargs, total=False):
 
 
 class OmDetTurboProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: DetrImageProcessorKwargs
     text_kwargs: OmDetTurboTextKwargs
     _defaults = {
         "text_kwargs": {
diff --git a/src/transformers/models/oneformer/image_processing_oneformer.py b/src/transformers/models/oneformer/image_processing_oneformer.py
index ccee798bc39e..c7dc8399b6f2 100644
--- a/src/transformers/models/oneformer/image_processing_oneformer.py
+++ b/src/transformers/models/oneformer/image_processing_oneformer.py
@@ -43,7 +43,7 @@
 
 try:
     from huggingface_hub import hf_hub_download
-    from huggingface_hub.utils import RepositoryNotFoundError
+    from huggingface_hub.errors import RepositoryNotFoundError
 except ImportError:
     hf_hub_download = None
     RepositoryNotFoundError = None
diff --git a/src/transformers/models/oneformer/modeling_oneformer.py b/src/transformers/models/oneformer/modeling_oneformer.py
index 57214df16d82..3c9d917c29a1 100644
--- a/src/transformers/models/oneformer/modeling_oneformer.py
+++ b/src/transformers/models/oneformer/modeling_oneformer.py
@@ -786,7 +786,6 @@ class OneFormerPixelDecoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     OneFormer's pixel level module output. It returns both the last and (optionally) the hidden states from the
@@ -794,6 +793,7 @@ class OneFormerPixelDecoderOutput(ModelOutput):
     Deformable Attention based decoder.
     """
 )
+@dataclass
 class OneFormerPixelLevelModuleOutput(ModelOutput):
     r"""
     encoder_features (List of `(torch.FloatTensor)`):
@@ -811,12 +811,12 @@ class OneFormerPixelLevelModuleOutput(ModelOutput):
     decoder_last_feature: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`OneFormerModel`]. This class returns all the needed hidden states to compute the logits.
     """
 )
+@dataclass
 class OneFormerModelOutput(ModelOutput):
     r"""
     encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -3120,6 +3120,13 @@ def forward(
         '👉 Panoptic Predictions Shape: [512, 683]'
         ```
         """
+        if mask_labels is not None:
+            target_device = pixel_values.device
+            mask_labels = [mask.to(target_device) for mask in mask_labels]
+
+        if class_labels is not None:
+            target_device = pixel_values.device
+            class_labels = [label.to(target_device) for label in class_labels]
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (
diff --git a/src/transformers/models/openai/modeling_openai.py b/src/transformers/models/openai/modeling_openai.py
index 0fc89733b282..3c5eba0f5006 100644
--- a/src/transformers/models/openai/modeling_openai.py
+++ b/src/transformers/models/openai/modeling_openai.py
@@ -271,12 +271,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.config.n_positions))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of models predicting if two sentences are consecutive or not.
     """
 )
+@dataclass
 class OpenAIGPTDoubleHeadsModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/ovis2/modeling_ovis2.py b/src/transformers/models/ovis2/modeling_ovis2.py
index 4b389e4c66b7..b9b7a1143765 100644
--- a/src/transformers/models/ovis2/modeling_ovis2.py
+++ b/src/transformers/models/ovis2/modeling_ovis2.py
@@ -41,8 +41,8 @@
 from .configuration_ovis2 import Ovis2Config, Ovis2VisionConfig
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithVisualIndicatorFeatures(BaseModelOutputWithPooling):
     r"""
     visual_indicator_features (`torch.FloatTensor` of shape `(batch_size, visual_indicator_size)`):
@@ -73,12 +73,12 @@ class Ovis2ModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Ovis2 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Ovis2CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -531,9 +531,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -581,11 +581,7 @@ def forward(
                     mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
 
                 if mask.any():
-                    inputs_embeds[mask] = (
-                        visual_indicator_features[i]
-                        .expand_as(inputs_embeds[mask])
-                        .to(inputs_embeds.device, inputs_embeds.dtype)
-                    )
+                    inputs_embeds[mask] = visual_indicator_features[i].to(inputs_embeds.device, inputs_embeds.dtype)
 
         outputs = self.language_model(
             attention_mask=attention_mask,
diff --git a/src/transformers/models/ovis2/modular_ovis2.py b/src/transformers/models/ovis2/modular_ovis2.py
index 74c1aa66b7ce..a3243790f50c 100644
--- a/src/transformers/models/ovis2/modular_ovis2.py
+++ b/src/transformers/models/ovis2/modular_ovis2.py
@@ -46,8 +46,8 @@ def hard_softmax(logits: torch.Tensor, dim: int):
     return ret
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithVisualIndicatorFeatures(BaseModelOutputWithPooling):
     r"""
     visual_indicator_features (`torch.FloatTensor` of shape `(batch_size, visual_indicator_size)`):
@@ -332,11 +332,7 @@ def forward(
                     mask = (input_ids == visual_indicator_id).to(inputs_embeds.device)
 
                 if mask.any():
-                    inputs_embeds[mask] = (
-                        visual_indicator_features[i]
-                        .expand_as(inputs_embeds[mask])
-                        .to(inputs_embeds.device, inputs_embeds.dtype)
-                    )
+                    inputs_embeds[mask] = visual_indicator_features[i].to(inputs_embeds.device, inputs_embeds.dtype)
 
         outputs = self.language_model(
             attention_mask=attention_mask,
diff --git a/src/transformers/models/ovis2/processing_ovis2.py b/src/transformers/models/ovis2/processing_ovis2.py
index acebbb4b2f84..9f60255c9ca5 100644
--- a/src/transformers/models/ovis2/processing_ovis2.py
+++ b/src/transformers/models/ovis2/processing_ovis2.py
@@ -18,12 +18,14 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_ovis2 import Ovis2ImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Ovis2ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Ovis2ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
diff --git a/src/transformers/models/owlv2/modeling_owlv2.py b/src/transformers/models/owlv2/modeling_owlv2.py
index e14c92a52754..b44970ef1299 100644
--- a/src/transformers/models/owlv2/modeling_owlv2.py
+++ b/src/transformers/models/owlv2/modeling_owlv2.py
@@ -168,12 +168,12 @@ def generalized_box_iou(boxes1, boxes2):
     return iou - (area - union) / area
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`Owlv2ForObjectDetection`].
     """
 )
+@dataclass
 class Owlv2ObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/owlvit/modeling_owlvit.py b/src/transformers/models/owlvit/modeling_owlvit.py
index fba1f05ed594..85b0578dd5c9 100644
--- a/src/transformers/models/owlvit/modeling_owlvit.py
+++ b/src/transformers/models/owlvit/modeling_owlvit.py
@@ -168,12 +168,12 @@ def generalized_box_iou(boxes1, boxes2):
     return iou - (area - union) / area
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`OwlViTForObjectDetection`].
     """
 )
+@dataclass
 class OwlViTObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
@@ -220,12 +220,12 @@ def to_tuple(self) -> tuple[Any]:
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`OwlViTForObjectDetection.image_guided_detection`].
     """
 )
+@dataclass
 class OwlViTImageGuidedObjectDetectionOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, num_patches, num_queries)`):
diff --git a/src/transformers/models/paddleocr_vl/convert_paddleocr_vl_to_hf.py b/src/transformers/models/paddleocr_vl/convert_paddleocr_vl_to_hf.py
new file mode 100644
index 000000000000..4d4728331f77
--- /dev/null
+++ b/src/transformers/models/paddleocr_vl/convert_paddleocr_vl_to_hf.py
@@ -0,0 +1,271 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import glob
+import re
+
+import torch
+from huggingface_hub import snapshot_download
+from PIL import Image
+from safetensors import safe_open
+
+from transformers import (
+    AutoProcessor,
+    PaddleOCRTextConfig,
+    PaddleOCRVisionConfig,
+    PaddleOCRVLConfig,
+    PaddleOCRVLForConditionalGeneration,
+)
+
+
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+    r"^visual\.": r"model.visual.",
+    r"^mlp_AR\.": r"model.projector.",
+    r"^model\.(?!visual\.|projector\.|language_model\.)": r"model.language_model.",
+}
+
+# Keys present in the original checkpoint that are not needed
+KEYS_TO_IGNORE = [
+    "packing_position_embedding",
+    "vision_model.head",
+]
+
+
+def convert_old_keys_to_new_keys(state_dict_keys):
+    output_dict = {}
+    if state_dict_keys is not None:
+        old_text = "\n".join(state_dict_keys)
+        new_text = old_text
+        for pattern, replacement in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+            if replacement is None:
+                new_text = re.sub(pattern, "", new_text)
+                continue
+            new_text = re.sub(pattern, replacement, new_text, flags=re.MULTILINE)
+        output_dict = dict(zip(old_text.split("\n"), new_text.split("\n")))
+    return output_dict
+
+
+def load_original_state_dict(model_id):
+    directory_path = snapshot_download(repo_id=model_id, allow_patterns=["*.safetensors"])
+
+    original_state_dict = {}
+    for path in sorted(glob.glob(f"{directory_path}/*.safetensors")):
+        with safe_open(path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                original_state_dict[key] = f.get_tensor(key)
+
+    return original_state_dict
+
+
+def get_paddleocr_vl_config():
+    vision_config = PaddleOCRVisionConfig(
+        hidden_size=1152,
+        intermediate_size=4304,
+        num_hidden_layers=27,
+        num_attention_heads=16,
+        num_channels=3,
+        image_size=384,
+        patch_size=14,
+        hidden_act="gelu_pytorch_tanh",
+        layer_norm_eps=1e-6,
+        attention_dropout=0.0,
+        spatial_merge_size=2,
+    )
+
+    text_config = PaddleOCRTextConfig(
+        vocab_size=103424,
+        hidden_size=1024,
+        intermediate_size=3072,
+        num_hidden_layers=18,
+        num_attention_heads=16,
+        num_key_value_heads=2,
+        hidden_act="silu",
+        max_position_embeddings=131072,
+        rms_norm_eps=1e-5,
+        use_cache=True,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=True,
+        use_bias=False,
+        head_dim=128,
+        rope_theta=500000.0,
+        rope_scaling={
+            "mrope_section": [16, 24, 24],
+            "rope_type": "default",
+            "type": "default",
+        },
+    )
+
+    config = PaddleOCRVLConfig(
+        vision_config=vision_config.to_dict(),
+        text_config=text_config.to_dict(),
+        image_token_id=100295,
+        video_token_id=101307,
+        vision_start_token_id=101305,
+        vision_end_token_id=101306,
+        tie_word_embeddings=True,
+    )
+
+    return config
+
+
+@torch.no_grad()
+def convert_paddleocr_vl_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False, verify_logits=True):
+    print(f"Loading original state dict from {model_name}...")
+    original_state_dict = load_original_state_dict(model_name)
+    print(f"Loaded {len(original_state_dict)} keys from original checkpoint.")
+
+    # 2. Convert keys
+    all_keys = list(original_state_dict.keys())
+    new_keys = convert_old_keys_to_new_keys(all_keys)
+
+    state_dict = {}
+    for old_key in all_keys:
+        new_key = new_keys[old_key]
+
+        if any(ignored in old_key for ignored in KEYS_TO_IGNORE):
+            print(f"  Skipping: {old_key}")
+            continue
+
+        state_dict[new_key] = original_state_dict[old_key]
+
+    embed_key = "model.language_model.embed_tokens.weight"
+    lm_head_key = "lm_head.weight"
+    if lm_head_key in state_dict and embed_key in state_dict:
+        if torch.equal(state_dict[lm_head_key], state_dict[embed_key]):
+            print("lm_head.weight is identical to embed_tokens.weight (will be tied after save).")
+        else:
+            print("WARNING: lm_head.weight differs from embed_tokens.weight.")
+
+    print(f"Converted state dict has {len(state_dict)} keys.")
+
+    config = get_paddleocr_vl_config()
+
+    print("Loading weights into PaddleOCRVLForConditionalGeneration...")
+    with torch.device("meta"):
+        model = PaddleOCRVLForConditionalGeneration(config)
+
+    model.load_state_dict(state_dict, strict=True, assign=True)
+    model.eval()
+    print("Checkpoint loaded successfully.")
+
+    print(f"Saving processor from {model_name}...")
+    processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
+    processor.save_pretrained(pytorch_dump_folder_path)
+
+    print(f"Saving converted model to {pytorch_dump_folder_path}...")
+    model.save_pretrained(pytorch_dump_folder_path)
+    print("Model saved successfully.")
+
+    if verify_logits:
+        print("Verifying logits between original and converted model...")
+        verify_model_outputs(model_name, pytorch_dump_folder_path, processor)
+
+    if push_to_hub:
+        print("Pushing model and processor to the hub...")
+        model.push_to_hub("PaddlePaddle/PaddleOCR-VL-hf")
+        processor.push_to_hub("PaddlePaddle/PaddleOCR-VL-hf")
+        print("Pushed to hub successfully.")
+
+
+def verify_model_outputs(original_model_name, converted_model_path, processor):
+    print("  Loading original model via native PaddleOCRVLForConditionalGeneration...")
+    original_model = PaddleOCRVLForConditionalGeneration.from_pretrained(
+        original_model_name,
+        torch_dtype=torch.bfloat16,
+    ).eval()
+
+    # Load converted model
+    print("  Loading converted model...")
+    converted_model = PaddleOCRVLForConditionalGeneration.from_pretrained(
+        converted_model_path,
+        torch_dtype=torch.bfloat16,
+    ).eval()
+
+    dummy_image = Image.new("RGB", (56, 56), color=(128, 100, 80))
+    messages = [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": dummy_image},
+                {"type": "text", "text": "OCR:"},
+            ],
+        }
+    ]
+    inputs = processor.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_dict=True,
+        return_tensors="pt",
+    )
+
+    print("  Running forward pass on original model...")
+    original_inputs = {k: v.to(original_model.device) for k, v in inputs.items()}
+    original_outputs = original_model(**original_inputs)
+
+    print("  Running forward pass on converted model...")
+    converted_inputs = {k: v.to(converted_model.device) for k, v in inputs.items()}
+    converted_outputs = converted_model(**converted_inputs)
+
+    # Compare logits
+    original_logits = original_outputs.logits
+    converted_logits = converted_outputs.logits
+
+    print(f"  Original logits shape: {original_logits.shape}")
+    print(f"  Converted logits shape: {converted_logits.shape}")
+    print(f"  Original logits sample: {original_logits[0, :3, :3]}")
+    print(f"  Converted logits sample: {converted_logits[0, :3, :3]}")
+
+    torch.testing.assert_close(original_logits, converted_logits, atol=1e-4, rtol=1e-4)
+    print("  Logits match! Conversion verified successfully.")
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        default="PaddlePaddle/PaddleOCR-VL",
+        type=str,
+        help="Hub ID of the original PaddleOCR-VL model.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        required=True,
+        type=str,
+        help="Path to the output directory where the converted model will be saved.",
+    )
+    parser.add_argument(
+        "--push_to_hub",
+        action="store_true",
+        help="Whether or not to push the converted model to the Hugging Face hub.",
+    )
+    parser.add_argument(
+        "--no_verify_logits",
+        action="store_true",
+        help="Skip logits verification between original and converted model.",
+    )
+
+    args = parser.parse_args()
+    convert_paddleocr_vl_checkpoint(
+        model_name=args.model_name,
+        pytorch_dump_folder_path=args.pytorch_dump_folder_path,
+        push_to_hub=args.push_to_hub,
+        verify_logits=not args.no_verify_logits,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py b/src/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py
index 0ae254feef39..3ed2c351b7a8 100644
--- a/src/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py
+++ b/src/transformers/models/paddleocr_vl/modeling_paddleocr_vl.py
@@ -973,12 +973,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class PaddleOCRVLModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -997,12 +997,12 @@ class PaddleOCRVLModelOutputWithPast(ModelOutput):
     rope_deltas: torch.LongTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for PaddleOCRVL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class PaddleOCRVLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1264,10 +1264,10 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0] * image_features.shape[1]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py b/src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py
index 02895d6e2576..19c1c264d4c9 100644
--- a/src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py
+++ b/src/transformers/models/paddleocr_vl/modular_paddleocr_vl.py
@@ -1014,10 +1014,10 @@ def get_placeholder_mask(
             special_image_mask = input_ids == self.config.image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0] * image_features.shape[1]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/paddleocr_vl/processing_paddleocr_vl.py b/src/transformers/models/paddleocr_vl/processing_paddleocr_vl.py
index 5a71289e0188..0e0f93733f63 100644
--- a/src/transformers/models/paddleocr_vl/processing_paddleocr_vl.py
+++ b/src/transformers/models/paddleocr_vl/processing_paddleocr_vl.py
@@ -27,9 +27,11 @@
 from ...image_utils import ImageInput
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from .image_processing_paddleocr_vl import PaddleOCRVLImageProcessorKwargs
 
 
 class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: PaddleOCRVLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
diff --git a/src/transformers/models/paligemma/modeling_paligemma.py b/src/transformers/models/paligemma/modeling_paligemma.py
index 6eeeaa6bd681..ffabe3d0e8e9 100644
--- a/src/transformers/models/paligemma/modeling_paligemma.py
+++ b/src/transformers/models/paligemma/modeling_paligemma.py
@@ -22,7 +22,7 @@
 from ...cache_utils import Cache
 from ...configuration_utils import PreTrainedConfig
 from ...generation import GenerationMixin
-from ...masking_utils import create_masks_for_generate
+from ...masking_utils import create_causal_mask, create_masks_for_generate, create_sliding_window_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
 from ...modeling_utils import PreTrainedModel
@@ -59,12 +59,12 @@ class PaligemmaModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for PaliGemma causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class PaliGemmaCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -100,29 +100,42 @@ def forward(self, image_features):
         return hidden_states
 
 
-def token_type_ids_mask_function(group_ids: torch.Tensor) -> Callable:
+def token_type_ids_mask_function(
+    token_type_ids: torch.Tensor | None,
+    image_group_ids: torch.Tensor | None,
+) -> Callable | None:
     """
     This function adds the correct offsets to the `q_idx` and `kv_idx` as the torch API can only accept lengths,
     not start and end indices.
-    Args:
-        group_ids (`torch.Tensor`):
-            A tensor of shape `(bs, len)` assigning each token to a vision group. Tokens with the same group
-            come from the same input image. Text is denoted by `-1`.
     """
+    # Do not return an additional mask in this case
+    if token_type_ids is None:
+        return None
 
     def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
-        seq_length = group_ids.shape[-1]
+        # If it's 1 for both query and key/value, we are in an image block
+        # NOTE: static cache shape goes beyond input seq length, while token_type_ids.shape[1] == input seq length
+        # Since vmap doesn't support `if statement` we workaround it with `torch.where`
+        safe_q_idx = torch.where(q_idx < token_type_ids.shape[1], q_idx, 0)
+        safe_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], kv_idx, 0)
+
+        token_type_ids_at_q_idx = token_type_ids[batch_idx, safe_q_idx]
+        token_type_ids_at_q_idx = torch.where(q_idx < token_type_ids.shape[1], token_type_ids_at_q_idx, 0)
+
+        token_type_ids_at_kv_idx = token_type_ids[batch_idx, safe_kv_idx]
+        token_type_ids_at_kv_idx = torch.where(kv_idx < token_type_ids.shape[1], token_type_ids_at_kv_idx, 0)
 
-        # clamp indices because with static cache they can go beyond `group_ids.shape[-1]`
-        q_idx_clamped = q_idx.clamp(max=seq_length - 1)
-        kv_idx_clamped = kv_idx.clamp(max=seq_length - 1)
+        image_group_ids_at_q_idx = image_group_ids[batch_idx, safe_q_idx]
+        image_group_ids_at_q_idx = torch.where(q_idx < image_group_ids.shape[1], image_group_ids_at_q_idx, -1)
 
-        # Unmask if the q and kv come from same group which is not -1 (i.e. non-text)
-        q_group = group_ids[batch_idx, q_idx_clamped]
-        kv_group = group_ids[batch_idx, kv_idx_clamped]
-        q_group = torch.where(q_idx < seq_length, q_group, -1)
-        kv_group = torch.where(kv_idx < seq_length, kv_group, -1)
-        return (q_group == kv_group) & (q_group >= 0)
+        image_group_ids_at_kv_idx = image_group_ids[batch_idx, safe_kv_idx]
+        image_group_ids_at_kv_idx = torch.where(kv_idx < image_group_ids.shape[1], image_group_ids_at_kv_idx, -1)
+
+        is_image_block = (token_type_ids_at_q_idx == 1) & (token_type_ids_at_kv_idx == 1)
+        same_image_block = image_group_ids_at_q_idx == image_group_ids_at_kv_idx
+
+        # This is bidirectional attention whenever we are dealing with image tokens
+        return is_image_block & same_image_block
 
     return inner_mask
 
@@ -160,10 +173,8 @@ def create_causal_mask_mapping(
     # from `forward` call. If users run a `forward` call, we have no option to infer `is_first_iteration` because users may be
     # running generation with custom loop. Thus we need to infer it in a `non-perfect` way
     # NOTE: Determining prefill in that case requires checking data values, which is not compile-compatible.
-    is_first_iteration = (
-        is_first_iteration
-        if is_first_iteration
-        else (past_key_values is None or not past_key_values.is_initialized or pixel_values is not None)
+    is_first_iteration = is_first_iteration or (
+        past_key_values is None or not past_key_values.is_initialized or pixel_values is not None
     )
 
     if is_first_iteration or not kwargs.get("use_cache", True):
@@ -191,9 +202,11 @@ def create_causal_mask_mapping(
         is_image = (token_type_ids == 1).to(inputs_embeds.device)
         is_previous_image = nn.functional.pad(is_image, (1, 0), value=0)[:, :-1]
         new_image_start = is_image & ~is_previous_image
-        group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
-        group_ids = torch.where(is_image, group_ids, torch.full_like(token_type_ids, -1))
-        mask_kwargs["or_mask_function"] = token_type_ids_mask_function(group_ids)
+        image_group_ids = torch.cumsum(new_image_start.int(), dim=1) - 1
+        image_group_ids = torch.where(is_image, image_group_ids, torch.full_like(token_type_ids, -1))
+        mask_kwargs["or_mask_function"] = token_type_ids_mask_function(
+            token_type_ids.to(inputs_embeds.device), image_group_ids
+        )
 
     return create_masks_for_generate(**mask_kwargs)
 
@@ -273,9 +286,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
@@ -353,21 +366,30 @@ def forward(
             )
             inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features)
 
-        # It may already have been prepared by e.g. `generate`
-        if not isinstance(causal_mask_mapping := attention_mask, dict):
-            causal_mask_mapping = create_causal_mask_mapping(
-                self.config,
-                inputs_embeds,
-                attention_mask,
-                past_key_values,
-                position_ids,
-                token_type_ids,
-                pixel_values,
-                is_training=self.training,
-            )
+        # Create the mask
+        mask_kwargs = {
+            "config": self.config.get_text_config(),
+            "inputs_embeds": inputs_embeds,
+            "attention_mask": attention_mask,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+        }
+        is_first_iteration = past_key_values is None or not past_key_values.is_initialized or pixel_values is not None
+        if token_type_ids is not None and is_first_iteration:
+            # Can attend bidirectionally in prefix and only causally in suffix
+            mask_kwargs["block_sequence_ids"] = torch.where(token_type_ids == 0, 0, -1)
+
+        # PG has no sliding window, only full attn. But PG2 needs sliding mask and full mask
+        causal_mask = create_causal_mask(**mask_kwargs)
+        if getattr(self.config.text_config, "sliding_window", None) is not None:
+            sliding_mask_kwargs = mask_kwargs.copy()
+            causal_mask = {
+                "full_attention": causal_mask,
+                "sliding_attention": create_sliding_window_causal_mask(**sliding_mask_kwargs),
+            }
 
         outputs = self.language_model(
-            attention_mask=causal_mask_mapping,
+            attention_mask=causal_mask,
             position_ids=position_ids,
             past_key_values=past_key_values,
             inputs_embeds=inputs_embeds,
@@ -535,16 +557,19 @@ def create_masks_for_generate(
         is_first_iteration: bool | None = False,
         **kwargs,
     ) -> dict:
-        # Uses the overwritten `create_masks_for_generate` with `token_type_ids` masking
-        return create_causal_mask_mapping(
-            config,
-            inputs_embeds,
-            attention_mask,
-            past_key_values,
-            position_ids,
-            token_type_ids,
-            is_first_iteration=is_first_iteration,
-            **{k: v for k, v in kwargs.items() if k != "pixel_values"},
+        group_ids = torch.full([*inputs_embeds.size()[:-1]], -1, device=inputs_embeds.device)
+        if token_type_ids is not None:
+            # First find where a new image block starts: 1 if image and previous not image
+            # The images cannot attend to future images, but can attend to all prev images and to itself bidirectionally
+            group_ids = torch.where(token_type_ids == 0, 0, -1)
+
+        return create_masks_for_generate(
+            config=config.get_text_config(),
+            inputs_embeds=inputs_embeds,
+            block_sequence_ids=group_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
         )
 
 
diff --git a/src/transformers/models/parakeet/__init__.py b/src/transformers/models/parakeet/__init__.py
index 5c54b2e2eadb..e8bbfe7faf45 100644
--- a/src/transformers/models/parakeet/__init__.py
+++ b/src/transformers/models/parakeet/__init__.py
@@ -21,7 +21,8 @@
     from .configuration_parakeet import *
     from .feature_extraction_parakeet import *
     from .modeling_parakeet import *
-    from .tokenization_parakeet_fast import *
+    from .processing_parakeet import *
+    from .tokenization_parakeet import *
 else:
     import sys
 
diff --git a/src/transformers/models/parakeet/configuration_parakeet.py b/src/transformers/models/parakeet/configuration_parakeet.py
index 6f4622ea3b2f..4b7c5b0fb526 100644
--- a/src/transformers/models/parakeet/configuration_parakeet.py
+++ b/src/transformers/models/parakeet/configuration_parakeet.py
@@ -11,7 +11,6 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""Parakeet model configuration."""
 
 from huggingface_hub.dataclasses import strict
 
@@ -43,21 +42,18 @@ class ParakeetEncoderConfig(PreTrainedConfig):
         Whether to scale the input embeddings.
 
     Example:
-        ```python
-        >>> from transformers import ParakeetEncoderModel, ParakeetEncoderConfig
-
-        >>> # Initializing a `ParakeetEncoder` configuration
-        >>> configuration = ParakeetEncoderConfig()
+    ```python
+    >>> from transformers import ParakeetEncoderModel, ParakeetEncoderConfig
 
-        >>> # Initializing a model from the configuration
-        >>> model = ParakeetEncoderModel(configuration)
+    >>> # Initializing a `ParakeetEncoder` configuration
+    >>> configuration = ParakeetEncoderConfig()
 
-        >>> # Accessing the model configuration
-        >>> configuration = model.config
-        ```
+    >>> # Initializing a model from the configuration
+    >>> model = ParakeetEncoderModel(configuration)
 
-    This configuration class is based on the ParakeetEncoder architecture from NVIDIA NeMo. You can find more details
-    and pre-trained models at [nvidia/parakeet-ctc-1.1b](https://huggingface.co/nvidia/parakeet-ctc-1.1b).
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
     """
 
     model_type = "parakeet_encoder"
@@ -135,4 +131,60 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-__all__ = ["ParakeetCTCConfig", "ParakeetEncoderConfig"]
+@auto_docstring(checkpoint="nvidia/parakeet-tdt-0.6b-v3")
+@strict
+class ParakeetTDTConfig(PreTrainedConfig):
+    r"""
+    decoder_hidden_size (`int`, *optional*, defaults to 640):
+        Hidden size of the LSTM prediction network and joint network.
+    num_decoder_layers (`int`, *optional*, defaults to 2):
+        Number of LSTM layers in the prediction network.
+    max_symbols_per_step (`int`, *optional*, defaults to 10):
+        Maximum number of symbols to emit per encoder time step during greedy decoding.
+    durations (`list[int]`, *optional*, defaults to `[0, 1, 2, 3, 4]`):
+        Token duration values that can be predicted. Each value represents how many frames a token or blank
+        emission spans.
+    encoder_config (`Union[dict, ParakeetEncoderConfig]`, *optional*):
+        The config object or dictionary of the encoder.
+    blank_token_id (`int`, *optional*, defaults to 8192):
+        Blank token id. Different from `pad_token_id` for TDT.
+
+    Example:
+    ```python
+    >>> from transformers import ParakeetForTDT, ParakeetTDTConfig
+
+    >>> # Initializing a Parakeet TDT configuration
+    >>> configuration = ParakeetTDTConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = ParakeetForTDT(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "parakeet_tdt"
+    sub_configs = {"encoder_config": ParakeetEncoderConfig}
+
+    vocab_size: int = 8193
+    decoder_hidden_size: int = 640
+    num_decoder_layers: int = 2
+    hidden_act: str = "relu"
+    max_symbols_per_step: int = 10
+    durations: list[int] | tuple[int, ...] = (0, 1, 2, 3, 4)
+    encoder_config: dict | PreTrainedConfig | None = None
+    pad_token_id: int = 2
+    blank_token_id: int = 8192
+    is_encoder_decoder: bool = True
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.encoder_config, dict):
+            self.encoder_config = ParakeetEncoderConfig(**self.encoder_config)
+        elif self.encoder_config is None:
+            self.encoder_config = ParakeetEncoderConfig()
+        self.initializer_range = self.encoder_config.initializer_range
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["ParakeetCTCConfig", "ParakeetEncoderConfig", "ParakeetTDTConfig"]
diff --git a/src/transformers/models/parakeet/convert_nemo_to_hf.py b/src/transformers/models/parakeet/convert_nemo_to_hf.py
index 2d4085e6d340..b1be27fe5dcf 100644
--- a/src/transformers/models/parakeet/convert_nemo_to_hf.py
+++ b/src/transformers/models/parakeet/convert_nemo_to_hf.py
@@ -24,11 +24,12 @@
 
 from transformers import (
     ParakeetCTCConfig,
-    ParakeetEncoder,
     ParakeetEncoderConfig,
     ParakeetFeatureExtractor,
     ParakeetForCTC,
+    ParakeetForTDT,
     ParakeetProcessor,
+    ParakeetTDTConfig,
     ParakeetTokenizer,
 )
 from transformers.convert_slow_tokenizer import ParakeetConverter
@@ -48,6 +49,15 @@
     r"linear_pos": r"relative_k_proj",
 }
 
+# Additional mappings for TDT decoder and joint network
+NEMO_TDT_WEIGHT_MAPPING = {
+    r"decoder\.prediction\.embed\.": r"decoder.embedding.",
+    r"decoder\.prediction\.dec_rnn\.lstm\.": r"decoder.lstm.",
+    r"joint\.enc\.": r"encoder_projector.",
+    r"joint\.pred\.": r"decoder.decoder_projector.",
+    r"joint\.joint_net\.2\.": r"joint.head.",
+}
+
 
 def convert_key(key, mapping):
     for pattern, replacement in mapping.items():
@@ -56,22 +66,12 @@ def convert_key(key, mapping):
 
 
 def extract_nemo_archive(nemo_file_path: str, extract_dir: str) -> dict[str, str]:
-    """
-    Extract .nemo file (tar archive) and return paths to important files.
-
-    Args:
-        nemo_file_path: Path to .nemo file
-        extract_dir: Directory to extract to
-
-    Returns:
-        Dictionary with paths to model.pt, model_config.yaml, etc.
-    """
+    """Extract .nemo file (tar archive) and return paths to important files."""
     print(f"Extracting NeMo archive: {nemo_file_path}")
 
     with tarfile.open(nemo_file_path, "r", encoding="utf-8") as tar:
         tar.extractall(extract_dir)
 
-    # Log all extracted files for debugging
     all_files = []
     for root, dirs, files in os.walk(extract_dir):
         for file in files:
@@ -80,14 +80,12 @@ def extract_nemo_archive(nemo_file_path: str, extract_dir: str) -> dict[str, str
 
     print(f"All extracted files: {[os.path.basename(f) for f in all_files]}")
 
-    # Find important files with more robust detection
     model_files = {}
     for root, dirs, files in os.walk(extract_dir):
         for file in files:
             file_path = os.path.join(root, file)
             file_lower = file.lower()
 
-            # Look for model weights with various common names
             if (
                 file.endswith(".pt")
                 or file.endswith(".pth")
@@ -102,26 +100,23 @@ def extract_nemo_archive(nemo_file_path: str, extract_dir: str) -> dict[str, str
                 model_files["model_weights"] = file_path
                 print(f"Found model weights: {file}")
 
-            # Look for config files
             elif (
                 file == "model_config.yaml"
                 or file == "config.yaml"
                 or (file.endswith(".yaml") and "config" in file_lower)
             ):
-                if "model_config" not in model_files:  # Prefer model_config.yaml
+                if "model_config" not in model_files:
                     model_files["model_config"] = file_path
                     print(f"Found config file: {file}")
                 if file == "model_config.yaml":
-                    model_files["model_config"] = file_path  # Override with preferred name
+                    model_files["model_config"] = file_path
 
-            # Look for vocabulary files
             elif (
                 file.endswith(".vocab")
                 or file.endswith(".model")
                 or file.endswith(".txt")
                 or ("tokenizer" in file_lower and (file.endswith(".vocab") or file.endswith(".model")))
             ):
-                # Prefer .vocab files over others
                 if "tokenizer_model_file" not in model_files or file.endswith(".model"):
                     model_files["tokenizer_model_file"] = file_path
                     print(f"Found tokenizer model file: {file}")
@@ -130,7 +125,6 @@ def extract_nemo_archive(nemo_file_path: str, extract_dir: str) -> dict[str, str
 
     print(f"Found model files: {list(model_files.keys())}")
 
-    # Validate that we found the required files
     if "model_weights" not in model_files:
         raise FileNotFoundError(
             f"Could not find model weights file in {nemo_file_path}. "
@@ -148,15 +142,27 @@ def extract_nemo_archive(nemo_file_path: str, extract_dir: str) -> dict[str, str
     return model_files
 
 
-def write_processor(nemo_config: dict, model_files, output_dir, push_to_repo_id=None):
+def write_processor(
+    nemo_config: dict, model_files, output_dir, model_type, push_to_repo_id=None, create_pr=True, revision=None
+):
     tokenizer_converted = ParakeetConverter(model_files["tokenizer_model_file"]).converted()
     tokenizer_converted_fast = ParakeetTokenizer(
         tokenizer_object=tokenizer_converted,
         clean_up_tokenization_spaces=False,
     )
-    tokenizer_converted_fast.add_tokens(
-        [AddedToken("<unk>", normalized=False, special=True), AddedToken("<pad>", normalized=False, special=True)]
-    )
+
+    if tokenizer_converted_fast.convert_tokens_to_ids("<unk>") is None:
+        # Normally CTC and TDT already have
+        tokenizer_converted_fast.add_tokens([AddedToken("<unk>", normalized=False, special=True)])
+        print(f"Added <unk> token at ID: {tokenizer_converted_fast.convert_tokens_to_ids('<unk>')}")
+    if tokenizer_converted_fast.convert_tokens_to_ids("<pad>") is None:
+        # Normally CTC doesn't have while TDT has at token id = 2
+        tokenizer_converted_fast.add_tokens([AddedToken("<pad>", normalized=False, special=True)])
+        print(f"Added <pad> token at ID: {tokenizer_converted_fast.convert_tokens_to_ids('<pad>')}")
+    if model_type == "tdt":
+        # TDT needs a separate blank token
+        tokenizer_converted_fast.add_tokens([AddedToken("<blank>", normalized=False, special=True)])
+        print(f"Added <blank> token at ID: {tokenizer_converted_fast.convert_tokens_to_ids('<blank>')}")
     tokenizer_converted_fast.add_special_tokens(
         {
             "pad_token": AddedToken("<pad>", normalized=False, special=True),
@@ -193,7 +199,6 @@ def write_processor(nemo_config: dict, model_files, output_dir, push_to_repo_id=
             raise ValueError(f"Key {key} not found in feature_extractor_keys_mapping")
 
     feature_extractor = ParakeetFeatureExtractor(**converted_feature_extractor_config)
-
     processor = ParakeetProcessor(
         feature_extractor=feature_extractor,
         tokenizer=tokenizer_converted_fast,
@@ -201,7 +206,12 @@ def write_processor(nemo_config: dict, model_files, output_dir, push_to_repo_id=
     processor.save_pretrained(output_dir)
 
     if push_to_repo_id:
-        processor.push_to_hub(push_to_repo_id)
+        commit_info = processor.push_to_hub(push_to_repo_id, create_pr=create_pr, revision=revision)
+        if create_pr and hasattr(commit_info, "pr_url") and commit_info.pr_url:
+            pr_num = commit_info.pr_url.rstrip("/").split("/")[-1]
+            return f"refs/pr/{pr_num}"
+
+    return revision
 
 
 def convert_encoder_config(nemo_config):
@@ -248,7 +258,6 @@ def convert_encoder_config(nemo_config):
             continue
         if key in encoder_config_keys_mapping:
             converted_encoder_config[encoder_config_keys_mapping[key]] = value
-            # NeMo uses 'use_bias' for both attention and convolution bias, but HF separates them
             if key == "use_bias":
                 converted_encoder_config["convolution_bias"] = value
         else:
@@ -262,7 +271,6 @@ def load_and_convert_state_dict(model_files):
     state_dict = torch.load(model_files["model_weights"], map_location="cpu", weights_only=True)
     converted_state_dict = {}
     for key, value in state_dict.items():
-        # Skip preprocessing weights (featurizer components)
         if key.endswith("featurizer.window") or key.endswith("featurizer.fb"):
             print(f"Skipping preprocessing weight: {key}")
             continue
@@ -272,7 +280,7 @@ def load_and_convert_state_dict(model_files):
     return converted_state_dict
 
 
-def write_ctc_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id=None):
+def write_ctc_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id=None, revision=None):
     """Write CTC model using encoder config and converted state dict."""
     model_config = ParakeetCTCConfig.from_encoder_config(encoder_config)
 
@@ -287,62 +295,117 @@ def write_ctc_model(encoder_config, converted_state_dict, output_dir, push_to_re
     model.save_pretrained(output_dir)
 
     if push_to_repo_id:
-        model.push_to_hub(push_to_repo_id)
+        model.push_to_hub(push_to_repo_id, revision=revision)
 
     del model
 
-    # Safety check: reload the converted model
     gc.collect()
     print("Reloading the model to check if it's saved correctly.")
     ParakeetForCTC.from_pretrained(output_dir, dtype=torch.bfloat16, device_map="auto")
     print("Model reloaded successfully.")
 
 
-def write_encoder_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id=None):
-    """Write encoder model using encoder config and converted state dict."""
-    # Filter to only encoder weights (exclude CTC head if present)
-    encoder_state_dict = {
-        k.replace("encoder.", "", 1) if k.startswith("encoder.") else k: v
-        for k, v in converted_state_dict.items()
-        if k.startswith("encoder.")
-    }
+def convert_tdt_config(nemo_config, encoder_config):
+    """Convert NeMo TDT config to HF TDT config."""
+    decoder_config = nemo_config["decoder"]
+    decoding_config = nemo_config["decoding"]
+    labels = nemo_config["labels"]
+    blank_token_id = len(labels)
+    vocab_size = len(labels) + 1  # +1 for blank token, which is added to tokenizer
+
+    prednet = decoder_config.get("prednet", {})
+    decoder_hidden_size = prednet.get("pred_hidden", 640)
+    num_decoder_layers = prednet.get("pred_rnn_layers", 2)
+    durations = decoding_config.get("durations", [0, 1, 2, 3, 4])
+    print(
+        f"TDT config: vocab_size={vocab_size} (including blank token), "
+        f"decoder_hidden={decoder_hidden_size}, "
+        f"decoder_layers={num_decoder_layers}, durations={durations}, "
+    )
+
+    return ParakeetTDTConfig(
+        vocab_size=vocab_size,
+        decoder_hidden_size=decoder_hidden_size,
+        num_decoder_layers=num_decoder_layers,
+        durations=durations,
+        hidden_act="relu",
+        max_symbols_per_step=10,
+        encoder_config=encoder_config.to_dict(),
+        pad_token_id=labels.index("<pad>"),
+        blank_token_id=blank_token_id,  # blank token is different from pad token for TDT
+    )
+
+
+def load_and_convert_tdt_state_dict(model_files, vocab_size):
+    """Load NeMo TDT state dict and convert keys to HF format, splitting combined head."""
+    state_dict = torch.load(model_files["model_weights"], map_location="cpu", weights_only=True)
+    converted_state_dict = {}
+
+    all_mappings = {**NEMO_TO_HF_WEIGHT_MAPPING, **NEMO_TDT_WEIGHT_MAPPING}
+
+    for key, value in state_dict.items():
+        if key.endswith("featurizer.window") or key.endswith("featurizer.fb"):
+            print(f"Skipping preprocessing weight: {key}")
+            continue
 
-    print("Loading the checkpoint in a Parakeet Encoder model (for TDT).")
+        converted_key = convert_key(key, all_mappings)
+        converted_state_dict[converted_key] = value
+
+    return converted_state_dict
+
+
+def write_tdt_model(nemo_config, encoder_config, model_files, output_dir, push_to_repo_id=None, revision=None):
+    """Write TDT model using encoder config, TDT config, and converted state dict."""
+    model_config = convert_tdt_config(nemo_config, encoder_config)
+    print(f"Converted TDT config: {model_config}")
+
+    converted_state_dict = load_and_convert_tdt_state_dict(model_files, model_config.vocab_size)
+
+    print("Loading the checkpoint in a Parakeet TDT model.")
     with torch.device("meta"):
-        model = ParakeetEncoder(encoder_config)
+        model = ParakeetForTDT(model_config)
+
+    missing_keys, unexpected_keys = model.load_state_dict(converted_state_dict, strict=False, assign=True)
+
+    if missing_keys:
+        print(f"Warning: Missing keys: {missing_keys}")
+    if unexpected_keys:
+        print(f"Warning: Unexpected keys: {unexpected_keys}")
+
+    if not missing_keys and not unexpected_keys:
+        print("All weights loaded successfully!")
 
-    model.load_state_dict(encoder_state_dict, strict=True, assign=True)
-    print("Checkpoint loaded successfully.")
     del model.config._name_or_path
 
+    model.generation_config.decoder_start_token_id = model.config.blank_token_id
+    model.generation_config.suppress_tokens = list(
+        range(model.config.vocab_size, model.config.vocab_size + len(model.config.durations))
+    )
+
     print("Saving the model.")
     model.save_pretrained(output_dir)
 
     if push_to_repo_id:
-        model.push_to_hub(push_to_repo_id)
+        model.push_to_hub(push_to_repo_id, revision=revision)
+
     del model
 
-    # Safety check: reload the converted model
     gc.collect()
     print("Reloading the model to check if it's saved correctly.")
-    ParakeetEncoder.from_pretrained(output_dir, dtype=torch.bfloat16, device_map="auto")
+    ParakeetForTDT.from_pretrained(output_dir, dtype=torch.bfloat16, device_map="auto")
     print("Model reloaded successfully.")
 
 
-def write_model(nemo_config, model_files, model_type, output_dir, push_to_repo_id=None):
+def write_model(nemo_config, model_files, model_type, output_dir, push_to_repo_id=None, revision=None):
     """Main model conversion function."""
-    # Step 1: Convert encoder config (shared across all model types)
     encoder_config = convert_encoder_config(nemo_config)
     print(f"Converted encoder config: {encoder_config}")
 
-    # Step 2: Load and convert state dict (shared across all model types)
-    converted_state_dict = load_and_convert_state_dict(model_files)
-
-    # Step 3: Write model based on type
-    if model_type == "encoder":
-        write_encoder_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id)
-    elif model_type == "ctc":
-        write_ctc_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id)
+    if model_type == "ctc":
+        converted_state_dict = load_and_convert_state_dict(model_files)
+        write_ctc_model(encoder_config, converted_state_dict, output_dir, push_to_repo_id, revision)
+    elif model_type == "tdt":
+        write_tdt_model(nemo_config, encoder_config, model_files, output_dir, push_to_repo_id, revision)
     else:
         raise ValueError(f"Model type {model_type} not supported.")
 
@@ -352,6 +415,8 @@ def main(
     output_dir,
     model_type,
     push_to_repo_id=None,
+    create_pr=True,
+    revision=None,
 ):
     nemo_filename = f"{hf_repo_id.split('/')[-1]}.nemo"
     filepath = cached_file(hf_repo_id, nemo_filename)
@@ -359,22 +424,62 @@ def main(
     model_files = extract_nemo_archive(filepath, os.path.dirname(filepath))
     nemo_config = yaml.load(open(model_files["model_config"], "r"), Loader=yaml.FullLoader)
 
-    write_processor(nemo_config, model_files, output_dir, push_to_repo_id)
-    write_model(nemo_config, model_files, model_type, output_dir, push_to_repo_id)
-
-
+    # When revision is given (e.g. "refs/pr/3"), both pushes target that existing PR branch.
+    # Otherwise, write_processor creates a new PR and returns its revision for write_model.
+    pr_revision = write_processor(
+        nemo_config,
+        model_files,
+        output_dir,
+        model_type,
+        push_to_repo_id,
+        create_pr=create_pr if revision is None else False,
+        revision=revision,
+    )
+    write_model(nemo_config, model_files, model_type, output_dir, push_to_repo_id, pr_revision)
+
+
+"""
+CTC conversion example:
+```bash
+python src/transformers/models/parakeet/convert_nemo_to_hf.py \
+    --hf_repo_id nvidia/parakeet-ctc-1.1b \
+    --model_type ctc \
+    --output_dir OUTPUT_DIR  \
+    --push_to_repo_id USERNAME/parakeet-ctc-1.1b
+```
+
+TDT conversion example:
+```bash
+python src/transformers/models/parakeet/convert_nemo_to_hf.py \
+    --hf_repo_id nvidia/parakeet-tdt-0.6b-v3 \
+    --model_type tdt \
+    --output_dir OUTPUT_DIR  \
+    --push_to_repo_id USERNAME/parakeet-tdt-0.6b-v3-hf
+```
+"""
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--hf_repo_id", required=True, help="Model repo on huggingface.co")
-    parser.add_argument(
-        "--model_type", required=True, choices=["encoder", "ctc"], help="Model type (`encoder`, `ctc`)"
-    )
+    parser.add_argument("--model_type", required=True, choices=["ctc", "tdt"], help="Model type (`ctc`, `tdt`)")
     parser.add_argument("--output_dir", required=True, help="Output directory for HuggingFace model")
     parser.add_argument("--push_to_repo_id", help="Repository ID to push the model to on the Hub")
+    parser.add_argument(
+        "--create_pr",
+        default=True,
+        action=argparse.BooleanOptionalAction,
+        help="Create a PR when pushing to the Hub (default: True). Use --no-create_pr to push directly.",
+    )
+    parser.add_argument(
+        "--revision",
+        default=None,
+        help='Push to an existing Hub PR branch (e.g. "refs/pr/3"). Overrides --create_pr.',
+    )
     args = parser.parse_args()
     main(
         args.hf_repo_id,
         args.output_dir,
         args.model_type,
         args.push_to_repo_id,
+        args.create_pr,
+        args.revision,
     )
diff --git a/src/transformers/models/parakeet/feature_extraction_parakeet.py b/src/transformers/models/parakeet/feature_extraction_parakeet.py
index c745d02c9629..bdf6fa4e3312 100644
--- a/src/transformers/models/parakeet/feature_extraction_parakeet.py
+++ b/src/transformers/models/parakeet/feature_extraction_parakeet.py
@@ -12,6 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import functools
+
 import numpy as np
 import torch
 
@@ -96,9 +98,28 @@ def __init__(
         )
         self.mel_filters = torch.from_numpy(mel_filters).to(torch.float32)
 
+    @staticmethod
+    @functools.cache
+    def _get_window(win_length: int, device: str) -> torch.Tensor:
+        return torch.hann_window(win_length, periodic=False, device=device)
+
+    @staticmethod
+    @functools.cache
+    def _get_mel_filters(feature_size: int, sampling_rate: int, n_fft: int, device: str) -> torch.Tensor:
+        mel_filters = librosa.filters.mel(
+            sr=sampling_rate,
+            n_fft=n_fft,
+            n_mels=feature_size,
+            fmin=0.0,
+            fmax=sampling_rate / 2,
+            norm="slaney",
+        )
+        return torch.from_numpy(mel_filters).to(device=device, dtype=torch.float32)
+
     def _torch_extract_fbank_features(self, waveform, device="cpu"):
         # spectrogram
-        window = torch.hann_window(self.win_length, periodic=False, device=device)
+        device = str(torch.device(device))
+        window = self._get_window(self.win_length, device)
         stft = torch.stft(
             waveform,
             self.n_fft,
@@ -108,21 +129,53 @@ def _torch_extract_fbank_features(self, waveform, device="cpu"):
             return_complex=True,
             pad_mode="constant",
         )
-        # Let's math original implementation
-        # magnitudes = torch.abs(stft) ** 2
-        magnitudes = torch.view_as_real(stft)
-        magnitudes = torch.sqrt(magnitudes.pow(2).sum(-1))
-        magnitudes = magnitudes.pow(2)
-
-        # log mel spectrogram
-        mel_filters = self.mel_filters.to(device)
+        mel_filters = self._get_mel_filters(self.feature_size, self.sampling_rate, self.n_fft, device)
+        return self._apply_mel_filters(stft, mel_filters)
+
+    @torch.compile(dynamic=True)
+    def _apply_mel_filters(self, stft_output: torch.Tensor, mel_filters: torch.Tensor) -> torch.Tensor:
+        magnitudes = stft_output.real.square() + stft_output.imag.square()
         mel_spec = mel_filters @ magnitudes
         mel_spec = torch.log(mel_spec + LOG_ZERO_GUARD_VALUE)
+        return mel_spec.permute(0, 2, 1)
+
+    @torch.compile(dynamic=True)
+    def _apply_preemphasis(self, input_features: torch.Tensor, audio_lengths: torch.Tensor) -> torch.Tensor:
+        if self.preemphasis is not None:
+            timemask = torch.arange(input_features.shape[1], device=input_features.device).unsqueeze(
+                0
+            ) < audio_lengths.unsqueeze(1)
+            input_features = torch.cat(
+                [input_features[:, :1], input_features[:, 1:] - self.preemphasis * input_features[:, :-1]], dim=1
+            )
+            input_features = input_features.masked_fill(~timemask, 0.0)
+        return input_features
 
-        # (batch_size, num_mel_filters, num_frames) -> (batch_size, num_frames, num_mel_filters)
-        mel_spec = mel_spec.permute(0, 2, 1)
+    @torch.compile(dynamic=True)
+    def _normalize_mel_features(
+        self, mel_features: torch.Tensor, audio_lengths: torch.Tensor
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # normalize mel features, ignoring padding
+        features_lengths = torch.floor_divide(audio_lengths + self.n_fft // 2 * 2 - self.n_fft, self.hop_length)
+        attention_mask = (
+            torch.arange(mel_features.shape[1], device=mel_features.device)[None, :] < features_lengths[:, None]
+        )
 
-        return mel_spec
+        mask = attention_mask.unsqueeze(-1)
+        lengths = attention_mask.sum(dim=1)
+        mel_features_masked = mel_features * mask
+        mean = (mel_features_masked.sum(dim=1) / lengths.unsqueeze(-1)).unsqueeze(1)
+        variance = ((mel_features_masked - mean) ** 2 * mask).sum(dim=1) / (lengths - 1).unsqueeze(-1)
+        std = torch.sqrt(variance).unsqueeze(1)
+        return (mel_features - mean) / (std + EPSILON) * mask, attention_mask
+
+    def _pad_raw_speech(self, raw_speech: list[torch.Tensor], max_len: int, device: str) -> torch.Tensor:
+        output = torch.full((len(raw_speech), max_len), self.padding_value, device=device, dtype=torch.float32)
+        dsts = [output[i, : raw_speech[i].shape[0]] for i in range(len(raw_speech))]
+        srcs = [s.squeeze(-1) for s in raw_speech]
+        # single kernel horizontal fusion
+        torch._foreach_copy_(dsts, srcs)
+        return output
 
     def __call__(
         self,
@@ -205,11 +258,18 @@ def __call__(
                 "Failing to do so can result in silent errors that might be hard to debug."
             )
 
+        device = device if device is not None else "cpu"
+
         # Convert to torch tensor
         if isinstance(raw_speech, np.ndarray):
-            raw_speech = torch.tensor(raw_speech)
-        elif isinstance(raw_speech, (list, tuple)) and isinstance(raw_speech[0], np.ndarray):
-            raw_speech = [torch.tensor(speech) for speech in raw_speech]
+            raw_speech = torch.as_tensor(raw_speech, device=device)
+        elif isinstance(raw_speech, (list, tuple)) and len(raw_speech) > 0:
+            if isinstance(raw_speech[0], np.ndarray):
+                raw_speech = [torch.as_tensor(speech, device=device) for speech in raw_speech]
+            elif isinstance(raw_speech[0], (float, int)):
+                raw_speech = torch.tensor(raw_speech, device=device, dtype=torch.float32)
+            elif isinstance(raw_speech[0], (list, tuple)):
+                raw_speech = [torch.tensor(speech, device=device, dtype=torch.float32) for speech in raw_speech]
 
         is_batched_torch = isinstance(raw_speech, torch.Tensor) and len(raw_speech.shape) > 1
         if is_batched_torch and len(raw_speech.shape) > 2:
@@ -217,61 +277,36 @@ def __call__(
                 f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                 "We will take the mean of the channels to convert to mono."
             )
-            raw_speech = raw_speech.mean(-1)
+            raw_speech = raw_speech.mean(1)
 
         is_batched_sequence = isinstance(raw_speech, (list, tuple))
         if is_batched_sequence:
-            for speech in raw_speech:
+            for i, speech in enumerate(raw_speech):
                 if len(speech.shape) > 1:
                     logger.warning(
                         f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                         "We will take the mean of the channels to convert to mono."
                     )
-                    speech = speech.mean(-1)
+                    raw_speech[i] = speech.mean(-1)
 
-        if is_batched_torch or is_batched_sequence:
-            raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech]
+        if is_batched_torch:
+            raw_speech = raw_speech.to(device=device, dtype=torch.float32)
+        elif is_batched_sequence:
+            raw_speech = [speech.to(device=device, dtype=torch.float32) for speech in raw_speech]
         else:
-            raw_speech = [raw_speech[:, None].to(torch.float32)]
-
-        audio_lengths = [len(speech) for speech in raw_speech]
-        batched_speech = BatchFeature({"input_features": raw_speech, "audio_lengths": audio_lengths})
-
-        padded_inputs = self.pad(
-            batched_speech,
-            padding=padding,
-            max_length=max_length,
-            truncation=truncation,
-            pad_to_multiple_of=pad_to_multiple_of,
-            return_tensors="pt",
-        )
-        input_features = padded_inputs.input_features.squeeze(-1)
+            raw_speech = [raw_speech.to(device=device, dtype=torch.float32)]
 
-        # preemphasis
-        if self.preemphasis is not None:
-            timemask = torch.arange(input_features.shape[1], device=input_features.device).unsqueeze(
-                0
-            ) < padded_inputs.audio_lengths.unsqueeze(1)
-            input_features = torch.cat(
-                [input_features[:, :1], input_features[:, 1:] - self.preemphasis * input_features[:, :-1]], dim=1
-            )
-            input_features = input_features.masked_fill(~timemask, 0.0)
+        audio_lengths = torch.tensor([len(speech) for speech in raw_speech], dtype=torch.long, device=device)
 
-        input_features = self._torch_extract_fbank_features(input_features, device)
-        features_lengths = torch.floor_divide(
-            padded_inputs.audio_lengths + self.n_fft // 2 * 2 - self.n_fft, self.hop_length
-        )
-        attention_mask = torch.arange(input_features.shape[1], device=device)[None, :] < features_lengths[:, None]
+        if isinstance(raw_speech, torch.Tensor):
+            input_features = raw_speech
+        else:
+            max_length = max(len(speech) for speech in raw_speech)
+            input_features = self._pad_raw_speech(raw_speech, max_length, device)
 
-        # normalize mel features, ignoring padding
-        mask = attention_mask.unsqueeze(-1)
-        input_features_masked = input_features * mask
-        mean = input_features_masked.sum(dim=1) / features_lengths.unsqueeze(-1)
-        mean = mean.unsqueeze(1)
-        variance = ((input_features_masked - mean) ** 2 * mask).sum(dim=1) / (features_lengths - 1).unsqueeze(-1)
-        std = torch.sqrt(variance).unsqueeze(1)
-        input_features = (input_features - mean) / (std + EPSILON)
-        input_features *= mask
+        input_features = self._apply_preemphasis(input_features, audio_lengths)
+        input_features = self._torch_extract_fbank_features(input_features, device)
+        input_features, attention_mask = self._normalize_mel_features(input_features, audio_lengths)
 
         return BatchFeature(
             data={
diff --git a/src/transformers/models/parakeet/generation_parakeet.py b/src/transformers/models/parakeet/generation_parakeet.py
new file mode 100644
index 000000000000..fe422f3dd3a8
--- /dev/null
+++ b/src/transformers/models/parakeet/generation_parakeet.py
@@ -0,0 +1,185 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from dataclasses import dataclass
+
+import torch
+
+from ...generation import GenerationMixin, StoppingCriteria
+from ...utils import ModelOutput
+
+
+@dataclass
+class ParakeetTDTGenerateOutput(ModelOutput):
+    """
+    Outputs of Parakeet TDT generation.
+
+    Args:
+        sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Generated token sequences (including blank tokens).
+        durations (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Per-step durations in frames. Combined with `sequences`, this is sufficient
+            to reconstruct full timestamp information (frame indices are the cumulative sum
+            of durations).
+        attentions (`tuple(tuple(torch.FloatTensor))`, *optional*):
+            Encoder attention weights per layer.
+        hidden_states (`tuple(tuple(torch.FloatTensor))`, *optional*):
+            Encoder hidden states per layer.
+    """
+
+    sequences: torch.LongTensor
+    durations: torch.LongTensor | None = None
+    attentions: tuple[tuple[torch.FloatTensor]] | None = None
+    hidden_states: tuple[tuple[torch.FloatTensor]] | None = None
+
+
+class EncoderExhaustedCriteria(StoppingCriteria):
+    """Stops generation when all batch elements have walked past their encoder output length."""
+
+    def __init__(self, model):
+        self.model = model
+
+    def __call__(self, input_ids, scores, **kwargs):
+        if self.model._encoder_finished is None:
+            return torch.zeros(input_ids.shape[0], dtype=torch.bool, device=input_ids.device)
+        return self.model._encoder_finished
+
+
+class ParakeetTDTGenerationMixin(GenerationMixin):
+    """Generation mixin for Parakeet TDT models.
+
+    Handles transducer-specific generation logic: encoder frame tracking,
+    duration accumulation, and encoder-exhaustion stopping.
+    """
+
+    def _get_stopping_criteria(self, *args, **kwargs):
+        criteria = super()._get_stopping_criteria(*args, **kwargs)
+        criteria.append(EncoderExhaustedCriteria(self))
+        return criteria
+
+    def _update_model_kwargs_for_generation(self, outputs, *args, **kwargs):
+        model_kwargs = super()._update_model_kwargs_for_generation(outputs, *args, **kwargs)
+
+        # Advance encoder frame pointer by the predicted duration
+        logits = outputs.logits[:, -1, :]
+        tokens = logits[:, : self.config.vocab_size].argmax(dim=-1)
+        durations = logits[:, self.config.vocab_size :].argmax(dim=-1)
+
+        # Only force forward progress (duration >= 1) for blank predictions;
+        blank_mask = tokens == self.config.blank_token_id
+        durations = torch.where(blank_mask & (durations == 0), torch.ones_like(durations), durations)
+        model_kwargs["encoder_frame_idxs"] = model_kwargs["encoder_frame_idxs"] + durations
+        self._step_durations.append(durations)
+
+        # Track which batch elements have exhausted their encoder frames.
+        self._encoder_finished = model_kwargs["encoder_frame_idxs"] >= model_kwargs["encoder_valid_lengths"]
+
+        return model_kwargs
+
+    def _prepare_generated_length(
+        self,
+        generation_config,
+        has_default_max_length,
+        has_default_min_length,
+        model_input_name,
+        input_ids_length,
+        inputs_tensor,
+    ):
+        # When the user hasn't explicitly set max_length/max_new_tokens, derive an upper
+        # bound from the encoder capacity. The actual stopping is handled by the
+        # encoder-exhaustion stopping criteria; this just sizes the output buffer.
+        if has_default_max_length and generation_config.max_new_tokens is None:
+            encoder_seq_len = self.encoder._get_subsampling_output_length(
+                torch.tensor([inputs_tensor.shape[1]], device=inputs_tensor.device)
+            ).item()
+            generation_config.max_length = self.max_symbols_per_step * encoder_seq_len
+            has_default_max_length = False  # prevent super() from overwriting
+        return super()._prepare_generated_length(
+            generation_config,
+            has_default_max_length,
+            has_default_min_length,
+            model_input_name,
+            input_ids_length,
+            inputs_tensor,
+        )
+
+    def _prepare_model_inputs(self, *args, **kwargs):
+        inputs, input_name, model_kwargs = super()._prepare_model_inputs(*args, **kwargs)
+
+        encoder_outputs = self.get_audio_features(
+            input_features=inputs,
+            attention_mask=model_kwargs.get("attention_mask", None),
+            output_attention_mask=True,
+        )
+        model_kwargs["encoder_outputs"] = encoder_outputs
+
+        if encoder_outputs.attention_mask is not None:
+            encoder_valid_lengths = encoder_outputs.attention_mask.sum(-1)
+        else:
+            batch_size = encoder_outputs.last_hidden_state.shape[0]
+            encoder_valid_lengths = torch.full(
+                (batch_size,),
+                encoder_outputs.last_hidden_state.shape[1],
+                dtype=torch.long,
+                device=encoder_outputs.last_hidden_state.device,
+            )
+        model_kwargs["encoder_valid_lengths"] = encoder_valid_lengths
+
+        model_kwargs["encoder_frame_idxs"] = torch.zeros(
+            inputs.shape[0],
+            device=inputs.device,
+            dtype=torch.long,
+        )
+
+        return inputs, input_name, model_kwargs
+
+    def _prepare_cache_for_generation(self, generation_config, model_kwargs, *args, **kwargs):
+        from .modeling_parakeet import ParakeetTDTDecoderCache
+
+        model_kwargs["decoder_cache"] = ParakeetTDTDecoderCache()
+
+    def prepare_inputs_for_generation(self, input_ids, *args, **kwargs):
+        from .modeling_parakeet import ParakeetEncoderModelOutput
+
+        model_inputs = super().prepare_inputs_for_generation(input_ids, *args, **kwargs)
+        encoder_frame_idxs = model_inputs.pop("encoder_frame_idxs").to(
+            model_inputs["encoder_outputs"].pooler_output.device
+        )
+
+        pooler_output = model_inputs["encoder_outputs"].pooler_output
+        batch_size, max_encoder_len = pooler_output.shape[0], pooler_output.shape[1]
+        encoder_frame_idxs = encoder_frame_idxs.clamp(max=max_encoder_len - 1)
+        model_inputs["encoder_outputs"] = ParakeetEncoderModelOutput(
+            pooler_output=pooler_output[torch.arange(batch_size), encoder_frame_idxs, None],
+        )
+
+        return model_inputs
+
+    def generate(self, inputs=None, generation_config=None, **kwargs):
+        # TODO @eustlb: this is temporary — we're going to modularize generate to allow doing this cleanly.
+        self._step_durations = []
+        self._encoder_finished = None
+
+        outputs = super().generate(inputs=inputs, generation_config=generation_config, **kwargs)
+        durations = torch.stack(self._step_durations, dim=1)  # (batch, steps)
+        # Prepend a zero duration for the decoder_start_token_id that super().generate() prepends to sequences
+        durations = torch.cat(
+            [torch.zeros(durations.shape[0], 1, dtype=durations.dtype, device=durations.device), durations], dim=1
+        )
+        del self._step_durations, self._encoder_finished
+
+        return ParakeetTDTGenerateOutput(
+            sequences=outputs.sequences if isinstance(outputs, ModelOutput) else outputs,
+            durations=durations,
+        )
diff --git a/src/transformers/models/parakeet/modeling_parakeet.py b/src/transformers/models/parakeet/modeling_parakeet.py
index 501a573f8494..4672dcab0cb2 100644
--- a/src/transformers/models/parakeet/modeling_parakeet.py
+++ b/src/transformers/models/parakeet/modeling_parakeet.py
@@ -27,35 +27,56 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...generation import CompileConfig, GenerationMixin, GenerationMode
 from ...integrations import use_kernel_func_from_hub, use_kernelized_func
 from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, CausalLMOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils import (
+    ModelOutput,
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    is_torchdynamo_compiling,
+    logging,
+)
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
-from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig
+from ..auto import AutoModel
+from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig, ParakeetTDTConfig
+from .generation_parakeet import ParakeetTDTGenerationMixin
+
+
+logger = logging.get_logger(__name__)
 
 
 @dataclass
 @auto_docstring(
     custom_intro="""
-    Extends [~modeling_outputs.BaseModelOutput] to include the output attention mask since sequence length is not preserved in the model's forward.
+    Extends [~modeling_outputs.BaseModelOutputWithPooling] to include the output attention mask since sequence length
+    is not preserved in the model's forward.
     """
 )
-class ParakeetEncoderModelOutput(BaseModelOutput):
+class ParakeetEncoderModelOutput(BaseModelOutputWithPooling):
     attention_mask: torch.Tensor | None = None
 
 
 class ParakeetEncoderRelPositionalEncoding(nn.Module):
-    """Relative positional encoding for Parakeet."""
-
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
     def __init__(self, config: ParakeetEncoderConfig, device=None):
         super().__init__()
         self.max_position_embeddings = config.max_position_embeddings
+        self.config = config
+        inv_freq = self.compute_default_relative_positional_parameters(config, device=device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @staticmethod
+    def compute_default_relative_positional_parameters(
+        config: ParakeetEncoderConfig | None = None,
+        device=None,
+    ) -> torch.Tensor:
         base = 10000.0
         inv_freq = 1.0 / (
             base
@@ -64,18 +85,11 @@ def __init__(self, config: ParakeetEncoderConfig, device=None):
                 / config.hidden_size
             )
         )
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        return inv_freq
 
     @torch.no_grad()
     def forward(self, hidden_states: torch.Tensor):
         seq_length = hidden_states.shape[1]
-        if seq_length > self.max_position_embeddings:
-            raise ValueError(
-                f"Sequence Length: {seq_length} has to be less or equal than "
-                f"config.max_position_embeddings {self.max_position_embeddings}."
-            )
-
         position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device)
         inv_freq_expanded = (
             self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device)
@@ -495,25 +509,17 @@ class ParakeetPreTrainedModel(PreTrainedModel):
     @torch.no_grad()
     def _init_weights(self, module):
         super()._init_weights(module)
-
-        if hasattr(self.config, "initializer_range"):
-            std = self.config.initializer_range
-        else:
-            # 0.02 is the standard default value across the library
-            std = getattr(self.config.get_text_config(), "initializer_range", 0.02)
+        std = getattr(self.config, "initializer_range", 0.02)
 
         if isinstance(module, ParakeetEncoderAttention):
-            # Initialize positional bias parameters
             init.normal_(module.bias_u, mean=0.0, std=std)
             init.normal_(module.bias_v, mean=0.0, std=std)
         elif isinstance(module, ParakeetEncoderRelPositionalEncoding):
-            inv_freq = 1.0 / (
-                10000.0 ** (torch.arange(0, self.config.hidden_size, 2, dtype=torch.int64) / self.config.hidden_size)
-            )
-            init.copy_(module.inv_freq, inv_freq)
+            buffer_value = module.compute_default_relative_positional_parameters(module.config)
+            init.copy_(module.inv_freq, buffer_value)
 
     def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
-        encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config
+        encoder_config = getattr(self.config, "encoder_config", self.config)
 
         kernel_size = encoder_config.subsampling_conv_kernel_size
         stride = encoder_config.subsampling_conv_stride
@@ -613,6 +619,7 @@ def forward(
             position_embeddings, p=self.dropout_positions, training=self.training
         )
 
+        output_mask = None
         if attention_mask is not None:
             output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
             attention_mask = output_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1)
@@ -642,9 +649,9 @@ def forward(
 
 
 @dataclass
-class ParakeetGenerateOutput(ModelOutput):
+class ParakeetCTCGenerateOutput(ModelOutput):
     """
-    Outputs of Parakeet models.
+    Outputs of Parakeet CTC model generation.
 
     Args:
         sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -668,17 +675,30 @@ class ParakeetGenerateOutput(ModelOutput):
     hidden_states: tuple[tuple[torch.FloatTensor]] | None = None
 
 
+@dataclass
+class ParakeetGenerateOutput(ParakeetCTCGenerateOutput):
+    """
+    Deprecated alias for ParakeetCTCGenerateOutput. Use ParakeetCTCGenerateOutput instead.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        logger.warning_once(
+            "`ParakeetGenerateOutput` is deprecated and removed starting from version 5.5.0; please use `ParakeetCTCGenerateOutput` instead.",
+        )
+
+
 @auto_docstring(
     custom_intro="""
     Parakeet Encoder with a Connectionist Temporal Classification (CTC) head.
     """
 )
-class ParakeetForCTC(ParakeetPreTrainedModel):
+class ParakeetForCTC(ParakeetPreTrainedModel, GenerationMixin):
     config: ParakeetCTCConfig
 
     def __init__(self, config: ParakeetCTCConfig):
         super().__init__(config)
-        self.encoder = ParakeetEncoder(config.encoder_config)
+        self.encoder = AutoModel.from_config(config.encoder_config)
         # Conv rather than linear to be consistent with NeMO decoding layer
         self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)
 
@@ -713,6 +733,8 @@ def forward(
         >>> print(outputs.loss)
         ```"""
 
+        if labels is not None:
+            kwargs.setdefault("output_attention_mask", True)
         encoder_outputs = self.encoder(
             input_features=input_features,
             attention_mask=attention_mask,
@@ -724,14 +746,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            # retrieve loss input_lengths from attention_mask
-            attention_mask = (
-                attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
-            )
-            input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
+            encoder_lengths = encoder_outputs.attention_mask.sum(-1)
 
-            # assuming that padded tokens are filled with -100
-            # when not being attended to
+            # assuming that padded tokens are filled with pad_token_id when not being attended to
             labels_mask = labels != self.config.pad_token_id
             target_lengths = labels_mask.sum(-1)
             flattened_targets = labels.masked_select(labels_mask)
@@ -743,7 +760,7 @@ def forward(
                 loss = nn.functional.ctc_loss(
                     log_probs,
                     flattened_targets,
-                    input_lengths,
+                    encoder_lengths,
                     target_lengths,
                     blank=self.config.pad_token_id,
                     reduction=self.config.ctc_loss_reduction,
@@ -763,9 +780,13 @@ def generate(
         input_features: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         return_dict_in_generate: bool = False,
+        compile_config: CompileConfig | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> ParakeetGenerateOutput | torch.LongTensor:
+    ) -> ParakeetCTCGenerateOutput | torch.LongTensor:
         r"""
+        compile_config ([`~generation.CompileConfig`], *optional*):
+            If provided, `torch.compile` will be applied to the forward calls in the decoding loop.
+
         Example:
 
         ```python
@@ -786,8 +807,10 @@ def generate(
         >>> print(transcription)
         ```
         """
+        model_forward = self.get_compiled_call(compile_config) if compile_config is not None else self.__call__
+
         kwargs["return_dict"] = True
-        outputs: CausalLMOutput = self.forward(
+        outputs: CausalLMOutput = model_forward(
             input_features=input_features,
             attention_mask=attention_mask,
             **kwargs,
@@ -802,7 +825,7 @@ def generate(
             sequences[~attention_mask] = self.config.pad_token_id
 
         if return_dict_in_generate:
-            return ParakeetGenerateOutput(
+            return ParakeetCTCGenerateOutput(
                 sequences=sequences,
                 logits=outputs.logits,
                 attentions=outputs.attentions,
@@ -812,4 +835,272 @@ def generate(
         return sequences
 
 
-__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"]
+class ParakeetTDTDecoderCache:
+    def __init__(self):
+        self.cache: torch.Tensor | None = None
+        self.hidden_state: torch.Tensor | None = None
+        self.cell_state: torch.Tensor | None = None
+        self.is_initialized: bool = False
+
+    def lazy_initialization(self, hidden_states, lstm_module):
+        self.cache = torch.zeros(
+            hidden_states.shape[0], 1, lstm_module.hidden_size, device=hidden_states.device, dtype=hidden_states.dtype
+        )
+        self.hidden_state = torch.zeros(
+            lstm_module.num_layers,
+            hidden_states.shape[0],
+            lstm_module.hidden_size,
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+        self.cell_state = torch.zeros(
+            lstm_module.num_layers,
+            hidden_states.shape[0],
+            lstm_module.hidden_size,
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+
+        if not is_torchdynamo_compiling():
+            torch._dynamo.mark_static_address(self.cache)
+            torch._dynamo.mark_static_address(self.hidden_state)
+            torch._dynamo.mark_static_address(self.cell_state)
+
+        self.is_initialized = True
+
+    def update(
+        self,
+        decoder_output,
+        hidden_state,
+        cell_state,
+        lstm_module=None,
+        mask=None,
+    ):
+        if not self.is_initialized and lstm_module is not None:
+            self.lazy_initialization(decoder_output, lstm_module)
+        elif not self.is_initialized:
+            raise ValueError(
+                "ParakeetTDTDecoderCache is not initialized. Make sure to provide lstm_module to the update method."
+            )
+
+        if mask is None:
+            self.hidden_state.copy_(hidden_state)
+            self.cell_state.copy_(cell_state)
+            self.cache.copy_(decoder_output)
+        else:
+            # Mask to update specific batch elements
+            mask = mask.to(decoder_output.device)
+            batch_size = decoder_output.shape[0]
+            mask_h = mask.view(1, batch_size, 1)
+            mask_d = mask.view(batch_size, 1, 1)
+            self.cache = torch.where(mask_d, decoder_output, self.cache)
+            self.hidden_state = torch.where(mask_h, hidden_state, self.hidden_state)
+            self.cell_state = torch.where(mask_h, cell_state, self.cell_state)
+
+
+class ParakeetTDTDecoder(nn.Module):
+    """LSTM-based prediction network for TDT."""
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__()
+        self.blank_token_id = config.blank_token_id
+        self.embedding = nn.Embedding(config.vocab_size, config.decoder_hidden_size)
+        self.lstm = nn.LSTM(
+            input_size=config.decoder_hidden_size,
+            hidden_size=config.decoder_hidden_size,
+            num_layers=config.num_decoder_layers,
+            batch_first=True,
+        )
+        self.decoder_projector = nn.Linear(config.decoder_hidden_size, config.decoder_hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        cache: ParakeetTDTDecoderCache | None = None,
+    ) -> torch.Tensor:
+        # All-blank fast path
+        if cache is not None and cache.is_initialized:
+            blank_mask = input_ids[:, -1] == self.blank_token_id
+            if blank_mask.all():
+                return cache.cache
+
+        hidden_cell_states = (
+            (cache.hidden_state, cache.cell_state) if cache is not None and cache.is_initialized else None
+        )
+        embeddings = self.embedding(input_ids)
+        lstm_output, (hidden_state, cell_state) = self.lstm(embeddings, hidden_cell_states)
+        decoder_output = self.decoder_projector(lstm_output)
+
+        if cache is not None:
+            mask = ~blank_mask if cache.is_initialized else None
+            cache.update(decoder_output, hidden_state, cell_state, lstm_module=self.lstm, mask=mask)
+            return cache.cache
+
+        return decoder_output
+
+
+class ParakeetTDTJointNetwork(nn.Module):
+    """Joint network that combines encoder and decoder outputs to predict tokens and durations."""
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__()
+        self.activation = ACT2FN[config.hidden_act]
+        self.head = nn.Linear(config.decoder_hidden_size, config.vocab_size + len(config.durations))
+        self.vocab_size = config.vocab_size
+
+    def forward(
+        self,
+        decoder_hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        joint_output = self.activation(encoder_hidden_states + decoder_hidden_states)
+        return self.head(joint_output)
+
+
+@dataclass
+class ParakeetTDTOutput(BaseModelOutputWithPooling):
+    """
+    Output of the Parakeet TDT forward pass.
+
+    Args:
+        loss (`torch.FloatTensor`, *optional*):
+            TDT loss, returned when `labels` are provided.
+        logits (`torch.FloatTensor`):
+            Joint token and duration logits. Shape is `(batch, T, U+1, vocab+durations)` for training
+            or `(batch, 1, 1, vocab+durations)` for single-step inference.
+        decoder_cache (`ParakeetTDTDecoderCache`, *optional*):
+            Decoder LSTM cache containing hidden state, cell state, and last output.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    decoder_cache: ParakeetTDTDecoderCache | None = None
+
+
+@auto_docstring(
+    custom_intro="""
+    Parakeet Encoder with a TDT (Token Duration Transducer) head.
+    """
+)
+class ParakeetForTDT(ParakeetPreTrainedModel, ParakeetTDTGenerationMixin):
+    config: ParakeetTDTConfig
+    _no_split_modules = ["ParakeetTDTDecoder"]
+    _supported_generation_modes = [GenerationMode.GREEDY_SEARCH]
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__(config)
+        self.encoder = AutoModel.from_config(config.encoder_config)
+        self.encoder_projector = nn.Linear(config.encoder_config.hidden_size, config.decoder_hidden_size)
+        self.decoder = ParakeetTDTDecoder(config)
+        self.joint = ParakeetTDTJointNetwork(config)
+        self.max_symbols_per_step = config.max_symbols_per_step  # used in generation
+
+        self.post_init()
+
+    @can_return_tuple
+    def get_audio_features(
+        self,
+        input_features: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ParakeetEncoderModelOutput:
+        encoder_outputs = self.encoder(
+            input_features=input_features,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        encoder_outputs.pooler_output = self.encoder_projector(encoder_outputs.last_hidden_state)
+        return encoder_outputs
+
+    @auto_docstring
+    @can_return_tuple
+    def forward(
+        self,
+        input_features: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_cache: ParakeetTDTDecoderCache | None = None,
+        use_decoder_cache: bool | None = None,
+        encoder_outputs: ParakeetEncoderModelOutput | tuple[torch.FloatTensor] | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ParakeetTDTOutput:
+        r"""
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
+            Decoder input token ids for single-step inference.
+        decoder_cache (`ParakeetTDTDecoderCache`, *optional*):
+            Decoder LSTM cache. When provided and initialized, the cached `decoder_output` is reused
+            (e.g. during blank-skipping) instead of running the decoder. When `input_ids` is provided,
+            the decoder runs and the cache is updated in-place.
+        use_decoder_cache (`bool`, *optional*):
+            Whether to use a decoder cache. When `True` and `decoder_cache` is `None`, a new cache
+            is created automatically during the forward pass.
+        encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
+            Pre-computed encoder outputs (last_hidden_state, pooler_output, hidden_states, attentions, attention_mask).
+            Can be a tuple or `ParakeetEncoderModelOutput`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, ParakeetForTDT
+        >>> from datasets import load_dataset, Audio
+
+        >>> model_id = "nvidia/parakeet-tdt-0.6b-v3"
+        >>> processor = AutoProcessor.from_pretrained(model_id)
+        >>> model = ParakeetForTDT.from_pretrained(model_id)
+
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+
+        >>> inputs = processor(ds[0]["audio"]["array"])
+        >>> outputs = model(**inputs)
+        ```
+        """
+        if encoder_outputs is None:
+            encoder_outputs = self.get_audio_features(
+                input_features=input_features,
+                attention_mask=attention_mask,
+                **kwargs,
+            )
+        elif not isinstance(encoder_outputs, ParakeetEncoderModelOutput):
+            encoder_outputs = ParakeetEncoderModelOutput(
+                last_hidden_state=encoder_outputs[0] if len(encoder_outputs) > 0 else None,
+                pooler_output=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                hidden_states=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+                attentions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
+                attention_mask=encoder_outputs[4] if len(encoder_outputs) > 4 else None,
+            )
+
+        if use_decoder_cache and decoder_cache is None:
+            decoder_cache = ParakeetTDTDecoderCache()
+
+        decoder_hidden_states = self.decoder(decoder_input_ids, cache=decoder_cache)
+        logits = self.joint(
+            encoder_hidden_states=encoder_outputs.pooler_output[:, :, None, :],
+            decoder_hidden_states=decoder_hidden_states[:, None, :, :],
+        ).squeeze(2)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                token_logits=logits[..., : self.config.vocab_size],
+                duration_logits=logits[..., self.config.vocab_size :],
+                labels=labels,
+                logit_lengths=encoder_outputs.attention_mask.sum(-1),
+                label_lengths=(labels != self.config.pad_token_id).sum(-1),
+                blank_token_id=self.config.blank_token_id,
+                durations=self.config.durations,
+            )
+
+        return ParakeetTDTOutput(
+            loss=loss,
+            logits=logits,
+            last_hidden_state=encoder_outputs.last_hidden_state,
+            pooler_output=encoder_outputs.pooler_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            decoder_cache=decoder_cache,
+        )
+
+
+__all__ = ["ParakeetForCTC", "ParakeetForTDT", "ParakeetEncoder", "ParakeetPreTrainedModel"]
diff --git a/src/transformers/models/parakeet/modular_parakeet.py b/src/transformers/models/parakeet/modular_parakeet.py
index b53d61a0c22d..22fce9362648 100644
--- a/src/transformers/models/parakeet/modular_parakeet.py
+++ b/src/transformers/models/parakeet/modular_parakeet.py
@@ -22,36 +22,57 @@
 
 from ... import initialization as init
 from ...activations import ACT2FN
+from ...generation import CompileConfig, GenerationMixin, GenerationMode
 from ...modeling_layers import GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutput, CausalLMOutput
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, CausalLMOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils import (
+    ModelOutput,
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    is_torchdynamo_compiling,
+    logging,
+)
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
 from ..fastspeech2_conformer.modeling_fastspeech2_conformer import FastSpeech2ConformerConvolutionModule
 from ..llama.modeling_llama import LlamaAttention, eager_attention_forward
-from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig
+from .configuration_parakeet import ParakeetCTCConfig, ParakeetEncoderConfig, ParakeetTDTConfig
+from .generation_parakeet import ParakeetTDTGenerationMixin
+
+
+logger = logging.get_logger(__name__)
 
 
 @dataclass
 @auto_docstring(
     custom_intro="""
-    Extends [~modeling_outputs.BaseModelOutput] to include the output attention mask since sequence length is not preserved in the model's forward.
+    Extends [~modeling_outputs.BaseModelOutputWithPooling] to include the output attention mask since sequence length
+    is not preserved in the model's forward.
     """
 )
-class ParakeetEncoderModelOutput(BaseModelOutput):
+class ParakeetEncoderModelOutput(BaseModelOutputWithPooling):
     attention_mask: torch.Tensor | None = None
 
 
 class ParakeetEncoderRelPositionalEncoding(nn.Module):
-    """Relative positional encoding for Parakeet."""
-
     inv_freq: torch.Tensor  # fix linting for `register_buffer`
 
     def __init__(self, config: ParakeetEncoderConfig, device=None):
         super().__init__()
         self.max_position_embeddings = config.max_position_embeddings
+        self.config = config
+        inv_freq = self.compute_default_relative_positional_parameters(config, device=device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+    @staticmethod
+    def compute_default_relative_positional_parameters(
+        config: ParakeetEncoderConfig | None = None,
+        device=None,
+    ) -> torch.Tensor:
         base = 10000.0
         inv_freq = 1.0 / (
             base
@@ -60,18 +81,11 @@ def __init__(self, config: ParakeetEncoderConfig, device=None):
                 / config.hidden_size
             )
         )
-
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        return inv_freq
 
     @torch.no_grad()
     def forward(self, hidden_states: torch.Tensor):
         seq_length = hidden_states.shape[1]
-        if seq_length > self.max_position_embeddings:
-            raise ValueError(
-                f"Sequence Length: {seq_length} has to be less or equal than "
-                f"config.max_position_embeddings {self.max_position_embeddings}."
-            )
-
         position_ids = torch.arange(seq_length - 1, -seq_length, -1, device=hidden_states.device)
         inv_freq_expanded = (
             self.inv_freq[None, :, None].float().expand(hidden_states.shape[0], -1, 1).to(hidden_states.device)
@@ -334,25 +348,17 @@ class ParakeetPreTrainedModel(PreTrainedModel):
     @torch.no_grad()
     def _init_weights(self, module):
         super()._init_weights(module)
-
-        if hasattr(self.config, "initializer_range"):
-            std = self.config.initializer_range
-        else:
-            # 0.02 is the standard default value across the library
-            std = getattr(self.config.get_text_config(), "initializer_range", 0.02)
+        std = getattr(self.config, "initializer_range", 0.02)
 
         if isinstance(module, ParakeetEncoderAttention):
-            # Initialize positional bias parameters
             init.normal_(module.bias_u, mean=0.0, std=std)
             init.normal_(module.bias_v, mean=0.0, std=std)
         elif isinstance(module, ParakeetEncoderRelPositionalEncoding):
-            inv_freq = 1.0 / (
-                10000.0 ** (torch.arange(0, self.config.hidden_size, 2, dtype=torch.int64) / self.config.hidden_size)
-            )
-            init.copy_(module.inv_freq, inv_freq)
+            buffer_value = module.compute_default_relative_positional_parameters(module.config)
+            init.copy_(module.inv_freq, buffer_value)
 
     def _get_subsampling_output_length(self, input_lengths: torch.Tensor):
-        encoder_config = self.config.encoder_config if isinstance(self.config, ParakeetCTCConfig) else self.config
+        encoder_config = getattr(self.config, "encoder_config", self.config)
 
         kernel_size = encoder_config.subsampling_conv_kernel_size
         stride = encoder_config.subsampling_conv_stride
@@ -452,6 +458,7 @@ def forward(
             position_embeddings, p=self.dropout_positions, training=self.training
         )
 
+        output_mask = None
         if attention_mask is not None:
             output_mask = self._get_output_attention_mask(attention_mask, target_length=hidden_states.shape[1])
             attention_mask = output_mask.unsqueeze(1).expand(-1, hidden_states.shape[1], -1)
@@ -481,9 +488,9 @@ def forward(
 
 
 @dataclass
-class ParakeetGenerateOutput(ModelOutput):
+class ParakeetCTCGenerateOutput(ModelOutput):
     """
-    Outputs of Parakeet models.
+    Outputs of Parakeet CTC model generation.
 
     Args:
         sequences (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -507,17 +514,30 @@ class ParakeetGenerateOutput(ModelOutput):
     hidden_states: tuple[tuple[torch.FloatTensor]] | None = None
 
 
+@dataclass
+class ParakeetGenerateOutput(ParakeetCTCGenerateOutput):
+    """
+    Deprecated alias for ParakeetCTCGenerateOutput. Use ParakeetCTCGenerateOutput instead.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        logger.warning_once(
+            "`ParakeetGenerateOutput` is deprecated and removed starting from version 5.5.0; please use `ParakeetCTCGenerateOutput` instead.",
+        )
+
+
 @auto_docstring(
     custom_intro="""
     Parakeet Encoder with a Connectionist Temporal Classification (CTC) head.
     """
 )
-class ParakeetForCTC(ParakeetPreTrainedModel):
+class ParakeetForCTC(ParakeetPreTrainedModel, GenerationMixin):
     config: ParakeetCTCConfig
 
     def __init__(self, config: ParakeetCTCConfig):
         super().__init__(config)
-        self.encoder = ParakeetEncoder(config.encoder_config)
+        self.encoder = AutoModel.from_config(config.encoder_config)
         # Conv rather than linear to be consistent with NeMO decoding layer
         self.ctc_head = nn.Conv1d(config.encoder_config.hidden_size, config.vocab_size, kernel_size=1)
 
@@ -552,6 +572,8 @@ def forward(
         >>> print(outputs.loss)
         ```"""
 
+        if labels is not None:
+            kwargs.setdefault("output_attention_mask", True)
         encoder_outputs = self.encoder(
             input_features=input_features,
             attention_mask=attention_mask,
@@ -563,14 +585,9 @@ def forward(
 
         loss = None
         if labels is not None:
-            # retrieve loss input_lengths from attention_mask
-            attention_mask = (
-                attention_mask if attention_mask is not None else torch.ones_like(input_features, dtype=torch.long)
-            )
-            input_lengths = self._get_subsampling_output_length(attention_mask.sum(-1))
+            encoder_lengths = encoder_outputs.attention_mask.sum(-1)
 
-            # assuming that padded tokens are filled with -100
-            # when not being attended to
+            # assuming that padded tokens are filled with pad_token_id when not being attended to
             labels_mask = labels != self.config.pad_token_id
             target_lengths = labels_mask.sum(-1)
             flattened_targets = labels.masked_select(labels_mask)
@@ -582,7 +599,7 @@ def forward(
                 loss = nn.functional.ctc_loss(
                     log_probs,
                     flattened_targets,
-                    input_lengths,
+                    encoder_lengths,
                     target_lengths,
                     blank=self.config.pad_token_id,
                     reduction=self.config.ctc_loss_reduction,
@@ -602,9 +619,13 @@ def generate(
         input_features: torch.Tensor,
         attention_mask: torch.Tensor | None = None,
         return_dict_in_generate: bool = False,
+        compile_config: CompileConfig | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> ParakeetGenerateOutput | torch.LongTensor:
+    ) -> ParakeetCTCGenerateOutput | torch.LongTensor:
         r"""
+        compile_config ([`~generation.CompileConfig`], *optional*):
+            If provided, `torch.compile` will be applied to the forward calls in the decoding loop.
+
         Example:
 
         ```python
@@ -625,8 +646,10 @@ def generate(
         >>> print(transcription)
         ```
         """
+        model_forward = self.get_compiled_call(compile_config) if compile_config is not None else self.__call__
+
         kwargs["return_dict"] = True
-        outputs: CausalLMOutput = self.forward(
+        outputs: CausalLMOutput = model_forward(
             input_features=input_features,
             attention_mask=attention_mask,
             **kwargs,
@@ -641,7 +664,7 @@ def generate(
             sequences[~attention_mask] = self.config.pad_token_id
 
         if return_dict_in_generate:
-            return ParakeetGenerateOutput(
+            return ParakeetCTCGenerateOutput(
                 sequences=sequences,
                 logits=outputs.logits,
                 attentions=outputs.attentions,
@@ -651,4 +674,272 @@ def generate(
         return sequences
 
 
-__all__ = ["ParakeetForCTC", "ParakeetEncoder", "ParakeetPreTrainedModel"]
+class ParakeetTDTDecoderCache:
+    def __init__(self):
+        self.cache: torch.Tensor | None = None
+        self.hidden_state: torch.Tensor | None = None
+        self.cell_state: torch.Tensor | None = None
+        self.is_initialized: bool = False
+
+    def lazy_initialization(self, hidden_states, lstm_module):
+        self.cache = torch.zeros(
+            hidden_states.shape[0], 1, lstm_module.hidden_size, device=hidden_states.device, dtype=hidden_states.dtype
+        )
+        self.hidden_state = torch.zeros(
+            lstm_module.num_layers,
+            hidden_states.shape[0],
+            lstm_module.hidden_size,
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+        self.cell_state = torch.zeros(
+            lstm_module.num_layers,
+            hidden_states.shape[0],
+            lstm_module.hidden_size,
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+
+        if not is_torchdynamo_compiling():
+            torch._dynamo.mark_static_address(self.cache)
+            torch._dynamo.mark_static_address(self.hidden_state)
+            torch._dynamo.mark_static_address(self.cell_state)
+
+        self.is_initialized = True
+
+    def update(
+        self,
+        decoder_output,
+        hidden_state,
+        cell_state,
+        lstm_module=None,
+        mask=None,
+    ):
+        if not self.is_initialized and lstm_module is not None:
+            self.lazy_initialization(decoder_output, lstm_module)
+        elif not self.is_initialized:
+            raise ValueError(
+                "ParakeetTDTDecoderCache is not initialized. Make sure to provide lstm_module to the update method."
+            )
+
+        if mask is None:
+            self.hidden_state.copy_(hidden_state)
+            self.cell_state.copy_(cell_state)
+            self.cache.copy_(decoder_output)
+        else:
+            # Mask to update specific batch elements
+            mask = mask.to(decoder_output.device)
+            batch_size = decoder_output.shape[0]
+            mask_h = mask.view(1, batch_size, 1)
+            mask_d = mask.view(batch_size, 1, 1)
+            self.cache = torch.where(mask_d, decoder_output, self.cache)
+            self.hidden_state = torch.where(mask_h, hidden_state, self.hidden_state)
+            self.cell_state = torch.where(mask_h, cell_state, self.cell_state)
+
+
+class ParakeetTDTDecoder(nn.Module):
+    """LSTM-based prediction network for TDT."""
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__()
+        self.blank_token_id = config.blank_token_id
+        self.embedding = nn.Embedding(config.vocab_size, config.decoder_hidden_size)
+        self.lstm = nn.LSTM(
+            input_size=config.decoder_hidden_size,
+            hidden_size=config.decoder_hidden_size,
+            num_layers=config.num_decoder_layers,
+            batch_first=True,
+        )
+        self.decoder_projector = nn.Linear(config.decoder_hidden_size, config.decoder_hidden_size)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        cache: ParakeetTDTDecoderCache | None = None,
+    ) -> torch.Tensor:
+        # All-blank fast path
+        if cache is not None and cache.is_initialized:
+            blank_mask = input_ids[:, -1] == self.blank_token_id
+            if blank_mask.all():
+                return cache.cache
+
+        hidden_cell_states = (
+            (cache.hidden_state, cache.cell_state) if cache is not None and cache.is_initialized else None
+        )
+        embeddings = self.embedding(input_ids)
+        lstm_output, (hidden_state, cell_state) = self.lstm(embeddings, hidden_cell_states)
+        decoder_output = self.decoder_projector(lstm_output)
+
+        if cache is not None:
+            mask = ~blank_mask if cache.is_initialized else None
+            cache.update(decoder_output, hidden_state, cell_state, lstm_module=self.lstm, mask=mask)
+            return cache.cache
+
+        return decoder_output
+
+
+class ParakeetTDTJointNetwork(nn.Module):
+    """Joint network that combines encoder and decoder outputs to predict tokens and durations."""
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__()
+        self.activation = ACT2FN[config.hidden_act]
+        self.head = nn.Linear(config.decoder_hidden_size, config.vocab_size + len(config.durations))
+        self.vocab_size = config.vocab_size
+
+    def forward(
+        self,
+        decoder_hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        joint_output = self.activation(encoder_hidden_states + decoder_hidden_states)
+        return self.head(joint_output)
+
+
+@dataclass
+class ParakeetTDTOutput(BaseModelOutputWithPooling):
+    """
+    Output of the Parakeet TDT forward pass.
+
+    Args:
+        loss (`torch.FloatTensor`, *optional*):
+            TDT loss, returned when `labels` are provided.
+        logits (`torch.FloatTensor`):
+            Joint token and duration logits. Shape is `(batch, T, U+1, vocab+durations)` for training
+            or `(batch, 1, 1, vocab+durations)` for single-step inference.
+        decoder_cache (`ParakeetTDTDecoderCache`, *optional*):
+            Decoder LSTM cache containing hidden state, cell state, and last output.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    decoder_cache: ParakeetTDTDecoderCache | None = None
+
+
+@auto_docstring(
+    custom_intro="""
+    Parakeet Encoder with a TDT (Token Duration Transducer) head.
+    """
+)
+class ParakeetForTDT(ParakeetPreTrainedModel, ParakeetTDTGenerationMixin):
+    config: ParakeetTDTConfig
+    _no_split_modules = ["ParakeetTDTDecoder"]
+    _supported_generation_modes = [GenerationMode.GREEDY_SEARCH]
+
+    def __init__(self, config: ParakeetTDTConfig):
+        super().__init__(config)
+        self.encoder = AutoModel.from_config(config.encoder_config)
+        self.encoder_projector = nn.Linear(config.encoder_config.hidden_size, config.decoder_hidden_size)
+        self.decoder = ParakeetTDTDecoder(config)
+        self.joint = ParakeetTDTJointNetwork(config)
+        self.max_symbols_per_step = config.max_symbols_per_step  # used in generation
+
+        self.post_init()
+
+    @can_return_tuple
+    def get_audio_features(
+        self,
+        input_features: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ParakeetEncoderModelOutput:
+        encoder_outputs = self.encoder(
+            input_features=input_features,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        encoder_outputs.pooler_output = self.encoder_projector(encoder_outputs.last_hidden_state)
+        return encoder_outputs
+
+    @auto_docstring
+    @can_return_tuple
+    def forward(
+        self,
+        input_features: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_cache: ParakeetTDTDecoderCache | None = None,
+        use_decoder_cache: bool | None = None,
+        encoder_outputs: ParakeetEncoderModelOutput | tuple[torch.FloatTensor] | None = None,
+        labels: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ParakeetTDTOutput:
+        r"""
+        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*):
+            Decoder input token ids for single-step inference.
+        decoder_cache (`ParakeetTDTDecoderCache`, *optional*):
+            Decoder LSTM cache. When provided and initialized, the cached `decoder_output` is reused
+            (e.g. during blank-skipping) instead of running the decoder. When `input_ids` is provided,
+            the decoder runs and the cache is updated in-place.
+        use_decoder_cache (`bool`, *optional*):
+            Whether to use a decoder cache. When `True` and `decoder_cache` is `None`, a new cache
+            is created automatically during the forward pass.
+        encoder_outputs (`tuple(torch.FloatTensor)`, *optional*):
+            Pre-computed encoder outputs (last_hidden_state, pooler_output, hidden_states, attentions, attention_mask).
+            Can be a tuple or `ParakeetEncoderModelOutput`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoProcessor, ParakeetForTDT
+        >>> from datasets import load_dataset, Audio
+
+        >>> model_id = "nvidia/parakeet-tdt-0.6b-v3"
+        >>> processor = AutoProcessor.from_pretrained(model_id)
+        >>> model = ParakeetForTDT.from_pretrained(model_id)
+
+        >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        >>> ds = ds.cast_column("audio", Audio(sampling_rate=processor.feature_extractor.sampling_rate))
+
+        >>> inputs = processor(ds[0]["audio"]["array"])
+        >>> outputs = model(**inputs)
+        ```
+        """
+        if encoder_outputs is None:
+            encoder_outputs = self.get_audio_features(
+                input_features=input_features,
+                attention_mask=attention_mask,
+                **kwargs,
+            )
+        elif not isinstance(encoder_outputs, ParakeetEncoderModelOutput):
+            encoder_outputs = ParakeetEncoderModelOutput(
+                last_hidden_state=encoder_outputs[0] if len(encoder_outputs) > 0 else None,
+                pooler_output=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                hidden_states=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+                attentions=encoder_outputs[3] if len(encoder_outputs) > 3 else None,
+                attention_mask=encoder_outputs[4] if len(encoder_outputs) > 4 else None,
+            )
+
+        if use_decoder_cache and decoder_cache is None:
+            decoder_cache = ParakeetTDTDecoderCache()
+
+        decoder_hidden_states = self.decoder(decoder_input_ids, cache=decoder_cache)
+        logits = self.joint(
+            encoder_hidden_states=encoder_outputs.pooler_output[:, :, None, :],
+            decoder_hidden_states=decoder_hidden_states[:, None, :, :],
+        ).squeeze(2)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                token_logits=logits[..., : self.config.vocab_size],
+                duration_logits=logits[..., self.config.vocab_size :],
+                labels=labels,
+                logit_lengths=encoder_outputs.attention_mask.sum(-1),
+                label_lengths=(labels != self.config.pad_token_id).sum(-1),
+                blank_token_id=self.config.blank_token_id,
+                durations=self.config.durations,
+            )
+
+        return ParakeetTDTOutput(
+            loss=loss,
+            logits=logits,
+            last_hidden_state=encoder_outputs.last_hidden_state,
+            pooler_output=encoder_outputs.pooler_output,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            decoder_cache=decoder_cache,
+        )
+
+
+__all__ = ["ParakeetForCTC", "ParakeetForTDT", "ParakeetEncoder", "ParakeetPreTrainedModel"]
diff --git a/src/transformers/models/parakeet/processing_parakeet.py b/src/transformers/models/parakeet/processing_parakeet.py
index 69734fb055af..85b63f396765 100644
--- a/src/transformers/models/parakeet/processing_parakeet.py
+++ b/src/transformers/models/parakeet/processing_parakeet.py
@@ -27,6 +27,7 @@ class ParakeetProcessorKwargs(ProcessingKwargs, total=False):
             "sampling_rate": 16000,
             "padding": "longest",
             "return_attention_mask": True,
+            "subsampling_factor": 8,
         },
         "text_kwargs": {
             "padding": True,
@@ -39,7 +40,13 @@ class ParakeetProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class ParakeetProcessor(ProcessorMixin):
-    def __init__(self, feature_extractor, tokenizer):
+    def __init__(self, feature_extractor, tokenizer, blank_token="<blank>"):
+        """
+        blank_token (`str`, *optional*, defaults to `"<blank>"`):
+            Blank token for TDT decoding.
+        """
+        self.blank_token = blank_token
+        self.blank_token_id = tokenizer.convert_tokens_to_ids(blank_token)
         super().__init__(feature_extractor, tokenizer)
 
     @auto_docstring
@@ -83,12 +90,78 @@ def __call__(
             return inputs
         else:
             inputs["labels"] = encodings["input_ids"]
+            # Prepend blank token to labels to form decoder_input_ids.
+            # The TDT decoder expects [blank, label_0, ..., label_{U-1}] as input,
+            if isinstance(text, str):
+                text = [text]
+            decoder_text = [self.blank_token + t for t in text]
+            decoder_encodings = self.tokenizer(decoder_text, **output_kwargs["text_kwargs"])
+            inputs["decoder_input_ids"] = decoder_encodings["input_ids"]
             return inputs
 
     @property
     def model_input_names(self):
         feature_extractor_input_names = self.feature_extractor.model_input_names
-        return feature_extractor_input_names + ["labels"]
+        return feature_extractor_input_names + ["labels", "decoder_input_ids"]
+
+    def decode(self, *args, durations=None, **kwargs):
+        """
+        Forward arguments to [`~PreTrainedTokenizer.decode`] and post-process the timestamps (if provided for TDT) as
+        in the NeMo library.
+        """
+        decoded = self.tokenizer.decode(*args, **kwargs)
+
+        if durations is not None:
+            token_ids = args[0]
+            # Derive per-step frame indices from cumulative sum of durations.
+            timestamps = durations.cumsum(dim=-1) - durations
+
+            output_kwargs = self._merge_kwargs(
+                ParakeetProcessorKwargs,
+                tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            )
+            frame_rate = (
+                self.feature_extractor.hop_length
+                / self.feature_extractor.sampling_rate
+                * output_kwargs["audio_kwargs"]["subsampling_factor"]
+            )
+            proc_timestamps = []
+            for batch_ids, batch_timestamps, batch_durations in zip(token_ids, timestamps, durations):
+                # See `compute_rnnt_timestamps` in NeMo: https://github.com/NVIDIA-NeMo/NeMo/blob/1692a8fb97e1aadc883cfadd2a57c4e8a1b793aa/nemo/collections/asr/parts/submodules/rnnt_decoding.py#L993
+                # Filter padding and blank tokens
+                skip_ids = {self.tokenizer.pad_token_id, self.blank_token_id}
+                non_blank_indices = [i for i, token_id in enumerate(batch_ids) if int(token_id) not in skip_ids]
+                non_blank_ids = [batch_ids[i] for i in non_blank_indices]
+                decoded_tokens = [self.tokenizer.decode([token_id]) for token_id in non_blank_ids]
+                timestamp_dict = [
+                    {
+                        "token": token_str,
+                        "start": int(batch_timestamps[i]),
+                        "end": int(batch_timestamps[i] + batch_durations[i]),
+                    }
+                    for token_str, i in zip(decoded_tokens, non_blank_indices)
+                ]
+                timestamp_dict = self._refine_timestamps_tdt(timestamp_dict)
+
+                # Convert to seconds
+                for offset in timestamp_dict:
+                    offset["start"] = offset["start"] * frame_rate
+                    offset["end"] = offset["end"] * frame_rate
+                proc_timestamps.append(timestamp_dict)
+
+            return decoded, proc_timestamps
+        return decoded
+
+    def _refine_timestamps_tdt(
+        self, char_offsets, supported_punctuation=["?", "'", "¡", "¿", "-", ":", ",", "%", "/", ".", "!"]
+    ):
+        for i, offset in enumerate(char_offsets):
+            # If token is a punctuation mark, set its start and end offset as start and end of previous token
+            if offset["token"] in supported_punctuation and i > 0:
+                offset["start"] = char_offsets[i - 1]["end"]
+                offset["end"] = offset["start"]
+
+        return char_offsets
 
 
 __all__ = ["ParakeetProcessor"]
diff --git a/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py b/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py
index 05766ab41153..3146261e3204 100644
--- a/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py
+++ b/src/transformers/models/patchtsmixer/modeling_patchtsmixer.py
@@ -1092,12 +1092,12 @@ def forward(
         return data, loc, scale
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for `PatchTSMixerEncoderOutput`, with potential hidden states.
     """
 )
+@dataclass
 class PatchTSMixerEncoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, d_model)`):
@@ -1179,12 +1179,12 @@ def forward(
         return PatchTSMixerEncoderOutput(last_hidden_state=last_hidden_state, hidden_states=hidden_states)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs, with potential hidden states.
     """
 )
+@dataclass
 class PatchTSMixerModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor`  of shape `(batch_size, num_channels, num_patches, d_model)`):
@@ -1313,12 +1313,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSMixerForPreTrainingOutput`].
     """
 )
+@dataclass
 class PatchTSMixerForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`):
@@ -1426,12 +1426,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSMixerForPredictionOutput`].
     """
 )
+@dataclass
 class PatchTSMixerForPredictionOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`):
@@ -1456,13 +1456,13 @@ class PatchTSMixerForPredictionOutput(ModelOutput):
     scale: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for time series model's predictions outputs that contains the sampled values from the chosen
     distribution.
     """
 )
+@dataclass
 class SamplePatchTSMixerPredictionOutput(ModelOutput):
     r"""
     sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, number_channels)`):
@@ -1472,13 +1472,13 @@ class SamplePatchTSMixerPredictionOutput(ModelOutput):
     sequences: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for time series model's predictions outputs that contains the sampled values from the chosen
     distribution.
     """
 )
+@dataclass
 class SamplePatchTSMixerRegressionOutput(ModelOutput):
     r"""
     sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, number_channels)`):
@@ -1736,12 +1736,12 @@ def generate(
         return SamplePatchTSMixerPredictionOutput(sequences=samples)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSMixerForTimeSeriesClassificationOutput`].
     """
 )
+@dataclass
 class PatchTSMixerForTimeSeriesClassificationOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`):
@@ -1870,12 +1870,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSMixerForRegressionOutput`].
     """
 )
+@dataclass
 class PatchTSMixerForRegressionOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `y` is provided, `torch.FloatTensor` of shape `()`):
diff --git a/src/transformers/models/patchtst/modeling_patchtst.py b/src/transformers/models/patchtst/modeling_patchtst.py
index b5f94bab3476..1cdd3f381b58 100755
--- a/src/transformers/models/patchtst/modeling_patchtst.py
+++ b/src/transformers/models/patchtst/modeling_patchtst.py
@@ -752,12 +752,12 @@ def forward(
         return BaseModelOutput(last_hidden_state=hidden_state, hidden_states=encoder_states, attentions=all_attentions)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs, with potential hidden states.
     """
 )
+@dataclass
 class PatchTSTModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_channels, num_patches, patch_length)`):
@@ -785,12 +785,12 @@ class PatchTSTModelOutput(ModelOutput):
     patch_input: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSTForPretraining`].
     """
 )
+@dataclass
 class PatchTSTForPretrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -805,12 +805,12 @@ class PatchTSTForPretrainingOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSTForRegression`].
     """
 )
+@dataclass
 class PatchTSTForRegressionOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -825,12 +825,12 @@ class PatchTSTForRegressionOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSTForPrediction`].
     """
 )
+@dataclass
 class PatchTSTForPredictionOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -857,12 +857,12 @@ class PatchTSTForPredictionOutput(ModelOutput):
     scale: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`PatchTSTForClassification`].
     """
 )
+@dataclass
 class PatchTSTForClassificationOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
@@ -878,13 +878,13 @@ class PatchTSTForClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for time series model's predictions outputs that contains the sampled values from the chosen
     distribution.
     """
 )
+@dataclass
 class SamplePatchTSTOutput(ModelOutput):
     r"""
     sequences (`torch.FloatTensor` of shape `(batch_size, num_samples, prediction_length, num_targets)`):
diff --git a/src/transformers/models/pe_audio/configuration_pe_audio.py b/src/transformers/models/pe_audio/configuration_pe_audio.py
index c7555d15b20c..2d1a81d08d74 100644
--- a/src/transformers/models/pe_audio/configuration_pe_audio.py
+++ b/src/transformers/models/pe_audio/configuration_pe_audio.py
@@ -118,6 +118,7 @@ class PeAudioConfig(PreTrainedConfig):
 
     text_config: dict | PreTrainedConfig | None = None
     audio_config: dict | PreTrainedConfig | None = None
+    tie_word_embeddings: bool = True
 
     def __post_init__(self, **kwargs):
         if isinstance(self.text_config, dict):
diff --git a/src/transformers/models/pe_audio/modeling_pe_audio.py b/src/transformers/models/pe_audio/modeling_pe_audio.py
index 5597b101dd81..6c9cce24ad44 100644
--- a/src/transformers/models/pe_audio/modeling_pe_audio.py
+++ b/src/transformers/models/pe_audio/modeling_pe_audio.py
@@ -560,8 +560,8 @@ def __init__(self, config: PeAudioEncoderConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -601,7 +601,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/pe_audio_video/modeling_pe_audio_video.py b/src/transformers/models/pe_audio_video/modeling_pe_audio_video.py
index f61b59b9c53c..e11a2d50c07d 100644
--- a/src/transformers/models/pe_audio_video/modeling_pe_audio_video.py
+++ b/src/transformers/models/pe_audio_video/modeling_pe_audio_video.py
@@ -464,8 +464,8 @@ def __init__(self, config: PeAudioVideoEncoderConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -505,7 +505,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -629,13 +629,90 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`PeAudioVideoModel`] when using text, audio, and/or video.
     """
 )
+@dataclass
 class PeAudioVideoOutput(ModelOutput):
+    r"""
+    audio_embeds (`torch.FloatTensor`, *optional*):
+        Audio modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    video_embeds (`torch.FloatTensor`, *optional*):
+        Video modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    audio_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint audio-video embeddings produced by a fusion module. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_audio_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-audio embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-video embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_audio_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-audio-video embeddings combining all three modalities. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    audio_plus_text_embeds (`torch.FloatTensor`, *optional*):
+        Combined audio and text embeddings (e.g., concatenation or additive fusion). Shape `(batch_size, sequence_length, hidden_size)`.
+
+    video_plus_text_embeds (`torch.FloatTensor`, *optional*):
+        Combined video and text embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_outputs (`MaskedLMOutput`, *optional*):
+        Model outputs for the text encoder. Includes hidden states, attentions, and optionally loss.
+
+    audio_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the audio encoder, including last hidden state and pooled output.
+
+    video_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the video encoder, including last hidden state and pooled output.
+
+    audio_video_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the joint audio-video encoder.
+
+    logits_audio_text (`torch.FloatTensor`, *optional*):
+        Similarity logits between audio and text embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_video_text (`torch.FloatTensor`, *optional*):
+        Similarity logits between video and text embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_audio_video (`torch.FloatTensor`, *optional*):
+        Similarity logits between audio and video embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_audio_video_text (`torch.FloatTensor`, *optional*):
+        Similarity logits across audio, video, and text modalities.
+
+    logits_audio_plus_text_video (`torch.FloatTensor`, *optional*):
+        Similarity logits between fused (audio + text) embeddings and video embeddings.
+
+    logits_video_plus_text_audio (`torch.FloatTensor`, *optional*):
+        Similarity logits between fused (video + text) embeddings and audio embeddings.
+
+    audio_text_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between audio and text representations.
+
+    video_text_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between video and text representations.
+
+    audio_video_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between audio and video representations.
+
+    audio_video_text_loss (`torch.FloatTensor`, *optional*):
+        Joint loss over audio, video, and text modalities.
+
+    audio_plus_text_video_loss (`torch.FloatTensor`, *optional*):
+        Loss between fused (audio + text) representations and video.
+
+    video_plus_text_audio_loss (`torch.FloatTensor`, *optional*):
+        Loss between fused (video + text) representations and audio.
+
+    loss (`torch.FloatTensor`, *optional*):
+        Combined loss for all modality-wise losses.
+    """
+
     # embeddings
     audio_embeds: torch.FloatTensor | None = None
     video_embeds: torch.FloatTensor | None = None
diff --git a/src/transformers/models/pe_audio_video/modular_pe_audio_video.py b/src/transformers/models/pe_audio_video/modular_pe_audio_video.py
index f93b0f8ebf3b..2904f53994c2 100644
--- a/src/transformers/models/pe_audio_video/modular_pe_audio_video.py
+++ b/src/transformers/models/pe_audio_video/modular_pe_audio_video.py
@@ -421,13 +421,90 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`PeAudioVideoModel`] when using text, audio, and/or video.
     """
 )
+@dataclass
 class PeAudioVideoOutput(ModelOutput):
+    r"""
+    audio_embeds (`torch.FloatTensor`, *optional*):
+        Audio modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    video_embeds (`torch.FloatTensor`, *optional*):
+        Video modality embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    audio_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint audio-video embeddings produced by a fusion module. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_audio_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-audio embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-video embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_audio_video_embeds (`torch.FloatTensor`, *optional*):
+        Joint text-audio-video embeddings combining all three modalities. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    audio_plus_text_embeds (`torch.FloatTensor`, *optional*):
+        Combined audio and text embeddings (e.g., concatenation or additive fusion). Shape `(batch_size, sequence_length, hidden_size)`.
+
+    video_plus_text_embeds (`torch.FloatTensor`, *optional*):
+        Combined video and text embeddings. Shape `(batch_size, sequence_length, hidden_size)`.
+
+    text_outputs (`MaskedLMOutput`, *optional*):
+        Model outputs for the text encoder. Includes hidden states, attentions, and optionally loss.
+
+    audio_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the audio encoder, including last hidden state and pooled output.
+
+    video_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the video encoder, including last hidden state and pooled output.
+
+    audio_video_outputs (`BaseModelOutputWithPooling`, *optional*):
+        Model outputs for the joint audio-video encoder.
+
+    logits_audio_text (`torch.FloatTensor`, *optional*):
+        Similarity logits between audio and text embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_video_text (`torch.FloatTensor`, *optional*):
+        Similarity logits between video and text embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_audio_video (`torch.FloatTensor`, *optional*):
+        Similarity logits between audio and video embeddings. Shape `(batch_size, batch_size)`.
+
+    logits_audio_video_text (`torch.FloatTensor`, *optional*):
+        Similarity logits across audio, video, and text modalities.
+
+    logits_audio_plus_text_video (`torch.FloatTensor`, *optional*):
+        Similarity logits between fused (audio + text) embeddings and video embeddings.
+
+    logits_video_plus_text_audio (`torch.FloatTensor`, *optional*):
+        Similarity logits between fused (video + text) embeddings and audio embeddings.
+
+    audio_text_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between audio and text representations.
+
+    video_text_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between video and text representations.
+
+    audio_video_loss (`torch.FloatTensor`, *optional*):
+        Contrastive loss computed between audio and video representations.
+
+    audio_video_text_loss (`torch.FloatTensor`, *optional*):
+        Joint loss over audio, video, and text modalities.
+
+    audio_plus_text_video_loss (`torch.FloatTensor`, *optional*):
+        Loss between fused (audio + text) representations and video.
+
+    video_plus_text_audio_loss (`torch.FloatTensor`, *optional*):
+        Loss between fused (video + text) representations and audio.
+
+    loss (`torch.FloatTensor`, *optional*):
+        Combined loss for all modality-wise losses.
+    """
+
     # embeddings
     audio_embeds: torch.FloatTensor | None = None
     video_embeds: torch.FloatTensor | None = None
diff --git a/src/transformers/models/pe_video/configuration_pe_video.py b/src/transformers/models/pe_video/configuration_pe_video.py
index d6e260d73389..b48b72daab5e 100644
--- a/src/transformers/models/pe_video/configuration_pe_video.py
+++ b/src/transformers/models/pe_video/configuration_pe_video.py
@@ -120,6 +120,7 @@ class PeVideoConfig(PreTrainedConfig):
 
     text_config: dict | PreTrainedConfig | None = None
     video_config: dict | PreTrainedConfig | None = None
+    tie_word_embeddings: bool = True
 
     def __post_init__(self, **kwargs):
         if isinstance(self.text_config, dict):
diff --git a/src/transformers/models/pe_video/modeling_pe_video.py b/src/transformers/models/pe_video/modeling_pe_video.py
index 5a712aa06028..f6f29b11f456 100644
--- a/src/transformers/models/pe_video/modeling_pe_video.py
+++ b/src/transformers/models/pe_video/modeling_pe_video.py
@@ -444,8 +444,8 @@ def __init__(self, config: PeVideoEncoderConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -485,7 +485,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/penguinvl/__init__.py b/src/transformers/models/penguinvl/__init__.py
new file mode 100644
index 000000000000..70dca7acf12f
--- /dev/null
+++ b/src/transformers/models/penguinvl/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_penguinvl import *
+    from .image_processing_penguinvl import *
+    from .modeling_penguinvl import *
+    from .processing_penguinvl import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/penguinvl/configuration_penguinvl.py b/src/transformers/models/penguinvl/configuration_penguinvl.py
new file mode 100644
index 000000000000..1361aa40dfcb
--- /dev/null
+++ b/src/transformers/models/penguinvl/configuration_penguinvl.py
@@ -0,0 +1,231 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/penguinvl/modular_penguinvl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_penguinvl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from ...configuration_utils import PreTrainedConfig, layer_type_validation
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="tencent/Penguin-VL-8B")
+class PenguinVLVisionConfig(PreTrainedConfig):
+    r"""
+    Configuration for the PenguinVL vision encoder.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 1024):
+            Dimensionality of the encoder hidden states.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            Number of key-value heads for grouped-query attention.
+        head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each attention head.
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of input channels.
+        patch_size (`int`, *optional*, defaults to 14):
+            The size of each image patch.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the encoder.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the rms normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in attention layers.
+        rope_scaling (`dict`, *optional*, defaults to `None`):
+            Dictionary containing the scaling configuration for the RoPE embeddings.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated normal initializer.
+    """
+
+    model_type = "penguinvl_vision"
+    base_config_key = "vision_encoder_config"
+
+    def __init__(
+        self,
+        hidden_size=1024,
+        intermediate_size=3072,
+        num_hidden_layers=28,
+        num_attention_heads=16,
+        num_key_value_heads=8,
+        max_position_embeddings=40960,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        head_dim=128,
+        num_channels=3,
+        patch_size=14,
+        hidden_act="silu",
+        rms_norm_eps=1e-6,
+        attention_dropout=0.0,
+        attention_bias=False,
+        rope_theta=1000000.0,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.head_dim = head_dim
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.hidden_act = hidden_act
+        self.rms_norm_eps = rms_norm_eps
+        self.attention_dropout = attention_dropout
+        self.attention_bias = attention_bias
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+
+        super().__init__(**kwargs)
+
+
+@auto_docstring(checkpoint="tencent/Penguin-VL-8B")
+class PenguinVLConfig(PreTrainedConfig):
+    r"""
+    Configuration for the PenguinVL model.
+
+    Args:
+        vision_encoder_config (`PenguinVLVisionConfig` or `dict`, *optional*):
+            Configuration for the vision encoder.
+        image_token_id (`int`, *optional*, defaults to 151669):
+            Token ID for the image placeholder token.
+        vision_projector_type (`str`, *optional*, defaults to `"mlp2x_gelu"`):
+            Type of the vision projector.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie word embeddings.
+    """
+
+    model_type = "penguinvl"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    # Default tensor parallel plan for base model `PenguinVL`
+    base_model_tp_plan = {
+        "layers.*.self_attn.q_proj": "colwise",
+        "layers.*.self_attn.k_proj": "colwise",
+        "layers.*.self_attn.v_proj": "colwise",
+        "layers.*.self_attn.q_norm": "replicated_with_grad_allreduce",
+        "layers.*.self_attn.k_norm": "replicated_with_grad_allreduce",
+        "layers.*.self_attn.o_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    sub_configs = {"vision_encoder_config": PenguinVLVisionConfig}
+
+    def __init__(
+        self,
+        vision_encoder_config=None,
+        image_token_id=151669,
+        vision_projector_type="mlp2x_gelu",
+        vocab_size: int | None = 151936,
+        hidden_size: int | None = 4096,
+        intermediate_size: int | None = 22016,
+        num_hidden_layers: int | None = 32,
+        num_attention_heads: int | None = 32,
+        num_key_value_heads: int | None = 32,
+        head_dim: int | None = 128,
+        hidden_act: str | None = "silu",
+        max_position_embeddings: int | None = 32768,
+        initializer_range: float | None = 0.02,
+        rms_norm_eps: float | None = 1e-6,
+        use_cache: bool | None = True,
+        tie_word_embeddings: bool | None = False,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        attention_bias: bool | None = False,
+        use_sliding_window: bool | None = False,
+        sliding_window: int | None = 4096,
+        max_window_layers: int | None = 28,
+        layer_types: list[str] | None = None,
+        attention_dropout: float | None = 0.0,
+        pad_token_id: int | None = None,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = None,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.use_sliding_window = use_sliding_window
+        self.sliding_window = sliding_window if self.use_sliding_window else None
+        self.max_window_layers = max_window_layers
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+
+        self.layer_types = layer_types
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention"
+                if self.sliding_window is not None and i >= self.max_window_layers
+                else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        layer_type_validation(self.layer_types, self.num_hidden_layers)
+
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.tie_word_embeddings = tie_word_embeddings
+        self.rope_parameters = rope_parameters
+
+        super().__init__(**kwargs)
+        if isinstance(vision_encoder_config, dict):
+            self.vision_encoder_config = self.sub_configs["vision_encoder_config"](**vision_encoder_config)
+        elif isinstance(vision_encoder_config, PreTrainedConfig):
+            self.vision_encoder_config = vision_encoder_config
+        elif vision_encoder_config is None:
+            self.vision_encoder_config = self.sub_configs["vision_encoder_config"]()
+        else:
+            raise ValueError(
+                f"vision_encoder_config must be dict or PreTrainedConfig, got {type(vision_encoder_config)}."
+            )
+
+        self.image_token_id = image_token_id
+        self.vision_projector_type = vision_projector_type
+
+
+__all__ = ["PenguinVLVisionConfig", "PenguinVLConfig"]
diff --git a/src/transformers/models/penguinvl/image_processing_penguinvl.py b/src/transformers/models/penguinvl/image_processing_penguinvl.py
new file mode 100644
index 000000000000..b98f15118cdc
--- /dev/null
+++ b/src/transformers/models/penguinvl/image_processing_penguinvl.py
@@ -0,0 +1,633 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/penguinvl/modular_penguinvl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_penguinvl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import numpy as np
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils import BaseImageProcessor
+from ...image_transforms import convert_to_rgb, resize, to_channel_dimension_format
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_scaled_image,
+    is_valid_image,
+    make_flat_list_of_images,
+    to_numpy_array,
+    validate_preprocess_arguments,
+)
+from ...processing_utils import ImagesKwargs
+from ...utils import TensorType, is_vision_available, logging
+from ...video_utils import VideoInput
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+logger = logging.get_logger(__name__)
+
+
+class PenguinVLImageProcessorKwargs(ImagesKwargs, total=False):
+    r"""
+    min_pixels (`int`, *optional*, defaults to `56 * 56`):
+        The min pixels of the image to resize the image.
+    max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
+        The max pixels of the image to resize the image.
+    patch_size (`int`, *optional*, defaults to 14):
+        The spatial patch size of the vision encoder.
+    temporal_patch_size (`int`, *optional*, defaults to 2):
+        The temporal patch size of the vision encoder.
+    merge_size (`int`, *optional*, defaults to 2):
+        The merge size of the vision encoder to llm encoder.
+    """
+
+    min_pixels: int
+    max_pixels: int
+    patch_size: int
+    temporal_patch_size: int
+    merge_size: int | list[int]
+    frame_types: list | None
+
+
+def smart_resize(
+    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = max(factor, math.floor(height / beta / factor) * factor)
+        w_bar = max(factor, math.floor(width / beta / factor) * factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+# ===================== Image Processor =====================
+
+
+def _make_batched_clips(images) -> list[list]:
+    r"""
+    Normalize visual inputs to a list of clips, where each clip is a list of frames.
+
+    - Single image: ``image`` -> ``[[image]]``
+    - List of images: ``[img1, img2]`` -> ``[[img1], [img2]]``
+    - Nested clips: ``[[img1], [f1, f2, f3]]`` -> ``[[img1], [f1, f2, f3]]``
+    """
+    if isinstance(images, list | tuple) and len(images) > 0:
+        if isinstance(images[0], list | tuple):
+            return [list(clip) for clip in images]
+        if all(is_valid_image(f) for f in images):
+            return [[img] for img in images]
+    if is_valid_image(images):
+        return [[images]]
+    raise ValueError(f"Could not make batched images from {type(images)}")
+
+
+def _simple_batched_resize(
+    images,
+    factor: int = 28,
+    min_tokens: int = 16,
+    max_tokens: int = 16384,
+    input_data_format=None,
+    frame_types=None,
+):
+    r"""
+    Compute per-frame target ``(h, w)`` for a clip using TRA (Temporal Redundancy-Aware)
+    token compression.
+
+    Key frames (type 0) retain higher resolution. Intermediate frames (type 1) are
+    allocated 1/16 of a key frame's area to reduce tokens while preserving temporal
+    coverage. When all frames fit within the token budget, the original (aligned)
+    resolution is kept for every frame.
+    """
+    min_pixels = min_tokens * factor * factor * 1.5
+    max_pixels = max_tokens * factor * factor * 0.95
+
+    first_image = images[0]
+    if is_vision_available() and isinstance(first_image, Image.Image):
+        width, height = first_image.size
+    else:
+        idf = input_data_format
+        if idf is None:
+            idf = infer_channel_dimension_format(first_image)
+        height, width = get_image_size(first_image, channel_dim=idf)
+
+    aspect_ratio = height / width
+    raw_area = height * width
+    num_frames = len(images)
+
+    if frame_types is not None:
+        ft_list = frame_types.tolist() if hasattr(frame_types, "tolist") else list(frame_types)
+        num_key = ft_list.count(0)
+        num_intermediate = ft_list.count(1)
+    else:
+        num_key = num_frames
+        num_intermediate = 0
+        ft_list = [0] * num_frames
+
+    def _dims_from_area(target_area, ar, fac):
+        w_new = math.sqrt(target_area / ar)
+        h_new = w_new * ar
+        return max(round(h_new / fac) * fac, fac), max(round(w_new / fac) * fac, fac)
+
+    def _ensure_min(h, w, min_p, ar):
+        if h * w < min_p:
+            w_f = math.sqrt(min_p / ar)
+            h_f = w_f * ar
+            h = math.ceil(h_f / factor) * factor
+            w = math.ceil(w_f / factor) * factor
+        return h, w
+
+    total_raw = num_frames * raw_area
+    key_area = raw_area
+    inter_area = raw_area
+
+    if total_raw > max_pixels:
+        eff = num_key + num_intermediate / 16.0
+        key_area = max_pixels / eff
+        inter_area = key_area / 16.0
+        if inter_area < min_pixels:
+            inter_area = min_pixels
+            key_area = (max_pixels - num_intermediate * min_pixels) / max(num_key, 1)
+        if key_area < min_pixels:
+            key_area = min_pixels
+
+    k_h, k_w = _dims_from_area(key_area, aspect_ratio, factor)
+    k_h, k_w = _ensure_min(k_h, k_w, min_pixels, aspect_ratio)
+
+    if num_intermediate > 0:
+        i_h, i_w = _dims_from_area(inter_area, aspect_ratio, factor)
+        i_h, i_w = _ensure_min(i_h, i_w, min_pixels, aspect_ratio)
+    else:
+        i_h, i_w = k_h, k_w
+
+    return [(i_h, i_w) if ft_list[i] == 1 else (k_h, k_w) for i in range(num_frames)]
+
+
+def _allocate_token_budget(clips, clip_merge_sizes, min_tokens, max_tokens, patch_size, input_data_format=None):
+    r"""Distribute ``max_tokens`` across clips proportionally to their raw token counts."""
+    clip_raw_tokens = []
+    for clip, ms in zip(clips, clip_merge_sizes):
+        first_frame = clip[0]
+        if is_vision_available() and isinstance(first_frame, Image.Image):
+            w, h = first_frame.size
+        else:
+            idf = input_data_format or infer_channel_dimension_format(first_frame)
+            h, w = get_image_size(first_frame, channel_dim=idf)
+        factor = patch_size * ms
+        clip_raw_tokens.append(len(clip) * h * w / (factor * factor))
+
+    total_raw = sum(clip_raw_tokens)
+    if total_raw <= max_tokens:
+        return [max_tokens] * len(clips)
+
+    return [max(min_tokens * len(clip), raw * max_tokens / total_raw) for clip, raw in zip(clips, clip_raw_tokens)]
+
+
+class PenguinVLImageProcessor(BaseImageProcessor):
+    r"""
+    Image processor for PenguinVL with dynamic resizing and TRA (Temporal Redundancy-Aware)
+    token compression for video frames.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image.
+        size (`dict[str, int] | None`, *optional*, defaults to `{"shortest_edge": 3136, "longest_edge": 3211264}`):
+            Size constraints for resizing. Must contain `shortest_edge` and `longest_edge` keys. When None, uses
+            `min_pixels` and `max_pixels` instead.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Resampling filter to use when resizing.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by `rescale_factor`.
+        rescale_factor (`float`, *optional*, defaults to `1/255`):
+            Scale factor for rescaling.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image.
+        image_mean (`list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Mean for normalization.
+        image_std (`list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Standard deviation for normalization.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+        min_pixels (`int`, *optional*, defaults to 3136):
+            Minimum pixels for resizing (equivalent to ``min_tokens * patch_size ** 2``).
+        max_pixels (`int`, *optional*, defaults to 3211264):
+            Maximum pixels for resizing (equivalent to ``max_tokens * patch_size ** 2``).
+        patch_size (`int`, *optional*, defaults to 14):
+            Spatial patch size of the vision encoder.
+        temporal_patch_size (`int`, *optional*, defaults to 1):
+            Temporal patch size of the vision encoder. Must be 1 for PenguinVL.
+        merge_size (`int`, *optional*, defaults to 1):
+            Default spatial merge size for token compression (1 for images, 2 for video).
+    """
+
+    model_input_names = ["pixel_values", "image_grid_thw", "image_merge_sizes"]
+    valid_kwargs = PenguinVLImageProcessorKwargs
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: int | float = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool = True,
+        min_pixels: int = 3136,
+        max_pixels: int = 3211264,
+        patch_size: int = 14,
+        temporal_patch_size: int = 1,
+        merge_size: int = 1,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        if size is not None:
+            if "shortest_edge" not in size or "longest_edge" not in size:
+                raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
+        else:
+            size = {"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 1280}
+        # backward compatibility: override size with min_pixels and max_pixels if they are provided
+        if min_pixels is not None:
+            size["shortest_edge"] = min_pixels
+        if max_pixels is not None:
+            size["longest_edge"] = max_pixels
+        self.min_pixels = size["shortest_edge"]
+        self.max_pixels = size["longest_edge"]
+        self.size = size
+
+        self.do_resize = do_resize
+        self.resample = resample
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+        self.do_convert_rgb = do_convert_rgb
+
+        if self.temporal_patch_size != 1:
+            raise ValueError("`temporal_patch_size` must be 1 for PenguinVL")
+
+    def _preprocess(
+        self,
+        images: ImageInput | VideoInput,
+        do_resize: bool | None = None,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling | None = None,
+        do_rescale: bool | None = None,
+        rescale_factor: float | None = None,
+        do_normalize: bool | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        patch_size: int | None = None,
+        temporal_patch_size: int | None = None,
+        merge_size: int | None = None,
+        do_convert_rgb: bool | None = None,
+        data_format: ChannelDimension | None = ChannelDimension.FIRST,
+        input_data_format: str | ChannelDimension | None = None,
+    ):
+        """
+        Preprocess an image or batch of images. Copy of the `preprocess` method from `CLIPImageProcessor`.
+
+        Args:
+            images (`ImageInput`):
+                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
+            vision_info (`list[Dict]`, *optional*):
+                Optional list of dictionaries containing additional information about vision inputs.
+            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
+                Whether to resize the image.
+            size (`dict[str, int]`, *optional*, defaults to `self.size`):
+                Size of the image after resizing. `shortest_edge` and `longest_edge` keys must be present.
+            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
+                Resampling filter to use if resizing the image. This can be one of the `PILImageResampling` enums.
+            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
+                Whether to rescale the image.
+            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
+                Scale factor to use if rescaling the image.
+            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
+                Whether to normalize the image.
+            image_mean (`float` or `list[float]`, *optional*, defaults to `self.image_mean`):
+                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            image_std (`float` or `list[float]`, *optional*, defaults to `self.image_std`):
+                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
+            patch_size (`int`, *optional*, defaults to `self.patch_size`):
+                The spatial patch size of the vision encoder.
+            temporal_patch_size (`int`, *optional*, defaults to `self.temporal_patch_size`):
+                The temporal patch size of the vision encoder.
+            merge_size (`int`, *optional*, defaults to `self.merge_size`):
+                The merge size of the vision encoder to llm encoder.
+            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
+                Whether to convert the image to RGB.
+            data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
+                The channel dimension format for the output image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - Unset: Use the channel dimension format of the input image.
+            input_data_format (`ChannelDimension` or `str`, *optional*):
+                The channel dimension format for the input image. Can be one of:
+                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
+                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
+                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
+        """
+        images = make_flat_list_of_images(images)
+
+        if do_convert_rgb:
+            images = [convert_to_rgb(image) for image in images]
+
+        # All transformations expect numpy arrays.
+        images = [to_numpy_array(image) for image in images]
+
+        if do_rescale and is_scaled_image(images[0]):
+            logger.warning_once(
+                "It looks like you are trying to rescale already rescaled images. If the input"
+                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
+            )
+        if input_data_format is None:
+            # We assume that all images have the same channel dimension format.
+            input_data_format = infer_channel_dimension_format(images[0])
+
+        height, width = get_image_size(images[0], channel_dim=input_data_format)
+        resized_height, resized_width = height, width
+        processed_images = []
+        for image in images:
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height,
+                    width,
+                    factor=patch_size * merge_size,
+                    min_pixels=size["shortest_edge"],
+                    max_pixels=size["longest_edge"],
+                )
+                image = resize(
+                    image, size=(resized_height, resized_width), resample=resample, input_data_format=input_data_format
+                )
+
+            if do_rescale:
+                image = self.rescale(image, scale=rescale_factor, input_data_format=input_data_format)
+
+            if do_normalize:
+                image = self.normalize(
+                    image=image, mean=image_mean, std=image_std, input_data_format=input_data_format
+                )
+
+            image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
+            processed_images.append(image)
+
+        patches = np.array(processed_images)
+        if data_format == ChannelDimension.LAST:
+            patches = patches.transpose(0, 3, 1, 2)
+        if patches.shape[0] % temporal_patch_size != 0:
+            repeats = np.repeat(
+                patches[-1][np.newaxis], temporal_patch_size - (patches.shape[0] % temporal_patch_size), axis=0
+            )
+            patches = np.concatenate([patches, repeats], axis=0)
+        channel = patches.shape[1]
+        grid_t = patches.shape[0] // temporal_patch_size
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        patches = patches.reshape(
+            grid_t,
+            temporal_patch_size,
+            channel,
+            grid_h // merge_size,
+            merge_size,
+            patch_size,
+            grid_w // merge_size,
+            merge_size,
+            patch_size,
+        )
+        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
+        flatten_patches = patches.reshape(
+            grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size
+        )
+
+        return flatten_patches, (grid_t, grid_h, grid_w)
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool | None = None,
+        size: dict[str, int] | None = None,
+        min_pixels: int | None = None,
+        max_pixels: int | None = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool | None = None,
+        rescale_factor: float | None = None,
+        do_normalize: bool | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool | None = None,
+        merge_size: int | list[int] | None = None,
+        frame_types: list | None = None,
+        return_tensors: str | TensorType | None = None,
+        data_format: ChannelDimension | None = ChannelDimension.FIRST,
+        input_data_format: str | ChannelDimension | None = None,
+    ):
+        r"""
+        Preprocess images or video clips with optional TRA key/intermediate frame compression.
+
+        Args:
+            images: Single image, list of images, or nested ``[[clip1_frames], [clip2_frames]]``.
+            merge_size: Spatial merge size. Can be ``int`` (all clips) or ``list[int]`` (per-clip).
+                Typically 1 for images and 2 for video.
+            frame_types: Per-clip frame type annotations. ``None`` means all key frames.
+                Each clip's frame_types is a list where 0 = key frame, 1 = intermediate frame.
+                Pass as ``[ft_clip1, ft_clip2, ...]`` or ``[ft_single_clip]``.
+        """
+        min_pixels = min_pixels if min_pixels is not None else self.min_pixels
+        max_pixels = max_pixels if max_pixels is not None else self.max_pixels
+
+        if size is not None:
+            if "shortest_edge" not in size or "longest_edge" not in size:
+                raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
+            min_pixels = size["shortest_edge"]
+        elif min_pixels is not None and max_pixels is not None:
+            # backward compatibility: override size with min_pixels and max_pixels if they are provided
+            size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
+        else:
+            size = {**self.size}
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        default_merge = merge_size if merge_size is not None else self.merge_size
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        validate_preprocess_arguments(
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        clips = _make_batched_clips(images)
+        num_clips = len(clips)
+
+        if isinstance(default_merge, list | tuple):
+            clip_merge_sizes = list(default_merge)
+        else:
+            clip_merge_sizes = [default_merge] * num_clips
+
+        if frame_types is None:
+            clip_frame_types = [None] * num_clips
+        elif isinstance(frame_types, list | tuple) and len(frame_types) > 0:
+            if isinstance(frame_types[0], list | tuple) or frame_types[0] is None:
+                clip_frame_types = list(frame_types)
+            else:
+                clip_frame_types = [frame_types] if num_clips == 1 else [None] * num_clips
+        else:
+            clip_frame_types = [None] * num_clips
+
+        ps2 = self.patch_size * self.patch_size
+        clip_budgets = _allocate_token_budget(
+            clips,
+            clip_merge_sizes,
+            min_tokens=self.min_pixels // ps2,
+            max_tokens=self.max_pixels // ps2,
+            patch_size=self.patch_size,
+            input_data_format=input_data_format,
+        )
+
+        pixel_values_list = []
+        grid_thw_list = []
+        merge_sizes_list = []
+        num_frames_per_clip = []
+
+        for clip, ms, ft, budget in zip(clips, clip_merge_sizes, clip_frame_types, clip_budgets):
+            factor = self.patch_size * ms
+            target_sizes = _simple_batched_resize(
+                clip,
+                factor=factor,
+                min_tokens=self.min_pixels // ps2,
+                max_tokens=budget,
+                input_data_format=input_data_format,
+                frame_types=ft,
+            )
+
+            clip_n = 0
+            for frame, target_size in zip(clip, target_sizes):
+                frame_convert_rgb = do_convert_rgb
+                frame_data_fmt = input_data_format
+                if do_resize:
+                    if do_convert_rgb:
+                        frame = convert_to_rgb(frame)
+                    frame = to_numpy_array(frame)
+                    if frame_data_fmt is None:
+                        frame_data_fmt = infer_channel_dimension_format(frame)
+                    rh, rw = int(target_size[0]), int(target_size[1])
+                    frame = resize(frame, size=(rh, rw), resample=resample, input_data_format=frame_data_fmt)
+                    frame_convert_rgb = False
+
+                patches, grid_thw = self._preprocess(
+                    frame,
+                    do_resize=False,
+                    size=size,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    patch_size=self.patch_size,
+                    temporal_patch_size=1,
+                    merge_size=ms,
+                    do_convert_rgb=frame_convert_rgb,
+                    data_format=data_format,
+                    input_data_format=frame_data_fmt,
+                )
+                pixel_values_list.append(patches)
+                grid_thw_list.append(grid_thw)
+                merge_sizes_list.append(ms)
+                clip_n += 1
+            num_frames_per_clip.append(clip_n)
+
+        pixel_values = np.concatenate(pixel_values_list, axis=0)
+        image_grid_thw = np.array(grid_thw_list)
+        image_merge_sizes = np.array(merge_sizes_list)
+
+        data = {
+            "pixel_values": pixel_values,
+            "image_grid_thw": image_grid_thw,
+            "image_merge_sizes": image_merge_sizes,
+            "num_frames_per_clip": num_frames_per_clip,
+        }
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of image patches per image.
+        """
+        min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"]
+        max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"]
+        patch_size = images_kwargs.get("patch_size", self.patch_size)
+        merge_size = images_kwargs.get("merge_size", self.merge_size)
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        return grid_h * grid_w
+
+
+__all__ = ["PenguinVLImageProcessor"]
diff --git a/src/transformers/models/penguinvl/image_processing_penguinvl_fast.py b/src/transformers/models/penguinvl/image_processing_penguinvl_fast.py
new file mode 100644
index 000000000000..b61d4d2a23eb
--- /dev/null
+++ b/src/transformers/models/penguinvl/image_processing_penguinvl_fast.py
@@ -0,0 +1,545 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/penguinvl/modular_penguinvl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_penguinvl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+from typing import Optional, Union
+
+import torch
+import torchvision.transforms.v2.functional as tvF
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_processing_utils_fast import BaseImageProcessorFast, group_images_by_shape, reorder_images
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_valid_image,
+)
+from ...processing_utils import Unpack
+from ...utils import TensorType, auto_docstring, is_vision_available
+from .image_processing_penguinvl import PenguinVLImageProcessorKwargs
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+def smart_resize(
+    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = max(factor, math.floor(height / beta / factor) * factor)
+        w_bar = max(factor, math.floor(width / beta / factor) * factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+# ===================== Image Processor =====================
+
+
+def _make_batched_clips(images) -> list[list]:
+    r"""
+    Normalize visual inputs to a list of clips, where each clip is a list of frames.
+
+    - Single image: ``image`` -> ``[[image]]``
+    - List of images: ``[img1, img2]`` -> ``[[img1], [img2]]``
+    - Nested clips: ``[[img1], [f1, f2, f3]]`` -> ``[[img1], [f1, f2, f3]]``
+    """
+    if isinstance(images, list | tuple) and len(images) > 0:
+        if isinstance(images[0], list | tuple):
+            return [list(clip) for clip in images]
+        if all(is_valid_image(f) for f in images):
+            return [[img] for img in images]
+    if is_valid_image(images):
+        return [[images]]
+    raise ValueError(f"Could not make batched images from {type(images)}")
+
+
+def _simple_batched_resize(
+    images,
+    factor: int = 28,
+    min_tokens: int = 16,
+    max_tokens: int = 16384,
+    input_data_format=None,
+    frame_types=None,
+):
+    r"""
+    Compute per-frame target ``(h, w)`` for a clip using TRA (Temporal Redundancy-Aware)
+    token compression.
+
+    Key frames (type 0) retain higher resolution. Intermediate frames (type 1) are
+    allocated 1/16 of a key frame's area to reduce tokens while preserving temporal
+    coverage. When all frames fit within the token budget, the original (aligned)
+    resolution is kept for every frame.
+    """
+    min_pixels = min_tokens * factor * factor * 1.5
+    max_pixels = max_tokens * factor * factor * 0.95
+
+    first_image = images[0]
+    if is_vision_available() and isinstance(first_image, Image.Image):
+        width, height = first_image.size
+    else:
+        idf = input_data_format
+        if idf is None:
+            idf = infer_channel_dimension_format(first_image)
+        height, width = get_image_size(first_image, channel_dim=idf)
+
+    aspect_ratio = height / width
+    raw_area = height * width
+    num_frames = len(images)
+
+    if frame_types is not None:
+        ft_list = frame_types.tolist() if hasattr(frame_types, "tolist") else list(frame_types)
+        num_key = ft_list.count(0)
+        num_intermediate = ft_list.count(1)
+    else:
+        num_key = num_frames
+        num_intermediate = 0
+        ft_list = [0] * num_frames
+
+    def _dims_from_area(target_area, ar, fac):
+        w_new = math.sqrt(target_area / ar)
+        h_new = w_new * ar
+        return max(round(h_new / fac) * fac, fac), max(round(w_new / fac) * fac, fac)
+
+    def _ensure_min(h, w, min_p, ar):
+        if h * w < min_p:
+            w_f = math.sqrt(min_p / ar)
+            h_f = w_f * ar
+            h = math.ceil(h_f / factor) * factor
+            w = math.ceil(w_f / factor) * factor
+        return h, w
+
+    total_raw = num_frames * raw_area
+    key_area = raw_area
+    inter_area = raw_area
+
+    if total_raw > max_pixels:
+        eff = num_key + num_intermediate / 16.0
+        key_area = max_pixels / eff
+        inter_area = key_area / 16.0
+        if inter_area < min_pixels:
+            inter_area = min_pixels
+            key_area = (max_pixels - num_intermediate * min_pixels) / max(num_key, 1)
+        if key_area < min_pixels:
+            key_area = min_pixels
+
+    k_h, k_w = _dims_from_area(key_area, aspect_ratio, factor)
+    k_h, k_w = _ensure_min(k_h, k_w, min_pixels, aspect_ratio)
+
+    if num_intermediate > 0:
+        i_h, i_w = _dims_from_area(inter_area, aspect_ratio, factor)
+        i_h, i_w = _ensure_min(i_h, i_w, min_pixels, aspect_ratio)
+    else:
+        i_h, i_w = k_h, k_w
+
+    return [(i_h, i_w) if ft_list[i] == 1 else (k_h, k_w) for i in range(num_frames)]
+
+
+def _allocate_token_budget(clips, clip_merge_sizes, min_tokens, max_tokens, patch_size, input_data_format=None):
+    r"""Distribute ``max_tokens`` across clips proportionally to their raw token counts."""
+    clip_raw_tokens = []
+    for clip, ms in zip(clips, clip_merge_sizes):
+        first_frame = clip[0]
+        if is_vision_available() and isinstance(first_frame, Image.Image):
+            w, h = first_frame.size
+        else:
+            idf = input_data_format or infer_channel_dimension_format(first_frame)
+            h, w = get_image_size(first_frame, channel_dim=idf)
+        factor = patch_size * ms
+        clip_raw_tokens.append(len(clip) * h * w / (factor * factor))
+
+    total_raw = sum(clip_raw_tokens)
+    if total_raw <= max_tokens:
+        return [max_tokens] * len(clips)
+
+    return [max(min_tokens * len(clip), raw * max_tokens / total_raw) for clip, raw in zip(clips, clip_raw_tokens)]
+
+
+@auto_docstring
+class PenguinVLImageProcessorFast(BaseImageProcessorFast):
+    r"""
+    Fast image processor for PenguinVL with dynamic per-clip resizing and TRA (Temporal
+    Redundancy-Aware) token compression for video frames.
+
+    Compared to the base Qwen2-VL fast processor this class:
+
+    * Supports **per-clip merge sizes** (``merge_size`` may be ``int`` or ``list[int]``).
+    * Applies TRA compression: key frames retain high resolution while intermediate
+      frames are allocated ~1/16 of the tokens.
+    * Returns ``image_merge_sizes`` and ``num_frames_per_clip`` alongside pixel values.
+    """
+
+    do_resize = True
+    resample = PILImageResampling.BICUBIC
+    size = {"shortest_edge": 56 * 56, "longest_edge": 28 * 28 * 1280}
+    do_rescale = True
+    do_normalize = True
+
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    do_convert_rgb = True
+    patch_size = 14
+    temporal_patch_size = 1
+    merge_size = 2
+    valid_kwargs = PenguinVLImageProcessorKwargs
+    model_input_names = ["pixel_values", "image_grid_thw", "image_merge_sizes"]
+
+    def __init__(self, **kwargs: Unpack[PenguinVLImageProcessorKwargs]):
+        size = kwargs.pop("size", None)
+        min_pixels = kwargs.pop("min_pixels", None)
+        max_pixels = kwargs.pop("max_pixels", None)
+        # backward compatibility: override size with min_pixels and max_pixels if they are provided
+        size = self.size if size is None else size
+        if min_pixels is not None:
+            size["shortest_edge"] = min_pixels
+            size.pop("min_pixels", None)
+        if max_pixels is not None:
+            size["longest_edge"] = max_pixels
+            size.pop("max_pixels", None)
+        if "shortest_edge" not in size or "longest_edge" not in size:
+            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
+
+        super().__init__(size=size, **kwargs)
+
+    def _further_process_kwargs(
+        self,
+        size: SizeDict | None = None,
+        min_pixels: int | None = None,
+        max_pixels: int | None = None,
+        **kwargs,
+    ) -> dict:
+        """
+        Update kwargs that need further processing before being validated
+        Can be overridden by subclasses to customize the processing of kwargs.
+        """
+        if min_pixels is not None and max_pixels is not None:
+            size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
+        elif size is not None:
+            if "shortest_edge" not in size or "longest_edge" not in size:
+                raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
+            min_pixels = size["shortest_edge"]
+            max_pixels = size["longest_edge"]
+        else:
+            size = {**self.size}
+
+        return super()._further_process_kwargs(size=size, **kwargs)
+
+    @auto_docstring
+    def preprocess(
+        self,
+        images: ImageInput,
+        **kwargs: Unpack[PenguinVLImageProcessorKwargs],
+    ) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def _preprocess_image_like_inputs(
+        self,
+        images: ImageInput,
+        do_convert_rgb: bool,
+        input_data_format: ChannelDimension,
+        device: Union[str, "torch.device"] | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Preprocess image-like inputs.
+        To be overridden by subclasses when image-like inputs other than images should be processed.
+        It can be used for segmentation maps, depth maps, etc.
+        """
+        if kwargs["temporal_patch_size"] != 1:
+            raise ValueError("`temporal_patch_size` must be 1 for PenguinVL")
+
+        merge_size_param = kwargs.pop("merge_size")
+        frame_types_param = kwargs.pop("frame_types", None)
+        size = kwargs["size"]
+        patch_size = kwargs["patch_size"]
+        do_resize = kwargs["do_resize"]
+        interpolation = kwargs["interpolation"]
+        do_rescale = kwargs["do_rescale"]
+        rescale_factor = kwargs["rescale_factor"]
+        do_normalize = kwargs["do_normalize"]
+        image_mean = kwargs["image_mean"]
+        image_std = kwargs["image_std"]
+        return_tensors = kwargs.get("return_tensors")
+
+        min_pixels = size["shortest_edge"]
+        max_pixels = size["longest_edge"]
+
+        clips = _make_batched_clips(images)
+        num_clips = len(clips)
+
+        if isinstance(merge_size_param, (list, tuple)):
+            clip_merge_sizes = list(merge_size_param)
+        else:
+            clip_merge_sizes = [merge_size_param] * num_clips
+
+        if frame_types_param is None:
+            clip_frame_types = [None] * num_clips
+        elif isinstance(frame_types_param, (list, tuple)) and len(frame_types_param) > 0:
+            if isinstance(frame_types_param[0], (list, tuple)) or frame_types_param[0] is None:
+                clip_frame_types = list(frame_types_param)
+            else:
+                clip_frame_types = [frame_types_param] if num_clips == 1 else [None] * num_clips
+        else:
+            clip_frame_types = [None] * num_clips
+
+        ps2 = patch_size * patch_size
+        min_tokens = min_pixels // ps2
+        max_tokens = max_pixels // ps2
+        clip_budgets = _allocate_token_budget(
+            clips,
+            clip_merge_sizes,
+            min_tokens,
+            max_tokens,
+            patch_size,
+        )
+
+        pixel_values_list = []
+        grid_thw_list = []
+        merge_sizes_list = []
+        num_frames_per_clip = []
+
+        for clip, ms, ft, budget in zip(clips, clip_merge_sizes, clip_frame_types, clip_budgets):
+            factor = patch_size * ms
+            target_sizes = _simple_batched_resize(
+                clip,
+                factor=factor,
+                min_tokens=min_tokens,
+                max_tokens=budget,
+                input_data_format=input_data_format,
+                frame_types=ft,
+            )
+
+            clip_n = 0
+            for frame, target_size in zip(clip, target_sizes):
+                frame_tensor = self._process_image(
+                    frame,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                    device=device,
+                )
+
+                if do_resize:
+                    frame_tensor = self.resize(
+                        frame_tensor,
+                        size=SizeDict(height=int(target_size[0]), width=int(target_size[1])),
+                        interpolation=interpolation,
+                    )
+
+                frame_tensor = self.rescale_and_normalize(
+                    frame_tensor.unsqueeze(0),
+                    do_rescale,
+                    rescale_factor,
+                    do_normalize,
+                    image_mean,
+                    image_std,
+                )
+
+                resized_height, resized_width = frame_tensor.shape[-2:]
+                grid_h = resized_height // patch_size
+                grid_w = resized_width // patch_size
+                channel = frame_tensor.shape[-3]
+
+                patches = frame_tensor.view(
+                    1,
+                    1,
+                    1,
+                    channel,
+                    grid_h // ms,
+                    ms,
+                    patch_size,
+                    grid_w // ms,
+                    ms,
+                    patch_size,
+                )
+                patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
+                flatten_patches = patches.reshape(
+                    grid_h * grid_w,
+                    channel * patch_size * patch_size,
+                )
+
+                pixel_values_list.append(flatten_patches)
+                grid_thw_list.append([1, grid_h, grid_w])
+                merge_sizes_list.append(ms)
+                clip_n += 1
+
+            num_frames_per_clip.append(clip_n)
+
+        pixel_values = torch.cat(pixel_values_list, dim=0)
+        image_grid_thw = torch.tensor(grid_thw_list)
+        image_merge_sizes = torch.tensor(merge_sizes_list)
+
+        return BatchFeature(
+            data={
+                "pixel_values": pixel_values,
+                "image_grid_thw": image_grid_thw,
+                "image_merge_sizes": image_merge_sizes,
+                "num_frames_per_clip": num_frames_per_clip,
+            },
+            tensor_type=return_tensors,
+        )
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        interpolation: Optional["tvF.InterpolationMode"],
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        patch_size: int,
+        temporal_patch_size: int,
+        merge_size: int,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        **kwargs,
+    ):
+        # Group images by size for batched resizing
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            height, width = stacked_images.shape[-2:]
+            if do_resize:
+                resized_height, resized_width = smart_resize(
+                    height,
+                    width,
+                    factor=patch_size * merge_size,
+                    min_pixels=size["shortest_edge"],
+                    max_pixels=size["longest_edge"],
+                )
+                stacked_images = self.resize(
+                    image=stacked_images,
+                    size=SizeDict(height=resized_height, width=resized_width),
+                    interpolation=interpolation,
+                )
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        # Group images by size for further processing
+        # Needed in case do_resize is False, or resize returns images with different sizes
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+        processed_images_grouped = {}
+        processed_grids = {}
+        for shape, stacked_images in grouped_images.items():
+            resized_height, resized_width = stacked_images.shape[-2:]
+            # Fused rescale and normalize
+            patches = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            if patches.ndim == 4:
+                # add a temporal dimension if we have images
+                patches = patches.unsqueeze(1)
+            if patches.shape[1] % temporal_patch_size != 0:
+                repeats = patches[:, -1:].repeat(1, temporal_patch_size - 1, 1, 1, 1)
+                patches = torch.cat([patches, repeats], dim=1)
+            batch_size, grid_t, channel = patches.shape[:3]
+            grid_t = grid_t // temporal_patch_size
+            grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+
+            patches = patches.view(
+                batch_size,
+                grid_t,
+                temporal_patch_size,
+                channel,
+                grid_h // merge_size,
+                merge_size,
+                patch_size,
+                grid_w // merge_size,
+                merge_size,
+                patch_size,
+            )
+            # Reorder dimensions to group grid and patch information for subsequent flattening.
+            # (batch, grid_t, grid_h, grid_w, merge_h, merge_w, channel, temp_patch_size, patch_h, patch_w)
+            patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
+            flatten_patches = patches.reshape(
+                batch_size,
+                grid_t * grid_h * grid_w,
+                channel * temporal_patch_size * patch_size * patch_size,
+            )
+
+            processed_images_grouped[shape] = flatten_patches
+            processed_grids[shape] = [[grid_t, grid_h, grid_w]] * batch_size
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+        processed_grids = reorder_images(processed_grids, grouped_images_index)
+        pixel_values = torch.cat(processed_images, dim=0)
+        image_grid_thw = torch.tensor(processed_grids)
+
+        return BatchFeature(
+            data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors
+        )
+
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Note: Do not remove this method! It is used by vLLM to infer the number of patches and placeholders
+        without an image input.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of image patches per image.
+        """
+        min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"]
+        max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"]
+        patch_size = images_kwargs.get("patch_size", self.patch_size)
+        merge_size = images_kwargs.get("merge_size", self.merge_size)
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        return grid_h * grid_w
+
+
+__all__ = ["PenguinVLImageProcessorFast"]
diff --git a/src/transformers/models/penguinvl/modeling_penguinvl.py b/src/transformers/models/penguinvl/modeling_penguinvl.py
new file mode 100644
index 000000000000..0833e0423bd8
--- /dev/null
+++ b/src/transformers/models/penguinvl/modeling_penguinvl.py
@@ -0,0 +1,1207 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/penguinvl/modular_penguinvl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_penguinvl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import re
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache
+from ...generation import GenerationMixin
+from ...integrations import use_kernel_forward_from_hub, use_kernel_func_from_hub, use_kernelized_func
+from ...masking_utils import create_causal_mask, create_sliding_window_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPast, BaseModelOutputWithPooling, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_penguinvl import PenguinVLConfig, PenguinVLVisionConfig
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class PenguinVLRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps: float = 1e-6) -> None:
+        """
+        PenguinVLRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight * hidden_states.to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class PenguinVLMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class PenguinVLVisionEmbeddings(nn.Module):
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.patch_size = config.patch_size
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states.view(-1, self.config.num_channels, self.patch_size, self.patch_size)
+        patch_embeds = self.patch_embedding(hidden_states)
+        embeddings = patch_embeds.view(-1, self.embed_dim)
+        return embeddings
+
+
+class PenguinVLVisionRotaryEmbedding(nn.Module):
+    r"""2D rotary position embedding for the vision encoder.
+
+    Produces per-token ``(cos, sin)`` of shape ``(total_seq, head_dim)`` where
+    the first ``head_dim / 2`` dimensions encode height positions and the last
+    ``head_dim / 2`` dimensions encode width positions.  Uses ``rotate_half``
+    coupling so that pair ``(i, i + head_dim/2)`` receives height rotation for
+    ``i < head_dim/2`` and width rotation otherwise.
+    """
+
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: PenguinVLVisionConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: PenguinVLVisionConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        r"""
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(2, position_ids.shape[1], -1, 1)
+        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (2, bs, 1, positions)
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+@use_kernel_func_from_hub("rotary_pos_emb")
+def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors.
+
+    Args:
+        q (`torch.Tensor`): The query tensor.
+        k (`torch.Tensor`): The key tensor.
+        cos (`torch.Tensor`): The cosine part of the rotary embedding.
+        sin (`torch.Tensor`): The sine part of the rotary embedding.
+        unsqueeze_dim (`int`, *optional*, defaults to 1):
+            The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
+            sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
+            that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
+            k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
+            cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
+            the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
+    Returns:
+        `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
+    """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    rope_section = [cos.shape[-1] // 2, cos.shape[-1] // 2]
+    cos = torch.cat([m[i % 2] for i, m in enumerate(cos.split(rope_section, dim=-1))], dim=-1).unsqueeze(unsqueeze_dim)
+    sin = torch.cat([m[i % 2] for i, m in enumerate(sin.split(rope_section, dim=-1))], dim=-1).unsqueeze(unsqueeze_dim)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class PenguinVLVisionAttention(nn.Module):
+    r"""Multi-headed attention with QK normalization for the vision encoder.
+
+    Inherits from Qwen3Attention; differs by: bidirectional (is_causal=False),
+    2D RoPE via apply_multimodal_rotary_pos_emb, and cu_seqlens for packed sequences.
+    """
+
+    def __init__(self, config: PenguinVLVisionConfig, layer_idx: int):
+        super().__init__()
+        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = False
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+        self.q_norm = PenguinVLRMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
+        self.k_norm = PenguinVLRMSNorm(
+            self.head_dim, eps=config.rms_norm_eps
+        )  # thus post q_norm does not need reshape
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        if is_flash_attention_requested(self.config):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, attn_weights = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+            ]
+            attn_outputs, attn_weights = [], []
+            for q, k, v in zip(*splits):
+                attn_output, attn_weight = attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )
+                attn_outputs.append(attn_output)
+                attn_weights.append(attn_weight)
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class PenguinVLVisionEncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PenguinVLVisionConfig, layer_idx: int):
+        super().__init__()
+        self.self_attn = PenguinVLVisionAttention(config, layer_idx)
+        self.mlp = PenguinVLMLP(config)
+        self.input_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class PenguinVLVisionEncoder(nn.Module):
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [PenguinVLVisionEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = PenguinVLVisionRotaryEmbedding(config=config)
+
+    def get_rope_index(self, grid_sizes, merge_sizes, position_ids):
+        position_ids = position_ids.contiguous()
+        batch_size = grid_sizes.shape[0]
+
+        # Vision Part: Generate 2D position indices for vision tokens
+        vision_pos_ids = []
+        for (t, h, w), merge_size in zip(grid_sizes, merge_sizes):
+            # Generate height position indices
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w).to(position_ids.device)
+            hpos_ids = hpos_ids.reshape(
+                h // merge_size,
+                merge_size,
+                w // merge_size,
+                merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            # Generate width position indices
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1).to(position_ids.device)
+            wpos_ids = wpos_ids.reshape(
+                h // merge_size,
+                merge_size,
+                w // merge_size,
+                merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+
+            # Stack height and width to create 2D positions
+            vision_pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+
+        num_start_idx = 0
+        for batch_idx in range(batch_size):
+            pos_len = vision_pos_ids[batch_idx].shape[0]
+            position_ids[:, 0, num_start_idx : num_start_idx + pos_len] = vision_pos_ids[batch_idx].permute(1, 0)
+            num_start_idx += pos_len
+
+        return position_ids
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutput:
+        r"""
+        hidden_states (`torch.Tensor`):
+            Input hidden states for the vision encoder.
+        cu_seqlens (`torch.Tensor`):
+            Cumulative sequence lengths for variable-length sequences in the batch.
+        grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+            Temporal, height and width dimensions of the feature grid for each image/video.
+        merge_sizes (`torch.Tensor` of shape `(num_images_or_videos,)`):
+            Spatial downsampling ratio for each image or video.
+        """
+        cache_position = torch.arange(0, hidden_states.shape[1], device=hidden_states.device)
+        position_ids = cache_position.view(1, 1, -1).expand(2, hidden_states.shape[0], -1)
+        position_ids = self.get_rope_index(grid_thw, merge_sizes, position_ids)
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+@use_kernelized_func(apply_rotary_pos_emb)
+class PenguinVLAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: PenguinVLConfig, layer_idx: int):
+        super().__init__()
+        self.layer_type = config.layer_types[layer_idx] if hasattr(config, "layer_types") else None
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+        )
+        self.q_norm = PenguinVLRMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
+        self.k_norm = PenguinVLRMSNorm(
+            self.head_dim, eps=config.rms_norm_eps
+        )  # thus post q_norm does not need reshape
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            sliding_window=self.sliding_window,  # diff with Llama
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class PenguinVLDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PenguinVLConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+
+        self.self_attn = PenguinVLAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = PenguinVLMLP(config)
+        self.input_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.attention_type = config.layer_types[layer_idx]
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = False,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class PenguinVLPreTrainedModel(PreTrainedModel):
+    config: PenguinVLConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["PenguinVLVisionEncoderLayer", "Qwen3DecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "hidden_states": PenguinVLDecoderLayer,
+        "attentions": PenguinVLAttention,
+    }
+    config_class = PenguinVLConfig
+
+
+class PenguinVLVisionModel(PenguinVLPreTrainedModel):
+    config_class = PenguinVLVisionConfig
+    main_input_name = "pixel_values"
+    _can_record_outputs = {
+        "hidden_states": PenguinVLVisionEncoderLayer,
+        "attentions": PenguinVLVisionAttention,
+    }
+
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__(config)
+        self.embeddings = PenguinVLVisionEmbeddings(config)
+        self.encoder = PenguinVLVisionEncoder(config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> PenguinVLVisionEmbeddings:
+        return self.embeddings.patch_embedding
+
+    def pixel_unshuffle(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+    ):
+        hidden_states_chunks = hidden_states.split(grid_thw.prod(dim=1).tolist(), dim=0)
+        outputs = []
+
+        for hidden_states, (t, h, w), merge_size in zip(hidden_states_chunks, grid_thw, merge_sizes):
+            c = hidden_states.shape[-1]
+            hidden_states = hidden_states.view(t, h // merge_size, w // merge_size, merge_size, merge_size, c).permute(
+                0, 1, 3, 2, 4, 5
+            )
+            hidden_states = hidden_states.reshape(t, h, w, c).permute(0, 3, 1, 2)
+            hidden_states = F.interpolate(hidden_states, size=(h // merge_size, w // merge_size), mode="bilinear")
+            hidden_states = hidden_states.permute(0, 2, 3, 1).view(-1, c)
+            outputs.append(hidden_states)
+
+        return torch.cat(outputs, dim=0)
+
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutput:
+        r"""
+        grid_thw (`torch.LongTensor` of shape `(num_images_or_videos, 3)`):
+            Temporal, height and width dimensions of the feature grid for each image/video.
+        merge_sizes (`torch.Tensor` of shape `(num_images_or_videos,)`):
+            Spatial downsampling ratio for each image or video.
+        """
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        hidden_states = self.embeddings(pixel_values.type(self.dtype))
+        encoder_outputs: BaseModelOutput = self.encoder(
+            hidden_states[None, ...],
+            cu_seqlens=cu_seqlens,
+            grid_thw=grid_thw,
+            merge_sizes=merge_sizes,
+            **kwargs,
+        )
+
+        last_hidden_state = encoder_outputs[0].squeeze(0)
+        last_hidden_state = self.pixel_unshuffle(last_hidden_state, grid_thw, merge_sizes)
+
+        return BaseModelOutput(last_hidden_state=last_hidden_state)
+
+
+# ===================== Projector =====================
+
+
+class PenguinVLProjector(nn.Module):
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__()
+        in_hidden_size = config.vision_encoder_config.hidden_size
+        out_hidden_size = config.hidden_size
+
+        projector_type = config.vision_projector_type
+        mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
+        if mlp_gelu_match:
+            mlp_depth = int(mlp_gelu_match.group(1))
+        else:
+            raise ValueError(f"Unknown projector type: {projector_type}")
+
+        modules = [nn.Linear(in_hidden_size, out_hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(out_hidden_size, out_hidden_size))
+        self.readout = nn.Sequential(*modules)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.readout(hidden_states)
+
+
+# ===================== Main Model =====================
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for PenguinVL outputs, with hidden states and attentions.
+    """
+)
+class PenguinVLModelOutputWithPast(ModelOutput):
+    r"""
+    past_key_values (`Cache`, *optional*):
+        Pre-computed hidden-states that can be used to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states produced by the vision encoder after projection.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class PenguinVLRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: PenguinVLConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: PenguinVLConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        """
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+@auto_docstring
+class PenguinVLLanguageModel(PenguinVLPreTrainedModel):
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [PenguinVLDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = PenguinVLRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+        self.has_sliding_layers = "sliding_attention" in self.config.layer_types
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if position_ids is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
+            position_ids = position_ids.unsqueeze(0)
+
+        # It may already have been prepared by e.g. `generate`
+        if not isinstance(causal_mask_mapping := attention_mask, dict):
+            # Prepare mask arguments
+            mask_kwargs = {
+                "config": self.config,
+                "inputs_embeds": inputs_embeds,
+                "attention_mask": attention_mask,
+                "past_key_values": past_key_values,
+                "position_ids": position_ids,
+            }
+            # Create the masks
+            causal_mask_mapping = {
+                "full_attention": create_causal_mask(**mask_kwargs),
+            }
+            # The sliding window alternating layers are not always activated depending on the config
+            if self.has_sliding_layers:
+                causal_mask_mapping["sliding_attention"] = create_sliding_window_causal_mask(**mask_kwargs)
+
+        hidden_states = inputs_embeds
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                attention_mask=causal_mask_mapping[decoder_layer.attention_type],
+                position_embeddings=position_embeddings,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values if use_cache else None,
+        )
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for PenguinVL causal language model outputs.
+    """
+)
+class PenguinVLCausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*):
+        Pre-computed hidden-states that can be used to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states produced by the vision encoder after projection.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class PenguinVLModel(PenguinVLPreTrainedModel):
+    _checkpoint_conversion_mapping = {
+        r"^vision_encoder\.vision_encoder\.": "vision_model.",
+        r"^vision_encoder\.": "vision_model.",
+        r"^vision_projector\.": "projector.",
+        r"^embed_tokens\.": "language_model.embed_tokens.",
+        r"^layers\.": "language_model.layers.",
+        r"^norm\.": "language_model.norm.",
+    }
+
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__(config)
+        self.vision_model = PenguinVLVisionModel._from_config(config.vision_encoder_config)
+        self.projector = PenguinVLProjector(config)
+        self.language_model = PenguinVLLanguageModel._from_config(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="Obtains image last hidden states from the vision model and applies multimodal projection."
+    )
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor,
+        image_merge_sizes: torch.LongTensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor`):
+            Pixel values for the vision encoder.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`):
+            Spatial downsampling ratio for each image.
+        """
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            grid_thw=image_grid_thw,
+            merge_sizes=image_merge_sizes,
+            return_dict=True,
+            **kwargs,
+        )
+        last_hidden_state = vision_outputs.last_hidden_state
+        image_embeds = self.projector(last_hidden_state)
+
+        split_sizes = image_grid_thw.prod(dim=1) // (image_merge_sizes**2)
+        image_embeds = torch.split(image_embeds, split_sizes.tolist())
+        vision_outputs.pooler_output = image_embeds
+
+        return vision_outputs
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor,
+    ):
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        return special_image_mask
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple | PenguinVLModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`, *optional*):
+            Spatial downsampling ratio for each image.
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        image_embeds = None
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(
+                pixel_values, image_grid_thw, image_merge_sizes, return_dict=True
+            ).pooler_output
+            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds)
+            num_mask_tokens = image_mask.sum() // inputs_embeds.shape[-1]
+            num_image_embeds = image_embeds.shape[0]
+            if num_mask_tokens != num_image_embeds:
+                raise ValueError(
+                    f"Number of image token positions ({num_mask_tokens}) does not match "
+                    f"number of image embeddings ({num_image_embeds}). "
+                    "Make sure the number of <image> tokens in your input matches the number of images/clips provided."
+                )
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return PenguinVLModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_embeds,
+        )
+
+
+class PenguinVLForConditionalGeneration(PenguinVLPreTrainedModel, GenerationMixin):
+    _checkpoint_conversion_mapping = {
+        r"^model\.vision_encoder\.vision_encoder\.": "model.vision_model.",
+        r"^model\.vision_encoder\.": "model.vision_model.",
+        r"^model\.vision_projector\.": "model.projector.",
+        r"^model\.embed_tokens\.": "model.language_model.embed_tokens.",
+        r"^model\.layers\.": "model.language_model.layers.",
+        r"^model\.norm\.": "model.language_model.norm.",
+    }
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__(config)
+        self.model = PenguinVLModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor,
+        image_merge_sizes: torch.LongTensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor`):
+            Pixel values for the vision encoder.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`):
+            Spatial downsampling ratio for each image.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple | PenguinVLCausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[0, ..., config.vocab_size]`
+            or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
+            only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`, *optional*):
+            Spatial downsampling ratio for each image.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return PenguinVLCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        is_first_iteration: bool | None = False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            use_cache=use_cache,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if not is_first_iteration and use_cache:
+            model_inputs["pixel_values"] = None
+
+        return model_inputs
+
+
+__all__ = [
+    "PenguinVLVisionModel",
+    "PenguinVLPreTrainedModel",
+    "PenguinVLLanguageModel",
+    "PenguinVLModel",
+    "PenguinVLForConditionalGeneration",
+]
diff --git a/src/transformers/models/penguinvl/modular_penguinvl.py b/src/transformers/models/penguinvl/modular_penguinvl.py
new file mode 100644
index 000000000000..c87e2b8a9c8a
--- /dev/null
+++ b/src/transformers/models/penguinvl/modular_penguinvl.py
@@ -0,0 +1,2000 @@
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch PenguinVL model."""
+
+import copy
+import math
+import re
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Optional, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...feature_extraction_utils import BatchFeature
+from ...generation import GenerationMixin
+from ...image_transforms import convert_to_rgb, resize
+from ...image_utils import (
+    IMAGENET_STANDARD_MEAN,
+    IMAGENET_STANDARD_STD,
+    ChannelDimension,
+    ImageInput,
+    PILImageResampling,
+    SizeDict,
+    get_image_size,
+    infer_channel_dimension_format,
+    is_valid_image,
+    load_image,
+    to_numpy_array,
+    validate_preprocess_arguments,
+)
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput
+from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, RopeParameters, dynamic_rope_update
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import (
+    TensorType,
+    auto_docstring,
+    can_return_tuple,
+    is_av_available,
+    is_cv2_available,
+    is_decord_available,
+    is_torchcodec_available,
+    is_torchvision_available,
+    is_vision_available,
+    logging,
+)
+from ...utils.generic import is_flash_attention_requested
+from ...utils.output_capturing import capture_outputs
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessor, Qwen2VLImageProcessorKwargs, smart_resize
+from ..qwen2_vl.image_processing_qwen2_vl_fast import Qwen2VLImageProcessorFast
+from ..qwen3.configuration_qwen3 import Qwen3Config
+from ..qwen3.modeling_qwen3 import (
+    Qwen3Attention,
+    Qwen3MLP,
+    Qwen3Model,
+    Qwen3PreTrainedModel,
+    Qwen3RMSNorm,
+    eager_attention_forward,
+    rotate_half,
+)
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="tencent/Penguin-VL-8B")
+class PenguinVLVisionConfig(PreTrainedConfig):
+    r"""
+    Configuration for the PenguinVL vision encoder.
+
+    Args:
+        hidden_size (`int`, *optional*, defaults to 1024):
+            Dimensionality of the encoder hidden states.
+        intermediate_size (`int`, *optional*, defaults to 3072):
+            Dimensionality of the "intermediate" (feed-forward) layer in the Transformer encoder.
+        num_hidden_layers (`int`, *optional*, defaults to 28):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 16):
+            Number of attention heads in the Transformer encoder.
+        num_key_value_heads (`int`, *optional*, defaults to 8):
+            Number of key-value heads for grouped-query attention.
+        head_dim (`int`, *optional*, defaults to 128):
+            Dimension of each attention head.
+        num_channels (`int`, *optional*, defaults to 3):
+            Number of input channels.
+        patch_size (`int`, *optional*, defaults to 14):
+            The size of each image patch.
+        hidden_act (`str`, *optional*, defaults to `"silu"`):
+            The non-linear activation function in the encoder.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-6):
+            The epsilon used by the rms normalization layers.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        attention_bias (`bool`, *optional*, defaults to `False`):
+            Whether to use bias in attention layers.
+        rope_scaling (`dict`, *optional*, defaults to `None`):
+            Dictionary containing the scaling configuration for the RoPE embeddings.
+        rope_theta (`float`, *optional*, defaults to 1000000.0):
+            The base period of the RoPE embeddings.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated normal initializer.
+    """
+
+    model_type = "penguinvl_vision"
+    base_config_key = "vision_encoder_config"
+
+    def __init__(
+        self,
+        hidden_size=1024,
+        intermediate_size=3072,
+        num_hidden_layers=28,
+        num_attention_heads=16,
+        num_key_value_heads=8,
+        max_position_embeddings=40960,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        head_dim=128,
+        num_channels=3,
+        patch_size=14,
+        hidden_act="silu",
+        rms_norm_eps=1e-6,
+        attention_dropout=0.0,
+        attention_bias=False,
+        rope_theta=1000000.0,
+        initializer_range=0.02,
+        **kwargs,
+    ):
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.max_position_embeddings = max_position_embeddings
+        self.head_dim = head_dim
+        self.num_channels = num_channels
+        self.patch_size = patch_size
+        self.hidden_act = hidden_act
+        self.rms_norm_eps = rms_norm_eps
+        self.attention_dropout = attention_dropout
+        self.attention_bias = attention_bias
+        self.rope_theta = rope_theta
+        self.initializer_range = initializer_range
+        if rope_parameters is None:
+            rope_parameters = {"rope_type": "default", "rope_theta": rope_theta}
+        self.rope_parameters = rope_parameters
+
+        super().__init__(**kwargs)
+
+
+@auto_docstring(checkpoint="tencent/Penguin-VL-8B")
+class PenguinVLConfig(Qwen3Config):
+    r"""
+    Configuration for the PenguinVL model.
+
+    Args:
+        vision_encoder_config (`PenguinVLVisionConfig` or `dict`, *optional*):
+            Configuration for the vision encoder.
+        image_token_id (`int`, *optional*, defaults to 151669):
+            Token ID for the image placeholder token.
+        vision_projector_type (`str`, *optional*, defaults to `"mlp2x_gelu"`):
+            Type of the vision projector.
+        tie_word_embeddings (`bool`, *optional*, defaults to `True`):
+            Whether to tie word embeddings.
+    """
+
+    model_type = "penguinvl"
+    sub_configs = {"vision_encoder_config": PenguinVLVisionConfig}
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vision_encoder_config=None,
+        image_token_id=151669,
+        vision_projector_type="mlp2x_gelu",
+        vocab_size: int | None = 151936,
+        hidden_size: int | None = 4096,
+        intermediate_size: int | None = 22016,
+        num_hidden_layers: int | None = 32,
+        num_attention_heads: int | None = 32,
+        num_key_value_heads: int | None = 32,
+        head_dim: int | None = 128,
+        hidden_act: str | None = "silu",
+        max_position_embeddings: int | None = 32768,
+        initializer_range: float | None = 0.02,
+        rms_norm_eps: float | None = 1e-6,
+        use_cache: bool | None = True,
+        tie_word_embeddings: bool | None = False,
+        rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
+        attention_bias: bool | None = False,
+        use_sliding_window: bool | None = False,
+        sliding_window: int | None = 4096,
+        max_window_layers: int | None = 28,
+        layer_types: list[str] | None = None,
+        attention_dropout: float | None = 0.0,
+        pad_token_id: int | None = None,
+        bos_token_id: int | None = None,
+        eos_token_id: int | None = None,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab_size=vocab_size,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_hidden_layers=num_hidden_layers,
+            num_attention_heads=num_attention_heads,
+            num_key_value_heads=num_key_value_heads,
+            head_dim=head_dim,
+            hidden_act=hidden_act,
+            max_position_embeddings=max_position_embeddings,
+            initializer_range=initializer_range,
+            rms_norm_eps=rms_norm_eps,
+            use_cache=use_cache,
+            tie_word_embeddings=tie_word_embeddings,
+            rope_parameters=rope_parameters,
+            attention_bias=attention_bias,
+            use_sliding_window=use_sliding_window,
+            sliding_window=sliding_window,
+            max_window_layers=max_window_layers,
+            layer_types=layer_types,
+            attention_dropout=attention_dropout,
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs,
+        )
+        if isinstance(vision_encoder_config, dict):
+            self.vision_encoder_config = self.sub_configs["vision_encoder_config"](**vision_encoder_config)
+        elif isinstance(vision_encoder_config, PreTrainedConfig):
+            self.vision_encoder_config = vision_encoder_config
+        elif vision_encoder_config is None:
+            self.vision_encoder_config = self.sub_configs["vision_encoder_config"]()
+        else:
+            raise ValueError(
+                f"vision_encoder_config must be dict or PreTrainedConfig, got {type(vision_encoder_config)}."
+            )
+
+        self.image_token_id = image_token_id
+        self.vision_projector_type = vision_projector_type
+        self.tie_word_embeddings = tie_word_embeddings
+
+
+# ===================== Vision Encoder =====================
+
+
+class PenguinVLRMSNorm(Qwen3RMSNorm):
+    pass
+
+
+class PenguinVLMLP(Qwen3MLP):
+    pass
+
+
+class PenguinVLVisionEmbeddings(nn.Module):
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.patch_size = config.patch_size
+        self.patch_embedding = nn.Conv2d(
+            in_channels=config.num_channels,
+            out_channels=self.embed_dim,
+            kernel_size=self.patch_size,
+            stride=self.patch_size,
+            padding="valid",
+        )
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = hidden_states.view(-1, self.config.num_channels, self.patch_size, self.patch_size)
+        patch_embeds = self.patch_embedding(hidden_states)
+        embeddings = patch_embeds.view(-1, self.embed_dim)
+        return embeddings
+
+
+class PenguinVLVisionRotaryEmbedding(nn.Module):
+    r"""2D rotary position embedding for the vision encoder.
+
+    Produces per-token ``(cos, sin)`` of shape ``(total_seq, head_dim)`` where
+    the first ``head_dim / 2`` dimensions encode height positions and the last
+    ``head_dim / 2`` dimensions encode width positions.  Uses ``rotate_half``
+    coupling so that pair ``(i, i + head_dim/2)`` receives height rotation for
+    ``i < head_dim/2`` and width rotation otherwise.
+    """
+
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: PenguinVLVisionConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+
+        self.rope_type = self.config.rope_parameters["rope_type"]
+        rope_init_fn: Callable = self.compute_default_rope_parameters
+        if self.rope_type != "default":
+            rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+        inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
+
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+
+    @staticmethod
+    def compute_default_rope_parameters(
+        config: PenguinVLVisionConfig | None = None,
+        device: Optional["torch.device"] = None,
+        seq_len: int | None = None,
+    ) -> tuple["torch.Tensor", float]:
+        r"""
+        Computes the inverse frequencies according to the original RoPE implementation
+        Args:
+            config ([`~transformers.PreTrainedConfig`]):
+                The model configuration.
+            device (`torch.device`):
+                The device to use for initialization of the inverse frequencies.
+            seq_len (`int`, *optional*):
+                The current sequence length. Unused for this type of RoPE.
+        Returns:
+            Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+            post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
+        """
+        base = config.rope_parameters["rope_theta"]
+        dim = getattr(config, "head_dim", None) or config.hidden_size // config.num_attention_heads
+
+        attention_factor = 1.0  # Unused in this type of RoPE
+
+        # Compute the inverse frequencies
+        inv_freq = 1.0 / (
+            base ** (torch.arange(0, dim, 2, dtype=torch.int64).to(device=device, dtype=torch.float) / dim)
+        )
+        return inv_freq, attention_factor
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(2, position_ids.shape[1], -1, 1)
+        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (2, bs, 1, positions)
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):
+    rope_section = [cos.shape[-1] // 2, cos.shape[-1] // 2]
+    cos = torch.cat([m[i % 2] for i, m in enumerate(cos.split(rope_section, dim=-1))], dim=-1).unsqueeze(unsqueeze_dim)
+    sin = torch.cat([m[i % 2] for i, m in enumerate(sin.split(rope_section, dim=-1))], dim=-1).unsqueeze(unsqueeze_dim)
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class PenguinVLVisionAttention(Qwen3Attention):
+    r"""Multi-headed attention with QK normalization for the vision encoder.
+
+    Inherits from Qwen3Attention; differs by: bidirectional (is_causal=False),
+    2D RoPE via apply_multimodal_rotary_pos_emb, and cu_seqlens for packed sequences.
+    """
+
+    def __init__(self, config: PenguinVLVisionConfig, layer_idx: int):
+        super().__init__(config, layer_idx)
+        self.is_causal = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        **kwargs,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        key_states = self.k_norm(self.k_proj(hidden_states).view(hidden_shape)).transpose(1, 2)
+        value_states = self.v_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        if is_flash_attention_requested(self.config):
+            max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max()
+            attn_output, attn_weights = attention_interface(
+                self,
+                query_states,
+                key_states,
+                value_states,
+                attention_mask=None,
+                scaling=self.scaling,
+                dropout=0.0 if not self.training else self.attention_dropout,
+                cu_seq_lens_q=cu_seqlens,
+                cu_seq_lens_k=cu_seqlens,
+                max_length_q=max_seqlen,
+                max_length_k=max_seqlen,
+                is_causal=False,
+                **kwargs,
+            )
+        else:
+            lengths = cu_seqlens[1:] - cu_seqlens[:-1]
+            splits = [
+                torch.split(tensor, lengths.tolist(), dim=2) for tensor in (query_states, key_states, value_states)
+            ]
+            attn_outputs, attn_weights = [], []
+            for q, k, v in zip(*splits):
+                attn_output, attn_weight = attention_interface(
+                    self,
+                    q,
+                    k,
+                    v,
+                    attention_mask=None,
+                    scaling=self.scaling,
+                    dropout=0.0 if not self.training else self.attention_dropout,
+                    is_causal=False,
+                    **kwargs,
+                )
+                attn_outputs.append(attn_output)
+                attn_weights.append(attn_weight)
+            attn_output = torch.cat(attn_outputs, dim=1)
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class PenguinVLVisionEncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PenguinVLVisionConfig, layer_idx: int):
+        super().__init__()
+        self.self_attn = PenguinVLVisionAttention(config, layer_idx)
+        self.mlp = PenguinVLMLP(config)
+        self.input_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states,
+            cu_seqlens=cu_seqlens,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class PenguinVLVisionEncoder(nn.Module):
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__()
+        self.layers = nn.ModuleList(
+            [PenguinVLVisionEncoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = PenguinVLRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = PenguinVLVisionRotaryEmbedding(config=config)
+
+    def get_rope_index(self, grid_sizes, merge_sizes, position_ids):
+        position_ids = position_ids.contiguous()
+        batch_size = grid_sizes.shape[0]
+
+        # Vision Part: Generate 2D position indices for vision tokens
+        vision_pos_ids = []
+        for (t, h, w), merge_size in zip(grid_sizes, merge_sizes):
+            # Generate height position indices
+            hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w).to(position_ids.device)
+            hpos_ids = hpos_ids.reshape(
+                h // merge_size,
+                merge_size,
+                w // merge_size,
+                merge_size,
+            )
+            hpos_ids = hpos_ids.permute(0, 2, 1, 3)
+            hpos_ids = hpos_ids.flatten()
+
+            # Generate width position indices
+            wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1).to(position_ids.device)
+            wpos_ids = wpos_ids.reshape(
+                h // merge_size,
+                merge_size,
+                w // merge_size,
+                merge_size,
+            )
+            wpos_ids = wpos_ids.permute(0, 2, 1, 3)
+            wpos_ids = wpos_ids.flatten()
+
+            # Stack height and width to create 2D positions
+            vision_pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1))
+
+        num_start_idx = 0
+        for batch_idx in range(batch_size):
+            pos_len = vision_pos_ids[batch_idx].shape[0]
+            position_ids[:, 0, num_start_idx : num_start_idx + pos_len] = vision_pos_ids[batch_idx].permute(1, 0)
+            num_start_idx += pos_len
+
+        return position_ids
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        cu_seqlens: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutput:
+        r"""
+        hidden_states (`torch.Tensor`):
+            Input hidden states for the vision encoder.
+        cu_seqlens (`torch.Tensor`):
+            Cumulative sequence lengths for variable-length sequences in the batch.
+        grid_thw (`torch.Tensor` of shape `(num_images_or_videos, 3)`):
+            Temporal, height and width dimensions of the feature grid for each image/video.
+        merge_sizes (`torch.Tensor` of shape `(num_images_or_videos,)`):
+            Spatial downsampling ratio for each image or video.
+        """
+        cache_position = torch.arange(0, hidden_states.shape[1], device=hidden_states.device)
+        position_ids = cache_position.view(1, 1, -1).expand(2, hidden_states.shape[0], -1)
+        position_ids = self.get_rope_index(grid_thw, merge_sizes, position_ids)
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(
+                hidden_states,
+                cu_seqlens=cu_seqlens,
+                position_embeddings=position_embeddings,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class PenguinVLPreTrainedModel(Qwen3PreTrainedModel):
+    config_class = PenguinVLConfig
+    _no_split_modules = ["PenguinVLVisionEncoderLayer", "Qwen3DecoderLayer"]
+
+
+class PenguinVLVisionModel(PenguinVLPreTrainedModel):
+    config_class = PenguinVLVisionConfig
+    main_input_name = "pixel_values"
+    _can_record_outputs = {
+        "hidden_states": PenguinVLVisionEncoderLayer,
+        "attentions": PenguinVLVisionAttention,
+    }
+
+    def __init__(self, config: PenguinVLVisionConfig):
+        super().__init__(config)
+        self.embeddings = PenguinVLVisionEmbeddings(config)
+        self.encoder = PenguinVLVisionEncoder(config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> PenguinVLVisionEmbeddings:
+        return self.embeddings.patch_embedding
+
+    def pixel_unshuffle(
+        self,
+        hidden_states: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+    ):
+        hidden_states_chunks = hidden_states.split(grid_thw.prod(dim=1).tolist(), dim=0)
+        outputs = []
+
+        for hidden_states, (t, h, w), merge_size in zip(hidden_states_chunks, grid_thw, merge_sizes):
+            c = hidden_states.shape[-1]
+            hidden_states = hidden_states.view(t, h // merge_size, w // merge_size, merge_size, merge_size, c).permute(
+                0, 1, 3, 2, 4, 5
+            )
+            hidden_states = hidden_states.reshape(t, h, w, c).permute(0, 3, 1, 2)
+            hidden_states = F.interpolate(hidden_states, size=(h // merge_size, w // merge_size), mode="bilinear")
+            hidden_states = hidden_states.permute(0, 2, 3, 1).view(-1, c)
+            outputs.append(hidden_states)
+
+        return torch.cat(outputs, dim=0)
+
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.Tensor,
+        grid_thw: torch.Tensor,
+        merge_sizes: torch.Tensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutput:
+        r"""
+        grid_thw (`torch.LongTensor` of shape `(num_images_or_videos, 3)`):
+            Temporal, height and width dimensions of the feature grid for each image/video.
+        merge_sizes (`torch.Tensor` of shape `(num_images_or_videos,)`):
+            Spatial downsampling ratio for each image or video.
+        """
+        cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum(
+            dim=0,
+            dtype=grid_thw.dtype if torch.jit.is_tracing() else torch.int32,
+        )
+        cu_seqlens = F.pad(cu_seqlens, (1, 0), value=0)
+
+        hidden_states = self.embeddings(pixel_values.type(self.dtype))
+        encoder_outputs: BaseModelOutput = self.encoder(
+            hidden_states[None, ...],
+            cu_seqlens=cu_seqlens,
+            grid_thw=grid_thw,
+            merge_sizes=merge_sizes,
+            **kwargs,
+        )
+
+        last_hidden_state = encoder_outputs[0].squeeze(0)
+        last_hidden_state = self.pixel_unshuffle(last_hidden_state, grid_thw, merge_sizes)
+
+        return BaseModelOutput(last_hidden_state=last_hidden_state)
+
+
+# ===================== Projector =====================
+
+
+class PenguinVLProjector(nn.Module):
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__()
+        in_hidden_size = config.vision_encoder_config.hidden_size
+        out_hidden_size = config.hidden_size
+
+        projector_type = config.vision_projector_type
+        mlp_gelu_match = re.match(r"^mlp(\d+)x_gelu$", projector_type)
+        if mlp_gelu_match:
+            mlp_depth = int(mlp_gelu_match.group(1))
+        else:
+            raise ValueError(f"Unknown projector type: {projector_type}")
+
+        modules = [nn.Linear(in_hidden_size, out_hidden_size)]
+        for _ in range(1, mlp_depth):
+            modules.append(nn.GELU())
+            modules.append(nn.Linear(out_hidden_size, out_hidden_size))
+        self.readout = nn.Sequential(*modules)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        return self.readout(hidden_states)
+
+
+# ===================== Main Model =====================
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for PenguinVL outputs, with hidden states and attentions.
+    """
+)
+class PenguinVLModelOutputWithPast(ModelOutput):
+    r"""
+    past_key_values (`Cache`, *optional*):
+        Pre-computed hidden-states that can be used to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states produced by the vision encoder after projection.
+    """
+
+    last_hidden_state: torch.FloatTensor = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class PenguinVLLanguageModel(Qwen3Model):
+    pass
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""
+    Base class for PenguinVL causal language model outputs.
+    """
+)
+class PenguinVLCausalLMOutputWithPast(ModelOutput):
+    r"""
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
+        Language modeling loss (for next-token prediction).
+    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+    past_key_values (`Cache`, *optional*):
+        Pre-computed hidden-states that can be used to speed up sequential decoding.
+    image_hidden_states (`torch.FloatTensor`, *optional*):
+        Hidden states produced by the vision encoder after projection.
+    """
+
+    loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: list[torch.FloatTensor] | None = None
+    hidden_states: tuple[torch.FloatTensor] | None = None
+    attentions: tuple[torch.FloatTensor] | None = None
+    image_hidden_states: torch.FloatTensor | None = None
+
+
+class PenguinVLModel(PenguinVLPreTrainedModel):
+    _checkpoint_conversion_mapping = {
+        r"^vision_encoder\.vision_encoder\.": "vision_model.",
+        r"^vision_encoder\.": "vision_model.",
+        r"^vision_projector\.": "projector.",
+        r"^embed_tokens\.": "language_model.embed_tokens.",
+        r"^layers\.": "language_model.layers.",
+        r"^norm\.": "language_model.norm.",
+    }
+
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__(config)
+        self.vision_model = PenguinVLVisionModel._from_config(config.vision_encoder_config)
+        self.projector = PenguinVLProjector(config)
+        self.language_model = PenguinVLLanguageModel._from_config(config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="Obtains image last hidden states from the vision model and applies multimodal projection."
+    )
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor,
+        image_merge_sizes: torch.LongTensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor`):
+            Pixel values for the vision encoder.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`):
+            Spatial downsampling ratio for each image.
+        """
+        vision_outputs = self.vision_model(
+            pixel_values=pixel_values,
+            grid_thw=image_grid_thw,
+            merge_sizes=image_merge_sizes,
+            return_dict=True,
+            **kwargs,
+        )
+        last_hidden_state = vision_outputs.last_hidden_state
+        image_embeds = self.projector(last_hidden_state)
+
+        split_sizes = image_grid_thw.prod(dim=1) // (image_merge_sizes**2)
+        image_embeds = torch.split(image_embeds, split_sizes.tolist())
+        vision_outputs.pooler_output = image_embeds
+
+        return vision_outputs
+
+    def get_placeholder_mask(
+        self,
+        input_ids: torch.LongTensor,
+        inputs_embeds: torch.FloatTensor,
+        image_features: torch.FloatTensor,
+    ):
+        if input_ids is None:
+            special_image_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_image_mask = special_image_mask.all(-1)
+        else:
+            special_image_mask = input_ids == self.config.image_token_id
+
+        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        return special_image_mask
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple | PenguinVLModelOutputWithPast:
+        r"""
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`, *optional*):
+            Spatial downsampling ratio for each image.
+        """
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        image_embeds = None
+        if pixel_values is not None:
+            image_embeds = self.get_image_features(
+                pixel_values, image_grid_thw, image_merge_sizes, return_dict=True
+            ).pooler_output
+            image_embeds = torch.cat(image_embeds, dim=0).to(inputs_embeds.device, inputs_embeds.dtype)
+            image_mask = self.get_placeholder_mask(input_ids, inputs_embeds=inputs_embeds, image_features=image_embeds)
+            num_mask_tokens = image_mask.sum() // inputs_embeds.shape[-1]
+            num_image_embeds = image_embeds.shape[0]
+            if num_mask_tokens != num_image_embeds:
+                raise ValueError(
+                    f"Number of image token positions ({num_mask_tokens}) does not match "
+                    f"number of image embeddings ({num_image_embeds}). "
+                    "Make sure the number of <image> tokens in your input matches the number of images/clips provided."
+                )
+            inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
+
+        outputs = self.language_model(
+            input_ids=None,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        return PenguinVLModelOutputWithPast(
+            last_hidden_state=outputs.last_hidden_state,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=image_embeds,
+        )
+
+
+class PenguinVLForConditionalGeneration(PenguinVLPreTrainedModel, GenerationMixin):
+    _checkpoint_conversion_mapping = {
+        r"^model\.vision_encoder\.vision_encoder\.": "model.vision_model.",
+        r"^model\.vision_encoder\.": "model.vision_model.",
+        r"^model\.vision_projector\.": "model.projector.",
+        r"^model\.embed_tokens\.": "model.language_model.embed_tokens.",
+        r"^model\.layers\.": "model.language_model.layers.",
+        r"^model\.norm\.": "model.language_model.norm.",
+    }
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: PenguinVLConfig):
+        super().__init__(config)
+        self.model = PenguinVLModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    @can_return_tuple
+    @auto_docstring
+    def get_image_features(
+        self,
+        pixel_values: torch.FloatTensor,
+        image_grid_thw: torch.LongTensor,
+        image_merge_sizes: torch.LongTensor,
+        **kwargs,
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor`):
+            Pixel values for the vision encoder.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`):
+            Spatial downsampling ratio for each image.
+        """
+        return self.model.get_image_features(
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> tuple | PenguinVLCausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should be in `[0, ..., config.vocab_size]`
+            or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is
+            only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        image_grid_thw (`torch.LongTensor` of shape `(num_images, 3)`, *optional*):
+            Temporal, height and width of feature shape for each image.
+        image_merge_sizes (`torch.Tensor` of shape `(num_images,)`, *optional*):
+            Spatial downsampling ratio for each image.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            return_dict=True,
+            cache_position=cache_position,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.lm_head(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        return PenguinVLCausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            image_hidden_states=outputs.image_hidden_states,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        attention_mask=None,
+        inputs_embeds=None,
+        cache_position=None,
+        position_ids=None,
+        use_cache=True,
+        pixel_values: torch.Tensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        image_merge_sizes: torch.LongTensor | None = None,
+        is_first_iteration: bool | None = False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            cache_position=cache_position,
+            position_ids=position_ids,
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
+            image_merge_sizes=image_merge_sizes,
+            use_cache=use_cache,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if not is_first_iteration and use_cache:
+            model_inputs["pixel_values"] = None
+
+        return model_inputs
+
+
+# ===================== Image Processor =====================
+
+
+def _make_batched_clips(images) -> list[list]:
+    r"""
+    Normalize visual inputs to a list of clips, where each clip is a list of frames.
+
+    - Single image: ``image`` -> ``[[image]]``
+    - List of images: ``[img1, img2]`` -> ``[[img1], [img2]]``
+    - Nested clips: ``[[img1], [f1, f2, f3]]`` -> ``[[img1], [f1, f2, f3]]``
+    """
+    if isinstance(images, list | tuple) and len(images) > 0:
+        if isinstance(images[0], list | tuple):
+            return [list(clip) for clip in images]
+        if all(is_valid_image(f) for f in images):
+            return [[img] for img in images]
+    if is_valid_image(images):
+        return [[images]]
+    raise ValueError(f"Could not make batched images from {type(images)}")
+
+
+def _simple_batched_resize(
+    images,
+    factor: int = 28,
+    min_tokens: int = 16,
+    max_tokens: int = 16384,
+    input_data_format=None,
+    frame_types=None,
+):
+    r"""
+    Compute per-frame target ``(h, w)`` for a clip using TRA (Temporal Redundancy-Aware)
+    token compression.
+
+    Key frames (type 0) retain higher resolution. Intermediate frames (type 1) are
+    allocated 1/16 of a key frame's area to reduce tokens while preserving temporal
+    coverage. When all frames fit within the token budget, the original (aligned)
+    resolution is kept for every frame.
+    """
+    min_pixels = min_tokens * factor * factor * 1.5
+    max_pixels = max_tokens * factor * factor * 0.95
+
+    first_image = images[0]
+    if is_vision_available() and isinstance(first_image, Image.Image):
+        width, height = first_image.size
+    else:
+        idf = input_data_format
+        if idf is None:
+            idf = infer_channel_dimension_format(first_image)
+        height, width = get_image_size(first_image, channel_dim=idf)
+
+    aspect_ratio = height / width
+    raw_area = height * width
+    num_frames = len(images)
+
+    if frame_types is not None:
+        ft_list = frame_types.tolist() if hasattr(frame_types, "tolist") else list(frame_types)
+        num_key = ft_list.count(0)
+        num_intermediate = ft_list.count(1)
+    else:
+        num_key = num_frames
+        num_intermediate = 0
+        ft_list = [0] * num_frames
+
+    def _dims_from_area(target_area, ar, fac):
+        w_new = math.sqrt(target_area / ar)
+        h_new = w_new * ar
+        return max(round(h_new / fac) * fac, fac), max(round(w_new / fac) * fac, fac)
+
+    def _ensure_min(h, w, min_p, ar):
+        if h * w < min_p:
+            w_f = math.sqrt(min_p / ar)
+            h_f = w_f * ar
+            h = math.ceil(h_f / factor) * factor
+            w = math.ceil(w_f / factor) * factor
+        return h, w
+
+    total_raw = num_frames * raw_area
+    key_area = raw_area
+    inter_area = raw_area
+
+    if total_raw > max_pixels:
+        eff = num_key + num_intermediate / 16.0
+        key_area = max_pixels / eff
+        inter_area = key_area / 16.0
+        if inter_area < min_pixels:
+            inter_area = min_pixels
+            key_area = (max_pixels - num_intermediate * min_pixels) / max(num_key, 1)
+        if key_area < min_pixels:
+            key_area = min_pixels
+
+    k_h, k_w = _dims_from_area(key_area, aspect_ratio, factor)
+    k_h, k_w = _ensure_min(k_h, k_w, min_pixels, aspect_ratio)
+
+    if num_intermediate > 0:
+        i_h, i_w = _dims_from_area(inter_area, aspect_ratio, factor)
+        i_h, i_w = _ensure_min(i_h, i_w, min_pixels, aspect_ratio)
+    else:
+        i_h, i_w = k_h, k_w
+
+    return [(i_h, i_w) if ft_list[i] == 1 else (k_h, k_w) for i in range(num_frames)]
+
+
+def _allocate_token_budget(clips, clip_merge_sizes, min_tokens, max_tokens, patch_size, input_data_format=None):
+    r"""Distribute ``max_tokens`` across clips proportionally to their raw token counts."""
+    clip_raw_tokens = []
+    for clip, ms in zip(clips, clip_merge_sizes):
+        first_frame = clip[0]
+        if is_vision_available() and isinstance(first_frame, Image.Image):
+            w, h = first_frame.size
+        else:
+            idf = input_data_format or infer_channel_dimension_format(first_frame)
+            h, w = get_image_size(first_frame, channel_dim=idf)
+        factor = patch_size * ms
+        clip_raw_tokens.append(len(clip) * h * w / (factor * factor))
+
+    total_raw = sum(clip_raw_tokens)
+    if total_raw <= max_tokens:
+        return [max_tokens] * len(clips)
+
+    return [max(min_tokens * len(clip), raw * max_tokens / total_raw) for clip, raw in zip(clips, clip_raw_tokens)]
+
+
+# ===================== KI Frame Extraction =====================
+
+_KI_PATCH = 14
+_KI_MIN_PIXELS = 10 * 14 * 14
+_KI_MAX_PIXELS = 10240 * 14 * 14
+_MIN_FRAME_SIMILARITY = 0.95
+
+
+# Adapted from Keye-VL
+def _get_frame_sim(
+    frame1: torch.Tensor,
+    frame2: torch.Tensor,
+    patch_size: int = 14,
+    threshold: float = 0.7,
+    epsilon: float = 1e-8,
+) -> float:
+    r"""Cosine similarity between two frames averaged over patches. Returns mean similarity in [0, 1]."""
+
+    def _to_comparison_tensor(tensor: torch.Tensor) -> torch.Tensor:
+        if is_cv2_available():
+            import cv2
+
+            arr = tensor.cpu().permute(1, 2, 0).numpy()
+            if arr.dtype in (np.float32, np.float64):
+                arr = arr.astype(np.uint8)
+            hsv = cv2.cvtColor(arr, cv2.COLOR_RGB2HSV)
+            return torch.from_numpy(hsv).permute(2, 0, 1).to(tensor.device).float()
+        return tensor.float()
+
+    f1 = _to_comparison_tensor(frame1)
+    f2 = _to_comparison_tensor(frame2)
+
+    c, H, W = f1.shape
+    h_patches = H // patch_size
+    w_patches = W // patch_size
+
+    def _to_patches(f):
+        f = f[:, : h_patches * patch_size, : w_patches * patch_size]
+        f = f.reshape(c, h_patches, patch_size, w_patches, patch_size)
+        f = f.permute(1, 3, 0, 2, 4).reshape(h_patches, w_patches, c * patch_size * patch_size)
+        return f.float()
+
+    patch1 = _to_patches(f1)
+    patch2 = _to_patches(f2)
+
+    norm1 = torch.norm(patch1, p=2, dim=-1, keepdim=True) + epsilon
+    norm2 = torch.norm(patch2, p=2, dim=-1, keepdim=True) + epsilon
+    cos_sim = (patch1 / norm1 * patch2 / norm2).sum(dim=-1)
+
+    both_near_zero = (norm1.squeeze(-1) < 0.01) & (norm2.squeeze(-1) < 0.01)
+    similar = torch.ones_like(cos_sim)
+    similar[~both_near_zero] = (cos_sim[~both_near_zero] > threshold).float()
+    return similar[~both_near_zero].float().mean().item()
+
+
+def _extract_ki_frames(
+    frames: torch.Tensor,
+    threshold: float = _MIN_FRAME_SIMILARITY,
+) -> list:
+    r"""
+    Label each frame as keyframe (0) or non-keyframe (1) by comparing to the
+    previous keyframe. First frame is always a keyframe; a new keyframe is chosen
+    when similarity drops below threshold.
+    """
+    if frames.dim() != 4:
+        raise ValueError("Frames must be 4D tensor [N, C, H, W]")
+    if frames.size(0) <= 1:
+        return [0] * frames.size(0)
+
+    _, _, h, w = frames.shape
+    rh, rw = smart_resize(h, w, factor=_KI_PATCH, min_pixels=_KI_MIN_PIXELS, max_pixels=_KI_MAX_PIXELS)
+    resized = F.interpolate(frames, (rh, rw), mode="bilinear", antialias=True).float()
+
+    indices = [0]
+    key = resized[0]
+    for i in range(1, resized.size(0)):
+        if _get_frame_sim(key, resized[i]) < threshold:
+            indices.append(i)
+            key = resized[i]
+
+    frame_types = torch.ones(frames.size(0), dtype=torch.int32)
+    frame_types[indices] = 0
+    return frame_types.tolist()
+
+
+class PenguinVLImageProcessorKwargs(Qwen2VLImageProcessorKwargs, total=False):
+    merge_size: int | list[int]
+    frame_types: list | None
+
+
+class PenguinVLImageProcessor(Qwen2VLImageProcessor):
+    r"""
+    Image processor for PenguinVL with dynamic resizing and TRA (Temporal Redundancy-Aware)
+    token compression for video frames.
+
+    Args:
+        do_resize (`bool`, *optional*, defaults to `True`):
+            Whether to resize the image.
+        size (`dict[str, int] | None`, *optional*, defaults to `{"shortest_edge": 3136, "longest_edge": 3211264}`):
+            Size constraints for resizing. Must contain `shortest_edge` and `longest_edge` keys. When None, uses
+            `min_pixels` and `max_pixels` instead.
+        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BICUBIC`):
+            Resampling filter to use when resizing.
+        do_rescale (`bool`, *optional*, defaults to `True`):
+            Whether to rescale the image by `rescale_factor`.
+        rescale_factor (`float`, *optional*, defaults to `1/255`):
+            Scale factor for rescaling.
+        do_normalize (`bool`, *optional*, defaults to `True`):
+            Whether to normalize the image.
+        image_mean (`list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Mean for normalization.
+        image_std (`list[float]`, *optional*, defaults to `[0.5, 0.5, 0.5]`):
+            Standard deviation for normalization.
+        do_convert_rgb (`bool`, *optional*, defaults to `True`):
+            Whether to convert the image to RGB.
+        min_pixels (`int`, *optional*, defaults to 3136):
+            Minimum pixels for resizing (equivalent to ``min_tokens * patch_size ** 2``).
+        max_pixels (`int`, *optional*, defaults to 3211264):
+            Maximum pixels for resizing (equivalent to ``max_tokens * patch_size ** 2``).
+        patch_size (`int`, *optional*, defaults to 14):
+            Spatial patch size of the vision encoder.
+        temporal_patch_size (`int`, *optional*, defaults to 1):
+            Temporal patch size of the vision encoder. Must be 1 for PenguinVL.
+        merge_size (`int`, *optional*, defaults to 1):
+            Default spatial merge size for token compression (1 for images, 2 for video).
+    """
+
+    model_input_names = ["pixel_values", "image_grid_thw", "image_merge_sizes"]
+    valid_kwargs = PenguinVLImageProcessorKwargs
+
+    def __init__(
+        self,
+        do_resize: bool = True,
+        size: dict[str, int] | None = None,
+        resample: PILImageResampling = PILImageResampling.BICUBIC,
+        do_rescale: bool = True,
+        rescale_factor: int | float = 1 / 255,
+        do_normalize: bool = True,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool = True,
+        min_pixels: int = 3136,
+        max_pixels: int = 3211264,
+        patch_size: int = 14,
+        temporal_patch_size: int = 1,
+        merge_size: int = 1,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+            do_rescale=do_rescale,
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_convert_rgb=do_convert_rgb,
+            min_pixels=min_pixels,
+            max_pixels=max_pixels,
+            patch_size=patch_size,
+            temporal_patch_size=temporal_patch_size,
+            merge_size=merge_size,
+            **kwargs,
+        )
+
+        self.image_mean = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
+        self.image_std = image_std if image_std is not None else IMAGENET_STANDARD_STD
+
+        if self.temporal_patch_size != 1:
+            raise ValueError("`temporal_patch_size` must be 1 for PenguinVL")
+
+    def preprocess(
+        self,
+        images: ImageInput,
+        do_resize: bool | None = None,
+        size: dict[str, int] | None = None,
+        min_pixels: int | None = None,
+        max_pixels: int | None = None,
+        resample: PILImageResampling = None,
+        do_rescale: bool | None = None,
+        rescale_factor: float | None = None,
+        do_normalize: bool | None = None,
+        image_mean: float | list[float] | None = None,
+        image_std: float | list[float] | None = None,
+        do_convert_rgb: bool | None = None,
+        merge_size: int | list[int] | None = None,
+        frame_types: list | None = None,
+        return_tensors: str | TensorType | None = None,
+        data_format: ChannelDimension | None = ChannelDimension.FIRST,
+        input_data_format: str | ChannelDimension | None = None,
+    ):
+        r"""
+        Preprocess images or video clips with optional TRA key/intermediate frame compression.
+
+        Args:
+            images: Single image, list of images, or nested ``[[clip1_frames], [clip2_frames]]``.
+            merge_size: Spatial merge size. Can be ``int`` (all clips) or ``list[int]`` (per-clip).
+                Typically 1 for images and 2 for video.
+            frame_types: Per-clip frame type annotations. ``None`` means all key frames.
+                Each clip's frame_types is a list where 0 = key frame, 1 = intermediate frame.
+                Pass as ``[ft_clip1, ft_clip2, ...]`` or ``[ft_single_clip]``.
+        """
+        min_pixels = min_pixels if min_pixels is not None else self.min_pixels
+        max_pixels = max_pixels if max_pixels is not None else self.max_pixels
+
+        if size is not None:
+            if "shortest_edge" not in size or "longest_edge" not in size:
+                raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
+            min_pixels = size["shortest_edge"]
+        elif min_pixels is not None and max_pixels is not None:
+            # backward compatibility: override size with min_pixels and max_pixels if they are provided
+            size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
+        else:
+            size = {**self.size}
+        do_resize = do_resize if do_resize is not None else self.do_resize
+        resample = resample if resample is not None else self.resample
+        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
+        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
+        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
+        image_mean = image_mean if image_mean is not None else self.image_mean
+        image_std = image_std if image_std is not None else self.image_std
+        default_merge = merge_size if merge_size is not None else self.merge_size
+        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
+
+        validate_preprocess_arguments(
+            rescale_factor=rescale_factor,
+            do_normalize=do_normalize,
+            image_mean=image_mean,
+            image_std=image_std,
+            do_resize=do_resize,
+            size=size,
+            resample=resample,
+        )
+
+        clips = _make_batched_clips(images)
+        num_clips = len(clips)
+
+        if isinstance(default_merge, list | tuple):
+            clip_merge_sizes = list(default_merge)
+        else:
+            clip_merge_sizes = [default_merge] * num_clips
+
+        if frame_types is None:
+            clip_frame_types = [None] * num_clips
+        elif isinstance(frame_types, list | tuple) and len(frame_types) > 0:
+            if isinstance(frame_types[0], list | tuple) or frame_types[0] is None:
+                clip_frame_types = list(frame_types)
+            else:
+                clip_frame_types = [frame_types] if num_clips == 1 else [None] * num_clips
+        else:
+            clip_frame_types = [None] * num_clips
+
+        ps2 = self.patch_size * self.patch_size
+        clip_budgets = _allocate_token_budget(
+            clips,
+            clip_merge_sizes,
+            min_tokens=self.min_pixels // ps2,
+            max_tokens=self.max_pixels // ps2,
+            patch_size=self.patch_size,
+            input_data_format=input_data_format,
+        )
+
+        pixel_values_list = []
+        grid_thw_list = []
+        merge_sizes_list = []
+        num_frames_per_clip = []
+
+        for clip, ms, ft, budget in zip(clips, clip_merge_sizes, clip_frame_types, clip_budgets):
+            factor = self.patch_size * ms
+            target_sizes = _simple_batched_resize(
+                clip,
+                factor=factor,
+                min_tokens=self.min_pixels // ps2,
+                max_tokens=budget,
+                input_data_format=input_data_format,
+                frame_types=ft,
+            )
+
+            clip_n = 0
+            for frame, target_size in zip(clip, target_sizes):
+                frame_convert_rgb = do_convert_rgb
+                frame_data_fmt = input_data_format
+                if do_resize:
+                    if do_convert_rgb:
+                        frame = convert_to_rgb(frame)
+                    frame = to_numpy_array(frame)
+                    if frame_data_fmt is None:
+                        frame_data_fmt = infer_channel_dimension_format(frame)
+                    rh, rw = int(target_size[0]), int(target_size[1])
+                    frame = resize(frame, size=(rh, rw), resample=resample, input_data_format=frame_data_fmt)
+                    frame_convert_rgb = False
+
+                patches, grid_thw = self._preprocess(
+                    frame,
+                    do_resize=False,
+                    size=size,
+                    resample=resample,
+                    do_rescale=do_rescale,
+                    rescale_factor=rescale_factor,
+                    do_normalize=do_normalize,
+                    image_mean=image_mean,
+                    image_std=image_std,
+                    patch_size=self.patch_size,
+                    temporal_patch_size=1,
+                    merge_size=ms,
+                    do_convert_rgb=frame_convert_rgb,
+                    data_format=data_format,
+                    input_data_format=frame_data_fmt,
+                )
+                pixel_values_list.append(patches)
+                grid_thw_list.append(grid_thw)
+                merge_sizes_list.append(ms)
+                clip_n += 1
+            num_frames_per_clip.append(clip_n)
+
+        pixel_values = np.concatenate(pixel_values_list, axis=0)
+        image_grid_thw = np.array(grid_thw_list)
+        image_merge_sizes = np.array(merge_sizes_list)
+
+        data = {
+            "pixel_values": pixel_values,
+            "image_grid_thw": image_grid_thw,
+            "image_merge_sizes": image_merge_sizes,
+            "num_frames_per_clip": num_frames_per_clip,
+        }
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+
+class PenguinVLImageProcessorFast(Qwen2VLImageProcessorFast):
+    r"""
+    Fast image processor for PenguinVL with dynamic per-clip resizing and TRA (Temporal
+    Redundancy-Aware) token compression for video frames.
+
+    Compared to the base Qwen2-VL fast processor this class:
+
+    * Supports **per-clip merge sizes** (``merge_size`` may be ``int`` or ``list[int]``).
+    * Applies TRA compression: key frames retain high resolution while intermediate
+      frames are allocated ~1/16 of the tokens.
+    * Returns ``image_merge_sizes`` and ``num_frames_per_clip`` alongside pixel values.
+    """
+
+    image_mean = IMAGENET_STANDARD_MEAN
+    image_std = IMAGENET_STANDARD_STD
+    temporal_patch_size = 1
+    valid_kwargs = PenguinVLImageProcessorKwargs
+    model_input_names = ["pixel_values", "image_grid_thw", "image_merge_sizes"]
+
+    def _preprocess_image_like_inputs(
+        self,
+        images: ImageInput,
+        do_convert_rgb: bool,
+        input_data_format: ChannelDimension,
+        device: Union[str, "torch.device"] | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        if kwargs["temporal_patch_size"] != 1:
+            raise ValueError("`temporal_patch_size` must be 1 for PenguinVL")
+
+        merge_size_param = kwargs.pop("merge_size")
+        frame_types_param = kwargs.pop("frame_types", None)
+        size = kwargs["size"]
+        patch_size = kwargs["patch_size"]
+        do_resize = kwargs["do_resize"]
+        interpolation = kwargs["interpolation"]
+        do_rescale = kwargs["do_rescale"]
+        rescale_factor = kwargs["rescale_factor"]
+        do_normalize = kwargs["do_normalize"]
+        image_mean = kwargs["image_mean"]
+        image_std = kwargs["image_std"]
+        return_tensors = kwargs.get("return_tensors")
+
+        min_pixels = size["shortest_edge"]
+        max_pixels = size["longest_edge"]
+
+        clips = _make_batched_clips(images)
+        num_clips = len(clips)
+
+        if isinstance(merge_size_param, (list, tuple)):
+            clip_merge_sizes = list(merge_size_param)
+        else:
+            clip_merge_sizes = [merge_size_param] * num_clips
+
+        if frame_types_param is None:
+            clip_frame_types = [None] * num_clips
+        elif isinstance(frame_types_param, (list, tuple)) and len(frame_types_param) > 0:
+            if isinstance(frame_types_param[0], (list, tuple)) or frame_types_param[0] is None:
+                clip_frame_types = list(frame_types_param)
+            else:
+                clip_frame_types = [frame_types_param] if num_clips == 1 else [None] * num_clips
+        else:
+            clip_frame_types = [None] * num_clips
+
+        ps2 = patch_size * patch_size
+        min_tokens = min_pixels // ps2
+        max_tokens = max_pixels // ps2
+        clip_budgets = _allocate_token_budget(
+            clips,
+            clip_merge_sizes,
+            min_tokens,
+            max_tokens,
+            patch_size,
+        )
+
+        pixel_values_list = []
+        grid_thw_list = []
+        merge_sizes_list = []
+        num_frames_per_clip = []
+
+        for clip, ms, ft, budget in zip(clips, clip_merge_sizes, clip_frame_types, clip_budgets):
+            factor = patch_size * ms
+            target_sizes = _simple_batched_resize(
+                clip,
+                factor=factor,
+                min_tokens=min_tokens,
+                max_tokens=budget,
+                input_data_format=input_data_format,
+                frame_types=ft,
+            )
+
+            clip_n = 0
+            for frame, target_size in zip(clip, target_sizes):
+                frame_tensor = self._process_image(
+                    frame,
+                    do_convert_rgb=do_convert_rgb,
+                    input_data_format=input_data_format,
+                    device=device,
+                )
+
+                if do_resize:
+                    frame_tensor = self.resize(
+                        frame_tensor,
+                        size=SizeDict(height=int(target_size[0]), width=int(target_size[1])),
+                        interpolation=interpolation,
+                    )
+
+                frame_tensor = self.rescale_and_normalize(
+                    frame_tensor.unsqueeze(0),
+                    do_rescale,
+                    rescale_factor,
+                    do_normalize,
+                    image_mean,
+                    image_std,
+                )
+
+                resized_height, resized_width = frame_tensor.shape[-2:]
+                grid_h = resized_height // patch_size
+                grid_w = resized_width // patch_size
+                channel = frame_tensor.shape[-3]
+
+                patches = frame_tensor.view(
+                    1,
+                    1,
+                    1,
+                    channel,
+                    grid_h // ms,
+                    ms,
+                    patch_size,
+                    grid_w // ms,
+                    ms,
+                    patch_size,
+                )
+                patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9)
+                flatten_patches = patches.reshape(
+                    grid_h * grid_w,
+                    channel * patch_size * patch_size,
+                )
+
+                pixel_values_list.append(flatten_patches)
+                grid_thw_list.append([1, grid_h, grid_w])
+                merge_sizes_list.append(ms)
+                clip_n += 1
+
+            num_frames_per_clip.append(clip_n)
+
+        pixel_values = torch.cat(pixel_values_list, dim=0)
+        image_grid_thw = torch.tensor(grid_thw_list)
+        image_merge_sizes = torch.tensor(merge_sizes_list)
+
+        return BatchFeature(
+            data={
+                "pixel_values": pixel_values,
+                "image_grid_thw": image_grid_thw,
+                "image_merge_sizes": image_merge_sizes,
+                "num_frames_per_clip": num_frames_per_clip,
+            },
+            tensor_type=return_tensors,
+        )
+
+
+# ===================== Processor =====================
+
+
+class PenguinVLProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+    }
+
+
+class PenguinVLProcessor(ProcessorMixin):
+    r"""
+    Processor for PenguinVL that wraps an image processor and a tokenizer.
+
+    Args:
+        image_processor (`PenguinVLImageProcessor`, *optional*):
+            The image processor.
+        tokenizer (`PreTrainedTokenizer`, *optional*):
+            The tokenizer.
+        image_token (`str`, *optional*, defaults to `"<image>"`):
+            The image placeholder token.
+        image_merge_size (`int`, *optional*, defaults to 1):
+            Spatial merge size for images.
+        video_merge_size (`int`, *optional*, defaults to 2):
+            Spatial merge size for video frames.
+        chat_template (`str`, *optional*):
+            A Jinja template for formatting conversations.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "PenguinVLImageProcessor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+    valid_kwargs = ["chat_template", "image_token", "image_merge_size", "video_merge_size"]
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        image_token="<image>",
+        image_merge_size: int = 1,
+        video_merge_size: int = 2,
+        chat_template=None,
+        **kwargs,
+    ):
+        self.image_token = image_token
+        self.image_merge_size = image_merge_size
+        self.video_merge_size = video_merge_size
+        if tokenizer is not None:
+            self.image_token_id = tokenizer.convert_tokens_to_ids(image_token)
+        super().__init__(image_processor=image_processor, tokenizer=tokenizer, chat_template=chat_template, **kwargs)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        frame_types: list | None = None,
+        **kwargs: Unpack[PenguinVLProcessorKwargs],
+    ) -> BatchFeature:
+        output_kwargs = self._merge_kwargs(
+            PenguinVLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = {}
+        num_frames_per_clip = None
+        if images is not None:
+            # Load images from URLs if needed (e.g. from apply_chat_template with return_dict=True)
+            def _load_if_url(x):
+                if isinstance(x, str) and (x.startswith("http://") or x.startswith("https://")):
+                    return load_image(x)
+                return x
+
+            def _load_images(imgs):
+                if isinstance(imgs, (list, tuple)):
+                    return [_load_images(item) for item in imgs]
+                return _load_if_url(imgs)
+
+            images = _load_images(images)
+            clips = _make_batched_clips(images)
+            merge_size = [self.video_merge_size if len(clip) > 1 else self.image_merge_size for clip in clips]
+            images_kwargs = {**output_kwargs.get("images_kwargs", {}), "merge_size": merge_size}
+            if frame_types is not None:
+                images_kwargs["frame_types"] = frame_types
+            image_inputs = self.image_processor(images=images, **images_kwargs)
+            image_grid_thw = image_inputs["image_grid_thw"]
+            image_merge_sizes = image_inputs["image_merge_sizes"]
+            num_frames_per_clip = image_inputs.pop("num_frames_per_clip", None)
+        else:
+            image_grid_thw = image_merge_sizes = []
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()
+
+        if images is not None:
+            total_image_tokens_in_text = sum(t.count(self.image_token) for t in text)
+            total_frames = int(sum(num_frames_per_clip)) if num_frames_per_clip is not None else len(image_grid_thw)
+
+            if total_image_tokens_in_text == total_frames:
+                frame_idx = 0
+                for i in range(len(text)):
+                    while self.image_token in text[i]:
+                        t, h, w = image_grid_thw[frame_idx]
+                        ms = image_merge_sizes[frame_idx]
+                        num_image_tokens = int(t * (h // ms) * (w // ms))
+                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                        frame_idx += 1
+                    text[i] = text[i].replace("<|placeholder|>", self.image_token)
+            else:
+                frame_idx = 0
+                clip_idx = 0
+                for i in range(len(text)):
+                    while self.image_token in text[i]:
+                        n_frames = num_frames_per_clip[clip_idx] if num_frames_per_clip is not None else 1
+                        num_image_tokens = 0
+                        for j in range(n_frames):
+                            t, h, w = image_grid_thw[frame_idx + j]
+                            ms = image_merge_sizes[frame_idx + j]
+                            num_image_tokens += int(t * (h // ms) * (w // ms))
+                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                        frame_idx += n_frames
+                        clip_idx += 1
+                    text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def _load_visual(self, source):
+        r"""Load a single image from URL, file:// path, local path, or pass through PIL images."""
+        if isinstance(source, str):
+            source = source.removeprefix("file://")
+            return load_image(source)
+        if is_vision_available() and isinstance(source, Image.Image):
+            return source
+        return source
+
+    def _load_video_frames(self, video_url, fps=1, max_frames=128):
+        r"""
+        Load frames from a video with fps-based sampling capped at max_frames,
+        then extract KI (key/intermediate) frame types.
+
+        Sampling logic:
+        - Read at ``fps`` frames per second (default 1).
+        - If the resulting frame count exceeds ``max_frames``, uniformly
+          subsample to ``max_frames`` frames.
+
+        Returns:
+            tuple: ``(frames, frame_types, timestamps)`` where *frames* is a
+            list of PIL images, *frame_types* is a list of ints (0 = keyframe,
+            1 = intermediate frame), and *timestamps* is a list of floats
+            (seconds) for each sampled frame.
+        """
+        from ...video_utils import load_video
+
+        _BACKEND_PRIORITY = ("decord", "opencv", "torchvision", "torchcodec", "pyav")
+        _BACKEND_AVAILABLE = {
+            "pyav": is_av_available,
+            "decord": is_decord_available,
+            "opencv": is_cv2_available,
+            "torchvision": is_torchvision_available,
+            "torchcodec": is_torchcodec_available,
+        }
+        backend = next(
+            (b for b in _BACKEND_PRIORITY if _BACKEND_AVAILABLE[b]()),
+            None,
+        )
+        if backend is None:
+            raise ImportError(
+                "No video backend available. Install one of: av (pyav), decord, opencv-python, torchvision, or torchcodec."
+            )
+
+        _fps = fps
+        _max = max_frames
+        _sampled_indices = []
+        _video_fps = [30.0]
+
+        def _sample_fn(metadata, **kwargs):
+            total = metadata.total_num_frames
+            video_fps = metadata.fps or 30.0
+            _video_fps[0] = video_fps
+            if total <= 0:
+                # Frame count unknown (not stored in container header); take consecutive frames up to _max
+                indices = np.arange(0, _max, dtype=int)
+            else:
+                num_at_target_fps = max(1, int(total / video_fps * _fps))
+                if num_at_target_fps <= _max:
+                    indices = np.arange(0, total, max(1, total / num_at_target_fps), dtype=int)
+                else:
+                    indices = np.linspace(0, total - 1, _max, dtype=int)
+            indices = indices[:_max]
+            _sampled_indices.extend(indices.tolist())
+            return indices
+
+        video_frames, _ = load_video(video_url, sample_indices_fn=_sample_fn, backend=backend)
+
+        if hasattr(video_frames, "numpy"):
+            video_frames = video_frames.numpy()
+        if not isinstance(video_frames, np.ndarray):
+            video_frames = np.stack([np.array(f) for f in video_frames])
+
+        frames_tensor = torch.from_numpy(video_frames.transpose(0, 3, 1, 2).copy()).float()
+        frame_types = _extract_ki_frames(frames_tensor)
+        timestamps = [idx / _video_fps[0] for idx in _sampled_indices]
+
+        if is_vision_available():
+            frames = [Image.fromarray(video_frames[i]) for i in range(len(video_frames))]
+        else:
+            frames = list(video_frames)
+
+        return frames, frame_types, timestamps
+
+    def _convert_messages_for_chat_template(self, messages):
+        r"""
+        Convert Qwen2-VL style messages for the Jinja chat template.
+
+        Image entries become ``{"type": "image"}``.  Video entries keep their
+        type and carry ``num_frames`` / ``timestamps`` so the template can emit
+        per-frame timestamp prefixes.  Call :meth:`process_vision_info` before
+        :meth:`apply_chat_template` to populate these fields automatically.
+
+        If ``num_frames`` is not present on a video entry (i.e.
+        :meth:`process_vision_info` was not called first), the entry falls back
+        to a plain ``{"type": "image"}`` for backward compatibility.
+        """
+        converted = copy.deepcopy(messages)
+        for message in converted:
+            content = message.get("content", [])
+            if isinstance(content, str):
+                continue
+            new_content = []
+            for item in content:
+                if not isinstance(item, dict):
+                    new_content.append(item)
+                    continue
+                if item.get("type") == "image":
+                    new_content.append({"type": "image"})
+                elif item.get("type") == "video":
+                    if "num_frames" in item:
+                        video_entry = {"type": "video", "num_frames": item["num_frames"]}
+                        if "timestamps" in item:
+                            video_entry["timestamps"] = item["timestamps"]
+                        new_content.append(video_entry)
+                    else:
+                        new_content.append({"type": "image"})
+                else:
+                    new_content.append(item)
+            message["content"] = new_content
+        return converted
+
+    def process_vision_info(
+        self,
+        messages: list[dict],
+        fps: int = 1,
+        max_frames: int = 128,
+    ) -> tuple[list, list] | tuple[None, None]:
+        r"""
+        Extract and load visual inputs from Qwen2-VL style conversation messages.
+
+        Walks through ``messages`` and collects images / video frames in order.
+        For video clips, frames are sampled at ``fps`` (default 1) and capped at
+        ``max_frames`` (default 128), then KI frame types are extracted.
+
+        Video content items in ``messages`` are enriched in-place with
+        ``num_frames`` and ``timestamps`` keys so that a subsequent call to
+        :meth:`apply_chat_template` can emit per-frame timestamp prefixes.
+        Call this method **before** :meth:`apply_chat_template`.
+
+        Supported content block formats::
+
+            {"type": "image", "image": "https://example.com/photo.jpg"}
+            {"type": "image", "image": "file:///path/to/image.png"}
+            {"type": "image", "image": <PIL.Image.Image>}
+            {"type": "video", "video": "https://example.com/clip.mp4"}
+            {"type": "video", "video": ["file:///path/frame1.jpg", ...], "timestamps": [0, ...]}
+            {"type": "video", "video": [<PIL.Image.Image>, ...], "timestamps": [0, ...]}
+
+        Args:
+            messages: Conversation in Qwen2-VL dict format. Video content items
+                are enriched in-place with ``num_frames`` and ``timestamps``.
+            fps: Frames per second for video sampling. Defaults to 1.
+            max_frames: Maximum number of frames per video. Defaults to 128.
+
+        Returns:
+            ``(visual_inputs, clip_frame_types)`` where *visual_inputs* is a
+            nested list of PIL images and *clip_frame_types* is a list of
+            per-clip frame type annotations (``None`` for images, ``list[int]``
+            for videos where 0 = keyframe, 1 = intermediate frame). Returns
+            ``(None, None)`` when no visual content is found.
+
+        Example::
+
+            images, frame_types = processor.process_vision_info(messages)
+            text = processor.apply_chat_template(messages, add_generation_prompt=True)
+            inputs = processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+        """
+        visual_inputs = []
+        clip_frame_types = []
+        for message in messages:
+            content = message.get("content", [])
+            if isinstance(content, str):
+                continue
+            for item in content:
+                if not isinstance(item, dict):
+                    continue
+                content_type = item.get("type")
+                if content_type == "image":
+                    source = item.get("image") or item.get("url") or item.get("path")
+                    if source is not None:
+                        img = self._load_visual(source)
+                        visual_inputs.append([img])
+                        clip_frame_types.append(None)
+                elif content_type == "video":
+                    video_data = item.get("video") or item.get("url") or item.get("path")
+                    if video_data is None:
+                        continue
+                    if isinstance(video_data, (list, tuple)):
+                        frames = [self._load_visual(f) for f in video_data]
+                        np_frames = np.stack([np.array(f) for f in frames])
+                        ft_tensor = torch.from_numpy(np_frames.transpose(0, 3, 1, 2).copy()).float()
+                        ft = _extract_ki_frames(ft_tensor)
+                        visual_inputs.append(frames)
+                        clip_frame_types.append(ft)
+                        item["num_frames"] = len(frames)
+                        if "timestamps" not in item:
+                            item["timestamps"] = []
+                    elif isinstance(video_data, str):
+                        frames, ft, timestamps = self._load_video_frames(video_data, fps=fps, max_frames=max_frames)
+                        visual_inputs.append(frames)
+                        clip_frame_types.append(ft)
+                        item["num_frames"] = len(frames)
+                        if "timestamps" not in item:
+                            item["timestamps"] = timestamps
+
+        if not visual_inputs:
+            return None, None
+        return visual_inputs, clip_frame_types
+
+    def apply_chat_template(self, conversation, chat_template=None, **kwargs):
+        kwargs.setdefault("image_token", self.image_token)
+        conversation = self._convert_messages_for_chat_template(conversation)
+        return super().apply_chat_template(conversation, chat_template, **kwargs)
+
+    def batch_decode(self, *args, **kwargs):
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = [
+    "PenguinVLVisionConfig",
+    "PenguinVLConfig",
+    "PenguinVLVisionModel",
+    "PenguinVLPreTrainedModel",
+    "PenguinVLLanguageModel",
+    "PenguinVLModel",
+    "PenguinVLForConditionalGeneration",
+    "PenguinVLProcessor",
+    "PenguinVLImageProcessor",
+    "PenguinVLImageProcessorFast",
+]
diff --git a/src/transformers/models/penguinvl/processing_penguinvl.py b/src/transformers/models/penguinvl/processing_penguinvl.py
new file mode 100644
index 000000000000..354de8c86ff3
--- /dev/null
+++ b/src/transformers/models/penguinvl/processing_penguinvl.py
@@ -0,0 +1,541 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/penguinvl/modular_penguinvl.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_penguinvl.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import math
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from ...feature_extraction_utils import BatchFeature
+from ...image_utils import ImageInput, is_valid_image, load_image
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import (
+    is_av_available,
+    is_cv2_available,
+    is_decord_available,
+    is_torchcodec_available,
+    is_torchvision_available,
+    is_vision_available,
+)
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+# ===================== Processor =====================
+
+
+class PenguinVLProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": False,
+        },
+    }
+
+
+def smart_resize(
+    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
+):
+    """Rescales the image so that the following conditions are met:
+
+    1. Both dimensions (height and width) are divisible by 'factor'.
+
+    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].
+
+    3. The aspect ratio of the image is maintained as closely as possible.
+
+    """
+    if max(height, width) / min(height, width) > 200:
+        raise ValueError(
+            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
+        )
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = max(factor, math.floor(height / beta / factor) * factor)
+        w_bar = max(factor, math.floor(width / beta / factor) * factor)
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+    return h_bar, w_bar
+
+
+# ===================== Image Processor =====================
+
+
+def _make_batched_clips(images) -> list[list]:
+    r"""
+    Normalize visual inputs to a list of clips, where each clip is a list of frames.
+
+    - Single image: ``image`` -> ``[[image]]``
+    - List of images: ``[img1, img2]`` -> ``[[img1], [img2]]``
+    - Nested clips: ``[[img1], [f1, f2, f3]]`` -> ``[[img1], [f1, f2, f3]]``
+    """
+    if isinstance(images, list | tuple) and len(images) > 0:
+        if isinstance(images[0], list | tuple):
+            return [list(clip) for clip in images]
+        if all(is_valid_image(f) for f in images):
+            return [[img] for img in images]
+    if is_valid_image(images):
+        return [[images]]
+    raise ValueError(f"Could not make batched images from {type(images)}")
+
+
+# ===================== KI Frame Extraction =====================
+
+_KI_PATCH = 14
+_KI_MIN_PIXELS = 10 * 14 * 14
+_KI_MAX_PIXELS = 10240 * 14 * 14
+_MIN_FRAME_SIMILARITY = 0.95
+
+
+# Adapted from Keye-VL
+def _get_frame_sim(
+    frame1: torch.Tensor,
+    frame2: torch.Tensor,
+    patch_size: int = 14,
+    threshold: float = 0.7,
+    epsilon: float = 1e-8,
+) -> float:
+    r"""Cosine similarity between two frames averaged over patches. Returns mean similarity in [0, 1]."""
+
+    def _to_comparison_tensor(tensor: torch.Tensor) -> torch.Tensor:
+        if is_cv2_available():
+            import cv2
+
+            arr = tensor.cpu().permute(1, 2, 0).numpy()
+            if arr.dtype in (np.float32, np.float64):
+                arr = arr.astype(np.uint8)
+            hsv = cv2.cvtColor(arr, cv2.COLOR_RGB2HSV)
+            return torch.from_numpy(hsv).permute(2, 0, 1).to(tensor.device).float()
+        return tensor.float()
+
+    f1 = _to_comparison_tensor(frame1)
+    f2 = _to_comparison_tensor(frame2)
+
+    c, H, W = f1.shape
+    h_patches = H // patch_size
+    w_patches = W // patch_size
+
+    def _to_patches(f):
+        f = f[:, : h_patches * patch_size, : w_patches * patch_size]
+        f = f.reshape(c, h_patches, patch_size, w_patches, patch_size)
+        f = f.permute(1, 3, 0, 2, 4).reshape(h_patches, w_patches, c * patch_size * patch_size)
+        return f.float()
+
+    patch1 = _to_patches(f1)
+    patch2 = _to_patches(f2)
+
+    norm1 = torch.norm(patch1, p=2, dim=-1, keepdim=True) + epsilon
+    norm2 = torch.norm(patch2, p=2, dim=-1, keepdim=True) + epsilon
+    cos_sim = (patch1 / norm1 * patch2 / norm2).sum(dim=-1)
+
+    both_near_zero = (norm1.squeeze(-1) < 0.01) & (norm2.squeeze(-1) < 0.01)
+    similar = torch.ones_like(cos_sim)
+    similar[~both_near_zero] = (cos_sim[~both_near_zero] > threshold).float()
+    return similar[~both_near_zero].float().mean().item()
+
+
+def _extract_ki_frames(
+    frames: torch.Tensor,
+    threshold: float = _MIN_FRAME_SIMILARITY,
+) -> list:
+    r"""
+    Label each frame as keyframe (0) or non-keyframe (1) by comparing to the
+    previous keyframe. First frame is always a keyframe; a new keyframe is chosen
+    when similarity drops below threshold.
+    """
+    if frames.dim() != 4:
+        raise ValueError("Frames must be 4D tensor [N, C, H, W]")
+    if frames.size(0) <= 1:
+        return [0] * frames.size(0)
+
+    _, _, h, w = frames.shape
+    rh, rw = smart_resize(h, w, factor=_KI_PATCH, min_pixels=_KI_MIN_PIXELS, max_pixels=_KI_MAX_PIXELS)
+    resized = F.interpolate(frames, (rh, rw), mode="bilinear", antialias=True).float()
+
+    indices = [0]
+    key = resized[0]
+    for i in range(1, resized.size(0)):
+        if _get_frame_sim(key, resized[i]) < threshold:
+            indices.append(i)
+            key = resized[i]
+
+    frame_types = torch.ones(frames.size(0), dtype=torch.int32)
+    frame_types[indices] = 0
+    return frame_types.tolist()
+
+
+class PenguinVLProcessor(ProcessorMixin):
+    r"""
+    Processor for PenguinVL that wraps an image processor and a tokenizer.
+
+    Args:
+        image_processor (`PenguinVLImageProcessor`, *optional*):
+            The image processor.
+        tokenizer (`PreTrainedTokenizer`, *optional*):
+            The tokenizer.
+        image_token (`str`, *optional*, defaults to `"<image>"`):
+            The image placeholder token.
+        image_merge_size (`int`, *optional*, defaults to 1):
+            Spatial merge size for images.
+        video_merge_size (`int`, *optional*, defaults to 2):
+            Spatial merge size for video frames.
+        chat_template (`str`, *optional*):
+            A Jinja template for formatting conversations.
+    """
+
+    attributes = ["image_processor", "tokenizer"]
+    image_processor_class = "PenguinVLImageProcessor"
+    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
+    valid_kwargs = ["chat_template", "image_token", "image_merge_size", "video_merge_size"]
+
+    def __init__(
+        self,
+        image_processor=None,
+        tokenizer=None,
+        image_token="<image>",
+        image_merge_size: int = 1,
+        video_merge_size: int = 2,
+        chat_template=None,
+        **kwargs,
+    ):
+        self.image_token = image_token
+        self.image_merge_size = image_merge_size
+        self.video_merge_size = video_merge_size
+        if tokenizer is not None:
+            self.image_token_id = tokenizer.convert_tokens_to_ids(image_token)
+        super().__init__(image_processor=image_processor, tokenizer=tokenizer, chat_template=chat_template, **kwargs)
+
+    def __call__(
+        self,
+        images: ImageInput = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
+        frame_types: list | None = None,
+        **kwargs: Unpack[PenguinVLProcessorKwargs],
+    ) -> BatchFeature:
+        output_kwargs = self._merge_kwargs(
+            PenguinVLProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = {}
+        num_frames_per_clip = None
+        if images is not None:
+            # Load images from URLs if needed (e.g. from apply_chat_template with return_dict=True)
+            def _load_if_url(x):
+                if isinstance(x, str) and (x.startswith("http://") or x.startswith("https://")):
+                    return load_image(x)
+                return x
+
+            def _load_images(imgs):
+                if isinstance(imgs, (list, tuple)):
+                    return [_load_images(item) for item in imgs]
+                return _load_if_url(imgs)
+
+            images = _load_images(images)
+            clips = _make_batched_clips(images)
+            merge_size = [self.video_merge_size if len(clip) > 1 else self.image_merge_size for clip in clips]
+            images_kwargs = {**output_kwargs.get("images_kwargs", {}), "merge_size": merge_size}
+            if frame_types is not None:
+                images_kwargs["frame_types"] = frame_types
+            image_inputs = self.image_processor(images=images, **images_kwargs)
+            image_grid_thw = image_inputs["image_grid_thw"]
+            image_merge_sizes = image_inputs["image_merge_sizes"]
+            num_frames_per_clip = image_inputs.pop("num_frames_per_clip", None)
+        else:
+            image_grid_thw = image_merge_sizes = []
+
+        if not isinstance(text, list):
+            text = [text]
+
+        text = text.copy()
+
+        if images is not None:
+            total_image_tokens_in_text = sum(t.count(self.image_token) for t in text)
+            total_frames = int(sum(num_frames_per_clip)) if num_frames_per_clip is not None else len(image_grid_thw)
+
+            if total_image_tokens_in_text == total_frames:
+                frame_idx = 0
+                for i in range(len(text)):
+                    while self.image_token in text[i]:
+                        t, h, w = image_grid_thw[frame_idx]
+                        ms = image_merge_sizes[frame_idx]
+                        num_image_tokens = int(t * (h // ms) * (w // ms))
+                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                        frame_idx += 1
+                    text[i] = text[i].replace("<|placeholder|>", self.image_token)
+            else:
+                frame_idx = 0
+                clip_idx = 0
+                for i in range(len(text)):
+                    while self.image_token in text[i]:
+                        n_frames = num_frames_per_clip[clip_idx] if num_frames_per_clip is not None else 1
+                        num_image_tokens = 0
+                        for j in range(n_frames):
+                            t, h, w = image_grid_thw[frame_idx + j]
+                            ms = image_merge_sizes[frame_idx + j]
+                            num_image_tokens += int(t * (h // ms) * (w // ms))
+                        text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
+                        frame_idx += n_frames
+                        clip_idx += 1
+                    text[i] = text[i].replace("<|placeholder|>", self.image_token)
+
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def _load_visual(self, source):
+        r"""Load a single image from URL, file:// path, local path, or pass through PIL images."""
+        if isinstance(source, str):
+            source = source.removeprefix("file://")
+            return load_image(source)
+        if is_vision_available() and isinstance(source, Image.Image):
+            return source
+        return source
+
+    def _load_video_frames(self, video_url, fps=1, max_frames=128):
+        r"""
+        Load frames from a video with fps-based sampling capped at max_frames,
+        then extract KI (key/intermediate) frame types.
+
+        Sampling logic:
+        - Read at ``fps`` frames per second (default 1).
+        - If the resulting frame count exceeds ``max_frames``, uniformly
+          subsample to ``max_frames`` frames.
+
+        Returns:
+            tuple: ``(frames, frame_types, timestamps)`` where *frames* is a
+            list of PIL images, *frame_types* is a list of ints (0 = keyframe,
+            1 = intermediate frame), and *timestamps* is a list of floats
+            (seconds) for each sampled frame.
+        """
+        from ...video_utils import load_video
+
+        _BACKEND_PRIORITY = ("decord", "opencv", "torchvision", "torchcodec", "pyav")
+        _BACKEND_AVAILABLE = {
+            "pyav": is_av_available,
+            "decord": is_decord_available,
+            "opencv": is_cv2_available,
+            "torchvision": is_torchvision_available,
+            "torchcodec": is_torchcodec_available,
+        }
+        backend = next(
+            (b for b in _BACKEND_PRIORITY if _BACKEND_AVAILABLE[b]()),
+            None,
+        )
+        if backend is None:
+            raise ImportError(
+                "No video backend available. Install one of: av (pyav), decord, opencv-python, torchvision, or torchcodec."
+            )
+
+        _fps = fps
+        _max = max_frames
+        _sampled_indices = []
+        _video_fps = [30.0]
+
+        def _sample_fn(metadata, **kwargs):
+            total = metadata.total_num_frames
+            video_fps = metadata.fps or 30.0
+            _video_fps[0] = video_fps
+            if total <= 0:
+                # Frame count unknown (not stored in container header); take consecutive frames up to _max
+                indices = np.arange(0, _max, dtype=int)
+            else:
+                num_at_target_fps = max(1, int(total / video_fps * _fps))
+                if num_at_target_fps <= _max:
+                    indices = np.arange(0, total, max(1, total / num_at_target_fps), dtype=int)
+                else:
+                    indices = np.linspace(0, total - 1, _max, dtype=int)
+            indices = indices[:_max]
+            _sampled_indices.extend(indices.tolist())
+            return indices
+
+        video_frames, _ = load_video(video_url, sample_indices_fn=_sample_fn, backend=backend)
+
+        if hasattr(video_frames, "numpy"):
+            video_frames = video_frames.numpy()
+        if not isinstance(video_frames, np.ndarray):
+            video_frames = np.stack([np.array(f) for f in video_frames])
+
+        frames_tensor = torch.from_numpy(video_frames.transpose(0, 3, 1, 2).copy()).float()
+        frame_types = _extract_ki_frames(frames_tensor)
+        timestamps = [idx / _video_fps[0] for idx in _sampled_indices]
+
+        if is_vision_available():
+            frames = [Image.fromarray(video_frames[i]) for i in range(len(video_frames))]
+        else:
+            frames = list(video_frames)
+
+        return frames, frame_types, timestamps
+
+    def _convert_messages_for_chat_template(self, messages):
+        r"""
+        Convert Qwen2-VL style messages for the Jinja chat template.
+
+        Image entries become ``{"type": "image"}``.  Video entries keep their
+        type and carry ``num_frames`` / ``timestamps`` so the template can emit
+        per-frame timestamp prefixes.  Call :meth:`process_vision_info` before
+        :meth:`apply_chat_template` to populate these fields automatically.
+
+        If ``num_frames`` is not present on a video entry (i.e.
+        :meth:`process_vision_info` was not called first), the entry falls back
+        to a plain ``{"type": "image"}`` for backward compatibility.
+        """
+        converted = copy.deepcopy(messages)
+        for message in converted:
+            content = message.get("content", [])
+            if isinstance(content, str):
+                continue
+            new_content = []
+            for item in content:
+                if not isinstance(item, dict):
+                    new_content.append(item)
+                    continue
+                if item.get("type") == "image":
+                    new_content.append({"type": "image"})
+                elif item.get("type") == "video":
+                    if "num_frames" in item:
+                        video_entry = {"type": "video", "num_frames": item["num_frames"]}
+                        if "timestamps" in item:
+                            video_entry["timestamps"] = item["timestamps"]
+                        new_content.append(video_entry)
+                    else:
+                        new_content.append({"type": "image"})
+                else:
+                    new_content.append(item)
+            message["content"] = new_content
+        return converted
+
+    def process_vision_info(
+        self,
+        messages: list[dict],
+        fps: int = 1,
+        max_frames: int = 128,
+    ) -> tuple[list, list] | tuple[None, None]:
+        r"""
+        Extract and load visual inputs from Qwen2-VL style conversation messages.
+
+        Walks through ``messages`` and collects images / video frames in order.
+        For video clips, frames are sampled at ``fps`` (default 1) and capped at
+        ``max_frames`` (default 128), then KI frame types are extracted.
+
+        Video content items in ``messages`` are enriched in-place with
+        ``num_frames`` and ``timestamps`` keys so that a subsequent call to
+        :meth:`apply_chat_template` can emit per-frame timestamp prefixes.
+        Call this method **before** :meth:`apply_chat_template`.
+
+        Supported content block formats::
+
+            {"type": "image", "image": "https://example.com/photo.jpg"}
+            {"type": "image", "image": "file:///path/to/image.png"}
+            {"type": "image", "image": <PIL.Image.Image>}
+            {"type": "video", "video": "https://example.com/clip.mp4"}
+            {"type": "video", "video": ["file:///path/frame1.jpg", ...], "timestamps": [0, ...]}
+            {"type": "video", "video": [<PIL.Image.Image>, ...], "timestamps": [0, ...]}
+
+        Args:
+            messages: Conversation in Qwen2-VL dict format. Video content items
+                are enriched in-place with ``num_frames`` and ``timestamps``.
+            fps: Frames per second for video sampling. Defaults to 1.
+            max_frames: Maximum number of frames per video. Defaults to 128.
+
+        Returns:
+            ``(visual_inputs, clip_frame_types)`` where *visual_inputs* is a
+            nested list of PIL images and *clip_frame_types* is a list of
+            per-clip frame type annotations (``None`` for images, ``list[int]``
+            for videos where 0 = keyframe, 1 = intermediate frame). Returns
+            ``(None, None)`` when no visual content is found.
+
+        Example::
+
+            images, frame_types = processor.process_vision_info(messages)
+            text = processor.apply_chat_template(messages, add_generation_prompt=True)
+            inputs = processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+        """
+        visual_inputs = []
+        clip_frame_types = []
+        for message in messages:
+            content = message.get("content", [])
+            if isinstance(content, str):
+                continue
+            for item in content:
+                if not isinstance(item, dict):
+                    continue
+                content_type = item.get("type")
+                if content_type == "image":
+                    source = item.get("image") or item.get("url") or item.get("path")
+                    if source is not None:
+                        img = self._load_visual(source)
+                        visual_inputs.append([img])
+                        clip_frame_types.append(None)
+                elif content_type == "video":
+                    video_data = item.get("video") or item.get("url") or item.get("path")
+                    if video_data is None:
+                        continue
+                    if isinstance(video_data, (list, tuple)):
+                        frames = [self._load_visual(f) for f in video_data]
+                        np_frames = np.stack([np.array(f) for f in frames])
+                        ft_tensor = torch.from_numpy(np_frames.transpose(0, 3, 1, 2).copy()).float()
+                        ft = _extract_ki_frames(ft_tensor)
+                        visual_inputs.append(frames)
+                        clip_frame_types.append(ft)
+                        item["num_frames"] = len(frames)
+                        if "timestamps" not in item:
+                            item["timestamps"] = []
+                    elif isinstance(video_data, str):
+                        frames, ft, timestamps = self._load_video_frames(video_data, fps=fps, max_frames=max_frames)
+                        visual_inputs.append(frames)
+                        clip_frame_types.append(ft)
+                        item["num_frames"] = len(frames)
+                        if "timestamps" not in item:
+                            item["timestamps"] = timestamps
+
+        if not visual_inputs:
+            return None, None
+        return visual_inputs, clip_frame_types
+
+    def apply_chat_template(self, conversation, chat_template=None, **kwargs):
+        kwargs.setdefault("image_token", self.image_token)
+        conversation = self._convert_messages_for_chat_template(conversation)
+        return super().apply_chat_template(conversation, chat_template, **kwargs)
+
+    def batch_decode(self, *args, **kwargs):
+        return self.tokenizer.batch_decode(*args, **kwargs)
+
+    def decode(self, *args, **kwargs):
+        return self.tokenizer.decode(*args, **kwargs)
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        image_processor_input_names = self.image_processor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
+
+
+__all__ = ["PenguinVLProcessor"]
diff --git a/src/transformers/models/perceiver/modeling_perceiver.py b/src/transformers/models/perceiver/modeling_perceiver.py
index 6b2850390e27..757a45eb8726 100755
--- a/src/transformers/models/perceiver/modeling_perceiver.py
+++ b/src/transformers/models/perceiver/modeling_perceiver.py
@@ -43,12 +43,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Perceiver base model's outputs, with potential hidden states, attentions and cross-attentions.
     """
 )
+@dataclass
 class PerceiverModelOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
@@ -62,12 +62,12 @@ class PerceiverModelOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Perceiver decoder outputs, with potential cross-attentions.
     """
 )
+@dataclass
 class PerceiverDecoderOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, num_labels)`):
@@ -78,12 +78,12 @@ class PerceiverDecoderOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Perceiver's masked language model outputs.
     """
 )
+@dataclass
 class PerceiverMaskedLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -99,13 +99,13 @@ class PerceiverMaskedLMOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Perceiver's outputs of sequence/image classification models, optical flow and multimodal
     autoencoding.
     """
 )
+@dataclass
 class PerceiverClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/perception_lm/modeling_perception_lm.py b/src/transformers/models/perception_lm/modeling_perception_lm.py
index 95982fe86532..33f277c1e767 100644
--- a/src/transformers/models/perception_lm/modeling_perception_lm.py
+++ b/src/transformers/models/perception_lm/modeling_perception_lm.py
@@ -127,12 +127,12 @@ class PerceptionLMModelOutputWithPast(BaseModelOutputWithPast):
     video_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for PerceptionLM causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class PerceptionLMCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -220,18 +220,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.size()[:-1].numel()}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.size()[:-1].numel()}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/perception_lm/modular_perception_lm.py b/src/transformers/models/perception_lm/modular_perception_lm.py
index 4c09a6d22a78..89f09232c296 100644
--- a/src/transformers/models/perception_lm/modular_perception_lm.py
+++ b/src/transformers/models/perception_lm/modular_perception_lm.py
@@ -188,18 +188,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.size()[:-1].numel()}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.size()[:-1].numel()}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/perception_lm/processing_perception_lm.py b/src/transformers/models/perception_lm/processing_perception_lm.py
index 56633fcc3856..f9b88bc52c13 100644
--- a/src/transformers/models/perception_lm/processing_perception_lm.py
+++ b/src/transformers/models/perception_lm/processing_perception_lm.py
@@ -22,12 +22,14 @@
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
 from ...video_utils import VideoInput
+from .image_processing_perception_lm_fast import PerceptionLMImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class PerceptionLMProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: PerceptionLMImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
diff --git a/src/transformers/models/persimmon/modeling_persimmon.py b/src/transformers/models/persimmon/modeling_persimmon.py
index e0516ed7da9a..800de6f101e1 100644
--- a/src/transformers/models/persimmon/modeling_persimmon.py
+++ b/src/transformers/models/persimmon/modeling_persimmon.py
@@ -114,7 +114,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/phi/modeling_phi.py b/src/transformers/models/phi/modeling_phi.py
index e3f97a01ee4c..cd904be742e6 100644
--- a/src/transformers/models/phi/modeling_phi.py
+++ b/src/transformers/models/phi/modeling_phi.py
@@ -89,7 +89,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/phi3/modeling_phi3.py b/src/transformers/models/phi3/modeling_phi3.py
index b07735f8a2e6..cd6add0a28ea 100644
--- a/src/transformers/models/phi3/modeling_phi3.py
+++ b/src/transformers/models/phi3/modeling_phi3.py
@@ -123,7 +123,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/phi3/modular_phi3.py b/src/transformers/models/phi3/modular_phi3.py
index 4229981cc0a8..4ec6d3c3c6dc 100644
--- a/src/transformers/models/phi3/modular_phi3.py
+++ b/src/transformers/models/phi3/modular_phi3.py
@@ -127,10 +127,19 @@ def forward(
         hidden_shape = (*input_shape, -1, self.head_dim)
 
         qkv = self.qkv_proj(hidden_states)
-        query_pos = self.config.num_attention_heads * self.head_dim
+
+        tp_degree = (
+            self.qkv_proj.weight.device_mesh.size(0)
+            if isinstance(self.qkv_proj.weight, torch.distributed.tensor.DTensor)
+            else 1
+        )
+        tp_sharded_attn_heads = self.config.num_attention_heads // tp_degree
+        tp_sharded_kv_heads = self.num_key_value_heads // tp_degree
+
+        query_pos = tp_sharded_attn_heads * self.head_dim
         query_states = qkv[..., :query_pos]
-        key_states = qkv[..., query_pos : query_pos + self.num_key_value_heads * self.head_dim]
-        value_states = qkv[..., query_pos + self.num_key_value_heads * self.head_dim :]
+        key_states = qkv[..., query_pos : query_pos + tp_sharded_kv_heads * self.head_dim]
+        value_states = qkv[..., query_pos + tp_sharded_kv_heads * self.head_dim :]
 
         query_states = query_states.view(hidden_shape).transpose(1, 2)
         key_states = key_states.view(hidden_shape).transpose(1, 2)
diff --git a/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py b/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
index 9ce98251e50e..3c3c1723a35a 100644
--- a/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
+++ b/src/transformers/models/phi4_multimodal/feature_extraction_phi4_multimodal.py
@@ -145,17 +145,17 @@ def __call__(
                 f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                 "We will take the mean of the channels to convert to mono."
             )
-            raw_speech = raw_speech.mean(-1)
+            raw_speech = raw_speech.mean(1)
 
         is_batched_sequence = isinstance(raw_speech, (list, tuple))
         if is_batched_sequence:
-            for speech in raw_speech:
+            for index, speech in enumerate(raw_speech):
                 if len(speech.shape) > 1:
                     logger.warning(
                         f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                         "We will take the mean of the channels to convert to mono."
                     )
-                    speech = speech.mean(-1)
+                    raw_speech[index] = speech.mean(0)
 
         if is_batched_torch or is_batched_sequence:
             raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech]
diff --git a/src/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py b/src/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py
index 9e6c0339098d..24f0672bc65d 100644
--- a/src/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py
+++ b/src/transformers/models/phi4_multimodal/modeling_phi4_multimodal.py
@@ -1475,7 +1475,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py b/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
index 325b27ed361c..dfef3c556d4d 100644
--- a/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
+++ b/src/transformers/models/phi4_multimodal/processing_phi4_multimodal.py
@@ -24,12 +24,14 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_phi4_multimodal_fast import Phi4MultimodalImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Phi4MultimodalProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Phi4MultimodalImageProcessorKwargs
     _defaults = {
         "audio_kwargs": {
             "device": "cpu",
diff --git a/src/transformers/models/phimoe/modeling_phimoe.py b/src/transformers/models/phimoe/modeling_phimoe.py
index 23bc944c522a..c2fcca077860 100644
--- a/src/transformers/models/phimoe/modeling_phimoe.py
+++ b/src/transformers/models/phimoe/modeling_phimoe.py
@@ -723,7 +723,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -731,7 +731,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -748,8 +750,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/pi0/configuration_pi0.py b/src/transformers/models/pi0/configuration_pi0.py
index ac4aa4dddb8c..cd1de0e26516 100644
--- a/src/transformers/models/pi0/configuration_pi0.py
+++ b/src/transformers/models/pi0/configuration_pi0.py
@@ -125,8 +125,8 @@ def __post_init__(self, **kwargs):
                 vocab_size=self.vlm_config.text_config.vocab_size,
             )
 
-        # Force bidirectional attention
-        self.dit_config.is_causal = False
+        # Force bidirectional attention for images in Paligemma
+        self.dit_config.is_causal = True
         self.dit_config.use_bidirectional_attention = True
         self.vlm_config.text_config.use_bidirectional_attention = True
         super().__post_init__(**kwargs)
diff --git a/src/transformers/models/pi0/image_processing_pi0.py b/src/transformers/models/pi0/image_processing_pi0.py
index c0cc0b6ea69a..29394eda97be 100644
--- a/src/transformers/models/pi0/image_processing_pi0.py
+++ b/src/transformers/models/pi0/image_processing_pi0.py
@@ -17,13 +17,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...image_processing_backends import TorchvisionBackend
 from ...image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD, PILImageResampling
 from ...utils import auto_docstring
+from ..siglip.image_processing_siglip import SiglipImageProcessor
 
 
 @auto_docstring
-class PI0ImageProcessor(TorchvisionBackend):
+class PI0ImageProcessor(SiglipImageProcessor):
     resample = PILImageResampling.BICUBIC
     image_mean = IMAGENET_STANDARD_MEAN
     image_std = IMAGENET_STANDARD_STD
diff --git a/src/transformers/models/pi0/modeling_pi0.py b/src/transformers/models/pi0/modeling_pi0.py
index 8fd8abe48d7b..4e227ff33190 100644
--- a/src/transformers/models/pi0/modeling_pi0.py
+++ b/src/transformers/models/pi0/modeling_pi0.py
@@ -19,7 +19,6 @@
 # limitations under the License.
 
 import math
-from collections.abc import Callable
 
 import torch
 import torch.nn.functional as F
@@ -27,7 +26,7 @@
 
 from ... import initialization as init
 from ...cache_utils import Cache
-from ...masking_utils import create_bidirectional_mask
+from ...masking_utils import create_causal_mask
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...utils import auto_docstring, can_return_tuple
@@ -101,15 +100,6 @@ def _init_weights(self, module):
             init.copy_(module.sinusoid_freq, module.compute_freqs(module.config))
 
 
-def blockwise_bidirectional_mask(block_boundaries: torch.Tensor) -> Callable:
-    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
-        q_block = torch.bucketize(q_idx, block_boundaries)
-        kv_block = torch.bucketize(kv_idx, block_boundaries)
-        return kv_block <= q_block
-
-    return inner_mask
-
-
 @auto_docstring
 class PI0Model(PI0PreTrainedModel):
     def __init__(self, config: PI0Config):
@@ -140,10 +130,7 @@ def embed_prefix(self, input_ids, pixel_values, pixel_attention_mask, attention_
         llm_input_ids[input_ids == self.config.vlm_config.image_token_id] = 0
         inputs_embeds = self.vlm.get_input_embeddings()(llm_input_ids)
         special_image_mask = (
-            (input_ids == self.config.vlm_config.image_token_id)
-            .unsqueeze(-1)
-            .expand_as(inputs_embeds)
-            .to(inputs_embeds.device)
+            (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1).to(inputs_embeds.device)
         )
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, total_image_features)
 
@@ -203,14 +190,19 @@ def forward(
         # We have three blocks: vlm-inputss, state and actions from which only 1 token is `state`
         # The mask should be bidirectional within each block and to prev blocks, but not to next blocks
         vlm_input_length = past_key_values.get_seq_length()
-        block_sizes = torch.tensor([vlm_input_length + 1, action_embeds.shape[1] - 1], device=action_embeds.device)
-        block_boundaries = torch.cumsum(block_sizes, dim=0) - 1
-        bidirectional_mask = create_bidirectional_mask(
+        block_sequence_ids = torch.cat(
+            [
+                torch.zeros(vlm_input_length + 1, device=action_embeds.device, dtype=torch.long),
+                torch.ones(action_embeds.shape[1] - 1, device=action_embeds.device, dtype=torch.long),
+            ]
+        )
+        block_sequence_ids = block_sequence_ids[None, :].repeat(action_embeds.shape[0], 1)
+        bidirectional_mask = create_causal_mask(
             config=self.config.dit_config,
             inputs_embeds=action_embeds,
             attention_mask=dit_attention_mask,
             past_key_values=past_key_values,
-            and_mask_function=blockwise_bidirectional_mask(block_boundaries),
+            block_sequence_ids=block_sequence_ids,
         )
 
         dit_output = self.dit(
@@ -357,13 +349,16 @@ def sample_actions(
             )
 
         # 2. Run VLM once and obtain prefix cache. Must infer positions here!
+        position_ids = None
         if attention_mask is not None:
             position_ids = attention_mask.cumsum(-1) - 1
         inputs_embeds = self.model.embed_prefix(input_ids, pixel_values, pixel_attention_mask)
+        token_type_ids = torch.zeros_like(inputs_embeds)[:, :, 0]
         past_key_values = self.model.vlm(
             inputs_embeds=inputs_embeds,
             attention_mask=attention_mask,
             position_ids=position_ids,
+            token_type_ids=token_type_ids,
             use_cache=True,
             return_dict=True,
         ).past_key_values
@@ -381,6 +376,7 @@ def sample_actions(
                 pixel_attention_mask=pixel_attention_mask,
                 attention_mask=attention_mask,
                 past_key_values=past_key_values,
+                **kwargs,
             )
 
             # We need to keep only the "vlm-prefix", no attention to past denoising steps!
diff --git a/src/transformers/models/pi0/modular_pi0.py b/src/transformers/models/pi0/modular_pi0.py
index f79ac3c2775a..84aa52bdcde2 100644
--- a/src/transformers/models/pi0/modular_pi0.py
+++ b/src/transformers/models/pi0/modular_pi0.py
@@ -27,7 +27,7 @@
 from ...configuration_utils import PreTrainedConfig
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, make_nested_list_of_images
-from ...masking_utils import create_bidirectional_mask
+from ...masking_utils import create_causal_mask
 from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import ProcessingKwargs, Unpack
@@ -274,8 +274,8 @@ def __post_init__(self, **kwargs):
                 vocab_size=self.vlm_config.text_config.vocab_size,
             )
 
-        # Force bidirectional attention
-        self.dit_config.is_causal = False
+        # Force bidirectional attention for images in Paligemma
+        self.dit_config.is_causal = True
         self.dit_config.use_bidirectional_attention = True
         self.vlm_config.text_config.use_bidirectional_attention = True
         super().__post_init__(**kwargs)
@@ -390,10 +390,7 @@ def embed_prefix(self, input_ids, pixel_values, pixel_attention_mask, attention_
         llm_input_ids[input_ids == self.config.vlm_config.image_token_id] = 0
         inputs_embeds = self.vlm.get_input_embeddings()(llm_input_ids)
         special_image_mask = (
-            (input_ids == self.config.vlm_config.image_token_id)
-            .unsqueeze(-1)
-            .expand_as(inputs_embeds)
-            .to(inputs_embeds.device)
+            (input_ids == self.config.vlm_config.image_token_id).unsqueeze(-1).to(inputs_embeds.device)
         )
         inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, total_image_features)
 
@@ -453,14 +450,19 @@ def forward(
         # We have three blocks: vlm-inputss, state and actions from which only 1 token is `state`
         # The mask should be bidirectional within each block and to prev blocks, but not to next blocks
         vlm_input_length = past_key_values.get_seq_length()
-        block_sizes = torch.tensor([vlm_input_length + 1, action_embeds.shape[1] - 1], device=action_embeds.device)
-        block_boundaries = torch.cumsum(block_sizes, dim=0) - 1
-        bidirectional_mask = create_bidirectional_mask(
+        block_sequence_ids = torch.cat(
+            [
+                torch.zeros(vlm_input_length + 1, device=action_embeds.device, dtype=torch.long),
+                torch.ones(action_embeds.shape[1] - 1, device=action_embeds.device, dtype=torch.long),
+            ]
+        )
+        block_sequence_ids = block_sequence_ids[None, :].repeat(action_embeds.shape[0], 1)
+        bidirectional_mask = create_causal_mask(
             config=self.config.dit_config,
             inputs_embeds=action_embeds,
             attention_mask=dit_attention_mask,
             past_key_values=past_key_values,
-            and_mask_function=blockwise_bidirectional_mask(block_boundaries),
+            block_sequence_ids=block_sequence_ids,
         )
 
         dit_output = self.dit(
@@ -607,13 +609,16 @@ def sample_actions(
             )
 
         # 2. Run VLM once and obtain prefix cache. Must infer positions here!
+        position_ids = None
         if attention_mask is not None:
             position_ids = attention_mask.cumsum(-1) - 1
         inputs_embeds = self.model.embed_prefix(input_ids, pixel_values, pixel_attention_mask)
+        token_type_ids = torch.zeros_like(inputs_embeds)[:, :, 0]
         past_key_values = self.model.vlm(
             inputs_embeds=inputs_embeds,
             attention_mask=attention_mask,
             position_ids=position_ids,
+            token_type_ids=token_type_ids,
             use_cache=True,
             return_dict=True,
         ).past_key_values
@@ -631,6 +636,7 @@ def sample_actions(
                 pixel_attention_mask=pixel_attention_mask,
                 attention_mask=attention_mask,
                 past_key_values=past_key_values,
+                **kwargs,
             )
 
             # We need to keep only the "vlm-prefix", no attention to past denoising steps!
diff --git a/src/transformers/models/pix2struct/processing_pix2struct.py b/src/transformers/models/pix2struct/processing_pix2struct.py
index 189c539daaf0..bef18d6566f8 100644
--- a/src/transformers/models/pix2struct/processing_pix2struct.py
+++ b/src/transformers/models/pix2struct/processing_pix2struct.py
@@ -19,9 +19,11 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import BatchEncoding, PreTokenizedInput, TextInput
 from ...utils import auto_docstring, logging
+from .image_processing_pix2struct import Pix2StructImageProcessorKwargs
 
 
 class Pix2StructProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Pix2StructImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/pixtral/processing_pixtral.py b/src/transformers/models/pixtral/processing_pixtral.py
index eef1dc674e7b..8d8d1d9ff374 100644
--- a/src/transformers/models/pixtral/processing_pixtral.py
+++ b/src/transformers/models/pixtral/processing_pixtral.py
@@ -32,12 +32,14 @@
 
 if is_vision_available():
     from .image_processing_pixtral import get_resize_output_image_size
+from .image_processing_pixtral import PixtralImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class PixtralProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: PixtralImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -97,7 +99,10 @@ def __init__(
         self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
         self.image_break_token_id = tokenizer.convert_tokens_to_ids(self.image_break_token)
         self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
-        self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
+
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
 
     @auto_docstring
     def __call__(
diff --git a/src/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py b/src/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py
index 7310bd55942e..9b536f4346d1 100644
--- a/src/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py
+++ b/src/transformers/models/pp_doclayout_v2/modeling_pp_doclayout_v2.py
@@ -966,12 +966,12 @@ class PPDocLayoutV2ForObjectDetectionOutput(ModelOutput):
     denoising_meta_values: dict | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the PP-DocLayoutV2 encoder-decoder model.
     """
 )
+@dataclass
 class PPDocLayoutV2ModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -1043,7 +1043,6 @@ def forward(self, x):
         return x
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the PPDocLayoutV2Decoder. This class adds two attributes to
@@ -1052,6 +1051,7 @@ def forward(self, x):
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class PPDocLayoutV2DecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
diff --git a/src/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py b/src/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py
index 5a59a6acddfd..03dc2680301c 100644
--- a/src/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py
+++ b/src/transformers/models/pp_doclayout_v3/modeling_pp_doclayout_v3.py
@@ -333,12 +333,12 @@ class PPDocLayoutV3DecoderOutput(ModelOutput):
     decoder_out_masks: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the PP-DocLayoutV3 model.
     """
 )
+@dataclass
 class PPDocLayoutV3ModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -1860,8 +1860,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class PPDocLayoutV3HybridEncoderOutput(BaseModelOutput):
     r"""
     mask_feat (`torch.FloatTensor` of shape `(batch_size, config.num_queries, 200, 200)`):
diff --git a/src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py b/src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py
index 6c5f1fd710dc..2cfdef0a7488 100644
--- a/src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py
+++ b/src/transformers/models/pp_doclayout_v3/modular_pp_doclayout_v3.py
@@ -1229,8 +1229,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class PPDocLayoutV3HybridEncoderOutput(BaseModelOutput):
     r"""
     mask_feat (`torch.FloatTensor` of shape `(batch_size, config.num_queries, 200, 200)`):
diff --git a/src/transformers/models/pp_formulanet/__init__.py b/src/transformers/models/pp_formulanet/__init__.py
new file mode 100644
index 000000000000..066f8084a4a3
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_pp_formulanet import *
+    from .image_processing_pp_formulanet import *
+    from .modeling_pp_formulanet import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/pp_formulanet/configuration_pp_formulanet.py b/src/transformers/models/pp_formulanet/configuration_pp_formulanet.py
new file mode 100644
index 000000000000..ffafc15f1206
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/configuration_pp_formulanet.py
@@ -0,0 +1,158 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_pp_formulanet.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetVisionConfig(PreTrainedConfig):
+    r"""
+    output_channels (`int`, *optional*, defaults to 256):
+        Dimensionality of the output channels in the Patch Encoder.
+    use_abs_pos (`bool`, *optional*, defaults to `True`):
+        Whether to use absolute position embedding.
+    use_rel_pos (`bool`, *optional*, defaults to `True`):
+        Whether to use relative position embedding.
+    window_size (`int`, *optional*, defaults to 14):
+        Window size for relative position.
+    global_attn_indexes (`list[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
+        The indexes of the global attention layers.
+    mlp_dim (`int`, *optional*, defaults to 3072):
+        The dimensionality of the MLP layer in the Transformer encoder.
+    """
+
+    base_config_key = "vision_config"
+    hidden_size: int = 768
+    output_channels: int = 256
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    num_channels: int = 3
+    image_size: int = 512
+    patch_size: int | list[int] | tuple[int, int] = 16
+    hidden_act: str = "gelu"
+    layer_norm_eps: float = 1e-06
+    attention_dropout: float | int = 0.0
+    initializer_range: float = 1e-10
+    qkv_bias: bool = True
+    use_abs_pos: bool = True
+    use_rel_pos: bool = True
+    window_size: int = 14
+    global_attn_indexes: list[int] | tuple[int, ...] = (2, 5, 8, 11)
+    mlp_dim: int = 3072
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetTextConfig(PreTrainedConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import PPFormulaNetTextConfig, PPFormulaNetTextModel
+
+    >>> # Initializing a PP_FORMULANET facebook/pp_formulanet-large-cc25 style configuration
+    >>> configuration = PPFormulaNetTextConfig()
+
+    >>> # Initializing a model (with random weights) from the facebook/pp_formulanet-large-cc25 style configuration
+    >>> model = PPFormulaNetTextModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "pp_formulanet"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_attention_heads": "encoder_attention_heads",
+        "hidden_size": "d_model",
+        "num_hidden_layers": "encoder_layers",
+    }
+    vocab_size: int = 50000
+    max_position_embeddings: int = 2560
+    encoder_layers: int = 12
+    encoder_attention_heads: int = 16
+    decoder_layers: int = 8
+    decoder_ffn_dim: int = 2048
+    decoder_attention_heads: int = 16
+    decoder_layerdrop: float | int = 0.0
+    use_cache: bool = True
+    is_encoder_decoder: bool = True
+    activation_function: str = "gelu"
+    d_model: int = 512
+    dropout: float | int = 0.1
+    attention_dropout: float | int = 0.0
+    activation_dropout: float | int = 0.0
+    init_std: float = 0.02
+    scale_embedding: bool = True
+    pad_token_id: int | None = 1
+    bos_token_id: int | None = 0
+    eos_token_id: int | list[int] | None = 2
+    decoder_start_token_id: int | None = 2
+    forced_eos_token_id: int | list[int] | None = 2
+    tie_word_embeddings: bool = False
+    base_config_key = "text_config"
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetConfig(PreTrainedConfig):
+    r"""
+    post_conv_in_channels (`int`, *optional*, defaults to 256):
+        Number of input channels for the post-encoder convolution layer.
+    post_conv_mid_channels (`int`, *optional*, defaults to 512):
+       Number of intermediate channels for the post-encoder convolution layer.
+    post_conv_out_channels (`int`, *optional*, defaults to 1024):
+        Number of output channels for the post-encoder convolution layer.
+    """
+
+    model_type = "pp_formulanet"
+    sub_configs = {"text_config": PPFormulaNetTextConfig, "vision_config": PPFormulaNetVisionConfig}
+
+    text_config: dict | PPFormulaNetTextConfig | None = None
+    vision_config: dict | PPFormulaNetVisionConfig | None = None
+    is_encoder_decoder: bool = True
+    post_conv_in_channels: int = 256
+    post_conv_out_channels: int = 1024
+    post_conv_mid_channels: int = 512
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.text_config, dict):
+            self.text_config = PPFormulaNetTextConfig(**self.text_config)
+        elif self.text_config is None:
+            logger.info("text_config is None. Initializing the PPFormulaNetTextConfig with default values.")
+            self.text_config = PPFormulaNetTextConfig()
+
+        if isinstance(self.vision_config, dict):
+            self.vision_config = PPFormulaNetVisionConfig(**self.vision_config)
+        elif self.vision_config is None:
+            logger.info("vision_config is None. Initializing the PPFormulaNetVisionConfig with default values.")
+            self.vision_config = PPFormulaNetVisionConfig()
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["PPFormulaNetConfig", "PPFormulaNetTextConfig", "PPFormulaNetVisionConfig"]
diff --git a/src/transformers/models/pp_formulanet/image_processing_pp_formulanet.py b/src/transformers/models/pp_formulanet/image_processing_pp_formulanet.py
new file mode 100644
index 000000000000..b3a5a5f5c6f7
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/image_processing_pp_formulanet.py
@@ -0,0 +1,298 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_pp_formulanet.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import torch
+from torchvision.transforms.v2 import functional as tvF
+
+from ...image_processing_backends import TorchvisionBackend
+from ...image_processing_utils import BatchFeature
+from ...image_transforms import get_resize_output_image_size, group_images_by_shape, reorder_images
+from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, SizeDict
+from ...processing_utils import ImagesKwargs, Unpack
+from ...utils import TensorType, auto_docstring
+from ...utils.import_utils import requires
+
+
+class PPFormulaNetImageProcessorKwargs(ImagesKwargs, total=False):
+    r"""
+    do_crop_margin (`bool`, *optional*, defaults to `self.do_crop_margin`):
+        Whether to crop the image margins.
+    do_thumbnail (`bool`, *optional*, defaults to `self.do_thumbnail`):
+        Whether to resize the image using thumbnail method.
+    do_align_long_axis (`bool`, *optional*, defaults to `self.do_align_long_axis`):
+        Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
+    """
+
+    do_crop_margin: bool
+    do_thumbnail: bool
+    do_align_long_axis: bool
+
+
+@auto_docstring
+@requires(backends=("torch",))
+class PPFormulaNetImageProcessor(TorchvisionBackend):
+    valid_kwargs = PPFormulaNetImageProcessorKwargs
+    resample = PILImageResampling.BILINEAR
+    image_mean = [0.7931, 0.7931, 0.7931]
+    image_std = [0.1738, 0.1738, 0.1738]
+    size = {"height": 768, "width": 768}
+    do_resize = True
+    do_normalize = True
+    do_thumbnail = True
+    do_align_long_axis = False
+    do_pad = True
+    do_rescale = True
+    do_crop_margin = True
+
+    def __init__(self, **kwargs: Unpack[PPFormulaNetImageProcessorKwargs]):
+        super().__init__(**kwargs)
+
+    @auto_docstring
+    def preprocess(self, images: ImageInput, **kwargs: Unpack[PPFormulaNetImageProcessorKwargs]) -> BatchFeature:
+        return super().preprocess(images, **kwargs)
+
+    def python_find_non_zero(
+        self,
+        image: "torch.Tensor",
+    ):
+        """This is a reimplementation of a findNonZero function equivalent to cv2."""
+
+        non_zero_indices = torch.nonzero(image, as_tuple=False)
+        idxvec = non_zero_indices[:, [2, 1]]
+        idxvec = idxvec.reshape(-1, 1, 2)
+        return idxvec
+
+    def python_bounding_rect(self, coordinates):
+        """This is a reimplementation of a BoundingRect function equivalent to cv2."""
+
+        min_values = torch.amin(coordinates, axis=(0, 1)).to(torch.int)
+        max_values = torch.amax(coordinates, axis=(0, 1)).to(torch.int)
+
+        x_min, y_min = min_values[0], min_values[1]
+        width = max_values[0] - x_min + 1
+        height = max_values[1] - y_min + 1
+        return x_min, y_min, width, height
+
+    def crop_margin(
+        self,
+        image: "torch.Tensor",
+        gray_threshold: int = 200,
+    ) -> "torch.Tensor":
+        """
+        Crops the margin of the image. Gray pixels are considered margin (i.e., pixels with a value below the
+        threshold).
+
+        Args:
+            image (`torch.Tensor`):
+                The image to be cropped.
+            gray_threshold (`int`, *optional*, defaults to `200`)
+                Value below which pixels are considered to be gray.
+        """
+        data = tvF.rgb_to_grayscale(image, num_output_channels=1)
+
+        max_val = torch.max(data)
+        min_val = torch.min(data)
+
+        if max_val == min_val:
+            return image
+        data = (data - min_val) / (max_val - min_val) * 255
+        gray = data < gray_threshold
+        coords = self.python_find_non_zero(gray)
+        x_min, y_min, width, height = self.python_bounding_rect(coords)
+        image = image[:, y_min : y_min + height, x_min : x_min + width]
+
+        return image
+
+    def align_long_axis(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+    ) -> "torch.Tensor":
+        """
+        Align the long axis of the image to the longest axis of the specified size.
+
+        Args:
+            image (`torch.Tensor`):
+                The image to be aligned.
+            size (`SizeDict`):
+                The size to align the long axis to.
+        Returns:
+            `torch.Tensor`: The aligned image.
+        """
+        input_height, input_width = image.shape[-2:]
+        output_height, output_width = size.height, size.width
+
+        if (output_width < output_height and input_width > input_height) or (
+            output_width > output_height and input_width < input_height
+        ):
+            image = torch.rot90(image, 3, dims=[1, 2])
+
+        return image
+
+    def thumbnail(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+    ) -> "torch.Tensor":
+        """
+        Resize the image to make a thumbnail. The image is resized so that no dimension is larger than any
+        corresponding dimension of the specified size.
+
+        Args:
+            image (`torch.tensor`):
+                The image to be resized.
+            size (`SizeDict`):
+                The size to resize the image to.
+        """
+
+        input_height, input_width = image.shape[-2:]
+        output_height, output_width = size.height, size.width
+
+        # We always resize to the smallest of either the input or output size.
+        height = min(input_height, output_height)
+        width = min(input_width, output_width)
+
+        if height == input_height and width == input_width:
+            return image
+
+        if input_height > input_width:
+            width = int(input_width * height / input_height)
+        elif input_width > input_height:
+            height = int(input_height * width / input_width)
+
+        new_size = (height, width)
+
+        return tvF.resize(image, new_size, interpolation=tvF.InterpolationMode.BICUBIC)
+
+    def pad_images(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+    ) -> "torch.Tensor":
+        """
+        Pads a batch of images to the specified size at the top, bottom, left and right.
+
+        Args:
+            image (`torch.tensor`):
+                The image to be padded.
+            size (`SizeDict`):
+                The size to pad the image to.
+        """
+        input_height, input_width = image.shape[-2:]
+        output_height, output_width = size.height, size.width
+
+        delta_width = output_width - input_width
+        delta_height = output_height - input_height
+
+        pad_top = delta_height // 2
+        pad_left = delta_width // 2
+
+        pad_bottom = delta_height - pad_top
+        pad_right = delta_width - pad_left
+
+        padding = (pad_left, pad_top, pad_right, pad_bottom)
+        return tvF.pad(image, padding)
+
+    def resize(
+        self,
+        image: "torch.Tensor",
+        size: SizeDict,
+        resample: "PILImageResampling | tvF.InterpolationMode | int | None" = None,
+        antialias: bool = True,
+        **kwargs,
+    ) -> "torch.Tensor":
+        """
+        Resize an image to `(size.height, size.width)`.
+
+        Args:
+            image (`torch.Tensor`):
+                Image to resize.
+            size (`SizeDict`):
+                Size of the output image.
+            resample (`PILImageResampling | tvF.InterpolationMode | int`, *optional*):
+                Resampling filter to use when resizing the image.
+        Returns:
+            `torch.Tensor`: The resized image.
+        """
+        shortest_edge = min(size.height, size.width)
+
+        new_size = get_resize_output_image_size(
+            image, size=shortest_edge, default_to_square=False, input_data_format=ChannelDimension.FIRST
+        )
+        return super().resize(
+            image, SizeDict(height=new_size[0], width=new_size[1]), resample=resample, antialias=antialias, **kwargs
+        )
+
+    def _preprocess(
+        self,
+        images: list["torch.Tensor"],
+        do_resize: bool,
+        size: SizeDict,
+        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
+        do_rescale: bool,
+        rescale_factor: float,
+        do_normalize: bool,
+        image_mean: float | list[float] | None,
+        image_std: float | list[float] | None,
+        do_pad: bool | None,
+        disable_grouping: bool | None,
+        return_tensors: str | TensorType | None,
+        do_align_long_axis: bool = False,
+        do_thumbnail: bool = True,
+        do_crop_margin: bool = True,
+        **kwargs,
+    ) -> BatchFeature:
+        # Crop images
+        if do_crop_margin:
+            images = [self.crop_margin(image) for image in images]
+
+        # Group images by size for batched resizing
+        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
+        resized_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            if do_align_long_axis:
+                stacked_images = self.align_long_axis(image=stacked_images, size=size)
+            if do_resize:
+                stacked_images = self.resize(image=stacked_images, size=size, resample=resample)
+            if do_thumbnail:
+                stacked_images = self.thumbnail(image=stacked_images, size=size)
+            if do_pad:
+                stacked_images = self.pad_images(image=stacked_images, size=size)
+            resized_images_grouped[shape] = stacked_images
+        resized_images = reorder_images(resized_images_grouped, grouped_images_index)
+
+        # Group images by size for further processing
+        # Needed in case do_resize is False, or resize returns images with different sizes
+        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
+        processed_images_grouped = {}
+        for shape, stacked_images in grouped_images.items():
+            # Fused rescale and normalize
+            stacked_images = self.rescale_and_normalize(
+                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
+            )
+            processed_images_grouped[shape] = stacked_images
+
+        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
+
+        return BatchFeature(data={"pixel_values": processed_images}, tensor_type=return_tensors)
+
+
+__all__ = ["PPFormulaNetImageProcessor"]
diff --git a/src/transformers/models/pp_formulanet/modeling_pp_formulanet.py b/src/transformers/models/pp_formulanet/modeling_pp_formulanet.py
new file mode 100644
index 000000000000..4bb816222b45
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/modeling_pp_formulanet.py
@@ -0,0 +1,1166 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_pp_formulanet.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+import math
+from collections.abc import Callable
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_bidirectional_mask, create_causal_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import (
+    BaseModelOutputWithPastAndCrossAttentions,
+    BaseModelOutputWithPooling,
+    Seq2SeqLMOutput,
+    Seq2SeqModelOutput,
+)
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import OutputRecorder, capture_outputs
+from .configuration_pp_formulanet import PPFormulaNetConfig, PPFormulaNetVisionConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+class PPFormulaNetPreTrainedModel(PreTrainedModel):
+    config: PPFormulaNetConfig
+    base_model_prefix = "model"
+    main_input_name = "pixel_values"
+    input_modalities = ("image",)
+    supports_gradient_checkpointing = True
+    _keep_in_fp32_modules_strict = []
+    _supports_sdpa = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        super()._init_weights(module)
+
+        # Initialize positional embeddings to zero (PPFormulaNetVisionModel holds pos_embed)
+        if isinstance(module, PPFormulaNetVisionModel):
+            if module.pos_embed is not None:
+                init.constant_(module.pos_embed, 0.0)
+
+        # Initialize relative positional embeddings to zero (PPFormulaNetVisionAttention holds rel_pos_h/w)
+        if isinstance(module, PPFormulaNetVisionAttention):
+            if module.use_rel_pos:
+                init.constant_(module.rel_pos_h, 0.0)
+                init.constant_(module.rel_pos_w, 0.0)
+
+
+# overrider for PPFormulaNetModel's encoder output
+@dataclass
+class PPFormulaNetVisionEncoderOutput(BaseModelOutputWithPooling):
+    pass
+
+
+class PPFormulaNetVisionAttention(nn.Module):
+    """Multi-head Attention block with relative position embeddings."""
+
+    def __init__(self, config, window_size):
+        super().__init__()
+        input_size = (
+            (config.image_size // config.patch_size, config.image_size // config.patch_size)
+            if window_size == 0
+            else (window_size, window_size)
+        )
+
+        self.num_attention_heads = config.num_attention_heads
+        head_dim = config.hidden_size // config.num_attention_heads
+        self.scale = head_dim**-0.5
+        self.dropout = config.attention_dropout
+
+        self.qkv = nn.Linear(config.hidden_size, config.hidden_size * 3, bias=config.qkv_bias)
+        self.proj = nn.Linear(config.hidden_size, config.hidden_size)
+
+        self.use_rel_pos = config.use_rel_pos
+        if self.use_rel_pos:
+            if input_size is None:
+                raise ValueError("Input size must be provided if using relative positional encoding.")
+
+            # initialize relative positional embeddings
+            self.rel_pos_h = nn.Parameter(torch.zeros(2 * input_size[0] - 1, head_dim))
+            self.rel_pos_w = nn.Parameter(torch.zeros(2 * input_size[1] - 1, head_dim))
+
+    def get_rel_pos(self, q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor:
+        """
+        Get relative positional embeddings according to the relative positions of
+            query and key sizes.
+
+        Args:
+            q_size (int):
+                size of the query.
+            k_size (int):
+                size of key k.
+            rel_pos (`torch.Tensor`):
+                relative position embeddings (L, channel).
+
+        Returns:
+            Extracted positional embeddings according to relative positions.
+        """
+        max_rel_dist = int(2 * max(q_size, k_size) - 1)
+        # Interpolate rel pos.
+        rel_pos_resized = F.interpolate(
+            rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
+            size=max_rel_dist,
+            mode="linear",
+        )
+        rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
+
+        # Scale the coords with short length if shapes for q and k are different.
+        q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
+        k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
+        relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
+
+        return rel_pos_resized[relative_coords.long()]
+
+    def get_decomposed_rel_pos(
+        self,
+        query: torch.Tensor,
+        rel_pos_h: torch.Tensor,
+        rel_pos_w: torch.Tensor,
+        q_size: tuple[int, int],
+        k_size: tuple[int, int],
+    ) -> torch.Tensor:
+        """
+        Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
+        https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py
+
+        Args:
+            query (`torch.Tensor`):
+                query q in the attention layer with shape (batch_size, query_height * query_width, channel).
+            rel_pos_h (`torch.Tensor`):
+                relative position embeddings (Lh, channel) for height axis.
+            rel_pos_w (`torch.Tensor`):
+                relative position embeddings (Lw, channel) for width axis.
+            q_size (tuple):
+                spatial sequence size of query q with (query_height, query_width).
+            k_size (tuple):
+                spatial sequence size of key k with (key_height, key_width).
+
+        Returns:
+            decomposed_rel_pos (`torch.Tensor`):
+                decomposed relative position embeddings.
+        """
+        query_height, query_width = q_size
+        key_height, key_width = k_size
+        relative_position_height = self.get_rel_pos(query_height, key_height, rel_pos_h)
+        relative_position_width = self.get_rel_pos(query_width, key_width, rel_pos_w)
+
+        batch_size, _, dim = query.shape
+        reshaped_query = query.reshape(batch_size, query_height, query_width, dim)
+        rel_h = torch.einsum("bhwc,hkc->bhwk", reshaped_query, relative_position_height)
+        rel_w = torch.einsum("bhwc,wkc->bhwk", reshaped_query, relative_position_width)
+
+        decomposed_rel_pos = rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
+
+        return decomposed_rel_pos
+
+    def forward(self, hidden_states: torch.Tensor, output_attentions=None) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_size, height, width, _ = hidden_states.shape
+        # qkv with shape (3, batch_size, nHead, height * width, channel)
+        qkv = (
+            self.qkv(hidden_states)
+            .reshape(batch_size, height * width, 3, self.num_attention_heads, -1)
+            .permute(2, 0, 3, 1, 4)
+        )
+        # q, k, v with shape (batch_size * nHead, height * width, channel)
+        query, key, value = qkv.reshape(3, batch_size * self.num_attention_heads, height * width, -1).unbind(0)
+
+        attn_weights = (query * self.scale) @ key.transpose(-2, -1)
+
+        if self.use_rel_pos:
+            decomposed_rel_pos = self.get_decomposed_rel_pos(
+                query, self.rel_pos_h, self.rel_pos_w, (height, width), (height, width)
+            )
+            decomposed_rel_pos = decomposed_rel_pos.reshape_as(attn_weights)
+            attn_weights = attn_weights + decomposed_rel_pos
+
+        attn_weights = torch.nn.functional.softmax(attn_weights, dtype=torch.float32, dim=-1).to(query.dtype)
+
+        attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
+
+        attn_output = (attn_probs @ value).reshape(batch_size, self.num_attention_heads, height, width, -1)
+        attn_output = attn_output.permute(0, 2, 3, 1, 4).reshape(batch_size, height, width, -1)
+
+        attn_output = self.proj(attn_output)
+        return attn_output, attn_weights
+
+
+class PPFormulaNetMultiModalProjector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            config.post_conv_in_channels, config.post_conv_mid_channels, kernel_size=3, stride=2, padding=1, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            config.post_conv_mid_channels,
+            config.post_conv_out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+        self.linear_1 = nn.Linear(config.post_conv_out_channels, config.post_conv_out_channels)
+        self.linear_2 = nn.Linear(config.post_conv_out_channels, config.text_config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]):
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class PPFormulaNetMLPBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.lin1 = nn.Linear(config.hidden_size, config.mlp_dim)
+        self.lin2 = nn.Linear(config.mlp_dim, config.hidden_size)
+        self.act = ACT2FN[config.hidden_act]
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.lin1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.lin2(hidden_states)
+        return hidden_states
+
+
+class PPFormulaNetVisionLayer(GradientCheckpointingLayer):
+    def __init__(self, config, window_size):
+        super().__init__()
+        self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attn = PPFormulaNetVisionAttention(config, window_size)
+        self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.mlp = PPFormulaNetMLPBlock(config)
+        self.window_size = window_size
+
+    def window_partition(self, hidden_states: torch.Tensor, window_size: int) -> tuple[torch.Tensor, tuple[int, int]]:
+        """
+        Args:
+        Partition into non-overlapping windows with padding if needed.
+            hidden_states (tensor): input tokens with [batch_size, height, width, channel]. window_size (int): window
+            size.
+
+        Returns:
+            windows: windows after partition with [batch_size * num_windows, window_size, window_size, channel].
+            (pad_height, pad_width): padded height and width before partition
+        """
+        batch_size, height, width, channel = hidden_states.shape
+
+        pad_h = (window_size - height % window_size) % window_size
+        pad_w = (window_size - width % window_size) % window_size
+        hidden_states = F.pad(hidden_states, (0, 0, 0, pad_w, 0, pad_h))
+        pad_height, pad_width = height + pad_h, width + pad_w
+
+        hidden_states = hidden_states.reshape(
+            batch_size, pad_height // window_size, window_size, pad_width // window_size, window_size, channel
+        )
+        windows = hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(-1, window_size, window_size, channel)
+        return windows, (pad_height, pad_width)
+
+    def window_unpartition(
+        self, windows: torch.Tensor, window_size: int, padding_shape: tuple[int, int], original_shape: tuple[int, int]
+    ) -> torch.Tensor:
+        """
+        Args:
+        Window unpartition into original sequences and removing padding.
+            hidden_states (tensor):
+                input tokens with [batch_size * num_windows, window_size, window_size, channel].
+            window_size (int):
+                window size.
+            padding_shape (Tuple):
+                padded height and width (pad_height, pad_width).
+            original_shape (Tuple): original height and width (height, width) before padding.
+
+        Returns:
+            hidden_states: unpartitioned sequences with [batch_size, height, width, channel].
+        """
+        pad_height, pad_width = padding_shape
+        height, width = original_shape
+        batch_size = windows.shape[0] // (pad_height * pad_width // window_size // window_size)
+        hidden_states = windows.reshape(
+            batch_size, pad_height // window_size, pad_width // window_size, window_size, window_size, -1
+        )
+        hidden_states = (
+            hidden_states.permute(0, 1, 3, 2, 4, 5).contiguous().reshape(batch_size, pad_height, pad_width, -1)
+        )
+
+        hidden_states = hidden_states[:, :height, :width, :].contiguous()
+        return hidden_states
+
+    def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]:
+        residual = hidden_states
+        hidden_states = self.layer_norm1(hidden_states)
+        # Window partition
+        if self.window_size > 0:
+            height, width = hidden_states.shape[1], hidden_states.shape[2]
+            hidden_states, padding_shape = self.window_partition(hidden_states, self.window_size)
+
+        hidden_states, attn_weights = self.attn(
+            hidden_states=hidden_states,
+        )
+        # Reverse window partition
+        if self.window_size > 0:
+            hidden_states = self.window_unpartition(hidden_states, self.window_size, padding_shape, (height, width))
+
+        hidden_states = residual + hidden_states
+        layernorm_output = self.layer_norm2(hidden_states)
+        hidden_states = hidden_states + self.mlp(layernorm_output)
+        return hidden_states
+
+
+class PPFormulaNetPatchEmbeddings(nn.Module):
+    """
+    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
+    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
+    Transformer.
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        image_size, patch_size = config.image_size, config.patch_size
+        num_channels, hidden_size = config.num_channels, config.hidden_size
+        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
+        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.num_patches = num_patches
+
+        self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
+
+    def forward(self, pixel_values):
+        batch_size, num_channels, height, width = pixel_values.shape
+        if num_channels != self.num_channels:
+            raise ValueError(
+                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
+            )
+        if height != self.image_size[0] or width != self.image_size[1]:
+            raise ValueError(
+                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+            )
+        embeddings = self.projection(pixel_values).permute(0, 2, 3, 1)
+        return embeddings
+
+
+class PPFormulaNetLayerNorm(nn.LayerNorm):
+    r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
+    The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
+    width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
+    """
+
+    def __init__(self, normalized_shape, *, eps=1e-6, data_format="channels_last", **kwargs):
+        super().__init__(normalized_shape, eps=eps, **kwargs)
+        if data_format not in ["channels_last", "channels_first"]:
+            raise NotImplementedError(f"Unsupported data format: {data_format}")
+        self.data_format = data_format
+
+    def forward(self, features: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            features: Tensor of shape (batch_size, channels, height, width) OR (batch_size, height, width, channels)
+        """
+        if self.data_format == "channels_first":
+            features = features.permute(0, 2, 3, 1)
+            features = super().forward(features)
+            features = features.permute(0, 3, 1, 2)
+        else:
+            features = super().forward(features)
+        return features
+
+
+class PPFormulaNetVisionNeck(nn.Module):
+    def __init__(self, config: PPFormulaNetVisionConfig):
+        super().__init__()
+        self.config = config
+
+        self.conv1 = nn.Conv2d(config.hidden_size, config.output_channels, kernel_size=1, bias=False)
+        self.layer_norm1 = PPFormulaNetLayerNorm(config.output_channels, data_format="channels_first")
+        self.conv2 = nn.Conv2d(config.output_channels, config.output_channels, kernel_size=3, padding=1, bias=False)
+        self.layer_norm2 = PPFormulaNetLayerNorm(config.output_channels, data_format="channels_first")
+
+    def forward(self, hidden_states):
+        hidden_states = hidden_states.permute(0, 3, 1, 2)
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.layer_norm1(hidden_states)
+
+        hidden_states = self.conv2(hidden_states)
+        hidden_states = self.layer_norm2(hidden_states)
+        return hidden_states
+
+
+class PPFormulaNetVisionModel(PPFormulaNetPreTrainedModel):
+    _can_record_outputs = {"hidden_states": PPFormulaNetVisionLayer, "attentions": PPFormulaNetVisionAttention}
+    input_modalities = ("image",)
+
+    def __init__(self, config: PPFormulaNetVisionConfig):
+        super().__init__(config)
+        self.config = config
+        self.image_size = config.image_size
+        self.patch_embed = PPFormulaNetPatchEmbeddings(config)
+
+        self.pos_embed = None
+        if config.use_abs_pos:
+            # Initialize absolute positional embedding with pretrain image size.
+            self.pos_embed = nn.Parameter(
+                torch.zeros(
+                    1,
+                    config.image_size // config.patch_size,
+                    config.image_size // config.patch_size,
+                    config.hidden_size,
+                )
+            )
+
+        self.layers = nn.ModuleList()
+        for i in range(config.num_hidden_layers):
+            layer = PPFormulaNetVisionLayer(
+                config,
+                window_size=config.window_size if i not in config.global_attn_indexes else 0,
+            )
+            self.layers.append(layer)
+
+        self.neck = PPFormulaNetVisionNeck(config)
+
+        self.gradient_checkpointing = False
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.patch_embed
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    def forward(
+        self, pixel_values: torch.FloatTensor | None = None, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | PPFormulaNetVisionEncoderOutput:
+        if pixel_values is None:
+            raise ValueError("You have to specify pixel_values")
+
+        hidden_states = self.patch_embed(pixel_values)
+        if self.pos_embed is not None:
+            hidden_states = hidden_states + self.pos_embed
+        for layer_module in self.layers:
+            hidden_states = layer_module(hidden_states)
+        hidden_states = self.neck(hidden_states)
+        return PPFormulaNetVisionEncoderOutput(
+            last_hidden_state=hidden_states,
+        )
+
+
+class PPFormulaNetLearnedPositionalEmbedding(nn.Embedding):
+    """
+    This module learns positional embeddings up to a fixed maximum size.
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int):
+        # PPFormulaNet is set up so that if padding_idx is specified then offset the embedding ids by 2
+        # and adjust num_embeddings appropriately. Other models don't have this hack
+        self.offset = 2
+        super().__init__(num_embeddings + self.offset, embedding_dim)
+
+    def forward(
+        self, input_ids: torch.Tensor, past_key_values_length: int = 0, position_ids: torch.Tensor | None = None
+    ):
+        """`input_ids' shape is expected to be [bsz x seqlen]."""
+
+        if position_ids is None:
+            bsz, seq_len = input_ids.shape[:2]
+            position_ids = torch.arange(
+                past_key_values_length, past_key_values_length + seq_len, dtype=torch.long, device=self.weight.device
+            ).expand(bsz, -1)
+        else:
+            position_ids = position_ids.unsqueeze(0)
+
+        return super().forward(position_ids + self.offset)
+
+
+class PPFormulaNetScaledWordEmbedding(nn.Embedding):
+    """
+    This module overrides nn.Embeddings' forward by multiplying with embeddings scale.
+    """
+
+    def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float | None = 1.0):
+        super().__init__(num_embeddings, embedding_dim, padding_idx)
+        self.embed_scale = embed_scale
+
+    def forward(self, input_ids: torch.Tensor):
+        return super().forward(input_ids) * self.embed_scale
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float | None = None,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    if scaling is None:
+        scaling = query.size(-1) ** -0.5
+
+    # Take the dot product between "query" and "key" to get the raw attention scores.
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class PPFormulaNetAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        is_causal: bool = False,
+        config: PPFormulaNetConfig | None = None,
+        layer_idx: int | None = None,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        self.config = config
+
+        if (self.head_dim * num_heads) != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+                f" and `num_heads`: {num_heads})."
+            )
+        self.scaling = self.head_dim**-0.5
+        self.is_decoder = is_decoder
+        self.is_causal = is_causal
+        self.layer_idx = layer_idx
+        if layer_idx is None and self.is_decoder:
+            logger.warning_once(
+                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+                "will lead to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        attention_mask: torch.Tensor | None = None,
+        # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+        # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+
+        # determine input shapes
+        input_shape = hidden_states.shape[:-1]
+
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        # get query proj
+        query_states = self.q_proj(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        is_updated = False
+        if past_key_values is not None:
+            if isinstance(past_key_values, EncoderDecoderCache):
+                is_updated = past_key_values.is_updated.get(self.layer_idx)
+                if is_cross_attention:
+                    # after the first generated id, we can subsequently re-use all key/value_states from cache
+                    curr_past_key_values = past_key_values.cross_attention_cache
+                else:
+                    curr_past_key_values = past_key_values.self_attention_cache
+            else:
+                curr_past_key_values = past_key_values
+
+        current_states = key_value_states if is_cross_attention else hidden_states
+        if is_cross_attention and past_key_values is not None and is_updated:
+            # reuse k,v, cross_attentions
+            key_states = curr_past_key_values.layers[self.layer_idx].keys
+            value_states = curr_past_key_values.layers[self.layer_idx].values
+        else:
+            key_states = self.k_proj(current_states)
+            value_states = self.v_proj(current_states)
+            kv_shape = (*current_states.shape[:-1], -1, self.head_dim)
+            key_states = key_states.view(kv_shape).transpose(1, 2)
+            value_states = value_states.view(kv_shape).transpose(1, 2)
+
+            if past_key_values is not None:
+                key_states, value_states = curr_past_key_values.update(key_states, value_states, self.layer_idx)
+                # set flag that curr layer for cross-attn is already updated so we can re-use in subsequent calls
+                if is_cross_attention and isinstance(past_key_values, EncoderDecoderCache):
+                    past_key_values.is_updated[self.layer_idx] = True
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class PPFormulaNetDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: PPFormulaNetConfig, layer_idx: int | None = None):
+        super().__init__()
+        self.embed_dim = config.d_model
+
+        self.self_attn = PPFormulaNetAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.decoder_attention_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            is_causal=True,
+            config=config,
+            layer_idx=layer_idx,
+        )
+        self.dropout = config.dropout
+        self.activation_fn = ACT2FN[config.activation_function]
+        self.activation_dropout = config.activation_dropout
+
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.encoder_attn = PPFormulaNetAttention(
+            self.embed_dim,
+            config.decoder_attention_heads,
+            dropout=config.attention_dropout,
+            is_decoder=True,
+            config=config,
+            layer_idx=layer_idx,
+        )
+        self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim)
+        self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        encoder_hidden_states: torch.Tensor | None = None,
+        encoder_attention_mask: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        use_cache: bool | None = True,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            encoder_hidden_states (`torch.FloatTensor`):
+                cross attention input to the layer of shape `(batch, seq_len, embed_dim)`
+            encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+            past_key_values (`Cache`): cached past key and value projection states
+        """
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            past_key_values=past_key_values,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+        hidden_states = residual + hidden_states
+
+        # Cross-Attention Block
+        if encoder_hidden_states is not None:
+            residual = hidden_states
+            hidden_states = self.encoder_attn_layer_norm(hidden_states)
+
+            hidden_states, _ = self.encoder_attn(
+                hidden_states=hidden_states,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+                past_key_values=past_key_values,
+                **kwargs,
+            )
+            hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+            hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.activation_fn(self.fc1(hidden_states))
+        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+        hidden_states = residual + hidden_states
+
+        return hidden_states
+
+
+@auto_docstring
+class PPFormulaNetTextModel(PPFormulaNetPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`PPFormulaNetTextModelLayer`]
+
+    Args:
+        config: PPFormulaNetConfig
+        embed_tokens (nn.Embedding): output embedding
+    """
+
+    _can_record_outputs = {
+        "hidden_states": PPFormulaNetDecoderLayer,
+        "attentions": OutputRecorder(PPFormulaNetAttention, index=1, layer_name="self_attn"),
+        "cross_attentions": OutputRecorder(PPFormulaNetAttention, index=1, layer_name="encoder_attn"),
+    }
+
+    def __init__(self, config: PPFormulaNetConfig):
+        super().__init__(config)
+        self.dropout = config.dropout
+        self.layerdrop = config.decoder_layerdrop
+        self.padding_idx = config.pad_token_id
+        self.max_target_positions = config.max_position_embeddings
+        embed_scale = math.sqrt(config.d_model) if config.scale_embedding else 1.0
+
+        self.embed_tokens = PPFormulaNetScaledWordEmbedding(
+            config.vocab_size, config.d_model, self.padding_idx, embed_scale=embed_scale
+        )
+
+        self.embed_positions = PPFormulaNetLearnedPositionalEmbedding(
+            config.max_position_embeddings,
+            config.d_model,
+        )
+        self.layers = nn.ModuleList(
+            [PPFormulaNetDecoderLayer(config, layer_idx=i) for i in range(config.decoder_layers)]
+        )
+        self.config = config
+
+        self.layernorm_embedding = nn.LayerNorm(config.d_model)
+        self.layer_norm = nn.LayerNorm(config.d_model)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        encoder_hidden_states: torch.FloatTensor | None = None,
+        encoder_attention_mask: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPastAndCrossAttentions:
+        r"""
+        Args:
+            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
+                provide it.
+
+                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+                [`PreTrainedTokenizer.__call__`] for details.
+
+                [What are input IDs?](../glossary#input-ids)
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*):
+                Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
+                of the decoder.
+            encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*):
+                Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. Mask values
+                selected in `[0, 1]`:
+
+                - 1 for tokens that are **not masked**,
+                - 0 for tokens that are **masked**.
+
+                [What are attention masks?](../glossary#attention-mask)
+            past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+                It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
+
+                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
+                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
+                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
+                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
+            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
+                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
+                than the model's internal embedding lookup matrix.
+        """
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        # initialize `past_key_values`
+        if use_cache and past_key_values is None:
+            past_key_values = (
+                EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config))
+                if encoder_hidden_states is not None or self.config.is_encoder_decoder
+                else DynamicCache(config=self.config)
+            )
+
+        batch_size, seq_length = inputs_embeds.size()[:-1]
+        past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
+        position_ids = torch.arange(seq_length, device=inputs_embeds.device) + past_key_values_length
+
+        if attention_mask is None and not is_torchdynamo_compiling():
+            # required mask seq length can be calculated via length of past cache
+            mask_seq_length = past_key_values_length + seq_length
+            attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
+
+        self_attn_cache = (
+            past_key_values.self_attention_cache
+            if isinstance(past_key_values, EncoderDecoderCache)
+            else past_key_values
+        )
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            past_key_values=self_attn_cache,
+        )
+        encoder_attention_mask = create_bidirectional_mask(
+            config=self.config,
+            inputs_embeds=inputs_embeds,
+            attention_mask=encoder_attention_mask,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+
+        # embed positions
+        position_ids = self.embed_positions(input, past_key_values_length, position_ids=position_ids)
+
+        hidden_states = inputs_embeds + position_ids.to(inputs_embeds.device)
+        hidden_states = self.layernorm_embedding(hidden_states)
+
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+
+        for idx, decoder_layer in enumerate(self.layers):
+            # add LayerDrop (see https://huggingface.co/papers/1909.11556 for description)
+            if self.training:
+                dropout_probability = torch.rand([])
+                if dropout_probability < self.layerdrop:
+                    continue
+
+            hidden_states = decoder_layer(
+                hidden_states,
+                causal_mask,
+                encoder_hidden_states,  # as a positional argument for gradient checkpointing
+                encoder_attention_mask=encoder_attention_mask,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                **kwargs,
+            )
+
+        hidden_states = self.layer_norm(hidden_states)
+
+        return BaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int):
+    """
+    Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that PPFormulaNet does not
+    have a single `decoder_start_token_id` in contrast to other Bart-like models.
+    """
+    prev_output_tokens = input_ids.clone()
+
+    if pad_token_id is None:
+        raise ValueError("self.model.config.pad_token_id has to be defined.")
+    # replace possible -100 values in labels by `pad_token_id`
+    prev_output_tokens.masked_fill_(prev_output_tokens == -100, pad_token_id)
+
+    index_of_eos = (prev_output_tokens.ne(pad_token_id).sum(dim=1) - 1).unsqueeze(-1)
+    decoder_start_tokens = prev_output_tokens.gather(1, index_of_eos).squeeze()
+    prev_output_tokens[:, 1:] = prev_output_tokens[:, :-1].clone()
+    prev_output_tokens[:, 0] = decoder_start_tokens
+
+    return prev_output_tokens
+
+
+class PPFormulaNetModel(PPFormulaNetPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.decoder = PPFormulaNetTextModel(config.text_config)
+        self.encoder = PPFormulaNetVisionModel(config=config.vision_config)
+        self.multi_modal_projector = PPFormulaNetMultiModalProjector(config)
+
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
+    )
+    def get_image_features(
+        self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+            The tensors corresponding to the input images.
+        """
+        image_outputs = self.encoder(pixel_values, **kwargs)
+        image_outputs.pooler_output = self.multi_modal_projector(image_outputs.last_hidden_state)
+
+        return image_outputs
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        encoder_outputs: list[torch.FloatTensor] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs,
+    ) -> tuple | Seq2SeqModelOutput:
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            decoder_input_ids = shift_tokens_right(input_ids, self.config.text_config.pad_token_id)
+
+        if (encoder_outputs is None) ^ (pixel_values is not None):
+            raise ValueError("You must specify exactly one of encoder_outputs or pixel_values")
+
+        if encoder_outputs is None:
+            encoder_outputs = self.get_image_features(pixel_values, **kwargs)
+        elif encoder_outputs.pooler_output is None:
+            encoder_outputs.pooler_output = self.multi_modal_projector(encoder_outputs.last_hidden_state)
+
+        image_features = encoder_outputs.pooler_output.to(self.decoder.device, self.decoder.dtype)
+
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=image_features,
+            past_key_values=past_key_values,
+            inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+
+@auto_docstring
+class PPFormulaNetForConditionalGeneration(PPFormulaNetPreTrainedModel, GenerationMixin):
+    def __init__(self, config: PPFormulaNetConfig):
+        super().__init__(config)
+        self.model = PPFormulaNetModel(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    @auto_docstring
+    def get_image_features(
+        self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        return self.model.get_image_features(pixel_values=pixel_values, **kwargs)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        encoder_outputs: list[torch.FloatTensor] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Seq2SeqLMOutput:
+        r"""
+        Example:
+
+        ```python
+        >>> from io import BytesIO
+
+        >>> import httpx
+        >>> from PIL import Image
+        >>> from transformers import AutoProcessor, PPFormulaNetForConditionalGeneration
+
+        >>> model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors"
+        >>> model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path, device_map="auto")
+        >>> processor = AutoProcessor.from_pretrained(model_path)
+
+        >>> image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
+        >>> image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB")
+        >>> inputs = processor(images=image, return_tensors="pt").to(model.device)
+        >>> outputs = model(**inputs)
+        >>> result = processor.post_process(outputs)
+        >>> print(result)
+        ['\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,']
+        ```"""
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            encoder_outputs=encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            # Pixel values are used only in the first iteration if available
+            # In subsequent iterations, they are already merged with text and cached
+            # NOTE: first iteration doesn't have to be prefill, it can be the first
+            # iteration with a question and cached system prompt (continue generate from cache)
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+    # override this function to compatible with `_prepare_encoder_decoder_kwargs_for_generation`
+    def get_encoder(self):
+        return self.model.get_encoder()
+
+
+__all__ = [
+    "PPFormulaNetModel",
+    "PPFormulaNetTextModel",
+    "PPFormulaNetVisionModel",
+    "PPFormulaNetForConditionalGeneration",
+    "PPFormulaNetPreTrainedModel",
+]
diff --git a/src/transformers/models/pp_formulanet/modular_pp_formulanet.py b/src/transformers/models/pp_formulanet/modular_pp_formulanet.py
new file mode 100644
index 000000000000..8819d6520bcf
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/modular_pp_formulanet.py
@@ -0,0 +1,549 @@
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+from dataclasses import dataclass
+
+import torch
+import torch.nn as nn
+from huggingface_hub.dataclasses import strict
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...image_processing_utils import BatchFeature
+from ...image_utils import (
+    ImageInput,
+)
+from ...modeling_outputs import (
+    BaseModelOutputWithPooling,
+    Seq2SeqLMOutput,
+    Seq2SeqModelOutput,
+)
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import (
+    ProcessingKwargs,
+    Unpack,
+)
+from ...utils import (
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    logging,
+)
+from ...utils.import_utils import requires
+from ..mbart.configuration_mbart import MBartConfig
+from ..mbart.modeling_mbart import MBartDecoder, shift_tokens_right
+from ..nougat.image_processing_nougat import NougatImageProcessor
+from ..nougat.processing_nougat import NougatProcessor
+from ..slanext.configuration_slanext import SLANeXtVisionConfig
+from ..slanext.modeling_slanext import (
+    SLANeXtPreTrainedModel,
+    SLANeXtVisionAttention,
+    SLANeXtVisionEncoder,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetVisionConfig(SLANeXtVisionConfig):
+    pass
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetTextConfig(MBartConfig):
+    base_config_key = "text_config"
+    vocab_size: int = 50000
+    max_position_embeddings: int = 2560
+    decoder_layers: int = 8
+    decoder_ffn_dim: int = 2048
+    d_model: int = 512
+    scale_embedding: bool = True
+    decoder_start_token_id: int | None = 2
+    tie_word_embeddings: bool = False
+    is_encoder_decoder: bool = True
+    classifier_dropout = AttributeError()
+    encoder_ffn_dim = AttributeError()
+    encoder_layerdrop = AttributeError()
+    is_decoder = AttributeError()
+
+
+@auto_docstring(checkpoint="PaddlePaddle/PPFormulaNet_plus-L_safetensors")
+@strict
+class PPFormulaNetConfig(PreTrainedConfig):
+    r"""
+    post_conv_in_channels (`int`, *optional*, defaults to 256):
+        Number of input channels for the post-encoder convolution layer.
+    post_conv_mid_channels (`int`, *optional*, defaults to 512):
+       Number of intermediate channels for the post-encoder convolution layer.
+    post_conv_out_channels (`int`, *optional*, defaults to 1024):
+        Number of output channels for the post-encoder convolution layer.
+    """
+
+    model_type = "pp_formulanet"
+    sub_configs = {"text_config": PPFormulaNetTextConfig, "vision_config": PPFormulaNetVisionConfig}
+
+    text_config: dict | PPFormulaNetTextConfig | None = None
+    vision_config: dict | PPFormulaNetVisionConfig | None = None
+    is_encoder_decoder: bool = True
+    post_conv_in_channels: int = 256
+    post_conv_out_channels: int = 1024
+    post_conv_mid_channels: int = 512
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.text_config, dict):
+            self.text_config = PPFormulaNetTextConfig(**self.text_config)
+        elif self.text_config is None:
+            logger.info("text_config is None. Initializing the PPFormulaNetTextConfig with default values.")
+            self.text_config = PPFormulaNetTextConfig()
+
+        if isinstance(self.vision_config, dict):
+            self.vision_config = PPFormulaNetVisionConfig(**self.vision_config)
+        elif self.vision_config is None:
+            logger.info("vision_config is None. Initializing the PPFormulaNetVisionConfig with default values.")
+            self.vision_config = PPFormulaNetVisionConfig()
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring
+@requires(backends=("torch",))
+class PPFormulaNetImageProcessor(NougatImageProcessor):
+    image_mean = [0.7931, 0.7931, 0.7931]
+    image_std = [0.1738, 0.1738, 0.1738]
+    size = {"height": 768, "width": 768}
+
+
+@auto_docstring
+class PPFormulaNetProcessor(NougatProcessor):
+    r"""
+    [`PPFormulaNetProcessor`] offers all the functionalities of [`PPFormulaNetImageProcessor`] and [`NougatTokenizer`]. See the
+    [`~PPFormulaNetProcessor.__call__`] and [`~PPFormulaNetProcessor.decode`] for more information.
+    """
+
+    def __call__(
+        self,
+        images: ImageInput,
+        **kwargs: Unpack[ProcessingKwargs],
+    ) -> BatchFeature:
+        """
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            ProcessingKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+        return BatchFeature({**image_inputs})
+
+    def normalize(self, s: str) -> str:
+        """Normalizes a string by removing unnecessary spaces.
+
+        Args:
+            s (str): String to normalize.
+
+        Returns:
+            str: Normalized string.
+        """
+        text_reg = r"(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})"
+        letter = r"[a-zA-Z]"
+        noletter = r"[\W_^\d]"
+        names = []
+        for x in re.findall(text_reg, s):
+            pattern = r"(\\[a-zA-Z]+)\s(?=\w)|\\[a-zA-Z]+\s(?=})"
+            matches = re.findall(pattern, x[0])
+            for m in matches:
+                if (
+                    m
+                    not in [
+                        "\\operatorname",
+                        "\\mathrm",
+                        "\\text",
+                        "\\mathbf",
+                    ]
+                    and m.strip() != ""
+                ):
+                    s = s.replace(m, m + "XXXXXXX")
+                    s = s.replace(" ", "")
+                    names.append(s)
+        if len(names) > 0:
+            s = re.sub(text_reg, lambda match: str(names.pop(0)), s)
+
+        rule_noletter_noletter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, noletter))
+        rule_noletter_letter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, letter))
+        rule_letter_noletter = re.compile(r"(%s)\s+?(%s)" % (letter, noletter))
+
+        news = s
+        while True:
+            s = news
+            news = rule_noletter_noletter.sub(r"\1\2", s)
+            news = rule_noletter_letter.sub(r"\1\2", news)
+            news = rule_letter_noletter.sub(r"\1\2", news)
+            if news == s:
+                break
+
+        return news.replace("XXXXXXX", " ")
+
+    def remove_chinese_text_wrapping(self, formula):
+        pattern = re.compile(r"\\text\s*{([^{}]*[\u4e00-\u9fff]+[^{}]*)}")
+
+        def replacer(match):
+            return match.group(1)
+
+        replaced_formula = pattern.sub(replacer, formula)
+        return replaced_formula.replace('"', "")
+
+    def post_process_generation(self, text: str) -> str:
+        """Post-processes a string by fixing text and normalizing it.
+
+        Args:
+            text (str): String to post-process.
+
+        Returns:
+            str: Post-processed string.
+        """
+        text = self.remove_chinese_text_wrapping(text)
+        try:
+            from ftfy import fix_text
+
+            text = fix_text(text)
+        except ImportError:
+            logger.warning_once(
+                "ftfy is not installed, skipping fix_text. "
+                "Output may contain unnormalized unicode, extra spaces, or escaped artifacts"
+            )
+        text = self.normalize(text)
+        return text
+
+    def post_process(self, generated_outputs, skip_special_tokens=True, **kwargs):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs)
+        return [self.post_process_generation(text) for text in generated_texts]
+
+
+class PPFormulaNetPreTrainedModel(SLANeXtPreTrainedModel):
+    _keep_in_fp32_modules_strict = []
+    base_model_prefix = "model"
+    _supports_sdpa = True
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        PreTrainedModel._init_weights(module)
+
+        # Initialize positional embeddings to zero (PPFormulaNetVisionModel holds pos_embed)
+        if isinstance(module, PPFormulaNetVisionModel):
+            if module.pos_embed is not None:
+                init.constant_(module.pos_embed, 0.0)
+
+        # Initialize relative positional embeddings to zero (PPFormulaNetVisionAttention holds rel_pos_h/w)
+        if isinstance(module, PPFormulaNetVisionAttention):
+            if module.use_rel_pos:
+                init.constant_(module.rel_pos_h, 0.0)
+                init.constant_(module.rel_pos_w, 0.0)
+
+
+# overrider for PPFormulaNetModel's encoder output
+@dataclass
+class PPFormulaNetVisionEncoderOutput(BaseModelOutputWithPooling):
+    pass
+
+
+class PPFormulaNetVisionAttention(SLANeXtVisionAttention):
+    pass
+
+
+class PPFormulaNetMultiModalProjector(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.conv1 = nn.Conv2d(
+            config.post_conv_in_channels, config.post_conv_mid_channels, kernel_size=3, stride=2, padding=1, bias=False
+        )
+        self.conv2 = nn.Conv2d(
+            config.post_conv_mid_channels,
+            config.post_conv_out_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False,
+        )
+        self.linear_1 = nn.Linear(config.post_conv_out_channels, config.post_conv_out_channels)
+        self.linear_2 = nn.Linear(config.post_conv_out_channels, config.text_config.hidden_size)
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]):
+        hidden_states = self.conv1(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        return hidden_states
+
+
+class PPFormulaNetVisionModel(SLANeXtVisionEncoder):
+    pass
+
+
+@auto_docstring
+class PPFormulaNetTextModel(MBartDecoder):
+    pass
+
+
+class PPFormulaNetModel(PPFormulaNetPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+
+        self.decoder = PPFormulaNetTextModel(config.text_config)
+        self.encoder = PPFormulaNetVisionModel(config=config.vision_config)
+        self.multi_modal_projector = PPFormulaNetMultiModalProjector(config)
+
+        self.post_init()
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="Obtains image last hidden states from the vision tower and apply multimodal projection."
+    )
+    def get_image_features(
+        self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`):
+            The tensors corresponding to the input images.
+        """
+        image_outputs = self.encoder(pixel_values, **kwargs)
+        image_outputs.pooler_output = self.multi_modal_projector(image_outputs.last_hidden_state)
+
+        return image_outputs
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        encoder_outputs: list[torch.FloatTensor] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs,
+    ) -> tuple | Seq2SeqModelOutput:
+        if decoder_input_ids is None and decoder_inputs_embeds is None:
+            decoder_input_ids = shift_tokens_right(input_ids, self.config.text_config.pad_token_id)
+
+        if (encoder_outputs is None) ^ (pixel_values is not None):
+            raise ValueError("You must specify exactly one of encoder_outputs or pixel_values")
+
+        if encoder_outputs is None:
+            encoder_outputs = self.get_image_features(pixel_values, **kwargs)
+        elif encoder_outputs.pooler_output is None:
+            encoder_outputs.pooler_output = self.multi_modal_projector(encoder_outputs.last_hidden_state)
+
+        image_features = encoder_outputs.pooler_output.to(self.decoder.device, self.decoder.dtype)
+
+        decoder_outputs = self.decoder(
+            input_ids=decoder_input_ids,
+            attention_mask=decoder_attention_mask,
+            encoder_hidden_states=image_features,
+            past_key_values=past_key_values,
+            inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        return Seq2SeqModelOutput(
+            last_hidden_state=decoder_outputs.last_hidden_state,
+            past_key_values=decoder_outputs.past_key_values,
+            decoder_hidden_states=decoder_outputs.hidden_states,
+            decoder_attentions=decoder_outputs.attentions,
+            cross_attentions=decoder_outputs.cross_attentions,
+            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
+            encoder_hidden_states=encoder_outputs.hidden_states,
+            encoder_attentions=encoder_outputs.attentions,
+        )
+
+
+@auto_docstring
+class PPFormulaNetForConditionalGeneration(PPFormulaNetPreTrainedModel, GenerationMixin):
+    def __init__(self, config: PPFormulaNetConfig):
+        super().__init__(config)
+        self.model = PPFormulaNetModel(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+
+        self.post_init()
+
+    def get_output_embeddings(self) -> nn.Module:
+        return self.lm_head
+
+    @auto_docstring
+    def get_image_features(
+        self, pixel_values: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> tuple | BaseModelOutputWithPooling:
+        return self.model.get_image_features(pixel_values=pixel_values, **kwargs)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values: torch.FloatTensor | None = None,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        decoder_input_ids: torch.LongTensor | None = None,
+        decoder_attention_mask: torch.LongTensor | None = None,
+        encoder_outputs: list[torch.FloatTensor] | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        decoder_inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Seq2SeqLMOutput:
+        r"""
+        Example:
+
+        ```python
+        >>> from io import BytesIO
+
+        >>> import httpx
+        >>> from PIL import Image
+        >>> from transformers import AutoProcessor, PPFormulaNetForConditionalGeneration
+
+        >>> model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors" # or "PaddlePaddle/PP-FormulaNet-L_safetensors"
+        >>> model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path, device_map="auto")
+        >>> processor = AutoProcessor.from_pretrained(model_path)
+
+        >>> image_url = "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
+        >>> image = Image.open(BytesIO(httpx.get(image_url).content)).convert("RGB")
+        >>> inputs = processor(images=image, return_tensors="pt").to(model.device)
+        >>> outputs = model(**inputs)
+        >>> result = processor.post_process(outputs)
+        >>> print(result)
+        ['\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,']
+        ```"""
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            encoder_outputs=encoder_outputs,
+            decoder_attention_mask=decoder_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            decoder_inputs_embeds=decoder_inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return Seq2SeqLMOutput(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self,
+        input_ids,
+        past_key_values=None,
+        inputs_embeds=None,
+        pixel_values=None,
+        attention_mask=None,
+        logits_to_keep=None,
+        is_first_iteration=False,
+        **kwargs,
+    ):
+        model_inputs = super().prepare_inputs_for_generation(
+            input_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            logits_to_keep=logits_to_keep,
+            is_first_iteration=is_first_iteration,
+            **kwargs,
+        )
+
+        if is_first_iteration or not kwargs.get("use_cache", True):
+            # Pixel values are used only in the first iteration if available
+            # In subsequent iterations, they are already merged with text and cached
+            # NOTE: first iteration doesn't have to be prefill, it can be the first
+            # iteration with a question and cached system prompt (continue generate from cache)
+            model_inputs["pixel_values"] = pixel_values
+
+        return model_inputs
+
+    # override this function to compatible with `_prepare_encoder_decoder_kwargs_for_generation`
+    def get_encoder(self):
+        return self.model.get_encoder()
+
+
+__all__ = [
+    "PPFormulaNetProcessor",
+    "PPFormulaNetImageProcessor",
+    "PPFormulaNetConfig",
+    "PPFormulaNetTextConfig",
+    "PPFormulaNetModel",
+    "PPFormulaNetTextModel",
+    "PPFormulaNetVisionModel",
+    "PPFormulaNetVisionConfig",
+    "PPFormulaNetForConditionalGeneration",
+    "PPFormulaNetPreTrainedModel",
+]
diff --git a/src/transformers/models/pp_formulanet/processing_pp_formulanet.py b/src/transformers/models/pp_formulanet/processing_pp_formulanet.py
new file mode 100644
index 000000000000..90671fcfa7d0
--- /dev/null
+++ b/src/transformers/models/pp_formulanet/processing_pp_formulanet.py
@@ -0,0 +1,167 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/pp_formulanet/modular_pp_formulanet.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_pp_formulanet.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+
+from ...image_processing_utils import BatchFeature
+from ...image_utils import ImageInput
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...utils import auto_docstring, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring
+class PPFormulaNetProcessor(ProcessorMixin):
+    r"""
+    [`PPFormulaNetProcessor`] offers all the functionalities of [`PPFormulaNetImageProcessor`] and [`NougatTokenizer`]. See the
+    [`~PPFormulaNetProcessor.__call__`] and [`~PPFormulaNetProcessor.decode`] for more information.
+    """
+
+    def __init__(self, image_processor, tokenizer):
+        super().__init__(image_processor, tokenizer)
+
+    @auto_docstring
+    def __call__(
+        self,
+        images: ImageInput,
+        **kwargs: Unpack[ProcessingKwargs],
+    ) -> BatchFeature:
+        """
+        Args:
+            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
+                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
+                tensor. Both channels-first and channels-last formats are supported.
+
+        Returns:
+            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
+
+            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
+        """
+        output_kwargs = self._merge_kwargs(
+            ProcessingKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
+        return BatchFeature({**image_inputs})
+
+    def post_process_generation(self, text: str) -> str:
+        """Post-processes a string by fixing text and normalizing it.
+
+        Args:
+            text (str): String to post-process.
+
+        Returns:
+            str: Post-processed string.
+        """
+        text = self.remove_chinese_text_wrapping(text)
+        try:
+            from ftfy import fix_text
+
+            text = fix_text(text)
+        except ImportError:
+            logger.warning_once(
+                "ftfy is not installed, skipping fix_text. "
+                "Output may contain unnormalized unicode, extra spaces, or escaped artifacts"
+            )
+        text = self.normalize(text)
+        return text
+
+    def normalize(self, s: str) -> str:
+        """Normalizes a string by removing unnecessary spaces.
+
+        Args:
+            s (str): String to normalize.
+
+        Returns:
+            str: Normalized string.
+        """
+        text_reg = r"(\\(operatorname|mathrm|text|mathbf)\s?\*? {.*?})"
+        letter = r"[a-zA-Z]"
+        noletter = r"[\W_^\d]"
+        names = []
+        for x in re.findall(text_reg, s):
+            pattern = r"(\\[a-zA-Z]+)\s(?=\w)|\\[a-zA-Z]+\s(?=})"
+            matches = re.findall(pattern, x[0])
+            for m in matches:
+                if (
+                    m
+                    not in [
+                        "\\operatorname",
+                        "\\mathrm",
+                        "\\text",
+                        "\\mathbf",
+                    ]
+                    and m.strip() != ""
+                ):
+                    s = s.replace(m, m + "XXXXXXX")
+                    s = s.replace(" ", "")
+                    names.append(s)
+        if len(names) > 0:
+            s = re.sub(text_reg, lambda match: str(names.pop(0)), s)
+
+        rule_noletter_noletter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, noletter))
+        rule_noletter_letter = re.compile(r"(?!\\ )(%s)\s+?(%s)" % (noletter, letter))
+        rule_letter_noletter = re.compile(r"(%s)\s+?(%s)" % (letter, noletter))
+
+        news = s
+        while True:
+            s = news
+            news = rule_noletter_noletter.sub(r"\1\2", s)
+            news = rule_noletter_letter.sub(r"\1\2", news)
+            news = rule_letter_noletter.sub(r"\1\2", news)
+            if news == s:
+                break
+
+        return news.replace("XXXXXXX", " ")
+
+    def remove_chinese_text_wrapping(self, formula):
+        pattern = re.compile(r"\\text\s*{([^{}]*[\u4e00-\u9fff]+[^{}]*)}")
+
+        def replacer(match):
+            return match.group(1)
+
+        replaced_formula = pattern.sub(replacer, formula)
+        return replaced_formula.replace('"', "")
+
+    def post_process(self, generated_outputs, skip_special_tokens=True, **kwargs):
+        """
+        Post-process the output of the model to decode the text.
+
+        Args:
+            generated_outputs (`torch.Tensor` or `np.ndarray`):
+                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
+                or `(sequence_length,)`.
+            skip_special_tokens (`bool`, *optional*, defaults to `True`):
+                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
+            **kwargs:
+                Additional arguments to be passed to the tokenizer's `batch_decode method`.
+
+        Returns:
+            `list[str]`: The decoded text.
+        """
+        generated_texts = self.batch_decode(generated_outputs, skip_special_tokens=skip_special_tokens, **kwargs)
+        return [self.post_process_generation(text) for text in generated_texts]
+
+
+__all__ = ["PPFormulaNetProcessor"]
diff --git a/src/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py b/src/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py
index ea7e8a0c223d..376a92670da2 100644
--- a/src/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py
+++ b/src/transformers/models/pp_ocrv5_mobile_rec/modeling_pp_ocrv5_mobile_rec.py
@@ -360,8 +360,8 @@ def forward(self, hidden_states: torch.FloatTensor, **kwargs: Unpack[Transformer
         return BaseModelOutputWithNoAttention(last_hidden_state=hidden_states, hidden_states=outputs.hidden_states)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class PPOCRV5MobileRecForTextRecognitionOutput(BaseModelOutputWithNoAttention):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py b/src/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py
index 9bffac55b779..9499373344ae 100644
--- a/src/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py
+++ b/src/transformers/models/pp_ocrv5_server_rec/modeling_pp_ocrv5_server_rec.py
@@ -343,8 +343,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class PPOCRV5ServerRecForTextRecognitionOutput(BaseModelOutputWithNoAttention):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py b/src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py
index 54e7d5f423eb..f0e4a973dbfb 100644
--- a/src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py
+++ b/src/transformers/models/pp_ocrv5_server_rec/modular_pp_ocrv5_server_rec.py
@@ -420,8 +420,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class PPOCRV5ServerRecForTextRecognitionOutput(BaseModelOutputWithNoAttention):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/prophetnet/modeling_prophetnet.py b/src/transformers/models/prophetnet/modeling_prophetnet.py
index 86089cd98914..dc76ae68c980 100644
--- a/src/transformers/models/prophetnet/modeling_prophetnet.py
+++ b/src/transformers/models/prophetnet/modeling_prophetnet.py
@@ -108,12 +108,12 @@ def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids)
     return main_relative_position_buckets, predict_relative_position_buckets
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for sequence-to-sequence language models outputs.
     """
 )
+@dataclass
 class ProphetNetSeq2SeqLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -159,13 +159,13 @@ class ProphetNetSeq2SeqLMOutput(ModelOutput):
     encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model encoder's outputs that also contains : pre-computed hidden states that can speed up sequential
     decoding.
     """
 )
+@dataclass
 class ProphetNetSeq2SeqModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
@@ -209,12 +209,12 @@ class ProphetNetSeq2SeqModelOutput(ModelOutput):
     encoder_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class ProphetNetDecoderModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, decoder_sequence_length, hidden_size)`):
@@ -253,12 +253,12 @@ class ProphetNetDecoderModelOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class ProphetNetDecoderLMOutput(ModelOutput):
     r"""
     ngram_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -1383,7 +1383,7 @@ def forward(
         attention_mask: torch.Tensor | None = None,
         decoder_input_ids: torch.Tensor | None = None,
         decoder_attention_mask: torch.BoolTensor | None = None,
-        encoder_outputs: tuple | None = None,
+        encoder_outputs: tuple | BaseModelOutput | None = None,
         past_key_values: Cache | None = None,
         inputs_embeds: torch.Tensor | None = None,
         decoder_inputs_embeds: torch.Tensor | None = None,
@@ -1442,6 +1442,12 @@ def forward(
                 output_hidden_states=output_hidden_states,
                 return_dict=return_dict,
             )
+        elif return_dict and isinstance(encoder_outputs, tuple):
+            encoder_outputs = BaseModelOutput(
+                last_hidden_state=encoder_outputs[0],
+                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
+                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
+            )
 
         # decoder outputs consists of (dec_features, past_key_values, dec_hidden, dec_attn)
         decoder_outputs = self.decoder(
diff --git a/src/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py b/src/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py
index 35868ccc6f47..9082d11f2a3f 100644
--- a/src/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py
+++ b/src/transformers/models/qianfan_ocr/modeling_qianfan_ocr.py
@@ -702,12 +702,12 @@ def pixel_shuffle(self, vision_features: torch.Tensor, scale_factor: float = 0.5
         return vision_features
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for QianfanOCR causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class QianfanOCRCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/qianfan_ocr/processing_qianfan_ocr.py b/src/transformers/models/qianfan_ocr/processing_qianfan_ocr.py
index f8d953bed7ac..525a2cf6134d 100644
--- a/src/transformers/models/qianfan_ocr/processing_qianfan_ocr.py
+++ b/src/transformers/models/qianfan_ocr/processing_qianfan_ocr.py
@@ -73,6 +73,10 @@ def __init__(
         self.video_token = None
         self.video_processor = None
 
+    @property
+    def image_token_ids(self) -> list[int]:
+        return [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
+
     def _insert_media_placeholders(
         self,
         text: list[str],
diff --git a/src/transformers/models/qwen2/modeling_qwen2.py b/src/transformers/models/qwen2/modeling_qwen2.py
index 9263e1d42937..9238202a42f0 100644
--- a/src/transformers/models/qwen2/modeling_qwen2.py
+++ b/src/transformers/models/qwen2/modeling_qwen2.py
@@ -33,11 +33,14 @@
 
 
 class Qwen2MLP(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config, layer_idx):
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
+        if hasattr(config, "layer_inter_size"):
+            self.intermediate_size = config.layer_inter_size[layer_idx]
+        else:
+            self.intermediate_size = config.intermediate_size
         self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
         self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
         self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
@@ -64,8 +67,8 @@ def __init__(self, config: Qwen2Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -105,7 +108,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -193,14 +196,18 @@ def __init__(self, config: Qwen2Config, layer_idx: int):
         self.config = config
         self.layer_idx = layer_idx
         self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        if hasattr(config, "layer_head_num"):
+            self.num_heads = config.layer_head_num[layer_idx]
+        else:
+            self.num_heads = config.num_attention_heads
+        self.num_key_value_groups = self.num_heads // config.num_key_value_heads
         self.scaling = self.head_dim**-0.5
         self.attention_dropout = config.attention_dropout
         self.is_causal = True
-        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=True)
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=True)
         self.k_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
         self.v_proj = nn.Linear(config.hidden_size, config.num_key_value_heads * self.head_dim, bias=True)
-        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
         self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
 
     def forward(
@@ -273,7 +280,7 @@ def __init__(self, config: Qwen2Config, layer_idx: int):
 
         self.self_attn = Qwen2Attention(config=config, layer_idx=layer_idx)
 
-        self.mlp = Qwen2MLP(config)
+        self.mlp = Qwen2MLP(config, layer_idx)
         self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
diff --git a/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py
index 1564d2b36de9..081823bf222f 100644
--- a/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/configuration_qwen2_5_omni.py
@@ -99,7 +99,12 @@ class Qwen2_5OmniAudioEncoderConfig(PreTrainedConfig):
     ```"""
 
     model_type = "qwen2_5_omni_audio_encoder"
-    attribute_map = {"num_hidden_layers": "encoder_layers"}
+    attribute_map = {
+        "num_hidden_layers": "encoder_layers",
+        "hidden_size": "d_model",
+        "num_attention_heads": "encoder_attention_heads",
+        "intermediate_size": "encoder_ffn_dim",
+    }
 
     num_mel_bins: int = 128
     encoder_layers: int = 32
diff --git a/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
index c8824b2f9730..623c90884f1f 100644
--- a/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/modeling_qwen2_5_omni.py
@@ -502,12 +502,12 @@ def get_rope_index(
 ############################
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1818,22 +1818,22 @@ def get_placeholder_mask(
             special_audio_mask = input_ids == self.config.audio_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
 
-        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_audio_mask = special_audio_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask, special_video_mask, special_audio_mask
 
     @can_return_tuple
@@ -2056,12 +2056,12 @@ def prepare_inputs_for_generation(
 ############################
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -2488,7 +2488,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -3858,7 +3858,7 @@ def generate(
         embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device)
         if thinker_kwargs.get("input_features") is not None:
             audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
-            audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            audio_mask = audio_ids_mask.unsqueeze(-1)
             audio_mask_tensor = torch.zeros(
                 [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
@@ -3867,7 +3867,7 @@ def generate(
             embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
         if thinker_kwargs.get("pixel_values") is not None:
             image_ids_mask = input_ids == self.config.thinker_config.image_token_index
-            image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            image_mask = image_ids_mask.unsqueeze(-1)
             image_mask_tensor = torch.zeros(
                 [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
@@ -3876,7 +3876,7 @@ def generate(
             embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
         if thinker_kwargs.get("pixel_values_videos") is not None:
             video_ids_mask = input_ids == self.config.thinker_config.video_token_index
-            video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            video_mask = video_ids_mask.unsqueeze(-1)
             video_mask_tensor = torch.zeros(
                 [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
diff --git a/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
index 4618b08cd574..27b86bc52606 100644
--- a/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/modular_qwen2_5_omni.py
@@ -1035,12 +1035,12 @@ def get_rope_index(
 ############################
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2.5OmniThinker causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2_5OmniThinkerCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1755,22 +1755,22 @@ def get_placeholder_mask(
             special_audio_mask = input_ids == self.config.audio_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
 
-        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_audio_mask = special_audio_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask, special_video_mask, special_audio_mask
 
     @can_return_tuple
@@ -1993,12 +1993,12 @@ def prepare_inputs_for_generation(
 ############################
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2.5OmniTalker causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2_5OmniTalkerCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -3693,7 +3693,7 @@ def generate(
         embeds_to_talker = thinker_result.hidden_states[0][0].clone().to(input_ids.device)
         if thinker_kwargs.get("input_features") is not None:
             audio_ids_mask = input_ids == self.config.thinker_config.audio_token_index
-            audio_mask = audio_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            audio_mask = audio_ids_mask.unsqueeze(-1)
             audio_mask_tensor = torch.zeros(
                 [audio_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
@@ -3702,7 +3702,7 @@ def generate(
             embeds_to_talker.masked_scatter_(audio_mask, audio_mask_tensor)
         if thinker_kwargs.get("pixel_values") is not None:
             image_ids_mask = input_ids == self.config.thinker_config.image_token_index
-            image_mask = image_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            image_mask = image_ids_mask.unsqueeze(-1)
             image_mask_tensor = torch.zeros(
                 [image_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
@@ -3711,7 +3711,7 @@ def generate(
             embeds_to_talker.masked_scatter_(image_mask, image_mask_tensor)
         if thinker_kwargs.get("pixel_values_videos") is not None:
             video_ids_mask = input_ids == self.config.thinker_config.video_token_index
-            video_mask = video_ids_mask.unsqueeze(-1).expand_as(embeds_to_talker)
+            video_mask = video_ids_mask.unsqueeze(-1)
             video_mask_tensor = torch.zeros(
                 [video_ids_mask.sum(), embeds_to_talker.shape[-1]],
                 dtype=embeds_to_talker.dtype,
diff --git a/src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py b/src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py
index 5f5b6584862a..37ea59c03d74 100644
--- a/src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py
+++ b/src/transformers/models/qwen2_5_omni/processing_qwen2_5_omni.py
@@ -26,6 +26,7 @@
 from ...tokenization_utils_base import AudioInput, PreTokenizedInput, TextInput
 from ...utils import auto_docstring
 from ...video_utils import VideoInput
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 # Redefine kwargs for videos because Qwen-Omni uses some kwargs for processing omni
@@ -77,6 +78,7 @@ class Qwen2_5_OmniVideosKwargs(VideosKwargs, total=False):
 
 
 class Qwen2_5OmniProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     videos_kwargs: Qwen2_5_OmniVideosKwargs
 
     _defaults = {
diff --git a/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py b/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
index 9e2812720d4c..aeff72aa7e5c 100644
--- a/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
+++ b/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
@@ -336,6 +336,10 @@ class Qwen2_5_VLPreTrainedModel(PreTrainedModel):
     _supports_attention_backend = True
 
     def _init_weights(self, module):
+        weight = getattr(module, "weight", None)
+        if weight is not None and not weight.is_floating_point():
+            return
+
         super()._init_weights(module)
         if isinstance(module, Qwen2_5_VisionRotaryEmbedding):
             inv_freq = 1.0 / (module.theta ** (torch.arange(0, module.dim, 2, dtype=torch.float) / module.dim))
@@ -518,12 +522,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen2_5_VLModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1208,18 +1212,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1352,12 +1356,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2_5_VL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2_5_VLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py b/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
index 038209892b6d..2a7837397948 100644
--- a/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
+++ b/src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py
@@ -29,13 +29,10 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache
 from ...configuration_utils import PreTrainedConfig
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
 from ...modeling_layers import GradientCheckpointingLayer
 from ...modeling_outputs import BaseModelOutputWithPooling
 from ...modeling_utils import PreTrainedModel
-from ...processing_utils import MultiModalData, ProcessingKwargs, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import ProcessingKwargs, Unpack
 from ...utils import auto_docstring, can_return_tuple, logging
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
@@ -785,140 +782,27 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
 
 
 class Qwen2_5_VLProcessor(Qwen2VLProcessor):
-    @property
-    def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        video_processor_input_names = self.video_processor.model_input_names
-        names_from_processor = list(
-            dict.fromkeys(tokenizer_input_names + image_processor_input_names + video_processor_input_names)
-        )
-        return names_from_processor + ["second_per_grid_ts", "mm_token_type_ids"]
-
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Qwen2_5_VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-
-        image_inputs = videos_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
+    def _process_videos(self, videos: VideoInput, **kwargs):
+        processed_data, video_replacements = super()._process_videos(videos, **kwargs)
+        video_grid_thw = processed_data["video_grid_thw"]
 
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            video_grid_thw = videos_inputs["video_grid_thw"]
+        video_metadata = processed_data["video_metadata"]
+        fps = [metadata.sampled_fps for metadata in video_metadata]
 
-            # Get video metadata
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-
-            fps = [metadata.sampled_fps for metadata in video_metadata]
-
-            if isinstance(fps, (int, float)):
-                second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
-            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
-                second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
-            else:
-                raise ValueError(
-                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
-                )
-            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if images is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if videos is not None:
-            merge_length = self.video_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_video_tokens = video_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
-
-    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
-        """
-        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
-        Args:
-            image_sizes (`list[list[int]]`, *optional*):
-                The input sizes formatted as (height, width) per each image.
-            video_sizes (`list[list[int]]`, *optional*):
-                The input sizes formatted as (num_frames, height, width) per each video.
-        Returns:
-            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
-            input modalities, along with other useful data.
-        """
+        if isinstance(fps, (int, float)):
+            second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+        elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+            second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
+        else:
+            raise ValueError(
+                f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+            )
+        processed_data["second_per_grid_ts"] = second_per_grid_ts
+        return processed_data, video_replacements
 
-        vision_data = {}
-        if image_sizes is not None:
-            images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {})
-            images_kwargs.update(kwargs)
-            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
-
-            num_image_patches = [
-                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
-                for image_size in image_sizes
-            ]
-            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
-            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
-
-        if video_sizes is not None:
-            videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {})
-            videos_kwargs.update(kwargs)
-            num_video_patches = [
-                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
-                for video_size in video_sizes
-            ]
-            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
-            vision_data["num_video_tokens"] = num_video_tokens
-
-        return MultiModalData(**vision_data)
+    @property
+    def model_input_names(self):
+        return super().model_input_names + ["second_per_grid_ts", "mm_token_type_ids"]
 
 
 __all__ = [
diff --git a/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py b/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
index 8873eb82557a..f921fc857a8c 100644
--- a/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
+++ b/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py
@@ -22,15 +22,14 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
 from ...video_utils import VideoInput
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -42,6 +41,8 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Qwen2_5_VLProcessor(ProcessorMixin):
+    valid_processor_kwargs = Qwen2_5_VLProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -57,93 +58,15 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         )
         super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Qwen2_5_VLProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Qwen2_5_VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
 
-        image_inputs = videos_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            video_grid_thw = videos_inputs["video_grid_thw"]
-
-            # Get video metadata
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-
-            fps = [metadata.sampled_fps for metadata in video_metadata]
-
-            if isinstance(fps, (int, float)):
-                second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
-            elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
-                second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
-            else:
-                raise ValueError(
-                    f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
-                )
-            videos_inputs.update({"second_per_grid_ts": second_per_grid_ts})
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if images is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if videos is not None:
-            merge_length = self.video_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_video_tokens = video_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_video_tokens = video_inputs["video_grid_thw"][video_idx].prod() // merge_length
+        return self.video_token * num_video_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
@@ -212,13 +135,25 @@ def post_process_image_text_to_text(
 
     @property
     def model_input_names(self):
-        tokenizer_input_names = self.tokenizer.model_input_names
-        image_processor_input_names = self.image_processor.model_input_names
-        video_processor_input_names = self.video_processor.model_input_names
-        names_from_processor = list(
-            dict.fromkeys(tokenizer_input_names + image_processor_input_names + video_processor_input_names)
-        )
-        return names_from_processor + ["second_per_grid_ts", "mm_token_type_ids"]
+        return super().model_input_names + ["second_per_grid_ts", "mm_token_type_ids"]
+
+    def _process_videos(self, videos: VideoInput, **kwargs):
+        processed_data, video_replacements = super()._process_videos(videos, **kwargs)
+        video_grid_thw = processed_data["video_grid_thw"]
+
+        video_metadata = processed_data["video_metadata"]
+        fps = [metadata.sampled_fps for metadata in video_metadata]
+
+        if isinstance(fps, (int, float)):
+            second_per_grid_ts = [self.video_processor.temporal_patch_size / fps] * len(video_grid_thw)
+        elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw):
+            second_per_grid_ts = [self.video_processor.temporal_patch_size / tmp for tmp in fps]
+        else:
+            raise ValueError(
+                f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number."
+            )
+        processed_data["second_per_grid_ts"] = second_per_grid_ts
+        return processed_data, video_replacements
 
 
 __all__ = ["Qwen2_5_VLProcessor"]
diff --git a/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py b/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py
index a617f33e6177..6aec9eace900 100644
--- a/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py
+++ b/src/transformers/models/qwen2_audio/configuration_qwen2_audio.py
@@ -42,7 +42,12 @@ class Qwen2AudioEncoderConfig(PreTrainedConfig):
     ```"""
 
     model_type = "qwen2_audio_encoder"
-    attribute_map = {"num_hidden_layers": "encoder_layers"}
+    attribute_map = {
+        "num_hidden_layers": "encoder_layers",
+        "hidden_size": "d_model",
+        "num_attention_heads": "encoder_attention_heads",
+        "intermediate_size": "encoder_ffn_dim",
+    }
 
     num_mel_bins: int = 128
     encoder_layers: int = 32
diff --git a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
index 442eab1edcd4..9f0cbf24a7ad 100644
--- a/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
+++ b/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py
@@ -38,12 +38,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2Audio causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2AudioCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -324,9 +324,23 @@ def forward(
     ):
         r"""
         Args:
-            attention_mask (`torch.Tensor`)`, *optional*):
-                Qwen2Audio does not support masking of the `input_features`, this argument is preserved for compatibility,
-                but it is not used. By default the silence in the input log mel spectrogram are ignored.
+            input_features (`torch.LongTensor` of shape `(batch_size, feature_size, sequence_length)`):
+                Float values of mel features extracted from the raw speech waveform. Raw speech waveform can be
+                obtained by loading a `.flac` or `.wav` audio file into an array of type `list[float]`, a
+                `numpy.ndarray` or a `torch.Tensor`, *e.g.* via the torchcodec library (`pip install torchcodec`) or
+                the soundfile library (`pip install soundfile`). To prepare the array into
+                `input_features`, the [`AutoFeatureExtractor`] should be used for extracting the mel features, padding
+                and conversion into a tensor of type `torch.FloatTensor`. See [`~WhisperFeatureExtractor.__call__`]
+            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`), *optional*):
+                attention mask used in the encoder stack (after the convolutional layers).
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            output_hidden_states (`bool`, *optional*):
+                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
+                for more detail.
+            return_dict (`bool`, *optional*):
+                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
         """
 
         expected_seq_length = self.config.max_source_positions * self.conv1.stride[0] * self.conv2.stride[0]
@@ -702,7 +716,7 @@ def forward(
                     feature_attention_mask.sum(-1)
                 )
                 batch_size, _, max_mel_seq_len = input_features.shape
-                max_seq_len = (max_mel_seq_len - 2) // 2 + 1
+                max_seq_len = (max_mel_seq_len - 1) // 2 + 1
                 # Create a sequence tensor of shape (batch_size, max_seq_len)
                 seq_range = (
                     torch.arange(0, max_seq_len, dtype=audio_feat_lengths.dtype, device=audio_feat_lengths.device)
@@ -754,7 +768,7 @@ def forward(
                         f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
                     )
                     special_audio_mask = (input_ids == self.config.audio_token_id).to(inputs_embeds.device)
-                    special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds)
+                    special_audio_mask = special_audio_mask.unsqueeze(-1)
                     audio_features = audio_features.to(inputs_embeds.device, inputs_embeds.dtype)
                     inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_features)
 
diff --git a/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py b/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
index d4150d0a74d7..3d4222cebb18 100644
--- a/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
+++ b/src/transformers/models/qwen2_moe/modeling_qwen2_moe.py
@@ -94,8 +94,8 @@ def __init__(self, config: Qwen2MoeConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -135,7 +135,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -343,8 +343,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -568,7 +568,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -576,7 +576,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -593,8 +595,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/qwen2_moe/modular_qwen2_moe.py b/src/transformers/models/qwen2_moe/modular_qwen2_moe.py
index deb615c9e7b6..af4ae9c1b3f7 100644
--- a/src/transformers/models/qwen2_moe/modular_qwen2_moe.py
+++ b/src/transformers/models/qwen2_moe/modular_qwen2_moe.py
@@ -99,8 +99,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
diff --git a/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py b/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py
index 70a4868eeee1..2d91880bed61 100644
--- a/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py
+++ b/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py
@@ -19,7 +19,6 @@
 """Image processor class for Qwen2-VL."""
 
 import math
-from collections.abc import Iterable
 
 import torch
 from torchvision.transforms.v2 import functional as tvF
@@ -122,21 +121,6 @@ def __init__(self, **kwargs: Unpack[Qwen2VLImageProcessorKwargs]):
 
         super().__init__(size=size, **kwargs)
 
-    def _standardize_kwargs(
-        self,
-        size: int | Iterable[int] | dict[str, int] | SizeDict | None = None,
-        min_pixels: int | None = None,
-        max_pixels: int | None = None,
-        **kwargs,
-    ) -> dict:
-        if min_pixels is not None and max_pixels is not None:
-            size = SizeDict(shortest_edge=min_pixels, longest_edge=max_pixels)
-        kwargs = super()._standardize_kwargs(size=size, **kwargs)
-        size = kwargs.get("size", self.size)
-        if not size.shortest_edge or not size.longest_edge:
-            raise ValueError("size must contain 'shortest_edge' and 'longest_edge' keys.")
-        return kwargs
-
     @auto_docstring
     def preprocess(
         self,
diff --git a/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py b/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
index 7ea940df2ae0..8a3c7ec49d9f 100644
--- a/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
+++ b/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py
@@ -59,12 +59,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen2VLModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -83,12 +83,12 @@ class Qwen2VLModelOutputWithPast(ModelOutput):
     rope_deltas: torch.LongTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen2VL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen2VLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1162,18 +1162,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/qwen2_vl/processing_qwen2_vl.py b/src/transformers/models/qwen2_vl/processing_qwen2_vl.py
index 9c38451e60e8..ad536f5113f4 100644
--- a/src/transformers/models/qwen2_vl/processing_qwen2_vl.py
+++ b/src/transformers/models/qwen2_vl/processing_qwen2_vl.py
@@ -20,18 +20,16 @@
 Processor class for Qwen2-VL.
 """
 
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from .image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Qwen2VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -42,6 +40,8 @@ class Qwen2VLProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Qwen2VLProcessor(ProcessorMixin):
+    valid_processor_kwargs = Qwen2VLProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -57,76 +57,15 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         )
         super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput | None = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput | None = None,
-        **kwargs: Unpack[Qwen2VLProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Qwen2VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
 
-        image_inputs = videos_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            video_grid_thw = videos_inputs["video_grid_thw"]
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-
-        if images is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if videos is not None:
-            merge_length = self.video_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    num_video_tokens = video_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.video_token, "<|placeholder|>" * num_video_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_video_tokens = video_inputs["video_grid_thw"][video_idx].prod() // merge_length
+        return self.video_token * num_video_tokens
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
@@ -195,9 +134,7 @@ def post_process_image_text_to_text(
 
     @property
     def model_input_names(self):
-        model_input_names = super().model_input_names
-        model_input_names.append("mm_token_type_ids")
-        return model_input_names
+        return super().model_input_names + ["mm_token_type_ids"]
 
 
 __all__ = ["Qwen2VLProcessor"]
diff --git a/src/transformers/models/qwen3/modeling_qwen3.py b/src/transformers/models/qwen3/modeling_qwen3.py
index 91715a33cf9d..756b02acefef 100644
--- a/src/transformers/models/qwen3/modeling_qwen3.py
+++ b/src/transformers/models/qwen3/modeling_qwen3.py
@@ -68,11 +68,14 @@ def extra_repr(self):
 
 
 class Qwen3MLP(nn.Module):
-    def __init__(self, config):
+    def __init__(self, config, layer_idx):
         super().__init__()
         self.config = config
         self.hidden_size = config.hidden_size
-        self.intermediate_size = config.intermediate_size
+        if hasattr(config, "layer_inter_size"):
+            self.intermediate_size = config.layer_inter_size[layer_idx]
+        else:
+            self.intermediate_size = config.intermediate_size
         self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
         self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
         self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
@@ -99,8 +102,8 @@ def __init__(self, config: Qwen3Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -140,7 +143,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -228,13 +231,17 @@ def __init__(self, config: Qwen3Config, layer_idx: int):
         self.config = config
         self.layer_idx = layer_idx
         self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
-        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        if hasattr(config, "layer_head_num"):
+            self.num_heads = config.layer_head_num[layer_idx]
+        else:
+            self.num_heads = config.num_attention_heads
+        self.num_key_value_groups = self.num_heads // config.num_key_value_heads
         self.scaling = self.head_dim**-0.5
         self.attention_dropout = config.attention_dropout
         self.is_causal = True
 
         self.q_proj = nn.Linear(
-            config.hidden_size, config.num_attention_heads * self.head_dim, bias=config.attention_bias
+            config.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias
         )
         self.k_proj = nn.Linear(
             config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
@@ -243,7 +250,7 @@ def __init__(self, config: Qwen3Config, layer_idx: int):
             config.hidden_size, config.num_key_value_heads * self.head_dim, bias=config.attention_bias
         )
         self.o_proj = nn.Linear(
-            config.num_attention_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
+            self.num_heads * self.head_dim, config.hidden_size, bias=config.attention_bias
         )
         self.q_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # unlike olmo, only on the head dim!
         self.k_norm = Qwen3RMSNorm(self.head_dim, eps=config.rms_norm_eps)  # thus post q_norm does not need reshape
@@ -298,7 +305,7 @@ def __init__(self, config: Qwen3Config, layer_idx: int):
 
         self.self_attn = Qwen3Attention(config=config, layer_idx=layer_idx)
 
-        self.mlp = Qwen3MLP(config)
+        self.mlp = Qwen3MLP(config, layer_idx)
         self.input_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.post_attention_layernorm = Qwen3RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
diff --git a/src/transformers/models/qwen3_5/configuration_qwen3_5.py b/src/transformers/models/qwen3_5/configuration_qwen3_5.py
index ae9eb8f86c6d..b63daf8e317e 100644
--- a/src/transformers/models/qwen3_5/configuration_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/configuration_qwen3_5.py
@@ -38,6 +38,14 @@ class Qwen3_5TextConfig(PreTrainedConfig):
         Number of key heads used in linear attention layers.
     linear_num_value_heads (`int`, *optional*, defaults to 32):
         Number of value heads used in linear attention layers.
+    mtp_num_hidden_layers (`int`, *optional*, defaults to 0):
+        Number of hidden layers in the Multi-Token Prediction (MTP) module. When set to 0, MTP is disabled.
+    mtp_loss_weight (`float`, *optional*, defaults to 0.0):
+        Weight for the MTP auxiliary loss. The total loss is computed as `main_loss + mtp_loss_weight * mtp_loss`.
+    output_mtp_loss (`bool`, *optional*, defaults to `False`):
+        Whether to return the MTP auxiliary loss in the model output. When `True`, the `mtp_loss` field in the
+        output will contain the MTP loss value, and it will be added to the main loss (weighted by
+        `mtp_loss_weight`) when `labels` are provided.
 
     ```python
     >>> from transformers import Qwen3_5TextModel, Qwen3_5TextConfig
@@ -100,6 +108,9 @@ class Qwen3_5TextConfig(PreTrainedConfig):
     eos_token_id: int | list[int] | None = None
     base_config_key = "text_config"
     ignore_keys_at_rope_validation = {"mrope_section", "mrope_interleaved"}
+    mtp_num_hidden_layers: int = 0
+    mtp_loss_weight: float = 0.0
+    output_mtp_loss: bool = False
 
     def __post_init__(self, **kwargs):
         kwargs.setdefault("partial_rotary_factor", 0.25)  # assign default for BC
@@ -144,6 +155,15 @@ class Qwen3_5VisionConfig(PreTrainedConfig):
 @strict
 class Qwen3_5Config(PreTrainedConfig):
     r"""
+    mtp_num_hidden_layers (`int`, *optional*, defaults to 0):
+        Number of hidden layers in the Multi-Token Prediction (MTP) module. When set to 0, MTP is disabled.
+    mtp_loss_weight (`float`, *optional*, defaults to 0.0):
+        Weight for the MTP auxiliary loss. The total loss is computed as `main_loss + mtp_loss_weight * mtp_loss`.
+    output_mtp_loss (`bool`, *optional*, defaults to `False`):
+        Whether to return the MTP auxiliary loss in the model output. When `True`, the `mtp_loss` field in the
+        output will contain the MTP loss value, and it will be added to the main loss (weighted by
+        `mtp_loss_weight`) when `labels` are provided.
+
     Example:
 
     ```python
@@ -171,6 +191,9 @@ class Qwen3_5Config(PreTrainedConfig):
     vision_start_token_id: int = 248053
     vision_end_token_id: int = 248054
     tie_word_embeddings: bool = False
+    mtp_num_hidden_layers: int = 0
+    mtp_loss_weight: float = 0.0
+    output_mtp_loss: bool = False
 
     def __post_init__(self, **kwargs):
         if isinstance(self.vision_config, dict):
diff --git a/src/transformers/models/qwen3_5/modeling_qwen3_5.py b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
index bad700952673..081cafd965a8 100644
--- a/src/transformers/models/qwen3_5/modeling_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modeling_qwen3_5.py
@@ -18,6 +18,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import itertools
 from collections.abc import Callable
 from dataclasses import dataclass
@@ -35,17 +36,12 @@
 from ...masking_utils import create_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
-from ...modeling_outputs import (
-    BaseModelOutputWithPast,
-    BaseModelOutputWithPooling,
-    CausalLMOutputWithPast,
-    ModelOutput,
-)
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, SequenceClassifierOutputWithPast
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
-from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
+from ...utils.generic import ModelOutput, is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
 from ...utils.import_utils import is_causal_conv1d_available, is_flash_linear_attention_available
 from ...utils.output_capturing import capture_outputs
 from .configuration_qwen3_5 import Qwen3_5Config, Qwen3_5TextConfig, Qwen3_5VisionConfig
@@ -739,6 +735,153 @@ def extra_repr(self):
         return f"{tuple(self.weight.shape)}, eps={self.eps}"
 
 
+@dataclass
+class Qwen3_5CausalLMOutputWithPast(ModelOutput):
+    r"""
+    Base class for Qwen3.5 causal language model (or autoregressive) outputs with MTP loss.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        mtp_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `output_mtp_loss=True`):
+            Multi-Token Prediction auxiliary loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used
+            to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding
+            layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+    """
+
+    loss: torch.FloatTensor | None = None
+    mtp_loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+
+
+@dataclass
+class Qwen3_5VLCausalLMOutputWithPast(ModelOutput):
+    r"""
+    Base class for Qwen3.5 vision-language causal language model (or autoregressive) outputs with MTP loss.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        mtp_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `output_mtp_loss=True`):
+            Multi-Token Prediction auxiliary loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used
+            to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding
+            layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+    """
+
+    loss: torch.FloatTensor | None = None
+    mtp_loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+    rope_deltas: torch.LongTensor | None = None
+
+
+class Qwen3_5MTPLayer(nn.Module):
+    def __init__(self, config: Qwen3_5TextConfig, layer_idx: int):
+        super().__init__()
+        mtp_config = copy.copy(config)
+        mtp_layer_types = list(getattr(config, "layer_types", ["full_attention"] * config.num_hidden_layers))
+        while len(mtp_layer_types) <= layer_idx:
+            mtp_layer_types.append("full_attention")
+        mtp_layer_types[layer_idx] = "full_attention"
+        mtp_config.layer_types = mtp_layer_types
+
+        self.input_layernorm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = Qwen3_5Attention(mtp_config, layer_idx=layer_idx)
+        self.post_attention_layernorm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = Qwen3_5MLP(mtp_config, config.intermediate_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_out, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + attn_out
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Qwen3_5MTP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        text_config = getattr(config, "text_config", config)
+
+        self.pre_fc_norm_hidden = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+        self.pre_fc_norm_embedding = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+        self.fc = nn.Linear(text_config.hidden_size * 2, text_config.hidden_size, bias=False)
+
+        mtp_num_layers = getattr(config, "mtp_num_hidden_layers", 1)
+
+        self.layers = nn.ModuleList(
+            [Qwen3_5MTPLayer(text_config, layer_idx=text_config.num_hidden_layers + i) for i in range(mtp_num_layers)]
+        )
+        self.norm = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_embeds: torch.Tensor,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        emb = self.pre_fc_norm_embedding(input_embeds)
+        h = self.pre_fc_norm_hidden(hidden_states)
+        fused = self.fc(torch.cat([emb, h], dim=-1))
+
+        for layer in self.layers:
+            fused = layer(
+                hidden_states=fused,
+                position_embeddings=position_embeddings,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                **kwargs,
+            )
+
+        return self.norm(fused)
+
+
 class Qwen3_5DecoderLayer(GradientCheckpointingLayer):
     def __init__(self, config: Qwen3_5TextConfig, layer_idx: int):
         super().__init__()
@@ -798,7 +941,7 @@ class Qwen3_5PreTrainedModel(PreTrainedModel):
     config: Qwen3_5Config
     base_model_prefix = "model"
     supports_gradient_checkpointing = True
-    _no_split_modules = ["Qwen3_5DecoderLayer", "Qwen3_5VisionBlock"]
+    _no_split_modules = ["Qwen3_5DecoderLayer", "Qwen3_5VisionBlock", "Qwen3_5MTPLayer"]
     _skip_keys_device_placement = "past_key_values"
     _supports_flash_attn = True
     _supports_sdpa = True
@@ -1193,12 +1336,12 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs)
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen3_5ModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1556,18 +1699,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1697,6 +1840,78 @@ def forward(
         )
 
 
+def _compute_qwen35_mtp_loss(
+    mtp: Qwen3_5MTP,
+    embed_tokens: nn.Embedding,
+    rotary_emb: Qwen3_5TextRotaryEmbedding,
+    lm_head: nn.Linear,
+    loss_function,
+    input_ids: torch.LongTensor,
+    main_hidden_states: torch.Tensor,
+    labels: torch.LongTensor,
+    vocab_size: int,
+    pad_token_id: int,
+    attention_mask: torch.Tensor | None = None,
+    position_ids: torch.LongTensor | None = None,
+) -> torch.Tensor:
+    inputs_embeds_for_pos = embed_tokens(input_ids)
+
+    if position_ids is None:
+        pos = torch.arange(inputs_embeds_for_pos.shape[1], device=inputs_embeds_for_pos.device)
+        pos = pos.view(1, 1, -1).expand(4, inputs_embeds_for_pos.shape[0], -1)
+    elif position_ids.ndim == 2:
+        pos = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
+    else:
+        pos = position_ids
+
+    if pos.ndim == 3 and pos.shape[0] == 4:
+        text_position_ids = pos[0]
+        mrope_position_ids = pos[1:]
+    else:
+        text_position_ids = None
+        mrope_position_ids = pos
+
+    position_embeddings = rotary_emb(inputs_embeds_for_pos, mrope_position_ids)
+
+    total_mtp_loss = torch.tensor(0.0, device=main_hidden_states.device, dtype=main_hidden_states.dtype)
+    current_hidden = main_hidden_states
+
+    for i in range(len(mtp.layers)):
+        shifted_input_ids = input_ids[:, 1:]
+        shifted_input_ids = F.pad(shifted_input_ids, (0, 1), value=pad_token_id)
+        input_embeds = embed_tokens(shifted_input_ids)
+
+        if text_position_ids is not None:
+            mtp_text_position_ids = torch.roll(text_position_ids, -1, dims=-1).clone()
+            mtp_text_position_ids[:, -1] = text_position_ids[:, -1]
+        else:
+            mtp_text_position_ids = None
+
+        mtp_hidden = mtp(
+            input_embeds=input_embeds,
+            hidden_states=current_hidden,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=mtp_text_position_ids,
+        )
+        mtp_logits = lm_head(mtp_hidden)
+
+        shift = i + 1
+        shifted_labels = torch.roll(labels, -shift, dims=1).clone()
+        shifted_labels[:, -shift:] = -100
+
+        layer_loss = loss_function(
+            logits=mtp_logits,
+            labels=shifted_labels,
+            vocab_size=vocab_size,
+        )
+
+        total_mtp_loss = total_mtp_loss + layer_loss
+        current_hidden = mtp_hidden
+
+    return total_mtp_loss / len(mtp.layers)
+
+
 @auto_docstring
 class Qwen3_5ForCausalLM(Qwen3_5PreTrainedModel, GenerationMixin):
     _tied_weights_keys = {"lm_head.weight": "model.embed_tokens.weight"}
@@ -1711,6 +1926,9 @@ def __init__(self, config):
         self.vocab_size = config.vocab_size
         self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
 
+        if getattr(config, "mtp_num_hidden_layers", 0) > 0:
+            self.mtp = Qwen3_5MTP(config)
+
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -1726,13 +1944,18 @@ def forward(
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
         logits_to_keep: int | torch.Tensor = 0,
+        output_mtp_loss: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> CausalLMOutputWithPast:
+    ) -> Qwen3_5CausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
             config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
             (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        output_mtp_loss (`bool`, *optional*):
+            Whether to return the MTP auxiliary loss. When `True`, the MTP loss is computed and returned in the
+            `mtp_loss` field of the output. If `labels` are provided, the MTP loss (weighted by `mtp_loss_weight`)
+            is also added to the main loss. If not specified, defaults to `config.output_mtp_loss`.
 
         Example:
 
@@ -1750,6 +1973,8 @@ def forward(
         >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
         "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
         ```"""
+        output_mtp_loss = output_mtp_loss if output_mtp_loss is not None else self.config.output_mtp_loss
+
         outputs: BaseModelOutputWithPast = self.model(
             input_ids=input_ids,
             attention_mask=attention_mask,
@@ -1761,54 +1986,64 @@ def forward(
         )
 
         hidden_states = outputs.last_hidden_state
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
         logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
+        mtp_loss = None
         if labels is not None:
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
 
-        return CausalLMOutputWithPast(
+        if (
+            output_mtp_loss
+            and labels is not None
+            and input_ids is not None
+            and getattr(self.config, "mtp_num_hidden_layers", 0) > 0
+            and hasattr(self, "mtp")
+        ):
+            mtp_loss = self._compute_mtp_loss(
+                input_ids=input_ids,
+                main_hidden_states=hidden_states,
+                labels=labels,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+            )
+            if loss is not None:
+                mtp_weight = getattr(self.config, "mtp_loss_weight", 0.0)
+                loss = loss + mtp_weight * mtp_loss
+
+        return Qwen3_5CausalLMOutputWithPast(
             loss=loss,
+            mtp_loss=mtp_loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
         )
 
-
-class Qwen3_5ForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
-    config: Qwen3_5TextConfig
-
-
-@dataclass
-@auto_docstring(
-    custom_intro="""
-    Base class for Qwen3_5 causal language model (or autoregressive) outputs.
-    """
-)
-class Qwen3_5CausalLMOutputWithPast(ModelOutput):
-    r"""
-    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
-        Language modeling loss (for next-token prediction).
-    logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
-        Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
-    past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
-        It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache).
-
-        Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
-        `past_key_values` input) to speed up sequential decoding.
-    rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
-        The rope index difference between sequence length and multimodal rope.
-    """
-
-    loss: torch.FloatTensor | None = None
-    logits: torch.FloatTensor | None = None
-    past_key_values: Cache | None = None
-    hidden_states: tuple[torch.FloatTensor] | None = None
-    attentions: tuple[torch.FloatTensor] | None = None
-    rope_deltas: torch.LongTensor | None = None
+    def _compute_mtp_loss(
+        self,
+        input_ids: torch.LongTensor,
+        main_hidden_states: torch.Tensor,
+        labels: torch.LongTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+    ) -> torch.Tensor:
+        pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else 0
+        return _compute_qwen35_mtp_loss(
+            mtp=self.mtp,
+            embed_tokens=self.model.embed_tokens,
+            rotary_emb=self.model.rotary_emb,
+            lm_head=self.lm_head,
+            loss_function=self.loss_function,
+            input_ids=input_ids,
+            main_hidden_states=main_hidden_states,
+            labels=labels,
+            vocab_size=self.config.vocab_size,
+            pad_token_id=pad_token_id,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+        )
 
 
 class Qwen3_5ForConditionalGeneration(Qwen3_5PreTrainedModel, GenerationMixin):
@@ -1822,6 +2057,9 @@ def __init__(self, config):
         self.model = Qwen3_5Model(config)
         self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
 
+        if getattr(config, "mtp_num_hidden_layers", 0) > 0:
+            self.mtp = Qwen3_5MTP(config)
+
         self.post_init()
 
     def get_input_embeddings(self):
@@ -1877,8 +2115,9 @@ def forward(
         video_grid_thw: torch.LongTensor | None = None,
         mm_token_type_ids: torch.IntTensor | None = None,
         logits_to_keep: int | torch.Tensor = 0,
+        output_mtp_loss: bool | None = None,
         **kwargs: Unpack[TransformersKwargs],
-    ) -> tuple | Qwen3_5CausalLMOutputWithPast:
+    ) -> tuple | Qwen3_5VLCausalLMOutputWithPast:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
@@ -1925,6 +2164,7 @@ def forward(
         >>> print(output_text)
         ```
         """
+        output_mtp_loss = output_mtp_loss if output_mtp_loss is not None else self.config.output_mtp_loss
 
         outputs = self.model(
             input_ids=input_ids,
@@ -1940,18 +2180,36 @@ def forward(
             **kwargs,
         )
 
-        hidden_states = outputs[0]
-
-        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        hidden_states = outputs.last_hidden_state
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
         logits = self.lm_head(hidden_states[:, slice_indices, :])
 
         loss = None
+        mtp_loss = None
         if labels is not None:
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
 
-        return Qwen3_5CausalLMOutputWithPast(
+        if (
+            output_mtp_loss
+            and labels is not None
+            and input_ids is not None
+            and getattr(self.config, "mtp_num_hidden_layers", 0) > 0
+            and hasattr(self, "mtp")
+        ):
+            mtp_loss = self._compute_mtp_loss(
+                input_ids=input_ids,
+                main_hidden_states=hidden_states,
+                labels=labels,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+            )
+            if loss is not None:
+                mtp_weight = getattr(self.config, "mtp_loss_weight", 0.0)
+                loss = loss + mtp_weight * mtp_loss
+
+        return Qwen3_5VLCausalLMOutputWithPast(
             loss=loss,
+            mtp_loss=mtp_loss,
             logits=logits,
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
@@ -2182,13 +2440,76 @@ def _expand_dict_for_generation(dict_to_expand):
 
         return input_ids, model_kwargs
 
+    def _compute_mtp_loss(
+        self,
+        input_ids: torch.LongTensor,
+        main_hidden_states: torch.Tensor,
+        labels: torch.LongTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+    ) -> torch.Tensor:
+        pad_token_id = self.config.text_config.pad_token_id if self.config.text_config.pad_token_id is not None else 0
+        return _compute_qwen35_mtp_loss(
+            mtp=self.mtp,
+            embed_tokens=self.model.language_model.embed_tokens,
+            rotary_emb=self.model.language_model.rotary_emb,
+            lm_head=self.lm_head,
+            loss_function=self.loss_function,
+            input_ids=input_ids,
+            main_hidden_states=main_hidden_states,
+            labels=labels,
+            vocab_size=self.config.text_config.vocab_size,
+            pad_token_id=pad_token_id,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+        )
+
+
+class Qwen3_5TextForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
+    config: Qwen3_5TextConfig
+    input_modalities = ("text",)
+
+
+class Qwen3_5ForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        mm_token_type_ids: torch.IntTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> SequenceClassifierOutputWithPast:
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            mm_token_type_ids=mm_token_type_ids,
+            **kwargs,
+        )
+
 
 __all__ = [
     "Qwen3_5VisionModel",
     "Qwen3_5TextModel",
     "Qwen3_5Model",
     "Qwen3_5ForCausalLM",
+    "Qwen3_5TextForSequenceClassification",
     "Qwen3_5ForSequenceClassification",
     "Qwen3_5ForConditionalGeneration",
     "Qwen3_5PreTrainedModel",
+    "Qwen3_5MTP",
+    "Qwen3_5CausalLMOutputWithPast",
+    "Qwen3_5VLCausalLMOutputWithPast",
 ]
diff --git a/src/transformers/models/qwen3_5/modular_qwen3_5.py b/src/transformers/models/qwen3_5/modular_qwen3_5.py
index 710b63a28dba..c9bf547dff90 100644
--- a/src/transformers/models/qwen3_5/modular_qwen3_5.py
+++ b/src/transformers/models/qwen3_5/modular_qwen3_5.py
@@ -13,6 +13,8 @@
 # limitations under the License.
 """PyTorch Qwen3.5 model."""
 
+import copy
+from dataclasses import dataclass
 from typing import Optional
 
 import torch
@@ -24,11 +26,11 @@
 from ...cache_utils import Cache, DynamicCache
 from ...masking_utils import create_causal_mask
 from ...modeling_layers import GenericForSequenceClassification, GradientCheckpointingLayer
-from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
+from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, SequenceClassifierOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
-from ...utils.generic import merge_with_config_defaults
+from ...utils.generic import ModelOutput, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..qwen3.modeling_qwen3 import Qwen3ForCausalLM
 from ..qwen3_next.configuration_qwen3_next import Qwen3NextConfig
@@ -69,6 +71,14 @@ class Qwen3_5TextConfig(Qwen3NextConfig):
         Number of key heads used in linear attention layers.
     linear_num_value_heads (`int`, *optional*, defaults to 32):
         Number of value heads used in linear attention layers.
+    mtp_num_hidden_layers (`int`, *optional*, defaults to 0):
+        Number of hidden layers in the Multi-Token Prediction (MTP) module. When set to 0, MTP is disabled.
+    mtp_loss_weight (`float`, *optional*, defaults to 0.0):
+        Weight for the MTP auxiliary loss. The total loss is computed as `main_loss + mtp_loss_weight * mtp_loss`.
+    output_mtp_loss (`bool`, *optional*, defaults to `False`):
+        Whether to return the MTP auxiliary loss in the model output. When `True`, the `mtp_loss` field in the
+        output will contain the MTP loss value, and it will be added to the main loss (weighted by
+        `mtp_loss_weight`) when `labels` are provided.
 
     ```python
     >>> from transformers import Qwen3_5TextModel, Qwen3_5TextConfig
@@ -105,6 +115,9 @@ class Qwen3_5TextConfig(Qwen3NextConfig):
     intermediate_size: int = 12288
     num_hidden_layers: int = 32
     num_key_value_heads: int = 4
+    mtp_num_hidden_layers: int = 0
+    mtp_loss_weight: float = 0.0
+    output_mtp_loss: bool = False
 
     decoder_sparse_step = AttributeError()
     norm_topk_prob = AttributeError()
@@ -138,6 +151,15 @@ class Qwen3_5VisionConfig(Qwen3VLVisionConfig):
 @strict
 class Qwen3_5Config(Qwen3VLConfig):
     r"""
+    mtp_num_hidden_layers (`int`, *optional*, defaults to 0):
+        Number of hidden layers in the Multi-Token Prediction (MTP) module. When set to 0, MTP is disabled.
+    mtp_loss_weight (`float`, *optional*, defaults to 0.0):
+        Weight for the MTP auxiliary loss. The total loss is computed as `main_loss + mtp_loss_weight * mtp_loss`.
+    output_mtp_loss (`bool`, *optional*, defaults to `False`):
+        Whether to return the MTP auxiliary loss in the model output. When `True`, the `mtp_loss` field in the
+        output will contain the MTP loss value, and it will be added to the main loss (weighted by
+        `mtp_loss_weight`) when `labels` are provided.
+
     Example:
 
     ```python
@@ -157,6 +179,9 @@ class Qwen3_5Config(Qwen3VLConfig):
     video_token_id: int = 248057
     vision_start_token_id: int = 248053
     vision_end_token_id: int = 248054
+    mtp_num_hidden_layers: int = 0
+    mtp_loss_weight: float = 0.0
+    output_mtp_loss: bool = False
 
 
 class Qwen3_5VisionRotaryEmbedding(Qwen3VLVisionRotaryEmbedding):
@@ -341,6 +366,225 @@ class Qwen3_5RMSNorm(Qwen3NextRMSNorm):
     pass
 
 
+@dataclass
+class Qwen3_5CausalLMOutputWithPast(ModelOutput):
+    r"""
+    Base class for Qwen3.5 causal language model (or autoregressive) outputs with MTP loss.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        mtp_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `output_mtp_loss=True`):
+            Multi-Token Prediction auxiliary loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used
+            to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding
+            layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+    """
+
+    loss: torch.FloatTensor | None = None
+    mtp_loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+
+
+@dataclass
+class Qwen3_5VLCausalLMOutputWithPast(ModelOutput):
+    r"""
+    Base class for Qwen3.5 vision-language causal language model (or autoregressive) outputs with MTP loss.
+
+    Args:
+        loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Language modeling loss (for next-token prediction).
+        mtp_loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `output_mtp_loss=True`):
+            Multi-Token Prediction auxiliary loss.
+        logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
+            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+        past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+            Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used
+            to speed up sequential decoding.
+        hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding
+            layer, + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`.
+        attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
+            Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads,
+            sequence_length, sequence_length)`.
+        rope_deltas (`torch.LongTensor` of shape `(batch_size, )`, *optional*):
+            The rope index difference between sequence length and multimodal rope.
+    """
+
+    loss: torch.FloatTensor | None = None
+    mtp_loss: torch.FloatTensor | None = None
+    logits: torch.FloatTensor | None = None
+    past_key_values: Cache | None = None
+    hidden_states: tuple[torch.FloatTensor, ...] | None = None
+    attentions: tuple[torch.FloatTensor, ...] | None = None
+    rope_deltas: torch.LongTensor | None = None
+
+
+class Qwen3_5MTPLayer(nn.Module):
+    def __init__(self, config: Qwen3_5TextConfig, layer_idx: int):
+        super().__init__()
+        mtp_config = copy.copy(config)
+        mtp_layer_types = list(getattr(config, "layer_types", ["full_attention"] * config.num_hidden_layers))
+        while len(mtp_layer_types) <= layer_idx:
+            mtp_layer_types.append("full_attention")
+        mtp_layer_types[layer_idx] = "full_attention"
+        mtp_config.layer_types = mtp_layer_types
+
+        self.input_layernorm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.self_attn = Qwen3_5Attention(mtp_config, layer_idx=layer_idx)
+        self.post_attention_layernorm = Qwen3_5RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.mlp = Qwen3_5MLP(mtp_config, config.intermediate_size)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        attn_out, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = residual + attn_out
+
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class Qwen3_5MTP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        text_config = getattr(config, "text_config", config)
+
+        self.pre_fc_norm_hidden = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+        self.pre_fc_norm_embedding = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+        self.fc = nn.Linear(text_config.hidden_size * 2, text_config.hidden_size, bias=False)
+
+        mtp_num_layers = getattr(config, "mtp_num_hidden_layers", 1)
+
+        self.layers = nn.ModuleList(
+            [Qwen3_5MTPLayer(text_config, layer_idx=text_config.num_hidden_layers + i) for i in range(mtp_num_layers)]
+        )
+        self.norm = Qwen3_5RMSNorm(text_config.hidden_size, eps=text_config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_embeds: torch.Tensor,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        emb = self.pre_fc_norm_embedding(input_embeds)
+        h = self.pre_fc_norm_hidden(hidden_states)
+        fused = self.fc(torch.cat([emb, h], dim=-1))
+
+        for layer in self.layers:
+            fused = layer(
+                hidden_states=fused,
+                position_embeddings=position_embeddings,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+                **kwargs,
+            )
+
+        return self.norm(fused)
+
+
+def _compute_qwen35_mtp_loss(
+    mtp: Qwen3_5MTP,
+    embed_tokens: nn.Embedding,
+    rotary_emb: Qwen3_5TextRotaryEmbedding,
+    lm_head: nn.Linear,
+    loss_function,
+    input_ids: torch.LongTensor,
+    main_hidden_states: torch.Tensor,
+    labels: torch.LongTensor,
+    vocab_size: int,
+    pad_token_id: int,
+    attention_mask: torch.Tensor | None = None,
+    position_ids: torch.LongTensor | None = None,
+) -> torch.Tensor:
+    inputs_embeds_for_pos = embed_tokens(input_ids)
+
+    if position_ids is None:
+        pos = torch.arange(inputs_embeds_for_pos.shape[1], device=inputs_embeds_for_pos.device)
+        pos = pos.view(1, 1, -1).expand(4, inputs_embeds_for_pos.shape[0], -1)
+    elif position_ids.ndim == 2:
+        pos = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
+    else:
+        pos = position_ids
+
+    if pos.ndim == 3 and pos.shape[0] == 4:
+        text_position_ids = pos[0]
+        mrope_position_ids = pos[1:]
+    else:
+        text_position_ids = None
+        mrope_position_ids = pos
+
+    position_embeddings = rotary_emb(inputs_embeds_for_pos, mrope_position_ids)
+
+    total_mtp_loss = torch.tensor(0.0, device=main_hidden_states.device, dtype=main_hidden_states.dtype)
+    current_hidden = main_hidden_states
+
+    for i in range(len(mtp.layers)):
+        shifted_input_ids = input_ids[:, 1:]
+        shifted_input_ids = F.pad(shifted_input_ids, (0, 1), value=pad_token_id)
+        input_embeds = embed_tokens(shifted_input_ids)
+
+        if text_position_ids is not None:
+            mtp_text_position_ids = torch.roll(text_position_ids, -1, dims=-1).clone()
+            mtp_text_position_ids[:, -1] = text_position_ids[:, -1]
+        else:
+            mtp_text_position_ids = None
+
+        mtp_hidden = mtp(
+            input_embeds=input_embeds,
+            hidden_states=current_hidden,
+            position_embeddings=position_embeddings,
+            attention_mask=attention_mask,
+            position_ids=mtp_text_position_ids,
+        )
+        mtp_logits = lm_head(mtp_hidden)
+
+        shift = i + 1
+        shifted_labels = torch.roll(labels, -shift, dims=1).clone()
+        shifted_labels[:, -shift:] = -100
+
+        layer_loss = loss_function(
+            logits=mtp_logits,
+            labels=shifted_labels,
+            vocab_size=vocab_size,
+        )
+
+        total_mtp_loss = total_mtp_loss + layer_loss
+        current_hidden = mtp_hidden
+
+    return total_mtp_loss / len(mtp.layers)
+
+
 class Qwen3_5DecoderLayer(GradientCheckpointingLayer):
     def __init__(self, config: Qwen3_5TextConfig, layer_idx: int):
         super().__init__()
@@ -398,7 +642,7 @@ def forward(
 
 class Qwen3_5PreTrainedModel(Qwen3NextPreTrainedModel):
     config: Qwen3_5Config
-    _no_split_modules = ["Qwen3_5DecoderLayer", "Qwen3_5VisionBlock"]
+    _no_split_modules = ["Qwen3_5DecoderLayer", "Qwen3_5VisionBlock", "Qwen3_5MTPLayer"]
     _can_record_outputs = {
         "hidden_states": Qwen3_5DecoderLayer,
         "attentions": Qwen3_5Attention,
@@ -667,12 +911,130 @@ def __init__(self, config):
         super().__init__(config)
         self.model = Qwen3_5TextModel(config)
 
+        if getattr(config, "mtp_num_hidden_layers", 0) > 0:
+            self.mtp = Qwen3_5MTP(config)
 
-class Qwen3_5ForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
-    config: Qwen3_5TextConfig
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        output_mtp_loss: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> Qwen3_5CausalLMOutputWithPast:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        output_mtp_loss (`bool`, *optional*):
+            Whether to return the MTP auxiliary loss. When `True`, the MTP loss is computed and returned in the
+            `mtp_loss` field of the output. If `labels` are provided, the MTP loss (weighted by `mtp_loss_weight`)
+            is also added to the main loss. If not specified, defaults to `config.output_mtp_loss`.
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, Qwen3_5ForCausalLM
+
+        >>> model = Qwen3_5ForCausalLM.from_pretrained("Qwen/Qwen3_5-8B")
+        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3_5-8B")
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        output_mtp_loss = output_mtp_loss if output_mtp_loss is not None else self.config.output_mtp_loss
+
+        outputs: BaseModelOutputWithPast = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        mtp_loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
+
+        if (
+            output_mtp_loss
+            and labels is not None
+            and input_ids is not None
+            and getattr(self.config, "mtp_num_hidden_layers", 0) > 0
+            and hasattr(self, "mtp")
+        ):
+            mtp_loss = self._compute_mtp_loss(
+                input_ids=input_ids,
+                main_hidden_states=hidden_states,
+                labels=labels,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+            )
+            if loss is not None:
+                mtp_weight = getattr(self.config, "mtp_loss_weight", 0.0)
+                loss = loss + mtp_weight * mtp_loss
+
+        return Qwen3_5CausalLMOutputWithPast(
+            loss=loss,
+            mtp_loss=mtp_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def _compute_mtp_loss(
+        self,
+        input_ids: torch.LongTensor,
+        main_hidden_states: torch.Tensor,
+        labels: torch.LongTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+    ) -> torch.Tensor:
+        pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else 0
+        return _compute_qwen35_mtp_loss(
+            mtp=self.mtp,
+            embed_tokens=self.model.embed_tokens,
+            rotary_emb=self.model.rotary_emb,
+            lm_head=self.lm_head,
+            loss_function=self.loss_function,
+            input_ids=input_ids,
+            main_hidden_states=main_hidden_states,
+            labels=labels,
+            vocab_size=self.config.vocab_size,
+            pad_token_id=pad_token_id,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+        )
 
 
 class Qwen3_5ForConditionalGeneration(Qwen3VLForConditionalGeneration):
+    def __init__(self, config):
+        super().__init__(config)
+
+        if getattr(config, "mtp_num_hidden_layers", 0) > 0:
+            self.mtp = Qwen3_5MTP(config)
+
     def get_video_features(
         self,
         **super_kwargs,
@@ -685,6 +1047,136 @@ def get_image_features(
     ) -> tuple | BaseModelOutputWithPooling:
         return super().get_image_features(**super_kwargs)
 
+    @can_return_tuple
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        mm_token_type_ids: torch.IntTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        output_mtp_loss: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | Qwen3_5VLCausalLMOutputWithPast:
+        output_mtp_loss = output_mtp_loss if output_mtp_loss is not None else self.config.output_mtp_loss
+
+        outputs = self.model(
+            input_ids=input_ids,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            position_ids=position_ids,
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            mm_token_type_ids=mm_token_type_ids,
+            **kwargs,
+        )
+
+        hidden_states = outputs.last_hidden_state
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        mtp_loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
+
+        if (
+            output_mtp_loss
+            and labels is not None
+            and input_ids is not None
+            and getattr(self.config, "mtp_num_hidden_layers", 0) > 0
+            and hasattr(self, "mtp")
+        ):
+            mtp_loss = self._compute_mtp_loss(
+                input_ids=input_ids,
+                main_hidden_states=hidden_states,
+                labels=labels,
+                attention_mask=attention_mask,
+                position_ids=position_ids,
+            )
+            if loss is not None:
+                mtp_weight = getattr(self.config, "mtp_loss_weight", 0.0)
+                loss = loss + mtp_weight * mtp_loss
+
+        return Qwen3_5VLCausalLMOutputWithPast(
+            loss=loss,
+            mtp_loss=mtp_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            rope_deltas=outputs.rope_deltas,
+        )
+
+    def _compute_mtp_loss(
+        self,
+        input_ids: torch.LongTensor,
+        main_hidden_states: torch.Tensor,
+        labels: torch.LongTensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+    ) -> torch.Tensor:
+        pad_token_id = self.config.text_config.pad_token_id if self.config.text_config.pad_token_id is not None else 0
+        return _compute_qwen35_mtp_loss(
+            mtp=self.mtp,
+            embed_tokens=self.model.language_model.embed_tokens,
+            rotary_emb=self.model.language_model.rotary_emb,
+            lm_head=self.lm_head,
+            loss_function=self.loss_function,
+            input_ids=input_ids,
+            main_hidden_states=main_hidden_states,
+            labels=labels,
+            vocab_size=self.config.text_config.vocab_size,
+            pad_token_id=pad_token_id,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+        )
+
+
+class Qwen3_5TextForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
+    config: Qwen3_5TextConfig
+    input_modalities = ("text",)
+
+
+class Qwen3_5ForSequenceClassification(GenericForSequenceClassification, Qwen3_5PreTrainedModel):
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        pixel_values: torch.Tensor | None = None,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        image_grid_thw: torch.LongTensor | None = None,
+        video_grid_thw: torch.LongTensor | None = None,
+        mm_token_type_ids: torch.IntTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> SequenceClassifierOutputWithPast:
+        return super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            pixel_values=pixel_values,
+            pixel_values_videos=pixel_values_videos,
+            image_grid_thw=image_grid_thw,
+            video_grid_thw=video_grid_thw,
+            mm_token_type_ids=mm_token_type_ids,
+            **kwargs,
+        )
+
 
 __all__ = [
     "Qwen3_5Config",
@@ -694,7 +1186,11 @@ def get_image_features(
     "Qwen3_5TextModel",
     "Qwen3_5Model",
     "Qwen3_5ForCausalLM",
+    "Qwen3_5TextForSequenceClassification",
     "Qwen3_5ForSequenceClassification",
     "Qwen3_5ForConditionalGeneration",
     "Qwen3_5PreTrainedModel",
+    "Qwen3_5MTP",
+    "Qwen3_5CausalLMOutputWithPast",
+    "Qwen3_5VLCausalLMOutputWithPast",
 ]
diff --git a/src/transformers/models/qwen3_5_moe/configuration_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/configuration_qwen3_5_moe.py
index f6f9594e0d73..65513958c4ef 100644
--- a/src/transformers/models/qwen3_5_moe/configuration_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/configuration_qwen3_5_moe.py
@@ -129,6 +129,8 @@ class Qwen3_5MoeVisionConfig(PreTrainedConfig):
         The output hidden size of the vision model.
     num_position_embeddings (`int`, *optional*, defaults to 2304):
         The maximum sequence length that this model might ever be used with
+    deepstack_visual_indexes (`list[int]`, *optional*, defaults to `[]`):
+        Indexes of layers for deepstack embeddings.
     """
 
     model_type = "qwen3_5_moe_vision"
@@ -145,6 +147,7 @@ class Qwen3_5MoeVisionConfig(PreTrainedConfig):
     temporal_patch_size: int | list[int] | tuple[int, int] = 2
     out_hidden_size: int = 3584
     num_position_embeddings: int = 2304
+    deepstack_visual_indexes: list[int] | tuple[int, ...] = ()
     initializer_range: float = 0.02
 
 
diff --git a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
index d7b45a276412..c2a4215f95c9 100644
--- a/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modeling_qwen3_5_moe.py
@@ -38,7 +38,6 @@
 from ...modeling_outputs import (
     BaseModelOutputWithPast,
     BaseModelOutputWithPooling,
-    ModelOutput,
     MoeCausalLMOutputWithPast,
     MoeModelOutputWithPast,
 )
@@ -46,7 +45,7 @@
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
 from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
-from ...utils.generic import is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
+from ...utils.generic import ModelOutput, is_flash_attention_requested, maybe_autocast, merge_with_config_defaults
 from ...utils.import_utils import is_causal_conv1d_available, is_flash_linear_attention_available
 from ...utils.output_capturing import OutputRecorder, capture_outputs
 from .configuration_qwen3_5_moe import Qwen3_5MoeConfig, Qwen3_5MoeTextConfig, Qwen3_5MoeVisionConfig
@@ -1286,12 +1285,12 @@ def forward(self, hidden_states: torch.Tensor, grid_thw: torch.Tensor, **kwargs)
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen3_5MoeModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1311,12 +1310,12 @@ class Qwen3_5MoeModelOutputWithPast(ModelOutput):
     router_logits: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen3_5Moe causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen3_5MoeCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1681,18 +1680,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py b/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
index 092643666aed..463a823cce82 100644
--- a/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
+++ b/src/transformers/models/qwen3_5_moe/modular_qwen3_5_moe.py
@@ -119,7 +119,12 @@ def __post_init__(self, **kwargs):
 @auto_docstring(checkpoint="Qwen/Qwen3.5-35B-A3B")
 @strict
 class Qwen3_5MoeVisionConfig(Qwen3_5VisionConfig):
-    pass
+    r"""
+    deepstack_visual_indexes (`list[int]`, *optional*, defaults to `[]`):
+        Indexes of layers for deepstack embeddings.
+    """
+
+    deepstack_visual_indexes: list[int] | tuple[int, ...] = ()
 
 
 @auto_docstring(checkpoint="Qwen/Qwen3.5-35B-A3B")
diff --git a/src/transformers/models/qwen3_asr/__init__.py b/src/transformers/models/qwen3_asr/__init__.py
new file mode 100644
index 000000000000..19df31aaf924
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/__init__.py
@@ -0,0 +1,29 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_qwen3_asr import *
+    from .feature_extraction_qwen3_asr import *
+    from .modeling_qwen3_asr import *
+    from .processing_qwen3_asr import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/qwen3_asr/configuration_qwen3_asr.py b/src/transformers/models/qwen3_asr/configuration_qwen3_asr.py
new file mode 100644
index 000000000000..7094098bca83
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/configuration_qwen3_asr.py
@@ -0,0 +1,166 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/qwen3_asr/modular_qwen3_asr.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_qwen3_asr.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring
+from ..auto import CONFIG_MAPPING, AutoConfig
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ASR-1.7B")
+@strict
+class Qwen3ASREncoderConfig(PreTrainedConfig):
+    r"""
+    max_source_positions (`int`, *optional*, defaults to 1500):
+        The maximum sequence length that this model might ever be used with.
+    n_window (`int`, *optional*, defaults to 50):
+        Half the number of mel frames in one encoder chunk. Each chunk processed by the conv stack has
+        ``2 * n_window`` mel frames (1 second of audio at 16 kHz with a 10 ms hop).
+    n_window_infer (`int`, *optional*, defaults to 800):
+        Number of mel frames worth of audio over which each attention window spans. Must be a multiple
+        of ``n_window * 2`` so attention windows align with encoder chunks.
+    downsample_hidden_size (`int`, *optional*, defaults to 480):
+        Hidden size of the convolutional downsampling stack.
+    output_dim (`int`, *optional*, defaults to 3584):
+        Dimensionality of the output.
+    """
+
+    model_type = "qwen3_asr_audio_encoder"
+    attribute_map = {"num_hidden_layers": "encoder_layers"}
+
+    num_mel_bins: int = 128
+    encoder_layers: int = 24
+    encoder_attention_heads: int = 16
+    encoder_ffn_dim: int = 4096
+    d_model: int = 1024
+    dropout: float | int = 0.0
+    attention_dropout: float | int = 0.0
+    activation_function: str = "gelu"
+    activation_dropout: float | int = 0.0
+    scale_embedding: bool = False
+    initializer_range: float = 0.02
+    max_source_positions: int = 1500
+
+    n_window: int = 50
+    output_dim: int = 3584
+    n_window_infer: int = 800
+    downsample_hidden_size: int = 480
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ASR-1.7B")
+@strict
+class Qwen3ASRConfig(PreTrainedConfig):
+    r"""
+    audio_token_id (`int`, *optional*, defaults to 151676):
+        The audio token id to encode the audio prompt.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRForConditionalGeneration, Qwen3ASRConfig
+
+    >>> # Initializing a Qwen3ASR style configuration
+    >>> configuration = Qwen3ASRConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = Qwen3ASRForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr"
+    sub_configs = {"audio_config": AutoConfig, "text_config": AutoConfig}
+
+    audio_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    audio_token_id: int = 151676
+    pad_token_id: int = 151645
+    eos_token_id: list[int] | tuple[int, ...] | int = (151643, 151645)
+    initializer_range: float = 0.02
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.audio_config, dict):
+            self.audio_config["model_type"] = self.audio_config.get("model_type", "qwen3_asr_audio_encoder")
+            self.audio_config = CONFIG_MAPPING[self.audio_config["model_type"]](**self.audio_config)
+        elif self.audio_config is None:
+            self.audio_config = CONFIG_MAPPING["qwen3_asr_audio_encoder"](
+                encoder_layers=24,
+                encoder_attention_heads=16,
+                encoder_ffn_dim=4096,
+                d_model=1024,
+                output_dim=2048,
+            )
+
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "qwen3")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["qwen3"](
+                hidden_size=2048,
+                intermediate_size=6144,
+                num_hidden_layers=28,
+                num_attention_heads=16,
+                num_key_value_heads=8,
+                head_dim=128,
+                max_position_embeddings=65536,
+                tie_word_embeddings=True,
+            )
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ForcedAligner-0.6B")
+@strict
+class Qwen3ForcedAlignerConfig(Qwen3ASRConfig):
+    r"""
+    num_timestamp_bins (`int`, *optional*, defaults to 5000):
+        Number of discrete timestamp bins the model can predict. Each bin corresponds
+        to a time offset of ``timestamp_segment_time`` milliseconds (set on the processor),
+        so the maximum representable duration is ``num_timestamp_bins * timestamp_segment_time`` ms
+        (e.g. 5000 * 80 ms = 400 s).
+    timestamp_token_id (`int`, *optional*, defaults to 151705):
+        Token ID of the ``<timestamp>`` marker in the tokenizer vocabulary. These markers
+        delimit word boundaries in the forced-alignment input sequence.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRForForcedAlignment, Qwen3ForcedAlignerConfig
+
+    >>> # Initializing a Qwen3ForcedAligner style configuration
+    >>> configuration = Qwen3ForcedAlignerConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = Qwen3ASRForForcedAlignment(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_forced_aligner"
+
+    num_timestamp_bins: int = 5000
+    timestamp_token_id: int = 151705
+
+
+__all__ = ["Qwen3ASREncoderConfig", "Qwen3ASRConfig", "Qwen3ForcedAlignerConfig"]
diff --git a/src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py b/src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py
new file mode 100644
index 000000000000..6075375986d5
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py
@@ -0,0 +1,388 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Convert Qwen3 ASR or Qwen3 Forced Aligner checkpoints to Hugging Face format.
+
+The script auto-detects the model type from the source checkpoint's config.json
+(by looking for a ``classify_num`` field inside ``thinker_config``).  You can
+also force the type with ``--model_type asr`` or ``--model_type forced_aligner``.
+
+Reproducible Usage
+==================
+
+1) Convert a Qwen3 ASR model:
+
+```
+python src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py \
+  --model_id Qwen/Qwen3-ASR-0.6B \
+  --dst_dir qwen3-asr-hf \
+  --push_to_hub <username-or-org>/Qwen3-ASR-0.6B
+```
+
+2) Convert a Qwen3 Forced Aligner model:
+
+```
+python src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py \
+  --model_id Qwen/Qwen3-ForcedAligner-0.6B \
+  --dst_dir qwen3-forced-aligner-hf \
+  --push_to_hub <username-or-org>/Qwen3-ForcedAligner-0.6B
+```
+
+3) Convert from a local directory with explicit model type:
+
+```
+python src/transformers/models/qwen3_asr/convert_qwen3_asr_to_hf.py \
+  --src_dir /path/to/local/model \
+  --dst_dir output-hf \
+  --model_type forced_aligner
+```
+"""
+
+import argparse
+import json
+import logging
+import re
+import shutil
+import tempfile
+from pathlib import Path
+from typing import Any
+
+import torch
+from huggingface_hub import snapshot_download
+from safetensors.torch import safe_open
+
+from transformers import (
+    AutoTokenizer,
+    GenerationConfig,
+    Qwen3ASRConfig,
+    Qwen3ASRFeatureExtractor,
+    Qwen3ASRForConditionalGeneration,
+    Qwen3ASRForForcedAlignment,
+    Qwen3ASRProcessor,
+    Qwen3ForcedAlignerConfig,
+)
+
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
+
+# fmt: off
+STATE_DICT_MAPPING_ASR = {
+    r"^thinker\.audio_tower\.": r"model.audio_tower.",
+    r"^thinker\.lm_head\.": r"lm_head.",
+    r"^thinker\.model\.": r"model.language_model.",
+}
+
+STATE_DICT_MAPPING_FORCED_ALIGNER = {
+    r"^thinker\.audio_tower\.": r"model.audio_tower.",
+    r"^thinker\.lm_head\.": r"classifier.",
+    r"^thinker\.model\.": r"model.language_model.",
+}
+# fmt: on
+
+
+def map_old_key_to_new(old_key: str, mapping: dict[str, str]) -> str:
+    """Map checkpoint keys to transformers model keys."""
+    new_key = old_key
+    for pattern, replacement in mapping.items():
+        new_key, n = re.subn(pattern, replacement, new_key)
+        if n > 0:
+            break
+    return new_key
+
+
+def convert_state_dict(original_state_dict: dict[str, Any], mapping: dict[str, str]) -> dict[str, Any]:
+    """Convert checkpoint state dict to transformers format."""
+    new_state_dict = {}
+    # `Qwen3ASRAudioAttention` inherits from `WhisperAttention`, which hardcodes `bias=False` on
+    # `k_proj` — drop the k_proj bias entries from the source checkpoint (they're mathematically
+    # redundant for softmax attention: a per-query constant that cancels out during softmax).
+    k_proj_bias_re = re.compile(r"audio_tower\.layers\.\d+\.self_attn\.k_proj\.bias$")
+    for old_key, tensor in original_state_dict.items():
+        new_key = map_old_key_to_new(old_key, mapping)
+        if k_proj_bias_re.search(new_key):
+            logger.debug(f"Dropping redundant k_proj bias: {old_key}")
+            continue
+        new_state_dict[new_key] = tensor
+        if old_key != new_key:
+            logger.debug(f"Converted: {old_key} -> {new_key}")
+    return new_state_dict
+
+
+def detect_model_type(src_root: Path) -> str:
+    """Auto-detect model type from the source checkpoint's config.json."""
+    config_path = src_root / "config.json"
+    with open(config_path, "r") as f:
+        config = json.load(f)
+
+    thinker = config.get("thinker_config", {})
+    if "classify_num" in thinker:
+        logger.info("Auto-detected model type: forced_aligner (found classify_num in thinker_config)")
+        return "forced_aligner"
+
+    logger.info("Auto-detected model type: asr (no classify_num in thinker_config)")
+    return "asr"
+
+
+def clean_config(src_root: Path, model_type: str) -> dict:
+    """Load and clean up the source config for transformers compatibility."""
+    config_path = src_root / "config.json"
+    with open(config_path, "r") as f:
+        model_config = json.load(f)
+
+    config_dict = model_config.copy()
+
+    # fmt: off
+    # Remove unused top-level keys
+    for key in ["support_languages"]:
+        config_dict.pop(key, None)
+
+    # Flatten thinker_config structure
+    if "thinker_config" in config_dict:
+        thinker_config = config_dict.pop("thinker_config")
+        if "audio_config" in thinker_config:
+            config_dict["audio_config"] = thinker_config["audio_config"]
+        if "text_config" in thinker_config:
+            config_dict["text_config"] = thinker_config["text_config"]
+        if "audio_token_id" in thinker_config:
+            config_dict["audio_token_id"] = thinker_config["audio_token_id"]
+        if "initializer_range" in thinker_config:
+            config_dict["initializer_range"] = thinker_config["initializer_range"]
+        # Forced aligner specific
+        if model_type == "forced_aligner" and "classify_num" in thinker_config:
+            config_dict["num_timestamp_bins"] = thinker_config["classify_num"]
+
+    # Audio config: strip non-standard fields
+    if "audio_config" in config_dict:
+        audio_unused = [
+            "_name_or_path", "architectures", "dtype", "model_type", "use_bfloat16", "add_cross_attention",
+            "chunk_size_feed_forward", "cross_attention_hidden_size", "decoder_start_token_id",
+            "finetuning_task", "id2label", "label2id", "is_decoder", "is_encoder_decoder",
+            "output_attentions", "output_hidden_states", "pad_token_id", "bos_token_id", "eos_token_id",
+            "prefix", "problem_type", "pruned_heads", "return_dict", "sep_token_id", "task_specific_params",
+            "tf_legacy_loss", "tie_encoder_decoder", "tie_word_embeddings", "tokenizer_class", "torchscript",
+        ]
+        for key in audio_unused:
+            config_dict["audio_config"].pop(key, None)
+
+    # Text config: strip non-standard fields + MoE fields + M-RoPE fields
+    if "text_config" in config_dict:
+        text_unused = [
+            "_name_or_path", "architectures", "dtype", "model_type", "use_bfloat16", "add_cross_attention",
+            "chunk_size_feed_forward", "cross_attention_hidden_size", "decoder_start_token_id",
+            "finetuning_task", "id2label", "label2id", "is_decoder", "is_encoder_decoder",
+            "output_attentions", "output_hidden_states", "prefix", "problem_type", "pruned_heads",
+            "return_dict", "sep_token_id", "task_specific_params", "tf_legacy_loss", "tie_encoder_decoder",
+            "tokenizer_class", "torchscript",
+            # MoE-specific fields
+            "decoder_sparse_step", "moe_intermediate_size", "num_experts_per_tok", "num_experts",
+            "norm_topk_prob", "output_router_logits", "router_aux_loss_coef", "mlp_only_layers",
+        ]
+        for key in text_unused:
+            config_dict["text_config"].pop(key, None)
+
+        # Strip M-RoPE fields from rope_scaling
+        rope_cfg = config_dict["text_config"].get("rope_scaling")
+        if isinstance(rope_cfg, dict):
+            for mrope_key in ["mrope_interleaved", "interleaved", "mrope_section", "type"]:
+                rope_cfg.pop(mrope_key, None)
+    # fmt: on
+
+    return config_dict
+
+
+# fmt: off
+FORCED_ALIGNER_CHAT_TEMPLATE = (
+    "{%- set ns = namespace(audio_tokens='', words=[]) -%}"
+    "{%- for m in messages -%}"
+    "{%- if m.content is not string -%}"
+    "{%- for c in m.content -%}"
+    "{%- if c.type == 'audio' or ('audio' in c) or ('audio_url' in c) -%}"
+    "{%- set ns.audio_tokens = ns.audio_tokens + '<|audio_start|><|audio_pad|><|audio_end|>' -%}"
+    "{%- endif -%}"
+    "{%- if c.type == 'text' and (c.text is defined) -%}"
+    "{%- set ns.words = ns.words + [c.text] -%}"
+    "{%- endif -%}"
+    "{%- endfor -%}"
+    "{%- endif -%}"
+    "{%- endfor -%}"
+    "{{- ns.audio_tokens + ns.words | join('<timestamp><timestamp>') + '<timestamp><timestamp>' -}}"
+)
+# fmt: on
+
+
+def write_processor(src_root: Path, dst_root: Path, model_type: str):
+    """Write processor (shared by both ASR and Forced Aligner)."""
+    tokenizer = AutoTokenizer.from_pretrained(src_root)
+
+    if model_type == "forced_aligner":
+        chat_template = FORCED_ALIGNER_CHAT_TEMPLATE
+    else:
+        # Load chat template from separate file if it exists
+        chat_template_file = src_root / "chat_template.json"
+        chat_template = None
+        if chat_template_file.exists():
+            logger.info("Loading chat template from %s", chat_template_file)
+            with open(chat_template_file, "r", encoding="utf-8") as f:
+                chat_template_data = json.load(f)
+                chat_template = chat_template_data.get("chat_template")
+
+    processor = Qwen3ASRProcessor(
+        feature_extractor=Qwen3ASRFeatureExtractor(),
+        tokenizer=tokenizer,
+        chat_template=chat_template,
+    )
+    processor.save_pretrained(str(dst_root))
+    logger.info("Processor saved to %s", dst_root)
+    return processor
+
+
+def load_state_dict(src_root: Path) -> dict[str, torch.Tensor]:
+    """Load safetensors state dict from source directory."""
+    state = {}
+    shard_files = sorted(src_root.glob("model-*.safetensors"))
+    single_file = src_root / "model.safetensors"
+
+    if shard_files:
+        logger.info("Found %d sharded safetensor files", len(shard_files))
+        safetensor_paths = shard_files
+    elif single_file.exists():
+        safetensor_paths = [single_file]
+    else:
+        raise FileNotFoundError(f"No safetensor files found in {src_root}")
+
+    for path in safetensor_paths:
+        logger.info("Loading %s", path.name)
+        with safe_open(path, framework="pt", device="cpu") as f:
+            for key in f.keys():
+                state[key] = f.get_tensor(key)
+
+    return state
+
+
+def write_asr_model(src_root: Path, dst_root: Path):
+    """Convert and write a Qwen3 ASR model."""
+    config_dict = clean_config(src_root, "asr")
+    config = Qwen3ASRConfig(**config_dict)
+    model = Qwen3ASRForConditionalGeneration(config).to(torch.bfloat16)
+
+    state = load_state_dict(src_root)
+    state = convert_state_dict(state, STATE_DICT_MAPPING_ASR)
+
+    load_res = model.load_state_dict(state, strict=True)
+    if load_res.missing_keys:
+        raise ValueError(f"Missing keys: {load_res.missing_keys}")
+    if load_res.unexpected_keys:
+        raise ValueError(f"Unexpected keys: {load_res.unexpected_keys}")
+
+    model.to(torch.bfloat16)
+    model.generation_config = GenerationConfig(
+        eos_token_id=(151643, 151645),
+        pad_token_id=151645,
+        do_sample=False,
+    )
+    model.save_pretrained(str(dst_root))
+    logger.info("ASR model saved to %s", dst_root)
+    return model
+
+
+def write_forced_aligner_model(src_root: Path, dst_root: Path):
+    """Convert and write a Qwen3 Forced Aligner model."""
+    config_dict = clean_config(src_root, "forced_aligner")
+    config = Qwen3ForcedAlignerConfig(**config_dict)
+    model = Qwen3ASRForForcedAlignment(config).to(torch.bfloat16)
+
+    state = load_state_dict(src_root)
+    state = convert_state_dict(state, STATE_DICT_MAPPING_FORCED_ALIGNER)
+
+    load_res = model.load_state_dict(state, strict=True)
+    if load_res.missing_keys:
+        raise ValueError(f"Missing keys: {load_res.missing_keys}")
+    if load_res.unexpected_keys:
+        raise ValueError(f"Unexpected keys: {load_res.unexpected_keys}")
+
+    model.to(torch.bfloat16)
+    model.save_pretrained(str(dst_root))
+    logger.info("Forced Aligner model saved to %s", dst_root)
+    return model
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(
+        description="Convert Qwen3 ASR or Qwen3 Forced Aligner checkpoints to Hugging Face format."
+    )
+    ap.add_argument("--model_id", default=None, type=str, help="Hugging Face model ID")
+    ap.add_argument("--src_dir", default=None, help="Source model root directory (alternative to --model_id)")
+    ap.add_argument("--dst_dir", required=True, help="Destination directory for converted model")
+    ap.add_argument(
+        "--model_type",
+        default=None,
+        choices=["asr", "forced_aligner"],
+        help="Model type to convert. If not specified, auto-detected from the source config.",
+    )
+    ap.add_argument("--push_to_hub", default=None, type=str, help="Push to Hub repo ID")
+    args = ap.parse_args()
+
+    # Determine source directory
+    if args.model_id:
+        logger.info("Downloading model from Hugging Face Hub: %s", args.model_id)
+        src_root = Path(tempfile.mkdtemp())
+        src_root = Path(snapshot_download(args.model_id, cache_dir=str(src_root)))
+        logger.info("Model downloaded to: %s", src_root)
+    elif args.src_dir:
+        src_root = Path(args.src_dir).resolve()
+    else:
+        raise ValueError("Either --model_id or --src_dir must be provided")
+
+    if not src_root.is_dir():
+        raise FileNotFoundError(f"Source directory not found: {src_root}")
+
+    # Auto-detect or use provided model type
+    model_type = args.model_type or detect_model_type(src_root)
+    logger.info("Converting model type: %s", model_type)
+
+    dst_root = Path(args.dst_dir).resolve()
+    if dst_root.exists():
+        logger.info("Removing existing destination directory: %s", dst_root)
+        shutil.rmtree(dst_root)
+
+    # Write processor (shared class, model-type-specific chat template)
+    processor = write_processor(src_root, dst_root, model_type)
+
+    # Write model
+    if model_type == "asr":
+        model = write_asr_model(src_root, dst_root)
+    else:
+        model = write_forced_aligner_model(src_root, dst_root)
+
+    # Optionally push to Hub
+    if args.push_to_hub:
+        logger.info("Pushing processor to the Hub ...")
+        processor.push_to_hub(args.push_to_hub)
+        logger.info("Pushing model to the Hub ...")
+        model.push_to_hub(args.push_to_hub)
+
+        # Verify upload
+        logger.info("Verifying upload by loading from Hub: %s", args.push_to_hub)
+        _ = Qwen3ASRProcessor.from_pretrained(args.push_to_hub)
+        if model_type == "asr":
+            _ = Qwen3ASRForConditionalGeneration.from_pretrained(args.push_to_hub)
+        else:
+            _ = Qwen3ASRForForcedAlignment.from_pretrained(args.push_to_hub)
+        logger.info("Verification successful!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/qwen3_asr/feature_extraction_qwen3_asr.py b/src/transformers/models/qwen3_asr/feature_extraction_qwen3_asr.py
new file mode 100644
index 000000000000..bf366fb9cb83
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/feature_extraction_qwen3_asr.py
@@ -0,0 +1,266 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+from ... import is_torch_available
+from ...audio_utils import mel_filter_bank, spectrogram, window_function
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import TensorType, logging
+
+
+if is_torch_available():
+    import torch
+
+
+logger = logging.get_logger(__name__)
+
+
+class Qwen3ASRFeatureExtractor(SequenceFeatureExtractor):
+    r"""
+    Constructs a Qwen3 ASR feature extractor.
+
+    Extracts 128-bin log-mel features from raw speech, then right-pads the mel time axis to a multiple of ``2 * n_window``.
+
+    Args:
+        feature_size (`int`, *optional*, defaults to 128):
+            Number of mel filter banks.
+        sampling_rate (`int`, *optional*, defaults to 16000):
+            Audio sampling rate in Hz.
+        hop_length (`int`, *optional*, defaults to 160):
+            Length of the overlapping windows for the STFT used to obtain the Mel Frequency coefficients.
+        chunk_length (`int`, *optional*, defaults to 30):
+            Maximum audio length (in seconds) used to trim/pad when ``padding="max_length"``.
+        n_fft (`int`, *optional*, defaults to 400):
+            Size of the Fourier transform.
+        padding_value (`float`, *optional*, defaults to 0.0):
+            Padding value used to pad the raw audio.
+        dither (`float`, *optional*, defaults to 0.0):
+            If non-zero, adds Gaussian noise (`std = dither`) to each STFT frame.
+        return_attention_mask (`bool`, *optional*, defaults to `False`):
+            Whether to return the attention mask corresponding to the padded mel frames. Recommended for batched inference.
+        n_window (`int`, *optional*, defaults to 50):
+            Half the mel-frame chunk size used for padding. The log-mel time axis is right-padded to a
+            multiple of ``2 * n_window``.
+    """
+
+    model_input_names = ["input_features"]
+
+    def __init__(
+        self,
+        feature_size=128,
+        sampling_rate=16000,
+        hop_length=160,
+        chunk_length=30,
+        n_fft=400,
+        padding_value=0.0,
+        dither=0.0,
+        return_attention_mask=False,
+        n_window=50,
+        **kwargs,
+    ):
+        super().__init__(
+            feature_size=feature_size,
+            sampling_rate=sampling_rate,
+            padding_value=padding_value,
+            return_attention_mask=return_attention_mask,
+            **kwargs,
+        )
+        self.n_fft = n_fft
+        self.hop_length = hop_length
+        self.chunk_length = chunk_length
+        self.n_samples = chunk_length * sampling_rate
+        self.nb_max_frames = self.n_samples // hop_length
+        self.sampling_rate = sampling_rate
+        self.dither = dither
+        self.n_window = n_window
+        self.mel_filters = mel_filter_bank(
+            num_frequency_bins=1 + n_fft // 2,
+            num_mel_filters=feature_size,
+            min_frequency=0.0,
+            max_frequency=8000.0,
+            sampling_rate=sampling_rate,
+            norm="slaney",
+            mel_scale="slaney",
+        )
+
+    def _np_extract_fbank_features(self, waveform_batch: np.ndarray, device: str) -> np.ndarray:
+        """Compute log-mel spectrograms using a NumPy STFT."""
+        if device != "cpu":
+            raise ValueError(
+                f"Got device `{device}` for feature extraction, but feature extraction on CUDA accelerator "
+                "devices requires torch, which is not installed. Either set `device='cpu'`, or "
+                "install torch according to the official instructions: https://pytorch.org/get-started/locally/"
+            )
+        log_spec_batch = []
+        for waveform in waveform_batch:
+            log_spec = spectrogram(
+                waveform,
+                window_function(self.n_fft, "hann"),
+                frame_length=self.n_fft,
+                hop_length=self.hop_length,
+                power=2.0,
+                dither=self.dither,
+                mel_filters=self.mel_filters,
+                log_mel="log10",
+            )
+            log_spec = log_spec[:, :-1]
+            log_spec = np.maximum(log_spec, log_spec.max() - 8.0)
+            log_spec = (log_spec + 4.0) / 4.0
+            log_spec_batch.append(log_spec)
+        return np.array(log_spec_batch)
+
+    def _torch_extract_fbank_features(self, waveform: np.ndarray, device: str = "cpu") -> np.ndarray:
+        """Compute log-mel spectrograms using PyTorch's (optionally GPU-accelerated) STFT."""
+        waveform = torch.from_numpy(waveform).to(device, torch.float32)
+        window = torch.hann_window(self.n_fft, device=device)
+
+        if self.dither != 0.0:
+            waveform += self.dither * torch.randn(waveform.shape, dtype=waveform.dtype, device=waveform.device)
+
+        stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
+        magnitudes = stft[..., :-1].abs() ** 2
+
+        mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32)
+        mel_spec = mel_filters.T @ magnitudes
+
+        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        if waveform.dim() == 2:
+            max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0]
+            log_spec = torch.maximum(log_spec, max_val - 8.0)
+        else:
+            log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+        log_spec = (log_spec + 4.0) / 4.0
+        if device != "cpu":
+            log_spec = log_spec.detach().cpu()
+        return log_spec.numpy()
+
+    def __call__(
+        self,
+        raw_speech: np.ndarray | list[float] | list[np.ndarray] | list[list[float]],
+        truncation: bool = True,
+        pad_to_multiple_of: int | None = None,
+        return_tensors: str | TensorType | None = None,
+        return_attention_mask: bool | None = None,
+        padding: str | None = "max_length",
+        max_length: int | None = None,
+        sampling_rate: int | None = None,
+        n_window: int | None = None,
+        device: str | None = "cpu",
+        **kwargs,
+    ) -> BatchFeature:
+        r"""
+        Prepare log-mel features from one or several audio sequences.
+
+        Args:
+            raw_speech (`np.ndarray`, `list[float]`, `list[np.ndarray]`, `list[list[float]]`):
+                The sequence or batch of sequences to be padded. Mono-channel audio only.
+            truncation (`bool`, *optional*, defaults to `True`):
+                Truncate audio longer than ``max_length`` samples.
+            pad_to_multiple_of (`int`, *optional*):
+                If set, pads the raw audio to a multiple of this value (in samples). Separate from
+                ``n_window``, which applies to the mel-frame axis.
+            return_tensors (`str` or [`~utils.TensorType`], *optional*):
+                Return format: ``'pt'`` for PyTorch tensors, ``'np'`` for NumPy arrays.
+            return_attention_mask (`bool`, *optional*):
+                Whether to return the mel-frame attention mask (recommended for batched inference).
+            padding (`str` or [`~utils.PaddingStrategy`], *optional*, defaults to `"max_length"`):
+                Padding strategy: ``"longest"``, ``"max_length"`` or ``"do_not_pad"``.
+            max_length (`int`, *optional*):
+                Maximum audio length (in samples) when ``padding="max_length"``.
+            sampling_rate (`int`, *optional*):
+                Sampling rate of ``raw_speech``. Must match the feature extractor's sampling rate.
+            n_window (`int`, *optional*):
+                Override the instance's ``n_window`` for this call. The mel axis is padded to a multiple
+                of ``2 * n_window``. Set to ``0`` to skip mel-axis padding entirely.
+            device (`str`, *optional*, defaults to `"cpu"`):
+                Device used to compute the log-mel spectrogram.
+        """
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
+                    f" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
+                    f" was sampled with {self.sampling_rate} and not {sampling_rate}."
+                )
+        else:
+            logger.warning(
+                f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        is_batched_numpy = isinstance(raw_speech, np.ndarray) and len(raw_speech.shape) > 1
+        if is_batched_numpy and len(raw_speech.shape) > 2:
+            raise ValueError(f"Only mono-channel audio is supported for input to {self}")
+        is_batched = is_batched_numpy or (
+            isinstance(raw_speech, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
+        )
+
+        if is_batched:
+            raw_speech = [np.asarray([speech], dtype=np.float32).T for speech in raw_speech]
+        elif not is_batched and not isinstance(raw_speech, np.ndarray):
+            raw_speech = np.asarray(raw_speech, dtype=np.float32)
+        elif isinstance(raw_speech, np.ndarray) and raw_speech.dtype is np.dtype(np.float64):
+            raw_speech = raw_speech.astype(np.float32)
+
+        # always return batch
+        if not is_batched:
+            raw_speech = [np.asarray([raw_speech]).T]
+
+        batched_speech = BatchFeature({"input_features": raw_speech})
+
+        padded_inputs = self.pad(
+            batched_speech,
+            padding=padding,
+            max_length=max_length if max_length else self.n_samples,
+            truncation=truncation,
+            pad_to_multiple_of=pad_to_multiple_of,
+            return_attention_mask=True,
+        )
+
+        input_features = padded_inputs.get("input_features").transpose(2, 0, 1)
+        extract_fbank_features = (
+            self._torch_extract_fbank_features if is_torch_available() else self._np_extract_fbank_features
+        )
+        input_features = extract_fbank_features(input_features[0], device)
+        padded_inputs["input_features"] = input_features
+
+        # Rescale raw-sample attention mask to mel-frame resolution.
+        rescaled_attention_mask = padded_inputs["attention_mask"][:, :: self.hop_length]
+        if padded_inputs["attention_mask"].shape[1] % self.hop_length != 0:
+            rescaled_attention_mask = rescaled_attention_mask[:, :-1]
+        padded_inputs["attention_mask"] = rescaled_attention_mask
+
+        # Right-pad the mel time axis to a multiple of `2 * n_window` (needed by `Qwen3ASREncoder`).
+        if n_window is None:
+            n_window = self.n_window
+        multiple = n_window * 2
+        if multiple and multiple > 1:
+            remainder = padded_inputs["input_features"].shape[-1] % multiple
+            if remainder:
+                pad = multiple - remainder
+                padded_inputs["input_features"] = np.pad(padded_inputs["input_features"], [(0, 0), (0, 0), (0, pad)])
+                padded_inputs["attention_mask"] = np.pad(padded_inputs["attention_mask"], [(0, 0), (0, pad)])
+
+        if not return_attention_mask:
+            padded_inputs.pop("attention_mask", None)
+
+        if return_tensors is not None:
+            padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
+
+        return padded_inputs
+
+
+__all__ = ["Qwen3ASRFeatureExtractor"]
diff --git a/src/transformers/models/qwen3_asr/modeling_qwen3_asr.py b/src/transformers/models/qwen3_asr/modeling_qwen3_asr.py
new file mode 100644
index 000000000000..b86d0abbe6d8
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/modeling_qwen3_asr.py
@@ -0,0 +1,777 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/qwen3_asr/modular_qwen3_asr.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_qwen3_asr.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from collections.abc import Callable
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...cache_utils import Cache, EncoderDecoderCache
+from ...generation import GenerationMixin
+from ...masking_utils import create_bidirectional_mask
+from ...modeling_flash_attention_utils import FlashAttentionKwargs
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast, TokenClassifierOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils.generic import is_flash_attention_requested, merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..auto import AutoModel
+from .configuration_qwen3_asr import Qwen3ASRConfig, Qwen3ASREncoderConfig, Qwen3ForcedAlignerConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring
+class Qwen3ASRPreTrainedModel(PreTrainedModel):
+    config: Qwen3ASRConfig
+    base_model_prefix = "model"
+    input_modalities = ("audio", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["Qwen3ASREncoderLayer", "Qwen3DecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, SinusoidsPositionEmbedding):
+            log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1)
+            inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float())
+            scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+            init.copy_(
+                module.positional_embedding,
+                torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+            )
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float | None = None,
+    dropout: float = 0.0,
+    **kwargs,
+):
+    if scaling is None:
+        scaling = query.size(-1) ** -0.5
+
+    attn_weights = torch.matmul(query, key.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1)
+
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+class Qwen3ASRAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        dropout: float = 0.0,
+        is_decoder: bool = False,
+        bias: bool = True,
+        is_causal: bool = False,
+        layer_idx: int | None = None,
+        config: Qwen3ASRConfig | None = None,
+    ):
+        super().__init__()
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.head_dim = embed_dim // num_heads
+        self.config = config
+
+        if (self.head_dim * num_heads) != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+                f" and `num_heads`: {num_heads})."
+            )
+        self.scaling = self.head_dim**-0.5
+        self.is_decoder = is_decoder
+        self.is_causal = is_causal
+
+        if layer_idx is None and is_decoder:
+            logger.warning_once(
+                f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
+                "will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+        self.layer_idx = layer_idx
+
+        self.k_proj = nn.Linear(embed_dim, embed_dim, bias=False)
+        self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+        self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        key_value_states: torch.Tensor | None = None,
+        past_key_values: Cache | None = None,
+        attention_mask: torch.Tensor | None = None,
+        output_attentions: bool = False,
+        # TODO: we need a refactor so that the different attention modules can get their specific kwargs
+        # ATM, we have mixed things encoder, decoder, and encoder-decoder attn
+        **kwargs: Unpack[FlashAttentionKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None, tuple[torch.Tensor] | None]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        # Scaling is susceptible to floating point arithmetics' inprecisions
+        # which can lead to different results (this is dependent from model
+        # to model, e.g. qwen3_asr is one such case). We therefore keep the
+        # original order of scaling to follow the original implementation
+        # and enforce no scaling (1.0) in the attention call below.
+        query_states = (self.q_proj(hidden_states) * self.scaling).view(hidden_shape).transpose(1, 2).contiguous()
+
+        # Check is encoder-decoder model is being used. Otherwise we'll get `DynamicCache`
+        if past_key_values is not None and isinstance(past_key_values, EncoderDecoderCache):
+            is_updated = past_key_values.is_updated.get(self.layer_idx)
+            if is_cross_attention:
+                # after the first generated id, we can subsequently re-use all key/value_states from cache
+                past_key_values.is_updated[self.layer_idx] = True
+                past_key_values = past_key_values.cross_attention_cache
+            else:
+                past_key_values = past_key_values.self_attention_cache
+
+        # use key_value_states if cross attention
+        current_states = key_value_states if key_value_states is not None else hidden_states
+        if is_cross_attention and past_key_values and is_updated:
+            # reuse k,v, cross_attentions
+            key_states = past_key_values.layers[self.layer_idx].keys
+            value_states = past_key_values.layers[self.layer_idx].values
+        else:
+            # Use the query's batch dimension for kv view so that a different-batch
+            # encoder output (e.g. in tests) gets absorbed into the sequence axis,
+            # preserving backward-compatible behaviour.
+            kv_shape = (input_shape[0], -1, self.num_heads, self.head_dim)
+            key_states = self.k_proj(current_states).view(kv_shape).transpose(1, 2).contiguous()
+            value_states = self.v_proj(current_states).view(kv_shape).transpose(1, 2).contiguous()
+            if past_key_values is not None:
+                key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.dropout,
+            scaling=1.0,
+            output_attentions=output_attentions,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class Qwen3ASREncoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: Qwen3ASRConfig):
+        super().__init__()
+        self.embed_dim = config.d_model
+
+        self.self_attn = Qwen3ASRAttention(
+            embed_dim=self.embed_dim,
+            num_heads=config.encoder_attention_heads,
+            dropout=config.attention_dropout,
+            config=config,
+        )
+        self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim)
+        self.dropout = config.dropout
+        self.activation_fn = ACT2FN[config.activation_function]
+        self.activation_dropout = config.activation_dropout
+        self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim)
+        self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim)
+        self.final_layer_norm = nn.LayerNorm(self.embed_dim)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`): attention mask of size
+                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+        """
+        residual = hidden_states
+        hidden_states = self.self_attn_layer_norm(hidden_states)
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            **kwargs,
+        )
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+        hidden_states = residual + hidden_states
+
+        residual = hidden_states
+        hidden_states = self.final_layer_norm(hidden_states)
+        hidden_states = self.activation_fn(self.fc1(hidden_states))
+        hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training)
+        hidden_states = self.fc2(hidden_states)
+        hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training)
+        hidden_states = residual + hidden_states
+
+        if hidden_states.dtype == torch.float16:
+            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
+            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
+
+        return hidden_states
+
+
+class SinusoidsPositionEmbedding(nn.Module):
+    def __init__(self, length, channels, max_timescale=10000):
+        super().__init__()
+        self.length = length
+        self.channels = channels
+        self.max_timescale = max_timescale
+        if channels % 2 != 0:
+            raise ValueError("SinusoidsPositionEmbedding needs even channels input")
+        log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1)
+        inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2).float())
+        scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+        self.register_buffer(
+            "positional_embedding",
+            torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+            persistent=False,
+        )
+
+    def forward(self, seqlen: int):
+        return self.positional_embedding[:seqlen, :]
+
+
+@auto_docstring(
+    custom_intro="""
+    The audio model for Qwen3 ASR without any head or projection on top.
+    """
+)
+class Qwen3ASREncoder(Qwen3ASRPreTrainedModel):
+    config: Qwen3ASREncoderConfig
+    main_input_name = "input_features"
+    input_modalities = "audio"
+    _no_split_modules = ["Qwen3ASREncoderLayer"]
+    _supports_sdpa = True
+    _can_record_outputs = {
+        "hidden_states": Qwen3ASREncoderLayer,
+        "attentions": Qwen3ASRAttention,
+    }
+    _can_compile_fullgraph = True
+
+    def __init__(self, config: Qwen3ASREncoderConfig):
+        super().__init__(config)
+        self.dropout = config.dropout
+
+        embed_dim = config.d_model
+        self.num_mel_bins = config.num_mel_bins
+        self.max_source_positions = config.max_source_positions
+        self.embed_scale = math.sqrt(embed_dim) if config.scale_embedding else 1.0
+        self.n_window = config.n_window
+        self.positional_embedding = SinusoidsPositionEmbedding(self.max_source_positions, embed_dim)
+        self.layers = nn.ModuleList([Qwen3ASREncoderLayer(config) for _ in range(config.encoder_layers)])
+        self.ln_post = nn.LayerNorm(config.d_model)
+        self.gradient_checkpointing = False
+        self.conv2d1 = nn.Conv2d(1, config.downsample_hidden_size, 3, 2, padding=1)
+        self.conv2d2 = nn.Conv2d(config.downsample_hidden_size, config.downsample_hidden_size, 3, 2, padding=1)
+        self.conv2d3 = nn.Conv2d(config.downsample_hidden_size, config.downsample_hidden_size, 3, 2, padding=1)
+        self.conv_out = nn.Linear(
+            config.downsample_hidden_size * ((((config.num_mel_bins + 1) // 2 + 1) // 2 + 1) // 2),
+            config.d_model,
+            bias=False,
+        )
+        self.proj1 = nn.Linear(config.d_model, config.d_model)
+        self.act = ACT2FN[config.activation_function]
+        self.proj2 = nn.Linear(config.d_model, config.output_dim)
+        self.n_window_infer = self.config.n_window_infer
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def _freeze_parameters(self):
+        for param in self.parameters():
+            param.requires_grad = False
+        self._requires_grad = False
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.conv2d1
+
+    def set_input_embeddings(self, value):
+        self.conv2d1 = value
+
+    def _prepare_attention_mask(self, inputs_tensor: torch.Tensor, cu_seqlens: torch.Tensor) -> torch.Tensor:
+        # Flash Attention 2 doesn't need a 4D mask and relies on `cu_seqlens/max_seqlen`
+        # NOTE: the created attention masl only approximates the ragged FA2 attention by
+        # allowing bidirectional attention within `cu_seqlens` blocks, and not attending between
+        # blocks. Though it will not be a 100% match for FA2's `varlen` path
+        if is_flash_attention_requested(self.config):
+            return None
+
+        seq_length = inputs_tensor.shape[0]
+        attention_mask = torch.full(
+            [1, 1, seq_length, seq_length],
+            torch.finfo(inputs_tensor.dtype).min,
+            device=inputs_tensor.device,
+            dtype=inputs_tensor.dtype,
+        )
+        for i in range(1, len(cu_seqlens)):
+            attention_mask[..., cu_seqlens[i - 1] : cu_seqlens[i], cu_seqlens[i - 1] : cu_seqlens[i]] = 0
+        return attention_mask
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        input_features: torch.Tensor,
+        input_features_mask: torch.Tensor,
+        **kwargs,
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        Args:
+            input_features (`torch.FloatTensor` of shape `(batch_size, num_mel_bins, padded_feature_length)`):
+                Log-mel features. `padded_feature_length` must be a multiple of `self.n_window * 2`.
+            input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+                1 for valid mel frames and 0 for padding.
+        """
+        batch_size, num_mel_bins, padded_feature_length = input_features.shape
+        chunk_len = self.n_window * 2
+        num_chunks = padded_feature_length // chunk_len
+
+        chunked = (
+            input_features.view(batch_size, num_mel_bins, num_chunks, chunk_len)
+            .permute(0, 2, 1, 3)
+            .reshape(batch_size * num_chunks, 1, num_mel_bins, chunk_len)
+        )
+
+        conv_out = F.gelu(self.conv2d1(chunked))
+        conv_out = F.gelu(self.conv2d2(conv_out))
+        conv_out = F.gelu(self.conv2d3(conv_out))
+        total_chunks, conv_channels, freq_bins, time_steps = conv_out.size()
+        conv_out = self.conv_out(
+            conv_out.permute(0, 3, 1, 2).contiguous().view(total_chunks, time_steps, conv_channels * freq_bins)
+        )
+        conv_out = conv_out + self.positional_embedding.positional_embedding[:time_steps, :].to(conv_out.dtype)
+        chunk_embeds = conv_out.view(batch_size, num_chunks, time_steps, -1)
+
+        # Mask out post-cnn positions that came from zero-padded mel frames.
+        chunk_mel_lens = input_features_mask.view(batch_size, num_chunks, chunk_len).sum(dim=-1)
+        chunk_post_cnn_lens = self._post_cnn_length(chunk_mel_lens)
+        post_cnn_positions = torch.arange(time_steps, device=input_features.device)
+        valid_post_cnn_mask = post_cnn_positions[None, None, :] < chunk_post_cnn_lens[:, :, None]
+        sequence_length = num_chunks * time_steps
+        hidden_states = chunk_embeds.reshape(batch_size, sequence_length, -1)
+        sequence_mask = valid_post_cnn_mask.reshape(batch_size, sequence_length).to(dtype=torch.long)
+
+        attention_mask = create_bidirectional_mask(
+            config=self.config,
+            inputs_embeds=hidden_states,
+            attention_mask=sequence_mask,
+        )
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states, attention_mask=attention_mask, **kwargs)
+            hidden_states = hidden_states * sequence_mask.to(hidden_states.dtype).unsqueeze(-1)
+
+        hidden_states = self.ln_post(hidden_states)
+        hidden_states = self.proj1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.proj2(hidden_states)
+        return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
+
+    def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, padding_side="right"):
+        """
+        Pads a sequence of tensors to their maximum length on indicated `padding_side`.
+        Then prepares a mask so that pad tokens are not attended to.
+        """
+        max_len = tensor_len.max()
+        dim = tensor_list[0].shape[0]
+        padded_tensor = torch.full(
+            size=(len(tensor_list), dim, max_len),
+            fill_value=padding_value,
+            dtype=self.dtype,
+            device=tensor_list[0].device,
+        )
+
+        batch_mask = torch.zeros(
+            (len(tensor_len), max_len),
+            dtype=torch.long,
+            device=padded_tensor.device,
+        )
+        for i, length in enumerate(tensor_len):
+            batch_mask[i, :length] = 1
+            padded_tensor[i, :, :length] = tensor_list[i]
+
+        feature_lens_after_cnn = (tensor_len - 1) // 2 + 1
+        max_len_after_cnn = feature_lens_after_cnn.max()
+        batch_mask_after_cnn = torch.zeros(
+            (len(tensor_len), max_len_after_cnn),
+            dtype=torch.long,
+            device=padded_tensor.device,
+        )
+        for i, length in enumerate(feature_lens_after_cnn):
+            batch_mask_after_cnn[i, :length] = 1
+        return (
+            padded_tensor,
+            batch_mask.unsqueeze(1),
+            batch_mask_after_cnn.bool(),
+        )
+
+    @staticmethod
+    def _post_cnn_length(lengths: torch.Tensor) -> torch.Tensor:
+        """Length after three (k=3, s=2, p=1) convolutions; zero-length input stays zero."""
+        for _ in range(3):
+            lengths = torch.where(lengths > 0, (lengths - 1) // 2 + 1, torch.zeros_like(lengths))
+        return lengths
+
+
+def _get_feat_extract_output_lengths(input_lengths, n_window=50):
+    """
+    Computes the output length of the convolutional layers and the output length of the audio encoder
+    """
+
+    chunk_len = n_window * 2
+    input_lengths_leave = input_lengths % chunk_len
+    feat_lengths = (input_lengths_leave - 1) // 2 + 1
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // chunk_len) * 13
+    return output_lengths
+
+
+class Qwen3ASRModel(Qwen3ASRPreTrainedModel):
+    def __init__(self, config: Qwen3ASRConfig):
+        super().__init__(config)
+        self.audio_tower = Qwen3ASREncoder(config.audio_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="This method is used to get the audio embeddings from input features (a log mel spectrogram)."
+    )
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+            1 for valid mel frames and 0 for padding.
+        """
+        audio_output = self.audio_tower(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+        audio_embeds = audio_output.last_hidden_state
+        input_lengths = input_features_mask.sum(-1).to(torch.long)
+        audio_token_lengths = _get_feat_extract_output_lengths(input_lengths, self.config.audio_config.n_window)
+        valid_mask = (
+            torch.arange(audio_embeds.shape[1], device=audio_embeds.device)[None, :] < audio_token_lengths[:, None]
+        )
+        audio_output.pooler_output = audio_embeds[valid_mask]
+        return audio_output
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ):
+        r"""
+        input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+            1 for valid mel frames and 0 for padding.
+        """
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if input_features is not None and input_ids is not None:
+            audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
+
+            # replace text-audio token placeholders with audio embeddings
+            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
+            inputs_embeds = inputs_embeds.masked_scatter(
+                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            )
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        return outputs
+
+
+@auto_docstring(
+    custom_intro="""
+    The Qwen3ASR model which consists of an audio encoder and a language model.
+    """
+)
+class Qwen3ASRForConditionalGeneration(Qwen3ASRPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: Qwen3ASRConfig):
+        super().__init__(config)
+        self.model = Qwen3ASRModel(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    @auto_docstring
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
+            Mask to avoid performing attention on padded feature indices.
+        """
+        return self.model.get_audio_features(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(self, *args, is_first_iteration: bool = False, **kwargs):
+        input_features = kwargs.pop("input_features", None)
+        input_features_mask = kwargs.pop("input_features_mask", None)
+
+        model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
+
+        if is_first_iteration or not model_inputs.get("use_cache", False):
+            if input_features is not None:
+                model_inputs["input_features"] = input_features
+            if input_features_mask is not None:
+                model_inputs["input_features_mask"] = input_features_mask
+
+        return model_inputs
+
+
+@auto_docstring(
+    custom_intro="""
+    The Qwen3 Forced Aligner model which consists of an audio encoder, a language model backbone,
+    and a token classification head for forced alignment.
+    """
+)
+class Qwen3ASRForForcedAlignment(Qwen3ASRPreTrainedModel):
+    config_class = Qwen3ForcedAlignerConfig
+
+    def __init__(self, config: Qwen3ForcedAlignerConfig):
+        super().__init__(config)
+        self.num_timestamp_bins = config.num_timestamp_bins
+        self.model = Qwen3ASRModel(config)
+        self.classifier = nn.Linear(config.text_config.hidden_size, config.num_timestamp_bins, bias=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        return self.model.get_audio_features(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> TokenClassifierOutput:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding feature indices.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the forced alignment loss. Indices should be in `[0, ..., config.num_timestamp_bins - 1]`.
+        """
+
+        outputs = self.model(
+            input_ids=input_ids,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.num_timestamp_bins)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "Qwen3ASREncoder",
+    "Qwen3ASRForConditionalGeneration",
+    "Qwen3ASRModel",
+    "Qwen3ASRPreTrainedModel",
+    "Qwen3ASRForForcedAlignment",
+]
diff --git a/src/transformers/models/qwen3_asr/modular_qwen3_asr.py b/src/transformers/models/qwen3_asr/modular_qwen3_asr.py
new file mode 100644
index 000000000000..3c5fb90b41d2
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/modular_qwen3_asr.py
@@ -0,0 +1,570 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from huggingface_hub.dataclasses import strict
+from torch import nn
+
+from ... import initialization as init
+from ...cache_utils import Cache
+from ...configuration_utils import PreTrainedConfig
+from ...generation import GenerationMixin
+from ...masking_utils import create_bidirectional_mask
+from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast, TokenClassifierOutput
+from ...modeling_utils import PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ..auto import CONFIG_MAPPING, AutoConfig, AutoModel
+from ..qwen2_5_omni.configuration_qwen2_5_omni import Qwen2_5OmniAudioEncoderConfig
+from ..qwen2_audio.modeling_qwen2_audio import Qwen2AudioPreTrainedModel
+from ..qwen3_omni_moe.modeling_qwen3_omni_moe import (
+    Qwen3OmniMoeAudioEncoder,
+    SinusoidsPositionEmbedding,
+    _get_feat_extract_output_lengths,
+)
+from ..whisper.modeling_whisper import WhisperAttention, WhisperEncoderLayer
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ASR-1.7B")
+@strict
+class Qwen3ASREncoderConfig(Qwen2_5OmniAudioEncoderConfig):
+    r"""
+    max_source_positions (`int`, *optional*, defaults to 1500):
+        The maximum sequence length that this model might ever be used with.
+    n_window (`int`, *optional*, defaults to 50):
+        Half the number of mel frames in one encoder chunk. Each chunk processed by the conv stack has
+        ``2 * n_window`` mel frames (1 second of audio at 16 kHz with a 10 ms hop).
+    n_window_infer (`int`, *optional*, defaults to 800):
+        Number of mel frames worth of audio over which each attention window spans. Must be a multiple
+        of ``n_window * 2`` so attention windows align with encoder chunks.
+    downsample_hidden_size (`int`, *optional*, defaults to 480):
+        Hidden size of the convolutional downsampling stack.
+    output_dim (`int`, *optional*, defaults to 3584):
+        Dimensionality of the output.
+    """
+
+    model_type = "qwen3_asr_audio_encoder"
+
+    n_window: int = 50
+    n_window_infer: int = 800
+    downsample_hidden_size: int = 480
+    encoder_layers: int = 24
+    encoder_attention_heads: int = 16
+    encoder_ffn_dim: int = 4096
+    d_model: int = 1024
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ASR-1.7B")
+@strict
+class Qwen3ASRConfig(PreTrainedConfig):
+    r"""
+    audio_token_id (`int`, *optional*, defaults to 151676):
+        The audio token id to encode the audio prompt.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRForConditionalGeneration, Qwen3ASRConfig
+
+    >>> # Initializing a Qwen3ASR style configuration
+    >>> configuration = Qwen3ASRConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = Qwen3ASRForConditionalGeneration(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_asr"
+    sub_configs = {"audio_config": AutoConfig, "text_config": AutoConfig}
+
+    audio_config: dict | PreTrainedConfig | None = None
+    text_config: dict | PreTrainedConfig | None = None
+    audio_token_id: int = 151676
+    pad_token_id: int = 151645
+    eos_token_id: list[int] | tuple[int, ...] | int = (151643, 151645)
+    initializer_range: float = 0.02
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self, **kwargs):
+        if isinstance(self.audio_config, dict):
+            self.audio_config["model_type"] = self.audio_config.get("model_type", "qwen3_asr_audio_encoder")
+            self.audio_config = CONFIG_MAPPING[self.audio_config["model_type"]](**self.audio_config)
+        elif self.audio_config is None:
+            self.audio_config = CONFIG_MAPPING["qwen3_asr_audio_encoder"](
+                encoder_layers=24,
+                encoder_attention_heads=16,
+                encoder_ffn_dim=4096,
+                d_model=1024,
+                output_dim=2048,
+            )
+
+        if isinstance(self.text_config, dict):
+            self.text_config["model_type"] = self.text_config.get("model_type", "qwen3")
+            self.text_config = CONFIG_MAPPING[self.text_config["model_type"]](**self.text_config)
+        elif self.text_config is None:
+            self.text_config = CONFIG_MAPPING["qwen3"](
+                hidden_size=2048,
+                intermediate_size=6144,
+                num_hidden_layers=28,
+                num_attention_heads=16,
+                num_key_value_heads=8,
+                head_dim=128,
+                max_position_embeddings=65536,
+                tie_word_embeddings=True,
+            )
+
+        super().__post_init__(**kwargs)
+
+
+@auto_docstring
+class Qwen3ASRPreTrainedModel(Qwen2AudioPreTrainedModel):
+    _no_split_modules = ["Qwen3ASREncoderLayer", "Qwen3DecoderLayer"]
+    _can_compile_fullgraph = True
+    _supports_attention_backend = True
+
+    def _init_weights(self, module):
+        PreTrainedModel._init_weights(self, module)
+        if isinstance(module, SinusoidsPositionEmbedding):
+            log_timescale_increment = np.log(module.max_timescale) / (module.channels // 2 - 1)
+            inv_timescales = torch.exp(-log_timescale_increment * torch.arange(module.channels // 2).float())
+            scaled_time = torch.arange(module.length)[:, np.newaxis] * inv_timescales[np.newaxis, :]
+            init.copy_(
+                module.positional_embedding,
+                torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1),
+            )
+
+
+class Qwen3ASRAttention(WhisperAttention):
+    pass
+
+
+class Qwen3ASREncoderLayer(WhisperEncoderLayer):
+    pass
+
+
+@auto_docstring(
+    custom_intro="""
+    The audio model for Qwen3 ASR without any head or projection on top.
+    """
+)
+class Qwen3ASREncoder(Qwen3OmniMoeAudioEncoder):
+    config: Qwen3ASREncoderConfig
+    _no_split_modules = ["Qwen3ASREncoderLayer"]
+    _can_compile_fullgraph = True
+    _can_record_outputs = {
+        "hidden_states": Qwen3ASREncoderLayer,
+        "attentions": Qwen3ASRAttention,
+    }
+
+    def __init__(self, config: Qwen3ASREncoderConfig):
+        super().__init__(config)
+        del self.conv_chunksize
+        self.layers = nn.ModuleList([Qwen3ASREncoderLayer(config) for _ in range(config.encoder_layers)])
+
+    @staticmethod
+    def _post_cnn_length(lengths: torch.Tensor) -> torch.Tensor:
+        """Length after three (k=3, s=2, p=1) convolutions; zero-length input stays zero."""
+        for _ in range(3):
+            lengths = torch.where(lengths > 0, (lengths - 1) // 2 + 1, torch.zeros_like(lengths))
+        return lengths
+
+    def forward(
+        self,
+        input_features: torch.Tensor,
+        input_features_mask: torch.Tensor,
+        **kwargs,
+    ) -> BaseModelOutputWithPooling:
+        r"""
+        Args:
+            input_features (`torch.FloatTensor` of shape `(batch_size, num_mel_bins, padded_feature_length)`):
+                Log-mel features. `padded_feature_length` must be a multiple of `self.n_window * 2`.
+            input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+                1 for valid mel frames and 0 for padding.
+        """
+        batch_size, num_mel_bins, padded_feature_length = input_features.shape
+        chunk_len = self.n_window * 2
+        num_chunks = padded_feature_length // chunk_len
+
+        chunked = (
+            input_features.view(batch_size, num_mel_bins, num_chunks, chunk_len)
+            .permute(0, 2, 1, 3)
+            .reshape(batch_size * num_chunks, 1, num_mel_bins, chunk_len)
+        )
+
+        conv_out = F.gelu(self.conv2d1(chunked))
+        conv_out = F.gelu(self.conv2d2(conv_out))
+        conv_out = F.gelu(self.conv2d3(conv_out))
+        total_chunks, conv_channels, freq_bins, time_steps = conv_out.size()
+        conv_out = self.conv_out(
+            conv_out.permute(0, 3, 1, 2).contiguous().view(total_chunks, time_steps, conv_channels * freq_bins)
+        )
+        conv_out = conv_out + self.positional_embedding.positional_embedding[:time_steps, :].to(conv_out.dtype)
+        chunk_embeds = conv_out.view(batch_size, num_chunks, time_steps, -1)
+
+        # Mask out post-cnn positions that came from zero-padded mel frames.
+        chunk_mel_lens = input_features_mask.view(batch_size, num_chunks, chunk_len).sum(dim=-1)
+        chunk_post_cnn_lens = self._post_cnn_length(chunk_mel_lens)
+        post_cnn_positions = torch.arange(time_steps, device=input_features.device)
+        valid_post_cnn_mask = post_cnn_positions[None, None, :] < chunk_post_cnn_lens[:, :, None]
+        sequence_length = num_chunks * time_steps
+        hidden_states = chunk_embeds.reshape(batch_size, sequence_length, -1)
+        sequence_mask = valid_post_cnn_mask.reshape(batch_size, sequence_length).to(dtype=torch.long)
+
+        attention_mask = create_bidirectional_mask(
+            config=self.config,
+            inputs_embeds=hidden_states,
+            attention_mask=sequence_mask,
+        )
+
+        for encoder_layer in self.layers:
+            hidden_states = encoder_layer(hidden_states, attention_mask=attention_mask, **kwargs)
+            hidden_states = hidden_states * sequence_mask.to(hidden_states.dtype).unsqueeze(-1)
+
+        hidden_states = self.ln_post(hidden_states)
+        hidden_states = self.proj1(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states = self.proj2(hidden_states)
+        return BaseModelOutputWithPooling(last_hidden_state=hidden_states)
+
+
+class Qwen3ASRModel(Qwen3ASRPreTrainedModel):
+    def __init__(self, config: Qwen3ASRConfig):
+        super().__init__(config)
+        self.audio_tower = Qwen3ASREncoder(config.audio_config)
+        self.language_model = AutoModel.from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.language_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.language_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring(
+        custom_intro="This method is used to get the audio embeddings from input features (a log mel spectrogram)."
+    )
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+            1 for valid mel frames and 0 for padding.
+        """
+        audio_output = self.audio_tower(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+        audio_embeds = audio_output.last_hidden_state
+        input_lengths = input_features_mask.sum(-1).to(torch.long)
+        audio_token_lengths = _get_feat_extract_output_lengths(input_lengths, self.config.audio_config.n_window)
+        valid_mask = (
+            torch.arange(audio_embeds.shape[1], device=audio_embeds.device)[None, :] < audio_token_lengths[:, None]
+        )
+        audio_output.pooler_output = audio_embeds[valid_mask]
+        return audio_output
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ):
+        r"""
+        input_features_mask (`torch.LongTensor` of shape `(batch_size, padded_feature_length)`):
+            1 for valid mel frames and 0 for padding.
+        """
+
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings()(input_ids)
+
+        if input_features is not None and input_ids is not None:
+            audio_embeds = self.get_audio_features(input_features, input_features_mask, return_dict=True).pooler_output
+
+            # replace text-audio token placeholders with audio embeddings
+            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
+            inputs_embeds = inputs_embeds.masked_scatter(
+                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            )
+
+        outputs = self.language_model(
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+        return outputs
+
+
+@auto_docstring(
+    custom_intro="""
+    The Qwen3ASR model which consists of an audio encoder and a language model.
+    """
+)
+class Qwen3ASRForConditionalGeneration(Qwen3ASRPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = {"lm_head.weight": "model.language_model.embed_tokens.weight"}
+
+    def __init__(self, config: Qwen3ASRConfig):
+        super().__init__(config)
+        self.model = Qwen3ASRModel(config)
+        self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    @auto_docstring
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`):
+            Mask to avoid performing attention on padded feature indices.
+        """
+        return self.model.get_audio_features(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CausalLMOutputWithPast:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding feature indices. Mask values selected in `[0, 1]`:
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+            config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+            (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+        """
+        outputs = self.model(
+            input_ids=input_ids,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(
+                logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size, **kwargs
+            )
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(self, *args, is_first_iteration: bool = False, **kwargs):
+        input_features = kwargs.pop("input_features", None)
+        input_features_mask = kwargs.pop("input_features_mask", None)
+
+        model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
+
+        if is_first_iteration or not model_inputs.get("use_cache", False):
+            if input_features is not None:
+                model_inputs["input_features"] = input_features
+            if input_features_mask is not None:
+                model_inputs["input_features_mask"] = input_features_mask
+
+        return model_inputs
+
+
+@auto_docstring(checkpoint="bezzam/Qwen3-ForcedAligner-0.6B")
+@strict
+class Qwen3ForcedAlignerConfig(Qwen3ASRConfig):
+    r"""
+    num_timestamp_bins (`int`, *optional*, defaults to 5000):
+        Number of discrete timestamp bins the model can predict. Each bin corresponds
+        to a time offset of ``timestamp_segment_time`` milliseconds (set on the processor),
+        so the maximum representable duration is ``num_timestamp_bins * timestamp_segment_time`` ms
+        (e.g. 5000 * 80 ms = 400 s).
+    timestamp_token_id (`int`, *optional*, defaults to 151705):
+        Token ID of the ``<timestamp>`` marker in the tokenizer vocabulary. These markers
+        delimit word boundaries in the forced-alignment input sequence.
+
+    Example:
+
+    ```python
+    >>> from transformers import Qwen3ASRForForcedAlignment, Qwen3ForcedAlignerConfig
+
+    >>> # Initializing a Qwen3ForcedAligner style configuration
+    >>> configuration = Qwen3ForcedAlignerConfig()
+
+    >>> # Initializing a model from the configuration
+    >>> model = Qwen3ASRForForcedAlignment(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "qwen3_forced_aligner"
+
+    num_timestamp_bins: int = 5000
+    timestamp_token_id: int = 151705
+
+
+@auto_docstring(
+    custom_intro="""
+    The Qwen3 Forced Aligner model which consists of an audio encoder, a language model backbone,
+    and a token classification head for forced alignment.
+    """
+)
+class Qwen3ASRForForcedAlignment(Qwen3ASRPreTrainedModel):
+    config_class = Qwen3ForcedAlignerConfig
+
+    def __init__(self, config: Qwen3ForcedAlignerConfig):
+        super().__init__(config)
+        self.num_timestamp_bins = config.num_timestamp_bins
+        self.model = Qwen3ASRModel(config)
+        self.classifier = nn.Linear(config.text_config.hidden_size, config.num_timestamp_bins, bias=False)
+
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        self.model.set_input_embeddings(value)
+
+    def get_audio_features(
+        self,
+        input_features: torch.FloatTensor,
+        input_features_mask: torch.LongTensor,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple | BaseModelOutputWithPooling:
+        return self.model.get_audio_features(
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            **kwargs,
+        )
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        input_features: torch.FloatTensor | None = None,
+        input_features_mask: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> TokenClassifierOutput:
+        r"""
+        input_features_mask (`torch.Tensor` of shape `(batch_size, feature_sequence_length)`, *optional*):
+            Mask to avoid performing attention on padding feature indices.
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the forced alignment loss. Indices should be in `[0, ..., config.num_timestamp_bins - 1]`.
+        """
+
+        outputs = self.model(
+            input_ids=input_ids,
+            input_features=input_features,
+            input_features_mask=input_features_mask,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            **kwargs,
+        )
+
+        hidden_states = outputs[0]
+        logits = self.classifier(hidden_states)
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.num_timestamp_bins)
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+__all__ = [
+    "Qwen3ASREncoderConfig",
+    "Qwen3ASRConfig",
+    "Qwen3ASREncoder",
+    "Qwen3ASRForConditionalGeneration",
+    "Qwen3ASRModel",
+    "Qwen3ASRPreTrainedModel",
+    "Qwen3ForcedAlignerConfig",
+    "Qwen3ASRForForcedAlignment",
+]
diff --git a/src/transformers/models/qwen3_asr/processing_qwen3_asr.py b/src/transformers/models/qwen3_asr/processing_qwen3_asr.py
new file mode 100644
index 000000000000..4e3724766efa
--- /dev/null
+++ b/src/transformers/models/qwen3_asr/processing_qwen3_asr.py
@@ -0,0 +1,603 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import re
+import unicodedata
+
+import numpy as np
+
+from ...audio_utils import AudioInput, make_list_of_audio
+from ...feature_extraction_utils import BatchFeature
+from ...processing_utils import ProcessingKwargs, ProcessorMixin
+from ...tokenization_utils_base import TextInput
+
+
+class Qwen3ASRProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": True,
+            "padding_side": "left",
+        },
+        "audio_kwargs": {
+            "sampling_rate": 16000,
+            "padding": True,
+            "truncation": False,
+            "return_attention_mask": True,
+            "n_window": 50,  # should match config.n_window
+        },
+        "common_kwargs": {"return_tensors": "pt"},
+    }
+
+
+def _get_feat_extract_output_lengths(input_lengths, n_window=50):
+    """
+    Computes the output length of the convolutional layers and the output length of the audio encoder
+    """
+
+    chunk_len = n_window * 2
+    input_lengths_leave = input_lengths % chunk_len
+    feat_lengths = (input_lengths_leave - 1) // 2 + 1
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // chunk_len) * 13
+    return output_lengths
+
+
+class Qwen3ASRProcessor(ProcessorMixin):
+    r"""
+    Constructs a Qwen3ASR processor.
+    [`Qwen3ASRProcessor`] offers all the functionalities of [`WhisperFeatureExtractor`], and [`Qwen2TokenizerFast`]. See the
+    [`~Qwen3ASRProcessor.__call__`] and [`~Qwen3ASRProcessor.decode`] for more information.
+
+    Args:
+        feature_extractor ([`WhisperFeatureExtractor`], *optional*):
+            The audio feature extractor.
+        tokenizer ([`Qwen2TokenizerFast`], *optional*):
+            The text tokenizer.
+        chat_template (`Optional[str]`, *optional*):
+            The Jinja template to use for formatting the conversation. If not provided, the default chat template is used.
+        timestamp_segment_time (`int`, *optional*, defaults to 80):
+            The segment time in milliseconds used for grouping timestamps during forced alignment. This should match the
+            value used during training of the forced aligner model.
+    """
+
+    def __init__(self, feature_extractor=None, tokenizer=None, chat_template=None, timestamp_segment_time: int = 80):
+        super().__init__(feature_extractor, tokenizer, chat_template=chat_template)
+        self.timestamp_segment_time = timestamp_segment_time
+        self.audio_token = self.tokenizer.audio_token
+        self.audio_token_id = self.tokenizer.convert_tokens_to_ids(self.audio_token)
+        self.audio_bos_token = self.tokenizer.audio_bos_token
+        self.audio_bos_token_id = self.tokenizer.convert_tokens_to_ids(self.audio_bos_token)
+        self.audio_eos_token = self.tokenizer.audio_eos_token
+        self.audio_eos_token_id = self.tokenizer.convert_tokens_to_ids(self.audio_eos_token)
+
+    def __call__(
+        self,
+        text: TextInput | list[TextInput],
+        audio: AudioInput,
+        output_labels: bool | None = False,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Main method to prepare one or several text sequence(s) and audio waveform(s) for the model.
+
+        Args:
+            text (`str`, `List[str]`):
+                The sequence or batch of sequences to be encoded.
+            audio (`np.ndarray`, `List[np.ndarray]`):
+                The audio or batch of audio to be prepared. Must be as many ``text``
+                inputs as ``audio`` inputs.
+            output_labels (bool, *optional*, default=False):
+                Whether to return labels for training.
+        """
+        call_kwargs = self._merge_kwargs(
+            Qwen3ASRProcessorKwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
+            **kwargs,
+        )
+
+        text_kwargs = call_kwargs["text_kwargs"]
+        audio_kwargs = call_kwargs["audio_kwargs"]
+        return_tensors = text_kwargs.get("return_tensors")
+        if return_tensors != "pt":
+            raise ValueError(f"{self.__class__.__name__} only supports `return_tensors='pt'`.")
+
+        if isinstance(text, str):
+            text = [text]
+
+        audio = make_list_of_audio(audio)
+        if len(text) != len(audio):
+            raise ValueError(f"Got {len(text)} text but {len(audio)} audios; they must match 1:1.")
+
+        # Prepare audio
+        data = self.feature_extractor(audio, **audio_kwargs)
+        data["input_features_mask"] = data.pop("attention_mask")
+
+        # Replace audio tokens in text
+        audio_lengths = (
+            _get_feat_extract_output_lengths(data["input_features_mask"].sum(-1), audio_kwargs["n_window"])
+            .cpu()
+            .numpy()
+        )
+        audio_token_pattern = re.compile(re.escape(self.audio_token))
+        for sample_idx, num_tokens in enumerate(audio_lengths):
+            text[sample_idx] = audio_token_pattern.sub(self.audio_token * int(num_tokens), text[sample_idx])
+
+        # Prepare text
+        text_inputs = self.tokenizer(text, **text_kwargs)
+        data.update(text_inputs)
+
+        if output_labels:
+            labels = data["input_ids"].clone()
+            labels[labels == self.audio_token_id] = -100
+            labels[labels == self.tokenizer.pad_token_id] = -100
+            labels[labels == self.audio_bos_token_id] = -100
+            labels[labels == self.audio_eos_token_id] = -100
+            data["labels"] = labels
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    @staticmethod
+    def _normalize_audio(audio: AudioInput) -> list:
+        """Normalize audio input(s) into a flat list."""
+        if isinstance(audio, str):
+            return [audio]
+        if isinstance(audio, (list, tuple)) and audio and all(isinstance(a, str) for a in audio):
+            return list(audio)
+        return make_list_of_audio(audio)
+
+    @staticmethod
+    def _normalize_languages(
+        language: str | list[str] | None, batch_size: int, allow_broadcast: bool = False
+    ) -> list[str | None]:
+        """Broadcast / validate a language argument to match batch_size."""
+        if language is None:
+            return [None] * batch_size
+        if isinstance(language, str):
+            return [language] * batch_size
+        if isinstance(language, (list, tuple)):
+            if allow_broadcast and len(language) == 1 and batch_size > 1:
+                return list(language) * batch_size
+            if len(language) != batch_size:
+                raise ValueError(f"Got {len(language)} language(s) for {batch_size} sample(s); counts must match.")
+            return list(language)
+        raise TypeError("`language` must be a string, a list of strings, or `None`.")
+
+    @staticmethod
+    def _audio_content_item(audio_item) -> dict:
+        """Build a chat-template content dict for a single audio item."""
+        if isinstance(audio_item, str):
+            return {"type": "audio", "path": audio_item}
+        return {"type": "audio", "audio": audio_item}
+
+    def apply_transcription_request(
+        self,
+        audio: AudioInput | list[AudioInput],
+        language: str | list[str] | None = None,
+        **kwargs,
+    ) -> BatchFeature:
+        """
+        Prepare inputs for automatic speech recognition without manually writing the chat template.
+
+        Args:
+            audio (`AudioInput` or `list[AudioInput]`):
+                Audio to transcribe. Can be a URL string, local path, numpy array, or a list of these.
+            language (`str` or `list[str]`, *optional*):
+                Language hint(s) to include in the system prompt (e.g. "English", "Chinese").
+                A list must be the same length as the audio batch.
+                When `None`, the model performs automatic language detection.
+            **kwargs:
+                Additional keyword arguments forwarded to
+                [`~Qwen3ASRProcessor.apply_chat_template`].
+
+        Returns:
+            [`BatchFeature`]: Processor outputs ready to be passed to
+            [`Qwen3ASRForConditionalGeneration.generate`].
+        """
+        audio_items = self._normalize_audio(audio)
+        batch_size = len(audio_items)
+        if batch_size == 0:
+            raise ValueError("`audio` must contain at least one sample.")
+        languages = self._normalize_languages(language, batch_size)
+
+        conversations = []
+        for lang, audio_item in zip(languages, audio_items):
+            messages = []
+            if lang is not None:
+                messages.append({"role": "system", "content": [{"type": "text", "text": lang}]})
+            messages.append({"role": "user", "content": [self._audio_content_item(audio_item)]})
+            conversations.append(messages)
+
+        return self.apply_chat_template(
+            conversations,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            **kwargs,
+        )
+
+    def decode(self, *args, return_format="raw", **kwargs):
+        """
+        Forward arguments to the tokenizer's decode and optionally parse the ASR output.
+
+        Qwen3 ASR outputs transcription in the format: ``language <LANG><asr_text>transcribed text``
+
+        Args:
+            return_format (`str`, *optional*, defaults to `"raw"`):
+                Options:
+
+                - ``"raw"``: Return raw decoded strings from the tokenizer.
+                - ``"parsed"``: Return a dict (or list of dicts) with ``"language"`` and ``"transcription"`` keys.
+                - ``"transcription_only"``: Extract only the transcribed text (after ``<asr_text>``).
+
+                ``skip_special_tokens`` is hard-set to ``True`` for ``"parsed"`` and ``"transcription_only"``.
+        """
+        valid_formats = ["raw", "parsed", "transcription_only"]
+        if return_format not in valid_formats:
+            raise ValueError(f"return_format must be one of {valid_formats}.")
+        if return_format != "raw":
+            kwargs["skip_special_tokens"] = True
+
+        decoded = self.tokenizer.decode(*args, **kwargs)
+        if return_format == "parsed":
+            decoded = self.parse_output(decoded)
+        elif return_format == "transcription_only":
+            decoded = self.extract_transcription(decoded)
+        return decoded
+
+    @staticmethod
+    def _parse_single_output(text: str) -> dict:
+        """Parse a single decoded ASR string into language + transcription."""
+        if "assistant\n" in text:
+            text = text.split("assistant\n", 1)[-1]
+        marker = "<asr_text>"
+        if marker not in text:
+            return {"language": None, "transcription": text}
+        prefix, transcription = text.split(marker, 1)
+        prefix = prefix.strip()
+        language = None
+        if prefix.startswith("language "):
+            language = prefix[len("language ") :].strip()
+        elif prefix:
+            language = prefix
+        return {"language": language, "transcription": transcription.strip()}
+
+    @staticmethod
+    def parse_output(text: str | list[str]) -> dict | list[dict]:
+        """
+        Parse Qwen3 ASR raw output into a structured dict.
+
+        The model outputs ``language <LANG><asr_text>transcribed text``.
+        This method returns a dict with ``"language"`` and ``"transcription"`` keys.
+
+        Args:
+            text (`str` or `list[str]`): Raw decoded output(s).
+
+        Returns:
+            `dict` or `list[dict]`: Parsed output(s). Each dict has keys
+            ``"language"`` (str or None) and ``"transcription"`` (str).
+            Returns the original string as the transcription if parsing fails.
+        """
+        if isinstance(text, str):
+            return Qwen3ASRProcessor._parse_single_output(text)
+        return [Qwen3ASRProcessor._parse_single_output(raw_text) for raw_text in text]
+
+    @staticmethod
+    def extract_transcription(text: str | list[str]) -> str | list[str]:
+        """
+        Extract transcription text from Qwen3 ASR raw output.
+
+        The model outputs ``language <LANG><asr_text>transcribed text``.
+        This method extracts the text after ``<asr_text>``.
+
+        Args:
+            text (`str` or `list[str]`): Raw decoded output(s).
+
+        Returns:
+            `str` or `list[str]`: Extracted transcription(s). Returns the
+            original string if ``<asr_text>`` is not found.
+        """
+        if isinstance(text, str):
+            return Qwen3ASRProcessor._parse_single_output(text)["transcription"]
+        return [Qwen3ASRProcessor._parse_single_output(raw_text)["transcription"] for raw_text in text]
+
+    @staticmethod
+    def _is_cjk_char(char: str) -> bool:
+        """
+        Return True for CJK ideograph characters.
+        Original: https://github.com/QwenLM/Qwen3-ASR/blob/c17a131fe028b2e428b6e80a33d30bb4fa57b8df/qwen_asr/inference/qwen3_forced_aligner.py#L62
+        """
+        codepoint = ord(char)
+        return (
+            (0x4E00 <= codepoint <= 0x9FFF)
+            or (0x3400 <= codepoint <= 0x4DBF)
+            or (0x20000 <= codepoint <= 0x2A6DF)
+            or (0x2A700 <= codepoint <= 0x2B73F)
+            or (0x2B740 <= codepoint <= 0x2B81F)
+            or (0x2B820 <= codepoint <= 0x2CEAF)
+            or (0xF900 <= codepoint <= 0xFAFF)
+            or (0x2F800 <= codepoint <= 0x2FA1F)
+        )
+
+    @staticmethod
+    def _is_kept_char(char: str) -> bool:
+        """Return True for characters kept during forced-alignment tokenisation."""
+        if char == "'":
+            return True
+        category = unicodedata.category(char)
+        return category.startswith("L") or category.startswith("N") or Qwen3ASRProcessor._is_cjk_char(char)
+
+    @staticmethod
+    def _clean_tokens(raw_tokens) -> list[str]:
+        """Filter each raw token to kept characters, dropping empty results."""
+        return [
+            cleaned
+            for token in raw_tokens
+            if (cleaned := "".join(char for char in token if Qwen3ASRProcessor._is_kept_char(char)))
+        ]
+
+    @staticmethod
+    def split_words_for_alignment(text: str | list[str], language: str | None = None) -> list[str]:
+        """
+        Split text into word-level tokens suitable for forced alignment.
+        Original: https://github.com/QwenLM/Qwen3-ASR/blob/c17a131fe028b2e428b6e80a33d30bb4fa57b8df/qwen_asr/inference/qwen3_forced_aligner.py#L101-L145
+
+        The tokenization strategy depends on the language:
+
+        - **Japanese**: Uses the ``nagisa`` library for morphological analysis
+          (install with ``pip install nagisa``).
+        - **Korean**: Uses the ``soynlp`` library for tokenization
+          (install with ``pip install soynlp``).
+        - **All other languages** (including Chinese): CJK characters are emitted
+          individually; space-delimited scripts produce whole words. Punctuation
+          is dropped.
+
+        Args:
+            text (`str`): Transcript text.
+            language (`str` or `None`, *optional*):
+                Language of the transcript (e.g. ``"Japanese"``, ``"Korean"``,
+                ``"English"``, ``"Chinese"``).  When ``None``, falls back to the
+                default CJK / space-based tokenizer.
+
+        Returns:
+            `list[str]`: Word-level tokens.
+        """
+        text = text.strip()
+        lang = language.lower() if language else ""
+
+        if lang == "japanese":
+            try:
+                import nagisa
+            except ImportError:
+                raise ImportError(
+                    "Japanese forced alignment requires the `nagisa` package. Install it with: pip install nagisa"
+                )
+            return Qwen3ASRProcessor._clean_tokens(nagisa.tagging(text).words)
+
+        if lang == "korean":
+            try:
+                from soynlp.tokenizer import LTokenizer
+            except ImportError:
+                raise ImportError(
+                    "Korean forced alignment requires the `soynlp` package. Install it with: pip install soynlp"
+                )
+            return Qwen3ASRProcessor._clean_tokens(LTokenizer().tokenize(text))
+
+        # Default: CJK characters individually, space-delimited words otherwise
+        tokens: list[str] = []
+        char_buffer: list[str] = []
+
+        def flush_buffer():
+            if char_buffer:
+                word = "".join(char_buffer)
+                if word:
+                    tokens.append(word)
+                char_buffer.clear()
+
+        for char in text:
+            if Qwen3ASRProcessor._is_cjk_char(char):
+                flush_buffer()
+                tokens.append(char)
+            elif char.isspace():
+                flush_buffer()
+            elif Qwen3ASRProcessor._is_kept_char(char):
+                char_buffer.append(char)
+        flush_buffer()
+        return tokens
+
+    @staticmethod
+    def _fix_timestamps(raw: np.ndarray) -> list[int]:
+        """
+        Monotonize predicted timestamps using longest increasing subsequence, then interpolate outliers.
+        Original: https://github.com/QwenLM/Qwen3-ASR/blob/c17a131fe028b2e428b6e80a33d30bb4fa57b8df/qwen_asr/inference/qwen3_forced_aligner.py#L147
+        """
+        data = raw.tolist()
+        num_values = len(data)
+        if num_values == 0:
+            return []
+
+        # Find longest increasing subsequence (LIS) via O(n²) DP
+        dp = [1] * num_values
+        parent = [-1] * num_values
+        for current in range(1, num_values):
+            for prev in range(current):
+                if data[prev] <= data[current] and dp[prev] + 1 > dp[current]:
+                    dp[current] = dp[prev] + 1
+                    parent[current] = prev
+
+        # Backtrack to get LIS indices
+        is_normal = [False] * num_values
+        trace_idx = dp.index(max(dp))
+        while trace_idx != -1:
+            is_normal[trace_idx] = True
+            trace_idx = parent[trace_idx]
+
+        # Interpolate non-LIS positions
+        result = data.copy()
+        block_start = 0
+        while block_start < num_values:
+            if is_normal[block_start]:
+                block_start += 1
+                continue
+            # Find contiguous block of outlier values [block_start, block_end)
+            block_end = block_start
+            while block_end < num_values and not is_normal[block_end]:
+                block_end += 1
+            block_len = block_end - block_start
+            left = next((result[pos] for pos in range(block_start - 1, -1, -1) if is_normal[pos]), None)
+            right = next((result[pos] for pos in range(block_end, num_values) if is_normal[pos]), None)
+            if block_len <= 2:
+                for pos in range(block_start, block_end):
+                    if left is None:
+                        result[pos] = right
+                    elif right is None:
+                        result[pos] = left
+                    else:
+                        result[pos] = left if (pos - (block_start - 1)) <= (block_end - pos) else right
+            else:
+                fill = left if left is not None else right
+                if left is not None and right is not None:
+                    step = (right - left) / (block_len + 1)
+                    for pos in range(block_start, block_end):
+                        result[pos] = left + step * (pos - block_start + 1)
+                elif fill is not None:
+                    for pos in range(block_start, block_end):
+                        result[pos] = fill
+            block_start = block_end
+
+        return [int(v) for v in result]
+
+    def prepare_forced_aligner_inputs(
+        self,
+        audio: AudioInput,
+        transcript: str | list[str],
+        language: str | list[str] | None = None,
+        **kwargs,
+    ) -> tuple[BatchFeature, list[list[str]]]:
+        """
+        Prepare inputs for the forced aligner model.
+
+        Args:
+            audio (`AudioInput`):
+                Audio input(s).  Accepts paths, URLs, numpy arrays, or a list of these.
+            transcript (`str` or `list[str]`):
+                Transcript(s) to align against the audio.
+            language (`str`, `list[str]`, or `None`, *optional*):
+                Language hint(s). Currently unused in tokenization but reserved for
+                language-specific tokenizers (e.g. Japanese, Korean).
+            **kwargs:
+                Additional keyword arguments forwarded to
+                [`~Qwen3ASRProcessor.apply_chat_template`].
+
+        Returns:
+            `tuple[BatchFeature, list[list[str]]]`:
+                - ``inputs``: A [`BatchFeature`] with ``input_ids``, ``attention_mask``,
+                  ``input_features``, and ``input_features_mask`` ready for the forced
+                  aligner model.
+                - ``word_lists``: A list (one per sample) of word-level token lists used
+                  to build the input. Pass these to
+                  [`~Qwen3ASRProcessor.decode_forced_alignment`] to pair timestamps
+                  with words.
+        """
+        if isinstance(transcript, str):
+            transcript = [transcript]
+
+        audio_items = self._normalize_audio(audio)
+        batch_size = len(audio_items)
+        if len(transcript) != batch_size:
+            raise ValueError(f"Got {len(transcript)} transcript(s) but {batch_size} audio(s); they must match 1:1.")
+
+        languages = self._normalize_languages(language, batch_size, allow_broadcast=True)
+        word_lists = [self.split_words_for_alignment(t, lang) for t, lang in zip(transcript, languages)]
+
+        conversations = []
+        for wl, audio_item in zip(word_lists, audio_items):
+            content = [self._audio_content_item(audio_item)]
+            content.extend({"type": "text", "text": word} for word in wl)
+            conversations.append([{"role": "user", "content": content}])
+
+        inputs = self.apply_chat_template(
+            conversations,
+            tokenize=True,
+            return_dict=True,
+            **kwargs,
+        )
+
+        attention_mask = inputs.get("attention_mask", None)
+        if attention_mask is not None:
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 0)
+            inputs["position_ids"] = position_ids
+
+        return inputs, word_lists
+
+    def decode_forced_alignment(
+        self,
+        logits,
+        input_ids,
+        word_lists: list[list[str]],
+        timestamp_token_id: int,
+        timestamp_segment_time: float | None = None,
+    ) -> list[list[dict]]:
+        """
+        Decode forced aligner model outputs into word-level timestamps.
+
+        Args:
+            logits (`torch.Tensor` of shape `(batch_size, seq_len, num_timestamp_bins)`):
+                Classification logits from [`Qwen3ASRForForcedAlignment`].
+            input_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`):
+                Input token IDs used for the forward pass.
+            word_lists (`list[list[str]]`):
+                Word-level token lists as returned by
+                [`~Qwen3ASRProcessor.prepare_forced_aligner_inputs`].
+            timestamp_token_id (`int`):
+                Token ID of the ``<timestamp>`` marker (from
+                ``model.config.timestamp_token_id``).
+            timestamp_segment_time (`float`, *optional*):
+                Milliseconds per timestamp class. If not provided, uses `self.timestamp_segment_time`.
+
+        Returns:
+            `list[list[dict]]`: One list per sample.  Each inner list contains dicts
+            with keys ``"text"`` (`str`), ``"start_time"`` (`float`, seconds), and
+            ``"end_time"`` (`float`, seconds).
+        """
+        if timestamp_segment_time is None:
+            timestamp_segment_time = self.timestamp_segment_time
+        pred_ids = logits.argmax(dim=-1)
+        batch_results = []
+
+        for sample_idx, word_list in enumerate(word_lists):
+            mask = input_ids[sample_idx] == timestamp_token_id
+            masked_pred = pred_ids[sample_idx][mask]
+            raw_ms = (masked_pred.float() * timestamp_segment_time).cpu().numpy()
+            fixed_ms = self._fix_timestamps(raw_ms)
+
+            items = [
+                {
+                    "text": word,
+                    "start_time": round(fixed_ms[word_idx * 2] / 1000.0, 3),
+                    "end_time": round(fixed_ms[word_idx * 2 + 1] / 1000.0, 3),
+                }
+                for word_idx, word in enumerate(word_list)
+            ]
+            batch_results.append(items)
+
+        return batch_results
+
+    @property
+    def model_input_names(self):
+        tokenizer_input_names = self.tokenizer.model_input_names
+        feature_extractor_input_names = self.feature_extractor.model_input_names
+        return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names + ["input_features_mask"]))
+
+
+__all__ = ["Qwen3ASRProcessor"]
diff --git a/src/transformers/models/qwen3_moe/modeling_qwen3_moe.py b/src/transformers/models/qwen3_moe/modeling_qwen3_moe.py
index ddf84fc575b7..1dcfb9a1a7a3 100644
--- a/src/transformers/models/qwen3_moe/modeling_qwen3_moe.py
+++ b/src/transformers/models/qwen3_moe/modeling_qwen3_moe.py
@@ -263,8 +263,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -440,7 +440,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -553,14 +553,14 @@ def load_balancing_loss_func(
     Returns:
         The auxiliary loss.
     """
-    if gate_logits is None or not isinstance(gate_logits, tuple):
+    if gate_logits is None or not isinstance(gate_logits, tuple) or len(gate_logits) == 0:
         return 0
 
     if isinstance(gate_logits, tuple):
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -568,13 +568,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -585,8 +589,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -688,7 +694,7 @@ def forward(
             loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
diff --git a/src/transformers/models/qwen3_moe/modular_qwen3_moe.py b/src/transformers/models/qwen3_moe/modular_qwen3_moe.py
index 0fd5b451959c..044364d41abc 100644
--- a/src/transformers/models/qwen3_moe/modular_qwen3_moe.py
+++ b/src/transformers/models/qwen3_moe/modular_qwen3_moe.py
@@ -31,7 +31,9 @@
     MixtralForCausalLM,
     MixtralModel,
     MixtralPreTrainedModel,
-    load_balancing_loss_func,
+)
+from ..mixtral.modeling_mixtral import (
+    load_balancing_loss_func as mixtral_load_balancing_loss_func,
 )
 from ..qwen2_moe.modeling_qwen2_moe import Qwen2MoeDecoderLayer, Qwen2MoeExperts, Qwen2MoeMLP, Qwen2MoeTopKRouter
 from ..qwen3.modeling_qwen3 import Qwen3Attention
@@ -41,6 +43,17 @@
 logger = logging.get_logger(__name__)
 
 
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor | tuple[torch.Tensor] | None,
+    num_experts: int | None = None,
+    top_k=2,
+    attention_mask: torch.Tensor | None = None,
+) -> torch.Tensor | int:
+    if isinstance(gate_logits, tuple) and len(gate_logits) == 0:
+        return 0
+    return mixtral_load_balancing_loss_func(gate_logits, num_experts, top_k, attention_mask)
+
+
 class Qwen3MoeAttention(Qwen3Attention):  # This is the main diff with qwen2Moe!
     def __init__(self, config: Qwen3MoeConfig, layer_idx: int):
         super().__init__(config, layer_idx)
@@ -162,7 +175,7 @@ def forward(
             loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
diff --git a/src/transformers/models/qwen3_next/modeling_qwen3_next.py b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
index 395f13d1420c..698e12f95b23 100644
--- a/src/transformers/models/qwen3_next/modeling_qwen3_next.py
+++ b/src/transformers/models/qwen3_next/modeling_qwen3_next.py
@@ -98,8 +98,8 @@ def __init__(self, config: Qwen3NextConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -141,7 +141,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -781,8 +781,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -1034,7 +1034,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1042,13 +1042,17 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
     else:
-        batch_size, sequence_length = attention_mask.shape
-        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+        num_hidden_layers = len(gate_logits)
+        batch_size = attention_mask.shape[0]
+        sequence_length = gate_logits[0].shape[0] // batch_size
+        attention_mask = attention_mask[:, -sequence_length:]
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
         expert_attention_mask = (
@@ -1059,8 +1063,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
diff --git a/src/transformers/models/qwen3_omni_moe/configuration_qwen3_omni_moe.py b/src/transformers/models/qwen3_omni_moe/configuration_qwen3_omni_moe.py
index 23413471c2b2..1cd303b7b1cf 100644
--- a/src/transformers/models/qwen3_omni_moe/configuration_qwen3_omni_moe.py
+++ b/src/transformers/models/qwen3_omni_moe/configuration_qwen3_omni_moe.py
@@ -28,26 +28,31 @@
 logger = logging.get_logger(__name__)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen2.5-Omni-7B")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeAudioEncoderConfig(PreTrainedConfig):
     r"""
     max_source_positions (`int`, *optional*, defaults to 1500):
         Maximum sequence length for the inputs
-    n_window (`int`, *optional*, defaults to 100):
-        Number of windwos
+    n_window (`int`, *optional*, defaults to 50):
+        Number of windows
     output_dim (`int`, *optional*, defaults to 3584):
         Dimensionality of the output
-    n_window_infer (`int`, *optional*, defaults to `400`):
+    n_window_infer (`int`, *optional*, defaults to `800`):
         Number of windows during inference
     conv_chunksize (`int`, *optional*, defaults to `500`):
         Chunk size of each input to convolutional layer
     downsample_hidden_size (`int`, *optional*, defaults to `480`):
-        Hidden size in donwsampling layer
+        Hidden size in downsampling layer
     """
 
     model_type = "qwen3_omni_moe_audio_encoder"
-    attribute_map = {"num_hidden_layers": "encoder_layers"}
+    attribute_map = {
+        "num_hidden_layers": "encoder_layers",
+        "hidden_size": "d_model",
+        "num_attention_heads": "encoder_attention_heads",
+        "intermediate_size": "encoder_ffn_dim",
+    }
 
     num_mel_bins: int = 128
     encoder_layers: int = 32
@@ -62,15 +67,14 @@ class Qwen3OmniMoeAudioEncoderConfig(PreTrainedConfig):
     initializer_range: float = 0.02
     max_source_positions: int = 1500
 
-    n_window: int = 100
+    n_window: int = 50
     output_dim: int = 3584
-
-    n_window_infer: int = 400
+    n_window_infer: int = 800
     conv_chunksize: int = 500
     downsample_hidden_size: int = 480
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeVisionEncoderConfig(PreTrainedConfig):
     r"""
@@ -100,7 +104,7 @@ class Qwen3OmniMoeVisionEncoderConfig(PreTrainedConfig):
     initializer_range: float = 0.02
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTextConfig(PreTrainedConfig):
     r"""
@@ -180,7 +184,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeThinkerConfig(PreTrainedConfig):
     r"""
@@ -247,7 +251,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3OmniMoeTalkerCodePredictor-8B")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTalkerCodePredictorConfig(PreTrainedConfig):
     r"""
@@ -315,7 +319,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTalkerTextConfig(PreTrainedConfig):
     r"""
@@ -408,7 +412,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTalkerConfig(PreTrainedConfig):
     r"""
@@ -503,7 +507,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeCode2WavConfig(PreTrainedConfig):
     r"""
@@ -559,7 +563,7 @@ def layer_types(self):
         return ["sliding_attention"] * self.num_hidden_layers
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeConfig(PreTrainedConfig):
     r"""
@@ -670,10 +674,10 @@ def get_text_config(self, decoder=False) -> "PreTrainedConfig":
 
 
 __all__ = [
+    "Qwen3OmniMoeAudioEncoderConfig",
     "Qwen3OmniMoeConfig",
     "Qwen3OmniMoeThinkerConfig",
     "Qwen3OmniMoeTalkerConfig",
-    "Qwen3OmniMoeAudioEncoderConfig",
     "Qwen3OmniMoeTalkerCodePredictorConfig",
     "Qwen3OmniMoeTalkerTextConfig",
     "Qwen3OmniMoeTextConfig",
diff --git a/src/transformers/models/qwen3_omni_moe/modeling_qwen3_omni_moe.py b/src/transformers/models/qwen3_omni_moe/modeling_qwen3_omni_moe.py
index 7b6c8b5b1bd4..6ab81423b976 100644
--- a/src/transformers/models/qwen3_omni_moe/modeling_qwen3_omni_moe.py
+++ b/src/transformers/models/qwen3_omni_moe/modeling_qwen3_omni_moe.py
@@ -74,8 +74,8 @@
 )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     r"""
     deepstack_features (`List[torch.FloatTensor]`, *optional*):
@@ -122,7 +122,7 @@ class Qwen3OmniMoePreTrainedModel(PreTrainedModel):
     @torch.no_grad()
     def _init_weights(self, module):
         super()._init_weights(module)
-        std = self.config.initializer_range
+        std = getattr(self.config, "initializer_range", 0.02)
         if isinstance(module, Qwen3OmniMoeThinkerTextSparseMoeBlock):
             init.normal_(module.experts.gate_up_proj, mean=0.0, std=std)
             init.normal_(module.experts.down_proj, mean=0.0, std=std)
@@ -142,14 +142,15 @@ def _init_weights(self, module):
             init.copy_(module.inv_freq, inv_freq)
 
 
-def _get_feat_extract_output_lengths(input_lengths):
+def _get_feat_extract_output_lengths(input_lengths, n_window=50):
     """
     Computes the output length of the convolutional layers and the output length of the audio encoder
     """
 
-    input_lengths_leave = input_lengths % 100
+    chunk_len = n_window * 2
+    input_lengths_leave = input_lengths % chunk_len
     feat_lengths = (input_lengths_leave - 1) // 2 + 1
-    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // chunk_len) * 13
     return output_lengths
 
 
@@ -348,7 +349,9 @@ def get_rope_index(
                     st_idx += bos_len
                     # Audio Only
                     if min_ed == ed_audio_start:
-                        audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                        audio_len = _get_feat_extract_output_lengths(
+                            audio_seqlens[audio_idx], self.config.audio_config.n_window
+                        )
                         llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
                         llm_pos_ids_list.append(llm_pos_ids)
 
@@ -392,7 +395,9 @@ def get_rope_index(
 
                     # Audio in Video
                     elif min_ed == ed_vision_start and ed_vision_start + 1 == ed_audio_start:
-                        audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                        audio_len = _get_feat_extract_output_lengths(
+                            audio_seqlens[audio_idx], self.config.audio_config.n_window
+                        )
                         audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
                         grid_t = video_grid_thw[video_idx][0]
                         grid_hs = video_grid_thw[:, 1]
@@ -708,7 +713,7 @@ def forward(
         aftercnn_lens (`torch.LongTensor` of shape `(batch_size,)`):
             mel length after cnn
         """
-        aftercnn_lens = _get_feat_extract_output_lengths(feature_lens)
+        aftercnn_lens = _get_feat_extract_output_lengths(feature_lens, self.n_window)
         chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long()
 
         chunk_lengths = torch.full((chunk_num.sum(),), self.n_window * 2, dtype=torch.long, device=feature_lens.device)
@@ -718,7 +723,7 @@ def forward(
 
         chunk_list = input_features.T.split(chunk_lengths.tolist(), dim=0)
         padded_feature = nn.utils.rnn.pad_sequence(chunk_list, batch_first=True).transpose(1, 2)
-        feature_lens_after_cnn = _get_feat_extract_output_lengths(chunk_lengths)
+        feature_lens_after_cnn = _get_feat_extract_output_lengths(chunk_lengths, self.n_window)
         padded_mask_after_cnn = nn.utils.rnn.pad_sequence(
             [torch.ones(length, dtype=torch.bool, device=padded_feature.device) for length in feature_lens_after_cnn],
             batch_first=True,
@@ -803,15 +808,6 @@ def padded_and_mask_function(self, tensor_list, tensor_len, padding_value=0, pad
             batch_mask_after_cnn.bool(),
         )
 
-    # Ignore copy
-    def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
-        """
-        Computes the output length of the convolutional layers and the output length of the audio encoder
-        """
-        input_lengths = (input_lengths - 1) // 2 + 1
-        output_lengths = (input_lengths - 2) // 2 + 1
-        return input_lengths, output_lengths
-
 
 def rotate_half(x):
     """Rotates half the hidden dims of the input."""
@@ -967,8 +963,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
         router_scores = router_top_value
@@ -1047,7 +1043,7 @@ class Qwen3OmniMoeVisionEncoder(Qwen3OmniMoePreTrainedModel):
     input_modalities = ("image", "video")
     _no_split_modules = ["Qwen3OmniMoeVisionBlock"]
     _can_record_outputs = {
-        "router_logits": OutputRecorder(Qwen3OmniMoeTextTopKRouter, layer_name="mlp.gate", index=0),
+        "router_logits": OutputRecorder(Qwen3OmniMoeTextTopKRouter, layer_name=r"mlp\.gate", index=0),
         "hidden_states": Qwen3OmniMoeVisionBlock,
         "attentions": Qwen3OmniMoeVisionAttention,
     }
@@ -1207,10 +1203,11 @@ def forward(
         Returns:
             `torch.Tensor`: hidden_states.
         """
+        input_dtype = hidden_states.dtype
         hidden_states = self.patch_embed(hidden_states)
 
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
-        hidden_states = hidden_states + pos_embeds
+        hidden_states = (hidden_states + pos_embeds).to(input_dtype)
 
         rotary_pos_emb = self.rot_pos_emb(grid_thw)
 
@@ -1400,8 +1397,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -1704,6 +1701,7 @@ def forward(
         past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
         # args for deepstack
         visual_pos_masks: torch.Tensor | None = None,
         deepstack_visual_embeds: list[torch.Tensor] | None = None,
@@ -1720,6 +1718,9 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
         # torch.jit.trace() doesn't support cache objects in the output
         if use_cache and past_key_values is None and not torch.jit.is_tracing():
             past_key_values = DynamicCache(config=self.config)
@@ -1727,11 +1728,15 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
+            position_ids = cache_position.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
         elif position_ids.ndim == 2:
             position_ids = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
 
@@ -1761,6 +1766,8 @@ def forward(
                 attention_mask=attention_mask,
                 position_ids=text_position_ids,
                 past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **kwargs,
             )
@@ -1839,7 +1846,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1847,7 +1854,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -1864,8 +1873,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -2022,22 +2033,22 @@ def get_placeholder_mask(
             special_audio_mask = input_ids == self.config.audio_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
 
-        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_audio_mask = special_audio_mask.unsqueeze(-1).to(inputs_embeds.device)
         return special_image_mask, special_video_mask, special_audio_mask
 
     @can_return_tuple
@@ -2238,7 +2249,7 @@ def forward(
             )
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
@@ -2497,8 +2508,8 @@ def __init__(self, config: Qwen3OmniMoeConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -2538,7 +2549,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -2770,8 +2781,8 @@ def __init__(self, config):
     def forward(self, hidden_states):
         hidden_states = hidden_states.reshape(-1, self.hidden_dim)
         router_logits = F.linear(hidden_states, self.weight)  # (seq_len, num_experts)
-        router_probs = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
-        router_top_value, router_indices = torch.topk(router_probs, self.top_k, dim=-1)  # (seq_len, top_k)
+        routing_weights = torch.nn.functional.softmax(router_logits, dtype=torch.float, dim=-1)
+        router_top_value, router_indices = torch.topk(routing_weights, self.top_k, dim=-1)  # (seq_len, top_k)
         if self.norm_topk_prob:
             router_top_value /= router_top_value.sum(dim=-1, keepdim=True)
         router_top_value = router_top_value.to(router_logits.dtype)
@@ -2934,6 +2945,7 @@ def forward(
         past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
         # args for deepstack
         visual_pos_masks: torch.Tensor | None = None,
         deepstack_visual_embeds: list[torch.Tensor] | None = None,
@@ -2950,6 +2962,9 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
         # torch.jit.trace() doesn't support cache objects in the output
         if use_cache and past_key_values is None and not torch.jit.is_tracing():
             past_key_values = DynamicCache(config=self.config)
@@ -2957,11 +2972,15 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
+            position_ids = cache_position.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
         elif position_ids.ndim == 2:
             position_ids = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
 
@@ -2991,6 +3010,8 @@ def forward(
                 attention_mask=attention_mask,
                 position_ids=text_position_ids,
                 past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **kwargs,
             )
@@ -3149,7 +3170,7 @@ def forward(
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
diff --git a/src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py b/src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py
index 2c78ad930eba..a07098ff3020 100644
--- a/src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py
+++ b/src/transformers/models/qwen3_omni_moe/modular_qwen3_omni_moe.py
@@ -103,8 +103,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     r"""
     deepstack_features (`List[torch.FloatTensor]`, *optional*):
@@ -114,45 +114,49 @@ class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     deepstack_features: list[torch.FloatTensor] | None = None
 
 
-def _get_feat_extract_output_lengths(input_lengths):
+def _get_feat_extract_output_lengths(input_lengths, n_window=50):
     """
     Computes the output length of the convolutional layers and the output length of the audio encoder
     """
 
-    input_lengths_leave = input_lengths % 100
+    chunk_len = n_window * 2
+    input_lengths_leave = input_lengths % chunk_len
     feat_lengths = (input_lengths_leave - 1) // 2 + 1
-    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // chunk_len) * 13
     return output_lengths
 
 
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
+@strict
 class Qwen3OmniMoeAudioEncoderConfig(Qwen2_5OmniAudioEncoderConfig):
     r"""
     max_source_positions (`int`, *optional*, defaults to 1500):
         Maximum sequence length for the inputs
-    n_window (`int`, *optional*, defaults to 100):
-        Number of windwos
+    n_window (`int`, *optional*, defaults to 50):
+        Number of windows
     output_dim (`int`, *optional*, defaults to 3584):
         Dimensionality of the output
-    n_window_infer (`int`, *optional*, defaults to `400`):
+    n_window_infer (`int`, *optional*, defaults to `800`):
         Number of windows during inference
     conv_chunksize (`int`, *optional*, defaults to `500`):
         Chunk size of each input to convolutional layer
     downsample_hidden_size (`int`, *optional*, defaults to `480`):
-        Hidden size in donwsampling layer
+        Hidden size in downsampling layer
     """
 
-    n_window_infer: int = 400
+    n_window: int = 50
+    n_window_infer: int = 800
     conv_chunksize: int = 500
     downsample_hidden_size: int = 480
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeVisionEncoderConfig(Qwen3VLMoeVisionConfig):
     pass
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTextConfig(PreTrainedConfig):
     r"""
@@ -232,7 +236,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeThinkerConfig(Qwen2_5OmniThinkerConfig):
     r"""
@@ -273,6 +277,8 @@ class Qwen3OmniMoeThinkerConfig(Qwen2_5OmniThinkerConfig):
     audio_end_token_id = AttributeError()
 
 
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
+@strict
 class Qwen3OmniMoeTalkerCodePredictorConfig(Qwen3Config):
     r"""
     num_code_groups (`int`, *optional*, defaults to 32):
@@ -294,6 +300,8 @@ def __post_init__(self, **kwargs):
         self.sliding_window = self.sliding_window
 
 
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
+@strict
 class Qwen3OmniMoeTalkerTextConfig(Qwen3MoeConfig):
     base_model_ep_plan = {
         "layers.*.mlp.gate": "ep_router",
@@ -317,7 +325,7 @@ def __post_init__(self, **kwargs):
         self.sliding_window = self.sliding_window
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeTalkerConfig(PreTrainedConfig):
     r"""
@@ -412,7 +420,7 @@ def __post_init__(self, **kwargs):
         super().__post_init__(**kwargs)
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeCode2WavConfig(PreTrainedConfig):
     r"""
@@ -468,7 +476,7 @@ def layer_types(self):
         return ["sliding_attention"] * self.num_hidden_layers
 
 
-@auto_docstring(checkpoint="Qwen/Qwen3-30B-A3B-Base")
+@auto_docstring(checkpoint="Qwen/Qwen3-Omni-30B-A3B-Instruct")
 @strict
 class Qwen3OmniMoeConfig(PreTrainedConfig):
     r"""
@@ -582,7 +590,7 @@ class Qwen3OmniMoePreTrainedModel(Qwen2_5OmniPreTrainedModel, PreTrainedModel):
     @torch.no_grad()
     def _init_weights(self, module):
         PreTrainedModel._init_weights(self, module)
-        std = self.config.initializer_range
+        std = getattr(self.config, "initializer_range", 0.02)
         if isinstance(module, Qwen3OmniMoeThinkerTextSparseMoeBlock):
             init.normal_(module.experts.gate_up_proj, mean=0.0, std=std)
             init.normal_(module.experts.down_proj, mean=0.0, std=std)
@@ -758,7 +766,9 @@ def get_rope_index(
                     st_idx += bos_len
                     # Audio Only
                     if min_ed == ed_audio_start:
-                        audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                        audio_len = _get_feat_extract_output_lengths(
+                            audio_seqlens[audio_idx], self.config.audio_config.n_window
+                        )
                         llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
                         llm_pos_ids_list.append(llm_pos_ids)
 
@@ -802,7 +812,9 @@ def get_rope_index(
 
                     # Audio in Video
                     elif min_ed == ed_vision_start and ed_vision_start + 1 == ed_audio_start:
-                        audio_len = _get_feat_extract_output_lengths(audio_seqlens[audio_idx])
+                        audio_len = _get_feat_extract_output_lengths(
+                            audio_seqlens[audio_idx], self.config.audio_config.n_window
+                        )
                         audio_llm_pos_ids = torch.arange(audio_len).view(1, -1).expand(3, -1) + st_idx
                         grid_t = video_grid_thw[video_idx][0]
                         grid_hs = video_grid_thw[:, 1]
@@ -894,6 +906,9 @@ def __init__(self, config: Qwen3OmniMoeAudioEncoderConfig):
         self.n_window_infer = self.config.n_window_infer
         self.conv_chunksize = self.config.conv_chunksize
 
+    def _get_feat_extract_output_lengths(self, input_lengths):
+        raise NotImplementedError("Using the standalone function _get_feat_extract_output_lengths instead.")
+
     def get_input_embeddings(self):
         return self.conv2d1
 
@@ -907,7 +922,7 @@ def forward(
         aftercnn_lens=None,
         **kwargs,
     ):
-        aftercnn_lens = _get_feat_extract_output_lengths(feature_lens)
+        aftercnn_lens = _get_feat_extract_output_lengths(feature_lens, self.n_window)
         chunk_num = torch.ceil(feature_lens / (self.n_window * 2)).long()
 
         chunk_lengths = torch.full((chunk_num.sum(),), self.n_window * 2, dtype=torch.long, device=feature_lens.device)
@@ -917,7 +932,7 @@ def forward(
 
         chunk_list = input_features.T.split(chunk_lengths.tolist(), dim=0)
         padded_feature = nn.utils.rnn.pad_sequence(chunk_list, batch_first=True).transpose(1, 2)
-        feature_lens_after_cnn = _get_feat_extract_output_lengths(chunk_lengths)
+        feature_lens_after_cnn = _get_feat_extract_output_lengths(chunk_lengths, self.n_window)
         padded_mask_after_cnn = nn.utils.rnn.pad_sequence(
             [torch.ones(length, dtype=torch.bool, device=padded_feature.device) for length in feature_lens_after_cnn],
             batch_first=True,
@@ -1308,7 +1323,7 @@ def forward(
             )
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
@@ -1741,7 +1756,7 @@ def forward(
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
 
         aux_loss = None
-        if output_router_logits:
+        if output_router_logits and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.num_experts,
@@ -2456,6 +2471,7 @@ class Qwen3OmniMoeProcessorKwargs(Qwen2_5OmniProcessorKwargs):
             },
         },
         "audio_kwargs": {
+            "n_window": 50,  # should match model config
             "sampling_rate": 16000,
             "padding": True,
             "truncation": False,
@@ -2564,6 +2580,7 @@ def __call__(
         position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds")
         use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video")
         fps = output_kwargs["videos_kwargs"].get("fps", 1.0)
+        n_window = output_kwargs["audio_kwargs"].pop("n_window", 50)
 
         if audio is not None:
             audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
@@ -2573,7 +2590,9 @@ def __call__(
             audio_inputs["input_features"] = audio_inputs.pop(
                 "input_features"
             )  # rename input_features to prevent conflicts later on
-            audio_lengths = iter(_get_feat_extract_output_lengths(audio_inputs["feature_attention_mask"].sum(-1)))
+            audio_lengths = iter(
+                _get_feat_extract_output_lengths(audio_inputs["feature_attention_mask"].sum(-1), n_window)
+            )
         else:
             audio_inputs = {}
             audio_lengths = iter([])
@@ -2625,10 +2644,10 @@ def apply_chat_template(self, conversations, chat_template=None, **kwargs):
 
 
 __all__ = [
+    "Qwen3OmniMoeAudioEncoderConfig",
     "Qwen3OmniMoeConfig",
     "Qwen3OmniMoeThinkerConfig",
     "Qwen3OmniMoeTalkerConfig",
-    "Qwen3OmniMoeAudioEncoderConfig",
     "Qwen3OmniMoeTalkerCodePredictorConfig",
     "Qwen3OmniMoeTalkerTextConfig",
     "Qwen3OmniMoeTextConfig",
diff --git a/src/transformers/models/qwen3_omni_moe/processing_qwen3_omni_moe.py b/src/transformers/models/qwen3_omni_moe/processing_qwen3_omni_moe.py
index f8fa23ee31ba..68031bae38d7 100644
--- a/src/transformers/models/qwen3_omni_moe/processing_qwen3_omni_moe.py
+++ b/src/transformers/models/qwen3_omni_moe/processing_qwen3_omni_moe.py
@@ -29,6 +29,7 @@
 from ...tokenization_utils_base import TextInput
 from ...utils import auto_docstring
 from ...video_utils import VideoInput, make_batched_videos
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 # Redefine kwargs for videos because Qwen-Omni uses some kwargs for processing omni
@@ -80,6 +81,7 @@ class Qwen3OmniMoeVideosKwargs(VideosKwargs, total=False):
 
 
 class Qwen3OmniMoeProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     videos_kwargs: Qwen3OmniMoeVideosKwargs
     _defaults = {
         "text_kwargs": {
@@ -96,6 +98,7 @@ class Qwen3OmniMoeProcessorKwargs(ProcessingKwargs, total=False):
             },
         },
         "audio_kwargs": {
+            "n_window": 50,  # should match model config
             "sampling_rate": 16000,
             "padding": True,
             "truncation": False,
@@ -104,14 +107,15 @@ class Qwen3OmniMoeProcessorKwargs(ProcessingKwargs, total=False):
     }
 
 
-def _get_feat_extract_output_lengths(input_lengths):
+def _get_feat_extract_output_lengths(input_lengths, n_window=50):
     """
     Computes the output length of the convolutional layers and the output length of the audio encoder
     """
 
-    input_lengths_leave = input_lengths % 100
+    chunk_len = n_window * 2
+    input_lengths_leave = input_lengths % chunk_len
     feat_lengths = (input_lengths_leave - 1) // 2 + 1
-    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // 100) * 13
+    output_lengths = ((feat_lengths - 1) // 2 + 1 - 1) // 2 + 1 + (input_lengths // chunk_len) * 13
     return output_lengths
 
 
@@ -151,6 +155,7 @@ def __call__(
         position_id_per_seconds = output_kwargs["videos_kwargs"].pop("position_id_per_seconds")
         use_audio_in_video = output_kwargs["videos_kwargs"].pop("use_audio_in_video")
         fps = output_kwargs["videos_kwargs"].get("fps", 1.0)
+        n_window = output_kwargs["audio_kwargs"].pop("n_window", 50)
 
         if audio is not None:
             audio_inputs = self.feature_extractor(audio, **output_kwargs["audio_kwargs"])
@@ -160,7 +165,9 @@ def __call__(
             audio_inputs["input_features"] = audio_inputs.pop(
                 "input_features"
             )  # rename input_features to prevent conflicts later on
-            audio_lengths = iter(_get_feat_extract_output_lengths(audio_inputs["feature_attention_mask"].sum(-1)))
+            audio_lengths = iter(
+                _get_feat_extract_output_lengths(audio_inputs["feature_attention_mask"].sum(-1), n_window)
+            )
         else:
             audio_inputs = {}
             audio_lengths = iter([])
diff --git a/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py b/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py
index 9522cb354789..e3351f7364f7 100644
--- a/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py
+++ b/src/transformers/models/qwen3_vl/modeling_qwen3_vl.py
@@ -45,8 +45,8 @@
 from .configuration_qwen3_vl import Qwen3VLConfig, Qwen3VLTextConfig, Qwen3VLVisionConfig
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     r"""
     deepstack_features (`List[torch.FloatTensor]`, *optional*):
@@ -564,12 +564,12 @@ def forward(
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen3VLModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -777,10 +777,11 @@ def forward(
         Returns:
             `torch.Tensor`: hidden_states.
         """
+        input_dtype = hidden_states.dtype
         hidden_states = self.patch_embed(hidden_states)
 
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
-        hidden_states = hidden_states + pos_embeds
+        hidden_states = (hidden_states + pos_embeds).to(input_dtype)
 
         rotary_pos_emb = self.rot_pos_emb(grid_thw)
 
@@ -861,6 +862,7 @@ def forward(
         past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
         # args for deepstack
         visual_pos_masks: torch.Tensor | None = None,
         deepstack_visual_embeds: list[torch.Tensor] | None = None,
@@ -877,6 +879,9 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
         # torch.jit.trace() doesn't support cache objects in the output
         if use_cache and past_key_values is None and not torch.jit.is_tracing():
             past_key_values = DynamicCache(config=self.config)
@@ -884,11 +889,15 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
+            position_ids = cache_position.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
         elif position_ids.ndim == 2:
             position_ids = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
 
@@ -918,6 +927,8 @@ def forward(
                 attention_mask=attention_mask,
                 position_ids=text_position_ids,
                 past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **kwargs,
             )
@@ -1190,18 +1201,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1362,12 +1373,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen3VL causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen3VLCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/qwen3_vl/modular_qwen3_vl.py b/src/transformers/models/qwen3_vl/modular_qwen3_vl.py
index e2b1dd42a68b..cc5ea977b22a 100644
--- a/src/transformers/models/qwen3_vl/modular_qwen3_vl.py
+++ b/src/transformers/models/qwen3_vl/modular_qwen3_vl.py
@@ -27,19 +27,15 @@
 from ...activations import ACT2FN
 from ...cache_utils import Cache, DynamicCache
 from ...configuration_utils import PreTrainedConfig
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
 from ...masking_utils import create_causal_mask
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling
 from ...modeling_rope_utils import RopeParameters, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import ProcessingKwargs, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring, can_return_tuple, logging
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
-from ...video_utils import VideoInput
 from ..llama.modeling_llama import LlamaRotaryEmbedding
 from ..qwen2_5_vl.modeling_qwen2_5_vl import (
     Qwen2_5_VLCausalLMOutputWithPast,
@@ -68,8 +64,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     r"""
     deepstack_features (`List[torch.FloatTensor]`, *optional*):
@@ -563,10 +559,11 @@ def forward(
         Returns:
             `torch.Tensor`: hidden_states.
         """
+        input_dtype = hidden_states.dtype
         hidden_states = self.patch_embed(hidden_states)
 
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
-        hidden_states = hidden_states + pos_embeds
+        hidden_states = (hidden_states + pos_embeds).to(input_dtype)
 
         rotary_pos_emb = self.rot_pos_emb(grid_thw)
 
@@ -645,6 +642,7 @@ def forward(
         past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
         # args for deepstack
         visual_pos_masks: torch.Tensor | None = None,
         deepstack_visual_embeds: list[torch.Tensor] | None = None,
@@ -661,6 +659,9 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
         # torch.jit.trace() doesn't support cache objects in the output
         if use_cache and past_key_values is None and not torch.jit.is_tracing():
             past_key_values = DynamicCache(config=self.config)
@@ -668,11 +669,15 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
+            position_ids = cache_position.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
         elif position_ids.ndim == 2:
             position_ids = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
 
@@ -702,6 +707,8 @@ def forward(
                 attention_mask=attention_mask,
                 position_ids=text_position_ids,
                 past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **kwargs,
             )
@@ -1198,112 +1205,33 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
             else tokenizer.convert_tokens_to_ids(self.vision_end_token)
         )
 
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Qwen3VLProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Qwen3VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
-        )
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-        else:
-            image_inputs = {}
-            image_grid_thw = None
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            video_grid_thw = videos_inputs["video_grid_thw"]
-            # If user has not requested video metadata, pop it
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-        else:
-            videos_inputs = {}
-            video_grid_thw = None
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if image_grid_thw is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_frames = video_inputs["video_grid_thw"][video_idx][0]
+        frame_seqlen = video_inputs["video_grid_thw"][video_idx][1:].prod() // merge_length
+        metadata = video_inputs["video_metadata"][video_idx]
+        video_placeholder = ""
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
 
-        if video_grid_thw is not None:
-            merge_length = self.video_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    metadata = video_metadata[index]
-                    if metadata.fps is None:
-                        logger.warning_once(
-                            "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
-                        )
-                        metadata.fps = 24 if metadata.fps is None else metadata.fps
-
-                    # if timestamps are not provided, calculate them
-                    curr_timestamp = self._calculate_timestamps(
-                        metadata.frames_indices,
-                        metadata.fps,
-                        self.video_processor.temporal_patch_size,
-                    )
+        # if timestamps are not provided, calculate them
+        curr_timestamp = self._calculate_timestamps(
+            metadata.frames_indices,
+            metadata.fps,
+            self.video_processor.temporal_patch_size,
+        )
 
-                    video_placeholder = ""
-                    frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
-                    for frame_idx in range(video_grid_thw[index][0]):
-                        curr_time = curr_timestamp[frame_idx]
-                        video_placeholder += f"<{curr_time:.1f} seconds>"
-                        video_placeholder += (
-                            self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
-                        )
-                    if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
-                        text[i] = text[i].replace(
-                            f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
-                        )
-                    else:
-                        # vllm may input video token directly
-                        text[i] = text[i].replace(self.video_token, video_placeholder, 1)
-                    index += 1
-
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+        for frame_idx in range(num_frames):
+            curr_time = curr_timestamp[frame_idx]
+            video_placeholder += f"<{curr_time:.1f} seconds>"
+            video_placeholder += self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
+        return video_placeholder
 
     def _calculate_timestamps(self, indices: list[int] | np.ndarray, video_fps: float, merge_size: int = 2):
         if not isinstance(indices, list):
diff --git a/src/transformers/models/qwen3_vl/processing_qwen3_vl.py b/src/transformers/models/qwen3_vl/processing_qwen3_vl.py
index 1ca435749ad2..bd8b0e9c2b5b 100644
--- a/src/transformers/models/qwen3_vl/processing_qwen3_vl.py
+++ b/src/transformers/models/qwen3_vl/processing_qwen3_vl.py
@@ -20,18 +20,16 @@
 
 import numpy as np
 
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from ..qwen2_vl.image_processing_qwen2_vl import Qwen2VLImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class Qwen3VLProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Qwen2VLImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -44,6 +42,8 @@ class Qwen3VLProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class Qwen3VLProcessor(ProcessorMixin):
+    valid_processor_kwargs = Qwen3VLProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -75,113 +75,38 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
             else tokenizer.convert_tokens_to_ids(self.vision_end_token)
         )
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[Qwen3VLProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            Qwen3VLProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        merge_length = self.video_processor.merge_size**2
+        num_frames = video_inputs["video_grid_thw"][video_idx][0]
+        frame_seqlen = video_inputs["video_grid_thw"][video_idx][1:].prod() // merge_length
+        metadata = video_inputs["video_metadata"][video_idx]
+        video_placeholder = ""
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 24 if metadata.fps is None else metadata.fps
+
+        # if timestamps are not provided, calculate them
+        curr_timestamp = self._calculate_timestamps(
+            metadata.frames_indices,
+            metadata.fps,
+            self.video_processor.temporal_patch_size,
         )
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-        else:
-            image_inputs = {}
-            image_grid_thw = None
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            video_grid_thw = videos_inputs["video_grid_thw"]
-            # If user has not requested video metadata, pop it
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-        else:
-            videos_inputs = {}
-            video_grid_thw = None
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-        if image_grid_thw is not None:
-            merge_length = self.image_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[index].prod() // merge_length
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if video_grid_thw is not None:
-            merge_length = self.video_processor.merge_size**2
-            index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    metadata = video_metadata[index]
-                    if metadata.fps is None:
-                        logger.warning_once(
-                            "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
-                        )
-                        metadata.fps = 24 if metadata.fps is None else metadata.fps
-
-                    # if timestamps are not provided, calculate them
-                    curr_timestamp = self._calculate_timestamps(
-                        metadata.frames_indices,
-                        metadata.fps,
-                        self.video_processor.temporal_patch_size,
-                    )
-
-                    video_placeholder = ""
-                    frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
-                    for frame_idx in range(video_grid_thw[index][0]):
-                        curr_time = curr_timestamp[frame_idx]
-                        video_placeholder += f"<{curr_time:.1f} seconds>"
-                        video_placeholder += (
-                            self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
-                        )
-                    if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
-                        text[i] = text[i].replace(
-                            f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
-                        )
-                    else:
-                        # vllm may input video token directly
-                        text[i] = text[i].replace(self.video_token, video_placeholder, 1)
-                    index += 1
-
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
 
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+        for frame_idx in range(num_frames):
+            curr_time = curr_timestamp[frame_idx]
+            video_placeholder += f"<{curr_time:.1f} seconds>"
+            video_placeholder += self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
+        return video_placeholder
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
@@ -250,9 +175,7 @@ def post_process_image_text_to_text(
 
     @property
     def model_input_names(self):
-        model_input_names = super().model_input_names
-        model_input_names.append("mm_token_type_ids")
-        return model_input_names
+        return super().model_input_names + ["mm_token_type_ids"]
 
     def _calculate_timestamps(self, indices: list[int] | np.ndarray, video_fps: float, merge_size: int = 2):
         if not isinstance(indices, list):
diff --git a/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py b/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py
index be248a160e7d..edc8994db2d3 100644
--- a/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py
+++ b/src/transformers/models/qwen3_vl_moe/modeling_qwen3_vl_moe.py
@@ -549,8 +549,8 @@ def forward(
         return hidden_states
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class BaseModelOutputWithDeepstackFeatures(BaseModelOutputWithPooling):
     r"""
     deepstack_features (`List[torch.FloatTensor]`, *optional*):
@@ -601,7 +601,7 @@ class Qwen3VLMoeVisionModel(Qwen3VLMoePreTrainedModel):
     input_modalities = ("image", "video")
     _no_split_modules = ["Qwen3VLMoeVisionBlock"]
     _can_record_outputs = {
-        "router_logits": OutputRecorder(Qwen3VLMoeTextTopKRouter, layer_name="mlp.gate", index=0),
+        "router_logits": OutputRecorder(Qwen3VLMoeTextTopKRouter, layer_name=r"mlp\.gate", index=0),
         "hidden_states": Qwen3VLMoeVisionBlock,
         "attentions": Qwen3VLMoeVisionAttention,
     }
@@ -761,10 +761,11 @@ def forward(
         Returns:
             `torch.Tensor`: hidden_states.
         """
+        input_dtype = hidden_states.dtype
         hidden_states = self.patch_embed(hidden_states)
 
         pos_embeds = self.fast_pos_embed_interpolate(grid_thw)
-        hidden_states = hidden_states + pos_embeds
+        hidden_states = (hidden_states + pos_embeds).to(input_dtype)
 
         rotary_pos_emb = self.rot_pos_emb(grid_thw)
 
@@ -934,6 +935,7 @@ def forward(
         past_key_values: Cache | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
         use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
         # args for deepstack
         visual_pos_masks: torch.Tensor | None = None,
         deepstack_visual_embeds: list[torch.Tensor] | None = None,
@@ -950,6 +952,9 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
+        if use_cache is None:
+            use_cache = self.config.use_cache
+
         # torch.jit.trace() doesn't support cache objects in the output
         if use_cache and past_key_values is None and not torch.jit.is_tracing():
             past_key_values = DynamicCache(config=self.config)
@@ -957,11 +962,15 @@ def forward(
         if inputs_embeds is None:
             inputs_embeds = self.embed_tokens(input_ids)
 
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
+            )
+
         # the hard coded `4` is for text, temporal, height and width.
         if position_ids is None:
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            position_ids = torch.arange(inputs_embeds.shape[1], device=inputs_embeds.device) + past_seen_tokens
-            position_ids = position_ids.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
+            position_ids = cache_position.view(1, 1, -1).expand(4, inputs_embeds.shape[0], -1)
         elif position_ids.ndim == 2:
             position_ids = position_ids[None, ...].expand(4, position_ids.shape[0], -1)
 
@@ -991,6 +1000,8 @@ def forward(
                 attention_mask=attention_mask,
                 position_ids=text_position_ids,
                 past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
                 position_embeddings=position_embeddings,
                 **kwargs,
             )
@@ -1022,12 +1033,12 @@ def _deepstack_process(
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Llava outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class Qwen3VLMoeModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -1047,12 +1058,12 @@ class Qwen3VLMoeModelOutputWithPast(ModelOutput):
     router_logits: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Qwen3VLMoe causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class Qwen3VLMoeCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1319,18 +1330,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -1527,7 +1538,7 @@ def load_balancing_loss_func(
         compute_device = gate_logits[0].device
         concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
 
-    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dtype=torch.float, dim=-1)
 
     _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
 
@@ -1535,7 +1546,9 @@ def load_balancing_loss_func(
 
     if attention_mask is None:
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0) / top_k
 
         # Compute the average probability of routing to these experts
         router_prob_per_expert = torch.mean(routing_weights, dim=0)
@@ -1552,8 +1565,10 @@ def load_balancing_loss_func(
         )
 
         # Compute the percentage of tokens routed to each experts
-        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
-            expert_attention_mask, dim=0
+        # Normalize by top_k so that sum(f_i) = 1, matching the distribution of P_i
+        # See: https://github.com/huggingface/transformers/issues/43688
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / (
+            torch.sum(expert_attention_mask, dim=0) * top_k
         )
 
         # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
@@ -1718,7 +1733,7 @@ def forward(
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
 
         aux_loss = None
-        if kwargs.get("output_router_logits", False):
+        if kwargs.get("output_router_logits", False) and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.config.text_config.num_experts,
diff --git a/src/transformers/models/qwen3_vl_moe/modular_qwen3_vl_moe.py b/src/transformers/models/qwen3_vl_moe/modular_qwen3_vl_moe.py
index 11534b395773..415b63d91f4b 100644
--- a/src/transformers/models/qwen3_vl_moe/modular_qwen3_vl_moe.py
+++ b/src/transformers/models/qwen3_vl_moe/modular_qwen3_vl_moe.py
@@ -234,7 +234,7 @@ class Qwen3VLMoeVisionBlock(Qwen3VLVisionBlock):
 
 class Qwen3VLMoeVisionModel(Qwen3VLVisionModel):
     _can_record_outputs = {
-        "router_logits": OutputRecorder(Qwen3VLMoeTextTopKRouter, layer_name="mlp.gate", index=0),
+        "router_logits": OutputRecorder(Qwen3VLMoeTextTopKRouter, layer_name=r"mlp\.gate", index=0),
         "hidden_states": Qwen3VLMoeVisionBlock,
         "attentions": Qwen3VLMoeVisionAttention,
     }
@@ -431,7 +431,7 @@ def forward(
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.text_config.vocab_size)
 
         aux_loss = None
-        if kwargs.get("output_router_logits", False):
+        if kwargs.get("output_router_logits", False) and self.training:
             aux_loss = load_balancing_loss_func(
                 outputs.router_logits,
                 self.config.text_config.num_experts,
diff --git a/src/transformers/models/rag/modeling_rag.py b/src/transformers/models/rag/modeling_rag.py
index 9b7ecc640e23..4762ebc6282c 100644
--- a/src/transformers/models/rag/modeling_rag.py
+++ b/src/transformers/models/rag/modeling_rag.py
@@ -33,12 +33,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for retriever augmented marginalized models outputs.
     """
 )
+@dataclass
 class RetrievAugLMMarginOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py b/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
index 6e9c072b8860..6596f5730bd9 100644
--- a/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
+++ b/src/transformers/models/recurrent_gemma/modeling_recurrent_gemma.py
@@ -123,7 +123,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/reformer/modeling_reformer.py b/src/transformers/models/reformer/modeling_reformer.py
index e8f7e74dc8ea..e1d72160db68 100755
--- a/src/transformers/models/reformer/modeling_reformer.py
+++ b/src/transformers/models/reformer/modeling_reformer.py
@@ -1858,12 +1858,12 @@ def _init_weights(self, module):
             init.constant_(module.mask_value_float32, -1e9)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ReformerModel`].
     """
 )
+@dataclass
 class ReformerModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
@@ -1886,12 +1886,12 @@ class ReformerModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`ReformerModelWithLMHead`].
     """
 )
+@dataclass
 class ReformerModelWithLMHeadOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/regnet/modeling_regnet.py b/src/transformers/models/regnet/modeling_regnet.py
index 6c43d79d7894..372422c07143 100644
--- a/src/transformers/models/regnet/modeling_regnet.py
+++ b/src/transformers/models/regnet/modeling_regnet.py
@@ -21,12 +21,12 @@
 from ... import initialization as init
 from ...activations import ACT2FN
 from ...modeling_outputs import (
-    BaseModelOutputWithNoAttention,
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_regnet import RegNetConfig
 
 
@@ -235,24 +235,11 @@ def __init__(self, config: RegNetConfig):
         for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
             self.stages.append(RegNetStage(config, in_channels, out_channels, depth=depth))
 
-    def forward(
-        self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
-    ) -> BaseModelOutputWithNoAttention:
-        hidden_states = () if output_hidden_states else None
-
+    def forward(self, hidden_state: Tensor) -> Tensor:
         for stage_module in self.stages:
-            if output_hidden_states:
-                hidden_states = hidden_states + (hidden_state,)
-
             hidden_state = stage_module(hidden_state)
 
-        if output_hidden_states:
-            hidden_states = hidden_states + (hidden_state,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_state, hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
+        return hidden_state
 
 
 @auto_docstring
@@ -261,6 +248,9 @@ class RegNetPreTrainedModel(PreTrainedModel):
     base_model_prefix = "regnet"
     main_input_name = "pixel_values"
     _no_split_modules = ["RegNetYLayer"]
+    _can_record_outputs = {
+        "hidden_states": RegNetStage,
+    }
 
     @torch.no_grad()
     def _init_weights(self, module):
@@ -283,7 +273,6 @@ def _init_weights(self, module):
 
 
 @auto_docstring
-# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
 class RegNetModel(RegNetPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
@@ -294,36 +283,22 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
         pixel_values: Tensor,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> BaseModelOutputWithPoolingAndNoAttention:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         embedding_output = self.embedder(pixel_values)
 
-        encoder_outputs = self.encoder(
-            embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
-        )
-
-        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.encoder(embedding_output)
 
         pooled_output = self.pooler(last_hidden_state)
 
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
         )
 
 
@@ -333,7 +308,6 @@ def forward(
     ImageNet.
     """
 )
-# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
 class RegNetForImageClassification(RegNetPreTrainedModel):
     def __init__(self, config):
         super().__init__(config)
@@ -347,13 +321,12 @@ def __init__(self, config):
         # initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> ImageClassifierOutputWithNoAttention:
         r"""
@@ -361,11 +334,9 @@ def forward(
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
-        outputs = self.regnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
+        outputs = self.regnet(pixel_values, **kwargs)
 
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+        pooled_output = outputs.pooler_output
 
         logits = self.classifier(pooled_output)
 
@@ -374,10 +345,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return (loss,) + output if loss is not None else output
-
         return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
 
 
diff --git a/src/transformers/models/resnet/modeling_resnet.py b/src/transformers/models/resnet/modeling_resnet.py
index 9e4ed8aecfa7..eefe61bea2de 100644
--- a/src/transformers/models/resnet/modeling_resnet.py
+++ b/src/transformers/models/resnet/modeling_resnet.py
@@ -23,13 +23,12 @@
 from ...backbone_utils import BackboneMixin, filter_output_hidden_states
 from ...modeling_outputs import (
     BackboneOutput,
-    BaseModelOutputWithNoAttention,
     BaseModelOutputWithPoolingAndNoAttention,
     ImageClassifierOutputWithNoAttention,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
-from ...utils.generic import can_return_tuple
+from ...utils import auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_resnet import ResNetConfig
 
 
@@ -219,27 +218,10 @@ def __init__(self, config: ResNetConfig):
         for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
             self.stages.append(ResNetStage(config, in_channels, out_channels, depth=depth))
 
-    def forward(
-        self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
-    ) -> BaseModelOutputWithNoAttention:
-        hidden_states = () if output_hidden_states else None
-
+    def forward(self, hidden_state: Tensor) -> Tensor:
         for stage_module in self.stages:
-            if output_hidden_states:
-                hidden_states = hidden_states + (hidden_state,)
-
             hidden_state = stage_module(hidden_state)
-
-        if output_hidden_states:
-            hidden_states = hidden_states + (hidden_state,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_state, hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(
-            last_hidden_state=hidden_state,
-            hidden_states=hidden_states,
-        )
+        return hidden_state
 
 
 @auto_docstring
@@ -250,6 +232,10 @@ class ResNetPreTrainedModel(PreTrainedModel):
     input_modalities = ("image",)
     _no_split_modules = ["ResNetConvLayer", "ResNetShortCut"]
 
+    _can_record_outputs = {
+        "hidden_states": ResNetStage,
+    }
+
     @torch.no_grad()
     def _init_weights(self, module):
         if isinstance(module, nn.Conv2d):
@@ -282,36 +268,22 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
         pixel_values: Tensor,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> BaseModelOutputWithPoolingAndNoAttention:
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         embedding_output = self.embedder(pixel_values)
 
-        encoder_outputs = self.encoder(
-            embedding_output, output_hidden_states=output_hidden_states, return_dict=return_dict
-        )
-
-        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.encoder(embedding_output)
 
         pooled_output = self.pooler(last_hidden_state)
 
-        if not return_dict:
-            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
-
         return BaseModelOutputWithPoolingAndNoAttention(
             last_hidden_state=last_hidden_state,
             pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
         )
 
 
@@ -334,13 +306,12 @@ def __init__(self, config):
         # initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> ImageClassifierOutputWithNoAttention:
         r"""
@@ -348,11 +319,9 @@ def forward(
             Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
+        outputs = self.resnet(pixel_values, **kwargs)
 
-        outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
-
-        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+        pooled_output = outputs.pooler_output
 
         logits = self.classifier(pooled_output)
 
@@ -361,10 +330,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return (loss,) + output if loss is not None else output
-
         return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
 
 
@@ -392,8 +357,6 @@ def __init__(self, config):
     def forward(
         self,
         pixel_values: Tensor,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> BackboneOutput:
         r"""
@@ -422,31 +385,20 @@ def forward(
         >>> list(feature_maps[-1].shape)
         [1, 2048, 7, 7]
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-
         embedding_output = self.embedder(pixel_values)
 
-        outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True)
+        outputs = self.encoder(embedding_output)
 
-        hidden_states = outputs.hidden_states
+        hidden_states = kwargs.get("output_collection", {}).get("hidden_states", ())
 
         feature_maps = ()
         for idx, stage in enumerate(self.stage_names):
             if stage in self.out_features:
                 feature_maps += (hidden_states[idx],)
 
-        if not return_dict:
-            output = (feature_maps,)
-            if output_hidden_states:
-                output += (outputs.hidden_states,)
-            return output
-
         return BackboneOutput(
             feature_maps=feature_maps,
-            hidden_states=outputs.hidden_states if output_hidden_states else None,
+            hidden_states=outputs.hidden_states if hasattr(outputs, "hidden_states") else None,
             attentions=None,
         )
 
diff --git a/src/transformers/models/rish_ai/README.md b/src/transformers/models/rish_ai/README.md
new file mode 100644
index 000000000000..a22a0846e881
--- /dev/null
+++ b/src/transformers/models/rish_ai/README.md
@@ -0,0 +1,155 @@
+# Rish AI
+
+## Model Description
+
+Rish AI is a cutting-edge Mixture of Experts (MoE) transformer model designed for efficient and scalable language understanding and generation. It features sparse routing with 7 experts per token, advanced rotary position embeddings, and optimized attention mechanisms.
+
+## Key Features
+
+- **Sparse Mixture of Experts**: 7 experts with 5 experts activated per token for optimal efficiency
+- **Rotary Position Embeddings**: Dynamic RoPE scaling for better long-context handling
+- **Grouped Query Attention**: Efficient attention with reduced key/value heads
+- **RMSNorm**: Improved normalization for stable training
+- **Load Balancing**: Automatic expert load balancing during training
+
+## Usage
+
+### Installation
+
+```bash
+pip install transformers
+```
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model and tokenizer
+model_name = "RishAILabs/RLLM-Base"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Prepare input
+text = "Hello, how are you?"
+inputs = tokenizer(text, return_tensors="pt")
+
+# Generate response
+outputs = model.generate(**inputs, max_length=50, do_sample=True, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Advanced Usage
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# Load model with specific configuration
+model = AutoModelForCausalLM.from_pretrained(
+    "RishAILabs/RLLM-Base",
+    torch_dtype=torch.bfloat16,  # For memory efficiency
+    device_map="auto"  # Automatic device placement
+)
+
+tokenizer = AutoTokenizer.from_pretrained("your-org/RishAI-1B-7B")
+
+# Multi-turn conversation
+conversation = [
+    {"role": "user", "content": "What is machine learning?"},
+    {"role": "assistant", "content": "Machine learning is a subset of AI..."},
+    {"role": "user", "content": "Can you give a practical example?"}
+]
+
+# Format conversation
+formatted_input = tokenizer.apply_chat_template(conversation, tokenize=False)
+inputs = tokenizer(formatted_input, return_tensors="pt")
+
+# Generate with controlled parameters
+outputs = model.generate(
+    **inputs,
+    max_length=200,
+    temperature=0.8,
+    top_p=0.9,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Model Configuration
+
+```python
+from transformers import RishAIConfig
+
+# Create custom configuration
+config = RishAIConfig(
+    vocab_size=100352,
+    hidden_size=4096,
+    num_hidden_layers=32,
+    num_attention_heads=32,
+    num_experts=7,           # Number of experts
+    num_experts_per_tok=5,   # Experts activated per token
+    max_position_embeddings=4096,
+    rope_scaling={"rope_type": "dynamic", "factor": 1.0}
+)
+
+# Initialize model with config
+from transformers import RishAIModel
+model = RishAIModel(config)
+```
+
+## Model Architecture
+
+### Sparse Mixture of Experts (MoE)
+- **Experts**: 7 specialized sub-networks
+- **Routing**: Top-5 expert selection per token
+- **Load Balancing**: Automatic expert utilization optimization
+
+### Attention Mechanism
+- **Grouped Query Attention**: Efficient key/value head reduction
+- **Rotary Embeddings**: Position-aware attention with dynamic scaling
+- **RMSNorm**: Stable layer normalization
+
+### Training Features
+- **Gradient Checkpointing**: Memory-efficient training
+- **Flash Attention**: Optimized attention computation
+- **Expert Parallelism**: Distributed expert training
+
+## Performance
+
+### Speed
+- **Inference**: Optimized for fast generation
+- **Training**: Efficient MoE routing and load balancing
+- **Memory**: Sparse activation reduces memory footprint
+
+### Quality
+- **Perplexity**: Competitive with state-of-the-art models
+- **Long Context**: Effective handling of 4K+ token sequences
+- **Multitask**: Strong performance across diverse tasks
+
+## Limitations
+
+- Requires significant computational resources for training
+- Memory usage scales with number of active experts
+- Best performance on modern GPUs with ample VRAM
+
+## Citation
+
+```bibtex
+@misc{rishailabs_2026,
+    author       = { RishAILabs },
+    title        = { RLLM-Base (Revision 552ee30) },
+    year         = 2026,
+    url          = { https://huggingface.co/RishAILabs/RLLM-Base },
+    doi          = { 10.57967/hf/7560 },
+    publisher    = { Hugging Face }
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 license.
diff --git a/src/transformers/models/rish_ai/__init__.py b/src/transformers/models/rish_ai/__init__.py
new file mode 100644
index 000000000000..7de8e449dc51
--- /dev/null
+++ b/src/transformers/models/rish_ai/__init__.py
@@ -0,0 +1,28 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_rish_ai import *  # noqa: F401, F403
+    from .modeling_rish_ai import *  # noqa: F401, F403
+    from .tokenization_rish_ai import *  # noqa: F401, F403
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/rish_ai/configuration_rish_ai.py b/src/transformers/models/rish_ai/configuration_rish_ai.py
new file mode 100644
index 000000000000..5313a4e985ff
--- /dev/null
+++ b/src/transformers/models/rish_ai/configuration_rish_ai.py
@@ -0,0 +1,169 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from transformers.configuration_utils import PretrainedConfig
+
+
+class RishAIConfig(PretrainedConfig):
+    r"""
+    Configuration class for RishAI models.
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 100352):
+            Vocabulary size of the RishAI model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`RishAIModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details, check out [this
+            paper](https://huggingface.co/papers/2305.13245). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 4096):
+            The maximum sequence length that this model might ever be used with.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*, defaults to 100277):
+            Padding token id.
+        bos_token_id (`int`, *optional*):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 100257):
+            End of stream token id.
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 500000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+        num_experts_per_tok (`int`, *optional*, defaults to 5):
+            Number of selected experts.
+        num_experts (`int`, *optional*, defaults to 7):
+            Number of routed experts.
+        output_router_logits (`bool`, *optional*, defaults to `False`):
+            Whether or not the router logits should be returned by the model. Enabling this will also
+            allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
+        router_aux_loss_coef (`float`, *optional*, defaults to 0.01):
+            The aux loss factor for the total loss.
+        norm_topk_prob (`bool`, *optional*, defaults to `False`):
+            Whether to normalize the topk probabilities.
+
+    Example:
+    ```python
+    >>> from transformers import RishAIConfig, RishAIModel
+
+    >>> # Initializing a RishAI rish_ai style configuration
+    >>> configuration = RishAIConfig()
+
+    >>> # Initializing a model from the RishAI style configuration
+    >>> model = RishAIModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "rish_ai"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100352,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=4096,
+        initializer_range=0.02,
+        rms_norm_eps=1e-06,
+        use_cache=True,
+        pad_token_id=100277,
+        bos_token_id=None,
+        eos_token_id=100257,
+        tie_word_embeddings=False,
+        rope_theta=500000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        num_experts_per_tok=5,
+        num_experts=7,
+        output_router_logits=False,
+        router_aux_loss_coef=0.01,
+        norm_topk_prob=False,
+        **kwargs,
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.num_experts_per_tok = num_experts_per_tok
+        self.num_experts = num_experts
+        self.output_router_logits = output_router_logits
+        self.router_aux_loss_coef = router_aux_loss_coef
+        self.norm_topk_prob = norm_topk_prob
+
+        # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
diff --git a/src/transformers/models/rish_ai/modeling_rish_ai.py b/src/transformers/models/rish_ai/modeling_rish_ai.py
new file mode 100644
index 000000000000..07e0c141eb08
--- /dev/null
+++ b/src/transformers/models/rish_ai/modeling_rish_ai.py
@@ -0,0 +1,638 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from collections.abc import Callable
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.generation import GenerationMixin
+from transformers.integrations import use_kernel_forward_from_hub
+from transformers.masking_utils import create_causal_mask
+from transformers.modeling_layers import GradientCheckpointingLayer
+from transformers.modeling_outputs import (
+    MoeCausalLMOutputWithPast,
+    MoeModelOutputWithPast,
+)
+from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
+from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from transformers.processing_utils import Unpack
+from transformers.utils import TransformersKwargs, auto_docstring, logging
+from transformers.utils.deprecation import deprecate_kwarg
+from transformers.utils.generic import OutputRecorder, check_model_inputs
+
+from .configuration_rish_ai import RishAIConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+@use_kernel_forward_from_hub("RMSNorm")
+class RishAIRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        RishAIRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+        return (self.weight * hidden_states).to(input_dtype)
+
+    def extra_repr(self):
+        return f"{tuple(self.weight.shape)}, eps={self.variance_epsilon}"
+
+
+class RishAIRotaryEmbedding(nn.Module):
+    inv_freq: torch.Tensor  # fix linting for `register_buffer`
+
+    def __init__(self, config: RishAIConfig, device=None):
+        super().__init__()
+        # BC: "rope_type" was originally "type"
+        if hasattr(config, "rope_scaling") and isinstance(config.rope_scaling, dict):
+            self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
+        else:
+            # Use the rope_type from config if available, otherwise default to 'dynamic'
+            self.rope_type = getattr(config, "rope_type", "dynamic")
+
+        # Ensure we have a valid rope_type
+        if self.rope_type not in ROPE_INIT_FUNCTIONS:
+            self.rope_type = "dynamic"  # fallback to dynamic if not found
+
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+
+        self.config = config
+        self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
+
+        inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device)
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = self.inv_freq
+
+    @torch.no_grad()
+    @dynamic_rope_update  # power user: used with advanced RoPE types (e.g. dynamic rope)
+    def forward(self, x, position_ids):
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)
+        position_ids_expanded = position_ids[:, None, :].float()
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+            return cos, sin
+
+
+class RishAIMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    scaling: float,
+    dropout: float = 0.0,
+    **kwargs: Unpack[TransformersKwargs],
+):
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+    if attention_mask is not None:
+        causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
+        attn_weights = attn_weights + causal_mask
+
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, attn_weights
+
+
+def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1):
+    """Applies Rotary Position Embedding to the query and key tensors."""
+    q_type, k_type = q.dtype, k.dtype
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed.to(q_type), k_embed.to(k_type)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+class RishAIAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: RishAIConfig, layer_idx: int | None = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.scaling = self.head_dim**-0.5
+        self.attention_dropout = config.attention_dropout
+        self.is_causal = True
+
+        self.q_proj = nn.Linear(
+            config.hidden_size,
+            config.num_attention_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.k_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.v_proj = nn.Linear(
+            config.hidden_size,
+            config.num_key_value_heads * self.head_dim,
+            bias=config.attention_bias,
+        )
+        self.o_proj = nn.Linear(
+            config.num_attention_heads * self.head_dim,
+            config.hidden_size,
+            bias=config.attention_bias,
+        )
+        self.q_norm = RishAIRMSNorm(config.num_attention_heads * self.head_dim, config.rms_norm_eps)
+        self.k_norm = RishAIRMSNorm(config.num_key_value_heads * self.head_dim, config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None,
+        past_key_values: Cache | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.head_dim)
+
+        query_states = self.q_norm(self.q_proj(hidden_states))
+        key_states = self.k_norm(self.k_proj(hidden_states))
+        value_states = self.v_proj(hidden_states)
+
+        query_states = query_states.view(hidden_shape).transpose(1, 2)
+        key_states = key_states.view(hidden_shape).transpose(1, 2)
+        value_states = value_states.view(hidden_shape).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
+
+        if past_key_values is not None:
+            # sin and cos are specific to RoPE models; cache_position needed for the static cache
+            cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
+            key_states, value_states = past_key_values.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            dropout=0.0 if not self.training else self.attention_dropout,
+            scaling=self.scaling,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        attn_output = self.o_proj(attn_output)
+        return attn_output, attn_weights
+
+
+class RishAISparseMoeBlock(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+        self.gate = nn.Linear(config.hidden_size, self.num_experts, bias=False)
+        self.experts = nn.ModuleList([RishAIMLP(config) for _ in range(self.num_experts)])
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        batch_size, sequence_length, hidden_dim = hidden_states.shape
+        hidden_states = hidden_states.view(-1, hidden_dim)
+        # router_logits: (batch * sequence_length, n_experts)
+        router_logits = self.gate(hidden_states)
+
+        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+        routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+        if self.norm_topk_prob:
+            routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
+        # we cast back to the input dtype
+        routing_weights = routing_weights.to(hidden_states.dtype)
+
+        final_hidden_states = torch.zeros(
+            (batch_size * sequence_length, hidden_dim),
+            dtype=hidden_states.dtype,
+            device=hidden_states.device,
+        )
+
+        # One hot encode the selected experts to create an expert mask
+        # this will be used to easily index which expert is going to be selected
+        expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+        # Loop over all available experts in the model and perform the computation on each expert
+        for expert_idx in range(self.num_experts):
+            expert_layer = self.experts[expert_idx]
+            idx, top_x = torch.where(expert_mask[expert_idx])
+
+            # Index the correct hidden states and compute the expert hidden state for
+            # the current expert. We need to make sure to multiply the output hidden
+            # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+            current_state = hidden_states[None, top_x].reshape(-1, hidden_dim)
+            current_hidden_states = expert_layer(current_state) * routing_weights[top_x, idx, None]
+
+            # However `index_add_` only support torch tensors for indexing so we'll use
+            # the `top_x` tensor here.
+            final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+        final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+        return final_hidden_states, router_logits
+
+
+class RishAIDecoderLayer(GradientCheckpointingLayer):
+    def __init__(self, config: RishAIConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = RishAIAttention(config=config, layer_idx=layer_idx)
+
+        self.mlp = RishAISparseMoeBlock(config)
+        self.post_attention_layernorm = RishAIRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_feedforward_layernorm = RishAIRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    @deprecate_kwarg("past_key_value", new_name="past_key_values", version="4.58")
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        cache_position: torch.LongTensor | None = None,
+        position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None,
+        **kwargs,
+    ) -> torch.FloatTensor:
+        residual = hidden_states
+
+        # Self Attention
+        hidden_states, _ = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            cache_position=cache_position,
+            position_embeddings=position_embeddings,
+            **kwargs,
+        )
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states, _ = self.mlp(hidden_states)
+        hidden_states = self.post_feedforward_layernorm(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+@auto_docstring
+class RishAIPreTrainedModel(PreTrainedModel):
+    config: RishAIConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["RishAIDecoderLayer"]
+    _skip_keys_device_placement = ["past_key_values"]
+    _supports_flash_attn = True
+    _supports_sdpa = True
+    _supports_flex_attn = True
+    _can_compile_fullgraph = False  # MoE models don't work with torch.compile (`torch.where(condition)` not supported)
+    _supports_attention_backend = True
+    _can_record_outputs = {
+        "router_logits": OutputRecorder(RishAISparseMoeBlock, index=1),
+        "hidden_states": RishAIDecoderLayer,
+        "attentions": RishAIAttention,
+    }
+
+
+@auto_docstring
+class RishAIModel(RishAIPreTrainedModel):
+    def __init__(self, config: RishAIConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [RishAIDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self.norm = RishAIRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = RishAIRotaryEmbedding(config=config)
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @check_model_inputs()
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        use_cache: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> MoeModelOutputWithPast:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        if use_cache and past_key_values is None:
+            past_key_values = DynamicCache(config=self.config)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids)
+
+        if cache_position is None:
+            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+            cache_position = torch.arange(
+                past_seen_tokens,
+                past_seen_tokens + inputs_embeds.shape[1],
+                device=inputs_embeds.device,
+            )
+        if position_ids is None:
+            position_ids = cache_position.unsqueeze(0)
+
+        causal_mask = create_causal_mask(
+            config=self.config,
+            input_embeds=inputs_embeds,
+            attention_mask=attention_mask,
+            cache_position=cache_position,
+            past_key_values=past_key_values,
+            position_ids=position_ids,
+        )
+
+        hidden_states = inputs_embeds
+
+        # create position embeddings to be shared across the decoder layers
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+
+        for decoder_layer in self.layers[: self.config.num_hidden_layers]:
+            hidden_states = decoder_layer(
+                hidden_states,
+                position_embeddings=position_embeddings,
+                attention_mask=causal_mask,
+                position_ids=position_ids,
+                past_key_values=past_key_values,
+                use_cache=use_cache,
+                cache_position=cache_position,
+                **kwargs,
+            )
+
+        hidden_states = self.norm(hidden_states)
+
+        return MoeModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=past_key_values,
+        )
+
+
+def load_balancing_loss_func(
+    gate_logits: torch.Tensor | tuple[torch.Tensor] | None,
+    num_experts: int | None = None,
+    top_k=2,
+    attention_mask: torch.Tensor | None = None,
+) -> torch.Tensor | int:
+    r"""
+    Computes the load balancing loss for the MoE router.
+
+    Args:
+        gate_logits:
+            Logits from the `gate`, should be a tuple of model.config.num_hidden_layers tensors of
+            shape [batch_size X sequence_length, num_experts].
+        num_experts:
+            Number of experts
+        top_k:
+            The number of experts to route per-token, can be also interpreted as the `top-k` routing
+            parameter.
+        attention_mask (`torch.Tensor`, *optional*):
+            The attention_mask used in forward function
+            shape [batch_size X sequence_length] if not None.
+
+    Returns:
+        The auxiliary loss.
+    """
+    if gate_logits is None or not isinstance(gate_logits, tuple):
+        return 0
+
+    if isinstance(gate_logits, tuple):
+        compute_device = gate_logits[0].device
+        concatenated_gate_logits = torch.cat([layer_gate.to(compute_device) for layer_gate in gate_logits], dim=0)
+
+    routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+    _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+    expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+    if attention_mask is None:
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.mean(routing_weights, dim=0)
+    else:
+        batch_size, sequence_length = attention_mask.shape
+        num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+        expert_attention_mask = (
+            attention_mask[None, :, :, None, None]
+            .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+            .reshape(-1, top_k, num_experts)
+            .to(compute_device)
+        )
+
+        # Compute the percentage of tokens routed to each experts
+        tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+            expert_attention_mask, dim=0
+        )
+
+        # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+        router_per_expert_attention_mask = (
+            attention_mask[None, :, :, None]
+            .expand(
+                (
+                    num_hidden_layers,
+                    batch_size,
+                    sequence_length,
+                    routing_weights.shape[1],
+                )
+            )
+            .reshape(-1, routing_weights.shape[1])
+            .to(compute_device)
+        )
+
+        # Compute the average probability of routing to these experts
+        router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+            router_per_expert_attention_mask, dim=0
+        )
+
+    device_index = routing_weights.device.index if routing_weights.device.index is not None else 0
+    rank = routing_weights.shape[1] * int(device_index)
+    overall_loss = torch.sum(
+        tokens_per_expert[:, rank : rank + routing_weights.shape[1]] * router_prob_per_expert.unsqueeze(0)
+    )
+    return overall_loss * num_experts
+
+
+class RishAICausalLM(RishAIPreTrainedModel, GenerationMixin):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = RishAIModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        self.router_aux_loss_coef = config.router_aux_loss_coef
+        self.num_experts = config.num_experts
+        self.num_experts_per_tok = config.num_experts_per_tok
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        past_key_values: Cache | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+        labels: torch.LongTensor | None = None,
+        use_cache: bool | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        output_router_logits: bool | None = None,
+        return_dict: bool | None = None,
+        cache_position: torch.LongTensor | None = None,
+        logits_to_keep: int | torch.Tensor = 0,
+        **kwargs,
+    ) -> tuple | MoeCausalLMOutputWithPast:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_router_logits = (
+            output_router_logits if output_router_logits is not None else self.config.output_router_logits
+        )
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            output_router_logits=output_router_logits,
+            return_dict=return_dict,
+            cache_position=cache_position,
+        )
+
+        hidden_states = outputs[0]
+        # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
+        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
+        logits = self.lm_head(hidden_states[:, slice_indices, :])
+
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(logits, labels, self.vocab_size, **kwargs)
+
+        aux_loss = None
+        if output_router_logits:
+            aux_loss = load_balancing_loss_func(
+                outputs.router_logits if return_dict else outputs[-1],
+                self.num_experts,
+                self.num_experts_per_tok,
+                attention_mask,
+            )
+            if labels is not None:
+                loss += self.router_aux_loss_coef * aux_loss.to(loss.device)  # make sure to reside in the same device
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            if output_router_logits:
+                output = (aux_loss,) + output
+            return (loss,) + output if loss is not None else output
+
+        return MoeCausalLMOutputWithPast(
+            loss=loss,
+            aux_loss=aux_loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            router_logits=outputs.router_logits,
+        )
diff --git a/src/transformers/models/rish_ai/tokenization_rish_ai.py b/src/transformers/models/rish_ai/tokenization_rish_ai.py
new file mode 100644
index 000000000000..5c6e949a4aa9
--- /dev/null
+++ b/src/transformers/models/rish_ai/tokenization_rish_ai.py
@@ -0,0 +1,176 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tokenization class for RishAI."""
+
+import json
+from pathlib import Path
+
+from transformers.tokenization_utils_base import PreTrainedTokenizerBase
+from transformers.utils import add_end_docstrings, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+@add_end_docstrings
+class RishAITokenizer(PreTrainedTokenizerBase):
+    """
+    Construct a RishAI tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    This tokenizer inherits from [`PreTrainedTokenizerBase`] which contains most of the main methods.
+    Users should refer to this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        merges_file (`str`):
+            Path to the merges file.
+        errors (`str`, *optional*, defaults to `"replace"`):
+            Paradigm to follow when decoding bytes to UTF-8. See
+            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
+        unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        bos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The beginning of sequence token.
+        eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
+            The end of sequence token.
+        pad_token (`str`, *optional*):
+            The token used for padding, for example when batching sequences of different lengths.
+        clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
+            extra spaces.
+        split_special_tokens (`bool`, *optional*, defaults to `False`):
+            Whether or not the special tokens should be split during the encoding.
+    """
+
+    vocab_files_names = {
+        "vocab_file": "vocab.json",
+        "merges_file": "merges.txt",
+    }
+    pretrained_vocab_files_map = {
+        "vocab_file": {},
+        "merges_file": {},
+    }
+    max_model_input_sizes = {"default": 4096}
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file=None,
+        merges_file=None,
+        errors="replace",
+        unk_token="<|endoftext|>",
+        bos_token="<|endoftext|>",
+        eos_token="<|endoftext|>",
+        pad_token=None,
+        clean_up_tokenization_spaces=False,
+        split_special_tokens=False,
+        **kwargs,
+    ):
+        # Set default special tokens if not provided
+        if pad_token is None:
+            pad_token = "<|endoftext|>"
+
+        super().__init__(
+            errors=errors,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            split_special_tokens=split_special_tokens,
+            **kwargs,
+        )
+
+        self.vocab_file = vocab_file
+        self.merges_file = merges_file
+
+        # Initialize vocabulary
+        self._vocab = {}
+        self._merges = []
+        self._bpe_ranks = {}
+
+        if vocab_file is not None and merges_file is not None:
+            self._load_vocab_and_merges(vocab_file, merges_file)
+
+    def _load_vocab_and_merges(self, vocab_file, merges_file):
+        """Load vocabulary and merges from files."""
+        # Load vocabulary
+        self._vocab = json.loads(Path(vocab_file).read_text(encoding="utf-8"))
+
+        # Load merges
+        self._merges = Path(merges_file).read_text(encoding="utf-8").split("\n")
+        self._merges = [merge for merge in self._merges if merge.strip()]
+
+        # Build BPE ranks
+        self._bpe_ranks = {merge: i for i, merge in enumerate(self._merges)}
+
+    @property
+    def vocab_size(self) -> int:
+        """Returns vocab size."""
+        return len(self._vocab)
+
+    def get_vocab(self) -> dict[str, int]:
+        """Returns vocab as a dict."""
+        return dict(self._vocab)
+
+    def _tokenize(self, text: str, **kwargs) -> list[str]:
+        """Tokenize a string."""
+        # Simple whitespace tokenization for now
+        # In a real implementation, this would use BPE
+        return text.split()
+
+    def _convert_token_to_id(self, token: str) -> int:
+        """Converts a token (str) to an id using the vocab."""
+        return self._vocab.get(token, self._vocab.get(self.unk_token, 0))
+
+    def _convert_id_to_token(self, index: int) -> str:
+        """Converts an index (integer) to a token (str) using the vocab."""
+        for token, idx in self._vocab.items():
+            if idx == index:
+                return token
+        return self.unk_token
+
+    def convert_tokens_to_string(self, tokens: list[str]) -> str:
+        """Converts a sequence of tokens (string) in a single string."""
+        # Simple detokenization - join with spaces
+        return " ".join(tokens)
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: str | None = None) -> tuple[str, str]:
+        """Save the vocabulary and merges files to a directory."""
+        if not self.can_save_slow_tokenizer:
+            raise ValueError(
+                "Your tokenizer does not have the necessary information to save the vocabulary. "
+                "Please use a tokenizer that has been trained with the correct parameters."
+            )
+
+        vocab_file = (filename_prefix + "-" if filename_prefix else "") + "vocab.json"
+        merges_file = (filename_prefix + "-" if filename_prefix else "") + "merges.txt"
+
+        vocab_file_path = f"{save_directory}/{vocab_file}"
+        merges_file_path = f"{save_directory}/{merges_file}"
+
+        with open(vocab_file_path, "w", encoding="utf-8") as f:
+            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
+
+        Path(merges_file_path).write_text("\n".join(self._merges), encoding="utf-8")
+
+        return vocab_file_path, merges_file_path
+
+    @property
+    def can_save_slow_tokenizer(self) -> bool:
+        """Check if the tokenizer can be saved."""
+        return self._vocab is not None and self._merges is not None
diff --git a/src/transformers/models/roberta/modeling_roberta.py b/src/transformers/models/roberta/modeling_roberta.py
index bf891b7dbfe7..f6efcba2282f 100644
--- a/src/transformers/models/roberta/modeling_roberta.py
+++ b/src/transformers/models/roberta/modeling_roberta.py
@@ -106,7 +106,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/roberta/modular_roberta.py b/src/transformers/models/roberta/modular_roberta.py
index a215c8e7a0c7..f84173f1b49c 100644
--- a/src/transformers/models/roberta/modular_roberta.py
+++ b/src/transformers/models/roberta/modular_roberta.py
@@ -83,7 +83,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py b/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
index 299d0565edc7..ea7e9e72eb08 100644
--- a/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
+++ b/src/transformers/models/roberta_prelayernorm/modeling_roberta_prelayernorm.py
@@ -102,7 +102,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/rt_detr/modeling_rt_detr.py b/src/transformers/models/rt_detr/modeling_rt_detr.py
index cc4326ed2178..c500a532de5e 100644
--- a/src/transformers/models/rt_detr/modeling_rt_detr.py
+++ b/src/transformers/models/rt_detr/modeling_rt_detr.py
@@ -47,7 +47,6 @@
 from .configuration_rt_detr import RTDetrConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RTDetrDecoder. This class adds two attributes to
@@ -56,6 +55,7 @@
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class RTDetrDecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -85,12 +85,12 @@ class RTDetrDecoderOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RT-DETR encoder-decoder model.
     """
 )
+@dataclass
 class RTDetrModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -143,12 +143,12 @@ class RTDetrModelOutput(ModelOutput):
     denoising_meta_values: dict | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`RTDetrForObjectDetection`].
     """
 )
+@dataclass
 class RTDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/rt_detr/modular_rt_detr.py b/src/transformers/models/rt_detr/modular_rt_detr.py
index cd4e8faf3fc2..7bc7663a72cb 100644
--- a/src/transformers/models/rt_detr/modular_rt_detr.py
+++ b/src/transformers/models/rt_detr/modular_rt_detr.py
@@ -662,7 +662,6 @@ def post_process_panoptic_segmentation(self):
         raise NotImplementedError("Panoptic segmentation post-processing is not implemented for RT-DETR yet.")
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RTDetrDecoder. This class adds two attributes to
@@ -671,6 +670,7 @@ def post_process_panoptic_segmentation(self):
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class RTDetrDecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -700,12 +700,12 @@ class RTDetrDecoderOutput(ModelOutput):
     cross_attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RT-DETR encoder-decoder model.
     """
 )
+@dataclass
 class RTDetrModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -758,12 +758,12 @@ class RTDetrModelOutput(ModelOutput):
     denoising_meta_values: dict | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`RTDetrForObjectDetection`].
     """
 )
+@dataclass
 class RTDetrObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py b/src/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py
index b5244ffda7f8..68ccff24e27c 100644
--- a/src/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py
+++ b/src/transformers/models/rt_detr_v2/modeling_rt_detr_v2.py
@@ -510,7 +510,6 @@ def _init_weights(self, module):
             init.copy_(module.n_points_scale, torch.tensor(n_points_scale, dtype=torch.float32))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RTDetrV2Decoder. This class adds two attributes to
@@ -519,6 +518,7 @@ def _init_weights(self, module):
     - a stacked tensor of intermediate reference points.
     """
 )
+@dataclass
 class RTDetrV2DecoderOutput(ModelOutput):
     r"""
     intermediate_hidden_states (`torch.FloatTensor` of shape `(batch_size, config.decoder_layers, num_queries, hidden_size)`):
@@ -664,12 +664,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of the RT-DETR encoder-decoder model.
     """
 )
+@dataclass
 class RTDetrV2ModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`):
@@ -1620,12 +1620,12 @@ def forward(self, x):
         return x
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`RTDetrV2ForObjectDetection`].
     """
 )
+@dataclass
 class RTDetrV2ObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/rwkv/modeling_rwkv.py b/src/transformers/models/rwkv/modeling_rwkv.py
index 5c7f008fc28e..22cfa84d2adf 100644
--- a/src/transformers/models/rwkv/modeling_rwkv.py
+++ b/src/transformers/models/rwkv/modeling_rwkv.py
@@ -27,12 +27,14 @@
 from ...utils import (
     ModelOutput,
     auto_docstring,
+    can_return_tuple,
     is_bitsandbytes_available,
     is_kernels_available,
     is_ninja_available,
     is_torch_cuda_available,
     logging,
 )
+from ...utils.output_capturing import OutputRecorder, capture_outputs
 from .configuration_rwkv import RwkvConfig
 
 
@@ -337,7 +339,7 @@ def __init__(self, config, layer_id):
         self.attention = RwkvSelfAttention(config, layer_id)
         self.feed_forward = RwkvFeedForward(config, layer_id)
 
-    def forward(self, hidden, state=None, use_cache=False, output_attentions=False):
+    def forward(self, hidden, state=None, use_cache=False):
         if self.layer_id == 0:
             hidden = self.pre_ln(hidden)
 
@@ -347,13 +349,15 @@ def forward(self, hidden, state=None, use_cache=False, output_attentions=False):
         feed_forward, state = self.feed_forward(self.ln2(hidden), state=state)
         hidden = hidden + feed_forward
 
-        outputs = (hidden, state)
-        if output_attentions:
-            outputs += (attention,)
-        else:
-            outputs += (None,)
+        # Rescale hidden states during inference when rescale_every is set
+        if (
+            not self.training
+            and self.config.rescale_every > 0
+            and (self.layer_id + 1) % self.config.rescale_every == 0
+        ):
+            hidden = hidden / 2
 
-        return outputs
+        return hidden, state
 
 
 @auto_docstring
@@ -364,6 +368,10 @@ class RwkvPreTrainedModel(PreTrainedModel):
     _keep_in_fp32_modules = ["time_decay", "time_first"]
     supports_gradient_checkpointing = True
     _is_stateful = True
+    _can_record_outputs = {
+        "hidden_states": RwkvBlock,
+        "attentions": OutputRecorder(target_class=RwkvSelfAttention, index=0),
+    }
 
     @torch.no_grad()
     def _init_weights(self, module: nn.Module):
@@ -442,12 +450,12 @@ def _init_weights(self, module: nn.Module):
             init.zeros_(module.bias)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for the RWKV model outputs.
     """
 )
+@dataclass
 class RwkvOutput(ModelOutput):
     r"""
     state (list of five `torch.FloatTensor` of shape `(batch_size, hidden_size, num_hidden_layers)`):
@@ -461,12 +469,12 @@ class RwkvOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class RwkvCausalLMOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -507,6 +515,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, new_embeddings):
         self.embeddings = new_embeddings
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
@@ -515,9 +524,6 @@ def forward(
         inputs_embeds: torch.FloatTensor | None = None,
         state: list[torch.FloatTensor] | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | RwkvOutput:
         r"""
@@ -539,12 +545,7 @@ def forward(
         use_cache (`bool`, *optional*):
             If set to `True`, the last state is returned and can be used to quickly generate the next logits.
         """
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
         use_cache = use_cache if use_cache is not None else (self.config.use_cache if not self.training else False)
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         if attention_mask is not None:
             logger.warning_once("`attention_mask` was passed, but it is unused in this model.")
@@ -579,39 +580,14 @@ def forward(
 
         hidden_states = inputs_embeds
 
-        all_self_attentions = () if output_attentions else None
-        all_hidden_states = () if output_hidden_states else None
-        for idx, block in enumerate(self.blocks):
-            hidden_states, state, attentions = block(
-                hidden_states, state=state, use_cache=use_cache, output_attentions=output_attentions
-            )
-
-            if (
-                self.layers_are_rescaled
-                and self.config.rescale_every > 0
-                and (idx + 1) % self.config.rescale_every == 0
-            ):
-                hidden_states = hidden_states / 2
-
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (hidden_states,)
-
-            if output_attentions:
-                all_self_attentions = all_self_attentions + (attentions,)
+        for block in self.blocks:
+            hidden_states, state = block(hidden_states, state=state, use_cache=use_cache)
 
         hidden_states = self.ln_out(hidden_states)
 
-        if output_hidden_states:
-            all_hidden_states = all_hidden_states + (hidden_states,)
-
-        if not return_dict:
-            return tuple(x for x in [hidden_states, state, all_hidden_states, all_self_attentions] if x is not None)
-
         return RwkvOutput(
             last_hidden_state=hidden_states,
             state=state,
-            hidden_states=all_hidden_states,
-            attentions=all_self_attentions,
         )
 
     def _rescale_layers(self):
@@ -683,6 +659,7 @@ def get_output_embeddings(self):
     def set_output_embeddings(self, new_embeddings):
         self.head = new_embeddings
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -692,9 +669,6 @@ def forward(
         state: list[torch.FloatTensor] | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         logits_to_keep: int | torch.Tensor = 0,
         **kwargs,
     ) -> tuple | RwkvCausalLMOutput:
@@ -721,19 +695,15 @@ def forward(
         use_cache (`bool`, *optional*):
             If set to `True`, the last state is returned and can be used to quickly generate the next logits.
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         rwkv_outputs = self.rwkv(
             input_ids,
             inputs_embeds=inputs_embeds,
             state=state,
             use_cache=use_cache,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        hidden_states = rwkv_outputs[0]
+        hidden_states = rwkv_outputs.last_hidden_state
         # Only compute necessary logits, and do not upcast them to float if we are not computing the loss
         slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep
         logits = self.head(hidden_states[:, slice_indices, :])
@@ -742,10 +712,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs)
 
-        if not return_dict:
-            output = (logits,) + rwkv_outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
         return RwkvCausalLMOutput(
             loss=loss,
             logits=logits,
diff --git a/src/transformers/models/sam/modeling_sam.py b/src/transformers/models/sam/modeling_sam.py
index 7638a2a8f8c0..d42358034c53 100644
--- a/src/transformers/models/sam/modeling_sam.py
+++ b/src/transformers/models/sam/modeling_sam.py
@@ -37,13 +37,13 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for sam vision model's outputs that also contains image embeddings obtained by applying the projection
     layer to the pooler_output.
     """
 )
+@dataclass
 class SamVisionEncoderOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -56,12 +56,12 @@ class SamVisionEncoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Segment-Anything model's output
     """
 )
+@dataclass
 class SamImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, num_masks)`):
diff --git a/src/transformers/models/sam2/modeling_sam2.py b/src/transformers/models/sam2/modeling_sam2.py
index 9bf33131633b..d4ccb9b20c0d 100644
--- a/src/transformers/models/sam2/modeling_sam2.py
+++ b/src/transformers/models/sam2/modeling_sam2.py
@@ -82,8 +82,8 @@ class Sam2VisionEncoderOutput(BaseModelOutputWithPooling):
     fpn_position_encoding: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class Sam2ImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
@@ -531,12 +531,12 @@ def forward(
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Hiera model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class Sam2HieraDetModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`):
diff --git a/src/transformers/models/sam2/modular_sam2.py b/src/transformers/models/sam2/modular_sam2.py
index d2ee38c524ae..4a67444a0046 100644
--- a/src/transformers/models/sam2/modular_sam2.py
+++ b/src/transformers/models/sam2/modular_sam2.py
@@ -289,8 +289,8 @@ class Sam2VisionEncoderOutput(BaseModelOutputWithPooling):
     fpn_position_encoding: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class Sam2ImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
@@ -610,12 +610,12 @@ def forward(
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Hiera model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class Sam2HieraDetModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, height, width, hidden_size)`):
diff --git a/src/transformers/models/sam2_video/modeling_sam2_video.py b/src/transformers/models/sam2_video/modeling_sam2_video.py
index 0f20ca5c75dc..57288c6802dd 100644
--- a/src/transformers/models/sam2_video/modeling_sam2_video.py
+++ b/src/transformers/models/sam2_video/modeling_sam2_video.py
@@ -614,8 +614,8 @@ def forward(self, hidden_states):
         return hidden_states
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2Video model's output.")
+@dataclass
 class Sam2VideoImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
@@ -655,8 +655,8 @@ class Sam2VideoImageSegmentationOutput(ModelOutput):
     object_pointer: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class Sam2VideoSegmentationOutput(ModelOutput):
     r"""
     object_ids (`list[int]`, *optional*):
diff --git a/src/transformers/models/sam2_video/modular_sam2_video.py b/src/transformers/models/sam2_video/modular_sam2_video.py
index 3ba37be88fe4..df61bfb604a1 100644
--- a/src/transformers/models/sam2_video/modular_sam2_video.py
+++ b/src/transformers/models/sam2_video/modular_sam2_video.py
@@ -892,8 +892,8 @@ class Sam2VideoImageSegmentationOutput(Sam2ImageSegmentationOutput):
     object_pointer: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class Sam2VideoSegmentationOutput(ModelOutput):
     r"""
     object_ids (`list[int]`, *optional*):
diff --git a/src/transformers/models/sam3/modeling_sam3.py b/src/transformers/models/sam3/modeling_sam3.py
index 0dee80edf5db..58b5d8d1d7bc 100644
--- a/src/transformers/models/sam3/modeling_sam3.py
+++ b/src/transformers/models/sam3/modeling_sam3.py
@@ -65,8 +65,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Sam3VisionEncoderOutput(BaseModelOutputWithPooling):
     r"""
     fpn_hidden_states (`tuple[torch.FloatTensor]`):
@@ -2024,7 +2024,7 @@ def __init__(self, config: Sam3MaskDecoderConfig):
     def forward(
         self,
         decoder_queries: torch.Tensor,
-        backbone_features: list[torch.Tensor],
+        backbone_features: torch.Tensor | list[torch.Tensor],
         encoder_hidden_states: torch.Tensor,
         prompt_features: torch.Tensor | None = None,
         prompt_mask: torch.Tensor | None = None,
@@ -2033,7 +2033,7 @@ def forward(
         """
         Args:
             decoder_queries: Decoder output queries [batch_size, num_queries, hidden_size]
-            backbone_features: List of backbone features to process through FPN
+            backbone_features: List of backbone features to process through FPN, or a single tensor for single-scale (see single-scale fallback logic below)
             encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size]
             prompt_features: Prompt features (text + geometry) for cross-attention [batch_size, prompt_len, hidden_size]
             prompt_mask: Padding mask [batch_size, prompt_len] where True=valid, False=padding
@@ -2041,6 +2041,55 @@ def forward(
         Returns:
             Sam3MaskDecoderOutput containing predicted masks and semantic segmentation.
         """
+
+        import warnings
+
+        # --- [Step 1] Input Normalization ---
+        # Ensure inputs are lists to satisfy downstream typing, even if single tensor provided.
+        if isinstance(backbone_features, torch.Tensor):
+            backbone_features = [backbone_features]
+
+        expected_levels = getattr(self.config, "num_multiscale_features", len(backbone_features))
+        actual_levels = len(backbone_features)
+
+        # --- [Step 2] Explicit Contract & Safety Check ---
+        if actual_levels != expected_levels:
+            if actual_levels == 1:
+                warnings.warn(
+                    f"Sam3MaskDecoder detected single-scale input (1 level), but config expects "
+                    f"{expected_levels} levels. Output will be generated using the provided scale only, "
+                    f"bypassing multi-scale fusion.",
+                    UserWarning,
+                )
+            else:
+                raise ValueError(
+                    f"Sam3MaskDecoder expects {expected_levels} feature levels or exactly 1 level "
+                    f"(single-scale mode). Received {actual_levels} levels."
+                )
+
+        # --- [Step 3] Adaptive Processing Logic ---
+        if actual_levels == 1:
+            # Single-scale path
+            x = (
+                self.input_projections[0](backbone_features[0])
+                if hasattr(self, "input_projections")
+                else backbone_features[0]
+            )
+            pos = self.image_position_embeddings[0] if hasattr(self, "image_position_embeddings") else 0
+            srcs = [x + pos]
+        else:
+            # Multi-scale path
+            srcs = []
+            for i in range(expected_levels):
+                src = (
+                    self.input_projections[i](backbone_features[i])
+                    if hasattr(self, "input_projections")
+                    else backbone_features[i]
+                )
+                pos = self.image_position_embeddings[i] if hasattr(self, "image_position_embeddings") else 0
+                srcs.append(src + pos)
+        pixel_embed = self.pixel_decoder(srcs)
+
         if prompt_features is not None:
             # Cross-attention: encoder features attend to prompt features
             residual = encoder_hidden_states
@@ -2064,12 +2113,6 @@ def forward(
             )
             encoder_hidden_states = residual + self.prompt_cross_attn_dropout(attn_output)
 
-        # Process backbone features through FPN to get pixel embeddings
-        pixel_embed = self._embed_pixels(
-            backbone_features=backbone_features,
-            encoder_hidden_states=encoder_hidden_states,
-        )
-
         # Predict instance masks via dot product between query embeddings and pixel embeddings
         instance_embeds = self.instance_projection(pixel_embed)
         mask_embeddings = self.mask_embedder(decoder_queries)
@@ -2083,39 +2126,6 @@ def forward(
             semantic_seg=semantic_seg,
         )
 
-    def _embed_pixels(
-        self,
-        backbone_features: list[torch.Tensor],
-        encoder_hidden_states: torch.Tensor,
-    ) -> torch.Tensor:
-        """
-        Embed pixels by combining backbone FPN features with encoder vision features.
-        The encoder vision features replace the finest-resolution backbone feature.
-
-        Args:
-            backbone_features: List of backbone features [batch_size, C, H_i, W_i]
-            encoder_hidden_states: Encoder outputs [batch_size, seq_len, hidden_size]
-
-        Returns:
-            Pixel embeddings [batch_size, hidden_size, H, W]
-        """
-        backbone_visual_feats = [feat.clone() for feat in backbone_features]
-
-        # Extract vision features from encoder output and reshape to spatial format
-        spatial_dim = backbone_features[-1].shape[-2] * backbone_features[-1].shape[-1]
-        encoder_visual_embed = encoder_hidden_states[:, :spatial_dim, :]
-        batch_size, _, hidden_size = encoder_visual_embed.shape
-        height, width = backbone_features[-1].shape[-2:]
-        encoder_visual_embed = encoder_visual_embed.transpose(1, 2).reshape(batch_size, hidden_size, height, width)
-
-        # Replace finest backbone feature with encoder vision features
-        backbone_visual_feats[-1] = encoder_visual_embed
-
-        # Process through FPN decoder
-        pixel_embed = self.pixel_decoder(backbone_visual_feats)
-
-        return pixel_embed
-
 
 class Sam3Model(Sam3PreTrainedModel):
     input_modalities = ["image", "text"]
diff --git a/src/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py b/src/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py
index 05a28e4bea2a..b79759b4f2ef 100644
--- a/src/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py
+++ b/src/transformers/models/sam3_lite_text/modeling_sam3_lite_text.py
@@ -418,8 +418,8 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class Sam3LiteTextVisionEncoderOutput(BaseModelOutputWithPooling):
     r"""
     fpn_hidden_states (`tuple[torch.FloatTensor]`):
diff --git a/src/transformers/models/sam3_tracker/modeling_sam3_tracker.py b/src/transformers/models/sam3_tracker/modeling_sam3_tracker.py
index cd7f674683d0..28b3b83f4afe 100644
--- a/src/transformers/models/sam3_tracker/modeling_sam3_tracker.py
+++ b/src/transformers/models/sam3_tracker/modeling_sam3_tracker.py
@@ -44,8 +44,8 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam3Tracker model's output.")
+@dataclass
 class Sam3TrackerImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
diff --git a/src/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py b/src/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py
index 9a5331f3506f..786e5b5b6769 100644
--- a/src/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py
+++ b/src/transformers/models/sam3_tracker_video/modeling_sam3_tracker_video.py
@@ -619,8 +619,8 @@ def forward(self, hidden_states):
         return hidden_states
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam3TrackerVideo model's output.")
+@dataclass
 class Sam3TrackerVideoImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, point_batch_size, num_masks)`):
@@ -660,8 +660,8 @@ class Sam3TrackerVideoImageSegmentationOutput(ModelOutput):
     object_pointer: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam2 model's output.")
+@dataclass
 class Sam3TrackerVideoSegmentationOutput(ModelOutput):
     r"""
     object_ids (`list[int]`, *optional*):
diff --git a/src/transformers/models/sam3_video/modeling_sam3_video.py b/src/transformers/models/sam3_video/modeling_sam3_video.py
index df6d7c028523..98d65489d787 100644
--- a/src/transformers/models/sam3_video/modeling_sam3_video.py
+++ b/src/transformers/models/sam3_video/modeling_sam3_video.py
@@ -457,8 +457,8 @@ def reset_state(self):
         self.cache.clear_all()
 
 
-@dataclass
 @auto_docstring(custom_intro="Base class for the Sam3Video model's output.")
+@dataclass
 class Sam3VideoSegmentationOutput(ModelOutput):
     r"""
     object_ids (`list[int]`, *optional*):
diff --git a/src/transformers/models/sam_hq/modeling_sam_hq.py b/src/transformers/models/sam_hq/modeling_sam_hq.py
index 83e558989b69..ef33622ce76e 100644
--- a/src/transformers/models/sam_hq/modeling_sam_hq.py
+++ b/src/transformers/models/sam_hq/modeling_sam_hq.py
@@ -42,13 +42,13 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for sam_hq vision model's outputs that also contains image embeddings obtained by applying the projection
     layer to the pooler_output.
     """
 )
+@dataclass
 class SamHQVisionEncoderOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -84,12 +84,12 @@ class SamHQMMaskDecoderOutputs(ModelOutput):
     mask_decoder_attentions: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Segment-Anything model's output
     """
 )
+@dataclass
 class SamHQImageSegmentationOutput(ModelOutput):
     r"""
     iou_scores (`torch.FloatTensor` of shape `(batch_size, num_masks)`):
@@ -414,7 +414,9 @@ class SamHQPositionalEmbedding(nn.Module):
     def __init__(self, config):
         super().__init__()
         self.scale = config.scale
-        self.positional_embedding = nn.Parameter(self.scale * torch.randn((2, config.num_pos_feats)))
+        self.positional_embedding = nn.Parameter(
+            self.scale * torch.randn((2, config.num_pos_feats)), requires_grad=False
+        )
 
     def forward(self, input_coords, input_shape=None):
         """Positionally encode points that are normalized to [0,1]."""
@@ -1246,6 +1248,9 @@ def __init__(self, config):
         config.mask_decoder_config._attn_implementation = config._attn_implementation
 
         self.mask_decoder = SamHQMaskDecoder(config.mask_decoder_config)
+
+        # Share positional embedding (matching original SAM-HQ architecture)
+        self.prompt_encoder.shared_embedding = self.shared_image_embedding
         self.post_init()
 
     def get_input_embeddings(self):
diff --git a/src/transformers/models/sam_hq/modular_sam_hq.py b/src/transformers/models/sam_hq/modular_sam_hq.py
index 5122ed9da2f6..4b4a46b67e45 100644
--- a/src/transformers/models/sam_hq/modular_sam_hq.py
+++ b/src/transformers/models/sam_hq/modular_sam_hq.py
@@ -395,8 +395,17 @@ def __init__(self, config):
 
         self.mask_decoder = SamHQMaskDecoder(config.mask_decoder_config)
 
+        # Share positional embedding (matching original SAM-HQ architecture)
+        self.prompt_encoder.shared_embedding = self.shared_image_embedding
+
         self.post_init()
 
+    def get_expanded_tied_weights_keys(self, all_submodels: bool = False) -> dict:
+        # Override needed because default requires tie_word_embeddings=True (for language models)
+        if self._tied_weights_keys is None:
+            return {}
+        return self._tied_weights_keys.copy()
+
     @torch.no_grad()
     def get_image_embeddings(
         self,
diff --git a/src/transformers/models/sarvam_mla/__init__.py b/src/transformers/models/sarvam_mla/__init__.py
new file mode 100644
index 000000000000..f9447754575c
--- /dev/null
+++ b/src/transformers/models/sarvam_mla/__init__.py
@@ -0,0 +1,26 @@
+# Copyright 2026 Sarvam AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_sarvam_mla import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/sarvam_mla/configuration_sarvam_mla.py b/src/transformers/models/sarvam_mla/configuration_sarvam_mla.py
new file mode 100644
index 000000000000..e4ee4061b338
--- /dev/null
+++ b/src/transformers/models/sarvam_mla/configuration_sarvam_mla.py
@@ -0,0 +1,118 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/sarvam_mla/modular_sarvam_mla.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_sarvam_mla.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 Sarvam AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...modeling_rope_utils import RopeParameters
+from ...utils import auto_docstring
+
+
+@auto_docstring(checkpoint="sarvamai/sarvam-105b")
+@strict(accept_kwargs=True)
+class SarvamMLAConfig(PreTrainedConfig):
+    r"""
+    n_group (`int`, *optional*, defaults to 16):
+        Number of groups for routed experts.
+    rope_interleave (`bool`, *optional*, defaults to `True`):
+        Whether to interleave the rotary position embeddings.
+    first_k_dense_replace (`int`, *optional*, defaults to 1):
+        Number of dense layers in shallow layers(embed->dense->moe->moe...->lm_head).
+                                                        \--k dense layers--/
+
+    Example:
+
+    ```python
+    >>> from transformers import SarvamMLAConfig
+
+    >>> # Initializing a SarvamMLA style configuration
+    >>> configuration = SarvamMLAConfig()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "sarvam_mla"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    base_model_tp_plan = {
+        "layers.*.mlp.experts.gate_up_proj": "packed_colwise",
+        "layers.*.mlp.experts.down_proj": "rowwise",
+        "layers.*.mlp.experts": "moe_tp_experts",
+        "layers.*.mlp.shared_experts.gate_proj": "colwise",
+        "layers.*.mlp.shared_experts.up_proj": "colwise",
+        "layers.*.mlp.shared_experts.down_proj": "rowwise",
+        "layers.*.mlp.gate_proj": "colwise",
+        "layers.*.mlp.up_proj": "colwise",
+        "layers.*.mlp.down_proj": "rowwise",
+    }
+    base_model_pp_plan = {
+        "embed_tokens": (["input_ids"], ["inputs_embeds"]),
+        "layers": (["hidden_states", "attention_mask"], ["hidden_states"]),
+        "norm": (["hidden_states"], ["hidden_states"]),
+    }
+    attribute_map = {
+        "num_local_experts": "n_routed_experts",
+    }
+
+    vocab_size: int = 262144
+    hidden_size: int = 4096
+    intermediate_size: int = 16384
+    moe_intermediate_size: int = 2048
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 64
+    num_key_value_heads: int | None = None
+    n_shared_experts: int = 1
+    n_routed_experts: int = 128
+    routed_scaling_factor: float = 2.5
+    kv_lora_rank: int = 512
+    q_lora_rank: int | None = None
+    qk_rope_head_dim: int = 64
+    v_head_dim: int | None = 128
+    qk_nope_head_dim: int = 128
+    n_group: int | None = 16
+    topk_group: int | None = 2
+    num_experts_per_tok: int | None = 8
+    first_k_dense_replace: int | None = 1
+    norm_topk_prob: bool | None = True
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 4096
+    initializer_range: float = 0.006
+    rms_norm_eps: float = 1e-6
+    use_cache: bool = True
+    pad_token_id: int | None = None
+    bos_token_id: int | None = 0
+    eos_token_id: int | list[int] | None = 1
+    pretraining_tp: int | None = 1
+    tie_word_embeddings: bool = False
+    rope_parameters: RopeParameters | dict | None = None
+    rope_interleave: bool | None = True
+    attention_bias: bool = False
+    attention_dropout: float | int | None = 0.0
+
+    def __post_init__(self, **kwargs):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        self.qk_head_dim = self.qk_nope_head_dim + self.qk_rope_head_dim
+        self.head_dim = self.qk_rope_head_dim
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["SarvamMLAConfig"]
diff --git a/src/transformers/models/sarvam_mla/modular_sarvam_mla.py b/src/transformers/models/sarvam_mla/modular_sarvam_mla.py
new file mode 100644
index 000000000000..a2a18e8f85fe
--- /dev/null
+++ b/src/transformers/models/sarvam_mla/modular_sarvam_mla.py
@@ -0,0 +1,64 @@
+# Copyright 2026 Sarvam AI and the HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from huggingface_hub.dataclasses import strict
+
+from ...utils import auto_docstring
+from ..deepseek_v3.configuration_deepseek_v3 import DeepseekV3Config
+
+
+@auto_docstring(checkpoint="sarvamai/sarvam-105b")
+@strict(accept_kwargs=True)
+class SarvamMLAConfig(DeepseekV3Config):
+    r"""
+    n_group (`int`, *optional*, defaults to 16):
+        Number of groups for routed experts.
+    rope_interleave (`bool`, *optional*, defaults to `True`):
+        Whether to interleave the rotary position embeddings.
+    first_k_dense_replace (`int`, *optional*, defaults to 1):
+        Number of dense layers in shallow layers(embed->dense->moe->moe...->lm_head).
+                                                        \--k dense layers--/
+
+    Example:
+
+    ```python
+    >>> from transformers import SarvamMLAConfig
+
+    >>> # Initializing a SarvamMLA style configuration
+    >>> configuration = SarvamMLAConfig()
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "sarvam_mla"
+
+    vocab_size: int = 262144
+    hidden_size: int = 4096
+    intermediate_size: int = 16384
+    num_hidden_layers: int = 32
+    num_attention_heads: int = 64
+    num_key_value_heads: int | None = None
+    n_routed_experts: int = 128
+    q_lora_rank: int | None = None
+    n_group: int | None = 16
+    topk_group: int | None = 2
+    first_k_dense_replace: int | None = 1
+    initializer_range: float = 0.006
+
+    def convert_rope_params_to_dict(self, **kwargs):
+        raise AttributeError("No BC behavior needed!")
+
+
+__all__ = ["SarvamMLAConfig"]
diff --git a/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py b/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
index e19c81de5fe6..9795a52dce97 100755
--- a/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
+++ b/src/transformers/models/seamless_m4t/modeling_seamless_m4t.py
@@ -76,13 +76,13 @@
 """
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class defining the generated outputs from [`SeamlessM4TModel`], [`SeamlessM4TForTextToText`],
     [`SeamlessM4TForTextToSpeech`], [`SeamlessM4TForSpeechToSpeech`] and [`SeamlessM4TForTextToSpeech`].
     """
 )
+@dataclass
 class SeamlessM4TGenerationOutput(ModelOutput):
     r"""
     waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
diff --git a/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py b/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
index 51a5fd456781..92ef692925c4 100644
--- a/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
+++ b/src/transformers/models/seamless_m4t_v2/modeling_seamless_m4t_v2.py
@@ -106,12 +106,12 @@ class SeamlessM4Tv2GenerationOutput(ModelOutput):
     unit_sequences: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class defining the outputs from [`SeamlessM4Tv2TextToUnitDecoder`].
     """
 )
+@dataclass
 class SeamlessM4Tv2TextToUnitDecoderOutput(ModelOutput):
     r"""
     padding_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
@@ -125,13 +125,13 @@ class SeamlessM4Tv2TextToUnitDecoderOutput(ModelOutput):
     padding_mask: torch.Tensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class defining the outputs from [`SeamlessM4Tv2TextToUnitForConditionalGeneration`] and
         [`SeamlessM4Tv2TextToUnitModel`].
     """
 )
+@dataclass
 class SeamlessM4Tv2TextToUnitOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
diff --git a/src/transformers/models/seed_oss/modeling_seed_oss.py b/src/transformers/models/seed_oss/modeling_seed_oss.py
index 1ebc8f10a272..9c24af77d591 100644
--- a/src/transformers/models/seed_oss/modeling_seed_oss.py
+++ b/src/transformers/models/seed_oss/modeling_seed_oss.py
@@ -344,7 +344,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/segformer/image_processing_pil_segformer.py b/src/transformers/models/segformer/image_processing_pil_segformer.py
index f1d0bb0f627b..771d70a6365c 100644
--- a/src/transformers/models/segformer/image_processing_pil_segformer.py
+++ b/src/transformers/models/segformer/image_processing_pil_segformer.py
@@ -138,10 +138,10 @@ def _preprocess_image_like_inputs(
 
     def reduce_label(self, image: np.ndarray) -> np.ndarray:
         """Reduce label values by 1, replacing 0 with 255."""
-        # Avoid using underflow conversion
-        image[image == 0] = 255
-        image = image - 1
-        image[image == 254] = 255
+        image = image.copy()
+        ignore_mask = (image == 0) | (image == 255)
+        image[ignore_mask] = 255
+        image[~ignore_mask] = image[~ignore_mask] - 1
         return image
 
     def _preprocess(
diff --git a/src/transformers/models/segformer/image_processing_segformer.py b/src/transformers/models/segformer/image_processing_segformer.py
index efc8c312953e..616895716a3f 100644
--- a/src/transformers/models/segformer/image_processing_segformer.py
+++ b/src/transformers/models/segformer/image_processing_segformer.py
@@ -138,9 +138,10 @@ def reduce_label(self, labels: list["torch.Tensor"]) -> list["torch.Tensor"]:
         """Reduce label values by 1, replacing 0 with 255."""
         for idx in range(len(labels)):
             label = labels[idx]
-            label = torch.where(label == 0, torch.tensor(255, dtype=label.dtype, device=label.device), label)
-            label = label - 1
-            label = torch.where(label == 254, torch.tensor(255, dtype=label.dtype, device=label.device), label)
+            ignore_mask = (label == 0) | (label == 255)
+            label = label.clone()
+            label[ignore_mask] = 255
+            label[~ignore_mask] = label[~ignore_mask] - 1
             labels[idx] = label
         return labels
 
diff --git a/src/transformers/models/segformer/modeling_segformer.py b/src/transformers/models/segformer/modeling_segformer.py
index df1430d5cad9..c858c8843680 100755
--- a/src/transformers/models/segformer/modeling_segformer.py
+++ b/src/transformers/models/segformer/modeling_segformer.py
@@ -23,6 +23,8 @@
 from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput, SemanticSegmenterOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import auto_docstring, logging
+from ...utils.generic import can_return_tuple
+from ...utils.output_capturing import capture_outputs
 from .configuration_segformer import SegformerConfig
 
 
@@ -402,6 +404,10 @@ class SegformerPreTrainedModel(PreTrainedModel):
     main_input_name = "pixel_values"
     input_modalities = ("image",)
 
+    @property
+    def _can_record_outputs(self) -> dict[str, str]:
+        return {"hidden_states": "SegformerEncoder", "attentions": "SegformerEncoder"}
+
 
 @auto_docstring
 class SegformerModel(SegformerPreTrainedModel):
@@ -455,6 +461,11 @@ def forward(
     """
 )
 class SegformerForImageClassification(SegformerPreTrainedModel):
+    _can_record_outputs = {
+        "hidden_states": "SegformerForImageClassification",
+        "attentions": "SegformerForImageClassification",
+    }
+
     def __init__(self, config):
         super().__init__(config)
 
@@ -467,7 +478,8 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
-    @auto_docstring
+    @can_return_tuple
+    @capture_outputs
     def forward(
         self,
         pixel_values: torch.FloatTensor | None = None,
@@ -483,7 +495,6 @@ def forward(
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.segformer(
             pixel_values,
@@ -510,10 +521,6 @@ def forward(
         if labels is not None:
             loss = self.loss_function(labels, logits, self.config)
 
-        if not return_dict:
-            output = (logits,) + outputs[1:]
-            return ((loss,) + output) if loss is not None else output
-
         return SegFormerImageClassifierOutput(
             loss=loss,
             logits=logits,
diff --git a/src/transformers/models/seggpt/modeling_seggpt.py b/src/transformers/models/seggpt/modeling_seggpt.py
index 0ba961989adc..1d8b095000e3 100644
--- a/src/transformers/models/seggpt/modeling_seggpt.py
+++ b/src/transformers/models/seggpt/modeling_seggpt.py
@@ -31,12 +31,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`SegGptEncoderOutput`].
     """
 )
+@dataclass
 class SegGptEncoderOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, patch_height, patch_width, hidden_size)`):
@@ -59,12 +59,12 @@ class SegGptEncoderOutput(ModelOutput):
     intermediate_hidden_states: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`SegGptImageSegmentationOutput`].
     """
 )
+@dataclass
 class SegGptImageSegmentationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/shieldgemma2/convert_shieldgemma2_weights_orbax_to_hf.py b/src/transformers/models/shieldgemma2/convert_shieldgemma2_weights_orbax_to_hf.py
index ec02efd26a44..2a740615203d 100644
--- a/src/transformers/models/shieldgemma2/convert_shieldgemma2_weights_orbax_to_hf.py
+++ b/src/transformers/models/shieldgemma2/convert_shieldgemma2_weights_orbax_to_hf.py
@@ -216,8 +216,6 @@ def convert_siglip_weight(
     else:
         raise ValueError(f"Unexpected path `{path}`.")
 
-    if "vision" in normalized_path:
-        print(normalized_path)
     return normalized_path, updated_weights
 
 
diff --git a/src/transformers/models/shieldgemma2/processing_shieldgemma2.py b/src/transformers/models/shieldgemma2/processing_shieldgemma2.py
index d23be1d6f941..e54ce6469785 100644
--- a/src/transformers/models/shieldgemma2/processing_shieldgemma2.py
+++ b/src/transformers/models/shieldgemma2/processing_shieldgemma2.py
@@ -18,6 +18,7 @@
 from ...image_utils import ImageInput
 from ...processing_utils import Unpack
 from ...utils import logging
+from ..gemma3.image_processing_gemma3 import Gemma3ImageProcessorKwargs
 from ..gemma3.processing_gemma3 import Gemma3Processor, Gemma3ProcessorKwargs
 
 
@@ -45,6 +46,7 @@
 
 
 class ShieldGemma2ProcessorKwargs(Gemma3ProcessorKwargs, total=False):
+    images_kwargs: Gemma3ImageProcessorKwargs
     policies: Sequence[str] | None
     custom_policies: Mapping[str, str] | None
     _defaults = {
diff --git a/src/transformers/models/siglip/modeling_siglip.py b/src/transformers/models/siglip/modeling_siglip.py
index 8309305f1fb1..4b50ffe6b534 100644
--- a/src/transformers/models/siglip/modeling_siglip.py
+++ b/src/transformers/models/siglip/modeling_siglip.py
@@ -78,8 +78,8 @@ class SiglipTextModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip
 class SiglipOutput(ModelOutput):
     r"""
@@ -457,14 +457,19 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutput:
         hidden_states = inputs_embeds
+        all_hidden_states = [hidden_states] if self.config.output_hidden_states else None
         for encoder_layer in self.layers:
             hidden_states = encoder_layer(
                 hidden_states,
                 attention_mask,
                 **kwargs,
             )
+            if all_hidden_states:
+                all_hidden_states.append(hidden_states)
 
-        return BaseModelOutput(last_hidden_state=hidden_states)
+        return BaseModelOutput(
+            last_hidden_state=hidden_states, hidden_states=tuple(all_hidden_states) if all_hidden_states else None
+        )
 
 
 @auto_docstring(
diff --git a/src/transformers/models/siglip2/modeling_siglip2.py b/src/transformers/models/siglip2/modeling_siglip2.py
index 83fab04d7518..d852ec433c02 100644
--- a/src/transformers/models/siglip2/modeling_siglip2.py
+++ b/src/transformers/models/siglip2/modeling_siglip2.py
@@ -41,12 +41,12 @@
 from .configuration_siglip2 import Siglip2Config, Siglip2TextConfig, Siglip2VisionConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states.
     """
 )
+@dataclass
 class Siglip2VisionOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -59,12 +59,12 @@ class Siglip2VisionOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for text model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class Siglip2TextOutput(ModelOutput):
     r"""
     text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
diff --git a/src/transformers/models/siglip2/processing_siglip2.py b/src/transformers/models/siglip2/processing_siglip2.py
index 2315eef2d016..1b4f3249a5cc 100644
--- a/src/transformers/models/siglip2/processing_siglip2.py
+++ b/src/transformers/models/siglip2/processing_siglip2.py
@@ -17,9 +17,11 @@
 
 from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from .image_processing_siglip2 import Siglip2ImageProcessorKwargs
 
 
 class Siglip2ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: Siglip2ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": "max_length",
diff --git a/src/transformers/models/slanet/modeling_slanet.py b/src/transformers/models/slanet/modeling_slanet.py
index 8ca95ad53d05..753db1a4f198 100644
--- a/src/transformers/models/slanet/modeling_slanet.py
+++ b/src/transformers/models/slanet/modeling_slanet.py
@@ -75,8 +75,8 @@ def _init_weights(self, module):
                             init.uniform_(layer.bias, -std, std)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class SLANetForTableRecognitionOutput(BaseModelOutputWithNoAttention):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/slanet/modular_slanet.py b/src/transformers/models/slanet/modular_slanet.py
index 19dfedb2901d..2c10a49fa822 100644
--- a/src/transformers/models/slanet/modular_slanet.py
+++ b/src/transformers/models/slanet/modular_slanet.py
@@ -118,8 +118,8 @@ def _init_weights(self, module):
                             init.uniform_(layer.bias, -std, std)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class SLANetForTableRecognitionOutput(BaseModelOutputWithNoAttention):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/slanext/modeling_slanext.py b/src/transformers/models/slanext/modeling_slanext.py
index a71b17e670e9..b767efbb8c25 100644
--- a/src/transformers/models/slanext/modeling_slanext.py
+++ b/src/transformers/models/slanext/modeling_slanext.py
@@ -364,13 +364,13 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.FloatTensor]:
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for slanext vision model's outputs that also contains image embeddings obtained by applying the projection
     layer to the pooler_output.
     """
 )
+@dataclass
 class SLANeXtVisionEncoderOutput(ModelOutput):
     r"""
     image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`):
@@ -597,8 +597,8 @@ def forward(
         return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class SLANeXtForTableRecognitionOutput(BaseModelOutput):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/slanext/modular_slanext.py b/src/transformers/models/slanext/modular_slanext.py
index 965e0da7fbe6..a63acd1e362d 100644
--- a/src/transformers/models/slanext/modular_slanext.py
+++ b/src/transformers/models/slanext/modular_slanext.py
@@ -267,8 +267,8 @@ def forward(
         return BaseModelOutput(last_hidden_state=structure_preds, hidden_states=structure_preds_list)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class SLANeXtForTableRecognitionOutput(BaseModelOutput):
     r"""
     head_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
diff --git a/src/transformers/models/smollm3/modeling_smollm3.py b/src/transformers/models/smollm3/modeling_smollm3.py
index 8d911e414b0f..476e279f59f5 100644
--- a/src/transformers/models/smollm3/modeling_smollm3.py
+++ b/src/transformers/models/smollm3/modeling_smollm3.py
@@ -62,8 +62,8 @@ def __init__(self, config: SmolLM3Config, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -103,7 +103,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/smolvlm/modeling_smolvlm.py b/src/transformers/models/smolvlm/modeling_smolvlm.py
index c2b6e23698cd..209ca06c0aaa 100644
--- a/src/transformers/models/smolvlm/modeling_smolvlm.py
+++ b/src/transformers/models/smolvlm/modeling_smolvlm.py
@@ -373,12 +373,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for SmolVLM model's outputs that may also contain a past key/values (to speed up sequential decoding).
     """
 )
+@dataclass
 class SmolVLMBaseModelOutputWithPast(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
@@ -530,7 +530,13 @@ def get_image_features(
             The attention mask indicating padded regions in the image.
         """
         batch_size, num_images, num_channels, height, width = pixel_values.shape
-        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
+        # Safely get dtype, handling DataParallel case where self.dtype might raise StopIteration
+        try:
+            target_dtype = self.dtype
+        except StopIteration:
+            # Fallback to pixel_values dtype if model has no floating point parameters
+            target_dtype = pixel_values.dtype if pixel_values.is_floating_point() else torch.float32
+        pixel_values = pixel_values.to(dtype=target_dtype)  # fp16 compatibility
         pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
 
         # Remove padding images - padding images are full 0.
@@ -629,7 +635,13 @@ def forward(
             ).pooler_output
             image_hidden_states = image_hidden_states.to(inputs_embeds.device)
         elif image_hidden_states is not None:
-            image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=inputs_embeds.device)
+            # Safely get dtype, handling DataParallel case where self.dtype might raise StopIteration
+            try:
+                target_dtype = self.dtype
+            except StopIteration:
+                # Fallback to image_hidden_states dtype if model has no floating point parameters
+                target_dtype = image_hidden_states.dtype if image_hidden_states.is_floating_point() else torch.float32
+            image_hidden_states = image_hidden_states.to(dtype=target_dtype, device=inputs_embeds.device)
 
         if image_hidden_states is not None:
             inputs_embeds = self.inputs_merger(
@@ -656,12 +668,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for Idefics causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class SmolVLMCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/smolvlm/modular_smolvlm.py b/src/transformers/models/smolvlm/modular_smolvlm.py
index cf91863c56a7..4e9fbee50d61 100644
--- a/src/transformers/models/smolvlm/modular_smolvlm.py
+++ b/src/transformers/models/smolvlm/modular_smolvlm.py
@@ -159,7 +159,13 @@ def get_image_features(
             The attention mask indicating padded regions in the image.
         """
         batch_size, num_images, num_channels, height, width = pixel_values.shape
-        pixel_values = pixel_values.to(dtype=self.dtype)  # fp16 compatibility
+        # Safely get dtype, handling DataParallel case where self.dtype might raise StopIteration
+        try:
+            target_dtype = self.dtype
+        except StopIteration:
+            # Fallback to pixel_values dtype if model has no floating point parameters
+            target_dtype = pixel_values.dtype if pixel_values.is_floating_point() else torch.float32
+        pixel_values = pixel_values.to(dtype=target_dtype)  # fp16 compatibility
         pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
 
         # Remove padding images - padding images are full 0.
@@ -252,7 +258,13 @@ def forward(
             ).pooler_output
             image_hidden_states = image_hidden_states.to(inputs_embeds.device)
         elif image_hidden_states is not None:
-            image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=inputs_embeds.device)
+            # Safely get dtype, handling DataParallel case where self.dtype might raise StopIteration
+            try:
+                target_dtype = self.dtype
+            except StopIteration:
+                # Fallback to image_hidden_states dtype if model has no floating point parameters
+                target_dtype = image_hidden_states.dtype if image_hidden_states.is_floating_point() else torch.float32
+            image_hidden_states = image_hidden_states.to(dtype=target_dtype, device=inputs_embeds.device)
 
         if image_hidden_states is not None:
             inputs_embeds = self.inputs_merger(
diff --git a/src/transformers/models/smolvlm/processing_smolvlm.py b/src/transformers/models/smolvlm/processing_smolvlm.py
index 0107ae31cbfe..af0dd91660da 100644
--- a/src/transformers/models/smolvlm/processing_smolvlm.py
+++ b/src/transformers/models/smolvlm/processing_smolvlm.py
@@ -24,6 +24,7 @@
 from ...tokenization_utils_base import BatchEncoding, TextInput
 from ...utils import auto_docstring, is_num2words_available, logging
 from ...video_utils import VideoInput
+from .image_processing_smolvlm import SmolVLMImageProcessorKwargs
 
 
 # Adapted from transformers.models.smolvlm.video_processing_smolvlm.DEFAULT_VIDEO_INTRO
@@ -98,6 +99,7 @@ def get_image_prompt_string(
 
 
 class SmolVLMProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: SmolVLMImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/solar_open/configuration_solar_open.py b/src/transformers/models/solar_open/configuration_solar_open.py
index ac0016aa7791..4055dfc8d4eb 100644
--- a/src/transformers/models/solar_open/configuration_solar_open.py
+++ b/src/transformers/models/solar_open/configuration_solar_open.py
@@ -31,6 +31,11 @@ class SolarOpenConfig(PreTrainedConfig):
     r"""
     n_group (`int`, *optional*, defaults to 1):
         Number of groups for routed experts.
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
     """
 
     model_type = "solar_open"
diff --git a/src/transformers/models/solar_open/modular_solar_open.py b/src/transformers/models/solar_open/modular_solar_open.py
index 90d4f0c389c0..4a1c546c9464 100644
--- a/src/transformers/models/solar_open/modular_solar_open.py
+++ b/src/transformers/models/solar_open/modular_solar_open.py
@@ -37,6 +37,11 @@ class SolarOpenConfig(Glm4MoeConfig):
     r"""
     n_group (`int`, *optional*, defaults to 1):
         Number of groups for routed experts.
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
     """
 
     model_type = "solar_open"
diff --git a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
index 638c1f7e838c..6a617ff9fbc2 100644
--- a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
+++ b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
@@ -22,7 +22,7 @@
 from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
 from ..auto.configuration_auto import AutoConfig
 from ..auto.modeling_auto import AutoModel, AutoModelForCausalLM
 from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
@@ -195,8 +195,7 @@ def from_encoder_decoder_pretrained(
                 All remaining positional arguments will be passed to the underlying model's `__init__` method.
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
-                `output_attentions=True`).
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
 
                 - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
                 - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
@@ -305,6 +304,7 @@ def from_encoder_decoder_pretrained(
         config.tie_word_embeddings = False
         return cls(encoder=encoder, decoder=decoder, config=config)
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -317,13 +317,10 @@ def forward(
         decoder_inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
         input_values: torch.FloatTensor | None = None,
         input_features: torch.FloatTensor | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput:
+    ) -> Seq2SeqLMOutput:
         r"""
         inputs (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, feature_dim)`, *optional*):
             Float values of input raw speech waveform or speech features. Values can be obtained by loading a `.flac`
@@ -388,8 +385,6 @@ def forward(
         >>> loss = model(input_values, labels=labels).loss
         >>> loss.backward()
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
 
         kwargs_decoder = {
@@ -397,6 +392,7 @@ def forward(
         }
         if "num_items_in_batch" in kwargs_encoder:
             kwargs_decoder["num_items_in_batch"] = kwargs_encoder.pop("num_items_in_batch", None)
+        kwargs_decoder = kwargs_decoder | {k: v for k, v in kwargs.items() if not k.startswith("decoder_")}
 
         if encoder_outputs is None:
             if inputs is None:
@@ -412,9 +408,6 @@ def forward(
             encoder_outputs = self.encoder(
                 inputs,
                 attention_mask=attention_mask,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
                 **kwargs_encoder,
             )
         elif isinstance(encoder_outputs, tuple):
@@ -449,27 +442,18 @@ def forward(
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=encoder_attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
             use_cache=use_cache,
             past_key_values=past_key_values,
-            return_dict=return_dict,
             **kwargs_decoder,
         )
 
         # Compute loss independent from decoder (as some shift the logits inside them)
         loss = None
         if labels is not None:
-            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
+            logits = decoder_outputs.logits if hasattr(decoder_outputs, "logits") else decoder_outputs[0]
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))
 
-        if not return_dict:
-            if loss is not None:
-                return (loss,) + decoder_outputs + encoder_outputs
-            else:
-                return decoder_outputs + encoder_outputs
-
         return Seq2SeqLMOutput(
             loss=loss,
             logits=decoder_outputs.logits,
diff --git a/src/transformers/models/speecht5/configuration_speecht5.py b/src/transformers/models/speecht5/configuration_speecht5.py
index 82646d9f8927..f49f1692cee1 100644
--- a/src/transformers/models/speecht5/configuration_speecht5.py
+++ b/src/transformers/models/speecht5/configuration_speecht5.py
@@ -216,6 +216,7 @@ def validate_architecture(self):
                 f" `len(config.conv_kernel) = {len(self.conv_kernel)}`."
             )
 
+    @property
     def inputs_to_logits_ratio(self):
         return functools.reduce(operator.mul, self.conv_stride, 1)
 
diff --git a/src/transformers/models/splinter/modeling_splinter.py b/src/transformers/models/splinter/modeling_splinter.py
index 48f6d18fbda6..ed7eee7c908a 100755
--- a/src/transformers/models/splinter/modeling_splinter.py
+++ b/src/transformers/models/splinter/modeling_splinter.py
@@ -560,12 +560,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of Splinter as a span selection model.
     """
 )
+@dataclass
 class SplinterForPreTrainingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when start and end positions are provided):
diff --git a/src/transformers/models/squeezebert/modeling_squeezebert.py b/src/transformers/models/squeezebert/modeling_squeezebert.py
index 5bf2890bcc9e..d543e5fe3f1c 100644
--- a/src/transformers/models/squeezebert/modeling_squeezebert.py
+++ b/src/transformers/models/squeezebert/modeling_squeezebert.py
@@ -31,10 +31,8 @@
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    auto_docstring,
-    logging,
-)
+from ...utils import auto_docstring, can_return_tuple, logging
+from ...utils.output_capturing import capture_outputs
 from .configuration_squeezebert import SqueezeBertConfig
 
 
@@ -208,7 +206,7 @@ def transpose_output(self, x):
         x = x.view(*new_x_shape)
         return x
 
-    def forward(self, hidden_states, attention_mask, output_attentions):
+    def forward(self, hidden_states, attention_mask, **kwargs):
         """
         expects hidden_states in [N, C, W] data layout.
 
@@ -238,10 +236,7 @@ def forward(self, hidden_states, attention_mask, output_attentions):
         context_layer = self.matmul_qkv(attention_probs, value_layer)
         context_layer = self.transpose_output(context_layer)
 
-        result = {"context_layer": context_layer}
-        if output_attentions:
-            result["attention_score"] = attention_score
-        return result
+        return context_layer, attention_score
 
 
 class SqueezeBertModule(nn.Module):
@@ -271,19 +266,15 @@ def __init__(self, config):
             cin=c2, cout=c3, groups=config.output_groups, dropout_prob=config.hidden_dropout_prob
         )
 
-    def forward(self, hidden_states, attention_mask, output_attentions):
-        att = self.attention(hidden_states, attention_mask, output_attentions)
-        attention_output = att["context_layer"]
+    def forward(self, hidden_states, attention_mask, **kwargs):
+        hidden_states_ncw = hidden_states.permute(0, 2, 1)
 
-        post_attention_output = self.post_attention(attention_output, hidden_states)
+        attention_output, _ = self.attention(hidden_states_ncw, attention_mask, **kwargs)
+        post_attention_output = self.post_attention(attention_output, hidden_states_ncw)
         intermediate_output = self.intermediate(post_attention_output)
         layer_output = self.output(intermediate_output, post_attention_output)
 
-        output_dict = {"feature_map": layer_output}
-        if output_attentions:
-            output_dict["attention_score"] = att["attention_score"]
-
-        return output_dict
+        return layer_output.permute(0, 2, 1)
 
 
 class SqueezeBertEncoder(nn.Module):
@@ -302,40 +293,12 @@ def forward(
         self,
         hidden_states,
         attention_mask=None,
-        output_attentions=False,
-        output_hidden_states=False,
-        return_dict=True,
+        **kwargs,
     ):
-        # [batch_size, sequence_length, hidden_size] --> [batch_size, hidden_size, sequence_length]
-        hidden_states = hidden_states.permute(0, 2, 1)
-
-        all_hidden_states = () if output_hidden_states else None
-        all_attentions = () if output_attentions else None
-
         for layer in self.layers:
-            if output_hidden_states:
-                hidden_states = hidden_states.permute(0, 2, 1)
-                all_hidden_states += (hidden_states,)
-                hidden_states = hidden_states.permute(0, 2, 1)
-
-            layer_output = layer.forward(hidden_states, attention_mask, output_attentions)
-
-            hidden_states = layer_output["feature_map"]
+            hidden_states = layer(hidden_states, attention_mask, **kwargs)
 
-            if output_attentions:
-                all_attentions += (layer_output["attention_score"],)
-
-        # [batch_size, hidden_size, sequence_length] --> [batch_size, sequence_length, hidden_size]
-        hidden_states = hidden_states.permute(0, 2, 1)
-
-        if output_hidden_states:
-            all_hidden_states += (hidden_states,)
-
-        if not return_dict:
-            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
-        return BaseModelOutput(
-            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
-        )
+        return BaseModelOutput(last_hidden_state=hidden_states)
 
 
 class SqueezeBertPooler(nn.Module):
@@ -404,6 +367,11 @@ class SqueezeBertPreTrainedModel(PreTrainedModel):
     config: SqueezeBertConfig
     base_model_prefix = "transformer"
 
+    _can_record_outputs = {
+        "hidden_states": SqueezeBertModule,
+        "attentions": SqueezeBertSelfAttention,
+    }
+
     @torch.no_grad()
     def _init_weights(self, module):
         """Initialize the weights"""
@@ -432,6 +400,7 @@ def get_input_embeddings(self):
     def set_input_embeddings(self, new_embeddings):
         self.embeddings.word_embeddings = new_embeddings
 
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
@@ -440,17 +409,8 @@ def forward(
         token_type_ids: torch.Tensor | None = None,
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.FloatTensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | BaseModelOutputWithPooling:
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
+    ) -> BaseModelOutputWithPooling:
         if input_ids is not None and inputs_embeds is not None:
             raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
         elif input_ids is not None:
@@ -476,21 +436,14 @@ def forward(
         encoder_outputs = self.encoder(
             hidden_states=embedding_output,
             attention_mask=extended_attention_mask,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
-        sequence_output = encoder_outputs[0]
+        sequence_output = encoder_outputs.last_hidden_state
         pooled_output = self.pooler(sequence_output)
 
-        if not return_dict:
-            return (sequence_output, pooled_output) + encoder_outputs[1:]
-
         return BaseModelOutputWithPooling(
             last_hidden_state=sequence_output,
             pooler_output=pooled_output,
-            hidden_states=encoder_outputs.hidden_states,
-            attentions=encoder_outputs.attentions,
         )
 
 
@@ -517,6 +470,7 @@ def set_output_embeddings(self, new_embeddings):
         self.cls.predictions.decoder = new_embeddings
         self.cls.predictions.bias = new_embeddings.bias
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -526,18 +480,14 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | MaskedLMOutput:
+    ) -> MaskedLMOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
             config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
             loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.transformer(
             input_ids,
@@ -545,12 +495,10 @@ def forward(
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        sequence_output = outputs[0]
+        sequence_output = outputs.last_hidden_state
         prediction_scores = self.cls(sequence_output)
 
         masked_lm_loss = None
@@ -558,10 +506,6 @@ def forward(
             loss_fct = CrossEntropyLoss()  # -100 index = padding token
             masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
-        if not return_dict:
-            output = (prediction_scores,) + outputs[2:]
-            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
-
         return MaskedLMOutput(
             loss=masked_lm_loss,
             logits=prediction_scores,
@@ -589,6 +533,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -598,18 +543,14 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | SequenceClassifierOutput:
+    ) -> SequenceClassifierOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
             Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
             config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
             `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
 
         outputs = self.transformer(
             input_ids,
@@ -617,13 +558,10 @@ def forward(
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        pooled_output = outputs[1]
-
+        pooled_output = outputs.pooler_output
         pooled_output = self.dropout(pooled_output)
         logits = self.classifier(pooled_output)
 
@@ -650,10 +588,6 @@ def forward(
                 loss_fct = BCEWithLogitsLoss()
                 loss = loss_fct(logits, labels)
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return SequenceClassifierOutput(
             loss=loss,
             logits=logits,
@@ -674,6 +608,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -683,11 +618,8 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | MultipleChoiceModelOutput:
+    ) -> MultipleChoiceModelOutput:
         r"""
         input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`):
             Indices of input sequence tokens in the vocabulary.
@@ -718,7 +650,6 @@ def forward(
             num_choices-1]` where *num_choices* is the size of the second dimension of the input tensors. (see
             *input_ids* above)
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
         num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
 
         input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
@@ -737,13 +668,10 @@ def forward(
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        pooled_output = outputs[1]
-
+        pooled_output = outputs.pooler_output
         pooled_output = self.dropout(pooled_output)
         logits = self.classifier(pooled_output)
         reshaped_logits = logits.view(-1, num_choices)
@@ -753,10 +681,6 @@ def forward(
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(reshaped_logits, labels)
 
-        if not return_dict:
-            output = (reshaped_logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return MultipleChoiceModelOutput(
             loss=loss,
             logits=reshaped_logits,
@@ -778,6 +702,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -787,30 +712,22 @@ def forward(
         position_ids: torch.Tensor | None = None,
         inputs_embeds: torch.Tensor | None = None,
         labels: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | TokenClassifierOutput:
+    ) -> TokenClassifierOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
             Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         outputs = self.transformer(
             input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        sequence_output = outputs[0]
-
+        sequence_output = outputs.last_hidden_state
         sequence_output = self.dropout(sequence_output)
         logits = self.classifier(sequence_output)
 
@@ -819,10 +736,6 @@ def forward(
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
 
-        if not return_dict:
-            output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return TokenClassifierOutput(
             loss=loss,
             logits=logits,
@@ -843,6 +756,7 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -853,26 +767,18 @@ def forward(
         inputs_embeds: torch.Tensor | None = None,
         start_positions: torch.Tensor | None = None,
         end_positions: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | QuestionAnsweringModelOutput:
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
+    ) -> QuestionAnsweringModelOutput:
         outputs = self.transformer(
             input_ids,
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
             inputs_embeds=inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        sequence_output = outputs[0]
-
+        sequence_output = outputs.last_hidden_state
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
         start_logits = start_logits.squeeze(-1).contiguous()
@@ -895,10 +801,6 @@ def forward(
             end_loss = loss_fct(end_logits, end_positions)
             total_loss = (start_loss + end_loss) / 2
 
-        if not return_dict:
-            output = (start_logits, end_logits) + outputs[2:]
-            return ((total_loss,) + output) if total_loss is not None else output
-
         return QuestionAnsweringModelOutput(
             loss=total_loss,
             start_logits=start_logits,
diff --git a/src/transformers/models/stablelm/modeling_stablelm.py b/src/transformers/models/stablelm/modeling_stablelm.py
index 9b9e0430e985..7a3e56320dd7 100755
--- a/src/transformers/models/stablelm/modeling_stablelm.py
+++ b/src/transformers/models/stablelm/modeling_stablelm.py
@@ -113,7 +113,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/starcoder2/modeling_starcoder2.py b/src/transformers/models/starcoder2/modeling_starcoder2.py
index 8b89a1d1745c..e50b24b55f43 100644
--- a/src/transformers/models/starcoder2/modeling_starcoder2.py
+++ b/src/transformers/models/starcoder2/modeling_starcoder2.py
@@ -319,7 +319,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/superglue/modeling_superglue.py b/src/transformers/models/superglue/modeling_superglue.py
index 4e9abde9b5cb..87f03a3dfc6f 100644
--- a/src/transformers/models/superglue/modeling_superglue.py
+++ b/src/transformers/models/superglue/modeling_superglue.py
@@ -145,7 +145,6 @@ def arange_like(x, dim: int) -> torch.Tensor:
     return x.new_ones(x.shape[dim]).cumsum(0) - 1
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of SuperGlue keypoint matching models. Due to the nature of keypoint detection and matching, the number
@@ -155,6 +154,7 @@ def arange_like(x, dim: int) -> torch.Tensor:
     information.
     """
 )
+@dataclass
 class SuperGlueKeypointMatchingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
diff --git a/src/transformers/models/superpoint/modeling_superpoint.py b/src/transformers/models/superpoint/modeling_superpoint.py
index 243315c7523c..473537b5ece0 100644
--- a/src/transformers/models/superpoint/modeling_superpoint.py
+++ b/src/transformers/models/superpoint/modeling_superpoint.py
@@ -19,16 +19,15 @@
 from torch import nn
 
 from transformers import PreTrainedModel
-from transformers.modeling_outputs import (
-    BaseModelOutputWithNoAttention,
-)
 from transformers.models.superpoint.configuration_superpoint import SuperPointConfig
 
 from ...utils import (
     ModelOutput,
     auto_docstring,
+    can_return_tuple,
     logging,
 )
+from ...utils.output_capturing import capture_outputs
 
 
 logger = logging.get_logger(__name__)
@@ -70,7 +69,6 @@ def max_pool(x):
     return torch.where(max_mask, scores, zeros)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of image point description models. Due to the nature of keypoint detection, the number of
@@ -80,6 +78,7 @@ def max_pool(x):
     and which are padding.
     """
 )
+@dataclass
 class SuperPointKeypointDescriptionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*):
@@ -165,26 +164,10 @@ def __init__(self, config: SuperPointConfig) -> None:
         )
         self.conv_blocks = nn.ModuleList(conv_blocks)
 
-    def forward(
-        self,
-        input,
-        output_hidden_states: bool | None = False,
-        return_dict: bool | None = True,
-    ) -> tuple | BaseModelOutputWithNoAttention:
-        all_hidden_states = () if output_hidden_states else None
-
+    def forward(self, input) -> torch.Tensor:
         for conv_block in self.conv_blocks:
             input = conv_block(input)
-            if output_hidden_states:
-                all_hidden_states = all_hidden_states + (input,)
-        output = input
-        if not return_dict:
-            return tuple(v for v in [output, all_hidden_states] if v is not None)
-
-        return BaseModelOutputWithNoAttention(
-            last_hidden_state=output,
-            hidden_states=all_hidden_states,
-        )
+        return input
 
 
 class SuperPointInterestPointDecoder(nn.Module):
@@ -326,6 +309,7 @@ class SuperPointPreTrainedModel(PreTrainedModel):
     main_input_name = "pixel_values"
     input_modalities = ("image",)
     supports_gradient_checkpointing = False
+    _can_record_outputs = {"hidden_states": SuperPointConvBlock}
 
     def extract_one_channel_pixel_values(self, pixel_values: torch.FloatTensor) -> torch.FloatTensor:
         """
@@ -370,13 +354,13 @@ def __init__(self, config: SuperPointConfig) -> None:
 
         self.post_init()
 
+    @can_return_tuple
+    @capture_outputs
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.FloatTensor,
         labels: torch.LongTensor | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
     ) -> tuple | SuperPointKeypointDescriptionOutput:
         r"""
@@ -403,33 +387,20 @@ def forward(
         if labels is not None:
             raise ValueError("SuperPoint does not support training for now.")
 
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         pixel_values = self.extract_one_channel_pixel_values(pixel_values)
 
         batch_size, _, height, width = pixel_values.shape
 
-        encoder_outputs = self.encoder(
-            pixel_values,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
-        )
-
-        last_hidden_state = encoder_outputs[0]
+        last_hidden_state = self.encoder(pixel_values)
 
-        list_keypoints_scores = [
-            self.keypoint_decoder(last_hidden_state[None, ...]) for last_hidden_state in last_hidden_state
-        ]
+        list_keypoints_scores = [self.keypoint_decoder(lhs[None, ...]) for lhs in last_hidden_state]
 
         list_keypoints = [keypoints_scores[0] for keypoints_scores in list_keypoints_scores]
         list_scores = [keypoints_scores[1] for keypoints_scores in list_keypoints_scores]
 
         list_descriptors = [
-            self.descriptor_decoder(last_hidden_state[None, ...], keypoints[None, ...])
-            for last_hidden_state, keypoints in zip(last_hidden_state, list_keypoints)
+            self.descriptor_decoder(lhs[None, ...], keypoints[None, ...])
+            for lhs, keypoints in zip(last_hidden_state, list_keypoints)
         ]
 
         maximum_num_keypoints = max(keypoints.shape[0] for keypoints in list_keypoints)
@@ -451,17 +422,12 @@ def forward(
         # Convert to relative coordinates
         keypoints = keypoints / torch.tensor([width, height], device=keypoints.device)
 
-        hidden_states = encoder_outputs[1] if output_hidden_states else None
-        if not return_dict:
-            return tuple(v for v in [loss, keypoints, scores, descriptors, mask, hidden_states] if v is not None)
-
         return SuperPointKeypointDescriptionOutput(
             loss=loss,
             keypoints=keypoints,
             scores=scores,
             descriptors=descriptors,
             mask=mask,
-            hidden_states=hidden_states,
         )
 
 
diff --git a/src/transformers/models/swin/modeling_swin.py b/src/transformers/models/swin/modeling_swin.py
index 37a2f5f81f13..f0070c094b0d 100644
--- a/src/transformers/models/swin/modeling_swin.py
+++ b/src/transformers/models/swin/modeling_swin.py
@@ -37,12 +37,12 @@
 # drop_path, SwinPatchEmbeddings, SwinPatchMerging and SwinDropPath are from the timm library.
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Swin encoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class SwinEncoderOutput(ModelOutput):
     r"""
     reshaped_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
@@ -59,12 +59,12 @@ class SwinEncoderOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Swin model's outputs that also contains a pooling of the last hidden states.
     """
 )
+@dataclass
 class SwinModelOutput(ModelOutput):
     r"""
     pooler_output (`torch.FloatTensor` of shape `(batch_size, hidden_size)`, *optional*, returned when `add_pooling_layer=True` is passed):
@@ -84,12 +84,12 @@ class SwinModelOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Swin masked image model outputs.
     """
 )
+@dataclass
 class SwinMaskedImageModelingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `bool_masked_pos` is provided):
@@ -111,12 +111,12 @@ class SwinMaskedImageModelingOutput(ModelOutput):
     reshaped_hidden_states: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Swin outputs for image classification.
     """
 )
+@dataclass
 class SwinImageClassifierOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/swin2sr/modeling_swin2sr.py b/src/transformers/models/swin2sr/modeling_swin2sr.py
index da2eb0ab5ed8..af1bd10b97dd 100644
--- a/src/transformers/models/swin2sr/modeling_swin2sr.py
+++ b/src/transformers/models/swin2sr/modeling_swin2sr.py
@@ -32,12 +32,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Swin2SR encoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class Swin2SREncoderOutput(ModelOutput):
     last_hidden_state: torch.FloatTensor | None = None
     hidden_states: tuple[torch.FloatTensor] | None = None
diff --git a/src/transformers/models/switch_transformers/modeling_switch_transformers.py b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
index 4f4124961a92..43cc3fee7bfd 100644
--- a/src/transformers/models/switch_transformers/modeling_switch_transformers.py
+++ b/src/transformers/models/switch_transformers/modeling_switch_transformers.py
@@ -87,12 +87,19 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         # https://huggingface.co/papers/2101.03961.
         # We also store the previous dtype to cast back the output to the previous dtype
         self.input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(self.dtype)
+
+        # Create a copy for applying jitter noise
+        routing_states = hidden_states.clone()
+        routing_states = routing_states.to(self.dtype)
+
         if self.training and self.jitter_noise > 0:
-            # Multiply the token inputs by the uniform distribution - adding some noise
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+            # Apply jitter noise only to the routing copy.
+            routing_states *= torch.empty_like(routing_states).uniform_(
+                1.0 - self.jitter_noise, 1.0 + self.jitter_noise
+            )
+
         self.classifier = self.classifier.to(self.dtype)
-        router_logits = self.classifier(hidden_states)
+        router_logits = self.classifier(routing_states)
 
         # Apply Softmax and cast back to the original `dtype`
         router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
@@ -562,7 +569,7 @@ def _init_weights(self, module):
             init.constant_(module.weight, factor * 1.0)
         elif isinstance(
             module,
-            (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
+            SwitchTransformersModel | SwitchTransformersForConditionalGeneration | SwitchTransformersEncoderModel,
         ):
             init.normal_(module.shared.weight, mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
@@ -618,8 +625,8 @@ def _shift_right(self, input_ids):
 class SwitchTransformersStack(SwitchTransformersPreTrainedModel):
     _can_record_outputs = {
         "hidden_states": SwitchTransformersBlock,
-        "attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name="layer.0"),
-        "cross_attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name="layer.1"),
+        "attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name=r"layer\.0"),
+        "cross_attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name=r"layer\.1"),
         "router_logits": OutputRecorder(SwitchTransformersTop1Router, index=2),
     }
 
diff --git a/src/transformers/models/switch_transformers/modular_switch_transformers.py b/src/transformers/models/switch_transformers/modular_switch_transformers.py
index 5c0f253cfb78..7056e8300894 100644
--- a/src/transformers/models/switch_transformers/modular_switch_transformers.py
+++ b/src/transformers/models/switch_transformers/modular_switch_transformers.py
@@ -154,12 +154,19 @@ def forward(self, hidden_states: torch.Tensor) -> tuple[torch.Tensor, torch.Tens
         # https://huggingface.co/papers/2101.03961.
         # We also store the previous dtype to cast back the output to the previous dtype
         self.input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(self.dtype)
+
+        # Create a copy for applying jitter noise
+        routing_states = hidden_states.clone()
+        routing_states = routing_states.to(self.dtype)
+
         if self.training and self.jitter_noise > 0:
-            # Multiply the token inputs by the uniform distribution - adding some noise
-            hidden_states *= torch.empty_like(hidden_states).uniform_(1.0 - self.jitter_noise, 1.0 + self.jitter_noise)
+            # Apply jitter noise only to the routing copy.
+            routing_states *= torch.empty_like(routing_states).uniform_(
+                1.0 - self.jitter_noise, 1.0 + self.jitter_noise
+            )
+
         self.classifier = self.classifier.to(self.dtype)
-        router_logits = self.classifier(hidden_states)
+        router_logits = self.classifier(routing_states)
 
         # Apply Softmax and cast back to the original `dtype`
         router_probs = nn.functional.softmax(router_logits, dim=-1, dtype=self.dtype).to(self.input_dtype)
@@ -342,7 +349,7 @@ def _init_weights(self, module):
             init.constant_(module.weight, factor * 1.0)
         elif isinstance(
             module,
-            (SwitchTransformersModel, SwitchTransformersForConditionalGeneration, SwitchTransformersEncoderModel),
+            SwitchTransformersModel | SwitchTransformersForConditionalGeneration | SwitchTransformersEncoderModel,
         ):
             init.normal_(module.shared.weight, mean=0.0, std=factor * 1.0)
             if hasattr(module, "lm_head") and not self.config.tie_word_embeddings:
@@ -398,8 +405,8 @@ def _shift_right(self, input_ids):
 class SwitchTransformersStack(SwitchTransformersPreTrainedModel):
     _can_record_outputs = {
         "hidden_states": SwitchTransformersBlock,
-        "attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name="layer.0"),
-        "cross_attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name="layer.1"),
+        "attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name=r"layer\.0"),
+        "cross_attentions": OutputRecorder(SwitchTransformersAttention, index=-1, layer_name=r"layer\.1"),
         "router_logits": OutputRecorder(SwitchTransformersTop1Router, index=2),
     }
 
diff --git a/src/transformers/models/t5/modeling_t5.py b/src/transformers/models/t5/modeling_t5.py
index 0f541c74069c..b7e8c34fc206 100644
--- a/src/transformers/models/t5/modeling_t5.py
+++ b/src/transformers/models/t5/modeling_t5.py
@@ -33,6 +33,7 @@
     Seq2SeqModelOutput,
     Seq2SeqQuestionAnsweringModelOutput,
     Seq2SeqSequenceClassifierOutput,
+    SequenceClassifierOutput,
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
@@ -1608,6 +1609,98 @@ def forward(
         )
 
 
+@auto_docstring
+class T5EncoderForSequenceClassification(T5PreTrainedModel):
+    keys_to_ignore_on_load_unexpected = [r"decoder"]
+
+    def __init__(self, config: T5Config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+
+        self.transformer = T5EncoderModel(config)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = T5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.model_parallel = False
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple[torch.Tensor] | SequenceClassifierOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Mask to avoid performing attention on padding token indices.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]  #  outputs.last_hidden_state
+        hidden_states = self.dropout(hidden_states)
+
+        sentence_representation = (hidden_states * attention_mask.unsqueeze(-1)).sum(dim=1)
+        sentence_representation /= attention_mask.sum(dim=1).unsqueeze(-1)
+
+        logits = self.classifier(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            if self.config.num_labels > 0 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                self.config.problem_type = "single_label_classification"
+            else:
+                self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                batch_size, _ = input_ids.shape
+                loss = loss_fct(logits.view(batch_size, self.num_labels), labels.view(batch_size, self.num_labels))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
 __all__ = [
     "T5EncoderModel",
     "T5ForConditionalGeneration",
@@ -1616,4 +1709,5 @@ def forward(
     "T5ForQuestionAnswering",
     "T5ForSequenceClassification",
     "T5ForTokenClassification",
+    "T5EncoderForSequenceClassification",
 ]
diff --git a/src/transformers/models/t5gemma/configuration_t5gemma.py b/src/transformers/models/t5gemma/configuration_t5gemma.py
index 9de40c832259..8f95f6c2a3e5 100644
--- a/src/transformers/models/t5gemma/configuration_t5gemma.py
+++ b/src/transformers/models/t5gemma/configuration_t5gemma.py
@@ -138,6 +138,7 @@ class T5GemmaConfig(PreTrainedConfig):
     attention_dropout: float | int = 0.0
     tie_word_embeddings: bool = True
     vocab_size: int = 256000
+    num_hidden_layers: int | None = None
 
     def __post_init__(self, **kwargs):
         if isinstance(self.encoder, dict):
@@ -161,6 +162,8 @@ def __post_init__(self, **kwargs):
         self.decoder.cross_attention_hidden_size = self.encoder.hidden_size
 
         self.initializer_range = kwargs.pop("initializer_range", self.decoder.initializer_range)
+        if self.num_hidden_layers is None:
+            self.num_hidden_layers = self.decoder.num_hidden_layers
 
         for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id"]:
             if special_token_key not in kwargs:
diff --git a/src/transformers/models/t5gemma/modeling_t5gemma.py b/src/transformers/models/t5gemma/modeling_t5gemma.py
index 1f41875c5def..e99913f8c00f 100644
--- a/src/transformers/models/t5gemma/modeling_t5gemma.py
+++ b/src/transformers/models/t5gemma/modeling_t5gemma.py
@@ -112,8 +112,8 @@ def __init__(self, config: T5GemmaConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -153,7 +153,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/t5gemma/modular_t5gemma.py b/src/transformers/models/t5gemma/modular_t5gemma.py
index 1c8846ad74b9..2a016d03d706 100644
--- a/src/transformers/models/t5gemma/modular_t5gemma.py
+++ b/src/transformers/models/t5gemma/modular_t5gemma.py
@@ -117,6 +117,7 @@ class T5GemmaConfig(PreTrainedConfig):
     attention_dropout: float | int = 0.0
     tie_word_embeddings: bool = True
     vocab_size: int = 256000
+    num_hidden_layers: int | None = None
 
     def __post_init__(self, **kwargs):
         if isinstance(self.encoder, dict):
@@ -140,6 +141,8 @@ def __post_init__(self, **kwargs):
         self.decoder.cross_attention_hidden_size = self.encoder.hidden_size
 
         self.initializer_range = kwargs.pop("initializer_range", self.decoder.initializer_range)
+        if self.num_hidden_layers is None:
+            self.num_hidden_layers = self.decoder.num_hidden_layers
 
         for special_token_key in ["bos_token_id", "pad_token_id", "eos_token_id"]:
             if special_token_key not in kwargs:
diff --git a/src/transformers/models/t5gemma2/modeling_t5gemma2.py b/src/transformers/models/t5gemma2/modeling_t5gemma2.py
index ad86febc367e..2c0fa7766ca8 100644
--- a/src/transformers/models/t5gemma2/modeling_t5gemma2.py
+++ b/src/transformers/models/t5gemma2/modeling_t5gemma2.py
@@ -637,7 +637,7 @@ def __init__(
     ):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
         self.eoi_token_index = eoi_token_index
         self.eoi_embedding = nn.Parameter(torch.zeros(self.embedding_dim))
 
@@ -913,10 +913,10 @@ def get_image_placeholder_mask(
             special_image_mask = input_ids == image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0] * image_features.shape[1]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/t5gemma2/modular_t5gemma2.py b/src/transformers/models/t5gemma2/modular_t5gemma2.py
index a1b80a81ef80..8b0ba064e16a 100644
--- a/src/transformers/models/t5gemma2/modular_t5gemma2.py
+++ b/src/transformers/models/t5gemma2/modular_t5gemma2.py
@@ -701,10 +701,10 @@ def get_image_placeholder_mask(
             special_image_mask = input_ids == image_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         n_image_features = image_features.shape[0] * image_features.shape[1]
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/tapas/modeling_tapas.py b/src/transformers/models/tapas/modeling_tapas.py
index e66335c13cc2..87765b17af7d 100644
--- a/src/transformers/models/tapas/modeling_tapas.py
+++ b/src/transformers/models/tapas/modeling_tapas.py
@@ -39,12 +39,12 @@
 CLOSE_ENOUGH_TO_LOG_ZERO = -10000.0
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`TapasForQuestionAnswering`].
     """
 )
+@dataclass
 class TableQuestionAnsweringOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` (and possibly `answer`, `aggregation_labels`, `numeric_values` and `numeric_values_scale` are provided)):
diff --git a/src/transformers/models/timesfm/modeling_timesfm.py b/src/transformers/models/timesfm/modeling_timesfm.py
index 219908c1e47c..9ca11c1bf411 100644
--- a/src/transformers/models/timesfm/modeling_timesfm.py
+++ b/src/transformers/models/timesfm/modeling_timesfm.py
@@ -19,9 +19,11 @@
 # limitations under the License.
 
 import math
+import warnings
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -36,13 +38,14 @@
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from .configuration_timesfm import TimesFmConfig
+from .xreg_utils import BatchedInContextXRegLinear, normalize
 
 
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFmOutput(BaseModelOutput):
     r"""
     loc (`torch.Tensor` of shape `(batch_size, )`):
@@ -55,8 +58,8 @@ class TimesFmOutput(BaseModelOutput):
     scale: torch.Tensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFmOutputForPrediction(BaseModelOutput):
     r"""
     mean_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
@@ -72,6 +75,26 @@ class TimesFmOutputForPrediction(BaseModelOutput):
     loss: torch.Tensor | float | None = None
 
 
+@dataclass
+@auto_docstring
+class TimesFmOutputForPredictionWithCovariates(TimesFmOutputForPrediction):
+    r"""
+    mean_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+        The mean predictions of the time series.
+    full_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+        The full predictions of the time series including the mean and the quantiles.
+    loss (`torch.Tensor` of shape `(1,)`, *optional*, returned when `future_values` is provided):
+        The loss of the TimesFM model.
+    xreg_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+        The predictions from the external regression (XReg) model using covariates.
+    combined_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+        The combined predictions from TimesFM and XReg models.
+    """
+
+    xreg_predictions: torch.Tensor | None = None
+    combined_predictions: torch.Tensor | None = None
+
+
 class TimesFmMLP(nn.Module):
     """Pax MLP in pytorch."""
 
@@ -381,8 +404,10 @@ def forward(
             Past values of the time series that serves as input to the model.
         past_values_padding (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
             The padding indicator of the time series.
-        freq (`torch.LongTensor` of shape `(batch_size,)`):
-            Frequency indices for the time series data.
+        freq (`torch.LongTensor` of shape `(batch_size,)` or `Sequence[int]`, *optional*):
+            Frequency indices for the time series data. Defaults to a zero tensor (high-frequency). A
+            sequence of ints is also accepted and converted to a tensor internally. Tensor inputs are
+            preferred and required for export.
         """
         # Reshape into patches (using view for efficiency)
         bsize = past_values.shape[0]
@@ -590,13 +615,20 @@ def __init__(self, config: TimesFmConfig):
         self.post_init()
 
     def _preprocess(
-        self, inputs: Sequence[torch.Tensor], freq: Sequence[int] | None = None, context_len: int | None = None
+        self,
+        inputs: torch.Tensor,
+        observed_mask: torch.Tensor,
+        freq: torch.Tensor | None = None,
+        context_len: int | None = None,
     ) -> tuple[torch.Tensor, ...]:
         """Pad/truncate input time series to `context_len` and build a padding mask.
 
         Args:
-            inputs: A list of 1d Tensors. Each Tensor is the context time series of a single forecast task.
-            freq: Optional list of frequencies (returned as a tensor when provided).
+            inputs: A 2D `torch.Tensor` of shape `(batch_size, sequence_length)`.
+            observed_mask: A 2D `torch.Tensor` of the same shape as `inputs` where `1` indicates an observed
+                value and `0` indicates a missing value. Missing positions are marked as padded in the
+                returned padding mask.
+            freq: Optional 1D `torch.Tensor` of frequency indices.
             context_len: Optional context length override (defaults to `self.context_len`).
 
         Returns:
@@ -605,25 +637,20 @@ def _preprocess(
         if context_len is None:
             context_len = self.context_len
 
-        input_ts, input_padding = [], []
-
-        for ts in inputs:
-            input_len = ts.shape[0]
-            padding = torch.zeros(input_len + self.horizon_len, dtype=ts.dtype, device=ts.device)
-            if input_len < context_len:
-                num_front_pad = context_len - input_len
-                ts = torch.cat([torch.zeros(num_front_pad, dtype=ts.dtype, device=ts.device), ts], dim=0)
-                padding = torch.cat([torch.ones(num_front_pad, dtype=ts.dtype, device=padding.device), padding], dim=0)
-            elif input_len > context_len:
-                ts = ts[-context_len:]
-                padding = padding[-(context_len + self.horizon_len) :]
+        x = inputs[:, -context_len:]
+        num_front_pad = context_len - x.shape[1]
+        x = F.pad(x, (num_front_pad, 0))
 
-            input_ts.append(ts)
-            input_padding.append(padding)
+        obs = observed_mask[:, -context_len:].to(dtype=x.dtype)
+        body_padding = 1 - obs
+        front_padding = torch.ones(x.shape[0], num_front_pad, dtype=x.dtype, device=x.device)
+        horizon_padding = torch.zeros(x.shape[0], self.horizon_len, dtype=x.dtype, device=x.device)
+        padding = torch.cat([front_padding, body_padding, horizon_padding], dim=1)
+        result = (x, padding)
 
-        result = (torch.stack(input_ts, dim=0), torch.stack(input_padding, dim=0))
         if freq is not None:
-            result = result + (torch.tensor(freq[: len(inputs)], dtype=torch.int32).reshape(-1, 1),)
+            freq_tensor = freq[: x.shape[0]].to(dtype=torch.int32)
+            result = result + (freq_tensor.reshape(-1, 1),)
         return result
 
     def _postprocess_output(
@@ -653,8 +680,9 @@ def _quantile_loss(self, predictions: torch.Tensor, targets: torch.Tensor) -> to
     @auto_docstring
     def forward(
         self,
-        past_values: Sequence[torch.Tensor],
-        freq: Sequence[torch.Tensor | int] | None = None,
+        past_values: Sequence[torch.Tensor] | torch.Tensor,
+        past_observed_mask: torch.Tensor | None = None,
+        freq: Sequence[int] | torch.Tensor | None = None,
         window_size: int | None = None,
         future_values: torch.Tensor | None = None,
         forecast_context_len: int | None = None,
@@ -664,9 +692,23 @@ def forward(
     ) -> TimesFmOutputForPrediction:
         r"""
         past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-            Past values of the time series that serves as input to the model.
-        freq (`torch.LongTensor` of shape `(batch_size,)`):
-            Frequency indices for the time series data.
+            Past values of the time series that serves as input to the model. A list of 1D tensors with
+            possibly differing lengths is also accepted (deprecated): each tensor is front-padded with zeros
+            and stacked into a 2D tensor. Tensor inputs are preferred and required for export.
+        past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask indicating which `past_values` were observed and which are padding/missing. Mask
+            values selected in `[0, 1]`:
+
+            - `1` for values that are **observed**,
+            - `0` for values that are **missing** (i.e. padded or NaNs that were replaced by zeros).
+
+            Defaults to a tensor of ones (everything observed). When `past_values` is passed as a list of
+            variable-length tensors, you should provide a matching `past_observed_mask` so the front-padding
+            zeros are not treated as observed values.
+        freq (`torch.LongTensor` of shape `(batch_size,)` or `Sequence[int]`, *optional*):
+            Frequency indices for the time series data. Defaults to a zero tensor (high-frequency). A
+            sequence of ints is also accepted and converted to a tensor internally. Tensor inputs are
+            preferred and required for export.
         window_size (`int`, *optional*):
             Window size of trend + residual decomposition. If None then we do not do decomposition.
         future_values (`torch.Tensor`, *optional*):
@@ -686,7 +728,7 @@ def forward(
 
         >>> model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch")
 
-        >>> forecast_input = [torch.linspace(0, 20, 100).sin(), torch.linspace(0, 20, 200).sin(), torch.linspace(0, 20, 400).sin()]
+        >>> forecast_input = torch.stack([torch.linspace(0, 20, 400).sin() for _ in range(3)])
         >>> frequency_input = torch.tensor([0, 1, 2], dtype=torch.long)
 
         >>> # Generate
@@ -701,27 +743,36 @@ def forward(
         else:
             fcontext_len = forecast_context_len
 
-        device = past_values[0].device
-
-        inputs = [ts[-fcontext_len:] for ts in past_values]
-        inp_min = torch.min(torch.stack([torch.min(ts) for ts in inputs]))
-
-        if window_size is not None:
-            new_inputs = []
-            new_freqs = []
-            for i, ts in enumerate(inputs):
-                new_inputs.extend(self._timesfm_moving_average(ts, window_size))
-                if freq is not None:
-                    new_freqs.extend([freq[i]] * 2)
-            inputs = new_inputs
-            if freq is not None:
-                freq = new_freqs
+        if isinstance(past_values, list):
+            warnings.warn(
+                "Passing `past_values` as a list of 1D tensors is deprecated and will be removed in a future "
+                "version. Please pass a 2D `torch.Tensor` of shape `(batch_size, sequence_length)` and, when "
+                "needed, a `past_observed_mask` of the same shape (1 = observed, 0 = padded/missing).",
+                FutureWarning,
+            )
+            past_values = self._past_values_to_tensor(past_values)
 
+        device = past_values.device
+        if past_observed_mask is None:
+            past_observed_mask = torch.ones_like(past_values)
         if freq is None:
             logger.info("No frequency provided via `freq`. Default to high (0).")
-            freq = [0] * len(inputs)
+            freq = torch.zeros(past_values.shape[0], dtype=torch.int32, device=device)
+        else:
+            freq = torch.as_tensor(freq, dtype=torch.int32, device=device)
+
+        inputs = past_values[:, -fcontext_len:]
+        observed_mask = past_observed_mask[:, -fcontext_len:].to(device=device)
+        sentinel = torch.full_like(inputs, torch.finfo(inputs.dtype).max)
+        inp_min = torch.where(observed_mask.bool(), inputs, sentinel).min()
 
-        input_ts, input_padding, inp_freq = self._preprocess(inputs, freq)
+        if window_size is not None:
+            trend, residual = self._timesfm_moving_average(inputs, window_size)
+            inputs = torch.stack([trend, residual], dim=1).view(2 * inputs.shape[0], -1)
+            observed_mask = torch.repeat_interleave(observed_mask, 2, dim=0)
+            freq = torch.repeat_interleave(freq, 2, dim=0)
+
+        input_ts, input_padding, inp_freq = self._preprocess(inputs, observed_mask, freq=freq)
         input_ts = input_ts.to(device)
         input_padding = input_padding.to(device)
         inp_freq = inp_freq.to(device)
@@ -793,16 +844,415 @@ def forward(
             loss=loss,
         )
 
+    @can_return_tuple
+    @auto_docstring
+    def forecast_with_covariates(
+        self,
+        past_values: Sequence[torch.Tensor],
+        dynamic_numerical_covariates: dict[str, Sequence[Sequence[float]]] | None = None,
+        dynamic_categorical_covariates: dict[str, Sequence[Sequence[int | str]]] | None = None,
+        static_numerical_covariates: dict[str, Sequence[float]] | None = None,
+        static_categorical_covariates: dict[str, Sequence[int | str]] | None = None,
+        freq: Sequence[torch.Tensor | int] | None = None,
+        window_size: int | None = None,
+        forecast_context_len: int | None = None,
+        xreg_mode: str = "xreg + timesfm",
+        normalize_xreg_target_per_input: bool = True,
+        ridge: float = 0.0,
+        truncate_negative: bool = False,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        future_values: torch.Tensor | None = None,
+    ) -> TimesFmOutputForPredictionWithCovariates:
+        r"""
+        Forecasts time series with external covariates using batched in-context regression.
+
+        This method combines TimesFM's forecasting capabilities with external regression (XReg)
+        on covariates to improve prediction accuracy. It supports both static and dynamic
+        covariates, with numerical and categorical types.
+
+        Args:
+            past_values (`Sequence[torch.Tensor]`):
+                Past values of the time series that serves as input to the model.
+            dynamic_numerical_covariates (`Dict[str, Sequence[Sequence[float]]]`, *optional*):
+                Dictionary mapping covariate names to sequences of numerical values for each
+                time series, covering both context and horizon periods.
+            dynamic_categorical_covariates (`Dict[str, Sequence[Sequence[Union[int, str]]]]`, *optional*):
+                Dictionary mapping covariate names to sequences of categorical values for each
+                time series, covering both context and horizon periods.
+            static_numerical_covariates (`Dict[str, Sequence[float]]`, *optional*):
+                Dictionary mapping covariate names to numerical values for each time series.
+            static_categorical_covariates (`Dict[str, Sequence[Union[int, str]]]`, *optional*):
+                Dictionary mapping covariate names to categorical values for each time series.
+            freq (`Sequence[Union[torch.Tensor, int]]`, *optional*):
+                Frequency indices for the time series data.
+            window_size (`int`, *optional*):
+                Window size of trend + residual decomposition. If None then we do not do decomposition.
+            forecast_context_len (`int`, *optional*):
+                Optional max context length.
+            xreg_mode (`str`, *optional*, defaults to `"xreg + timesfm"`):
+                Mode for combining TimesFM and XReg predictions. Options:
+                - "xreg + timesfm": Fit linear model on targets first, then forecast residuals with TimesFM
+                - "timesfm + xreg": Forecast with TimesFM first, then fit linear model on residuals
+            normalize_xreg_target_per_input (`bool`, *optional*, defaults to `True`):
+                Whether to normalize the XReg targets per input series.
+            ridge (`float`, *optional*, defaults to 0.0):
+                Ridge regularization parameter for the linear regression.
+            truncate_negative (`bool`, *optional*, defaults to `False`):
+                Truncate to only non-negative values if any of the contexts have non-negative values.
+            output_attentions (`bool`, *optional*):
+                Whether to output the attentions.
+            output_hidden_states (`bool`, *optional*):
+                Whether to output the hidden states.
+            return_dict (`bool`, *optional*):
+                Whether to return a dictionary or a tuple.
+            future_values (`torch.Tensor`, *optional*):
+                Optional future time series values to compute a training loss. Shape should be `(batch_size, horizon)`
+                matching the produced horizon from covariates (or model horizon if not provided).
+
+        Returns:
+            [`TimesFmOutputForPredictionWithCovariates`]: The output containing both TimesFM
+            predictions and covariate-based predictions.
+
+        Example:
+            ```python
+            >>> from transformers import TimesFmModelForPrediction
+            >>> import torch
+
+            >>> model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch")
+
+            >>> # Prepare time series data
+            >>> past_values = [torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])]
+
+            >>> # Add covariates
+            >>> dynamic_numerical = {"temperature": [[20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0]]}
+            >>> static_categorical = {"store_type": ["supermarket"]}
+
+            >>> # Generate forecast with covariates
+            >>> outputs = model.forecast_with_covariates(
+            ...     past_values=past_values,
+            ...     dynamic_numerical_covariates=dynamic_numerical,
+            ...     static_categorical_covariates=static_categorical,
+            ...     ridge=0.1,
+            ... )
+            >>> combined_forecast = outputs.combined_predictions
+            ```
+        """
+        if not (
+            dynamic_numerical_covariates
+            or dynamic_categorical_covariates
+            or static_numerical_covariates
+            or static_categorical_covariates
+        ):
+            raise ValueError(
+                "At least one of dynamic_numerical_covariates, dynamic_categorical_covariates, "
+                "static_numerical_covariates, static_categorical_covariates must be provided."
+            )
+
+        if xreg_mode not in ["xreg + timesfm", "timesfm + xreg"]:
+            raise ValueError(f"xreg_mode must be 'xreg + timesfm' or 'timesfm + xreg', got '{xreg_mode}'")
+
+        # Get device from the first input tensor
+        device = past_values[0].device
+
+        # Set default values
+        if output_attentions is None:
+            output_attentions = self.config.output_attentions
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
+        if return_dict is None:
+            return_dict = self.config.use_return_dict
+
+        if forecast_context_len is None:
+            fcontext_len = self.context_len
+        else:
+            fcontext_len = forecast_context_len
+
+        if freq is None:
+            logger.info("No frequency provided via `freq`. Default to high (0).")
+            freq = [0] * len(past_values)
+
+        # Convert past_values to lists for easier processing
+        inputs = [ts[-fcontext_len:].cpu().float().numpy().tolist() for ts in past_values]
+
+        # Track the lengths for XReg processing
+        input_lens = [len(inp) for inp in inputs]
+        train_lens = []
+        test_lens = []
+
+        for i, input_len in enumerate(input_lens):
+            if xreg_mode == "timesfm + xreg":
+                # For fitting residuals, no TimesFM forecast on the first patch
+                train_lens.append(max(0, input_len - self.config.patch_length))
+            elif xreg_mode == "xreg + timesfm":
+                train_lens.append(input_len)
+
+            # Determine horizon length from dynamic covariates
+            if dynamic_numerical_covariates:
+                test_len = len(list(dynamic_numerical_covariates.values())[0][i]) - input_len
+            elif dynamic_categorical_covariates:
+                test_len = len(list(dynamic_categorical_covariates.values())[0][i]) - input_len
+            else:
+                test_len = self.horizon_len
+
+            if test_len > self.horizon_len:
+                raise ValueError(f"Forecast horizon ({test_len}) exceeds model horizon ({self.horizon_len})")
+            test_lens.append(test_len)
+
+        # Prepare covariates for XReg
+        train_dynamic_numerical_covariates = {}
+        test_dynamic_numerical_covariates = {}
+        train_dynamic_categorical_covariates = {}
+        test_dynamic_categorical_covariates = {}
+
+        # Split dynamic covariates
+        if dynamic_numerical_covariates:
+            for cov_name, cov_values in dynamic_numerical_covariates.items():
+                train_dynamic_numerical_covariates[cov_name] = []
+                test_dynamic_numerical_covariates[cov_name] = []
+                for input_len, train_len, cov_value in zip(input_lens, train_lens, cov_values):
+                    train_dynamic_numerical_covariates[cov_name].append(cov_value[(input_len - train_len) : input_len])
+                    test_dynamic_numerical_covariates[cov_name].append(cov_value[input_len:])
+
+        if dynamic_categorical_covariates:
+            for cov_name, cov_values in dynamic_categorical_covariates.items():
+                train_dynamic_categorical_covariates[cov_name] = []
+                test_dynamic_categorical_covariates[cov_name] = []
+                for input_len, train_len, cov_value in zip(input_lens, train_lens, cov_values):
+                    train_dynamic_categorical_covariates[cov_name].append(
+                        cov_value[(input_len - train_len) : input_len]
+                    )
+                    test_dynamic_categorical_covariates[cov_name].append(cov_value[input_len:])
+
+        # Execute XReg mode
+        if xreg_mode == "timesfm + xreg":
+            # First get TimesFM forecast, then fit XReg on residuals
+            timesfm_output = self.forward(
+                past_values=past_values,
+                freq=freq,
+                window_size=window_size,
+                forecast_context_len=forecast_context_len,
+                return_forecast_on_context=True,
+                truncate_negative=truncate_negative,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            # Calculate residuals:
+            mean_outputs = timesfm_output.mean_predictions  # keep as torch for grad flow
+            targets = []
+            # Slicing: use fixed horizon_start based on forecast_context_len
+            horizon_start = max(0, fcontext_len - self.config.patch_length)
+
+            for i, (input_ts, mean_output, train_len) in enumerate(zip(inputs, mean_outputs, train_lens)):
+                if train_len > 0:
+                    # compute on CPU/NumPy only for target arrays; does not affect autograd
+                    input_segment = np.array(input_ts)[-train_len:]
+                    context_prediction = (
+                        mean_output[(horizon_start - train_len) : horizon_start].detach().cpu().numpy()
+                    )
+                    target_residuals = input_segment - context_prediction
+                    targets.append(target_residuals.tolist())
+                else:
+                    targets.append([])
+
+            # Normalize if requested
+            per_instance_stats = None
+            if normalize_xreg_target_per_input:
+                targets, per_instance_stats = normalize(targets)
+
+        else:  # "xreg + timesfm"
+            # First fit XReg on targets, then forecast residuals with TimesFM
+            targets = [np.array(inp)[-train_len:].tolist() for inp, train_len in zip(inputs, train_lens)]
+
+            # Normalize if requested
+            per_instance_stats = None
+            if normalize_xreg_target_per_input:
+                targets, per_instance_stats = normalize(targets)
+
+        # Fit XReg model
+        xreg_model = BatchedInContextXRegLinear(
+            targets=targets,
+            train_lens=train_lens,
+            test_lens=test_lens,
+            train_dynamic_numerical_covariates=train_dynamic_numerical_covariates,
+            test_dynamic_numerical_covariates=test_dynamic_numerical_covariates,
+            train_dynamic_categorical_covariates=train_dynamic_categorical_covariates,
+            test_dynamic_categorical_covariates=test_dynamic_categorical_covariates,
+            static_numerical_covariates=static_numerical_covariates,
+            static_categorical_covariates=static_categorical_covariates,
+        )
+
+        if xreg_mode == "xreg + timesfm":
+            # Get both predictions and predictions on context
+            xreg_result = xreg_model.fit(
+                ridge=ridge,
+                one_hot_encoder_drop="first" if ridge == 0 else None,
+                debug_info=True,
+                device=device,
+                assert_covariates=True,
+            )
+            xreg_predictions, xreg_on_context, _, _, _ = xreg_result
+
+            # Calculate residuals and forecast with TimesFM
+            residual_inputs = []
+            for i, (target, xreg_context) in enumerate(zip(targets, xreg_on_context)):
+                if len(target) > 0 and len(xreg_context) > 0:
+                    residual = np.array(target) - np.array(xreg_context)
+                    residual_inputs.append(torch.tensor(residual, dtype=next(self.parameters()).dtype, device=device))
+                else:
+                    residual_inputs.append(past_values[i])
+
+            # Forecast residuals with TimesFM
+            timesfm_output = self.forward(
+                past_values=residual_inputs,
+                freq=freq,
+                window_size=window_size,
+                forecast_context_len=forecast_context_len,
+                return_forecast_on_context=True,
+                truncate_negative=False,  # Don't truncate residuals
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+        else:  # "timesfm + xreg"
+            # Just get XReg predictions
+            xreg_predictions = xreg_model.fit(
+                ridge=ridge,
+                one_hot_encoder_drop="first" if ridge == 0 else None,
+                device=device,
+                assert_covariates=True,
+            )
+
+        # Convert to tensors with proper padding
+        max_horizon = max(test_lens)
+        batch_size = len(past_values)
+
+        model_dtype = next(self.parameters()).dtype
+        xreg_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+        mean_predictions_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+        combined_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+
+        # Fill tensors from XReg outputs and sliced TimesFM predictions (torch ops to keep grad)
+        for i, (xreg_out, test_len) in enumerate(zip(xreg_predictions, test_lens)):
+            xreg_tensor[i, :test_len] = torch.tensor(xreg_out, dtype=model_dtype, device=device)
+            # Take the forecast portion from TimesFM predictions
+            horizon_start = max(0, fcontext_len - self.config.patch_length)
+            horizon_end = min(timesfm_output.mean_predictions.shape[1], horizon_start + test_len)
+            timesfm_forecast = timesfm_output.mean_predictions[i, horizon_start:horizon_end]
+            # Ensure same length by padding if needed
+            if len(timesfm_forecast) < test_len:
+                last_val = (
+                    timesfm_forecast[-1]
+                    if len(timesfm_forecast) > 0
+                    else torch.tensor(0.0, device=device, dtype=timesfm_forecast.dtype)
+                )
+                pad_len = test_len - len(timesfm_forecast)
+                padding = last_val.repeat(pad_len)
+                timesfm_forecast = torch.cat([timesfm_forecast, padding])
+            mean_predictions_tensor[i, :test_len] = timesfm_forecast
+
+        # Combine predictions in normalized space, then denormalize.
+        # This matches the reference: combined = timesfm_forecast + xreg, then _renormalize(combined).
+        if xreg_mode == "timesfm + xreg":
+            # xreg was fit on residuals (targets - timesfm_context) in normalized space.
+            # Denormalize xreg before adding to timesfm horizon forecast (which is in original units).
+            if normalize_xreg_target_per_input and per_instance_stats:
+                for i, test_len in enumerate(test_lens):
+                    mean_i, std_i = per_instance_stats[i]
+                    if test_len == 0:
+                        continue
+                    xreg_tensor[i, :test_len] = xreg_tensor[i, :test_len] * float(std_i) + float(mean_i)
+            for i, tl in enumerate(test_lens):
+                if tl > 0:
+                    combined_tensor[i, :tl] = mean_predictions_tensor[i, :tl] + xreg_tensor[i, :tl]
+        else:
+            # "xreg + timesfm": both timesfm and xreg forecasts are in normalized space.
+            # Combine first, then denormalize the combined result.
+            for i, tl in enumerate(test_lens):
+                if tl == 0:
+                    continue
+                # Add in normalized space
+                combined_tensor[i, :tl] = mean_predictions_tensor[i, :tl] + xreg_tensor[i, :tl]
+            if normalize_xreg_target_per_input and per_instance_stats:
+                for i, tl in enumerate(test_lens):
+                    if tl == 0:
+                        continue
+                    mean_i, std_i = per_instance_stats[i]
+                    combined_tensor[i, :tl] = combined_tensor[i, :tl] * float(std_i) + float(mean_i)
+                    xreg_tensor[i, :tl] = xreg_tensor[i, :tl] * float(std_i) + float(mean_i)
+                    mean_predictions_tensor[i, :tl] = mean_predictions_tensor[i, :tl] * float(std_i) + float(mean_i)
+
+        # Apply truncation if requested
+        if truncate_negative:
+            inp_min = min(torch.min(ts) for ts in past_values)
+            if inp_min >= 0:
+                combined_tensor = torch.maximum(combined_tensor, torch.tensor(0.0, device=device))
+                xreg_tensor = torch.maximum(xreg_tensor, torch.tensor(0.0, device=device))
+
+        # Compute training loss if labels provided (always on combined)
+        loss = None
+        if future_values is not None:
+            # Build mask using per-series horizon lengths
+            mask = torch.zeros_like(combined_tensor, dtype=combined_tensor.dtype, device=device)
+            for i, tl in enumerate(test_lens):
+                if tl > 0:
+                    mask[i, :tl] = 1.0
+            denom = torch.clamp(mask.sum(), min=1.0)
+
+            if future_values.shape[1] < combined_tensor.shape[1]:
+                raise ValueError(
+                    f"future_values width {future_values.shape[1]} < expected horizon {combined_tensor.shape[1]}"
+                )
+
+            # MSE on combined prediction
+            mse_loss = (((combined_tensor - future_values[:, : mask.shape[1]]) ** 2) * mask).sum() / denom
+
+            # Quantile loss: shift TimesFM quantiles by XReg predictions (both in original units)
+            q_losses = []
+            for i, tl in enumerate(test_lens):
+                if tl == 0:
+                    continue
+                h_start = max(0, fcontext_len - self.config.patch_length)
+                h_end = min(timesfm_output.full_predictions.shape[1], h_start + tl)
+                timesfm_quants = timesfm_output.full_predictions[i, h_start:h_end, 1:]
+                shifted_quants = timesfm_quants + xreg_tensor[i, :tl].unsqueeze(-1)
+                q_losses.append(self._quantile_loss(shifted_quants, future_values[i, :tl]))
+            quantile_loss = torch.stack(q_losses).mean() if q_losses else torch.tensor(0.0, device=device)
+            loss = mse_loss + quantile_loss
+
+        # Create output
+        output = TimesFmOutputForPredictionWithCovariates(
+            last_hidden_state=timesfm_output.last_hidden_state,
+            attentions=timesfm_output.attentions if output_attentions else None,
+            hidden_states=timesfm_output.hidden_states if output_hidden_states else None,
+            mean_predictions=mean_predictions_tensor,
+            full_predictions=timesfm_output.full_predictions,
+            loss=loss,
+            xreg_predictions=xreg_tensor,
+            combined_predictions=combined_tensor,
+        )
+
+        return output if return_dict else tuple(output.values())
+
+    @staticmethod
+    def _past_values_to_tensor(past_values: Sequence[torch.Tensor]) -> torch.Tensor:
+        """Convert a list of variable-length 1D tensors into a 2D tensor of shape `(batch_size, max_len)`
+        by left-padding each entry with zeros. Equivalent to `torch.nn.utils.rnn.pad_sequence(past_values,
+        batch_first=True, padding_side="left")`, re-implemented here because `padding_side` requires
+        `torch>=2.5`.
+        """
+        max_len = max(ts.shape[0] for ts in past_values)
+        return torch.stack([F.pad(ts, (max_len - ts.shape[0], 0)) for ts in past_values], dim=0)
+
     @staticmethod
-    def _timesfm_moving_average(arr: torch.Tensor, window_size: int) -> list[torch.Tensor]:
-        """Calculates the moving average using PyTorch's convolution function."""
-        # Pad with zeros to handle initial window positions
+    def _timesfm_moving_average(arr: torch.Tensor, window_size: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """Calculates the moving average using PyTorch's convolution function. `arr` shape: `(B, T)`."""
         arr_padded = F.pad(arr, (window_size - 1, 0), "constant", 0)
-        # Create a convolution kernel
         kernel = torch.ones(window_size, dtype=arr.dtype, device=arr.device) / window_size
-        # Apply convolution to calculate the moving average
-        smoothed_arr = F.conv1d(arr_padded.view(1, 1, -1), kernel.view(1, 1, -1)).squeeze()
-        return [smoothed_arr, arr - smoothed_arr]
+        kernel = kernel.view(1, 1, -1)
+        smoothed_arr = F.conv1d(arr_padded.unsqueeze(1), kernel).squeeze(1)
+        return smoothed_arr, arr - smoothed_arr
 
 
 __all__ = ["TimesFmModelForPrediction", "TimesFmPreTrainedModel", "TimesFmModel"]
diff --git a/src/transformers/models/timesfm/modular_timesfm.py b/src/transformers/models/timesfm/modular_timesfm.py
index ca53ec7dd668..468aed20fef0 100644
--- a/src/transformers/models/timesfm/modular_timesfm.py
+++ b/src/transformers/models/timesfm/modular_timesfm.py
@@ -14,9 +14,11 @@
 """PyTorch TimesFM model."""
 
 import math
+import warnings
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -32,13 +34,14 @@
 from ..llama.modeling_llama import LlamaRMSNorm
 from ..phi4_multimodal.modeling_phi4_multimodal import simple_eager_attention_forward
 from .configuration_timesfm import TimesFmConfig
+from .xreg_utils import BatchedInContextXRegLinear, normalize
 
 
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFmOutput(BaseModelOutput):
     r"""
     loc (`torch.Tensor` of shape `(batch_size, )`):
@@ -51,8 +54,8 @@ class TimesFmOutput(BaseModelOutput):
     scale: torch.Tensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFmOutputForPrediction(BaseModelOutput):
     r"""
     mean_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
@@ -68,6 +71,26 @@ class TimesFmOutputForPrediction(BaseModelOutput):
     loss: torch.Tensor | float | None = None
 
 
+@dataclass
+@auto_docstring
+class TimesFmOutputForPredictionWithCovariates(TimesFmOutputForPrediction):
+    r"""
+    mean_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+        The mean predictions of the time series.
+    full_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+        The full predictions of the time series including the mean and the quantiles.
+    loss (`torch.Tensor` of shape `(1,)`, *optional*, returned when `future_values` is provided):
+        The loss of the TimesFM model.
+    xreg_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+        The predictions from the external regression (XReg) model using covariates.
+    combined_predictions (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+        The combined predictions from TimesFM and XReg models.
+    """
+
+    xreg_predictions: torch.Tensor | None = None
+    combined_predictions: torch.Tensor | None = None
+
+
 class TimesFmMLP(nn.Module):
     """Pax MLP in pytorch."""
 
@@ -338,8 +361,10 @@ def forward(
             Past values of the time series that serves as input to the model.
         past_values_padding (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
             The padding indicator of the time series.
-        freq (`torch.LongTensor` of shape `(batch_size,)`):
-            Frequency indices for the time series data.
+        freq (`torch.LongTensor` of shape `(batch_size,)` or `Sequence[int]`, *optional*):
+            Frequency indices for the time series data. Defaults to a zero tensor (high-frequency). A
+            sequence of ints is also accepted and converted to a tensor internally. Tensor inputs are
+            preferred and required for export.
         """
         # Reshape into patches (using view for efficiency)
         bsize = past_values.shape[0]
@@ -547,13 +572,20 @@ def __init__(self, config: TimesFmConfig):
         self.post_init()
 
     def _preprocess(
-        self, inputs: Sequence[torch.Tensor], freq: Sequence[int] | None = None, context_len: int | None = None
+        self,
+        inputs: torch.Tensor,
+        observed_mask: torch.Tensor,
+        freq: torch.Tensor | None = None,
+        context_len: int | None = None,
     ) -> tuple[torch.Tensor, ...]:
         """Pad/truncate input time series to `context_len` and build a padding mask.
 
         Args:
-            inputs: A list of 1d Tensors. Each Tensor is the context time series of a single forecast task.
-            freq: Optional list of frequencies (returned as a tensor when provided).
+            inputs: A 2D `torch.Tensor` of shape `(batch_size, sequence_length)`.
+            observed_mask: A 2D `torch.Tensor` of the same shape as `inputs` where `1` indicates an observed
+                value and `0` indicates a missing value. Missing positions are marked as padded in the
+                returned padding mask.
+            freq: Optional 1D `torch.Tensor` of frequency indices.
             context_len: Optional context length override (defaults to `self.context_len`).
 
         Returns:
@@ -562,25 +594,20 @@ def _preprocess(
         if context_len is None:
             context_len = self.context_len
 
-        input_ts, input_padding = [], []
-
-        for ts in inputs:
-            input_len = ts.shape[0]
-            padding = torch.zeros(input_len + self.horizon_len, dtype=ts.dtype, device=ts.device)
-            if input_len < context_len:
-                num_front_pad = context_len - input_len
-                ts = torch.cat([torch.zeros(num_front_pad, dtype=ts.dtype, device=ts.device), ts], dim=0)
-                padding = torch.cat([torch.ones(num_front_pad, dtype=ts.dtype, device=padding.device), padding], dim=0)
-            elif input_len > context_len:
-                ts = ts[-context_len:]
-                padding = padding[-(context_len + self.horizon_len) :]
+        x = inputs[:, -context_len:]
+        num_front_pad = context_len - x.shape[1]
+        x = F.pad(x, (num_front_pad, 0))
 
-            input_ts.append(ts)
-            input_padding.append(padding)
+        obs = observed_mask[:, -context_len:].to(dtype=x.dtype)
+        body_padding = 1 - obs
+        front_padding = torch.ones(x.shape[0], num_front_pad, dtype=x.dtype, device=x.device)
+        horizon_padding = torch.zeros(x.shape[0], self.horizon_len, dtype=x.dtype, device=x.device)
+        padding = torch.cat([front_padding, body_padding, horizon_padding], dim=1)
+        result = (x, padding)
 
-        result = (torch.stack(input_ts, dim=0), torch.stack(input_padding, dim=0))
         if freq is not None:
-            result = result + (torch.tensor(freq[: len(inputs)], dtype=torch.int32).reshape(-1, 1),)
+            freq_tensor = freq[: x.shape[0]].to(dtype=torch.int32)
+            result = result + (freq_tensor.reshape(-1, 1),)
         return result
 
     def _postprocess_output(
@@ -610,8 +637,9 @@ def _quantile_loss(self, predictions: torch.Tensor, targets: torch.Tensor) -> to
     @auto_docstring
     def forward(
         self,
-        past_values: Sequence[torch.Tensor],
-        freq: Sequence[torch.Tensor | int] | None = None,
+        past_values: Sequence[torch.Tensor] | torch.Tensor,
+        past_observed_mask: torch.Tensor | None = None,
+        freq: Sequence[int] | torch.Tensor | None = None,
         window_size: int | None = None,
         future_values: torch.Tensor | None = None,
         forecast_context_len: int | None = None,
@@ -621,9 +649,23 @@ def forward(
     ) -> TimesFmOutputForPrediction:
         r"""
         past_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
-            Past values of the time series that serves as input to the model.
-        freq (`torch.LongTensor` of shape `(batch_size,)`):
-            Frequency indices for the time series data.
+            Past values of the time series that serves as input to the model. A list of 1D tensors with
+            possibly differing lengths is also accepted (deprecated): each tensor is front-padded with zeros
+            and stacked into a 2D tensor. Tensor inputs are preferred and required for export.
+        past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask indicating which `past_values` were observed and which are padding/missing. Mask
+            values selected in `[0, 1]`:
+
+            - `1` for values that are **observed**,
+            - `0` for values that are **missing** (i.e. padded or NaNs that were replaced by zeros).
+
+            Defaults to a tensor of ones (everything observed). When `past_values` is passed as a list of
+            variable-length tensors, you should provide a matching `past_observed_mask` so the front-padding
+            zeros are not treated as observed values.
+        freq (`torch.LongTensor` of shape `(batch_size,)` or `Sequence[int]`, *optional*):
+            Frequency indices for the time series data. Defaults to a zero tensor (high-frequency). A
+            sequence of ints is also accepted and converted to a tensor internally. Tensor inputs are
+            preferred and required for export.
         window_size (`int`, *optional*):
             Window size of trend + residual decomposition. If None then we do not do decomposition.
         future_values (`torch.Tensor`, *optional*):
@@ -643,7 +685,7 @@ def forward(
 
         >>> model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch")
 
-        >>> forecast_input = [torch.linspace(0, 20, 100).sin(), torch.linspace(0, 20, 200).sin(), torch.linspace(0, 20, 400).sin()]
+        >>> forecast_input = torch.stack([torch.linspace(0, 20, 400).sin() for _ in range(3)])
         >>> frequency_input = torch.tensor([0, 1, 2], dtype=torch.long)
 
         >>> # Generate
@@ -658,27 +700,36 @@ def forward(
         else:
             fcontext_len = forecast_context_len
 
-        device = past_values[0].device
-
-        inputs = [ts[-fcontext_len:] for ts in past_values]
-        inp_min = torch.min(torch.stack([torch.min(ts) for ts in inputs]))
-
-        if window_size is not None:
-            new_inputs = []
-            new_freqs = []
-            for i, ts in enumerate(inputs):
-                new_inputs.extend(self._timesfm_moving_average(ts, window_size))
-                if freq is not None:
-                    new_freqs.extend([freq[i]] * 2)
-            inputs = new_inputs
-            if freq is not None:
-                freq = new_freqs
+        if isinstance(past_values, list):
+            warnings.warn(
+                "Passing `past_values` as a list of 1D tensors is deprecated and will be removed in a future "
+                "version. Please pass a 2D `torch.Tensor` of shape `(batch_size, sequence_length)` and, when "
+                "needed, a `past_observed_mask` of the same shape (1 = observed, 0 = padded/missing).",
+                FutureWarning,
+            )
+            past_values = self._past_values_to_tensor(past_values)
 
+        device = past_values.device
+        if past_observed_mask is None:
+            past_observed_mask = torch.ones_like(past_values)
         if freq is None:
             logger.info("No frequency provided via `freq`. Default to high (0).")
-            freq = [0] * len(inputs)
+            freq = torch.zeros(past_values.shape[0], dtype=torch.int32, device=device)
+        else:
+            freq = torch.as_tensor(freq, dtype=torch.int32, device=device)
+
+        inputs = past_values[:, -fcontext_len:]
+        observed_mask = past_observed_mask[:, -fcontext_len:].to(device=device)
+        sentinel = torch.full_like(inputs, torch.finfo(inputs.dtype).max)
+        inp_min = torch.where(observed_mask.bool(), inputs, sentinel).min()
 
-        input_ts, input_padding, inp_freq = self._preprocess(inputs, freq)
+        if window_size is not None:
+            trend, residual = self._timesfm_moving_average(inputs, window_size)
+            inputs = torch.stack([trend, residual], dim=1).view(2 * inputs.shape[0], -1)
+            observed_mask = torch.repeat_interleave(observed_mask, 2, dim=0)
+            freq = torch.repeat_interleave(freq, 2, dim=0)
+
+        input_ts, input_padding, inp_freq = self._preprocess(inputs, observed_mask, freq=freq)
         input_ts = input_ts.to(device)
         input_padding = input_padding.to(device)
         inp_freq = inp_freq.to(device)
@@ -750,16 +801,415 @@ def forward(
             loss=loss,
         )
 
+    @can_return_tuple
+    @auto_docstring
+    def forecast_with_covariates(
+        self,
+        past_values: Sequence[torch.Tensor],
+        dynamic_numerical_covariates: dict[str, Sequence[Sequence[float]]] | None = None,
+        dynamic_categorical_covariates: dict[str, Sequence[Sequence[int | str]]] | None = None,
+        static_numerical_covariates: dict[str, Sequence[float]] | None = None,
+        static_categorical_covariates: dict[str, Sequence[int | str]] | None = None,
+        freq: Sequence[torch.Tensor | int] | None = None,
+        window_size: int | None = None,
+        forecast_context_len: int | None = None,
+        xreg_mode: str = "xreg + timesfm",
+        normalize_xreg_target_per_input: bool = True,
+        ridge: float = 0.0,
+        truncate_negative: bool = False,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+        future_values: torch.Tensor | None = None,
+    ) -> TimesFmOutputForPredictionWithCovariates:
+        r"""
+        Forecasts time series with external covariates using batched in-context regression.
+
+        This method combines TimesFM's forecasting capabilities with external regression (XReg)
+        on covariates to improve prediction accuracy. It supports both static and dynamic
+        covariates, with numerical and categorical types.
+
+        Args:
+            past_values (`Sequence[torch.Tensor]`):
+                Past values of the time series that serves as input to the model.
+            dynamic_numerical_covariates (`Dict[str, Sequence[Sequence[float]]]`, *optional*):
+                Dictionary mapping covariate names to sequences of numerical values for each
+                time series, covering both context and horizon periods.
+            dynamic_categorical_covariates (`Dict[str, Sequence[Sequence[Union[int, str]]]]`, *optional*):
+                Dictionary mapping covariate names to sequences of categorical values for each
+                time series, covering both context and horizon periods.
+            static_numerical_covariates (`Dict[str, Sequence[float]]`, *optional*):
+                Dictionary mapping covariate names to numerical values for each time series.
+            static_categorical_covariates (`Dict[str, Sequence[Union[int, str]]]`, *optional*):
+                Dictionary mapping covariate names to categorical values for each time series.
+            freq (`Sequence[Union[torch.Tensor, int]]`, *optional*):
+                Frequency indices for the time series data.
+            window_size (`int`, *optional*):
+                Window size of trend + residual decomposition. If None then we do not do decomposition.
+            forecast_context_len (`int`, *optional*):
+                Optional max context length.
+            xreg_mode (`str`, *optional*, defaults to `"xreg + timesfm"`):
+                Mode for combining TimesFM and XReg predictions. Options:
+                - "xreg + timesfm": Fit linear model on targets first, then forecast residuals with TimesFM
+                - "timesfm + xreg": Forecast with TimesFM first, then fit linear model on residuals
+            normalize_xreg_target_per_input (`bool`, *optional*, defaults to `True`):
+                Whether to normalize the XReg targets per input series.
+            ridge (`float`, *optional*, defaults to 0.0):
+                Ridge regularization parameter for the linear regression.
+            truncate_negative (`bool`, *optional*, defaults to `False`):
+                Truncate to only non-negative values if any of the contexts have non-negative values.
+            output_attentions (`bool`, *optional*):
+                Whether to output the attentions.
+            output_hidden_states (`bool`, *optional*):
+                Whether to output the hidden states.
+            return_dict (`bool`, *optional*):
+                Whether to return a dictionary or a tuple.
+            future_values (`torch.Tensor`, *optional*):
+                Optional future time series values to compute a training loss. Shape should be `(batch_size, horizon)`
+                matching the produced horizon from covariates (or model horizon if not provided).
+
+        Returns:
+            [`TimesFmOutputForPredictionWithCovariates`]: The output containing both TimesFM
+            predictions and covariate-based predictions.
+
+        Example:
+            ```python
+            >>> from transformers import TimesFmModelForPrediction
+            >>> import torch
+
+            >>> model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch")
+
+            >>> # Prepare time series data
+            >>> past_values = [torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])]
+
+            >>> # Add covariates
+            >>> dynamic_numerical = {"temperature": [[20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0]]}
+            >>> static_categorical = {"store_type": ["supermarket"]}
+
+            >>> # Generate forecast with covariates
+            >>> outputs = model.forecast_with_covariates(
+            ...     past_values=past_values,
+            ...     dynamic_numerical_covariates=dynamic_numerical,
+            ...     static_categorical_covariates=static_categorical,
+            ...     ridge=0.1,
+            ... )
+            >>> combined_forecast = outputs.combined_predictions
+            ```
+        """
+        if not (
+            dynamic_numerical_covariates
+            or dynamic_categorical_covariates
+            or static_numerical_covariates
+            or static_categorical_covariates
+        ):
+            raise ValueError(
+                "At least one of dynamic_numerical_covariates, dynamic_categorical_covariates, "
+                "static_numerical_covariates, static_categorical_covariates must be provided."
+            )
+
+        if xreg_mode not in ["xreg + timesfm", "timesfm + xreg"]:
+            raise ValueError(f"xreg_mode must be 'xreg + timesfm' or 'timesfm + xreg', got '{xreg_mode}'")
+
+        # Get device from the first input tensor
+        device = past_values[0].device
+
+        # Set default values
+        if output_attentions is None:
+            output_attentions = self.config.output_attentions
+        if output_hidden_states is None:
+            output_hidden_states = self.config.output_hidden_states
+        if return_dict is None:
+            return_dict = self.config.use_return_dict
+
+        if forecast_context_len is None:
+            fcontext_len = self.context_len
+        else:
+            fcontext_len = forecast_context_len
+
+        if freq is None:
+            logger.info("No frequency provided via `freq`. Default to high (0).")
+            freq = [0] * len(past_values)
+
+        # Convert past_values to lists for easier processing
+        inputs = [ts[-fcontext_len:].cpu().float().numpy().tolist() for ts in past_values]
+
+        # Track the lengths for XReg processing
+        input_lens = [len(inp) for inp in inputs]
+        train_lens = []
+        test_lens = []
+
+        for i, input_len in enumerate(input_lens):
+            if xreg_mode == "timesfm + xreg":
+                # For fitting residuals, no TimesFM forecast on the first patch
+                train_lens.append(max(0, input_len - self.config.patch_length))
+            elif xreg_mode == "xreg + timesfm":
+                train_lens.append(input_len)
+
+            # Determine horizon length from dynamic covariates
+            if dynamic_numerical_covariates:
+                test_len = len(list(dynamic_numerical_covariates.values())[0][i]) - input_len
+            elif dynamic_categorical_covariates:
+                test_len = len(list(dynamic_categorical_covariates.values())[0][i]) - input_len
+            else:
+                test_len = self.horizon_len
+
+            if test_len > self.horizon_len:
+                raise ValueError(f"Forecast horizon ({test_len}) exceeds model horizon ({self.horizon_len})")
+            test_lens.append(test_len)
+
+        # Prepare covariates for XReg
+        train_dynamic_numerical_covariates = {}
+        test_dynamic_numerical_covariates = {}
+        train_dynamic_categorical_covariates = {}
+        test_dynamic_categorical_covariates = {}
+
+        # Split dynamic covariates
+        if dynamic_numerical_covariates:
+            for cov_name, cov_values in dynamic_numerical_covariates.items():
+                train_dynamic_numerical_covariates[cov_name] = []
+                test_dynamic_numerical_covariates[cov_name] = []
+                for input_len, train_len, cov_value in zip(input_lens, train_lens, cov_values):
+                    train_dynamic_numerical_covariates[cov_name].append(cov_value[(input_len - train_len) : input_len])
+                    test_dynamic_numerical_covariates[cov_name].append(cov_value[input_len:])
+
+        if dynamic_categorical_covariates:
+            for cov_name, cov_values in dynamic_categorical_covariates.items():
+                train_dynamic_categorical_covariates[cov_name] = []
+                test_dynamic_categorical_covariates[cov_name] = []
+                for input_len, train_len, cov_value in zip(input_lens, train_lens, cov_values):
+                    train_dynamic_categorical_covariates[cov_name].append(
+                        cov_value[(input_len - train_len) : input_len]
+                    )
+                    test_dynamic_categorical_covariates[cov_name].append(cov_value[input_len:])
+
+        # Execute XReg mode
+        if xreg_mode == "timesfm + xreg":
+            # First get TimesFM forecast, then fit XReg on residuals
+            timesfm_output = self.forward(
+                past_values=past_values,
+                freq=freq,
+                window_size=window_size,
+                forecast_context_len=forecast_context_len,
+                return_forecast_on_context=True,
+                truncate_negative=truncate_negative,
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+
+            # Calculate residuals:
+            mean_outputs = timesfm_output.mean_predictions  # keep as torch for grad flow
+            targets = []
+            # Slicing: use fixed horizon_start based on forecast_context_len
+            horizon_start = max(0, fcontext_len - self.config.patch_length)
+
+            for i, (input_ts, mean_output, train_len) in enumerate(zip(inputs, mean_outputs, train_lens)):
+                if train_len > 0:
+                    # compute on CPU/NumPy only for target arrays; does not affect autograd
+                    input_segment = np.array(input_ts)[-train_len:]
+                    context_prediction = (
+                        mean_output[(horizon_start - train_len) : horizon_start].detach().cpu().numpy()
+                    )
+                    target_residuals = input_segment - context_prediction
+                    targets.append(target_residuals.tolist())
+                else:
+                    targets.append([])
+
+            # Normalize if requested
+            per_instance_stats = None
+            if normalize_xreg_target_per_input:
+                targets, per_instance_stats = normalize(targets)
+
+        else:  # "xreg + timesfm"
+            # First fit XReg on targets, then forecast residuals with TimesFM
+            targets = [np.array(inp)[-train_len:].tolist() for inp, train_len in zip(inputs, train_lens)]
+
+            # Normalize if requested
+            per_instance_stats = None
+            if normalize_xreg_target_per_input:
+                targets, per_instance_stats = normalize(targets)
+
+        # Fit XReg model
+        xreg_model = BatchedInContextXRegLinear(
+            targets=targets,
+            train_lens=train_lens,
+            test_lens=test_lens,
+            train_dynamic_numerical_covariates=train_dynamic_numerical_covariates,
+            test_dynamic_numerical_covariates=test_dynamic_numerical_covariates,
+            train_dynamic_categorical_covariates=train_dynamic_categorical_covariates,
+            test_dynamic_categorical_covariates=test_dynamic_categorical_covariates,
+            static_numerical_covariates=static_numerical_covariates,
+            static_categorical_covariates=static_categorical_covariates,
+        )
+
+        if xreg_mode == "xreg + timesfm":
+            # Get both predictions and predictions on context
+            xreg_result = xreg_model.fit(
+                ridge=ridge,
+                one_hot_encoder_drop="first" if ridge == 0 else None,
+                debug_info=True,
+                device=device,
+                assert_covariates=True,
+            )
+            xreg_predictions, xreg_on_context, _, _, _ = xreg_result
+
+            # Calculate residuals and forecast with TimesFM
+            residual_inputs = []
+            for i, (target, xreg_context) in enumerate(zip(targets, xreg_on_context)):
+                if len(target) > 0 and len(xreg_context) > 0:
+                    residual = np.array(target) - np.array(xreg_context)
+                    residual_inputs.append(torch.tensor(residual, dtype=next(self.parameters()).dtype, device=device))
+                else:
+                    residual_inputs.append(past_values[i])
+
+            # Forecast residuals with TimesFM
+            timesfm_output = self.forward(
+                past_values=residual_inputs,
+                freq=freq,
+                window_size=window_size,
+                forecast_context_len=forecast_context_len,
+                return_forecast_on_context=True,
+                truncate_negative=False,  # Don't truncate residuals
+                output_attentions=output_attentions,
+                output_hidden_states=output_hidden_states,
+            )
+        else:  # "timesfm + xreg"
+            # Just get XReg predictions
+            xreg_predictions = xreg_model.fit(
+                ridge=ridge,
+                one_hot_encoder_drop="first" if ridge == 0 else None,
+                device=device,
+                assert_covariates=True,
+            )
+
+        # Convert to tensors with proper padding
+        max_horizon = max(test_lens)
+        batch_size = len(past_values)
+
+        model_dtype = next(self.parameters()).dtype
+        xreg_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+        mean_predictions_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+        combined_tensor = torch.zeros(batch_size, max_horizon, dtype=model_dtype, device=device)
+
+        # Fill tensors from XReg outputs and sliced TimesFM predictions (torch ops to keep grad)
+        for i, (xreg_out, test_len) in enumerate(zip(xreg_predictions, test_lens)):
+            xreg_tensor[i, :test_len] = torch.tensor(xreg_out, dtype=model_dtype, device=device)
+            # Take the forecast portion from TimesFM predictions
+            horizon_start = max(0, fcontext_len - self.config.patch_length)
+            horizon_end = min(timesfm_output.mean_predictions.shape[1], horizon_start + test_len)
+            timesfm_forecast = timesfm_output.mean_predictions[i, horizon_start:horizon_end]
+            # Ensure same length by padding if needed
+            if len(timesfm_forecast) < test_len:
+                last_val = (
+                    timesfm_forecast[-1]
+                    if len(timesfm_forecast) > 0
+                    else torch.tensor(0.0, device=device, dtype=timesfm_forecast.dtype)
+                )
+                pad_len = test_len - len(timesfm_forecast)
+                padding = last_val.repeat(pad_len)
+                timesfm_forecast = torch.cat([timesfm_forecast, padding])
+            mean_predictions_tensor[i, :test_len] = timesfm_forecast
+
+        # Combine predictions in normalized space, then denormalize.
+        # This matches the reference: combined = timesfm_forecast + xreg, then _renormalize(combined).
+        if xreg_mode == "timesfm + xreg":
+            # xreg was fit on residuals (targets - timesfm_context) in normalized space.
+            # Denormalize xreg before adding to timesfm horizon forecast (which is in original units).
+            if normalize_xreg_target_per_input and per_instance_stats:
+                for i, test_len in enumerate(test_lens):
+                    mean_i, std_i = per_instance_stats[i]
+                    if test_len == 0:
+                        continue
+                    xreg_tensor[i, :test_len] = xreg_tensor[i, :test_len] * float(std_i) + float(mean_i)
+            for i, tl in enumerate(test_lens):
+                if tl > 0:
+                    combined_tensor[i, :tl] = mean_predictions_tensor[i, :tl] + xreg_tensor[i, :tl]
+        else:
+            # "xreg + timesfm": both timesfm and xreg forecasts are in normalized space.
+            # Combine first, then denormalize the combined result.
+            for i, tl in enumerate(test_lens):
+                if tl == 0:
+                    continue
+                # Add in normalized space
+                combined_tensor[i, :tl] = mean_predictions_tensor[i, :tl] + xreg_tensor[i, :tl]
+            if normalize_xreg_target_per_input and per_instance_stats:
+                for i, tl in enumerate(test_lens):
+                    if tl == 0:
+                        continue
+                    mean_i, std_i = per_instance_stats[i]
+                    combined_tensor[i, :tl] = combined_tensor[i, :tl] * float(std_i) + float(mean_i)
+                    xreg_tensor[i, :tl] = xreg_tensor[i, :tl] * float(std_i) + float(mean_i)
+                    mean_predictions_tensor[i, :tl] = mean_predictions_tensor[i, :tl] * float(std_i) + float(mean_i)
+
+        # Apply truncation if requested
+        if truncate_negative:
+            inp_min = min(torch.min(ts) for ts in past_values)
+            if inp_min >= 0:
+                combined_tensor = torch.maximum(combined_tensor, torch.tensor(0.0, device=device))
+                xreg_tensor = torch.maximum(xreg_tensor, torch.tensor(0.0, device=device))
+
+        # Compute training loss if labels provided (always on combined)
+        loss = None
+        if future_values is not None:
+            # Build mask using per-series horizon lengths
+            mask = torch.zeros_like(combined_tensor, dtype=combined_tensor.dtype, device=device)
+            for i, tl in enumerate(test_lens):
+                if tl > 0:
+                    mask[i, :tl] = 1.0
+            denom = torch.clamp(mask.sum(), min=1.0)
+
+            if future_values.shape[1] < combined_tensor.shape[1]:
+                raise ValueError(
+                    f"future_values width {future_values.shape[1]} < expected horizon {combined_tensor.shape[1]}"
+                )
+
+            # MSE on combined prediction
+            mse_loss = (((combined_tensor - future_values[:, : mask.shape[1]]) ** 2) * mask).sum() / denom
+
+            # Quantile loss: shift TimesFM quantiles by XReg predictions (both in original units)
+            q_losses = []
+            for i, tl in enumerate(test_lens):
+                if tl == 0:
+                    continue
+                h_start = max(0, fcontext_len - self.config.patch_length)
+                h_end = min(timesfm_output.full_predictions.shape[1], h_start + tl)
+                timesfm_quants = timesfm_output.full_predictions[i, h_start:h_end, 1:]
+                shifted_quants = timesfm_quants + xreg_tensor[i, :tl].unsqueeze(-1)
+                q_losses.append(self._quantile_loss(shifted_quants, future_values[i, :tl]))
+            quantile_loss = torch.stack(q_losses).mean() if q_losses else torch.tensor(0.0, device=device)
+            loss = mse_loss + quantile_loss
+
+        # Create output
+        output = TimesFmOutputForPredictionWithCovariates(
+            last_hidden_state=timesfm_output.last_hidden_state,
+            attentions=timesfm_output.attentions if output_attentions else None,
+            hidden_states=timesfm_output.hidden_states if output_hidden_states else None,
+            mean_predictions=mean_predictions_tensor,
+            full_predictions=timesfm_output.full_predictions,
+            loss=loss,
+            xreg_predictions=xreg_tensor,
+            combined_predictions=combined_tensor,
+        )
+
+        return output if return_dict else tuple(output.values())
+
+    @staticmethod
+    def _past_values_to_tensor(past_values: Sequence[torch.Tensor]) -> torch.Tensor:
+        """Convert a list of variable-length 1D tensors into a 2D tensor of shape `(batch_size, max_len)`
+        by left-padding each entry with zeros. Equivalent to `torch.nn.utils.rnn.pad_sequence(past_values,
+        batch_first=True, padding_side="left")`, re-implemented here because `padding_side` requires
+        `torch>=2.5`.
+        """
+        max_len = max(ts.shape[0] for ts in past_values)
+        return torch.stack([F.pad(ts, (max_len - ts.shape[0], 0)) for ts in past_values], dim=0)
+
     @staticmethod
-    def _timesfm_moving_average(arr: torch.Tensor, window_size: int) -> list[torch.Tensor]:
-        """Calculates the moving average using PyTorch's convolution function."""
-        # Pad with zeros to handle initial window positions
+    def _timesfm_moving_average(arr: torch.Tensor, window_size: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """Calculates the moving average using PyTorch's convolution function. `arr` shape: `(B, T)`."""
         arr_padded = F.pad(arr, (window_size - 1, 0), "constant", 0)
-        # Create a convolution kernel
         kernel = torch.ones(window_size, dtype=arr.dtype, device=arr.device) / window_size
-        # Apply convolution to calculate the moving average
-        smoothed_arr = F.conv1d(arr_padded.view(1, 1, -1), kernel.view(1, 1, -1)).squeeze()
-        return [smoothed_arr, arr - smoothed_arr]
+        kernel = kernel.view(1, 1, -1)
+        smoothed_arr = F.conv1d(arr_padded.unsqueeze(1), kernel).squeeze(1)
+        return smoothed_arr, arr - smoothed_arr
 
 
 __all__ = ["TimesFmModelForPrediction", "TimesFmPreTrainedModel", "TimesFmModel"]
diff --git a/src/transformers/models/timesfm/xreg_utils.py b/src/transformers/models/timesfm/xreg_utils.py
new file mode 100644
index 000000000000..e553f6e9db54
--- /dev/null
+++ b/src/transformers/models/timesfm/xreg_utils.py
@@ -0,0 +1,357 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Helper utilities for TimesFM covariates and in-context regression."""
+
+import itertools
+from collections.abc import Mapping, Sequence
+from typing import Any
+
+import numpy as np
+import torch
+
+from ...utils import is_sklearn_available
+
+
+_Category = int | str
+
+_TOL = 1e-6
+
+
+def _unnest(nested: Sequence[Sequence[Any]]) -> np.ndarray:
+    """Flatten a nested sequence into a 1D numpy array."""
+    return np.array(list(itertools.chain.from_iterable(nested)))
+
+
+def _repeat(elements: Sequence[Any], counts: Sequence[int]) -> np.ndarray:
+    """Repeat elements according to counts."""
+    return np.array(list(itertools.chain.from_iterable(map(itertools.repeat, elements, counts))))
+
+
+def normalize(targets: list[np.ndarray], eps: float = _TOL) -> tuple[list[np.ndarray], list[tuple[float, float]]]:
+    """Normalize each target series independently.
+
+    Args:
+        targets: List of target arrays to normalize.
+        eps: Small value for numerical stability.
+
+    Returns:
+        Normalized targets and their statistics (mean, std) for denormalization.
+    """
+    normalized = []
+    stats = []
+
+    for target in targets:
+        target = np.array(target)
+        mean = np.mean(target)
+        std = np.std(target)
+        if std < eps:
+            std = 1.0
+        normalized.append((target - mean) / std)
+        stats.append((mean, std))
+
+    return normalized, stats
+
+
+class _BatchedInContextXRegBase:
+    """Base class for in-context regression with covariates.
+
+    Handles the formatting and validation of covariates for batched
+    in-context regression used with TimesFM.
+    """
+
+    def __init__(
+        self,
+        targets: Sequence[Sequence[float]],
+        train_lens: Sequence[int],
+        test_lens: Sequence[int],
+        train_dynamic_numerical_covariates: Mapping[str, Sequence[Sequence[float]]] | None = None,
+        train_dynamic_categorical_covariates: Mapping[str, Sequence[Sequence[_Category]]] | None = None,
+        test_dynamic_numerical_covariates: Mapping[str, Sequence[Sequence[float]]] | None = None,
+        test_dynamic_categorical_covariates: Mapping[str, Sequence[Sequence[_Category]]] | None = None,
+        static_numerical_covariates: Mapping[str, Sequence[float]] | None = None,
+        static_categorical_covariates: Mapping[str, Sequence[_Category]] | None = None,
+    ) -> None:
+        self.targets = targets
+        self.train_lens = train_lens
+        self.test_lens = test_lens
+
+        self.train_dynamic_numerical_covariates = train_dynamic_numerical_covariates or {}
+        self.train_dynamic_categorical_covariates = train_dynamic_categorical_covariates or {}
+        self.test_dynamic_numerical_covariates = test_dynamic_numerical_covariates or {}
+        self.test_dynamic_categorical_covariates = test_dynamic_categorical_covariates or {}
+        self.static_numerical_covariates = static_numerical_covariates or {}
+        self.static_categorical_covariates = static_categorical_covariates or {}
+
+    def _assert_covariates(self, assert_covariate_shapes: bool = False) -> None:
+        """Validate covariate consistency and shapes."""
+        # Check that train and test dynamic covariates are paired
+        if (self.train_dynamic_numerical_covariates and not self.test_dynamic_numerical_covariates) or (
+            not self.train_dynamic_numerical_covariates and self.test_dynamic_numerical_covariates
+        ):
+            raise ValueError(
+                "train_dynamic_numerical_covariates and test_dynamic_numerical_covariates "
+                "must be both present or both absent."
+            )
+
+        if (self.train_dynamic_categorical_covariates and not self.test_dynamic_categorical_covariates) or (
+            not self.train_dynamic_categorical_covariates and self.test_dynamic_categorical_covariates
+        ):
+            raise ValueError(
+                "train_dynamic_categorical_covariates and test_dynamic_categorical_covariates "
+                "must be both present or both absent."
+            )
+
+        # Check that keys match between train and test
+        for dict_a, dict_b, dict_a_name, dict_b_name in [
+            (
+                self.train_dynamic_numerical_covariates,
+                self.test_dynamic_numerical_covariates,
+                "train_dynamic_numerical_covariates",
+                "test_dynamic_numerical_covariates",
+            ),
+            (
+                self.train_dynamic_categorical_covariates,
+                self.test_dynamic_categorical_covariates,
+                "train_dynamic_categorical_covariates",
+                "test_dynamic_categorical_covariates",
+            ),
+        ]:
+            if w := set(dict_a.keys()) - set(dict_b.keys()):
+                raise ValueError(f"{dict_a_name} has keys not present in {dict_b_name}: {w}")
+            if w := set(dict_b.keys()) - set(dict_a.keys()):
+                raise ValueError(f"{dict_b_name} has keys not present in {dict_a_name}: {w}")
+
+        if not assert_covariate_shapes:
+            return
+
+        if len(self.targets) != len(self.train_lens):
+            raise ValueError("targets and train_lens must have the same number of elements.")
+
+        if len(self.train_lens) != len(self.test_lens):
+            raise ValueError("train_lens and test_lens must have the same number of elements.")
+
+        for i, (target, train_len) in enumerate(zip(self.targets, self.train_lens)):
+            if len(target) != train_len:
+                raise ValueError(f"targets[{i}] has length {len(target)} != expected {train_len}.")
+
+        for key, values in self.static_numerical_covariates.items():
+            if len(values) != len(self.train_lens):
+                raise ValueError(
+                    f"static_numerical_covariates['{key}'] has {len(values)} examples "
+                    f"!= expected {len(self.train_lens)}."
+                )
+
+        for key, values in self.static_categorical_covariates.items():
+            if len(values) != len(self.train_lens):
+                raise ValueError(
+                    f"static_categorical_covariates['{key}'] has {len(values)} examples "
+                    f"!= expected {len(self.train_lens)}."
+                )
+
+        for lens, dict_cov, dict_cov_name in [
+            (self.train_lens, self.train_dynamic_numerical_covariates, "train_dynamic_numerical_covariates"),
+            (self.train_lens, self.train_dynamic_categorical_covariates, "train_dynamic_categorical_covariates"),
+            (self.test_lens, self.test_dynamic_numerical_covariates, "test_dynamic_numerical_covariates"),
+            (self.test_lens, self.test_dynamic_categorical_covariates, "test_dynamic_categorical_covariates"),
+        ]:
+            for key, cov_values in dict_cov.items():
+                if len(cov_values) != len(lens):
+                    raise ValueError(
+                        f"{dict_cov_name}['{key}'] has {len(cov_values)} examples != expected {len(lens)}."
+                    )
+                for i, cov_value in enumerate(cov_values):
+                    if len(cov_value) != lens[i]:
+                        raise ValueError(
+                            f"{dict_cov_name}['{key}'][{i}] has length {len(cov_value)} != expected {lens[i]}."
+                        )
+
+    def _create_covariate_matrix(
+        self,
+        one_hot_encoder_drop: str | None = "first",
+        use_intercept: bool = True,
+        assert_covariates: bool = False,
+        assert_covariate_shapes: bool = False,
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """Create target vector and covariate matrices for regression.
+
+        Returns:
+            Tuple of (target_vector, train_covariate_matrix, test_covariate_matrix).
+        """
+        if assert_covariates:
+            self._assert_covariates(assert_covariate_shapes)
+
+        x_train, x_test = [], []
+
+        # Process numerical features
+        for name in sorted(self.train_dynamic_numerical_covariates):
+            x_train.append(_unnest(self.train_dynamic_numerical_covariates[name])[:, np.newaxis])
+            x_test.append(_unnest(self.test_dynamic_numerical_covariates[name])[:, np.newaxis])
+
+        for name in sorted(self.static_numerical_covariates):
+            covs = self.static_numerical_covariates[name]
+            x_train.append(_repeat(covs, self.train_lens)[:, np.newaxis])
+            x_test.append(_repeat(covs, self.test_lens)[:, np.newaxis])
+
+        # Normalize numerical features if present
+        if x_train:
+            x_train = np.concatenate(x_train, axis=1)
+            x_test = np.concatenate(x_test, axis=1)
+
+            x_mean = np.mean(x_train, axis=0, keepdims=True)
+            x_std = np.where((w := np.std(x_train, axis=0, keepdims=True)) > _TOL, w, 1.0)
+            x_train = [(x_train - x_mean) / x_std]
+            x_test = [(x_test - x_mean) / x_std]
+
+        # Process categorical features
+        if not is_sklearn_available():
+            raise ImportError("sklearn is required for covariate support. Install it with: pip install scikit-learn")
+        from sklearn import preprocessing
+
+        one_hot_encoder = preprocessing.OneHotEncoder(
+            drop=one_hot_encoder_drop,
+            sparse_output=False,
+            handle_unknown="ignore",
+        )
+
+        for name in sorted(self.train_dynamic_categorical_covariates.keys()):
+            ohe_train = _unnest(self.train_dynamic_categorical_covariates[name])[:, np.newaxis]
+            ohe_test = _unnest(self.test_dynamic_categorical_covariates[name])[:, np.newaxis]
+            x_train.append(np.array(one_hot_encoder.fit_transform(ohe_train)))
+            x_test.append(np.array(one_hot_encoder.transform(ohe_test)))
+
+        for name in sorted(self.static_categorical_covariates.keys()):
+            covs = self.static_categorical_covariates[name]
+            ohe = one_hot_encoder.fit_transform(np.array(covs)[:, np.newaxis])
+            x_train.append(_repeat(ohe, self.train_lens))
+            x_test.append(_repeat(ohe, self.test_lens))
+
+        # Concatenate all features
+        x_train = np.concatenate(x_train, axis=1) if x_train else np.zeros((sum(self.train_lens), 0))
+        x_test = np.concatenate(x_test, axis=1) if x_test else np.zeros((sum(self.test_lens), 0))
+
+        # Add intercept if requested
+        if use_intercept:
+            x_train = np.pad(x_train, ((0, 0), (1, 0)), constant_values=1.0)
+            x_test = np.pad(x_test, ((0, 0), (1, 0)), constant_values=1.0)
+
+        return _unnest(self.targets), x_train, x_test
+
+
+class BatchedInContextXRegLinear(_BatchedInContextXRegBase):
+    """Linear regression model for in-context covariates.
+
+    Implements batched ridge regression that can be used with TimesFM for
+    incorporating covariates into forecasts.
+    """
+
+    def fit(
+        self,
+        ridge: float = 0.0,
+        one_hot_encoder_drop: str | None = "first",
+        use_intercept: bool = True,
+        max_rows_per_col: int = 0,
+        max_rows_per_col_sample_seed: int = 42,
+        debug_info: bool = False,
+        assert_covariates: bool = False,
+        assert_covariate_shapes: bool = False,
+        device: torch.device | None = None,
+    ) -> list[np.ndarray] | tuple[list[np.ndarray], list[np.ndarray], torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Fit a linear regression model with optional ridge regularization.
+
+        Args:
+            ridge: Ridge regularization parameter (L2 penalty).
+            one_hot_encoder_drop: Strategy for dropping columns in one-hot encoding.
+            use_intercept: Whether to add an intercept term.
+            max_rows_per_col: Maximum ratio of rows to columns for stability (0 for no limit).
+            max_rows_per_col_sample_seed: Random seed for sampling rows.
+            debug_info: Whether to return predictions on context and debug tensors.
+            assert_covariates: Whether to validate covariates.
+            assert_covariate_shapes: Whether to validate covariate shapes.
+            device: PyTorch device to use for computation.
+
+        Returns:
+            If debug_info is False: List of predictions for each series.
+            If debug_info is True: Tuple of (predictions, predictions_on_context,
+                                            coefficients, train_matrix, test_matrix).
+        """
+        y, x_train, x_test = self._create_covariate_matrix(
+            one_hot_encoder_drop=one_hot_encoder_drop,
+            use_intercept=use_intercept,
+            assert_covariates=assert_covariates,
+            assert_covariate_shapes=assert_covariate_shapes,
+        )
+
+        if device is None:
+            device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+        y_tensor = torch.tensor(y, dtype=torch.float32, device=device)
+        x_train_tensor = torch.tensor(x_train, dtype=torch.float32, device=device)
+        x_test_tensor = torch.tensor(x_test, dtype=torch.float32, device=device)
+
+        # Subsample rows if the matrix is too tall relative to its width
+        if max_rows_per_col > 0 and x_train.shape[0] > max_rows_per_col * x_train.shape[1]:
+            np.random.seed(max_rows_per_col_sample_seed)
+            n_samples = max_rows_per_col * x_train.shape[1]
+            indices = np.random.choice(x_train.shape[0], n_samples, replace=False)
+            indices_tensor = torch.tensor(indices, device=device)
+            x_train_tensor = x_train_tensor[indices_tensor]
+            y_tensor = y_tensor[indices_tensor]
+
+        # Solve linear regression
+        if x_train_tensor.shape[1] == 0:
+            predictions_flat = torch.zeros(x_test_tensor.shape[0], device=device)
+            predictions_on_context_flat = torch.zeros(len(y), device=device)
+            coeffs = torch.zeros(0, device=device)
+        else:
+            xtx = x_train_tensor.T @ x_train_tensor
+            if ridge > 0:
+                xtx = xtx + ridge * torch.eye(xtx.shape[0], device=device)
+
+            xty = x_train_tensor.T @ y_tensor
+
+            try:
+                coeffs = torch.linalg.solve(xtx, xty)
+            except torch.linalg.LinAlgError:
+                result = torch.linalg.lstsq(x_train_tensor, y_tensor, rcond=None)
+                coeffs = result.solution[: x_train_tensor.shape[1]]
+
+            predictions_flat = x_test_tensor @ coeffs
+
+            x_train_full = torch.tensor(x_train, dtype=torch.float32, device=device)
+            predictions_on_context_flat = x_train_full @ coeffs
+
+        predictions_flat = predictions_flat.cpu().numpy()
+        predictions_on_context_flat = predictions_on_context_flat.cpu().numpy()
+
+        # Reshape predictions to match original batch structure
+        predictions = []
+        predictions_on_context = []
+
+        test_start = 0
+        train_start = 0
+        for train_len, test_len in zip(self.train_lens, self.test_lens):
+            predictions.append(predictions_flat[test_start : test_start + test_len])
+            predictions_on_context.append(predictions_on_context_flat[train_start : train_start + train_len])
+            test_start += test_len
+            train_start += train_len
+
+        if debug_info:
+            return (
+                predictions,
+                predictions_on_context,
+                coeffs.cpu() if x_train_tensor.shape[1] > 0 else coeffs,
+                x_train_tensor.cpu(),
+                x_test_tensor.cpu(),
+            )
+        return predictions
diff --git a/src/transformers/models/timesfm2_5/modeling_timesfm2_5.py b/src/transformers/models/timesfm2_5/modeling_timesfm2_5.py
index e7b4e799d20b..2cebb25a7488 100644
--- a/src/transformers/models/timesfm2_5/modeling_timesfm2_5.py
+++ b/src/transformers/models/timesfm2_5/modeling_timesfm2_5.py
@@ -19,6 +19,7 @@
 # limitations under the License.
 
 import math
+import warnings
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
 from typing import Optional
@@ -42,8 +43,8 @@
 from .configuration_timesfm2_5 import TimesFm2_5Config
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFm2_5Output(BaseModelOutput):
     r"""
     context_mu (`torch.Tensor` of shape `(batch_size, num_patches)`):
@@ -59,8 +60,8 @@ class TimesFm2_5Output(BaseModelOutput):
     context_sigma: torch.Tensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFm2_5OutputForPrediction(BaseModelOutput):
     r"""
     mean_predictions (`torch.Tensor` of shape `(batch_size, horizon_length)`):
@@ -584,12 +585,15 @@ def forward(
         """
         batch_size, seq_len = past_values.shape
         patch_len = self.config.patch_length
+        torch._check(seq_len % patch_len == 0)
 
         if past_values_padding is None:
             past_values_padding = torch.zeros_like(past_values, dtype=torch.long)
+        else:
+            past_values_padding = past_values_padding.narrow(1, 0, seq_len)
 
-        patched_inputs = past_values.view(batch_size, -1, patch_len)
-        patched_masks = past_values_padding[:, :seq_len].view(batch_size, -1, patch_len)
+        patched_inputs = past_values.unflatten(-1, (-1, patch_len))
+        patched_masks = past_values_padding.unflatten(-1, (-1, patch_len))
         patched_masks_bool = patched_masks >= 0.5
 
         count = past_values.new_zeros(batch_size)
@@ -682,13 +686,20 @@ def __init__(self, config: TimesFm2_5Config):
         self.post_init()
 
     def _preprocess(
-        self, inputs: Sequence[torch.Tensor], freq: Sequence[int] | None = None, context_len: int | None = None
+        self,
+        inputs: torch.Tensor,
+        observed_mask: torch.Tensor,
+        freq: torch.Tensor | None = None,
+        context_len: int | None = None,
     ) -> tuple[torch.Tensor, ...]:
         """Pad/truncate input time series to `context_len` and build a padding mask.
 
         Args:
-            inputs: A list of 1d Tensors. Each Tensor is the context time series of a single forecast task.
-            freq: Optional list of frequencies (returned as a tensor when provided).
+            inputs: A 2D `torch.Tensor` of shape `(batch_size, sequence_length)`.
+            observed_mask: A 2D `torch.Tensor` of the same shape as `inputs` where `1` indicates an observed
+                value and `0` indicates a missing value. Missing positions are marked as padded in the
+                returned padding mask.
+            freq: Optional 1D `torch.Tensor` of frequency indices.
             context_len: Optional context length override (defaults to `self.context_len`).
 
         Returns:
@@ -697,25 +708,20 @@ def _preprocess(
         if context_len is None:
             context_len = self.context_len
 
-        input_ts, input_padding = [], []
-
-        for ts in inputs:
-            input_len = ts.shape[0]
-            padding = torch.zeros(input_len + self.horizon_len, dtype=ts.dtype, device=ts.device)
-            if input_len < context_len:
-                num_front_pad = context_len - input_len
-                ts = torch.cat([torch.zeros(num_front_pad, dtype=ts.dtype, device=ts.device), ts], dim=0)
-                padding = torch.cat([torch.ones(num_front_pad, dtype=ts.dtype, device=padding.device), padding], dim=0)
-            elif input_len > context_len:
-                ts = ts[-context_len:]
-                padding = padding[-(context_len + self.horizon_len) :]
+        x = inputs[:, -context_len:]
+        num_front_pad = context_len - x.shape[1]
+        x = F.pad(x, (num_front_pad, 0))
 
-            input_ts.append(ts)
-            input_padding.append(padding)
+        obs = observed_mask[:, -context_len:].to(dtype=x.dtype)
+        body_padding = 1 - obs
+        front_padding = torch.ones(x.shape[0], num_front_pad, dtype=x.dtype, device=x.device)
+        horizon_padding = torch.zeros(x.shape[0], self.horizon_len, dtype=x.dtype, device=x.device)
+        padding = torch.cat([front_padding, body_padding, horizon_padding], dim=1)
+        result = (x, padding)
 
-        result = (torch.stack(input_ts, dim=0), torch.stack(input_padding, dim=0))
         if freq is not None:
-            result = result + (torch.tensor(freq[: len(inputs)], dtype=torch.int32).reshape(-1, 1),)
+            freq_tensor = freq[: x.shape[0]].to(dtype=torch.int32)
+            result = result + (freq_tensor.reshape(-1, 1),)
         return result
 
     def _postprocess_output(
@@ -745,7 +751,8 @@ def _quantile_loss(self, predictions: torch.Tensor, targets: torch.Tensor) -> to
     @auto_docstring
     def forward(
         self,
-        past_values: Sequence[torch.Tensor],
+        past_values: Sequence[torch.Tensor] | torch.Tensor,
+        past_observed_mask: torch.Tensor | None = None,
         window_size: int | None = None,
         future_values: torch.Tensor | None = None,
         forecast_context_len: int | None = None,
@@ -754,8 +761,20 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> TimesFm2_5OutputForPrediction:
         r"""
-        past_values (`Sequence[torch.Tensor]`):
-            Past values of the time series that serves as input to the model. Each tensor is a 1D time series.
+        past_values (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+            Past values of the time series that serves as input to the model. A list of 1D tensors with
+            possibly differing lengths is also accepted (deprecated): each tensor is front-padded with zeros
+            and stacked into a 2D tensor. Tensor inputs are preferred and required for export.
+        past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask indicating which `past_values` were observed and which are padding/missing. Mask
+            values selected in `[0, 1]`:
+
+            - `1` for values that are **observed**,
+            - `0` for values that are **missing** (i.e. padded or NaNs that were replaced by zeros).
+
+            Defaults to a tensor of ones (everything observed). When `past_values` is passed as a list of
+            variable-length tensors, you should provide a matching `past_observed_mask` so the front-padding
+            zeros are not treated as observed values.
         window_size (`int`, *optional*):
             Window size of trend + residual decomposition. If `None`, decomposition is not applied.
         future_values (`torch.Tensor`, *optional*):
@@ -769,23 +788,36 @@ def forward(
             `config.force_flip_invariance`.
         """
         forecast_context_len = forecast_context_len or self.context_len
-        device = past_values[0].device
 
-        inputs = [ts[-forecast_context_len:] for ts in past_values]
-        input_min = torch.min(torch.stack([torch.min(ts) for ts in inputs]))
+        if isinstance(past_values, list):
+            warnings.warn(
+                "Passing `past_values` as a list of 1D tensors is deprecated and will be removed in a future "
+                "version. Please pass a 2D `torch.Tensor` of shape `(batch_size, sequence_length)` and, when "
+                "needed, a `past_observed_mask` of the same shape (1 = observed, 0 = padded/missing).",
+                FutureWarning,
+            )
+            past_values = self._past_values_to_tensor(past_values)
+
+        device = past_values.device
+        if past_observed_mask is None:
+            past_observed_mask = torch.ones_like(past_values)
+
+        inputs = past_values[:, -forecast_context_len:]
+        observed_mask = past_observed_mask[:, -forecast_context_len:].to(device=device)
+        sentinel = torch.full_like(inputs, torch.finfo(inputs.dtype).max)
+        input_min = torch.where(observed_mask.bool(), inputs, sentinel).min()
 
         if window_size is not None:
-            new_inputs: list[torch.Tensor] = []
-            for ts in inputs:
-                new_inputs.extend(self._timesfm_moving_average(ts, window_size))
-            inputs = new_inputs
+            trend, residual = self._timesfm2_5_moving_average(inputs, window_size)
+            inputs = torch.stack([trend, residual], dim=1).view(2 * inputs.shape[0], -1)
+            observed_mask = torch.repeat_interleave(observed_mask, 2, dim=0)
 
         if truncate_negative is None:
             truncate_negative = self.config.infer_is_positive
         if force_flip_invariance is None:
             force_flip_invariance = self.config.force_flip_invariance
 
-        input_ts, input_padding = self._preprocess(inputs, context_len=forecast_context_len)
+        input_ts, input_padding = self._preprocess(inputs, observed_mask, context_len=forecast_context_len)
         input_ts = input_ts.to(device)
         input_padding = input_padding.to(device)
 
@@ -864,15 +896,23 @@ def _flip_quantiles(x: torch.Tensor) -> torch.Tensor:
         )
 
     @staticmethod
-    def _timesfm2_5_moving_average(arr: torch.Tensor, window_size: int) -> list[torch.Tensor]:
-        """Calculates the moving average using PyTorch's convolution function."""
-        # Pad with zeros to handle initial window positions
+    def _past_values_to_tensor(past_values: Sequence[torch.Tensor]) -> torch.Tensor:
+        """Convert a list of variable-length 1D tensors into a 2D tensor of shape `(batch_size, max_len)`
+        by left-padding each entry with zeros. Equivalent to `torch.nn.utils.rnn.pad_sequence(past_values,
+        batch_first=True, padding_side="left")`, re-implemented here because `padding_side` requires
+        `torch>=2.5`.
+        """
+        max_len = max(ts.shape[0] for ts in past_values)
+        return torch.stack([F.pad(ts, (max_len - ts.shape[0], 0)) for ts in past_values], dim=0)
+
+    @staticmethod
+    def _timesfm2_5_moving_average(arr: torch.Tensor, window_size: int) -> tuple[torch.Tensor, torch.Tensor]:
+        """Calculates the moving average using PyTorch's convolution function. `arr` shape: `(B, T)`."""
         arr_padded = F.pad(arr, (window_size - 1, 0), "constant", 0)
-        # Create a convolution kernel
         kernel = torch.ones(window_size, dtype=arr.dtype, device=arr.device) / window_size
-        # Apply convolution to calculate the moving average
-        smoothed_arr = F.conv1d(arr_padded.view(1, 1, -1), kernel.view(1, 1, -1)).squeeze()
-        return [smoothed_arr, arr - smoothed_arr]
+        kernel = kernel.view(1, 1, -1)
+        smoothed_arr = F.conv1d(arr_padded.unsqueeze(1), kernel).squeeze(1)
+        return smoothed_arr, arr - smoothed_arr
 
     def _decode_and_project(
         self,
diff --git a/src/transformers/models/timesfm2_5/modular_timesfm2_5.py b/src/transformers/models/timesfm2_5/modular_timesfm2_5.py
index 3a912d07946b..8a44ce999b88 100644
--- a/src/transformers/models/timesfm2_5/modular_timesfm2_5.py
+++ b/src/transformers/models/timesfm2_5/modular_timesfm2_5.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import math
+import warnings
 from collections.abc import Callable, Sequence
 from dataclasses import dataclass
 
@@ -109,8 +110,8 @@ class TimesFm2_5Config(TimesFmConfig):
     max_timescale = AttributeError()
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFm2_5Output(TimesFmOutput):
     r"""
     context_mu (`torch.Tensor` of shape `(batch_size, num_patches)`):
@@ -123,8 +124,8 @@ class TimesFm2_5Output(TimesFmOutput):
     context_sigma: torch.Tensor | None = None
 
 
-@dataclass
 @auto_docstring
+@dataclass
 class TimesFm2_5OutputForPrediction(TimesFmOutputForPrediction):
     r"""
     mean_predictions (`torch.Tensor` of shape `(batch_size, horizon_length)`):
@@ -387,12 +388,15 @@ def forward(
         """
         batch_size, seq_len = past_values.shape
         patch_len = self.config.patch_length
+        torch._check(seq_len % patch_len == 0)
 
         if past_values_padding is None:
             past_values_padding = torch.zeros_like(past_values, dtype=torch.long)
+        else:
+            past_values_padding = past_values_padding.narrow(1, 0, seq_len)
 
-        patched_inputs = past_values.view(batch_size, -1, patch_len)
-        patched_masks = past_values_padding[:, :seq_len].view(batch_size, -1, patch_len)
+        patched_inputs = past_values.unflatten(-1, (-1, patch_len))
+        patched_masks = past_values_padding.unflatten(-1, (-1, patch_len))
         patched_masks_bool = patched_masks >= 0.5
 
         count = past_values.new_zeros(batch_size)
@@ -532,7 +536,8 @@ def _decode_and_project(
     @auto_docstring
     def forward(
         self,
-        past_values: Sequence[torch.Tensor],
+        past_values: Sequence[torch.Tensor] | torch.Tensor,
+        past_observed_mask: torch.Tensor | None = None,
         window_size: int | None = None,
         future_values: torch.Tensor | None = None,
         forecast_context_len: int | None = None,
@@ -541,8 +546,20 @@ def forward(
         **kwargs: Unpack[TransformersKwargs],
     ) -> TimesFm2_5OutputForPrediction:
         r"""
-        past_values (`Sequence[torch.Tensor]`):
-            Past values of the time series that serves as input to the model. Each tensor is a 1D time series.
+        past_values (`torch.Tensor` of shape `(batch_size, sequence_length)`):
+            Past values of the time series that serves as input to the model. A list of 1D tensors with
+            possibly differing lengths is also accepted (deprecated): each tensor is front-padded with zeros
+            and stacked into a 2D tensor. Tensor inputs are preferred and required for export.
+        past_observed_mask (`torch.BoolTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Boolean mask indicating which `past_values` were observed and which are padding/missing. Mask
+            values selected in `[0, 1]`:
+
+            - `1` for values that are **observed**,
+            - `0` for values that are **missing** (i.e. padded or NaNs that were replaced by zeros).
+
+            Defaults to a tensor of ones (everything observed). When `past_values` is passed as a list of
+            variable-length tensors, you should provide a matching `past_observed_mask` so the front-padding
+            zeros are not treated as observed values.
         window_size (`int`, *optional*):
             Window size of trend + residual decomposition. If `None`, decomposition is not applied.
         future_values (`torch.Tensor`, *optional*):
@@ -556,23 +573,36 @@ def forward(
             `config.force_flip_invariance`.
         """
         forecast_context_len = forecast_context_len or self.context_len
-        device = past_values[0].device
 
-        inputs = [ts[-forecast_context_len:] for ts in past_values]
-        input_min = torch.min(torch.stack([torch.min(ts) for ts in inputs]))
+        if isinstance(past_values, list):
+            warnings.warn(
+                "Passing `past_values` as a list of 1D tensors is deprecated and will be removed in a future "
+                "version. Please pass a 2D `torch.Tensor` of shape `(batch_size, sequence_length)` and, when "
+                "needed, a `past_observed_mask` of the same shape (1 = observed, 0 = padded/missing).",
+                FutureWarning,
+            )
+            past_values = self._past_values_to_tensor(past_values)
+
+        device = past_values.device
+        if past_observed_mask is None:
+            past_observed_mask = torch.ones_like(past_values)
+
+        inputs = past_values[:, -forecast_context_len:]
+        observed_mask = past_observed_mask[:, -forecast_context_len:].to(device=device)
+        sentinel = torch.full_like(inputs, torch.finfo(inputs.dtype).max)
+        input_min = torch.where(observed_mask.bool(), inputs, sentinel).min()
 
         if window_size is not None:
-            new_inputs: list[torch.Tensor] = []
-            for ts in inputs:
-                new_inputs.extend(self._timesfm_moving_average(ts, window_size))
-            inputs = new_inputs
+            trend, residual = self._timesfm2_5_moving_average(inputs, window_size)
+            inputs = torch.stack([trend, residual], dim=1).view(2 * inputs.shape[0], -1)
+            observed_mask = torch.repeat_interleave(observed_mask, 2, dim=0)
 
         if truncate_negative is None:
             truncate_negative = self.config.infer_is_positive
         if force_flip_invariance is None:
             force_flip_invariance = self.config.force_flip_invariance
 
-        input_ts, input_padding = self._preprocess(inputs, context_len=forecast_context_len)
+        input_ts, input_padding = self._preprocess(inputs, observed_mask, context_len=forecast_context_len)
         input_ts = input_ts.to(device)
         input_padding = input_padding.to(device)
 
diff --git a/src/transformers/models/timm_backbone/modeling_timm_backbone.py b/src/transformers/models/timm_backbone/modeling_timm_backbone.py
index c60606a7657e..d615ab358e07 100644
--- a/src/transformers/models/timm_backbone/modeling_timm_backbone.py
+++ b/src/transformers/models/timm_backbone/modeling_timm_backbone.py
@@ -14,14 +14,13 @@
 
 
 import torch
-from torch import Tensor, nn
+from torch import nn
 
 from ... import initialization as init
-from ...backbone_utils import BackboneMixin, filter_output_hidden_states
+from ...backbone_utils import BackboneMixin
 from ...modeling_outputs import BackboneOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import is_timm_available, requires_backends
-from ...utils.generic import can_return_tuple
+from ...utils import can_return_tuple, is_timm_available, requires_backends
 from .configuration_timm_backbone import TimmBackboneConfig
 
 
@@ -72,8 +71,8 @@ def __init__(self, config, **kwargs):
         if getattr(config, "freeze_batch_norm_2d", False):
             self.freeze_batch_norm_2d()
 
-        # These are used to control the output of the model when called. If output_hidden_states is True, then
-        # return_layers is modified to include all layers.
+        # These are used to control the output of the model when called. If hidden states are requested (via
+        # config or kwargs), return_layers is modified to include all layers.
         self._return_layers = {
             layer["module"]: str(layer["index"]) for layer in self._backbone.feature_info.get_dicts()
         }
@@ -118,20 +117,13 @@ def _init_weights(self, module):
                 init.zeros_(module.num_batches_tracked)
 
     @can_return_tuple
-    @filter_output_hidden_states
     def forward(
         self,
         pixel_values: torch.FloatTensor,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> BackboneOutput | tuple[Tensor, ...]:
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+    ) -> BackboneOutput:
+        output_hidden_states = kwargs.pop("output_hidden_states", self.config.output_hidden_states)
+        output_attentions = kwargs.pop("output_attentions", self.config.output_attentions)
 
         if output_attentions:
             raise ValueError("Cannot output attentions for timm backbones at the moment")
@@ -149,12 +141,6 @@ def forward(
         feature_maps = tuple(feature_maps)
         hidden_states = tuple(hidden_states) if hidden_states is not None else None
 
-        if not return_dict:
-            output = (feature_maps,)
-            if output_hidden_states:
-                output = output + (hidden_states,)
-            return output
-
         return BackboneOutput(feature_maps=feature_maps, hidden_states=hidden_states, attentions=None)
 
 
diff --git a/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py b/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py
index 17e87cb8f959..6f956901df21 100644
--- a/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py
+++ b/src/transformers/models/timm_wrapper/modeling_timm_wrapper.py
@@ -28,13 +28,13 @@
     import timm
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output class for models TimmWrapperModel, containing the last hidden states, an optional pooled output,
     and optional hidden states.
     """
 )
+@dataclass
 class TimmWrapperModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor`):
diff --git a/src/transformers/models/tvp/processing_tvp.py b/src/transformers/models/tvp/processing_tvp.py
index b72f6be48c02..f6f056eefe7c 100644
--- a/src/transformers/models/tvp/processing_tvp.py
+++ b/src/transformers/models/tvp/processing_tvp.py
@@ -17,9 +17,11 @@
 
 from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from .image_processing_tvp import TvpImageProcessorKwargs
 
 
 class TvpProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: TvpImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "truncation": True,
diff --git a/src/transformers/models/udop/modeling_udop.py b/src/transformers/models/udop/modeling_udop.py
index dceadc547e86..00f21c22b2bf 100644
--- a/src/transformers/models/udop/modeling_udop.py
+++ b/src/transformers/models/udop/modeling_udop.py
@@ -50,13 +50,13 @@
 logger = logging.getLogger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for the model's outputs that may also contain a past key/values (to speed up sequential decoding). Includes
     an additional attention mask.
     """
 )
+@dataclass
 class BaseModelOutputWithAttentionMask(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
diff --git a/src/transformers/models/udop/processing_udop.py b/src/transformers/models/udop/processing_udop.py
index 707b5693a2d5..805512997006 100644
--- a/src/transformers/models/udop/processing_udop.py
+++ b/src/transformers/models/udop/processing_udop.py
@@ -22,6 +22,7 @@
 from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import auto_docstring
+from ..layoutlmv3.image_processing_layoutlmv3 import LayoutLMv3ImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
@@ -33,6 +34,7 @@ class UdopTextKwargs(TextKwargs, total=False):
 
 
 class UdopProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: LayoutLMv3ImageProcessorKwargs
     text_kwargs: UdopTextKwargs
     _defaults = {
         "text_kwargs": {
diff --git a/src/transformers/models/umt5/modeling_umt5.py b/src/transformers/models/umt5/modeling_umt5.py
index 281ed39fda76..32d6c16ec052 100644
--- a/src/transformers/models/umt5/modeling_umt5.py
+++ b/src/transformers/models/umt5/modeling_umt5.py
@@ -33,6 +33,7 @@
     Seq2SeqModelOutput,
     Seq2SeqQuestionAnsweringModelOutput,
     Seq2SeqSequenceClassifierOutput,
+    SequenceClassifierOutput,
     TokenClassifierOutput,
 )
 from ...modeling_utils import PreTrainedModel
@@ -1619,6 +1620,99 @@ def forward(
         )
 
 
+@auto_docstring
+class UMT5EncoderForSequenceClassification(UMT5PreTrainedModel):
+    keys_to_ignore_on_load_unexpected = [r"decoder"]
+
+    # Copied from transformers.models.t5.modeling_t5.T5EncoderForSequenceClassification.__init__ with T5->UMT5
+    def __init__(self, config: UMT5Config):
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+
+        self.transformer = UMT5EncoderModel(config)
+        self.dropout = nn.Dropout(config.classifier_dropout)
+        self.classifier = UMT5ClassificationHead(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+        self.model_parallel = False
+
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        labels: torch.Tensor | None = None,
+        output_attentions: bool | None = None,
+        output_hidden_states: bool | None = None,
+        return_dict: bool | None = None,
+    ) -> tuple[torch.Tensor] | SequenceClassifierOutput:
+        r"""
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. T5 is a model with relative position embeddings so you
+            should be able to pad the inputs on both the right and the left.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for detail.
+
+            [What are input IDs?](../glossary#input-ids)
+
+            To know more on how to prepare `input_ids` for pretraining take a look a [T5 Training](./t5#training).
+        attention_mask (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Mask to avoid performing attention on padding token indices.
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.transformer(
+            input_ids,
+            attention_mask=attention_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]  #  outputs.last_hidden_state
+        hidden_states = self.dropout(hidden_states)
+
+        sentence_representation = (hidden_states * attention_mask.unsqueeze(-1)).sum(dim=1)
+        sentence_representation /= attention_mask.sum(dim=1).unsqueeze(-1)
+
+        logits = self.classifier(sentence_representation)
+
+        loss = None
+        if labels is not None:
+            if self.config.num_labels > 0 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                self.config.problem_type = "single_label_classification"
+            else:
+                self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                batch_size, _ = input_ids.shape
+                loss = loss_fct(logits.view(batch_size, self.num_labels), labels.view(batch_size, self.num_labels))
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return ((loss,) + output) if loss is not None else output
+
+        return SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
 __all__ = [
     "UMT5EncoderModel",
     "UMT5ForConditionalGeneration",
@@ -1627,4 +1721,5 @@ def forward(
     "UMT5ForTokenClassification",
     "UMT5Model",
     "UMT5PreTrainedModel",
+    "UMT5EncoderForSequenceClassification",
 ]
diff --git a/src/transformers/models/unispeech/modeling_unispeech.py b/src/transformers/models/unispeech/modeling_unispeech.py
index e1ee81f42950..03103760140c 100755
--- a/src/transformers/models/unispeech/modeling_unispeech.py
+++ b/src/transformers/models/unispeech/modeling_unispeech.py
@@ -50,12 +50,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`UniSpeechForPreTrainingOutput`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class UniSpeechForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/unispeech/modular_unispeech.py b/src/transformers/models/unispeech/modular_unispeech.py
index 6c94a57f0973..fce518d13a65 100644
--- a/src/transformers/models/unispeech/modular_unispeech.py
+++ b/src/transformers/models/unispeech/modular_unispeech.py
@@ -40,12 +40,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`UniSpeechForPreTrainingOutput`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class UniSpeechForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py b/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
index c23fdcf16420..565852237d06 100755
--- a/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
+++ b/src/transformers/models/unispeech_sat/modeling_unispeech_sat.py
@@ -53,12 +53,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`UniSpeechSatForPreTrainingOutput`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class UniSpeechSatForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/unispeech_sat/modular_unispeech_sat.py b/src/transformers/models/unispeech_sat/modular_unispeech_sat.py
index c445c42b9139..b90470695df1 100644
--- a/src/transformers/models/unispeech_sat/modular_unispeech_sat.py
+++ b/src/transformers/models/unispeech_sat/modular_unispeech_sat.py
@@ -45,12 +45,12 @@
 _HIDDEN_STATES_START_POSITION = 2
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`UniSpeechSatForPreTrainingOutput`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class UniSpeechSatForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when model is in train mode, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/univnet/modeling_univnet.py b/src/transformers/models/univnet/modeling_univnet.py
index a6b8eee85e7f..3696bacc9c2e 100644
--- a/src/transformers/models/univnet/modeling_univnet.py
+++ b/src/transformers/models/univnet/modeling_univnet.py
@@ -20,20 +20,20 @@
 
 from ...modeling_outputs import ModelOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
 from .configuration_univnet import UnivNetConfig
 
 
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output class for the [`UnivNetModel`], which includes the generated audio waveforms and the original unpadded
     lengths of those waveforms (so that the padding can be removed by [`UnivNetModel.batch_decode`]).
     """
 )
+@dataclass
 class UnivNetModelOutput(ModelOutput):
     r"""
     waveforms (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
@@ -467,6 +467,7 @@ def __init__(self, config: UnivNetConfig):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -474,9 +475,8 @@ def forward(
         noise_sequence: torch.FloatTensor | None = None,
         padding_mask: torch.FloatTensor | None = None,
         generator: torch.Generator | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple[torch.FloatTensor] | UnivNetModelOutput:
+    ) -> UnivNetModelOutput:
         r"""
         noise_sequence (`torch.FloatTensor`, *optional*):
             Tensor containing a noise sequence of standard Gaussian noise. Can be batched and of shape `(batch_size,
@@ -516,8 +516,6 @@ def forward(
          [1, 140288]
          ```
         """
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         # Resolve batch sizes for noise_sequence and spectrogram
         spectrogram_batched = input_features.dim() == 3
         if not spectrogram_batched:
@@ -582,10 +580,6 @@ def forward(
             # Padding is always contiguous and added on the right
             waveform_lengths = torch.sum(padding_mask, dim=1)
 
-        if not return_dict:
-            outputs = (waveform, waveform_lengths)
-            return outputs
-
         return UnivNetModelOutput(
             waveforms=waveform,
             waveform_lengths=waveform_lengths,
diff --git a/src/transformers/models/upernet/modeling_upernet.py b/src/transformers/models/upernet/modeling_upernet.py
index 98dd697da205..4aae4616de73 100644
--- a/src/transformers/models/upernet/modeling_upernet.py
+++ b/src/transformers/models/upernet/modeling_upernet.py
@@ -20,7 +20,7 @@
 from ...backbone_utils import load_backbone
 from ...modeling_outputs import SemanticSegmenterOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring
+from ...utils import auto_docstring, can_return_tuple
 from .configuration_upernet import UperNetConfig
 
 
@@ -290,16 +290,14 @@ def __init__(self, config):
         # Initialize weights and apply final processing
         self.post_init()
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
         pixel_values: torch.Tensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
         labels: torch.Tensor | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple | SemanticSegmenterOutput:
+    ) -> SemanticSegmenterOutput:
         r"""
         labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
             Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
@@ -330,15 +328,11 @@ def forward(
         if labels is not None and self.config.num_labels == 1:
             raise ValueError("The number of labels should be greater than one")
 
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-        output_hidden_states = (
-            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
-        )
-        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        # Pass output flags from config to backbone when not in kwargs (e.g. config.output_hidden_states = True)
+        kwargs.setdefault("output_hidden_states", self.config.output_hidden_states)
+        kwargs.setdefault("output_attentions", self.config.output_attentions)
 
-        outputs = self.backbone.forward_with_filtered_kwargs(
-            pixel_values, output_hidden_states=output_hidden_states, output_attentions=output_attentions
-        )
+        outputs = self.backbone.forward_with_filtered_kwargs(pixel_values, **kwargs)
         features = outputs.feature_maps
 
         logits = self.decode_head(features)
@@ -360,13 +354,6 @@ def forward(
                 auxiliary_loss = loss_fct(auxiliary_logits, labels)
                 loss += self.config.auxiliary_loss_weight * auxiliary_loss
 
-        if not return_dict:
-            if output_hidden_states:
-                output = (logits,) + outputs[1:]
-            else:
-                output = (logits,) + outputs[2:]
-            return ((loss,) + output) if loss is not None else output
-
         return SemanticSegmenterOutput(
             loss=loss,
             logits=logits,
diff --git a/src/transformers/models/vaultgemma/modeling_vaultgemma.py b/src/transformers/models/vaultgemma/modeling_vaultgemma.py
index f0a2e48d20b8..deafcb42a26f 100644
--- a/src/transformers/models/vaultgemma/modeling_vaultgemma.py
+++ b/src/transformers/models/vaultgemma/modeling_vaultgemma.py
@@ -288,8 +288,8 @@ def __init__(self, config: VaultGemmaConfig, device=None):
             rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type]
         inv_freq, self.attention_scaling = rope_init_fn(self.config, device)
 
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-        self.register_buffer("original_inv_freq", inv_freq.clone(), persistent=False)
+        self.inv_freq = nn.parameter.Buffer(inv_freq, persistent=False)
+        self.original_inv_freq = nn.parameter.Buffer(inv_freq.clone(), persistent=False)
 
     @staticmethod
     def compute_default_rope_parameters(
@@ -329,7 +329,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
@@ -345,7 +345,7 @@ class VaultGemmaTextScaledWordEmbedding(nn.Embedding):
     def __init__(self, num_embeddings: int, embedding_dim: int, padding_idx: int, embed_scale: float = 1.0):
         super().__init__(num_embeddings, embedding_dim, padding_idx)
         self.scalar_embed_scale = embed_scale
-        self.register_buffer("embed_scale", torch.tensor(embed_scale), persistent=False)
+        self.embed_scale = nn.parameter.Buffer(torch.tensor(embed_scale), persistent=False)
 
     def forward(self, input_ids: torch.Tensor):
         return super().forward(input_ids) * self.embed_scale.to(self.weight.dtype)
@@ -377,6 +377,13 @@ def _init_weights(self, module):
             init.zeros_(module.weight)
         elif isinstance(module, VaultGemmaTextScaledWordEmbedding):
             init.constant_(module.embed_scale, module.scalar_embed_scale)
+        if isinstance(module, VaultGemmaRotaryEmbedding):
+            rope_init_fn = module.compute_default_rope_parameters
+            if module.rope_type != "default":
+                rope_init_fn = ROPE_INIT_FUNCTIONS[module.rope_type]
+            inv_freq, _ = rope_init_fn(module.config)
+            init.copy_(module.inv_freq, inv_freq)
+            init.copy_(module.original_inv_freq, inv_freq)
 
 
 @auto_docstring
diff --git a/src/transformers/models/vibevoice_asr/modeling_vibevoice_asr.py b/src/transformers/models/vibevoice_asr/modeling_vibevoice_asr.py
index 3d26d0fbe9f3..b66dd15b2cb1 100644
--- a/src/transformers/models/vibevoice_asr/modeling_vibevoice_asr.py
+++ b/src/transformers/models/vibevoice_asr/modeling_vibevoice_asr.py
@@ -28,7 +28,13 @@
 from ...modeling_outputs import BaseModelOutputWithPooling, CausalLMOutputWithPast
 from ...modeling_utils import PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling
+from ...utils import (
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    is_torchdynamo_compiling,
+    torch_compilable_check,
+)
 from ..auto import AutoModel, AutoModelForCausalLM
 from .configuration_vibevoice_asr import VibeVoiceAsrConfig
 
@@ -363,6 +369,30 @@ def get_audio_features(
 
         return BaseModelOutputWithPooling(last_hidden_state=acoustic_latents, pooler_output=combined_features)
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
diff --git a/src/transformers/models/video_llama_3/configuration_video_llama_3.py b/src/transformers/models/video_llama_3/configuration_video_llama_3.py
index d25f1266284c..55230680d477 100644
--- a/src/transformers/models/video_llama_3/configuration_video_llama_3.py
+++ b/src/transformers/models/video_llama_3/configuration_video_llama_3.py
@@ -82,6 +82,12 @@ def __post_init__(self, **kwargs):
         elif self.text_config is None:
             self.text_config = CONFIG_MAPPING["qwen2"]()
 
+        # The default value is `False` but this config is used with many model types
+        # Attr `tie_word_embeddings` was saved in text config for those models, so we
+        # need an ugly workaround and forward-pass the attr from text config
+        if not self.tie_word_embeddings and self.text_config.tie_word_embeddings:
+            self.tie_word_embeddings = self.text_config.tie_word_embeddings
+
         super().__post_init__(**kwargs)
 
 
diff --git a/src/transformers/models/video_llama_3/modeling_video_llama_3.py b/src/transformers/models/video_llama_3/modeling_video_llama_3.py
index 26d89b313167..9fe774babc8d 100644
--- a/src/transformers/models/video_llama_3/modeling_video_llama_3.py
+++ b/src/transformers/models/video_llama_3/modeling_video_llama_3.py
@@ -495,12 +495,12 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLLaMA3 outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class VideoLlama3ModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -628,18 +628,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0]}",
             )
         return special_image_mask, special_video_mask
@@ -722,12 +722,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLLaMA3 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class VideoLlama3CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/video_llama_3/modular_video_llama_3.py b/src/transformers/models/video_llama_3/modular_video_llama_3.py
index c4a9e40bc8f0..04e41f2d2000 100644
--- a/src/transformers/models/video_llama_3/modular_video_llama_3.py
+++ b/src/transformers/models/video_llama_3/modular_video_llama_3.py
@@ -30,15 +30,13 @@
     IMAGENET_STANDARD_MEAN,
     IMAGENET_STANDARD_STD,
     ChannelDimension,
-    ImageInput,
     PILImageResampling,
     SizeDict,
     get_image_size,
 )
 from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ModelOutput
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
-from ...processing_utils import Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import ProcessorMixin, Unpack
 from ...utils import (
     TensorType,
     auto_docstring,
@@ -48,7 +46,6 @@
 from ...utils.generic import is_flash_attention_requested, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ...video_utils import (
-    VideoInput,
     group_videos_by_shape,
     reorder_videos,
 )
@@ -66,13 +63,13 @@
     eager_attention_forward,
 )
 from ..qwen2_vl.processing_qwen2_vl import (
-    Qwen2VLProcessor,
     Qwen2VLProcessorKwargs,
 )
 from ..qwen2_vl.video_processing_qwen2_vl import (
     Qwen2VLVideoProcessor,
     Qwen2VLVideoProcessorInitKwargs,
 )
+from ..qwen3_vl.processing_qwen3_vl import Qwen3VLProcessor
 from ..siglip.configuration_siglip import SiglipVisionConfig
 from ..siglip.modeling_siglip import (
     SiglipAttention,
@@ -118,6 +115,12 @@ def __post_init__(self, **kwargs):
         elif self.text_config is None:
             self.text_config = CONFIG_MAPPING["qwen2"]()
 
+        # The default value is `False` but this config is used with many model types
+        # Attr `tie_word_embeddings` was saved in text config for those models, so we
+        # need an ugly workaround and forward-pass the attr from text config
+        if not self.tie_word_embeddings and self.text_config.tie_word_embeddings:
+            self.tie_word_embeddings = self.text_config.tie_word_embeddings
+
         super().__post_init__(**kwargs)
 
 
@@ -458,12 +461,12 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
         return hidden_states
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLLaMA3 outputs, with hidden states and attentions.
     """
 )
+@dataclass
 class VideoLlama3ModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -643,12 +646,12 @@ def forward(
         )
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLLaMA3 causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class VideoLlama3CausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -1018,94 +1021,54 @@ class VideoLlama3ProcessorKwargs(Qwen2VLProcessorKwargs):
     }
 
 
-class VideoLlama3Processor(Qwen2VLProcessor):
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[VideoLlama3ProcessorKwargs],
-    ) -> BatchFeature:
-        output_kwargs = self._merge_kwargs(
-            VideoLlama3ProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
+class VideoLlama3Processor(Qwen3VLProcessor):
+    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
+        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
+        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
+        self.image_token_id = (
+            tokenizer.image_token_id
+            if getattr(tokenizer, "image_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.image_token)
         )
+        self.video_token_id = (
+            tokenizer.video_token_id
+            if getattr(tokenizer, "video_token_id", None)
+            else tokenizer.convert_tokens_to_ids(self.video_token)
+        )
+        ProcessorMixin.__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
 
-        image_inputs = videos_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-            image_merge_sizes = image_inputs["image_merge_sizes"]
-        else:
-            image_grid_thw = image_merge_sizes = []
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            num_video_tokens = [
-                grid_thw.prod() // merge_size**2
-                for grid_thw, merge_size in zip(videos_inputs["video_grid_thw"], videos_inputs["video_merge_sizes"])
-            ]
-            video_compression_masks = videos_inputs["video_compression_mask"].split(num_video_tokens)
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-            timestamps = []
-            for metadata in video_metadata:
-                if metadata.fps is None:
-                    logger.warning_once(
-                        "VideoLLaMA4 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                        "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                        "Defaulting to `fps=1`. Please provide `video_metadata` for more accurate results."
-                    )
-                metadata.fps = 1 if metadata.fps is None else metadata.fps
-                timestamps.append(metadata.timestamps)
-        else:
-            video_compression_masks = timestamps = []
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-
-        if images is not None:
-            image_index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[image_index].prod() // (image_merge_sizes[image_index] ** 2)
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    image_index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if videos is not None:
-            video_index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    frame_compression_masks = video_compression_masks[video_index].split(
-                        len(video_compression_masks[video_index]) // len(timestamps[video_index])
-                    )
-                    num_frame_tokens = [x.sum() for x in frame_compression_masks]
-                    frame_prompts = [
-                        f"Time {t:.1f}s:" + "<|placeholder|>" * n
-                        for n, t in zip(num_frame_tokens, timestamps[video_index])
-                    ]
-                    text[i] = text[i].replace(self.video_token, ",".join(frame_prompts), 1)
-                    video_index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        num_video_tokens = [
+            grid_thw.prod() // merge_size**2
+            for grid_thw, merge_size in zip(video_inputs["video_grid_thw"], video_inputs["video_merge_sizes"])
+        ]
+        video_compression_masks = video_inputs["video_compression_mask"].split(num_video_tokens)
+        metadata = video_inputs["video_metadata"][video_idx]
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "VideoLLaMA3 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=1`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 1 if metadata.fps is None else metadata.fps
 
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+        frame_compression_masks = video_compression_masks[video_idx].split(
+            len(video_compression_masks[video_idx]) // len(metadata.timestamps)
+        )
+        num_frame_tokens = [x.sum() for x in frame_compression_masks]
+        video_placeholder = [
+            f"Time {t:.1f}s:" + self.video_token * n for n, t in zip(num_frame_tokens, metadata.timestamps)
+        ]
 
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+        return ",".join(video_placeholder)
 
     def model_input_names(self):
         raise AttributeError("VideoLlama doesn't need to override it")
 
+    def _calculate_timestamps(self):
+        raise AttributeError("VideoLlama doesn't need this method")
+
 
 class VideoLlama3ImageProcessorKwargs(Qwen2VLImageProcessorKwargs):
     pass
diff --git a/src/transformers/models/video_llama_3/processing_video_llama_3.py b/src/transformers/models/video_llama_3/processing_video_llama_3.py
index 7916d7e41d8e..f3f4baf76b87 100644
--- a/src/transformers/models/video_llama_3/processing_video_llama_3.py
+++ b/src/transformers/models/video_llama_3/processing_video_llama_3.py
@@ -17,18 +17,16 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from ...feature_extraction_utils import BatchFeature
-from ...image_utils import ImageInput
-from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
-from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring, logging
-from ...video_utils import VideoInput
+from .image_processing_video_llama_3 import VideoLlama3ImageProcessorKwargs
 
 
 logger = logging.get_logger(__name__)
 
 
 class VideoLlama3ProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: VideoLlama3ImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "padding": False,
@@ -40,6 +38,8 @@ class VideoLlama3ProcessorKwargs(ProcessingKwargs, total=False):
 
 @auto_docstring
 class VideoLlama3Processor(ProcessorMixin):
+    valid_processor_kwargs = VideoLlama3ProcessorKwargs
+
     def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
         self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
         self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
@@ -55,103 +55,36 @@ def __init__(self, image_processor=None, tokenizer=None, video_processor=None, c
         )
         super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
 
-    @auto_docstring
-    def __call__(
-        self,
-        images: ImageInput = None,
-        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
-        videos: VideoInput = None,
-        **kwargs: Unpack[VideoLlama3ProcessorKwargs],
-    ) -> BatchFeature:
-        r"""
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] with the following fields:
-
-            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
-            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
-              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
-              `None`).
-            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
-            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
-            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
-            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
-        """
-        output_kwargs = self._merge_kwargs(
-            VideoLlama3ProcessorKwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
-            **kwargs,
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        merge_length = self.image_processor.merge_size**2
+        num_image_tokens = image_inputs["image_grid_thw"][image_idx].prod() // merge_length
+        return self.image_token * num_image_tokens
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        num_video_tokens = [
+            grid_thw.prod() // merge_size**2
+            for grid_thw, merge_size in zip(video_inputs["video_grid_thw"], video_inputs["video_merge_sizes"])
+        ]
+        video_compression_masks = video_inputs["video_compression_mask"].split(num_video_tokens)
+        metadata = video_inputs["video_metadata"][video_idx]
+
+        if metadata.fps is None:
+            logger.warning_once(
+                "VideoLLaMA3 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
+                "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
+                "Defaulting to `fps=1`. Please provide `video_metadata` for more accurate results."
+            )
+        metadata.fps = 1 if metadata.fps is None else metadata.fps
+
+        frame_compression_masks = video_compression_masks[video_idx].split(
+            len(video_compression_masks[video_idx]) // len(metadata.timestamps)
         )
+        num_frame_tokens = [x.sum() for x in frame_compression_masks]
+        video_placeholder = [
+            f"Time {t:.1f}s:" + self.video_token * n for n, t in zip(num_frame_tokens, metadata.timestamps)
+        ]
 
-        image_inputs = videos_inputs = {}
-        if images is not None:
-            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
-            image_grid_thw = image_inputs["image_grid_thw"]
-            image_merge_sizes = image_inputs["image_merge_sizes"]
-        else:
-            image_grid_thw = image_merge_sizes = []
-
-        if videos is not None:
-            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"])
-            num_video_tokens = [
-                grid_thw.prod() // merge_size**2
-                for grid_thw, merge_size in zip(videos_inputs["video_grid_thw"], videos_inputs["video_merge_sizes"])
-            ]
-            video_compression_masks = videos_inputs["video_compression_mask"].split(num_video_tokens)
-            if not kwargs.get("return_metadata"):
-                video_metadata = videos_inputs.pop("video_metadata")
-            else:
-                video_metadata = videos_inputs["video_metadata"]
-            timestamps = []
-            for metadata in video_metadata:
-                if metadata.fps is None:
-                    logger.warning_once(
-                        "VideoLLaMA4 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
-                        "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
-                        "Defaulting to `fps=1`. Please provide `video_metadata` for more accurate results."
-                    )
-                metadata.fps = 1 if metadata.fps is None else metadata.fps
-                timestamps.append(metadata.timestamps)
-        else:
-            video_compression_masks = timestamps = []
-
-        if not isinstance(text, list):
-            text = [text]
-
-        text = text.copy()  # below lines change text in-place
-
-        if images is not None:
-            image_index = 0
-            for i in range(len(text)):
-                while self.image_token in text[i]:
-                    num_image_tokens = image_grid_thw[image_index].prod() // (image_merge_sizes[image_index] ** 2)
-                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1)
-                    image_index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.image_token)
-
-        if videos is not None:
-            video_index = 0
-            for i in range(len(text)):
-                while self.video_token in text[i]:
-                    frame_compression_masks = video_compression_masks[video_index].split(
-                        len(video_compression_masks[video_index]) // len(timestamps[video_index])
-                    )
-                    num_frame_tokens = [x.sum() for x in frame_compression_masks]
-                    frame_prompts = [
-                        f"Time {t:.1f}s:" + "<|placeholder|>" * n
-                        for n, t in zip(num_frame_tokens, timestamps[video_index])
-                    ]
-                    text[i] = text[i].replace(self.video_token, ",".join(frame_prompts), 1)
-                    video_index += 1
-                text[i] = text[i].replace("<|placeholder|>", self.video_token)
-
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
-
-        if return_mm_token_type_ids:
-            text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
-        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
+        return ",".join(video_placeholder)
 
     def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
         """
diff --git a/src/transformers/models/video_llava/modeling_video_llava.py b/src/transformers/models/video_llava/modeling_video_llava.py
index 102ac455a47d..f6891c5204da 100644
--- a/src/transformers/models/video_llava/modeling_video_llava.py
+++ b/src/transformers/models/video_llava/modeling_video_llava.py
@@ -35,12 +35,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLlava base model outputs.
     """
 )
+@dataclass
 class VideoLlavaModelOutputWithPast(ModelOutput):
     r"""
     past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
@@ -64,12 +64,12 @@ class VideoLlavaModelOutputWithPast(ModelOutput):
     video_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VideoLlava causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class VideoLlavaCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -285,18 +285,18 @@ def get_placeholder_mask(
             special_video_mask = input_ids == self.config.video_token_id
 
         n_image_tokens = special_image_mask.sum()
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         if image_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_image_mask].numel() == image_features.numel(),
+                n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
                 f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {image_features.shape[0] * image_features.shape[1]}",
             )
 
         n_video_tokens = special_video_mask.sum()
-        special_video_mask = special_video_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_video_mask = special_video_mask.unsqueeze(-1).to(inputs_embeds.device)
         if video_features is not None:
             torch_compilable_check(
-                inputs_embeds[special_video_mask].numel() == video_features.numel(),
+                n_video_tokens * inputs_embeds.shape[-1] == video_features.numel(),
                 f"Video features and video tokens do not match, tokens: {n_video_tokens}, features: {video_features.shape[0] * video_features.shape[1]}",
             )
         return special_image_mask, special_video_mask
diff --git a/src/transformers/models/videomae/modeling_videomae.py b/src/transformers/models/videomae/modeling_videomae.py
index b86d0f10fbb7..7adf3dcb9c38 100755
--- a/src/transformers/models/videomae/modeling_videomae.py
+++ b/src/transformers/models/videomae/modeling_videomae.py
@@ -38,12 +38,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for VideoMAEDecoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class VideoMAEDecoderOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`):
@@ -55,12 +55,12 @@ class VideoMAEDecoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class VideoMAEForPreTrainingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/videomt/convert_videomt_to_hf.py b/src/transformers/models/videomt/convert_videomt_to_hf.py
index ab717083b8fa..e1bda7db8fa9 100644
--- a/src/transformers/models/videomt/convert_videomt_to_hf.py
+++ b/src/transformers/models/videomt/convert_videomt_to_hf.py
@@ -37,27 +37,31 @@
 import sys
 import tempfile
 import types
+from io import BytesIO
 from pathlib import Path
 
 import torch
 import torch.nn as nn
-from huggingface_hub import hf_hub_download
+from huggingface_hub import HfApi, hf_hub_download
 
-from transformers import VideomtConfig, VideomtForUniversalSegmentation
+from transformers import VideomtConfig, VideomtForUniversalSegmentation, VideomtVideoProcessor
 
 
 MODEL_REPO_ID = "tue-mps/VidEoMT"
+MODEL_ZOO_URL = "https://github.com/tue-mps/videomt/blob/master/model_zoo/dinov2.md"
+PAPER_URL = "https://huggingface.co/papers/2602.17807"
+DEFAULT_HUB_NAMESPACE = "tue-mps"
 
 # fmt: off
 CHECKPOINT_CONFIGS = {
-    "yt_2019_vit_small_52.8.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-small-ytvis2019", "dataset": "ytvis_2019"},
-    "yt_2019_vit_base_58.2.pth":    {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-base-ytvis2019",  "dataset": "ytvis_2019"},
-    "yt_2019_vit_large_68.6.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2019", "dataset": "ytvis_2019"},
-    "yt_2021_vit_large_63.1.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2021", "dataset": "ytvis_2021"},
-    "yt_2022_vit_large_42.6.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2022", "dataset": "ytvis_2021"},
-    "ovis_vit_large_52.5.pth":      {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ovis",      "dataset": "ovis"},
-    "vipseg_vit_large_55.2.pth":    {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-vipseg",    "dataset": "vipseg"},
-    "vspw_vit_large_95.0_64.9.pth": {"image_size": 1280, "num_frames": 2, "hub_name": "videomt-dinov2-large-vspw",      "dataset": "vipseg"},
+    "yt_2019_vit_small_52.8.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-small-ytvis2019", "dataset": "ytvis_2019", "dataset_name": "YouTube-VIS 2019", "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-S", "metrics": {"AP": "52.8", "AR@10": "62.2", "FPS": "294"}},
+    "yt_2019_vit_base_58.2.pth":    {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-base-ytvis2019",  "dataset": "ytvis_2019", "dataset_name": "YouTube-VIS 2019", "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-B", "metrics": {"AP": "58.2", "AR@10": "66.5", "FPS": "251"}},
+    "yt_2019_vit_large_68.6.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2019", "dataset": "ytvis_2019", "dataset_name": "YouTube-VIS 2019", "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-L", "metrics": {"AP": "68.6", "AR@10": "73.9", "FPS": "160"}},
+    "yt_2021_vit_large_63.1.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2021", "dataset": "ytvis_2021", "dataset_name": "YouTube-VIS 2021", "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-L", "metrics": {"AP": "63.1", "AR@10": "68.1", "FPS": "160"}},
+    "yt_2022_vit_large_42.6.pth":   {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ytvis2022", "dataset": "ytvis_2021", "dataset_name": "YouTube-VIS 2022", "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-L", "metrics": {"AP^L": "42.6", "AR^L@10": "48.1", "FPS": "161"}},
+    "ovis_vit_large_52.5.pth":      {"image_size": 640,  "num_frames": 2, "hub_name": "videomt-dinov2-large-ovis",      "dataset": "ovis",       "dataset_name": "OVIS",             "task": "video instance segmentation", "task_tag": "video-instance-segmentation", "variant": "VidEoMT-L", "metrics": {"AP": "52.5", "AR@10": "57.5", "FPS": "115"}},
+    "vipseg_vit_large_55.2.pth":    {"image_size": 1280, "num_frames": 2, "hub_name": "videomt-dinov2-large-vipseg",    "dataset": "vipseg",     "dataset_name": "VIPSeg",           "task": "video panoptic segmentation", "task_tag": "video-panoptic-segmentation", "variant": "VidEoMT-L", "metrics": {"VPQ": "55.2", "STQ": "48.9", "FPS": "75"}},
+    "vspw_vit_large_95.0_64.9.pth": {"image_size": 1280, "num_frames": 2, "hub_name": "videomt-dinov2-large-vspw",      "dataset": "vipseg",     "dataset_name": "VSPW",             "task": "video semantic segmentation", "task_tag": "video-semantic-segmentation", "variant": "VidEoMT-L", "metrics": {"mVC_16": "95.0", "mIoU": "64.9", "FPS": "73"}},
 }
 
 YTVIS_2019_ID2LABEL = {
@@ -179,6 +183,79 @@ def infer_backbone_model_name(checkpoint_filename: str) -> str:
     raise ValueError(f"Could not infer timm backbone model from checkpoint name '{checkpoint_filename}'.")
 
 
+def infer_backbone_display_name(checkpoint_filename: str) -> str:
+    if "vit_small" in checkpoint_filename:
+        return "DINOv2 ViT-S/14 with 4 register tokens"
+    if "vit_base" in checkpoint_filename:
+        return "DINOv2 ViT-B/14 with 4 register tokens"
+    if "vit_large" in checkpoint_filename:
+        return "DINOv2 ViT-L/14 with 4 register tokens"
+    raise ValueError(f"Could not infer backbone display name from checkpoint name '{checkpoint_filename}'.")
+
+
+def resolve_hub_repo_id(checkpoint_filename: str, hub_namespace: str | None) -> str:
+    hub_name = CHECKPOINT_CONFIGS[checkpoint_filename]["hub_name"]
+    if "/" in hub_name or hub_namespace is None:
+        return hub_name
+    return f"{hub_namespace}/{hub_name}"
+
+
+def build_model_card(checkpoint_filename: str, hub_repo_id: str, image_size: int, num_frames: int) -> str:
+    checkpoint_config = CHECKPOINT_CONFIGS[checkpoint_filename]
+    metric_rows = "\n".join(
+        f"| {metric_name} | {metric_value} |" for metric_name, metric_value in checkpoint_config["metrics"].items()
+    )
+    backbone_model_name = infer_backbone_display_name(checkpoint_filename)
+
+    return f"""---
+library_name: transformers
+pipeline_tag: image-segmentation
+tags:
+- transformers
+- videomt
+- video-segmentation
+- {checkpoint_config["task_tag"]}
+- dinov2
+---
+
+# {checkpoint_config["variant"]} on {checkpoint_config["dataset_name"]}
+
+This repository contains the Hugging Face Transformers conversion of the official VidEoMT checkpoint
+`{checkpoint_filename}` from [tue-mps/VidEoMT]({MODEL_ZOO_URL}).
+
+## Model details
+
+- Architecture: VidEoMT with a {backbone_model_name} backbone
+- Task: {checkpoint_config["task"]}
+- Dataset: {checkpoint_config["dataset_name"]}
+- Input resolution: {image_size} x {image_size}
+- Number of frames: {num_frames}
+- Paper: [Your ViT is Secretly Also a Video Segmentation Model]({PAPER_URL})
+
+## Reported metrics
+
+| Metric | Value |
+| --- | --- |
+{metric_rows}
+
+The metrics above are the numbers reported by the authors in the [official model zoo]({MODEL_ZOO_URL}).
+
+## Usage
+
+```python
+from transformers import AutoModelForUniversalSegmentation, AutoVideoProcessor
+
+model_id = "{hub_repo_id}"
+processor = AutoVideoProcessor.from_pretrained(model_id)
+model = AutoModelForUniversalSegmentation.from_pretrained(model_id)
+```
+
+Use `processor.post_process_instance_segmentation`,
+`processor.post_process_panoptic_segmentation`, or
+`processor.post_process_semantic_segmentation` depending on the target task.
+"""
+
+
 def _build_reference_load_dict(
     original_state_dict: dict[str, torch.Tensor], reference_state_dict: dict[str, torch.Tensor]
 ) -> tuple[dict[str, torch.Tensor], list[str]]:
@@ -375,6 +452,7 @@ def convert_checkpoint(
     verify: bool = False,
     reference_repo_path: str | None = None,
     push_to_hub: bool = False,
+    hub_namespace: str | None = DEFAULT_HUB_NAMESPACE,
 ) -> None:
     checkpoint_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=checkpoint_filename)
     checkpoint = torch.load(checkpoint_path, map_location="cpu")
@@ -396,6 +474,7 @@ def convert_checkpoint(
         config.label2id = {v: k for k, v in id2label.items()}
 
     model = VideomtForUniversalSegmentation(config)
+    processor = VideomtVideoProcessor(size={"height": image_size, "width": image_size})
     converted_state_dict, consumed_keys = convert_state_dict(original_state_dict)
 
     load_info = model.load_state_dict(converted_state_dict, strict=False)
@@ -437,9 +516,15 @@ def convert_checkpoint(
         if query_updater_keys:
             print("note=unconverted_query_updater_keys_detected; temporal-frame forward parity may differ")
 
+    hub_repo_id = resolve_hub_repo_id(checkpoint_filename, hub_namespace)
+    model_card = build_model_card(checkpoint_filename, hub_repo_id, image_size=image_size, num_frames=num_frames)
+
     if output_dir is not None:
+        output_dir_path = Path(output_dir)
+        output_dir_path.mkdir(parents=True, exist_ok=True)
         model.save_pretrained(output_dir)
-        config.save_pretrained(output_dir)
+        processor.save_pretrained(output_dir)
+        output_dir_path.joinpath("README.md").write_text(model_card)
         print(f"saved_to={output_dir}")
 
     if push_to_hub:
@@ -448,9 +533,17 @@ def convert_checkpoint(
             raise ValueError(
                 f"Cannot push to Hub: checkpoint '{checkpoint_filename}' has no entry in CHECKPOINT_CONFIGS."
             )
-        hub_name = ckpt_cfg["hub_name"]
-        model.push_to_hub(hub_name)
-        print(f"pushed_to_hub={hub_name}")
+        api = HfApi()
+        api.create_repo(repo_id=hub_repo_id, repo_type="model", exist_ok=True)
+        model.push_to_hub(hub_repo_id)
+        processor.push_to_hub(hub_repo_id)
+        api.upload_file(
+            repo_id=hub_repo_id,
+            repo_type="model",
+            path_or_fileobj=BytesIO(model_card.encode("utf-8")),
+            path_in_repo="README.md",
+        )
+        print(f"pushed_to_hub={hub_repo_id}")
 
     if verify:
         verify_ok = verify_conversion_against_github_reference(
@@ -594,6 +687,12 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--reference-repo-path", type=str, default=None)
     parser.add_argument("--all", action="store_true", help="Convert all supported DINOv2 checkpoints")
     parser.add_argument("--push-to-hub", action="store_true", help="Push converted models to the Hugging Face Hub")
+    parser.add_argument(
+        "--hub-namespace",
+        type=str,
+        default=DEFAULT_HUB_NAMESPACE,
+        help="Namespace used when `hub_name` is not already fully qualified",
+    )
     args = parser.parse_args()
 
     if not args.all and args.checkpoint_filename is None:
@@ -623,6 +722,7 @@ def main() -> None:
             verify=args.verify,
             reference_repo_path=args.reference_repo_path,
             push_to_hub=args.push_to_hub,
+            hub_namespace=args.hub_namespace,
         )
 
 
diff --git a/src/transformers/models/videoprism/__init__.py b/src/transformers/models/videoprism/__init__.py
new file mode 100644
index 000000000000..4360a00e206d
--- /dev/null
+++ b/src/transformers/models/videoprism/__init__.py
@@ -0,0 +1,30 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_videoprism import *
+    from .modeling_videoprism import *
+    from .processing_videoprism import *
+    from .tokenization_videoprism import *
+    from .video_processing_videoprism import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)
diff --git a/src/transformers/models/videoprism/configuration_videoprism.py b/src/transformers/models/videoprism/configuration_videoprism.py
new file mode 100644
index 000000000000..293773da836d
--- /dev/null
+++ b/src/transformers/models/videoprism/configuration_videoprism.py
@@ -0,0 +1,162 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/videoprism/modular_videoprism.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_videoprism.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from huggingface_hub.dataclasses import strict
+
+from ...configuration_utils import PreTrainedConfig
+from ...utils import auto_docstring, logging
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="google/videoprism-base-f16r288")
+@strict
+class VideoPrismVisionConfig(PreTrainedConfig):
+    r"""
+    num_frames (`int`, *optional*, defaults to 16):
+        The number of frames in the input video.
+    tubelet_size (`List[int]`, *optional*, defaults to `[1, 18, 18]`):
+        The size of the tubelet patch.
+    num_spatial_layers (`int`, *optional*, defaults to 12):
+        Number of spatial transformer blocks.
+    num_temporal_layers (`int`, *optional*, defaults to 4):
+        Number of temporal transformer blocks.
+    attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
+        Softcapping constant for attention logits.
+    num_auxiliary_layers (`int`, *optional*, defaults to 2):
+        Number of auxiliary layers. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel.
+    apply_l2norm (`bool`, *optional*, defaults to `True`):
+        Whether to apply L2 normalization to the output. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel.
+    """
+
+    model_type = "videoprism_vision_model"
+
+    image_size: int | list[int] | tuple[int, int] = 288
+    num_frames: int = 16
+    tubelet_size: list[int] | tuple[int, ...] = (1, 18, 18)
+    num_channels: int = 3
+    hidden_size: int = 768
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    intermediate_size: int = 3072
+    hidden_act: str = "gelu_python"
+    hidden_dropout_prob: float = 0.0
+    attention_probs_dropout_prob: float = 0.0
+    initializer_range: float = 0.02
+    layer_norm_eps: float = 1e-06
+    qkv_bias: bool = True
+    base_config_key = "vision_config"
+    num_spatial_layers: int = 12
+    num_temporal_layers: int = 4
+    attn_logit_softcapping: float = 50.0
+    num_auxiliary_layers: int = 2
+    apply_l2norm: bool = True
+
+
+@auto_docstring(checkpoint="google/videoprism-lvt-base-f16r288")
+@strict
+class VideoPrismTextConfig(PreTrainedConfig):
+    r"""
+    apply_l2norm (`bool`, *optional*, defaults to `True`):
+        Whether to apply L2 normalization to the output of VideoPrismTextEncoder.
+    attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
+        Softcapping constant for attention logits.
+    """
+
+    model_type = "videoprism_text_model"
+    base_config_key = "text_config"
+
+    vocab_size: int = 32000
+    hidden_size: int = 768
+    intermediate_size: int = 3072
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    max_position_embeddings: int = 64
+    hidden_act: str = "relu"
+    layer_norm_eps: float = 1e-6
+    # This differs from `CLIPTokenizer`'s default and from openai/videoprism
+    # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538
+    pad_token_id: int | None = 1
+    bos_token_id: int | None = 49406
+    eos_token_id: int | list[int] | None = 49407
+    attention_probs_dropout_prob: float | int = 0.0
+    apply_l2norm: bool = True
+    qkv_bias: bool = True
+    hidden_dropout_prob: float = 0.0
+    initializer_range: float = 0.02
+    attn_logit_softcapping: float = 50.0
+
+
+@auto_docstring(
+    checkpoint="google/videoprism-lvt-base-f16r288",
+    custom_intro="""
+    This is the configuration class to store the configuration of a [`VideoPrismClipModel`]. It is used to instantiate a
+    VideoPrismClipModel according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the VideoPrism
+    [google/videoprism-lvt-base-f16r288](https://huggingface.co/google/videoprism-lvt-base-f16r288) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    """,
+)
+@strict
+class VideoPrismConfig(PreTrainedConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import VideoPrismClipModel, VideoPrismConfig
+
+    >>> # Initializing a VideoPrismConfig with default values
+    >>> configuration = VideoPrismConfig()
+
+    >>> # Initializing a VideoPrismClipModel with the configuration
+    >>> model = VideoPrismClipModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    model_type = "videoprism"
+    sub_configs = {"text_config": VideoPrismTextConfig, "vision_config": VideoPrismVisionConfig}
+
+    text_config: dict | PreTrainedConfig | None = None
+    vision_config: dict | PreTrainedConfig | None = None
+
+    def __post_init__(self, **kwargs):
+        if self.text_config is None:
+            self.text_config = VideoPrismTextConfig()
+            logger.info("`text_config` is `None`. Initializing the `VideoPrismTextConfig` with default values.")
+        elif isinstance(self.text_config, dict):
+            self.text_config = VideoPrismTextConfig(**self.text_config)
+
+        if self.vision_config is None:
+            self.vision_config = VideoPrismVisionConfig()
+            logger.info("`vision_config` is `None`. initializing the `VideoPrismVisionConfig` with default values.")
+        elif isinstance(self.vision_config, dict):
+            self.vision_config = VideoPrismVisionConfig(**self.vision_config)
+
+        super().__post_init__(**kwargs)
+
+
+__all__ = ["VideoPrismVisionConfig", "VideoPrismTextConfig", "VideoPrismConfig"]
diff --git a/src/transformers/models/videoprism/convert_videoprism_weights_to_hf.py b/src/transformers/models/videoprism/convert_videoprism_weights_to_hf.py
new file mode 100644
index 000000000000..2b857ea157a1
--- /dev/null
+++ b/src/transformers/models/videoprism/convert_videoprism_weights_to_hf.py
@@ -0,0 +1,584 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import os
+import re
+
+import mediapy
+import numpy as np
+import torch
+from huggingface_hub import hf_hub_download
+from safetensors.torch import load_file, save_file
+
+from transformers import (
+    AutoModel,
+    AutoTokenizer,
+    VideoPrismConfig,
+    VideoPrismTextConfig,
+    VideoPrismVisionConfig,
+)
+from transformers.models.codegen.modeling_codegen import create_sinusoidal_positions
+from transformers.models.videoprism.modeling_videoprism import VideoPrismClipModel, VideoPrismVisionModel
+
+
+torch.set_printoptions(precision=10)
+
+# backbone refers to VideoPrismVisionModel, lvt (original name) refers to VideoPrismClipModel
+COMMON_CONFIG_PARAMS = {
+    "backbone_base": {
+        "hidden_size": 768,
+        "intermediate_size": 3072,
+        "num_attention_heads": 12,
+        "num_frames": 16,
+        "num_spatial_layers": 12,
+        "num_temporal_layers": 4,
+    },
+    "backbone_large": {
+        "hidden_size": 1024,
+        "intermediate_size": 4096,
+        "num_attention_heads": 16,
+        "num_frames": 8,
+        "num_spatial_layers": 24,
+        "num_temporal_layers": 4,
+    },
+    "lvt_base": {
+        "vision_config": {
+            "hidden_size": 768,
+            "intermediate_size": 3072,
+            "num_attention_heads": 12,
+            "num_frames": 16,
+            "num_spatial_layers": 12,
+            "num_temporal_layers": 4,
+            "num_auxiliary_layers": 2,
+        },
+        "text_config": {
+            "hidden_size": 768,
+            "intermediate_size": 3072,
+            "num_attention_heads": 12,
+            "num_text_layers": 12,
+        },
+    },
+    "lvt_large": {
+        "vision_config": {
+            "hidden_size": 1024,
+            "intermediate_size": 4096,
+            "num_attention_heads": 16,
+            "num_frames": 8,
+            "num_spatial_layers": 24,
+            "num_temporal_layers": 4,
+            "num_auxiliary_layers": 2,
+        },
+        "text_config": {
+            "hidden_size": 1024,
+            "intermediate_size": 4096,
+            "num_attention_heads": 16,
+            "num_text_layers": 12,
+        },
+    },
+}
+
+SENTENCES = [
+    [262, 266, 768, 267, 1376, 14293, 259],
+    [262, 266, 768, 267, 2865, 259],
+    [262, 266, 768, 267, 1376, 20682, 259],
+    [262, 266, 768, 267, 1376, 289, 10691, 259],
+    [262, 266, 768, 267, 4605, 259],
+]
+
+ORIGINAL_CHECKPOINTS = {
+    "backbone_base": {
+        "repo_id": "google/videoprism-base-f16r288",
+        "filename": "flax_base_f16r288_repeated.npz",
+        "new_checkpoint_name": "videoprism-base-f16r288",
+    },
+    "backbone_large": {
+        "repo_id": "google/videoprism-large-f8r288",
+        "filename": "flax_large_f8r288_repeated.npz",
+        "new_checkpoint_name": "videoprism-large-f8r288",
+    },
+    "lvt_base": {
+        "repo_id": "google/videoprism-lvt-base-f16r288",
+        "filename": "flax_lvt_base_f16r288_repeated.npz",
+        "new_checkpoint_name": "videoprism-lvt-base-f16r288",
+    },
+    "lvt_large": {
+        "repo_id": "google/videoprism-lvt-large-f8r288",
+        "filename": "flax_lvt_large_f8r288_repeated.npz",
+        "new_checkpoint_name": "videoprism-lvt-large-f8r288",
+    },
+}
+
+EXPECTED_OUTPUTS = {
+    "backbone_base": torch.tensor(
+        [
+            [0.11648951, 0.4568253, 0.19288044],
+            [0.28420594, -0.04224018, 0.377879],
+            [0.24594213, -0.3914095, -0.30516925],
+        ]
+    ),
+    "backbone_large": torch.tensor(
+        [
+            [0.39503154, 0.07308281, 0.21407786],
+            [0.4963156, -0.02489206, 0.49198192],
+            [-0.41461205, 0.24869855, 0.25285226],
+        ]
+    ),
+    "lvt_base": {
+        "vision": torch.tensor(
+            [
+                -0.01940615,
+                -0.04830061,
+                0.0069022,
+                0.02915299,
+                -0.05897291,
+                0.02168823,
+                -0.01471708,
+                -0.00971614,
+                -0.00220576,
+            ]
+        ),
+        "text": torch.tensor(
+            [
+                [-0.00802545, 0.00931361, 0.01555958],
+                [0.02245245, 0.00010197, -0.01073526],
+                [-0.02258418, 0.00133927, -0.01555064],
+                [0.01056228, 0.01835608, -0.01539922],
+                [-0.00366718, 0.00370416, 0.00800336],
+            ]
+        ),
+    },
+    "lvt_large": {
+        "vision": torch.tensor(
+            [
+                -0.00077759,
+                0.00582959,
+                -0.00158949,
+                0.04192347,
+                -0.01581791,
+                0.02410023,
+                -0.00364033,
+                -0.02118852,
+                0.00181754,
+            ]
+        ),
+        "text": torch.tensor(
+            [
+                [0.00454123, -0.02623128, -0.00612541],
+                [-0.00042687, -0.0018771, 0.01664249],
+                [0.02318677, -0.02984732, 0.00270805],
+                [-0.02054974, 0.00793169, 0.00964476],
+                [-0.00214194, -0.02825877, 0.01981462],
+            ]
+        ),
+    },
+}
+
+ORIGINAL_TO_CONVERTED_KEY_MAPPING = {
+    # Vision Encoder
+    r"params(/vision_encoder)?/patch_projection/linear/(bias|kernel)": r"video_model.vision_encoder.spatial_embeddings.patch_embeddings.projection.\2",
+    r"params(/vision_encoder)?/(spatial|temporal)_pos_emb/emb_var": r"video_model.vision_encoder.\2_embeddings.position_embeddings",
+    r"params(/vision_encoder)?/(spatial|temporal)_encoder/transformers_stack/x_layers/ff_layer/ffn_layer1/linear/(bias|kernel)": r"video_model.vision_encoder.\2_encoder.layer.intermediate.dense.\3",
+    r"params(/vision_encoder)?/(spatial|temporal)_encoder/transformers_stack/x_layers/ff_layer/ffn_layer2/linear/(bias|kernel)": r"video_model.vision_encoder.\2_encoder.layer.output.dense.\3",
+    r"params(/vision_encoder)?/(spatial|temporal)_encoder/transformers_stack/x_layers/ff_layer/layer_norm/(bias|scale)": r"video_model.vision_encoder.\2_encoder.layer.layernorm_after.\3",
+    r"params(/vision_encoder)?/(spatial|temporal)_encoder/transformers_stack/x_layers/layer_norm/(bias|scale)": r"video_model.vision_encoder.\2_encoder.layer.layernorm_before.\3",
+    r"params(/vision_encoder)?/(spatial|temporal)_encoder/transformers_stack/x_layers/self_attention/(key|post|query|value)/(b|w)": r"video_model.vision_encoder.\2_encoder.layer.attention.attention.\3.\4",
+    r"params(/vision_encoder)?/(spatial|temporal)_ln/(bias|scale)": r"video_model.vision_encoder.layernorm\2.\3",
+    # Auxiliary Encoder
+    r"params/auxiliary_encoder/transformers_stack/x_layers/ff_layer/ffn_layer1/linear/(bias|kernel)": r"video_model.auxiliary_encoder.layer.intermediate.dense.\1",
+    r"params/auxiliary_encoder/transformers_stack/x_layers/ff_layer/layer_norm/(bias|scale)": r"video_model.auxiliary_encoder.layer.layernorm_after.\1",
+    r"params/auxiliary_encoder/transformers_stack/x_layers/layer_norm/(bias|scale)": r"video_model.auxiliary_encoder.layer.layernorm_before.\1",
+    r"params/auxiliary_encoder/transformers_stack/x_layers/self_attention/(key|post|query|value)/(b|w)": r"video_model.auxiliary_encoder.layer.attention.attention.\1.\2",
+    r"params/auxiliary_encoder/transformers_stack/x_layers/ff_layer/ffn_layer2/linear/(bias|kernel)": r"video_model.auxiliary_encoder.layer.output.dense.\1",
+    # Attention Pooler
+    r"params/contrastive_vision_pooler/pooling_attention/(query|key|value|post)/(b|w)": r"video_model.contrastive_vision_pooler.\1.\2",
+    r"params/contrastive_vision_pooler/pooling_attention/per_dim_scale/per_dim_scale": r"video_model.contrastive_vision_pooler.per_dim_scale",
+    r"params/contrastive_vision_pooler/pooling_attention_layer_norm/(bias|scale)": r"video_model.contrastive_vision_pooler.layernorm.\1",
+    r"params/contrastive_vision_pooler/pooling_attention_query": r"video_model.contrastive_vision_pooler.pooling_attention_query",
+    # Text Encoder
+    r"params/text_encoder/cls_emb": r"text_model.cls_emb",
+    r"params/text_encoder/token_emb/emb_var": r"text_model.embeddings.token_embedding.weight",
+    r"params/text_encoder/unimodal_ln/(bias|scale)": r"text_model.layernorm.\1",
+    r"params/text_encoder/unimodal_transformer/x_layers/ff_layer/ffn_layer1/linear/(bias|kernel)": r"text_model.text_encoder.layer.intermediate.dense.\1",
+    r"params/text_encoder/unimodal_transformer/x_layers/ff_layer/ffn_layer2/linear/(bias|kernel)": r"text_model.text_encoder.layer.output.dense.\1",
+    r"params/text_encoder/unimodal_transformer/x_layers/ff_layer/layer_norm/(bias|scale)": r"text_model.text_encoder.layer.layernorm_after.\1",
+    r"params/text_encoder/unimodal_transformer/x_layers/layer_norm/(bias|scale)": r"text_model.text_encoder.layer.layernorm_before.\1",
+    r"params/text_encoder/unimodal_transformer/x_layers/self_attention/(query|key|value|post)/(b|w)": r"text_model.text_encoder.layer.attention.attention.\1.\2",
+}
+
+
+def download_flax_weights(checkpoint_info):
+    # Download the weights file
+    file = hf_hub_download(repo_id=checkpoint_info["repo_id"], filename=checkpoint_info["filename"])
+    state_dict = np.load(file)
+    return state_dict
+
+
+def transform_block_params(key, param, hidden_size):
+    if re.fullmatch(
+        r"params(/vision_encoder)?/(spatial|temporal|auxiliary|text)_encoder/(transformers_stack|unimodal_transformer)/x_layers/self_attention/(key|query|value)/w",
+        key,
+    ):
+        new_param = param.reshape(hidden_size, -1).T
+
+    elif re.fullmatch(
+        r"params(/vision_encoder)?/(spatial|temporal|auxiliary|text)_encoder/(transformers_stack|unimodal_transformer)/x_layers/self_attention/post/w",
+        key,
+    ):
+        new_param = param.reshape(hidden_size, -1)
+
+    elif re.fullmatch(
+        r"params(/vision_encoder)?/(spatial|temporal|auxiliary|text)_encoder/(transformers_stack|unimodal_transformer)/x_layers/self_attention/(key|post|query|value)/b",
+        key,
+    ):
+        new_param = param.reshape(-1)
+
+    elif re.fullmatch(
+        r"params(/vision_encoder)?/(spatial|temporal|auxiliary|text)_encoder/(transformers_stack|unimodal_transformer)/x_layers/ff_layer/ffn_layer([12])/linear/kernel",
+        key,
+    ):
+        new_param = param.T
+
+    else:
+        new_param = param
+
+    return new_param
+
+
+def transform_remaining_params(key, param, hidden_size):
+    # Vision Encoder specific transformations
+    if re.fullmatch(r"params(/vision_encoder)?/patch_projection/linear/kernel", key):
+        # Hard-coded number of patches
+        new_param = param.T.reshape(hidden_size, 1, 18, 18, 3).transpose(0, 4, 1, 2, 3)
+
+    elif re.fullmatch(r"params(/vision_encoder)?/(spatial|temporal)_pos_emb/emb_var", key):
+        new_param = np.expand_dims(param, 0)
+
+    # Contrastive Vision Pooler specific transformations
+    elif re.fullmatch(r"params/contrastive_vision_pooler/pooling_attention_query", key):
+        new_param = param.reshape(1, 1, -1)
+
+    elif re.fullmatch(r"params/contrastive_vision_pooler/pooling_attention/(query|key|value)/w", key):
+        new_param = param.reshape(hidden_size, -1).T
+
+    elif re.fullmatch(r"params/contrastive_vision_pooler/pooling_attention/post/w", key):
+        new_param = param.reshape(hidden_size, -1)
+
+    elif re.fullmatch(r"params/contrastive_vision_pooler/pooling_attention/(query|key|value|post)/b", key):
+        new_param = param.reshape(-1)
+
+    else:
+        new_param = param
+
+    return new_param
+
+
+def convert_params(flax_state_dict, model_name):
+    # Convert flax parameters to HF-Pytorch format
+    new_state_dict = {}
+    if "lvt" in model_name:
+        vision_config = COMMON_CONFIG_PARAMS[model_name]["vision_config"]
+        hidden_size = vision_config["hidden_size"]
+    else:
+        config = COMMON_CONFIG_PARAMS[model_name]
+        hidden_size = config["hidden_size"]
+
+    for key in flax_state_dict:
+        for original_pattern, new_pattern in ORIGINAL_TO_CONVERTED_KEY_MAPPING.items():
+            if re.fullmatch(original_pattern, key):
+                try:
+                    new_key = re.sub(original_pattern, new_pattern, key)
+                except Exception as e:
+                    print(f"Error processing key: {key}")
+                    raise e
+
+                # Additional substitutions
+                new_key = re.sub(r"\.scale$", ".weight", new_key)
+                new_key = re.sub(r"attention\.post", "output.dense", new_key)
+                new_key = re.sub(r"contrastive_vision_pooler\.post", "contrastive_vision_pooler.projection", new_key)
+                new_key = re.sub(r"\.b$", ".bias", new_key)
+                new_key = re.sub(r"\.w$|\.kernel$", ".weight", new_key)
+                new_key = re.sub(r"layernormspatial", "layernorm1", new_key)
+                new_key = re.sub(r"layernormtemporal", "layernorm2", new_key)
+                new_key = re.sub(r"vision_encoder", "backbone", new_key)
+
+                if "lvt" not in model_name:
+                    new_key = new_key.replace("video_model.backbone.", "")
+
+                param = flax_state_dict[key]
+                if "layer." in new_key and param.ndim > 1:
+                    # Split weights and biases layerwise
+                    for layer in range(param.shape[0]):
+                        layer_key = new_key.replace("layer.", f"layer.{layer}.")
+                        new_param = transform_block_params(key, param[layer], hidden_size)
+                        new_state_dict[layer_key] = torch.tensor(new_param).contiguous()
+
+                else:
+                    # Transformation of non-layerwise parameters
+                    new_param = transform_remaining_params(key, param, hidden_size)
+                    new_state_dict[new_key] = torch.tensor(new_param).contiguous()
+
+    # Last step is to add the buffers named "scale", "positional_embedding" and "position_ids"
+    if "lvt" in model_name:
+        # scale (used inside VideoPrismMultiheadAttentionPoolingHead)
+        # dim is the dimension of a single attention head, which is hidden_size / num_attention_heads
+        dim = int(vision_config["intermediate_size"] / vision_config["num_attention_heads"])
+        r_softplus_0 = 1.442695041
+        scale = torch.tensor(r_softplus_0 / (dim**0.5))
+        new_state_dict["video_model.contrastive_vision_pooler.scale"] = scale
+
+        # positional_embedding
+        text_config = COMMON_CONFIG_PARAMS[model_name]["text_config"]
+        num_pos, dim = 64, text_config["hidden_size"]  # Hardcoded num_pos
+        positional_embedding = create_sinusoidal_positions(num_pos, dim)
+        new_state_dict["text_model.embeddings.position_embedding"] = positional_embedding
+
+        # position_ids
+        new_state_dict["text_model.embeddings.position_ids"] = torch.arange(num_pos).expand((1, -1))
+
+    return new_state_dict
+
+
+def read_and_preprocess_video(  # This function is from the original repo
+    filename: str, target_num_frames: int, target_frame_size: tuple[int, int]
+):
+    """Reads and preprocesses a video."""
+
+    frames = mediapy.read_video(filename)
+
+    # Sample to target number of frames.
+    frame_indices = np.linspace(0, len(frames), num=target_num_frames, endpoint=False, dtype=np.int32)
+    frames = np.array([frames[i] for i in frame_indices])
+
+    # Resize to target size.
+    original_height, original_width = frames.shape[-3:-1]
+    target_height, target_width = target_frame_size
+    assert original_height * target_width == original_width * target_height, (
+        "Currently does not support aspect ratio mismatch."
+    )
+    frames = mediapy.resize_video(frames, shape=target_frame_size)
+
+    # Normalize pixel values to [0.0, 1.0].
+    frames = mediapy.to_float01(frames)
+
+    return frames
+
+
+def get_tokenizer(checkpoint_name=None):
+    TEXT_QUERY_CSV = "playing drums,sitting,playing flute,playing at playground,concert"  # @param {type: "string"}
+    PROMPT_TEMPLATE = "a video of {}."
+
+    text_queries = TEXT_QUERY_CSV.split(",")
+    text_queries = [PROMPT_TEMPLATE.format(t) for t in text_queries]
+
+    tokenizer = AutoTokenizer.from_pretrained("MHRDYN7/" + checkpoint_name)
+
+    return tokenizer, text_queries
+
+
+def pad_and_stack(input_ids_list, pad_token_id=0, max_length=None):
+    """
+    Pads a list of input ID tensors to the same length and stacks them into a single tensor.
+    """
+    if max_length is None:
+        max_length = max(len(ids) for ids in input_ids_list)
+
+    padded_tensors = []
+    for i, ids in enumerate(input_ids_list):
+        padded = ids + [pad_token_id] * (max_length - len(ids))
+        padded_tensors.append(torch.tensor(padded, dtype=torch.long))
+
+    return torch.stack(padded_tensors)
+
+
+def ids_to_attention_mask(input_ids: torch.Tensor, pad_token_id: int = 0) -> torch.Tensor:
+    return (input_ids != pad_token_id).long()
+
+
+@torch.no_grad()
+def convert_videoprism_checkpoint(
+    model_name="lvt_base",
+    pytorch_dump_folder_path="checkpoints/",
+    convert=False,
+    load_model=True,
+    from_pretrained=False,
+    from_tokenizer=False,
+    load_video=True,
+    inference=True,
+    upload=False,
+):
+    checkpoint = ORIGINAL_CHECKPOINTS[model_name]
+
+    if "lvt" in model_name:
+        vision_config = VideoPrismVisionConfig(**COMMON_CONFIG_PARAMS[model_name]["vision_config"])
+        text_config = VideoPrismTextConfig(**COMMON_CONFIG_PARAMS[model_name]["text_config"])
+    else:
+        vision_config = VideoPrismVisionConfig(**COMMON_CONFIG_PARAMS[model_name])
+
+    checkpoint_name = checkpoint["new_checkpoint_name"]
+    checkpoint_path = os.path.join(pytorch_dump_folder_path, f"{checkpoint_name}.safetensors")
+
+    if convert:
+        flax_checkpoint = download_flax_weights(checkpoint)
+        hf_checkpoint = convert_params(flax_checkpoint, model_name)
+        save_file(hf_checkpoint, checkpoint_path, metadata={"format": "safetensors"})
+
+    if load_model:
+        if not from_pretrained:
+            model_config = vision_config if "lvt" not in model_name else VideoPrismConfig(text_config, vision_config)
+            model = (
+                VideoPrismVisionModel(model_config) if "lvt" not in model_name else VideoPrismClipModel(model_config)
+            )
+
+            model.config._attn_implementation = "eager"
+            state_dict = load_file(checkpoint_path)
+            model.load_state_dict(state_dict)
+        else:
+            model = AutoModel.from_pretrained("MHRDYN7/" + checkpoint_name)  # Hard-coded username of the contributer
+            model.config._attn_implementation = "eager"
+            model_config = model.config
+
+    if load_video:
+        VIDEO_FILE_PATH = "./src/transformers/models/videoprism/water_bottle_drumming.mp4"
+        NUM_FRAMES = model_config.num_frames if "lvt" not in model_name else vision_config.num_frames
+        FRAME_SIZE = 288
+        frames = read_and_preprocess_video(
+            VIDEO_FILE_PATH,
+            target_num_frames=NUM_FRAMES,
+            target_frame_size=[FRAME_SIZE, FRAME_SIZE],
+        )
+
+        input_vid = torch.tensor(frames).unsqueeze(0).permute(0, 1, 4, 2, 3)
+
+    if inference:
+        model.eval()
+        if "lvt" not in model_name:
+            outputs = model(input_vid)
+            logits = outputs.last_hidden_state[0, :3, :3]
+            assert torch.allclose(logits, EXPECTED_OUTPUTS[model_name], atol=1e-5), (
+                "The converted model logits do not match the expected logits."
+            )
+            print("Inference successful and logits match expected outputs.")
+
+        else:
+            if from_tokenizer:
+                tokenizer, text_queries = get_tokenizer(checkpoint_name=checkpoint_name)
+                outputs = tokenizer(text_queries, max_length=64, padding="max_length", return_tensors="pt")
+                input_ids, mask = outputs["input_ids"], outputs["attention_mask"]
+            else:
+                input_ids = pad_and_stack(SENTENCES, pad_token_id=0, max_length=64)
+                mask = ids_to_attention_mask(input_ids)
+            outputs = model(input_vid, input_ids, mask)
+            video_logits = outputs.video_embeds[0, :9]
+            text_logits = outputs.text_embeds[:, :3]
+            assert torch.allclose(video_logits, EXPECTED_OUTPUTS[model_name]["vision"], atol=1e-5), (
+                "The converted model video logits do not match the expected logits."
+            )
+            assert torch.allclose(text_logits, EXPECTED_OUTPUTS[model_name]["text"], atol=1e-4), (
+                "The converted model text logits do not match the expected logits."
+            )
+            print("Inference successful and logits match expected outputs.")
+
+    if upload:
+        repo_id = f"MHRDYN7/{checkpoint_name}"
+        model.push_to_hub(repo_id)
+        print(f"Uploaded the model to the Hugging Face hub at {repo_id}.")
+
+
+def main():
+    """
+    Typical workflow
+    1. Convert and check a model out of the keys of `ORIGINAL_CHECKPOINTS` dictionary
+        - Set model_name="MODEL_NAME", convert=True (saves locally), load_model=True,
+        from_pretrained=False (loads local checkpoint), load_video=True, inference=True (compares to expected outputs).
+    2. If outputs match perfectly, upload the model to hub, run the script with
+        - upload=True, convert=False, inference=False.
+    3. If a checkpoint from hub needs to be teseted set
+        - convert=False, from_pretrained=True, load_video=True, inference=True
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model_name",
+        default="backbone_large",
+        type=str,
+        choices=ORIGINAL_CHECKPOINTS.keys(),
+        help="Name of the model you'd like to convert.",
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        default="./src/transformers/models/videoprism/checkpoints/",
+        type=str,
+        help="Path to the output PyTorch model directory.",
+    )
+    parser.add_argument(
+        "--convert",
+        default=True,
+        type=bool,
+        help="Whether to convert the original Flax checkpoint to Hugging Face format.",
+    )
+    parser.add_argument(
+        "--load_model",
+        default=True,
+        type=bool,
+        help="Whether to load the converted model for inference.",
+    )
+    parser.add_argument(
+        "--from_pretrained",
+        default=False,
+        type=bool,
+        help="Whether to load the model weights from the Hugging Face hub if load_model=True. Loads local checkpoint (not in cache dir) if False.",
+    )
+    parser.add_argument(
+        "--from_tokenizer",
+        default=True,
+        type=bool,
+        help="Whether to use AutoTokenizer from the Hugging Face hub. Uses custom input_ids if False.",
+    )
+    parser.add_argument(
+        "--load_video",
+        default=True,
+        type=bool,
+        help="Whether to load and preprocess the sample video for inference.",
+    )
+    parser.add_argument(
+        "--inference",
+        default=True,
+        type=bool,
+        help="Whether to run inference on the loaded model and compare outputs to expected outputs.",
+    )
+    parser.add_argument(
+        "--upload",
+        default=True,
+        type=bool,
+        help="Whether to upload the converted model to the Hugging Face hub.",
+    )
+
+    args = parser.parse_args()
+
+    convert_videoprism_checkpoint(
+        model_name=args.model_name,
+        pytorch_dump_folder_path=args.pytorch_dump_folder_path,
+        convert=args.convert,
+        load_model=args.load_model,
+        from_pretrained=args.from_pretrained,
+        from_tokenizer=args.from_tokenizer,
+        load_video=args.load_video,
+        inference=args.inference,
+        upload=args.upload,
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/transformers/models/videoprism/modeling_videoprism.py b/src/transformers/models/videoprism/modeling_videoprism.py
new file mode 100644
index 000000000000..4d9662a3ead9
--- /dev/null
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+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/videoprism/modular_videoprism.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_videoprism.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ... import initialization as init
+from ...activations import ACT2FN
+from ...masking_utils import create_causal_mask
+from ...modeling_layers import GradientCheckpointingLayer
+from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, torch_int
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from .configuration_videoprism import VideoPrismConfig, VideoPrismTextConfig, VideoPrismVisionConfig
+
+
+@dataclass
+@auto_docstring(custom_intro="""Base class for model outputs that include spatial and temporal states.""")
+class BaseModelOutputWithSpatialAndTemporalStates(ModelOutput):
+    r"""
+    temporal_hidden_state (`torch.FloatTensor`, *optional*):
+        The last hidden state of the temporal encoder, typically of shape
+        `(batch_size * num_patches, num_frames, hidden_size)`.
+    spatial_hidden_state (`torch.FloatTensor`, *optional*):
+        The last hidden state of the spatial encoder, typically of shape
+        `(batch_size * num_frames, num_patches, hidden_size)`.
+    """
+
+    last_hidden_state: torch.FloatTensor
+    temporal_hidden_state: torch.FloatTensor | None = None
+    spatial_hidden_state: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(custom_intro="""Base class for VideoPrismVideoModel outputs.""")
+class VideoPrismVideoOutput(ModelOutput):
+    r"""
+    video_last_hidden_state (`torch.FloatTensor`):
+        The pooled video embeddings after the attention pooling head, typically of shape
+        `(batch_size, 1, hidden_size)`.
+    auxiliary_output (`BaseModelOutput`, *optional*):
+        The output of the auxiliary encoder. Its `last_hidden_state` is typically of shape
+        `(batch_size, num_patches * num_frames, hidden_size)`.
+    attention_pooling_output (`tuple(torch.FloatTensor, torch.FloatTensor)`, *optional*):
+        The output tuple of [`VideoPrismMultiheadAttentionPoolingHead`] containing the pooled tensor of shape
+        `(batch_size, 1, hidden_size)` and the attention probabilities of shape
+        `(batch_size, num_attention_heads, 1, num_patches * num_frames)`.
+    """
+
+    video_last_hidden_state: torch.FloatTensor
+    auxiliary_output: BaseModelOutput | None = None
+    attention_pooling_output: tuple[torch.FloatTensor, torch.FloatTensor] | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""Base class for VideoPrismClipModel outputs.""",
+)
+class VideoPrismClipOutput(ModelOutput):
+    r"""
+    logits_per_video (`torch.FloatTensor` of shape `(video_batch_size, text_batch_size)`):
+        The scaled dot product scores between `video_embeds` and `text_embeds`. This represents the video-text
+        similarity scores.
+    logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, video_batch_size)`):
+        The scaled dot product scores between `text_embeds` and `video_embeds`. This represents the text-video
+        similarity scores.
+    video_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`):
+        The video embeddings obtained by applying the projection layer to the pooled output of [`VideoPrismVideoModel`].
+    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`):
+        The text embeddings obtained by applying the projection layer to the pooled output of [`VideoPrismTextModel`].
+    video_model_output (`VideoPrismVideoOutput`):
+        The output of the [`VideoPrismVideoModel`].
+    text_model_output (`BaseModelOutput`):
+        The output of the [`VideoPrismTextModel`].
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+        Contrastive loss for video-text similarity.
+    """
+
+    logits_per_video: torch.FloatTensor | None = None
+    logits_per_text: torch.FloatTensor | None = None
+    video_embeds: torch.FloatTensor | None = None
+    text_embeds: torch.FloatTensor | None = None
+    video_model_output: VideoPrismVideoOutput = None
+    text_model_output: BaseModelOutput = None
+    loss: torch.FloatTensor | None = None
+
+    def to_tuple(self) -> tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_model_output", "video_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+class VideoPrismTubeletEmbeddings(nn.Module):
+    """
+    VideoPrism Tubelet Embeddings.
+
+    The authors of Videoprism use the Factorized Encoder architecture, i.e. "Model 2", introduced in the VIVIT paper (https://huggingface.co/papers/2103.15691).
+    This differs from Vivit by using a convolution of `tubelet_size=(1, 18, 18)`, which is essntially a 2d convolution in the spatial dimension.
+    The temporal dimension is also merged with the `batch_size` in order to make sure the image embeddings have no temporal component, unlike Vivit.
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.num_frames = config.num_frames
+        self.image_size = (
+            config.image_size if isinstance(config.image_size, tuple) else (config.image_size, config.image_size)
+        )
+        self.patch_size = config.tubelet_size
+        self.embed_dim = config.hidden_size
+
+        self.projection = nn.Conv3d(
+            config.num_channels, config.hidden_size, kernel_size=config.tubelet_size, stride=config.tubelet_size
+        )
+        self.pos_emb_shape = [self.image_size[0] // self.patch_size[1], self.image_size[1] // self.patch_size[2]]
+        self.num_patches = self.pos_emb_shape[0] * self.pos_emb_shape[1]
+
+    def forward(self, pixel_values_videos: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
+        batch_size, num_frames, num_channels, height, width = pixel_values_videos.shape
+        if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
+            raise ValueError(
+                f"Image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}). Set interpolate_pos_encoding=True to automatically resize the model position embeddings."
+            )
+        # permute to (batch_size, num_channels, num_frames, height, width)
+        pixel_values_videos = pixel_values_videos.transpose(1, 2)
+        hidden_states = self.projection(pixel_values_videos)
+        # flatten the spatial part and permute to (batch_size, num_frames, num_patches, hidden_dim)
+        hidden_states = hidden_states.flatten(3).permute(0, 2, 3, 1)
+        # combine batch and time dimension
+        batch_size, num_frames, num_patches, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.reshape(batch_size * num_frames, num_patches, hidden_size)
+
+        return hidden_states
+
+
+class VideoPrismSpatialEmbeddings(nn.Module):
+    """
+    VideoPrism Spatial Embeddings.
+
+    Creates embeddings from a video using VideoPrismSpatialTubeletEmbeddings and adds positional embeddings.
+    This module differs from Vivit model
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.patch_embeddings = VideoPrismTubeletEmbeddings(config)
+        self.position_embeddings = nn.Parameter(torch.zeros(1, self.patch_embeddings.num_patches, config.hidden_size))
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.patch_size = config.tubelet_size[1:]
+        self.tubelet_size = config.tubelet_size
+
+    # Adapted from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """
+
+        num_patches = embeddings.shape[1]
+        num_positions = self.position_embeddings.shape[1]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+            return self.position_embeddings
+
+        dim = embeddings.shape[-1]
+
+        num_row_patches = height // self.patch_size[0]
+        num_col_patches = width // self.patch_size[1]
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = self.position_embeddings.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        # This differs from Vivit by using bilinear mode instead of bicubic.
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(num_row_patches, num_col_patches),
+            mode="bilinear",
+            antialias=True,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def forward(
+        self,
+        pixel_values_videos: torch.Tensor,
+        interpolate_pos_encoding: bool | None = False,
+    ) -> torch.Tensor:
+        batch, frames, channel, height, width = pixel_values_videos.shape
+        embeddings = self.patch_embeddings(pixel_values_videos, interpolate_pos_encoding)
+        # no cls token is added unlike Vivit
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embeddings
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+class VideoPrismTemporalEmbeddings(nn.Module):
+    """
+    VideoPrism Temporal Embeddings.
+
+    Receives embeddings from spatial encoder, reshapes the hidden state to
+    (batch_size * num_patches, num_frames, hidden_size) and adds positional embeddings.
+    This module is only used in the VideoPrism architecture and not available in Vivit.
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+
+        self.position_embeddings = nn.Parameter(torch.zeros(1, self.config.num_frames, config.hidden_size))
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    # Adapted from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """
+        target_emb_length = embeddings.shape[1]
+        source_emb_length = self.position_embeddings.shape[1]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and target_emb_length == source_emb_length:
+            return self.position_embeddings
+
+        source_emb = self.position_embeddings
+        dim = embeddings.shape[-1]
+        source_emb = source_emb.unsqueeze(1)
+        source_emb = nn.functional.interpolate(
+            source_emb,
+            size=(target_emb_length, dim),
+            mode="bilinear",
+            antialias=True,
+        )
+
+        return source_emb.squeeze(1)
+
+    def forward(
+        self,
+        pixel_values_videos: torch.Tensor,
+        input_shape: torch.Size,
+        interpolate_pos_encoding: bool | None = False,
+    ) -> torch.Tensor:
+        if input_shape is not None:
+            batch, frames, channel, height, width = input_shape
+        _, features, dim = pixel_values_videos.shape
+        hidden_states = pixel_values_videos.view(batch, frames, features, dim)
+        hidden_states = hidden_states.transpose(2, 1)
+        embeddings = hidden_states.reshape(batch * features, frames, dim)
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings)
+        else:
+            embeddings = embeddings + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+def eager_attention_forward(
+    module: nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: torch.Tensor | None,
+    dropout: float | int = 0.0,
+    scaling: float | None = None,
+    softcap: float | None = None,
+    **kwargs,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    if scaling is None:
+        scaling = module.head_dim**-0.5
+
+    key_states = repeat_kv(key, module.num_key_value_groups)
+    value_states = repeat_kv(value, module.num_key_value_groups)
+
+    attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+
+    if softcap is not None:
+        attn_weights = attn_weights / softcap
+        attn_weights = torch.tanh(attn_weights)
+        attn_weights = attn_weights * softcap
+    if attention_mask is not None:
+        attn_weights = attn_weights + attention_mask
+
+    # upcast attention to fp32
+    attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+    attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
+    attn_output = torch.matmul(attn_weights, value_states)
+    attn_output = attn_output.transpose(1, 2).contiguous()
+    return attn_output, attn_weights
+
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+class VideoPrismSelfAttention(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig | VideoPrismTextConfig):
+        super().__init__()
+        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
+            raise ValueError(
+                f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
+                f"heads {config.num_attention_heads}."
+            )
+
+        self.config = config
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.dropout_prob = config.attention_probs_dropout_prob
+        self.scaling = self.attention_head_size**-0.5
+        self.is_causal = False
+
+        self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
+        self.num_key_value_groups = 1.0
+        self.attn_logit_softcapping = self.config.attn_logit_softcapping
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.attention_head_size)
+
+        query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            scaling=self.scaling,
+            dropout=0.0 if not self.training else self.dropout_prob,
+            softcap=self.attn_logit_softcapping,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        return attn_output, attn_weights
+
+
+class VideoPrismSelfOutput(nn.Module):
+    """
+    The residual connection is defined in VideoPrismLayer instead of here (as is the case with other models), due to the
+    layernorm applied before each block.
+    """
+
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        return hidden_states
+
+
+class VideoPrismAttention(nn.Module):
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__()
+        self.attention = VideoPrismSelfAttention(config)
+        self.output = VideoPrismSelfOutput(config)
+
+    def forward(
+        self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> torch.Tensor:
+        self_attn_output, _ = self.attention(hidden_states, attention_mask, **kwargs)
+        output = self.output(self_attn_output, hidden_states)
+        return output
+
+
+class VideoPrismLayerNorm(nn.LayerNorm):
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # a custom layernorm formula with gamma -> gamma + 1 is used in this model
+        return F.layer_norm(hidden_states, self.normalized_shape, self.weight + 1, self.bias, self.eps)
+
+
+class VideoPrismIntermediate(nn.Module):
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        if isinstance(config.hidden_act, str):
+            self.intermediate_act_fn = ACT2FN[config.hidden_act]
+        else:
+            self.intermediate_act_fn = config.hidden_act
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.intermediate_act_fn(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+
+        return hidden_states
+
+
+class VideoPrismOutput(nn.Module):
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__()
+        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+
+    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.dropout(hidden_states)
+        hidden_states = hidden_states + input_tensor
+        return hidden_states
+
+
+class VideoPrismLayer(GradientCheckpointingLayer):
+    """This corresponds to the EncoderBlock class in the scenic/videoprism implementation."""
+
+    def __init__(self, config: VideoPrismVisionConfig | VideoPrismTextConfig):
+        super().__init__()
+        self.attention = VideoPrismAttention(config)
+        self.intermediate = VideoPrismIntermediate(config)
+        self.output = VideoPrismOutput(config)
+        self.layernorm_before = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm_after = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        hidden_states_norm = self.layernorm_before(hidden_states)
+        attention_output = self.attention(hidden_states_norm, attention_mask, **kwargs)
+
+        # first residual connection
+        hidden_states = attention_output + hidden_states
+
+        # in VideoPrism, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+
+        # second residual connection is done here
+        layer_output = self.output(layer_output, hidden_states)
+
+        return layer_output
+
+
+class VideoPrismSpatialEncoder(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_spatial_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class VideoPrismTemporalEncoder(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_temporal_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class VideoPrismAuxiliaryEncoder(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_auxiliary_layers)])
+        self.gradient_checkpointing = False
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states, attention_mask, **kwargs)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class VideoPrismTextEncoder(nn.Module):
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__()
+        self.config = config
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+        self.is_causal = True
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states, attention_mask, **kwargs)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+def create_sinusoidal_positions(num_pos: int, dim: int) -> torch.Tensor:
+    inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, dtype=torch.int64) / dim))
+    sinusoid_inp = torch.einsum("i , j -> i j", torch.arange(num_pos, dtype=torch.int64).float(), inv_freq).float()
+    return torch.cat((torch.sin(sinusoid_inp), torch.cos(sinusoid_inp)), dim=1)
+
+
+@auto_docstring
+class VideoPrismPreTrainedModel(PreTrainedModel):
+    config: VideoPrismConfig
+    base_model_prefix = "videoprism"
+    main_input_name = "pixel_values_videos"
+    input_modalities = ("video", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "VideoPrismSpatialEmbeddings",
+        "VideoPrismTemporalEmbeddings",
+        "VideoPrismSpatialEncoder",
+        "VideoPrismTemporalEncoder",
+        "VideoPrismAuxiliaryEncoder",
+        "VideoPrismTextEncoder",
+        "VideoPrismMultiheadAttentionPoolingHead",
+    ]
+    _supports_sdpa = False
+    _supports_flash_attn = True
+    _supports_attention_backend = True
+    _supports_flex_attention = True
+    _can_record_outputs = {
+        "hidden_states": VideoPrismLayer,
+        "attentions": VideoPrismSelfAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, (nn.Linear, nn.Conv3d)):
+            init.lecun_normal_(module.weight)
+
+        elif isinstance(module, VideoPrismSpatialEmbeddings):
+            init.lecun_normal_(module.position_embeddings)
+
+        elif isinstance(module, VideoPrismTemporalEmbeddings):
+            init.lecun_normal_(module.position_embeddings)
+
+        elif isinstance(module, VideoPrismMultiheadAttentionPoolingHead):
+            init.zeros_(module.per_dim_scale)
+            init.lecun_normal_(module.pooling_attention_query)
+            scale = module.scale.new_tensor(1.442695041 / (module.dim**0.5))
+            init.copy_(module.scale, scale)
+
+        elif isinstance(module, VideoPrismTextEmbeddings):
+            position_embedding = create_sinusoidal_positions(
+                module.config.max_position_embeddings, module.config.hidden_size
+            ).to(device=module.position_embedding.device, dtype=module.position_embedding.dtype)
+            init.copy_(module.position_embedding, position_embedding)
+            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
+
+        elif isinstance(module, VideoPrismTextModel):
+            init.normal_(module.embeddings.token_embedding.weight, std=module.config.hidden_size**-0.5)
+            init.normal_(module.cls_emb, std=module.config.hidden_size**-0.5)
+
+
+@auto_docstring(
+    custom_intro="""
+    The bare VideoPrism vision encoder outputting raw hidden-states without any specific head on top. This model is the backbone encoder used in VideoPrismVideoModel.
+    """
+)
+class VideoPrismVisionModel(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+    input_modalities = ("video",)
+    base_model_prefix = "vision_model"
+    _input_embed_layer = "patch_embedding"
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.layernorm1 = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm2 = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.spatial_embeddings = VideoPrismSpatialEmbeddings(config)
+        self.temporal_embeddings = VideoPrismTemporalEmbeddings(config)
+        self.spatial_encoder = VideoPrismSpatialEncoder(config)
+        self.temporal_encoder = VideoPrismTemporalEncoder(config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.spatial_embeddings.patch_embeddings = value
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithSpatialAndTemporalStates:
+        if pixel_values_videos is None:
+            raise ValueError("You have to specify pixel_values_videos")
+
+        input_shape = pixel_values_videos.shape
+
+        # spatial
+        spatial_embeds = self.spatial_embeddings(pixel_values_videos, interpolate_pos_encoding)
+        spatial_encoder_outputs: BaseModelOutput = self.spatial_encoder(hidden_states=spatial_embeds, **kwargs)
+        spatial_sequence_output = spatial_encoder_outputs.last_hidden_state
+        features = self.layernorm1(spatial_sequence_output)
+
+        # temporal
+        temporal_embeds = self.temporal_embeddings(features, input_shape, interpolate_pos_encoding)
+        temporal_encoder_outputs: BaseModelOutput = self.temporal_encoder(hidden_states=temporal_embeds, **kwargs)
+        temporal_sequence_output = temporal_encoder_outputs.last_hidden_state
+        features = self.layernorm2(temporal_sequence_output)
+
+        # final reshape
+        _, num_frames, dim = features.shape
+        features = features.view(input_shape[0], -1, num_frames, dim).transpose(1, 2).contiguous()
+        _, num_frames, num_patches, dim = features.shape
+        features = features.view(input_shape[0], num_frames * num_patches, -1)
+
+        return BaseModelOutputWithSpatialAndTemporalStates(
+            last_hidden_state=features,
+            temporal_hidden_state=temporal_sequence_output,
+            spatial_hidden_state=spatial_sequence_output,
+        )
+
+
+class VideoPrismMultiheadAttentionPoolingHead(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.num_attention_heads = self.config.num_attention_heads
+        self.attention_head_size = int(self.config.intermediate_size / self.config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.dropout_prob = self.config.attention_probs_dropout_prob
+        self.num_key_value_groups = 1.0
+        # PerDimScale
+        self.dim = int(self.config.intermediate_size / self.config.num_attention_heads)
+        self.per_dim_scale = nn.Parameter(torch.zeros(self.dim))
+        r_softplus_0 = 1.442695041
+        scale = torch.tensor(r_softplus_0 / (self.dim**0.5))
+        self.register_buffer("scale", scale)
+        self.is_causal = False
+        self.pooling_attention_query = nn.Parameter(torch.zeros(1, 1, self.config.hidden_size))
+        self.query = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.key = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.value = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.projection = nn.Linear(self.config.intermediate_size, self.config.hidden_size, bias=self.config.qkv_bias)
+        self.layernorm = VideoPrismLayerNorm(self.config.hidden_size, eps=self.config.layer_norm_eps)
+        self.dim = int(self.config.intermediate_size / self.config.num_attention_heads)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+        batch_size, seq_length, hidden_size = hidden_states.shape
+        query = self.pooling_attention_query.expand(batch_size, -1, -1)
+        query_layer = (
+            self.query(query).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+        )
+        softplus = nn.functional.softplus(self.per_dim_scale)
+        scale = self.scale.to(query_layer.dtype) * softplus
+        query_layer = query_layer * scale.expand(*query_layer.shape)
+
+        key_layer = (
+            self.key(hidden_states)
+            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+            .transpose(1, 2)
+        )
+        value_layer = (
+            self.value(hidden_states)
+            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+            .transpose(1, 2)
+        )
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        context_layer, attention_probs = attention_interface(
+            self,
+            query_layer,
+            key_layer,
+            value_layer,
+            attention_mask,
+            is_causal=self.is_causal,
+            scaling=1.0,
+            dropout=0.0 if not self.training else self.dropout_prob,
+            softcap=None,
+            **kwargs,
+        )
+
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.reshape(new_context_layer_shape)
+        outputs = self.projection(context_layer)
+        outputs = self.layernorm(outputs)
+        return (outputs, attention_probs)
+
+
+class VideoPrismTextEmbeddings(nn.Module):
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
+        self.register_buffer(
+            "position_embedding", create_sinusoidal_positions(config.max_position_embeddings, config.hidden_size)
+        )
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.token_embedding(input_ids)
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, : inputs_embeds.shape[1]]
+
+        inputs_embeds = inputs_embeds * self.config.hidden_size**0.5
+        position_embeddings = self.position_embedding[position_ids].to(dtype=inputs_embeds.dtype)
+        embeddings = inputs_embeds + position_embeddings
+
+        return embeddings
+
+
+def l2norm(x: torch.FloatTensor, dim: int = -1, eps: float = 1e-6):
+    """This function is intended to align with the l2norm implementation in the FLA library."""
+    inv_norm = torch.rsqrt((x * x).sum(dim=dim, keepdim=True) + eps)
+    return x * inv_norm
+
+
+@auto_docstring(
+    custom_intro="""
+    The bare VideoPrism text encoder outputting last hidden states without any specific head on top. This model is used in VideoPrismClipModel.
+    """
+)
+class VideoPrismTextModel(VideoPrismPreTrainedModel):
+    config: VideoPrismTextConfig
+    input_modalities = ("text",)
+    base_model_prefix = "text_model"
+    main_input_name = "input_ids"
+    _no_split_modules = ["VideoPrismTextEmbeddings", "VideoPrismLayer"]
+    _input_embed_layer = "token_embedding"
+
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = VideoPrismTextEmbeddings(self.config)
+        self.text_encoder = VideoPrismTextEncoder(self.config)
+        self.cls_emb = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        self.layernorm = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.normalize = config.apply_l2norm
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
+        batch_size, seq_len, dim = hidden_states.shape
+        cls_emb = self.cls_emb * (self.config.hidden_size**0.5)
+        cls_emb = cls_emb.expand(hidden_states.shape[0], -1, -1)
+        features = torch.cat((hidden_states, cls_emb), dim=1)
+
+        if attention_mask is not None:
+            cls_padding = torch.ones(batch_size, 1, device=attention_mask.device, dtype=attention_mask.dtype)
+            attention_mask = torch.cat((attention_mask, cls_padding), dim=1)
+            attention_mask = create_causal_mask(
+                config=self.config,
+                inputs_embeds=features,
+                attention_mask=attention_mask,
+                past_key_values=None,
+            )
+
+        text_encoder_output = self.text_encoder(features, attention_mask)
+        features = text_encoder_output.last_hidden_state
+        features = self.layernorm(features)
+        text_embeddings = features[:, -1]
+
+        if self.normalize:
+            text_embeddings = l2norm(text_embeddings, dim=-1)
+
+        return BaseModelOutput(
+            last_hidden_state=text_embeddings,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism video model consisting of the vision encoder backbone with auxiliary encoder layers and an attention pooling head on top. This model is used in VideoPrismClipModel.
+    """
+)
+class VideoPrismVideoModel(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.backbone = VideoPrismVisionModel._from_config(config)
+        self.auxiliary_encoder = VideoPrismAuxiliaryEncoder(config)
+        self.contrastive_vision_pooler = VideoPrismMultiheadAttentionPoolingHead(config)
+        self.normalize = config.apply_l2norm
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.backbone.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.backbone.spatial_embeddings.patch_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> VideoPrismVideoOutput:
+        backbone_outputs = self.backbone(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        video_features = backbone_outputs.last_hidden_state
+        auxiliary_output = self.auxiliary_encoder(video_features)
+        auxiliary_output_features = auxiliary_output.last_hidden_state
+        contrastive_vision_pooler_output = self.contrastive_vision_pooler(auxiliary_output_features, **kwargs)
+        video_embeddings = contrastive_vision_pooler_output[0]
+        if self.normalize:
+            video_embeddings = l2norm(video_embeddings, dim=-1)
+
+        return VideoPrismVideoOutput(
+            video_last_hidden_state=video_embeddings,
+            auxiliary_output=auxiliary_output,
+            attention_pooling_output=contrastive_vision_pooler_output,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism model for video-text contrastive learning. This model consists of a VideoPrismVideoModel and a VideoPrismTextModel, and computes similarity scores between video and text inputs.
+    """
+)
+class VideoPrismClipModel(VideoPrismPreTrainedModel):
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__(config)
+        self.video_model = VideoPrismVideoModel._from_config(config.vision_config)
+        self.text_model = VideoPrismTextModel._from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.text_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.text_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        temperature: float | None = None,
+        return_loss: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> VideoPrismClipOutput:
+        r"""
+        temperature (`float`, *optional*):
+            A temperature scalar to scale the similarity scores. If not provided, no scaling is applied.
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        """
+
+        video_model_outputs = self.video_model(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        text_model_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
+
+        video_embeddings = video_model_outputs.video_last_hidden_state
+        text_embeddings = text_model_outputs.last_hidden_state
+        video_emb_dim = video_embeddings[0].shape[-1]
+        text_emb_dim = text_embeddings[0].shape[-1]
+
+        video_embeds = video_embeddings.reshape(-1, video_emb_dim)
+        text_embeds = text_embeddings.reshape(-1, text_emb_dim)
+        similarity_matrix = torch.matmul(video_embeds, text_embeds.T)
+
+        if temperature is not None:
+            similarity_matrix /= temperature
+
+        logits_per_video = torch.exp(similarity_matrix)
+        logits_per_text = logits_per_video.T
+        logits_per_video = logits_per_video / torch.sum(logits_per_video, dim=0, keepdims=True)
+        logits_per_text = logits_per_text / torch.sum(logits_per_text, dim=0, keepdims=True)
+
+        # adopted from siglip
+        loss = None
+        if return_loss:
+            # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip.py#L287
+            eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device)
+            m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye
+            loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text)
+            nll = -torch.sum(loglik, dim=-1)
+            loss = nll.mean()
+
+        return VideoPrismClipOutput(
+            logits_per_video=logits_per_video,
+            logits_per_text=logits_per_text,
+            video_embeds=video_embeds,
+            text_embeds=text_embeds,
+            video_model_output=video_model_outputs,
+            text_model_output=text_model_outputs,
+            loss=loss,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism Model transformer with a video classification head on top (a linear layer on top of the attention pooler).
+    """
+)
+class VideoPrismForVideoClassification(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+    input_modalities = ("video",)
+    base_model_prefix = "vision_model"
+    _input_embed_layer = "patch_embedding"
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        if not isinstance(config, VideoPrismVisionConfig):
+            raise TypeError(
+                f"`config` is expected to be of type `VideoPrismVisionConfig` but is of type {type(config)}."
+            )
+        super().__init__(config)
+        self.config = config
+        self.encoder = VideoPrismVisionModel._from_config(self.config)
+        self.contrastive_vision_pooler = VideoPrismMultiheadAttentionPoolingHead(self.config)
+        self.classifier = nn.Linear(self.config.hidden_size, self.config.num_labels)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.encoder.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.encoder.spatial_embeddings.patch_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        labels: torch.LongTensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ImageClassifierOutput:
+        encoder_outputs = self.encoder(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        sequence_output = encoder_outputs.last_hidden_state
+        pooled_output = self.contrastive_vision_pooler(sequence_output, **kwargs)[0]
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(labels, logits, self.config, **kwargs)
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=encoder_outputs.last_hidden_state,
+        )
+
+
+__all__ = [
+    "VideoPrismVisionModel",
+    "VideoPrismPreTrainedModel",
+    "VideoPrismVideoModel",
+    "VideoPrismTextModel",
+    "VideoPrismClipModel",
+    "VideoPrismForVideoClassification",
+]
diff --git a/src/transformers/models/videoprism/modular_videoprism.py b/src/transformers/models/videoprism/modular_videoprism.py
new file mode 100644
index 000000000000..7bdb26f4e704
--- /dev/null
+++ b/src/transformers/models/videoprism/modular_videoprism.py
@@ -0,0 +1,1076 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from collections.abc import Callable
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub.dataclasses import strict
+
+from ... import initialization as init
+from ...masking_utils import create_causal_mask
+from ...modeling_outputs import BaseModelOutput, ImageClassifierOutput
+from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
+from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...utils import ModelOutput, TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_int
+from ...utils.generic import merge_with_config_defaults
+from ...utils.output_capturing import capture_outputs
+from ..codegen.modeling_codegen import create_sinusoidal_positions
+from ..gemma2.modeling_gemma2 import eager_attention_forward
+from ..qwen3_next.modeling_qwen3_next import l2norm
+from ..siglip.configuration_siglip import SiglipConfig, SiglipTextConfig
+from ..t5.tokenization_t5 import T5Tokenizer
+from ..vivit.configuration_vivit import VivitConfig
+from ..vivit.modeling_vivit import (
+    VivitAttention,
+    VivitEmbeddings,
+    VivitEncoder,
+    VivitLayer,
+    VivitSelfAttention,
+    VivitTubeletEmbeddings,
+)
+
+
+logger = logging.get_logger(__name__)
+
+
+@auto_docstring(checkpoint="google/videoprism-base-f16r288")
+@strict
+class VideoPrismVisionConfig(VivitConfig):
+    r"""
+    num_frames (`int`, *optional*, defaults to 16):
+        The number of frames in the input video.
+    tubelet_size (`List[int]`, *optional*, defaults to `[1, 18, 18]`):
+        The size of the tubelet patch.
+    num_spatial_layers (`int`, *optional*, defaults to 12):
+        Number of spatial transformer blocks.
+    num_temporal_layers (`int`, *optional*, defaults to 4):
+        Number of temporal transformer blocks.
+    attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
+        Softcapping constant for attention logits.
+    num_auxiliary_layers (`int`, *optional*, defaults to 2):
+        Number of auxiliary layers. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel.
+    apply_l2norm (`bool`, *optional*, defaults to `True`):
+        Whether to apply L2 normalization to the output. This is used in the VideoPrismVideoModel that is a part of VideoPrismClipModel.
+    """
+
+    model_type = "videoprism_vision_model"
+    base_config_key = "vision_config"
+
+    image_size: int | list[int] | tuple[int, int] = 288
+    num_frames: int = 16
+    tubelet_size: list[int] | tuple[int, ...] = (1, 18, 18)
+    num_channels: int = 3
+    num_spatial_layers: int = 12
+    num_temporal_layers: int = 4
+    num_attention_heads: int = 12
+    intermediate_size: int = 3072
+    hidden_act: str = "gelu_python"
+    hidden_dropout_prob: float = 0.0
+    attention_probs_dropout_prob: float = 0.0
+    initializer_range: float = 0.02
+    layer_norm_eps: float = 1e-06
+    qkv_bias: bool = True
+    attn_logit_softcapping: float = 50.0
+    num_auxiliary_layers: int = 2
+    apply_l2norm: bool = True
+
+
+@auto_docstring(checkpoint="google/videoprism-lvt-base-f16r288")
+@strict
+class VideoPrismTextConfig(SiglipTextConfig):
+    r"""
+    apply_l2norm (`bool`, *optional*, defaults to `True`):
+        Whether to apply L2 normalization to the output of VideoPrismTextEncoder.
+    attn_logit_softcapping (`float`, *optional*, defaults to 50.0):
+        Softcapping constant for attention logits.
+    """
+
+    vocab_size: int = 32000
+    hidden_size: int = 768
+    intermediate_size: int = 3072
+    num_hidden_layers: int = 12
+    num_attention_heads: int = 12
+    max_position_embeddings: int = 64
+    hidden_act: str = "relu"
+    layer_norm_eps: float = 1e-6
+    attention_probs_dropout_prob: float | int = 0.0
+    apply_l2norm: bool = True
+    qkv_bias: bool = True
+    hidden_dropout_prob: float = 0.0
+    initializer_range: float = 0.02
+    attn_logit_softcapping: float = 50.0
+    attention_dropout = AttributeError()
+    projection_size = AttributeError()
+
+    def __post_init__(self, **kwargs):
+        raise AttributeError("Not used here")
+
+
+@auto_docstring(
+    checkpoint="google/videoprism-lvt-base-f16r288",
+    custom_intro="""
+    This is the configuration class to store the configuration of a [`VideoPrismClipModel`]. It is used to instantiate a
+    VideoPrismClipModel according to the specified arguments, defining the model architecture. Instantiating a
+    configuration with the defaults will yield a similar configuration to that of the VideoPrism
+    [google/videoprism-lvt-base-f16r288](https://huggingface.co/google/videoprism-lvt-base-f16r288) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+    """,
+)
+@strict
+class VideoPrismConfig(SiglipConfig):
+    r"""
+    Example:
+
+    ```python
+    >>> from transformers import VideoPrismClipModel, VideoPrismConfig
+
+    >>> # Initializing a VideoPrismConfig with default values
+    >>> configuration = VideoPrismConfig()
+
+    >>> # Initializing a VideoPrismClipModel with the configuration
+    >>> model = VideoPrismClipModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```
+    """
+
+    initializer_factor = AttributeError()
+
+
+class VideoPrismTokenizer(T5Tokenizer):
+    r"""
+    Constructs a VideoPrism tokenizer, which is essentially a T5 tokenizer without its postprocessor
+    (appending an EOS token at the end of the sequence).
+
+    This tokenizer inherits from [`T5Tokenizer`] which contains most of the main methods. Users should refer to this
+    superclass for more information regarding those methods.
+    """
+
+    def __init__(
+        self,
+        vocab: str | list[tuple[str, float]] | None = None,
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        _spm_precompiled_charsmap=None,
+        extra_ids=100,
+        additional_special_tokens=None,
+        **kwargs,
+    ):
+        super().__init__(
+            vocab=vocab,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            _spm_precompiled_charsmap=_spm_precompiled_charsmap,
+            extra_ids=extra_ids,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+        # VideoPrism does not append an EOS token by default
+        self._tokenizer.post_processor = None
+
+
+class VideoPrismProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": "max_length",
+            "truncation": True,
+            "max_length": 64,
+        },
+        "video_kwargs": {
+            "size": {"height": 288, "width": 288},
+            "do_normalize": False,
+        },
+    }
+
+
+@auto_docstring
+class VideoPrismProcessor(ProcessorMixin):
+    valid_processor_kwargs = VideoPrismProcessorKwargs
+
+    def __init__(self, video_processor=None, tokenizer=None):
+        super().__init__(video_processor, tokenizer)
+
+
+@dataclass
+@auto_docstring(custom_intro="""Base class for model outputs that include spatial and temporal states.""")
+class BaseModelOutputWithSpatialAndTemporalStates(ModelOutput):
+    r"""
+    temporal_hidden_state (`torch.FloatTensor`, *optional*):
+        The last hidden state of the temporal encoder, typically of shape
+        `(batch_size * num_patches, num_frames, hidden_size)`.
+    spatial_hidden_state (`torch.FloatTensor`, *optional*):
+        The last hidden state of the spatial encoder, typically of shape
+        `(batch_size * num_frames, num_patches, hidden_size)`.
+    """
+
+    last_hidden_state: torch.FloatTensor
+    temporal_hidden_state: torch.FloatTensor | None = None
+    spatial_hidden_state: torch.FloatTensor | None = None
+
+
+@dataclass
+@auto_docstring(custom_intro="""Base class for VideoPrismVideoModel outputs.""")
+class VideoPrismVideoOutput(ModelOutput):
+    r"""
+    video_last_hidden_state (`torch.FloatTensor`):
+        The pooled video embeddings after the attention pooling head, typically of shape
+        `(batch_size, 1, hidden_size)`.
+    auxiliary_output (`BaseModelOutput`, *optional*):
+        The output of the auxiliary encoder. Its `last_hidden_state` is typically of shape
+        `(batch_size, num_patches * num_frames, hidden_size)`.
+    attention_pooling_output (`tuple(torch.FloatTensor, torch.FloatTensor)`, *optional*):
+        The output tuple of [`VideoPrismMultiheadAttentionPoolingHead`] containing the pooled tensor of shape
+        `(batch_size, 1, hidden_size)` and the attention probabilities of shape
+        `(batch_size, num_attention_heads, 1, num_patches * num_frames)`.
+    """
+
+    video_last_hidden_state: torch.FloatTensor
+    auxiliary_output: BaseModelOutput | None = None
+    attention_pooling_output: tuple[torch.FloatTensor, torch.FloatTensor] | None = None
+
+
+@dataclass
+@auto_docstring(
+    custom_intro="""Base class for VideoPrismClipModel outputs.""",
+)
+class VideoPrismClipOutput(ModelOutput):
+    r"""
+    logits_per_video (`torch.FloatTensor` of shape `(video_batch_size, text_batch_size)`):
+        The scaled dot product scores between `video_embeds` and `text_embeds`. This represents the video-text
+        similarity scores.
+    logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, video_batch_size)`):
+        The scaled dot product scores between `text_embeds` and `video_embeds`. This represents the text-video
+        similarity scores.
+    video_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`):
+        The video embeddings obtained by applying the projection layer to the pooled output of [`VideoPrismVideoModel`].
+    text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)`):
+        The text embeddings obtained by applying the projection layer to the pooled output of [`VideoPrismTextModel`].
+    video_model_output (`VideoPrismVideoOutput`):
+        The output of the [`VideoPrismVideoModel`].
+    text_model_output (`BaseModelOutput`):
+        The output of the [`VideoPrismTextModel`].
+    loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
+        Contrastive loss for video-text similarity.
+    """
+
+    logits_per_video: torch.FloatTensor | None = None
+    logits_per_text: torch.FloatTensor | None = None
+    video_embeds: torch.FloatTensor | None = None
+    text_embeds: torch.FloatTensor | None = None
+    video_model_output: VideoPrismVideoOutput = None
+    text_model_output: BaseModelOutput = None
+    loss: torch.FloatTensor | None = None
+
+    def to_tuple(self) -> tuple[Any]:
+        return tuple(
+            self[k] if k not in ["text_model_output", "video_model_output"] else getattr(self, k).to_tuple()
+            for k in self.keys()
+        )
+
+
+class VideoPrismTubeletEmbeddings(VivitTubeletEmbeddings):
+    """
+    VideoPrism Tubelet Embeddings.
+
+    The authors of Videoprism use the Factorized Encoder architecture, i.e. "Model 2", introduced in the VIVIT paper (https://huggingface.co/papers/2103.15691).
+    This differs from Vivit by using a convolution of `tubelet_size=(1, 18, 18)`, which is essntially a 2d convolution in the spatial dimension.
+    The temporal dimension is also merged with the `batch_size` in order to make sure the image embeddings have no temporal component, unlike Vivit.
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        del self.num_patches
+        self.image_size = (
+            config.image_size if isinstance(config.image_size, tuple) else (config.image_size, config.image_size)
+        )
+        self.pos_emb_shape = [self.image_size[0] // self.patch_size[1], self.image_size[1] // self.patch_size[2]]
+        self.num_patches = self.pos_emb_shape[0] * self.pos_emb_shape[1]
+
+    def forward(self, pixel_values_videos: torch.Tensor, interpolate_pos_encoding: bool = False) -> torch.Tensor:
+        batch_size, num_frames, num_channels, height, width = pixel_values_videos.shape
+        if not interpolate_pos_encoding and (height != self.image_size[0] or width != self.image_size[1]):
+            raise ValueError(
+                f"Image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]}). Set interpolate_pos_encoding=True to automatically resize the model position embeddings."
+            )
+        # permute to (batch_size, num_channels, num_frames, height, width)
+        pixel_values_videos = pixel_values_videos.transpose(1, 2)
+        hidden_states = self.projection(pixel_values_videos)
+        # flatten the spatial part and permute to (batch_size, num_frames, num_patches, hidden_dim)
+        hidden_states = hidden_states.flatten(3).permute(0, 2, 3, 1)
+        # combine batch and time dimension
+        batch_size, num_frames, num_patches, hidden_size = hidden_states.shape
+        hidden_states = hidden_states.reshape(batch_size * num_frames, num_patches, hidden_size)
+
+        return hidden_states
+
+
+class VideoPrismSpatialEmbeddings(VivitEmbeddings):
+    """
+    VideoPrism Spatial Embeddings.
+
+    Creates embeddings from a video using VideoPrismSpatialTubeletEmbeddings and adds positional embeddings.
+    This module differs from Vivit model
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        del self.cls_token
+        self.tubelet_size = config.tubelet_size
+        self.position_embeddings = nn.Parameter(torch.zeros(1, self.patch_embeddings.num_patches, config.hidden_size))
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
+        """
+        This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
+        images. This method is also adapted to support torch.jit tracing.
+
+        Adapted from:
+        - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
+        - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
+        """
+
+        num_patches = embeddings.shape[1]
+        num_positions = self.position_embeddings.shape[1]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
+            return self.position_embeddings
+
+        dim = embeddings.shape[-1]
+
+        num_row_patches = height // self.patch_size[0]
+        num_col_patches = width // self.patch_size[1]
+
+        sqrt_num_positions = torch_int(num_positions**0.5)
+        patch_pos_embed = self.position_embeddings.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
+        patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
+
+        # This differs from Vivit by using bilinear mode instead of bicubic.
+        patch_pos_embed = nn.functional.interpolate(
+            patch_pos_embed,
+            size=(num_row_patches, num_col_patches),
+            mode="bilinear",
+            antialias=True,
+        )
+
+        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
+        return patch_pos_embed
+
+    def forward(
+        self,
+        pixel_values_videos: torch.Tensor,
+        interpolate_pos_encoding: bool | None = False,
+    ) -> torch.Tensor:
+        batch, frames, channel, height, width = pixel_values_videos.shape
+        embeddings = self.patch_embeddings(pixel_values_videos, interpolate_pos_encoding)
+        # no cls token is added unlike Vivit
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+        else:
+            embeddings = embeddings + self.position_embeddings
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+
+class VideoPrismTemporalEmbeddings(VivitEmbeddings):
+    """
+    VideoPrism Temporal Embeddings.
+
+    Receives embeddings from spatial encoder, reshapes the hidden state to
+    (batch_size * num_patches, num_frames, hidden_size) and adds positional embeddings.
+    This module is only used in the VideoPrism architecture and not available in Vivit.
+    """
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        del self.cls_token
+        del self.patch_embeddings
+        del self.patch_size
+
+        self.position_embeddings = nn.Parameter(torch.zeros(1, self.config.num_frames, config.hidden_size))
+
+    def interpolate_pos_encoding(self, embeddings: torch.Tensor) -> torch.Tensor:
+        target_emb_length = embeddings.shape[1]
+        source_emb_length = self.position_embeddings.shape[1]
+
+        # always interpolate when tracing to ensure the exported model works for dynamic input shapes
+        if not torch.jit.is_tracing() and target_emb_length == source_emb_length:
+            return self.position_embeddings
+
+        source_emb = self.position_embeddings
+        dim = embeddings.shape[-1]
+        source_emb = source_emb.unsqueeze(1)
+        source_emb = nn.functional.interpolate(
+            source_emb,
+            size=(target_emb_length, dim),
+            mode="bilinear",
+            antialias=True,
+        )
+
+        return source_emb.squeeze(1)
+
+    def forward(
+        self,
+        pixel_values_videos: torch.Tensor,
+        input_shape: torch.Size,
+        interpolate_pos_encoding: bool | None = False,
+    ) -> torch.Tensor:
+        if input_shape is not None:
+            batch, frames, channel, height, width = input_shape
+        _, features, dim = pixel_values_videos.shape
+        hidden_states = pixel_values_videos.view(batch, frames, features, dim)
+        hidden_states = hidden_states.transpose(2, 1)
+        embeddings = hidden_states.reshape(batch * features, frames, dim)
+
+        # add positional encoding to each token
+        if interpolate_pos_encoding:
+            embeddings = embeddings + self.interpolate_pos_encoding(embeddings)
+        else:
+            embeddings = embeddings + self.position_embeddings
+        embeddings = self.dropout(embeddings)
+        return embeddings
+
+
+class VideoPrismSelfAttention(VivitSelfAttention):
+    def __init__(self, config: VideoPrismVisionConfig | VideoPrismTextConfig):
+        super().__init__(config)
+        self.num_key_value_groups = 1.0
+        self.attn_logit_softcapping = self.config.attn_logit_softcapping
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        input_shape = hidden_states.shape[:-1]
+        hidden_shape = (*input_shape, -1, self.attention_head_size)
+
+        query_states = self.query(hidden_states).view(hidden_shape).transpose(1, 2)
+        key_states = self.key(hidden_states).view(hidden_shape).transpose(1, 2)
+        value_states = self.value(hidden_states).view(hidden_shape).transpose(1, 2)
+
+        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS.get_interface(
+            self.config._attn_implementation, eager_attention_forward
+        )
+
+        attn_output, attn_weights = attention_interface(
+            self,
+            query_states,
+            key_states,
+            value_states,
+            attention_mask,
+            scaling=self.scaling,
+            dropout=0.0 if not self.training else self.dropout_prob,
+            softcap=self.attn_logit_softcapping,
+            **kwargs,
+        )
+
+        attn_output = attn_output.reshape(*input_shape, -1).contiguous()
+        return attn_output, attn_weights
+
+
+class VideoPrismAttention(VivitAttention):
+    def forward(
+        self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, **kwargs: Unpack[TransformersKwargs]
+    ) -> torch.Tensor:
+        self_attn_output, _ = self.attention(hidden_states, attention_mask, **kwargs)
+        output = self.output(self_attn_output, hidden_states)
+        return output
+
+
+class VideoPrismLayerNorm(nn.LayerNorm):
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        # a custom layernorm formula with gamma -> gamma + 1 is used in this model
+        return F.layer_norm(hidden_states, self.normalized_shape, self.weight + 1, self.bias, self.eps)
+
+
+class VideoPrismLayer(VivitLayer):
+    def __init__(self, config: VideoPrismVisionConfig | VideoPrismTextConfig):
+        super().__init__(config)
+        del self.chunk_size_feed_forward
+        del self.seq_len_dim
+        self.layernorm_after = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm_before = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> torch.Tensor:
+        hidden_states_norm = self.layernorm_before(hidden_states)
+        attention_output = self.attention(hidden_states_norm, attention_mask, **kwargs)
+
+        # first residual connection
+        hidden_states = attention_output + hidden_states
+
+        # in VideoPrism, layernorm is also applied after self-attention
+        layer_output = self.layernorm_after(hidden_states)
+        layer_output = self.intermediate(layer_output)
+
+        # second residual connection is done here
+        layer_output = self.output(layer_output, hidden_states)
+
+        return layer_output
+
+
+class VideoPrismSpatialEncoder(VivitEncoder):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_spatial_layers)])
+
+
+class VideoPrismTemporalEncoder(VivitEncoder):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_temporal_layers)])
+
+
+class VideoPrismAuxiliaryEncoder(VivitEncoder):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_auxiliary_layers)])
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states, attention_mask, **kwargs)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+class VideoPrismTextEncoder(VivitEncoder):
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__(config)
+        self.layer = nn.ModuleList([VideoPrismLayer(config) for _ in range(config.num_hidden_layers)])
+        self.gradient_checkpointing = False
+        self.is_causal = True
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        for i, layer_module in enumerate(self.layer):
+            hidden_states = layer_module(hidden_states, attention_mask, **kwargs)
+
+        return BaseModelOutput(last_hidden_state=hidden_states)
+
+
+@auto_docstring
+class VideoPrismPreTrainedModel(PreTrainedModel):
+    config: VideoPrismConfig
+    base_model_prefix = "videoprism"
+    main_input_name = "pixel_values_videos"
+    input_modalities = ("video", "text")
+    supports_gradient_checkpointing = True
+    _no_split_modules = [
+        "VideoPrismSpatialEmbeddings",
+        "VideoPrismTemporalEmbeddings",
+        "VideoPrismSpatialEncoder",
+        "VideoPrismTemporalEncoder",
+        "VideoPrismAuxiliaryEncoder",
+        "VideoPrismTextEncoder",
+        "VideoPrismMultiheadAttentionPoolingHead",
+    ]
+    _supports_sdpa = False
+    _supports_flash_attn = True
+    _supports_attention_backend = True
+    _supports_flex_attention = True
+    _can_record_outputs = {
+        "hidden_states": VideoPrismLayer,
+        "attentions": VideoPrismSelfAttention,
+    }
+
+    @torch.no_grad()
+    def _init_weights(self, module):
+        super()._init_weights(module)
+        if isinstance(module, (nn.Linear, nn.Conv3d)):
+            init.lecun_normal_(module.weight)
+
+        elif isinstance(module, VideoPrismSpatialEmbeddings):
+            init.lecun_normal_(module.position_embeddings)
+
+        elif isinstance(module, VideoPrismTemporalEmbeddings):
+            init.lecun_normal_(module.position_embeddings)
+
+        elif isinstance(module, VideoPrismMultiheadAttentionPoolingHead):
+            init.zeros_(module.per_dim_scale)
+            init.lecun_normal_(module.pooling_attention_query)
+            scale = module.scale.new_tensor(1.442695041 / (module.dim**0.5))
+            init.copy_(module.scale, scale)
+
+        elif isinstance(module, VideoPrismTextEmbeddings):
+            position_embedding = create_sinusoidal_positions(
+                module.config.max_position_embeddings, module.config.hidden_size
+            ).to(device=module.position_embedding.device, dtype=module.position_embedding.dtype)
+            init.copy_(module.position_embedding, position_embedding)
+            init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
+
+        elif isinstance(module, VideoPrismTextModel):
+            init.normal_(module.embeddings.token_embedding.weight, std=module.config.hidden_size**-0.5)
+            init.normal_(module.cls_emb, std=module.config.hidden_size**-0.5)
+
+
+@auto_docstring(
+    custom_intro="""
+    The bare VideoPrism vision encoder outputting raw hidden-states without any specific head on top. This model is the backbone encoder used in VideoPrismVideoModel.
+    """
+)
+class VideoPrismVisionModel(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+    input_modalities = ("video",)
+    base_model_prefix = "vision_model"
+    _input_embed_layer = "patch_embedding"
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.layernorm1 = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.layernorm2 = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.spatial_embeddings = VideoPrismSpatialEmbeddings(config)
+        self.temporal_embeddings = VideoPrismTemporalEmbeddings(config)
+        self.spatial_encoder = VideoPrismSpatialEncoder(config)
+        self.temporal_encoder = VideoPrismTemporalEncoder(config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.spatial_embeddings.patch_embeddings = value
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutputWithSpatialAndTemporalStates:
+        if pixel_values_videos is None:
+            raise ValueError("You have to specify pixel_values_videos")
+
+        input_shape = pixel_values_videos.shape
+
+        # spatial
+        spatial_embeds = self.spatial_embeddings(pixel_values_videos, interpolate_pos_encoding)
+        spatial_encoder_outputs: BaseModelOutput = self.spatial_encoder(hidden_states=spatial_embeds, **kwargs)
+        spatial_sequence_output = spatial_encoder_outputs.last_hidden_state
+        features = self.layernorm1(spatial_sequence_output)
+
+        # temporal
+        temporal_embeds = self.temporal_embeddings(features, input_shape, interpolate_pos_encoding)
+        temporal_encoder_outputs: BaseModelOutput = self.temporal_encoder(hidden_states=temporal_embeds, **kwargs)
+        temporal_sequence_output = temporal_encoder_outputs.last_hidden_state
+        features = self.layernorm2(temporal_sequence_output)
+
+        # final reshape
+        _, num_frames, dim = features.shape
+        features = features.view(input_shape[0], -1, num_frames, dim).transpose(1, 2).contiguous()
+        _, num_frames, num_patches, dim = features.shape
+        features = features.view(input_shape[0], num_frames * num_patches, -1)
+
+        return BaseModelOutputWithSpatialAndTemporalStates(
+            last_hidden_state=features,
+            temporal_hidden_state=temporal_sequence_output,
+            spatial_hidden_state=spatial_sequence_output,
+        )
+
+
+class VideoPrismMultiheadAttentionPoolingHead(nn.Module):
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__()
+        self.config = config
+        self.num_attention_heads = self.config.num_attention_heads
+        self.attention_head_size = int(self.config.intermediate_size / self.config.num_attention_heads)
+        self.all_head_size = self.num_attention_heads * self.attention_head_size
+        self.dropout_prob = self.config.attention_probs_dropout_prob
+        self.num_key_value_groups = 1.0
+        # PerDimScale
+        self.dim = int(self.config.intermediate_size / self.config.num_attention_heads)
+        self.per_dim_scale = nn.Parameter(torch.zeros(self.dim))
+        r_softplus_0 = 1.442695041
+        scale = torch.tensor(r_softplus_0 / (self.dim**0.5))
+        self.register_buffer("scale", scale)
+        self.is_causal = False
+        self.pooling_attention_query = nn.Parameter(torch.zeros(1, 1, self.config.hidden_size))
+        self.query = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.key = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.value = nn.Linear(self.config.hidden_size, self.config.intermediate_size, bias=self.config.qkv_bias)
+        self.projection = nn.Linear(self.config.intermediate_size, self.config.hidden_size, bias=self.config.qkv_bias)
+        self.layernorm = VideoPrismLayerNorm(self.config.hidden_size, eps=self.config.layer_norm_eps)
+        self.dim = int(self.config.intermediate_size / self.config.num_attention_heads)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.LongTensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> tuple[torch.FloatTensor, torch.FloatTensor]:
+        batch_size, seq_length, hidden_size = hidden_states.shape
+        query = self.pooling_attention_query.expand(batch_size, -1, -1)
+        query_layer = (
+            self.query(query).view(batch_size, -1, self.num_attention_heads, self.attention_head_size).transpose(1, 2)
+        )
+        softplus = nn.functional.softplus(self.per_dim_scale)
+        scale = self.scale.to(query_layer.dtype) * softplus
+        query_layer = query_layer * scale.expand(*query_layer.shape)
+
+        key_layer = (
+            self.key(hidden_states)
+            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+            .transpose(1, 2)
+        )
+        value_layer = (
+            self.value(hidden_states)
+            .view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
+            .transpose(1, 2)
+        )
+
+        attention_interface: Callable = eager_attention_forward
+        if self.config._attn_implementation != "eager":
+            attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
+
+        context_layer, attention_probs = attention_interface(
+            self,
+            query_layer,
+            key_layer,
+            value_layer,
+            attention_mask,
+            is_causal=self.is_causal,
+            scaling=1.0,
+            dropout=0.0 if not self.training else self.dropout_prob,
+            softcap=None,
+            **kwargs,
+        )
+
+        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
+        context_layer = context_layer.reshape(new_context_layer_shape)
+        outputs = self.projection(context_layer)
+        outputs = self.layernorm(outputs)
+        return (outputs, attention_probs)
+
+
+class VideoPrismTextEmbeddings(nn.Module):
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__()
+        self.config = config
+        embed_dim = config.hidden_size
+        self.token_embedding = nn.Embedding(config.vocab_size, embed_dim)
+        self.register_buffer(
+            "position_embedding", create_sinusoidal_positions(config.max_position_embeddings, config.hidden_size)
+        )
+        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)))
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        position_ids: torch.LongTensor | None = None,
+        inputs_embeds: torch.FloatTensor | None = None,
+    ) -> torch.Tensor:
+        if inputs_embeds is None:
+            inputs_embeds = self.token_embedding(input_ids)
+
+        if position_ids is None:
+            position_ids = self.position_ids[:, : inputs_embeds.shape[1]]
+
+        inputs_embeds = inputs_embeds * self.config.hidden_size**0.5
+        position_embeddings = self.position_embedding[position_ids].to(dtype=inputs_embeds.dtype)
+        embeddings = inputs_embeds + position_embeddings
+
+        return embeddings
+
+
+@auto_docstring(
+    custom_intro="""
+    The bare VideoPrism text encoder outputting last hidden states without any specific head on top. This model is used in VideoPrismClipModel.
+    """
+)
+class VideoPrismTextModel(VideoPrismPreTrainedModel):
+    config: VideoPrismTextConfig
+    input_modalities = ("text",)
+    base_model_prefix = "text_model"
+    main_input_name = "input_ids"
+    _no_split_modules = ["VideoPrismTextEmbeddings", "VideoPrismLayer"]
+    _input_embed_layer = "token_embedding"
+
+    def __init__(self, config: VideoPrismTextConfig):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = VideoPrismTextEmbeddings(self.config)
+        self.text_encoder = VideoPrismTextEncoder(self.config)
+        self.cls_emb = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
+        self.layernorm = VideoPrismLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.normalize = config.apply_l2norm
+        self.post_init()
+
+    @merge_with_config_defaults
+    @capture_outputs(tie_last_hidden_states=False)
+    @auto_docstring
+    def forward(
+        self,
+        input_ids: torch.LongTensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+        inputs_embeds: torch.Tensor | None = None,
+        position_ids: torch.Tensor | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> BaseModelOutput:
+        if (input_ids is None) ^ (inputs_embeds is not None):
+            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
+
+        hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids, inputs_embeds=inputs_embeds)
+        batch_size, seq_len, dim = hidden_states.shape
+        cls_emb = self.cls_emb * (self.config.hidden_size**0.5)
+        cls_emb = cls_emb.expand(hidden_states.shape[0], -1, -1)
+        features = torch.cat((hidden_states, cls_emb), dim=1)
+
+        if attention_mask is not None:
+            cls_padding = torch.ones(batch_size, 1, device=attention_mask.device, dtype=attention_mask.dtype)
+            attention_mask = torch.cat((attention_mask, cls_padding), dim=1)
+            attention_mask = create_causal_mask(
+                config=self.config,
+                inputs_embeds=features,
+                attention_mask=attention_mask,
+                past_key_values=None,
+            )
+
+        text_encoder_output = self.text_encoder(features, attention_mask)
+        features = text_encoder_output.last_hidden_state
+        features = self.layernorm(features)
+        text_embeddings = features[:, -1]
+
+        if self.normalize:
+            text_embeddings = l2norm(text_embeddings, dim=-1)
+
+        return BaseModelOutput(
+            last_hidden_state=text_embeddings,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism video model consisting of the vision encoder backbone with auxiliary encoder layers and an attention pooling head on top. This model is used in VideoPrismClipModel.
+    """
+)
+class VideoPrismVideoModel(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        super().__init__(config)
+        self.backbone = VideoPrismVisionModel._from_config(config)
+        self.auxiliary_encoder = VideoPrismAuxiliaryEncoder(config)
+        self.contrastive_vision_pooler = VideoPrismMultiheadAttentionPoolingHead(config)
+        self.normalize = config.apply_l2norm
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.backbone.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.backbone.spatial_embeddings.patch_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> VideoPrismVideoOutput:
+        backbone_outputs = self.backbone(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        video_features = backbone_outputs.last_hidden_state
+        auxiliary_output = self.auxiliary_encoder(video_features)
+        auxiliary_output_features = auxiliary_output.last_hidden_state
+        contrastive_vision_pooler_output = self.contrastive_vision_pooler(auxiliary_output_features, **kwargs)
+        video_embeddings = contrastive_vision_pooler_output[0]
+        if self.normalize:
+            video_embeddings = l2norm(video_embeddings, dim=-1)
+
+        return VideoPrismVideoOutput(
+            video_last_hidden_state=video_embeddings,
+            auxiliary_output=auxiliary_output,
+            attention_pooling_output=contrastive_vision_pooler_output,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism model for video-text contrastive learning. This model consists of a VideoPrismVideoModel and a VideoPrismTextModel, and computes similarity scores between video and text inputs.
+    """
+)
+class VideoPrismClipModel(VideoPrismPreTrainedModel):
+    def __init__(self, config: VideoPrismConfig):
+        super().__init__(config)
+        self.video_model = VideoPrismVideoModel._from_config(config.vision_config)
+        self.text_model = VideoPrismTextModel._from_config(config.text_config)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.text_model.get_input_embeddings()
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.text_model.set_input_embeddings(value)
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        temperature: float | None = None,
+        return_loss: bool | None = None,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> VideoPrismClipOutput:
+        r"""
+        temperature (`float`, *optional*):
+            A temperature scalar to scale the similarity scores. If not provided, no scaling is applied.
+        return_loss (`bool`, *optional*):
+            Whether or not to return the contrastive loss.
+        """
+
+        video_model_outputs = self.video_model(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        text_model_outputs = self.text_model(input_ids=input_ids, attention_mask=attention_mask, **kwargs)
+
+        video_embeddings = video_model_outputs.video_last_hidden_state
+        text_embeddings = text_model_outputs.last_hidden_state
+        video_emb_dim = video_embeddings[0].shape[-1]
+        text_emb_dim = text_embeddings[0].shape[-1]
+
+        video_embeds = video_embeddings.reshape(-1, video_emb_dim)
+        text_embeds = text_embeddings.reshape(-1, text_emb_dim)
+        similarity_matrix = torch.matmul(video_embeds, text_embeds.T)
+
+        if temperature is not None:
+            similarity_matrix /= temperature
+
+        logits_per_video = torch.exp(similarity_matrix)
+        logits_per_text = logits_per_video.T
+        logits_per_video = logits_per_video / torch.sum(logits_per_video, dim=0, keepdims=True)
+        logits_per_text = logits_per_text / torch.sum(logits_per_text, dim=0, keepdims=True)
+
+        # adopted from siglip
+        loss = None
+        if return_loss:
+            # Adapted from https://github.com/google-research/big_vision/blob/01edb81a4716f93a48be43b3a4af14e29cdb3a7f/big_vision/trainers/proj/image_text/siglip.py#L287
+            eye = torch.eye(logits_per_text.size(0), device=logits_per_text.device)
+            m1_diag1 = -torch.ones_like(logits_per_text) + 2 * eye
+            loglik = torch.nn.functional.logsigmoid(m1_diag1 * logits_per_text)
+            nll = -torch.sum(loglik, dim=-1)
+            loss = nll.mean()
+
+        return VideoPrismClipOutput(
+            logits_per_video=logits_per_video,
+            logits_per_text=logits_per_text,
+            video_embeds=video_embeds,
+            text_embeds=text_embeds,
+            video_model_output=video_model_outputs,
+            text_model_output=text_model_outputs,
+            loss=loss,
+        )
+
+
+@auto_docstring(
+    custom_intro="""
+    VideoPrism Model transformer with a video classification head on top (a linear layer on top of the attention pooler).
+    """
+)
+class VideoPrismForVideoClassification(VideoPrismPreTrainedModel):
+    config: VideoPrismVisionConfig
+    input_modalities = ("video",)
+    base_model_prefix = "vision_model"
+    _input_embed_layer = "patch_embedding"
+
+    def __init__(self, config: VideoPrismVisionConfig):
+        if not isinstance(config, VideoPrismVisionConfig):
+            raise TypeError(
+                f"`config` is expected to be of type `VideoPrismVisionConfig` but is of type {type(config)}."
+            )
+        super().__init__(config)
+        self.config = config
+        self.encoder = VideoPrismVisionModel._from_config(self.config)
+        self.contrastive_vision_pooler = VideoPrismMultiheadAttentionPoolingHead(self.config)
+        self.classifier = nn.Linear(self.config.hidden_size, self.config.num_labels)
+        self.post_init()
+
+    def get_input_embeddings(self) -> nn.Module:
+        return self.encoder.spatial_embeddings.patch_embeddings
+
+    def set_input_embeddings(self, value: nn.Module):
+        self.encoder.spatial_embeddings.patch_embeddings = value
+
+    @can_return_tuple
+    @auto_docstring
+    def forward(
+        self,
+        pixel_values_videos: torch.FloatTensor,
+        labels: torch.LongTensor | None = None,
+        interpolate_pos_encoding: bool | None = False,
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> ImageClassifierOutput:
+        encoder_outputs = self.encoder(
+            pixel_values_videos=pixel_values_videos, interpolate_pos_encoding=interpolate_pos_encoding, **kwargs
+        )
+        sequence_output = encoder_outputs.last_hidden_state
+        pooled_output = self.contrastive_vision_pooler(sequence_output, **kwargs)[0]
+        logits = self.classifier(pooled_output)
+        loss = None
+        if labels is not None:
+            loss = self.loss_function(labels, logits, self.config, **kwargs)
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=encoder_outputs.last_hidden_state,
+        )
+
+
+__all__ = [
+    "VideoPrismVisionConfig",
+    "VideoPrismTextConfig",
+    "VideoPrismConfig",
+    "VideoPrismVisionModel",
+    "VideoPrismPreTrainedModel",
+    "VideoPrismVideoModel",
+    "VideoPrismTextModel",
+    "VideoPrismClipModel",
+    "VideoPrismForVideoClassification",
+    "VideoPrismTokenizer",
+    "VideoPrismProcessor",
+]
diff --git a/src/transformers/models/videoprism/processing_videoprism.py b/src/transformers/models/videoprism/processing_videoprism.py
new file mode 100644
index 000000000000..253ca154d5a5
--- /dev/null
+++ b/src/transformers/models/videoprism/processing_videoprism.py
@@ -0,0 +1,48 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/videoprism/modular_videoprism.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_videoprism.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+from ...processing_utils import ProcessingKwargs, ProcessorMixin
+from ...utils import auto_docstring
+
+
+class VideoPrismProcessorKwargs(ProcessingKwargs, total=False):
+    _defaults = {
+        "text_kwargs": {
+            "padding": "max_length",
+            "truncation": True,
+            "max_length": 64,
+        },
+        "video_kwargs": {
+            "size": {"height": 288, "width": 288},
+            "do_normalize": False,
+        },
+    }
+
+
+@auto_docstring
+class VideoPrismProcessor(ProcessorMixin):
+    valid_processor_kwargs = VideoPrismProcessorKwargs
+
+    def __init__(self, video_processor=None, tokenizer=None):
+        super().__init__(video_processor, tokenizer)
+
+
+__all__ = ["VideoPrismProcessor"]
diff --git a/src/transformers/models/videoprism/tokenization_videoprism.py b/src/transformers/models/videoprism/tokenization_videoprism.py
new file mode 100644
index 000000000000..a0b37e54f9cd
--- /dev/null
+++ b/src/transformers/models/videoprism/tokenization_videoprism.py
@@ -0,0 +1,128 @@
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+#           This file was automatically generated from src/transformers/models/videoprism/modular_videoprism.py.
+#               Do NOT edit this file manually as any edits will be overwritten by the generation of
+#             the file from the modular. If any change should be done, please apply the change to the
+#                          modular_videoprism.py file directly. One of our CI enforces this.
+#                🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import re
+
+from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
+from tokenizers.models import Unigram
+
+from ...tokenization_utils_tokenizers import TokenizersBackend
+
+
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
+
+
+class VideoPrismTokenizer(TokenizersBackend):
+    r"""
+    Constructs a VideoPrism tokenizer, which is essentially a T5 tokenizer without its postprocessor
+    (appending an EOS token at the end of the sequence).
+
+    This tokenizer inherits from [`T5Tokenizer`] which contains most of the main methods. Users should refer to this
+    superclass for more information regarding those methods.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+    model = Unigram
+
+    def __init__(
+        self,
+        vocab: str | list[tuple[str, float]] | None = None,
+        eos_token="</s>",
+        unk_token="<unk>",
+        pad_token="<pad>",
+        _spm_precompiled_charsmap=None,
+        extra_ids=100,
+        additional_special_tokens=None,
+        **kwargs,
+    ):
+        self._extra_ids = extra_ids
+
+        # Handle extra_ids and additional_special_tokens
+        if additional_special_tokens is not None:
+            extra_tokens = [x for x in additional_special_tokens if "<extra_id_" in str(x)]
+            if len(extra_tokens) < 1:
+                additional_special_tokens += [f"<extra_id_{i}>" for i in range(extra_ids)]
+            elif extra_ids > 0 and extra_ids != len(extra_tokens):
+                raise ValueError(
+                    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
+                    " provided to VideoPrismTokenizer. In this case the additional_special_tokens must include the extra_ids"
+                    " tokens"
+                )
+        else:
+            extra_tokens = [f"<extra_id_{i}>" for i in range(extra_ids)]
+            additional_special_tokens = extra_tokens
+
+        # VIDEOPRISM vocab structure: <pad>=0, </s>=1, <unk>=2, then regular vocab, then extra_ids in reverse
+        if vocab is not None:
+            self._vocab_scores = vocab
+        else:
+            self._vocab_scores = [
+                (str(pad_token), 0.0),
+                (str(eos_token), 0.0),
+                (str(unk_token), 0.0),
+                ("▁", -2.0),  # Space token
+            ]
+            for i in range(extra_ids - 1, -1, -1):
+                self._vocab_scores.append((f"<extra_id_{i}>", 0.0))
+
+        self._tokenizer = Tokenizer(
+            Unigram(
+                self._vocab_scores,
+                unk_id=2,
+                byte_fallback=False,
+            )
+        )
+
+        if _spm_precompiled_charsmap is not None:
+            self._tokenizer.normalizer = normalizers.Precompiled(_spm_precompiled_charsmap)
+
+        self._tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
+            [
+                pre_tokenizers.WhitespaceSplit(),
+                pre_tokenizers.Metaspace(replacement="▁", prepend_scheme="always", split=True),
+            ]
+        )
+        self._tokenizer.decoder = decoders.Metaspace(replacement="▁", prepend_scheme="always", split=True)
+
+        super().__init__(
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            extra_ids=extra_ids,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+        # VideoPrism does not append an EOS token by default
+        self._tokenizer.post_processor = None
+
+    def get_sentinel_tokens(self):
+        """Get the list of sentinel tokens (extra_id tokens) from additional_special_tokens."""
+        return list(
+            set(filter(lambda x: bool(re.search(r"<extra_id_\d+>", x)) is not None, self.additional_special_tokens))
+        )
+
+    def get_sentinel_token_ids(self):
+        """Get the token IDs for sentinel tokens."""
+        return [self.convert_tokens_to_ids(token) for token in self.get_sentinel_tokens()]
+
+
+__all__ = ["VideoPrismTokenizer"]
diff --git a/src/transformers/models/vilt/modeling_vilt.py b/src/transformers/models/vilt/modeling_vilt.py
index 5e973f7502d1..a5959d840ff2 100755
--- a/src/transformers/models/vilt/modeling_vilt.py
+++ b/src/transformers/models/vilt/modeling_vilt.py
@@ -40,12 +40,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of [`ViltForImagesAndTextClassification`].
     """
 )
+@dataclass
 class ViltForImagesAndTextClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/vilt/processing_vilt.py b/src/transformers/models/vilt/processing_vilt.py
index be47b2e6ee75..cbf6bd820032 100644
--- a/src/transformers/models/vilt/processing_vilt.py
+++ b/src/transformers/models/vilt/processing_vilt.py
@@ -17,9 +17,11 @@
 
 from ...processing_utils import ProcessingKwargs, ProcessorMixin
 from ...utils import auto_docstring
+from .image_processing_vilt import ViltImageProcessorKwargs
 
 
 class ViltProcessorKwargs(ProcessingKwargs, total=False):
+    images_kwargs: ViltImageProcessorKwargs
     _defaults = {
         "text_kwargs": {
             "add_special_tokens": True,
diff --git a/src/transformers/models/vipllava/modeling_vipllava.py b/src/transformers/models/vipllava/modeling_vipllava.py
index b09d9eff34fe..1ee41bd7969a 100644
--- a/src/transformers/models/vipllava/modeling_vipllava.py
+++ b/src/transformers/models/vipllava/modeling_vipllava.py
@@ -56,12 +56,12 @@ class VipLlavaModelOutputWithPast(BaseModelOutputWithPast):
     image_hidden_states: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for VipLlava causal language model (or autoregressive) outputs.
     """
 )
+@dataclass
 class VipLlavaCausalLMOutputWithPast(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -203,9 +203,9 @@ def get_placeholder_mask(
 
         n_image_tokens = special_image_mask.sum()
         n_image_features = image_features.shape[0] * image_features.shape[1]
-        special_image_mask = special_image_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        special_image_mask = special_image_mask.unsqueeze(-1).to(inputs_embeds.device)
         torch_compilable_check(
-            inputs_embeds[special_image_mask].numel() == image_features.numel(),
+            n_image_tokens * inputs_embeds.shape[-1] == image_features.numel(),
             f"Image features and image tokens do not match, tokens: {n_image_tokens}, features: {n_image_features}",
         )
         return special_image_mask
diff --git a/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py b/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py
index 337c6d090736..31ff9d210232 100644
--- a/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py
+++ b/src/transformers/models/vision_encoder_decoder/modeling_vision_encoder_decoder.py
@@ -22,7 +22,7 @@
 from ...generation import GenerationMixin
 from ...modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
 from ...modeling_utils import PreTrainedModel
-from ...utils import auto_docstring, logging
+from ...utils import auto_docstring, can_return_tuple, logging
 from ..auto.configuration_auto import AutoConfig
 from ..auto.modeling_auto import AutoModel, AutoModelForCausalLM
 from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig
@@ -188,8 +188,7 @@ def from_encoder_decoder_pretrained(
                 All remaining positional arguments will be passed to the underlying model's `__init__` method.
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
-                `output_attentions=True`).
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
 
                 - To update the encoder configuration, use the prefix *encoder_* for each configuration parameter.
                 - To update the decoder configuration, use the prefix *decoder_* for each configuration parameter.
@@ -298,6 +297,7 @@ def from_encoder_decoder_pretrained(
         config.tie_word_embeddings = False
         return cls(encoder=encoder, decoder=decoder, config=config)
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -309,11 +309,8 @@ def forward(
         decoder_inputs_embeds: torch.FloatTensor | None = None,
         labels: torch.LongTensor | None = None,
         use_cache: bool | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
         **kwargs,
-    ) -> tuple[torch.FloatTensor] | Seq2SeqLMOutput:
+    ) -> Seq2SeqLMOutput:
         r"""
         decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
             Indices of decoder input sequence tokens in the vocabulary.
@@ -372,13 +369,18 @@ def forward(
         >>> generated_ids = model.generate(pixel_values)
         >>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         kwargs_encoder = {argument: value for argument, value in kwargs.items() if not argument.startswith("decoder_")}
 
         kwargs_decoder = {
             argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
         }
+        # output_attentions and output_hidden_states apply to both encoder and decoder
+        kwargs_decoder.setdefault(
+            "output_attentions", kwargs_encoder.get("output_attentions", self.config.output_attentions)
+        )
+        kwargs_decoder.setdefault(
+            "output_hidden_states", kwargs_encoder.get("output_hidden_states", self.config.output_hidden_states)
+        )
 
         if encoder_outputs is None:
             if pixel_values is None:
@@ -386,9 +388,6 @@ def forward(
 
             encoder_outputs = self.encoder(
                 pixel_values=pixel_values,
-                output_attentions=output_attentions,
-                output_hidden_states=output_hidden_states,
-                return_dict=return_dict,
                 **kwargs_encoder,
             )
         elif isinstance(encoder_outputs, tuple):
@@ -418,28 +417,19 @@ def forward(
             encoder_hidden_states=encoder_hidden_states,
             encoder_attention_mask=encoder_attention_mask,
             inputs_embeds=decoder_inputs_embeds,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
             use_cache=use_cache,
             past_key_values=past_key_values,
-            return_dict=return_dict,
             **kwargs_decoder,
         )
 
         # Compute loss independent from decoder (as some shift the logits inside them)
         loss = None
         if labels is not None:
-            logits = decoder_outputs.logits if return_dict else decoder_outputs[0]
+            logits = decoder_outputs.logits if hasattr(decoder_outputs, "logits") else decoder_outputs[0]
 
             loss_fct = CrossEntropyLoss()
             loss = loss_fct(logits.reshape(-1, self.decoder.config.vocab_size), labels.reshape(-1))
 
-        if not return_dict:
-            if loss is not None:
-                return (loss,) + decoder_outputs + encoder_outputs
-            else:
-                return decoder_outputs + encoder_outputs
-
         return Seq2SeqLMOutput(
             loss=loss,
             logits=decoder_outputs.logits,
diff --git a/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py b/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py
index 8572003e80ea..eda45cc24729 100755
--- a/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py
+++ b/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py
@@ -168,6 +168,7 @@ def get_image_features(
 
         return vision_outputs
 
+    @can_return_tuple
     @auto_docstring
     def forward(
         self,
@@ -177,11 +178,8 @@ def forward(
         position_ids: torch.LongTensor | None = None,
         return_loss: bool | None = None,
         token_type_ids: torch.LongTensor | None = None,
-        output_attentions: bool | None = None,
-        output_hidden_states: bool | None = None,
-        return_dict: bool | None = None,
-        **kwargs,
-    ) -> tuple[torch.Tensor] | CLIPOutput:
+        **kwargs: Unpack[TransformersKwargs],
+    ) -> CLIPOutput:
         r"""
         return_loss (`bool`, *optional*):
             Whether or not to return the contrastive loss.
@@ -239,13 +237,9 @@ def forward(
         >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
         >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
         ```"""
-        return_dict = return_dict if return_dict is not None else self.config.return_dict
-
         vision_outputs = self.vision_model(
             pixel_values=pixel_values,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
         text_outputs = self.text_model(
@@ -253,15 +247,13 @@ def forward(
             attention_mask=attention_mask,
             token_type_ids=token_type_ids,
             position_ids=position_ids,
-            output_attentions=output_attentions,
-            output_hidden_states=output_hidden_states,
-            return_dict=return_dict,
+            **kwargs,
         )
 
-        image_embeds = vision_outputs[1]  # pooler_output
+        image_embeds = vision_outputs.pooler_output
         image_embeds = self.visual_projection(image_embeds)
 
-        text_embeds = text_outputs[1]  # pooler_output
+        text_embeds = text_outputs.pooler_output
         text_embeds = self.text_projection(text_embeds)
 
         # normalized features
@@ -277,10 +269,6 @@ def forward(
         if return_loss:
             loss = image_text_contrastive_loss(logits_per_text)
 
-        if not return_dict:
-            output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
-            return ((loss,) + output) if loss is not None else output
-
         return CLIPOutput(
             loss=loss,
             logits_per_image=logits_per_image,
@@ -323,8 +311,7 @@ def from_vision_text_pretrained(
                 All remaining positional arguments will be passed to the underlying model's `__init__` method.
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
-                Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
-                `output_attentions=True`).
+                Can be used to update the configuration object (after it being loaded) and initiate the model.
 
                 - To update the text configuration, use the prefix *text_* for each configuration parameter.
                 - To update the vision configuration, use the prefix *vision_* for each configuration parameter.
diff --git a/src/transformers/models/visual_bert/modeling_visual_bert.py b/src/transformers/models/visual_bert/modeling_visual_bert.py
index 60dfb0785b98..c1de02ba1fce 100755
--- a/src/transformers/models/visual_bert/modeling_visual_bert.py
+++ b/src/transformers/models/visual_bert/modeling_visual_bert.py
@@ -464,12 +464,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`VisualBertForPreTraining`].
     """
 )
+@dataclass
 class VisualBertForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `labels` is provided, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/vit/image_processing_vit.py b/src/transformers/models/vit/image_processing_vit.py
index 4116cc1e597c..1f63d18a108c 100644
--- a/src/transformers/models/vit/image_processing_vit.py
+++ b/src/transformers/models/vit/image_processing_vit.py
@@ -18,7 +18,7 @@
 
 
 class ViTImageProcessor(TorchvisionBackend):
-    resample = PILImageResampling.BILINEAR
+    resample = PILImageResampling.BICUBIC
     image_mean = IMAGENET_STANDARD_MEAN
     image_std = IMAGENET_STANDARD_STD
     size = {"height": 224, "width": 224}
diff --git a/src/transformers/models/vit_mae/modeling_vit_mae.py b/src/transformers/models/vit_mae/modeling_vit_mae.py
index ff148bae99f2..3a928a4f47d4 100755
--- a/src/transformers/models/vit_mae/modeling_vit_mae.py
+++ b/src/transformers/models/vit_mae/modeling_vit_mae.py
@@ -37,12 +37,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for ViTMAEModel's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class ViTMAEModelOutput(ModelOutput):
     r"""
     mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
@@ -58,12 +58,12 @@ class ViTMAEModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for ViTMAEDecoder's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class ViTMAEDecoderOutput(ModelOutput):
     r"""
     logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, patch_size ** 2 * num_channels)`):
@@ -75,12 +75,12 @@ class ViTMAEDecoderOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for ViTMAEForPreTraining's outputs, with potential hidden states and attentions.
     """
 )
+@dataclass
 class ViTMAEForPreTrainingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/vitmatte/modeling_vitmatte.py b/src/transformers/models/vitmatte/modeling_vitmatte.py
index 63b6e33e0f7a..0919775b0c63 100644
--- a/src/transformers/models/vitmatte/modeling_vitmatte.py
+++ b/src/transformers/models/vitmatte/modeling_vitmatte.py
@@ -25,12 +25,12 @@
 from .configuration_vitmatte import VitMatteConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of image matting models.
     """
 )
+@dataclass
 class ImageMattingOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/vitpose/modeling_vitpose.py b/src/transformers/models/vitpose/modeling_vitpose.py
index 3e47f66bd03e..bb737b8a99ec 100644
--- a/src/transformers/models/vitpose/modeling_vitpose.py
+++ b/src/transformers/models/vitpose/modeling_vitpose.py
@@ -33,12 +33,12 @@
 # General docstring
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Class for outputs of pose estimation models.
     """
 )
+@dataclass
 class VitPoseEstimatorOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/models/vits/modeling_vits.py b/src/transformers/models/vits/modeling_vits.py
index b8d318ca4e26..8ce1411bed5e 100644
--- a/src/transformers/models/vits/modeling_vits.py
+++ b/src/transformers/models/vits/modeling_vits.py
@@ -36,12 +36,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Describes the outputs for the VITS model, with potential hidden states and attentions.
     """
 )
+@dataclass
 class VitsModelOutput(ModelOutput):
     r"""
     waveform (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):
@@ -60,12 +60,12 @@ class VitsModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Describes the outputs for the VITS text encoder model, with potential hidden states and attentions.
     """
 )
+@dataclass
 class VitsTextEncoderOutput(ModelOutput):
     r"""
     prior_means (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
diff --git a/src/transformers/models/vivit/modeling_vivit.py b/src/transformers/models/vivit/modeling_vivit.py
index 792dc05ae03b..c1299c94a82b 100755
--- a/src/transformers/models/vivit/modeling_vivit.py
+++ b/src/transformers/models/vivit/modeling_vivit.py
@@ -64,7 +64,7 @@ def forward(self, pixel_values: torch.Tensor, interpolate_pos_encoding: bool = F
         batch_size, num_frames, num_channels, height, width = pixel_values.shape
         if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):
             raise ValueError(
-                f"Image image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
+                f"Image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
             )
 
         # permute to (batch_size, num_channels, num_frames, height, width)
@@ -86,7 +86,7 @@ class VivitEmbeddings(nn.Module):
 
     def __init__(self, config: VivitConfig):
         super().__init__()
-
+        self.config = config
         self.cls_token = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
         self.patch_embeddings = VivitTubeletEmbeddings(config)
 
@@ -95,7 +95,6 @@ def __init__(self, config: VivitConfig):
         )
         self.dropout = nn.Dropout(config.hidden_dropout_prob)
         self.patch_size = config.tubelet_size[1:]
-        self.config = config
 
     # Adapted from transformers.models.vit.modeling_vit.ViTEmbeddings.interpolate_pos_encoding
     def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
@@ -344,7 +343,7 @@ def __init__(self, config: VivitConfig):
         self.layer = nn.ModuleList([VivitLayer(config) for _ in range(config.num_hidden_layers)])
         self.gradient_checkpointing = False
 
-    def forward(self, hidden_states: torch.Tensor) -> BaseModelOutput:
+    def forward(self, hidden_states: torch.Tensor, **kwargs: Unpack[TransformersKwargs]) -> BaseModelOutput:
         for i, layer_module in enumerate(self.layer):
             hidden_states = layer_module(hidden_states)
 
@@ -411,7 +410,7 @@ def __init__(self, config: VivitConfig, add_pooling_layer: bool = True):
         # Initialize weights and apply final processing
         self.post_init()
 
-    def get_input_embeddings(self):
+    def get_input_embeddings(self) -> VivitTubeletEmbeddings:
         return self.embeddings.patch_embeddings
 
     @merge_with_config_defaults
diff --git a/src/transformers/models/vjepa2/configuration_vjepa2.py b/src/transformers/models/vjepa2/configuration_vjepa2.py
index c81a230bca66..735982222502 100644
--- a/src/transformers/models/vjepa2/configuration_vjepa2.py
+++ b/src/transformers/models/vjepa2/configuration_vjepa2.py
@@ -43,6 +43,22 @@ class VJEPA2Config(PreTrainedConfig):
         Initialize the mask tokens in the predictor with 0.
     pred_mlp_ratio (`float`, *optional*, defaults to 4.0):
         Ratio of the hidden size of the MLPs used in Predictor relative to the `pred_hidden_size`.
+    use_rope_interleave (`bool`, *optional*, defaults to `False`):
+        Use corrected RoPE implementation with `repeat_interleave` (V-JEPA 2.1) instead of `repeat` (V-JEPA 2).
+    use_modality_embeddings (`bool`, *optional*, defaults to `False`):
+        Add learnable modality embeddings (`img_mod_embed`/`video_mod_embed`) to patch embeddings.
+    interpolate_rope (`bool`, *optional*, defaults to `False`):
+        Scale RoPE positions for flexible resolution handling.
+    return_all_tokens (`bool`, *optional*, defaults to `False`):
+        Whether the predictor returns both predicted and context tokens via a separate projection.
+    img_temporal_dim_size (`int`, *optional*, defaults to `None`):
+        When set, creates a separate image patch embedding with `tubelet_size=1`.
+    teacher_embed_dim (`int`, *optional*, defaults to `None`):
+        Teacher embedding dimension for distilled models. Controls predictor output projection size.
+    n_output_distillation (`int`, *optional*, defaults to 0):
+        Number of distillation output layers. Controls predictor embed architecture (>1 uses MLP).
+    hierarchical_layers (`list[int]`, *optional*, defaults to `None`):
+        Encoder layer indices for hierarchical feature extraction with per-layer norms.
 
     Example:
 
@@ -84,6 +100,14 @@ class VJEPA2Config(PreTrainedConfig):
     pred_num_mask_tokens: int = 10
     pred_zero_init_mask_tokens: bool = True
     pred_mlp_ratio: int | float = 4.0
+    use_rope_interleave: bool = False
+    use_modality_embeddings: bool = False
+    interpolate_rope: bool = False
+    return_all_tokens: bool = False
+    img_temporal_dim_size: int | None = None
+    teacher_embed_dim: int | None = None
+    n_output_distillation: int = 0
+    hierarchical_layers: list[int] | None = None
 
 
 __all__ = ["VJEPA2Config"]
diff --git a/src/transformers/models/vjepa2/convert_vjepa2_to_hf.py b/src/transformers/models/vjepa2/convert_vjepa2_to_hf.py
index d4decd46df7d..6be3aee00a23 100644
--- a/src/transformers/models/vjepa2/convert_vjepa2_to_hf.py
+++ b/src/transformers/models/vjepa2/convert_vjepa2_to_hf.py
@@ -36,6 +36,11 @@
     "vit_huge": "facebook/vjepa2-vith-fpc64-256",
     "vit_giant": "facebook/vjepa2-vitg-fpc64-256",
     "vit_giant_384": "facebook/vjepa2-vitg-fpc64-384",
+    # provisional names pending Meta's Hub upload (facebookresearch/vjepa2#137)
+    "vit_base_2_1_384": "facebook/vjepa2.1-vitb-fpc64-384",
+    "vit_large_2_1_384": "facebook/vjepa2.1-vitl-fpc64-384",
+    "vit_giant_2_1_384": "facebook/vjepa2.1-vitg-fpc64-384",
+    "vit_gigantic_2_1_384": "facebook/vjepa2.1-vitG-fpc64-384",
 }
 
 S3_MODELS = {
@@ -43,6 +48,10 @@
     "vit_huge": "https://dl.fbaipublicfiles.com/vjepa2/vith.pt",
     "vit_giant": "https://dl.fbaipublicfiles.com/vjepa2/vitg.pt",
     "vit_giant_384": "https://dl.fbaipublicfiles.com/vjepa2/vitg-384.pt",
+    "vit_base_2_1_384": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2_1_vitb_dist_vitG_384.pt",
+    "vit_large_2_1_384": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2_1_vitl_dist_vitG_384.pt",
+    "vit_giant_2_1_384": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2_1_vitg_384.pt",
+    "vit_gigantic_2_1_384": "https://dl.fbaipublicfiles.com/vjepa2/vjepa2_1_vitG_384.pt",
 }
 
 TOKEN = os.environ.get("HF_TOKEN", None)
@@ -102,6 +111,89 @@ def get_vjepa2_config(model_name):
             pred_num_hidden_layers=12,
             pred_num_mask_tokens=10,
         )
+    # V-JEPA 2.1 models
+    elif model_name == "vit_base_2_1_384":
+        return VJEPA2Config(
+            crop_size=384,
+            frames_per_clip=64,
+            hidden_size=768,
+            num_attention_heads=12,
+            num_hidden_layers=12,
+            mlp_ratio=4,
+            pred_hidden_size=384,
+            pred_num_attention_heads=12,
+            pred_num_hidden_layers=12,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            teacher_embed_dim=1664,
+            n_output_distillation=1,
+            hierarchical_layers=[2, 5, 8, 11],
+        )
+    elif model_name == "vit_large_2_1_384":
+        return VJEPA2Config(
+            crop_size=384,
+            frames_per_clip=64,
+            hidden_size=1024,
+            num_attention_heads=16,
+            num_hidden_layers=24,
+            mlp_ratio=4,
+            pred_hidden_size=384,
+            pred_num_attention_heads=12,
+            pred_num_hidden_layers=12,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            teacher_embed_dim=1664,
+            n_output_distillation=1,
+            hierarchical_layers=[5, 11, 17, 23],
+        )
+    elif model_name == "vit_giant_2_1_384":
+        return VJEPA2Config(
+            crop_size=384,
+            frames_per_clip=64,
+            hidden_size=1408,
+            num_attention_heads=22,
+            num_hidden_layers=40,
+            mlp_ratio=48 / 11,
+            pred_hidden_size=384,
+            pred_num_attention_heads=12,
+            pred_num_hidden_layers=24,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            n_output_distillation=4,
+            hierarchical_layers=[9, 19, 29, 39],
+        )
+    elif model_name == "vit_gigantic_2_1_384":
+        return VJEPA2Config(
+            crop_size=384,
+            frames_per_clip=64,
+            hidden_size=1664,
+            num_attention_heads=26,
+            num_hidden_layers=48,
+            mlp_ratio=64 / 13,
+            pred_hidden_size=384,
+            pred_num_attention_heads=12,
+            pred_num_hidden_layers=24,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            n_output_distillation=4,
+            hierarchical_layers=[11, 23, 37, 47],
+        )
     else:
         raise ValueError("Model not supported")
 
@@ -117,10 +209,18 @@ def convert_encoder_keys(model_state_dict, og_encoder_state_dict, config):
             key = key.replace("attn.", "attention.")
         if key == "pos_embed":
             key = "encoder.embeddings.position_embeddings"
-        if "patch_embed." in key:
+        if "patch_embed." in key and not key.startswith("patch_embed_img."):
             key = key.replace("patch_embed.", "encoder.embeddings.patch_embeddings.")
+        if key.startswith("patch_embed_img."):
+            key = key.replace("patch_embed_img.", "encoder.embeddings.patch_embeddings_img.")
+        if key.startswith("norms_block."):
+            key = "encoder." + key
         if key.startswith("norm."):
             key = key.replace("norm.", "encoder.layernorm.")
+        if key == "img_mod_embed":
+            key = "encoder.embeddings.img_mod_embed"
+        if key == "video_mod_embed":
+            key = "encoder.embeddings.video_mod_embed"
         if "qkv." in key:
             prefix, suffix = key.split("qkv")
             if "bias" in suffix:
@@ -147,7 +247,6 @@ def convert_predictor_keys(model_state_dict, og_predictor_state_dict, config):
     emb_dim = config.pred_hidden_size
     if "predictor_pos_embed" in og_predictor_state_dict:
         del og_predictor_state_dict["predictor_pos_embed"]
-    # update predictor weights
     mask_tokens = {}
     mask_token_keys_to_delete = []
     for key, val in og_predictor_state_dict.copy().items():
@@ -164,10 +263,15 @@ def convert_predictor_keys(model_state_dict, og_predictor_state_dict, config):
         if "mask_tokens." in key:
             mask_tokens[key.split("mask_tokens.")[-1]] = val
             mask_token_keys_to_delete.append(key)
-            # key = key.replace("mask_tokens.", "predictor.embeddings.mask_tokens.")
         if key.startswith("predictor_norm."):
             key = key.replace("predictor_norm.", "predictor.layernorm.")
-        if key.startswith("predictor_proj."):
+        if key == "img_mod_embed":
+            key = "predictor.embeddings.img_mod_embed"
+        if key == "video_mod_embed":
+            key = "predictor.embeddings.video_mod_embed"
+        if key.startswith("predictor_proj_context."):
+            key = key.replace("predictor_proj_context.", "predictor.proj_context.")
+        elif key.startswith("predictor_proj."):
             key = key.replace("predictor_proj.", "predictor.proj.")
         if "qkv." in key:
             prefix, suffix = key.split("qkv")
@@ -220,6 +324,10 @@ def upload_original_ckpts(model_name):
         print("Uploading complete")
 
 
+def _is_2_1_model(model_name):
+    return "2_1" in model_name
+
+
 @torch.no_grad()
 def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub=False):
     """
@@ -227,8 +335,13 @@ def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, pus
     """
     config = get_vjepa2_config(model_name)
 
+    if _is_2_1_model(model_name):
+        hub_name = "vjepa2_1_" + model_name.replace("_2_1", "")
+    else:
+        hub_name = "vjepa2_" + model_name
+
     # load original model from torch hub
-    original_encoder, original_predictor = torch.hub.load(HUB_REPO, "vjepa2_" + model_name, source=HUB_SOURCE)
+    original_encoder, original_predictor = torch.hub.load(HUB_REPO, hub_name, source=HUB_SOURCE)
     original_encoder.eval()
     original_predictor.eval()
     original_preprocessor = torch.hub.load(
@@ -273,6 +386,9 @@ def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, pus
         original_predictor = original_predictor.to(device="cuda", dtype=torch.float32)
         model = model.to(device="cuda", dtype=torch.float32)
         # forward
+        is_2_1 = _is_2_1_model(model_name)
+        if is_2_1 and config.n_output_distillation > 1:
+            original_encoder.return_hierarchical = True
         original_encoder_outputs = original_encoder(pixel_values_videos.permute(0, 2, 1, 3, 4))
         B, N, _ = original_encoder_outputs.shape
         # test full mask
@@ -282,7 +398,15 @@ def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, pus
         outputs = model(pixel_values_videos, context_mask=context_mask, target_mask=predictor_mask)
         assert torch.allclose(outputs.last_hidden_state, original_encoder_outputs, atol=1e-3)
         predictor_outputs = outputs.predictor_output
-        assert torch.allclose(predictor_outputs.last_hidden_state, original_predictor_outputs, atol=1e-3)
+        if is_2_1 and config.return_all_tokens:
+            og_target, og_context = original_predictor_outputs
+            N_ctxt = context_mask[0].shape[1]
+            hf_context = predictor_outputs.last_hidden_state[:, :N_ctxt]
+            hf_target = predictor_outputs.last_hidden_state[:, N_ctxt:]
+            assert torch.allclose(hf_target, og_target, atol=1e-2)
+            assert torch.allclose(hf_context, og_context, atol=1e-2)
+        else:
+            assert torch.allclose(predictor_outputs.last_hidden_state, original_predictor_outputs, atol=1e-3)
         # test partial mask
         window_size = 256
         mask = torch.arange(N, device=pixel_values_videos.device).unsqueeze(0)
@@ -296,7 +420,15 @@ def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, pus
         outputs = model(pixel_values_videos, context_mask=context_mask, target_mask=predictor_mask)
         assert torch.allclose(outputs.last_hidden_state, original_encoder_outputs, atol=1e-3)
         predictor_outputs = outputs.predictor_output
-        assert torch.allclose(predictor_outputs.last_hidden_state, original_predictor_outputs, atol=1e-3)
+        if is_2_1 and config.return_all_tokens:
+            og_target, og_context = original_predictor_outputs
+            N_ctxt = context_mask[0].shape[1]
+            hf_context = predictor_outputs.last_hidden_state[:, :N_ctxt]
+            hf_target = predictor_outputs.last_hidden_state[:, N_ctxt:]
+            assert torch.allclose(hf_target, og_target, atol=1e-2)
+            assert torch.allclose(hf_context, og_context, atol=1e-2)
+        else:
+            assert torch.allclose(predictor_outputs.last_hidden_state, original_predictor_outputs, atol=1e-3)
 
     print("Looks ok!")
 
@@ -325,6 +457,10 @@ def convert_and_test_vjepa2_checkpoint(model_name, pytorch_dump_folder_path, pus
             "vit_huge",
             "vit_giant",
             "vit_giant_384",
+            "vit_base_2_1_384",
+            "vit_large_2_1_384",
+            "vit_giant_2_1_384",
+            "vit_gigantic_2_1_384",
         ],
         help="Name of the model you'd like to convert.",
     )
diff --git a/src/transformers/models/vjepa2/modeling_vjepa2.py b/src/transformers/models/vjepa2/modeling_vjepa2.py
index ff469faa1599..551b30bb668a 100644
--- a/src/transformers/models/vjepa2/modeling_vjepa2.py
+++ b/src/transformers/models/vjepa2/modeling_vjepa2.py
@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import copy
 from collections.abc import Callable
 from dataclasses import dataclass
 
@@ -32,12 +33,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     VJEPA Predictor outputs that also contains the masked encoder outputs
     """
 )
+@dataclass
 class VJEPA2WithMaskedInputPredictorOutput(ModelOutput):
     r"""
     masked_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, returned when `context_mask` is provided which is applied on VJEPA2Encoder outputs):
@@ -53,13 +54,13 @@ class VJEPA2WithMaskedInputPredictorOutput(ModelOutput):
     target_hidden_state: torch.FloatTensor | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     VJEPA outputs that also contains the masked encoder outputs
     Optionally contains the predictor outputs
     """
 )
+@dataclass
 class VJEPA2WithMaskedInputModelOutput(ModelOutput):
     r"""
     masked_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*, returned when `context_mask` is provided which is applied on VJEPA2Encoder outputs):
@@ -128,24 +129,44 @@ def __init__(self, config: VJEPA2Config, hidden_size: int = 1024):
         self.hidden_size = hidden_size
         self.patch_embeddings = VJEPA2PatchEmbeddings3D(config, hidden_size=hidden_size)
 
+        if config.img_temporal_dim_size is not None:
+            img_config = copy.copy(config)
+            img_config.tubelet_size = 1
+            self.patch_embeddings_img = VJEPA2PatchEmbeddings3D(img_config, hidden_size=hidden_size)
+        else:
+            self.patch_embeddings_img = None
+
+        if config.use_modality_embeddings:
+            self.img_mod_embed = nn.Parameter(torch.zeros(1, 1, hidden_size))
+            self.video_mod_embed = nn.Parameter(torch.zeros(1, 1, hidden_size))
+
         self.num_patches = self.patch_embeddings.num_patches
         self.patch_size = config.patch_size
 
     def forward(self, pixel_values_videos: torch.Tensor) -> torch.Tensor:
         num_frames = pixel_values_videos.shape[1]
 
-        # Swap `frames` and `channels` dims, the result is:
         # (batch_size, channels, num_frames, height, width)
         pixel_values_videos = pixel_values_videos.permute(0, 2, 1, 3, 4)
 
-        # For some cases, if the input vision (image/video) consists of num_frames < tubelet_size,
-        # then embedding lookup fails. In these cases, we duplicate the frames.
-        if num_frames < self.config.tubelet_size:
-            pixel_values_videos = pixel_values_videos.repeat(1, 1, self.config.tubelet_size, 1, 1)
+        is_image = self.config.img_temporal_dim_size is not None and num_frames == self.config.img_temporal_dim_size
 
-        target_dtype = self.patch_embeddings.proj.weight.dtype
-        pixel_values_videos = pixel_values_videos.to(dtype=target_dtype)
-        embeddings = self.patch_embeddings(pixel_values_videos)
+        if is_image and self.patch_embeddings_img is not None:
+            target_dtype = self.patch_embeddings_img.proj.weight.dtype
+            pixel_values_videos = pixel_values_videos.to(dtype=target_dtype)
+            embeddings = self.patch_embeddings_img(pixel_values_videos)
+        else:
+            if num_frames < self.config.tubelet_size:
+                pixel_values_videos = pixel_values_videos.repeat(1, 1, self.config.tubelet_size, 1, 1)
+            target_dtype = self.patch_embeddings.proj.weight.dtype
+            pixel_values_videos = pixel_values_videos.to(dtype=target_dtype)
+            embeddings = self.patch_embeddings(pixel_values_videos)
+
+        if self.config.use_modality_embeddings:
+            if is_image:
+                embeddings = embeddings + self.img_mod_embed
+            else:
+                embeddings = embeddings + self.video_mod_embed
 
         return embeddings
 
@@ -177,25 +198,24 @@ def eager_attention_forward(
     return attn_output, attn_weights
 
 
-def rotate_queries_or_keys(x, pos):
+def rotate_queries_or_keys(x, pos, use_interleave=False):
     B, num_heads, N, D = x.size()
 
-    # similar to inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim))
-    # they are computing this every time. instead HF style is to compute the inv_freq once and store it
-    # -- compute angle for each position
     omega = torch.arange(D // 2, dtype=x.dtype, device=x.device)
     omega /= D / 2.0
     omega = 1.0 / 10000**omega  # (D/2,)
     freq = pos.unsqueeze(-1) * omega  # (..., N, D/2), outer product
 
-    # -- build rotation matrix and apply
     emb_sin = freq.sin()  # (..., N, D/2)
     emb_cos = freq.cos()  # (..., N, D/2)
 
-    emb_sin = emb_sin.repeat(1, 1, 1, 2)
-    emb_cos = emb_cos.repeat(1, 1, 1, 2)
+    if use_interleave:
+        emb_sin = emb_sin.repeat_interleave(2, dim=-1)
+        emb_cos = emb_cos.repeat_interleave(2, dim=-1)
+    else:
+        emb_sin = emb_sin.repeat(1, 1, 1, 2)
+        emb_cos = emb_cos.repeat(1, 1, 1, 2)
 
-    # --
     y = x.unflatten(-1, (-1, 2))
     y1, y2 = y.unbind(dim=-1)
 
@@ -210,11 +230,13 @@ def __init__(
         config: VJEPA2Config,
         hidden_size: int = 1024,
         num_attention_heads: int = 16,
+        is_predictor: bool = False,
     ):
         super().__init__()
         self.config = config
         self.hidden_size = hidden_size
         self.num_attention_heads = num_attention_heads
+        self.is_predictor = is_predictor
         if hidden_size % num_attention_heads != 0:
             raise ValueError(
                 f"The hidden size {(hidden_size,)} is not a multiple of the number of attention "
@@ -234,6 +256,8 @@ def __init__(
 
         self.grid_size = self.config.crop_size // self.config.patch_size
         self.grid_depth = self.config.frames_per_clip // self.config.tubelet_size
+        # matches Meta's hardcoded RoPE reference resolution (256 for patch_size=16)
+        self.pretrained_grid_size = 256 // self.config.patch_size
 
         self.d_dim = int(2 * ((self.attention_head_size // 3) // 2))
         self.h_dim = int(2 * ((self.attention_head_size // 3) // 2))
@@ -259,33 +283,34 @@ def get_position_ids(self, x, masks=None):
         device = x.device
         token_size = x.size(1)
 
-        # Note: when masks is none, we use a 1d id instead of Bxnum_attention_heads mask,
-        # as 1d vector is broadcasted to the correct shapes.
         if masks is not None:
             ids = masks.unsqueeze(1).repeat(1, self.num_attention_heads, 1)
         else:
             ids = torch.arange(token_size, device=device)
-        # change to allow for extrapolation
         tokens_per_frame = int(self.grid_size * self.grid_size)
         frame_ids = self._get_frame_pos(ids)
-        # --
         tokens_per_row = self.grid_size
         height_ids = self._get_height_pos(ids)
-        # --
-        # Remove frame component from ids (1st term) and height component (2nd term)
         width_ids = (ids - tokens_per_frame * frame_ids) - tokens_per_row * height_ids
+
+        if self.config.interpolate_rope and not self.is_predictor and self.grid_size > 1:
+            h_scale = (self.pretrained_grid_size - 1.0) / max(self.grid_size - 1.0, 1.0)
+            w_scale = (self.pretrained_grid_size - 1.0) / max(self.grid_size - 1.0, 1.0)
+            height_ids = height_ids.float() * h_scale
+            width_ids = width_ids.float() * w_scale
+
         return frame_ids, height_ids, width_ids
 
     def apply_rotary_embeddings(self, qk, pos_ids):
+        use_interleave = self.config.use_rope_interleave
         d_mask, h_mask, w_mask = pos_ids
         s = 0
-        qkd = rotate_queries_or_keys(qk[..., s : s + self.d_dim], pos=d_mask)
+        qkd = rotate_queries_or_keys(qk[..., s : s + self.d_dim], pos=d_mask, use_interleave=use_interleave)
         s += self.d_dim
-        qkh = rotate_queries_or_keys(qk[..., s : s + self.h_dim], pos=h_mask)
+        qkh = rotate_queries_or_keys(qk[..., s : s + self.h_dim], pos=h_mask, use_interleave=use_interleave)
         s += self.h_dim
-        qkw = rotate_queries_or_keys(qk[..., s : s + self.w_dim], pos=w_mask)
+        qkw = rotate_queries_or_keys(qk[..., s : s + self.w_dim], pos=w_mask, use_interleave=use_interleave)
         s += self.w_dim
-        # Combine rotated dimension
         if s < self.attention_head_size:
             qkr = qk[..., s:]
             qk = torch.cat([qkd, qkh, qkw, qkr], dim=-1)
@@ -386,6 +411,7 @@ def __init__(
         hidden_size: int = 1024,
         num_attention_heads: int = 16,
         mlp_ratio: float = 4.0,
+        is_predictor: bool = False,
     ):
         super().__init__()
         self.config = config
@@ -394,7 +420,7 @@ def __init__(
         self.mlp_ratio = mlp_ratio
 
         self.norm1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
-        self.attention = VJEPA2RopeAttention(config, hidden_size, num_attention_heads)
+        self.attention = VJEPA2RopeAttention(config, hidden_size, num_attention_heads, is_predictor=is_predictor)
         self.drop_path = VJEPA2DropPath(drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
         self.norm2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_eps)
         self.mlp = VJEPA2MLP(config, hidden_size=hidden_size, mlp_ratio=mlp_ratio)
@@ -446,21 +472,47 @@ def __init__(self, config: VJEPA2Config):
                 for i in range(config.num_hidden_layers)
             ]
         )
-        self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        if config.hierarchical_layers is not None:
+            self.norms_block = nn.ModuleList(
+                [nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) for _ in config.hierarchical_layers]
+            )
+            n_dist = config.n_output_distillation if config.n_output_distillation > 0 else 1
+            self._extraction_layers = config.hierarchical_layers[-n_dist:]
+            self.layernorm = None
+        else:
+            self.norms_block = None
+            self._extraction_layers = None
+            self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
         self.gradient_checkpointing = False
 
     def forward(
         self,
         pixel_values_videos: torch.Tensor | None = None,
+        return_hierarchical: bool = True,
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutput:
         hidden_states = self.embeddings(pixel_values_videos)
 
+        hierarchical_outputs = []
+
         for i, layer_module in enumerate(self.layer):
             layer_outputs = layer_module(hidden_states, None, **kwargs)
             hidden_states = layer_outputs[0]
 
-        hidden_states = self.layernorm(hidden_states)
+            if self.norms_block is not None and self._extraction_layers is not None:
+                if i in self._extraction_layers:
+                    norm_idx = self.config.hierarchical_layers.index(i)
+                    hierarchical_outputs.append(self.norms_block[norm_idx](hidden_states))
+
+        if self.norms_block is not None:
+            if return_hierarchical and len(hierarchical_outputs) > 1:
+                hidden_states = torch.cat(hierarchical_outputs, dim=-1)
+            elif hierarchical_outputs:
+                hidden_states = hierarchical_outputs[-1]
+        elif self.layernorm is not None:
+            hidden_states = self.layernorm(hidden_states)
 
         return BaseModelOutput(
             last_hidden_state=hidden_states,
@@ -493,12 +545,28 @@ def __init__(self, config: VJEPA2Config):
         super().__init__()
 
         self.config = config
-        self.predictor_embeddings = nn.Linear(config.hidden_size, config.pred_hidden_size)
+
+        n_dist = config.n_output_distillation if config.n_output_distillation > 0 else 1
+        encoder_output_dim = config.hidden_size * n_dist
+
+        if config.n_output_distillation > 1:
+            self.predictor_embeddings = nn.Sequential(
+                nn.Linear(encoder_output_dim, config.hidden_size, bias=True),
+                nn.GELU(),
+                nn.Linear(config.hidden_size, config.pred_hidden_size, bias=True),
+            )
+        else:
+            self.predictor_embeddings = nn.Linear(config.hidden_size, config.pred_hidden_size)
+
         self.num_mask_tokens = 0
         self.zero_init_mask_tokens = config.pred_zero_init_mask_tokens
         self.num_mask_tokens = config.pred_num_mask_tokens
         self.mask_tokens = nn.Parameter(torch.zeros(self.num_mask_tokens, 1, 1, config.pred_hidden_size))
 
+        if config.use_modality_embeddings:
+            self.img_mod_embed = nn.Parameter(torch.zeros(1, 1, config.pred_hidden_size))
+            self.video_mod_embed = nn.Parameter(torch.zeros(1, 1, config.pred_hidden_size))
+
         self.patch_size = config.patch_size
         self.config = config
 
@@ -576,12 +644,26 @@ def __init__(self, config: VJEPA2Config):
                     hidden_size=config.pred_hidden_size,
                     num_attention_heads=config.pred_num_attention_heads,
                     mlp_ratio=config.pred_mlp_ratio,
+                    is_predictor=True,
                 )
                 for i in range(config.pred_num_hidden_layers)
             ]
         )
         self.layernorm = nn.LayerNorm(config.pred_hidden_size, eps=config.layer_norm_eps)
-        self.proj = nn.Linear(config.pred_hidden_size, config.hidden_size, bias=True)
+
+        n_hier = len(config.hierarchical_layers) if config.hierarchical_layers else 1
+        if config.teacher_embed_dim is not None:
+            out_embed_dim = config.teacher_embed_dim // n_hier
+        else:
+            out_embed_dim = config.hidden_size
+        proj_output_dim = n_hier * out_embed_dim
+
+        self.proj = nn.Linear(config.pred_hidden_size, proj_output_dim, bias=True)
+
+        if config.return_all_tokens:
+            self.proj_context = nn.Linear(config.pred_hidden_size, proj_output_dim, bias=True)
+        else:
+            self.proj_context = None
 
     def sort_tokens(self, hidden_states, position_masks, argsort):
         # gather position masks
@@ -607,28 +689,38 @@ def forward(
         encoder_hidden_states: torch.Tensor,
         context_mask: list[torch.Tensor],
         target_mask: list[torch.Tensor],
+        is_image: bool = False,
         **kwargs: Unpack[TransformersKwargs],
     ) -> BaseModelOutput:
-        # mask out the encoder hidden states
-        # this is implemented here as in VJEPA training a separate encoder is used for target
         encoder_hidden_states = apply_masks(encoder_hidden_states, context_mask)
         _, N_ctxt, D = encoder_hidden_states.shape
         hidden_states, position_masks = self.embeddings(encoder_hidden_states, context_mask, target_mask)
 
-        # Put tokens in sorted order
         argsort = torch.argsort(position_masks, dim=1)  # [B, N]
         hidden_states, position_masks = self.sort_tokens(hidden_states, position_masks, argsort)
 
+        if self.config.use_modality_embeddings and hasattr(self.embeddings, "video_mod_embed"):
+            if is_image:
+                hidden_states = hidden_states + self.embeddings.img_mod_embed
+            else:
+                hidden_states = hidden_states + self.embeddings.video_mod_embed
+
         for i, layer_module in enumerate(self.layer):
             layer_outputs = layer_module(hidden_states, position_masks, **kwargs)
             hidden_states = layer_outputs[0]
 
         hidden_states = self.layernorm(hidden_states)
-        # unsort and extract the predicted tokens
         hidden_states = self.unsort_tokens(hidden_states, argsort)
-        hidden_states = hidden_states[:, N_ctxt:]
-        # projection
-        hidden_states = self.proj(hidden_states)
+
+        if self.config.return_all_tokens and self.proj_context is not None:
+            context_tokens = hidden_states[:, :N_ctxt]
+            target_tokens = hidden_states[:, N_ctxt:]
+            target_tokens = self.proj(target_tokens)
+            context_tokens = self.proj_context(context_tokens)
+            hidden_states = torch.cat([context_tokens, target_tokens], dim=1)
+        else:
+            hidden_states = hidden_states[:, N_ctxt:]
+            hidden_states = self.proj(hidden_states)
 
         return BaseModelOutput(
             last_hidden_state=hidden_states,
@@ -861,8 +953,8 @@ class VJEPA2PreTrainedModel(PreTrainedModel):
     _supports_sdpa = True
     _supports_flash_attn = True
     _can_record_outputs = {
-        "hidden_states": OutputRecorder(VJEPA2Layer, layer_name="encoder.layer"),
-        "attentions": OutputRecorder(VJEPA2RopeAttention, index=1, layer_name="encoder.layer"),
+        "hidden_states": OutputRecorder(VJEPA2Layer, layer_name=r"encoder\.layer"),
+        "attentions": OutputRecorder(VJEPA2RopeAttention, index=1, layer_name=r"encoder\.layer"),
     }
 
     @torch.no_grad()
@@ -933,15 +1025,22 @@ def forward(
         if pixel_values_videos is None:
             raise ValueError("You have to specify pixel_values_videos")
 
+        is_image = (
+            self.config.img_temporal_dim_size is not None
+            and pixel_values_videos.shape[1] == self.config.img_temporal_dim_size
+        )
+
+        needs_hierarchical = not skip_predictor and self.config.n_output_distillation > 1
         encoder_outputs: BaseModelOutput = self.encoder(
             pixel_values_videos=pixel_values_videos,
+            return_hierarchical=needs_hierarchical,
             **kwargs,
         )
         sequence_output = encoder_outputs.last_hidden_state
 
         if context_mask is None and target_mask is None:
             B = pixel_values_videos.size(0)
-            N = sequence_output.size(1)  # ensure we are using dynamic patch size
+            N = sequence_output.size(1)
             context_mask = [torch.arange(N, device=pixel_values_videos.device).unsqueeze(0).repeat((B, 1))]
             target_mask = [torch.arange(N, device=pixel_values_videos.device).unsqueeze(0).repeat((B, 1))]
 
@@ -950,6 +1049,7 @@ def forward(
                 encoder_hidden_states=sequence_output,
                 context_mask=context_mask,
                 target_mask=target_mask,
+                is_image=is_image,
                 **kwargs,
             )
             predictor_output = VJEPA2WithMaskedInputPredictorOutput(
@@ -985,6 +1085,13 @@ class VJEPA2ForVideoClassification(VJEPA2PreTrainedModel):
     def __init__(self, config: VJEPA2Config):
         super().__init__(config)
 
+        if config.n_output_distillation > 1:
+            raise ValueError(
+                f"Classification heads for hierarchical distillation outputs "
+                f"(n_output_distillation={config.n_output_distillation}) are not yet supported. "
+                f"Use VJEPA2Model for feature extraction instead."
+            )
+
         self.num_labels = config.num_labels
         self.vjepa2 = VJEPA2Model(config)
 
diff --git a/src/transformers/models/voxtral/modeling_voxtral.py b/src/transformers/models/voxtral/modeling_voxtral.py
index 76da78cc558f..54466321b79e 100644
--- a/src/transformers/models/voxtral/modeling_voxtral.py
+++ b/src/transformers/models/voxtral/modeling_voxtral.py
@@ -32,7 +32,7 @@
 from ...modeling_outputs import BaseModelOutputWithPast, BaseModelOutputWithPooling, CausalLMOutputWithPast
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, logging, torch_compilable_check
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..auto import AutoModel, AutoModelForCausalLM
@@ -418,6 +418,30 @@ def get_audio_features(
 
         return audio_outputs
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -473,10 +497,10 @@ def forward(
             audio_embeds = self.get_audio_features(input_features, return_dict=True).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: BaseModelOutputWithPast = self.language_model(
             attention_mask=attention_mask,
diff --git a/src/transformers/models/voxtral/modular_voxtral.py b/src/transformers/models/voxtral/modular_voxtral.py
index c7b2c53e16d4..02e8e2806a0f 100644
--- a/src/transformers/models/voxtral/modular_voxtral.py
+++ b/src/transformers/models/voxtral/modular_voxtral.py
@@ -25,7 +25,7 @@
     CausalLMOutputWithPast,
 )
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple
+from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, torch_compilable_check
 from ...utils.generic import merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..auto import AutoModel, AutoModelForCausalLM
@@ -187,6 +187,30 @@ def get_audio_features(
 
         return audio_outputs
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -242,10 +266,10 @@ def forward(
             audio_embeds = self.get_audio_features(input_features, return_dict=True).pooler_output
 
             # replace text-audio token placeholders with audio embeddings
-            audio_token_mask = (input_ids == self.config.audio_token_id).unsqueeze(-1)
-            inputs_embeds = inputs_embeds.masked_scatter(
-                audio_token_mask.to(inputs_embeds.device), audio_embeds.to(inputs_embeds.device)
+            special_audio_mask = self.get_placeholder_mask(
+                input_ids, inputs_embeds=inputs_embeds, audio_features=audio_embeds
             )
+            inputs_embeds = inputs_embeds.masked_scatter(special_audio_mask, audio_embeds.to(inputs_embeds.device))
 
         outputs: BaseModelOutputWithPast = self.language_model(
             attention_mask=attention_mask,
diff --git a/src/transformers/models/voxtral_realtime/feature_extraction_voxtral_realtime.py b/src/transformers/models/voxtral_realtime/feature_extraction_voxtral_realtime.py
index 58355f3c0d7c..f13006f6b198 100644
--- a/src/transformers/models/voxtral_realtime/feature_extraction_voxtral_realtime.py
+++ b/src/transformers/models/voxtral_realtime/feature_extraction_voxtral_realtime.py
@@ -203,17 +203,17 @@ def __call__(
                 f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                 "We will take the mean of the channels to convert to mono."
             )
-            raw_speech = raw_speech.mean(-1)
+            raw_speech = raw_speech.mean(1)
 
         is_batched_sequence = isinstance(raw_speech, (list, tuple))
         if is_batched_sequence:
-            for speech in raw_speech:
+            for index, speech in enumerate(raw_speech):
                 if len(speech.shape) > 1:
                     logger.warning(
                         f"Only mono-channel audio is supported for input to {self.__class__.__name__}. "
                         "We will take the mean of the channels to convert to mono."
                     )
-                    speech = speech.mean(-1)
+                    raw_speech[index] = speech.mean(0)
 
         if is_batched_torch or is_batched_sequence:
             raw_speech = [speech[:, None].to(torch.float32) for speech in raw_speech]
diff --git a/src/transformers/models/voxtral_realtime/modeling_voxtral_realtime.py b/src/transformers/models/voxtral_realtime/modeling_voxtral_realtime.py
index 07325b0ea559..13e243e23f25 100644
--- a/src/transformers/models/voxtral_realtime/modeling_voxtral_realtime.py
+++ b/src/transformers/models/voxtral_realtime/modeling_voxtral_realtime.py
@@ -39,7 +39,14 @@
 from ...modeling_rope_utils import ROPE_INIT_FUNCTIONS, dynamic_rope_update
 from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
 from ...processing_utils import Unpack
-from ...utils import TransformersKwargs, auto_docstring, can_return_tuple, is_torchdynamo_compiling, logging
+from ...utils import (
+    TransformersKwargs,
+    auto_docstring,
+    can_return_tuple,
+    is_torchdynamo_compiling,
+    logging,
+    torch_compilable_check,
+)
 from ...utils.generic import maybe_autocast, merge_with_config_defaults
 from ...utils.output_capturing import capture_outputs
 from ..auto import AutoModel
@@ -1007,6 +1014,30 @@ def get_audio_features(
 
         return audio_outputs
 
+    def get_placeholder_mask(
+        self, input_ids: torch.LongTensor, inputs_embeds: torch.FloatTensor, audio_features: torch.FloatTensor
+    ):
+        """
+        Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is
+        equal to the length of multimodal features. If the lengths are different, an error is raised.
+        """
+        if input_ids is None:
+            special_audio_mask = inputs_embeds == self.get_input_embeddings()(
+                torch.tensor(self.config.audio_token_id, dtype=torch.long, device=inputs_embeds.device)
+            )
+            special_audio_mask = special_audio_mask.all(-1)
+        else:
+            special_audio_mask = input_ids == self.config.audio_token_id
+
+        n_audio_tokens = special_audio_mask.sum()
+        n_audio_features = audio_features.shape[0]
+        special_audio_mask = special_audio_mask.unsqueeze(-1).expand_as(inputs_embeds).to(inputs_embeds.device)
+        torch_compilable_check(
+            inputs_embeds[special_audio_mask].numel() == audio_features.numel(),
+            f"Audio features and audio tokens do not match, tokens: {n_audio_tokens}, features: {n_audio_features}",
+        )
+        return special_audio_mask
+
     @can_return_tuple
     @auto_docstring
     def forward(
@@ -1024,6 +1055,7 @@ def forward(
         use_cache: bool | None = None,
         logits_to_keep: int | torch.Tensor = 0,
         num_delay_tokens: int | torch.Tensor = None,
+        audio_embeds: torch.FloatTensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> VoxtralRealtimeCausalLMOutputWithPast:
         r"""
@@ -1035,6 +1067,11 @@ def forward(
             Optionally, instead of passing `input_features` you can choose to directly pass an embedded representation for the encoder.
         num_delay_tokens (`int` or `torch.Tensor`, *optional*):
             Number of delay tokens used when preparing inputs, see [`~VoxtralRealtimeProcessor`] for more details.
+        audio_embeds (`torch.FloatTensor`, *optional*):
+            Pre-computed audio embeddings (after encoder and projector). When provided, the audio encoder is
+            skipped and these embeddings are added directly to the text input embeddings. This is used internally
+            by `generate` when `precompute_audio_embeds=True` (the default) to avoid running the encoder
+            iteratively.
 
         Example:
 
@@ -1060,13 +1097,16 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if (input_features is None) ^ (encoder_inputs_embeds is not None):
+        if audio_embeds is None and (input_features is None) ^ (encoder_inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_features or encoder_inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.get_input_embeddings()(input_ids)
 
-        if input_features is not None or encoder_inputs_embeds is not None:
+        audio_outputs = None
+        if audio_embeds is not None:
+            inputs_embeds += audio_embeds.to(inputs_embeds.device)
+        elif input_features is not None or encoder_inputs_embeds is not None:
             audio_outputs = self.get_audio_features(
                 input_features=input_features,
                 encoder_inputs_embeds=encoder_inputs_embeds,
@@ -1111,23 +1151,29 @@ def forward(
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
-            encoder_past_key_values=audio_outputs.past_key_values if use_cache else None,
-            padding_cache=audio_outputs.padding_cache if use_cache else None,
+            encoder_past_key_values=audio_outputs.past_key_values if use_cache and audio_outputs is not None else None,
+            padding_cache=audio_outputs.padding_cache if use_cache and audio_outputs is not None else None,
         )
 
     def prepare_inputs_for_generation(
         self,
         *args,
         encoder_inputs_embeds: torch.Tensor | None = None,
+        audio_embeds: torch.Tensor | None = None,
+        precompute_audio_embeds: bool = True,
         **kwargs,
     ):
         model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
 
-        if encoder_inputs_embeds is not None:
-            past_key_values = model_inputs.get("past_key_values")
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            current_seq_len = model_inputs.get("position_ids").shape[-1]
+        past_key_values = model_inputs.get("past_key_values")
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        current_seq_len = model_inputs.get("position_ids").shape[-1]
 
+        if audio_embeds is not None:
+            start_idx = past_seen_tokens
+            end_idx = past_seen_tokens + current_seq_len
+            model_inputs["audio_embeds"] = audio_embeds[:, start_idx:end_idx, :]
+        elif encoder_inputs_embeds is not None:
             start_idx = past_seen_tokens * self.config.downsample_factor
             end_idx = (past_seen_tokens + current_seq_len) * self.config.downsample_factor
             model_inputs["encoder_inputs_embeds"] = encoder_inputs_embeds[:, start_idx:end_idx, :]
@@ -1142,9 +1188,18 @@ def _prepare_model_inputs(
     ) -> tuple[torch.Tensor, str | None, dict[str, torch.Tensor]]:
         inputs, input_name, model_kwargs = super()._prepare_model_inputs(inputs, bos_token_id, model_kwargs)
 
+        precompute_audio_embeds = model_kwargs.pop("precompute_audio_embeds", True)
+
         input_features = model_kwargs.get("input_features")
         if input_features is not None and not isinstance(input_features, GeneratorType):
-            model_kwargs["encoder_inputs_embeds"] = self.audio_tower.embedder(model_kwargs.pop("input_features"))
+            if precompute_audio_embeds:
+                audio_outputs = self.get_audio_features(
+                    input_features=model_kwargs.pop("input_features"),
+                    return_dict=True,
+                )
+                model_kwargs["audio_embeds"] = audio_outputs.pooler_output
+            else:
+                model_kwargs["encoder_inputs_embeds"] = self.audio_tower.embedder(model_kwargs.pop("input_features"))
 
         elif isinstance(input_features, GeneratorType):
             input_features_generator = model_kwargs.pop("input_features")
@@ -1241,6 +1296,8 @@ def _prepare_generation_config(
         generation_config,
         **kwargs,
     ):
+        precompute_audio_embeds = kwargs.pop("precompute_audio_embeds", True)
+
         # Check if user explicitly provided max_length or max_new_tokens BEFORE
         # the base class applies defaults
         user_set_max_length = kwargs.get("max_length") is not None or (
@@ -1251,6 +1308,7 @@ def _prepare_generation_config(
         )
 
         generation_config, model_kwargs = super()._prepare_generation_config(generation_config, **kwargs)
+        model_kwargs["precompute_audio_embeds"] = precompute_audio_embeds
 
         input_features = model_kwargs.get("input_features")
         if input_features is not None and not isinstance(input_features, GeneratorType):
diff --git a/src/transformers/models/voxtral_realtime/modular_voxtral_realtime.py b/src/transformers/models/voxtral_realtime/modular_voxtral_realtime.py
index edad37679927..a12be9022f50 100644
--- a/src/transformers/models/voxtral_realtime/modular_voxtral_realtime.py
+++ b/src/transformers/models/voxtral_realtime/modular_voxtral_realtime.py
@@ -589,6 +589,7 @@ def forward(
         use_cache: bool | None = None,
         logits_to_keep: int | torch.Tensor = 0,
         num_delay_tokens: int | torch.Tensor = None,
+        audio_embeds: torch.FloatTensor | None = None,
         **kwargs: Unpack[TransformersKwargs],
     ) -> VoxtralRealtimeCausalLMOutputWithPast:
         r"""
@@ -600,6 +601,11 @@ def forward(
             Optionally, instead of passing `input_features` you can choose to directly pass an embedded representation for the encoder.
         num_delay_tokens (`int` or `torch.Tensor`, *optional*):
             Number of delay tokens used when preparing inputs, see [`~VoxtralRealtimeProcessor`] for more details.
+        audio_embeds (`torch.FloatTensor`, *optional*):
+            Pre-computed audio embeddings (after encoder and projector). When provided, the audio encoder is
+            skipped and these embeddings are added directly to the text input embeddings. This is used internally
+            by `generate` when `precompute_audio_embeds=True` (the default) to avoid running the encoder
+            iteratively.
 
         Example:
 
@@ -625,13 +631,16 @@ def forward(
         if (input_ids is None) ^ (inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
 
-        if (input_features is None) ^ (encoder_inputs_embeds is not None):
+        if audio_embeds is None and (input_features is None) ^ (encoder_inputs_embeds is not None):
             raise ValueError("You must specify exactly one of input_features or encoder_inputs_embeds")
 
         if inputs_embeds is None:
             inputs_embeds = self.get_input_embeddings()(input_ids)
 
-        if input_features is not None or encoder_inputs_embeds is not None:
+        audio_outputs = None
+        if audio_embeds is not None:
+            inputs_embeds += audio_embeds.to(inputs_embeds.device)
+        elif input_features is not None or encoder_inputs_embeds is not None:
             audio_outputs = self.get_audio_features(
                 input_features=input_features,
                 encoder_inputs_embeds=encoder_inputs_embeds,
@@ -676,23 +685,29 @@ def forward(
             past_key_values=outputs.past_key_values,
             hidden_states=outputs.hidden_states,
             attentions=outputs.attentions,
-            encoder_past_key_values=audio_outputs.past_key_values if use_cache else None,
-            padding_cache=audio_outputs.padding_cache if use_cache else None,
+            encoder_past_key_values=audio_outputs.past_key_values if use_cache and audio_outputs is not None else None,
+            padding_cache=audio_outputs.padding_cache if use_cache and audio_outputs is not None else None,
         )
 
     def prepare_inputs_for_generation(
         self,
         *args,
         encoder_inputs_embeds: torch.Tensor | None = None,
+        audio_embeds: torch.Tensor | None = None,
+        precompute_audio_embeds: bool = True,
         **kwargs,
     ):
         model_inputs = super().prepare_inputs_for_generation(*args, **kwargs)
 
-        if encoder_inputs_embeds is not None:
-            past_key_values = model_inputs.get("past_key_values")
-            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
-            current_seq_len = model_inputs.get("position_ids").shape[-1]
+        past_key_values = model_inputs.get("past_key_values")
+        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
+        current_seq_len = model_inputs.get("position_ids").shape[-1]
 
+        if audio_embeds is not None:
+            start_idx = past_seen_tokens
+            end_idx = past_seen_tokens + current_seq_len
+            model_inputs["audio_embeds"] = audio_embeds[:, start_idx:end_idx, :]
+        elif encoder_inputs_embeds is not None:
             start_idx = past_seen_tokens * self.config.downsample_factor
             end_idx = (past_seen_tokens + current_seq_len) * self.config.downsample_factor
             model_inputs["encoder_inputs_embeds"] = encoder_inputs_embeds[:, start_idx:end_idx, :]
@@ -707,9 +722,18 @@ def _prepare_model_inputs(
     ) -> tuple[torch.Tensor, str | None, dict[str, torch.Tensor]]:
         inputs, input_name, model_kwargs = GenerationMixin._prepare_model_inputs(inputs, bos_token_id, model_kwargs)
 
+        precompute_audio_embeds = model_kwargs.pop("precompute_audio_embeds", True)
+
         input_features = model_kwargs.get("input_features")
         if input_features is not None and not isinstance(input_features, GeneratorType):
-            model_kwargs["encoder_inputs_embeds"] = self.audio_tower.embedder(model_kwargs.pop("input_features"))
+            if precompute_audio_embeds:
+                audio_outputs = self.get_audio_features(
+                    input_features=model_kwargs.pop("input_features"),
+                    return_dict=True,
+                )
+                model_kwargs["audio_embeds"] = audio_outputs.pooler_output
+            else:
+                model_kwargs["encoder_inputs_embeds"] = self.audio_tower.embedder(model_kwargs.pop("input_features"))
 
         elif isinstance(input_features, GeneratorType):
             input_features_generator = model_kwargs.pop("input_features")
@@ -806,6 +830,8 @@ def _prepare_generation_config(
         generation_config,
         **kwargs,
     ):
+        precompute_audio_embeds = kwargs.pop("precompute_audio_embeds", True)
+
         # Check if user explicitly provided max_length or max_new_tokens BEFORE
         # the base class applies defaults
         user_set_max_length = kwargs.get("max_length") is not None or (
@@ -816,6 +842,7 @@ def _prepare_generation_config(
         )
 
         generation_config, model_kwargs = GenerationMixin._prepare_generation_config(generation_config, **kwargs)
+        model_kwargs["precompute_audio_embeds"] = precompute_audio_embeds
 
         input_features = model_kwargs.get("input_features")
         if input_features is not None and not isinstance(input_features, GeneratorType):
diff --git a/src/transformers/models/wav2vec2/modeling_wav2vec2.py b/src/transformers/models/wav2vec2/modeling_wav2vec2.py
index 08442dad50b8..274a03365710 100755
--- a/src/transformers/models/wav2vec2/modeling_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_wav2vec2.py
@@ -63,12 +63,12 @@
 _HIDDEN_STATES_START_POSITION = 2
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`Wav2Vec2ForPreTraining`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class Wav2Vec2ForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
index 354146cedb55..9f35e5db42ed 100644
--- a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
+++ b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
@@ -31,12 +31,12 @@
 from .configuration_wav2vec2_conformer import Wav2Vec2ConformerConfig
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class Wav2Vec2ConformerForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/wav2vec2_conformer/modular_wav2vec2_conformer.py b/src/transformers/models/wav2vec2_conformer/modular_wav2vec2_conformer.py
index a0bd70a14976..f02ce539d228 100644
--- a/src/transformers/models/wav2vec2_conformer/modular_wav2vec2_conformer.py
+++ b/src/transformers/models/wav2vec2_conformer/modular_wav2vec2_conformer.py
@@ -35,12 +35,12 @@
 _HIDDEN_STATES_START_POSITION = 2
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`Wav2Vec2ConformerForPreTraining`], with potential hidden states and attentions.
     """
 )
+@dataclass
 class Wav2Vec2ConformerForPreTrainingOutput(ModelOutput):
     r"""
     loss (*optional*, returned when `sample_negative_indices` are passed, `torch.FloatTensor` of shape `(1,)`):
diff --git a/src/transformers/models/whisper/feature_extraction_whisper.py b/src/transformers/models/whisper/feature_extraction_whisper.py
index 4151a3824dfd..ffff3ba1ab6b 100644
--- a/src/transformers/models/whisper/feature_extraction_whisper.py
+++ b/src/transformers/models/whisper/feature_extraction_whisper.py
@@ -22,10 +22,43 @@
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
 from ...utils import TensorType, logging
+from ...utils.import_utils import requires_backends
 
 
 if is_torch_available():
     import torch
+    from torch import nn
+
+    class _WhisperFeatureExtractorModule(nn.Module):
+        def __init__(self, feature_extractor: "WhisperFeatureExtractor"):
+            super().__init__()
+            self.n_fft = feature_extractor.n_fft
+            self.hop_length = feature_extractor.hop_length
+            self.dither = feature_extractor.dither
+            self.register_buffer("window", torch.hann_window(self.n_fft))
+            self.register_buffer("mel_filters", torch.from_numpy(feature_extractor.mel_filters).float())
+
+        def forward(self, waveform):
+            # Note: it would be better to dither the chunked waveform,
+            # so overlapping signal does not get the same dithering.
+            # But, chunking is happening inside pytorch, so it is here.
+            if self.dither != 0.0:
+                waveform += self.dither * torch.randn(waveform.shape, dtype=waveform.dtype, device=waveform.device)
+
+            stft = torch.stft(waveform, self.n_fft, self.hop_length, window=self.window, return_complex=True)
+            magnitudes = stft[..., :-1].abs() ** 2
+
+            mel_spec = self.mel_filters.T @ magnitudes
+
+            log_spec = torch.clamp(mel_spec, min=1e-10).log10()
+            if waveform.dim() == 2:
+                max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0]
+                log_spec = torch.maximum(log_spec, max_val - 8.0)
+            else:
+                log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
+            log_spec = (log_spec + 4.0) / 4.0
+            return log_spec
+
 
 logger = logging.get_logger(__name__)
 
@@ -102,17 +135,18 @@ def __init__(
             mel_scale="slaney",
         )
 
-    def _np_extract_fbank_features(self, waveform_batch: np.ndarray, device: str) -> np.ndarray:
+    def to_exportable_module(self) -> "nn.Module":
+        """
+        Returns an exportable version of the feature extractor, which can be used with `torch.export`.
+        """
+        requires_backends(self, "torch")
+        return _WhisperFeatureExtractorModule(self)
+
+    def _np_extract_fbank_features(self, waveform_batch: np.ndarray) -> np.ndarray:
         """
         Compute the log-mel spectrogram of the provided audio, gives similar results to Whisper's original torch
         implementation with 1e-5 tolerance.
         """
-        if device != "cpu":
-            raise ValueError(
-                f"Got device `{device}` for feature extraction, but feature extraction on CUDA accelerator "
-                "devices requires torch, which is not installed. Either set `device='cpu'`, or "
-                "install torch according to the official instructions: https://pytorch.org/get-started/locally/"
-            )
         log_spec_batch = []
         for waveform in waveform_batch:
             log_spec = spectrogram(
@@ -138,27 +172,8 @@ def _torch_extract_fbank_features(self, waveform: np.ndarray, device: str = "cpu
         yielding results similar to cpu computing with 1e-5 tolerance.
         """
         waveform = torch.from_numpy(waveform).to(device, torch.float32)
-        window = torch.hann_window(self.n_fft, device=device)
-
-        # Note: it would be better to dither the chunked waveform,
-        # so overlapping signal does not get the same dithering.
-        # But, chunking is happening inside pytorch, so it is here.
-        if self.dither != 0.0:
-            waveform += self.dither * torch.randn(waveform.shape, dtype=waveform.dtype, device=waveform.device)
-
-        stft = torch.stft(waveform, self.n_fft, self.hop_length, window=window, return_complex=True)
-        magnitudes = stft[..., :-1].abs() ** 2
-
-        mel_filters = torch.from_numpy(self.mel_filters).to(device, torch.float32)
-        mel_spec = mel_filters.T @ magnitudes
-
-        log_spec = torch.clamp(mel_spec, min=1e-10).log10()
-        if waveform.dim() == 2:
-            max_val = log_spec.max(dim=2, keepdim=True)[0].max(dim=1, keepdim=True)[0]
-            log_spec = torch.maximum(log_spec, max_val - 8.0)
-        else:
-            log_spec = torch.maximum(log_spec, log_spec.max() - 8.0)
-        log_spec = (log_spec + 4.0) / 4.0
+        module = self.to_exportable_module().to(device)
+        log_spec = module(waveform)
         if device != "cpu":
             log_spec = log_spec.detach().cpu()
         return log_spec.numpy()
@@ -314,10 +329,10 @@ def __call__(
         # make sure list is in array format
         input_features = padded_inputs.get("input_features").transpose(2, 0, 1)
 
-        extract_fbank_features = (
-            self._torch_extract_fbank_features if is_torch_available() else self._np_extract_fbank_features
-        )
-        input_features = extract_fbank_features(input_features[0], device)
+        if is_torch_available() and device != "cpu":
+            input_features = self._torch_extract_fbank_features(input_features[0], device=device)
+        else:
+            input_features = self._np_extract_fbank_features(input_features[0])
 
         if isinstance(input_features[0], list):
             padded_inputs["input_features"] = [np.asarray(feature, dtype=np.float32) for feature in input_features]
diff --git a/src/transformers/models/whisper/generation_whisper.py b/src/transformers/models/whisper/generation_whisper.py
index 1f9c9843d34a..fa48c30ecf7e 100644
--- a/src/transformers/models/whisper/generation_whisper.py
+++ b/src/transformers/models/whisper/generation_whisper.py
@@ -893,6 +893,7 @@ def generate(
                     idx=i,
                     return_token_timestamps=return_token_timestamps,
                     decoder_input_ids=decoder_input_ids,
+                    max_frames=max_frames[i],
                 )
 
                 seek[prev_i] += segment_offset
@@ -1060,11 +1061,15 @@ def generate_with_fallback(
             new_decoder_input_ids = []
             new_decoder_attention_mask = []
 
+            eos_token_id = generation_config.eos_token_id
+            if isinstance(eos_token_id, int):
+                eos_token_id = [eos_token_id]
+
             for i, seek_sequence in enumerate(seek_sequences):
                 # remove all padding tokens, except for the eos token
                 if seek_sequence[-1] == generation_config.pad_token_id:
                     num_paddings = (seek_sequence == generation_config.pad_token_id).sum()
-                    if generation_config.pad_token_id == generation_config.eos_token_id:
+                    if eos_token_id is not None and generation_config.pad_token_id in eos_token_id:
                         # we do not remove the eos token id since it is needed for avg logprob calculation in _need_fallback
                         num_paddings -= 1
                     if num_paddings != 0:
@@ -1082,7 +1087,7 @@ def generate_with_fallback(
                 )
 
                 # remove eos token
-                if seek_sequence[-1] == generation_config.eos_token_id:
+                if eos_token_id is not None and seek_sequence[-1].item() in eos_token_id:
                     seek_sequence = seek_sequence[:-1]
 
                 seek_sequence_list[fallback_index_map[i]] = seek_sequence
@@ -1986,6 +1991,7 @@ def _retrieve_segment(
         idx,
         return_token_timestamps,
         decoder_input_ids,
+        max_frames,
     ):
         # find the predicted "end of segment" predictions of Whisper
         # "end of segment" predictions occur whenever Whisper predicts a timestamp token
@@ -2055,6 +2061,16 @@ def _retrieve_segment(
                 last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(
                     torch.float32 if device.type == "mps" else torch.float64
                 )
+                add_time_offset = torch.round(time_offset[prev_idx] / time_precision).to(seek_sequence.dtype)
+                if (add_time_offset != 0).any():
+                    seek_sequence[timestamp_tokens] += add_time_offset
+                    # Ensure the added offset does not exceed the chunk length; otherwise, the timestamp may surpass Whisper's hard token id limit at <|30.00|>.
+                    max_timestamp_token_id = timestamp_begin + int(max_frames * 0.01 / time_precision)
+                    seek_sequence = seek_sequence.clamp(max=max_timestamp_token_id)
+                    if isinstance(seek_outputs[0], torch.Tensor):
+                        seek_outputs[idx][idx_offset : idx_offset + len(seek_sequence)] = seek_sequence
+                    elif isinstance(seek_outputs[0], dict):
+                        seek_outputs[idx]["sequences"][idx_offset : idx_offset + len(seek_sequence)] = seek_sequence
             segments = [
                 {
                     "start": time_offset[prev_idx],
diff --git a/src/transformers/models/whisper/processing_whisper.py b/src/transformers/models/whisper/processing_whisper.py
index 1d1b33f3c155..7eb97c24f7e3 100644
--- a/src/transformers/models/whisper/processing_whisper.py
+++ b/src/transformers/models/whisper/processing_whisper.py
@@ -30,28 +30,17 @@ def get_decoder_prompt_ids(self, task=None, language=None, no_timestamps=True):
     @auto_docstring
     def __call__(self, *args, **kwargs):
         audio = kwargs.pop("audio", None)
-        sampling_rate = kwargs.pop("sampling_rate", None)
         text = kwargs.pop("text", None)
+
+        # for BC
         if len(args) > 0:
             audio = args[0]
             args = args[1:]
 
-        if audio is None and text is None:
-            raise ValueError("You need to specify either an `audio` or `text` input to process.")
-
-        if audio is not None:
-            inputs = self.feature_extractor(audio, *args, sampling_rate=sampling_rate, **kwargs)
+        outputs = super().__call__(audio=audio, text=text, **kwargs)
         if text is not None:
-            encodings = self.tokenizer(text, **kwargs)
-
-        if text is None:
-            return inputs
-
-        elif audio is None:
-            return encodings
-        else:
-            inputs["labels"] = encodings["input_ids"]
-            return inputs
+            outputs["labels"] = outputs["input_ids"]
+        return outputs
 
     def get_prompt_ids(self, text: str, return_tensors="np"):
         return self.tokenizer.get_prompt_ids(text, return_tensors=return_tensors)
diff --git a/src/transformers/models/whisper/tokenization_whisper.py b/src/transformers/models/whisper/tokenization_whisper.py
index 1c56d1da765d..75da14860554 100644
--- a/src/transformers/models/whisper/tokenization_whisper.py
+++ b/src/transformers/models/whisper/tokenization_whisper.py
@@ -493,23 +493,26 @@ def decode(
             remove_diacritics=remove_diacritics,
             **kwargs,
         )
+
+        # decode/ batch decode is now unified
+        is_batch = isinstance(text, list)
+        texts = text if is_batch else [text]
+        token_ids = token_ids if is_batch else [token_ids]
+
         if decode_with_timestamps:
-            # legacy method to decode timestamps when not included in the tokenizer vocabulary
-            text = self._decode_with_timestamps(
-                filtered_ids, time_precision=time_precision, skip_special_tokens=skip_special_tokens
-            )
+            texts = [
+                self._decode_with_timestamps(t, time_precision=time_precision, skip_special_tokens=skip_special_tokens)
+                for t in texts
+            ]
         else:
-            # Handle both single string and batch (list of strings) outputs
-            if isinstance(text, list):
-                text = [self._filter_timestamp_ids(t) for t in text]
-            else:
-                text = self._filter_timestamp_ids(text)
+            texts = [self._filter_timestamp_ids(t) for t in texts]
 
-        # retrieve offsets
         if output_offsets:
-            offsets = self._compute_offsets(token_ids, time_precision=time_precision)
-            return {"text": text, "offsets": offsets}
-        return text
+            offsets = [self._compute_offsets(t, time_precision=time_precision) for t in token_ids]
+            results = [{"text": t, "offsets": o} for t, o in zip(texts, offsets)]
+            return results if is_batch else results[0]
+
+        return texts if is_batch else texts[0]
 
     def _decode(
         self, *args, normalize: bool = False, basic_normalize: bool = False, remove_diacritics: bool = False, **kwargs
diff --git a/src/transformers/models/xlm/modeling_xlm.py b/src/transformers/models/xlm/modeling_xlm.py
index 9a2bee1d183d..7466f5d95960 100755
--- a/src/transformers/models/xlm/modeling_xlm.py
+++ b/src/transformers/models/xlm/modeling_xlm.py
@@ -79,12 +79,12 @@ def get_masks(slen, lengths, causal, padding_mask=None):
     return mask, attn_mask
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of question answering models using a [`~modeling_utils.XLMSQuADHead`].
     """
 )
+@dataclass
 class XLMSquadHeadOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
@@ -638,12 +638,12 @@ def _init_weights(self, module):
             init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1)))
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Base class for outputs of question answering models using a `XLMSQuADHead`.
     """
 )
+@dataclass
 class XLMForQuestionAnsweringOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
diff --git a/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py b/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
index 76653e7f644c..bce50bffb07a 100644
--- a/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
+++ b/src/transformers/models/xlm_roberta/modeling_xlm_roberta.py
@@ -106,7 +106,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/xlnet/modeling_xlnet.py b/src/transformers/models/xlnet/modeling_xlnet.py
index 702989331287..ce29b5dea44b 100755
--- a/src/transformers/models/xlnet/modeling_xlnet.py
+++ b/src/transformers/models/xlnet/modeling_xlnet.py
@@ -652,12 +652,12 @@ def _init_weights(self, module):
             init.normal_(module.mask_emb, mean=0.0, std=self.config.initializer_range)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetModel`].
     """
 )
+@dataclass
 class XLNetModelOutput(ModelOutput):
     r"""
     last_hidden_state (`torch.FloatTensor` of shape `(batch_size, num_predict, hidden_size)`):
@@ -677,12 +677,12 @@ class XLNetModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetLMHeadModel`].
     """
 )
+@dataclass
 class XLNetLMHeadModelOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
@@ -705,12 +705,12 @@ class XLNetLMHeadModelOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetForSequenceClassification`].
     """
 )
+@dataclass
 class XLNetForSequenceClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `label` is provided):
@@ -730,12 +730,12 @@ class XLNetForSequenceClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetForTokenClassificationOutput`].
     """
 )
+@dataclass
 class XLNetForTokenClassificationOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -755,12 +755,12 @@ class XLNetForTokenClassificationOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetForMultipleChoice`].
     """
 )
+@dataclass
 class XLNetForMultipleChoiceOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
@@ -782,12 +782,12 @@ class XLNetForMultipleChoiceOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetForQuestionAnsweringSimple`].
     """
 )
+@dataclass
 class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
@@ -810,12 +810,12 @@ class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
     attentions: tuple[torch.FloatTensor, ...] | None = None
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`XLNetForQuestionAnswering`].
     """
 )
+@dataclass
 class XLNetForQuestionAnsweringOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
diff --git a/src/transformers/models/xmod/modeling_xmod.py b/src/transformers/models/xmod/modeling_xmod.py
index 79ef73d34254..5e77ecc3d611 100644
--- a/src/transformers/models/xmod/modeling_xmod.py
+++ b/src/transformers/models/xmod/modeling_xmod.py
@@ -101,7 +101,7 @@ def forward(
         if token_type_ids is None:
             if hasattr(self, "token_type_ids"):
                 # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0])
-                buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1)
+                buffered_token_type_ids = self.token_type_ids.to(position_ids.device).expand(position_ids.shape[0], -1)
                 buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids)
                 token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length)
             else:
diff --git a/src/transformers/models/yolos/modeling_yolos.py b/src/transformers/models/yolos/modeling_yolos.py
index f44e11638806..3b1849cd2196 100755
--- a/src/transformers/models/yolos/modeling_yolos.py
+++ b/src/transformers/models/yolos/modeling_yolos.py
@@ -34,12 +34,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Output type of [`YolosForObjectDetection`].
     """
 )
+@dataclass
 class YolosObjectDetectionOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)):
diff --git a/src/transformers/models/youtu/configuration_youtu.py b/src/transformers/models/youtu/configuration_youtu.py
index 6d9f2cef1f96..a9b868c5b10a 100644
--- a/src/transformers/models/youtu/configuration_youtu.py
+++ b/src/transformers/models/youtu/configuration_youtu.py
@@ -35,6 +35,11 @@
 @strict
 class YoutuConfig(PreTrainedConfig):
     r"""
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
     rope_interleave (`bool`, *optional*, defaults to `True`):
         Whether to interleave the rotary position embeddings.
     embedding_initializer_range (`float`, *optional*):
diff --git a/src/transformers/models/youtu/modeling_youtu.py b/src/transformers/models/youtu/modeling_youtu.py
index d40bef358da6..261f13351798 100644
--- a/src/transformers/models/youtu/modeling_youtu.py
+++ b/src/transformers/models/youtu/modeling_youtu.py
@@ -289,7 +289,7 @@ def __init__(self, config: YoutuConfig, layer_idx: int):
             self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.qk_head_dim, bias=False)
         else:
             self.q_a_proj = nn.Linear(config.hidden_size, config.q_lora_rank, bias=config.attention_bias)
-            self.q_a_layernorm = YoutuRMSNorm(config.q_lora_rank)
+            self.q_a_layernorm = YoutuRMSNorm(config.q_lora_rank, eps=config.rms_norm_eps)
             self.q_b_proj = nn.Linear(config.q_lora_rank, self.num_heads * self.qk_head_dim, bias=False)
 
         self.kv_a_proj_with_mqa = nn.Linear(
@@ -297,7 +297,7 @@ def __init__(self, config: YoutuConfig, layer_idx: int):
             self.kv_lora_rank + self.qk_rope_head_dim,
             bias=config.attention_bias,
         )
-        self.kv_a_layernorm = YoutuRMSNorm(self.kv_lora_rank)
+        self.kv_a_layernorm = YoutuRMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
         self.kv_b_proj = nn.Linear(
             self.kv_lora_rank,
             self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
diff --git a/src/transformers/models/youtu/modular_youtu.py b/src/transformers/models/youtu/modular_youtu.py
index b2de3a2df0a5..ca2b06849d5f 100644
--- a/src/transformers/models/youtu/modular_youtu.py
+++ b/src/transformers/models/youtu/modular_youtu.py
@@ -45,6 +45,11 @@
 @strict
 class YoutuConfig(DeepseekV3Config):
     r"""
+    num_nextn_predict_layers (`int`, *optional*, defaults to 0):
+        Number of Multi-Token Prediction (MTP) modules appended after the base
+        transformer. When `0`, the model behaves as a standard decoder. When `>0`,
+        each extra module predicts one additional future token at inference time
+        (speculative decoding via `generate(..., use_mtp=True)`).
     rope_interleave (`bool`, *optional*, defaults to `True`):
         Whether to interleave the rotary position embeddings.
     embedding_initializer_range (`float`, *optional*):
diff --git a/src/transformers/models/yue/feature_extraction_yue.py b/src/transformers/models/yue/feature_extraction_yue.py
new file mode 100644
index 000000000000..6c45393db39f
--- /dev/null
+++ b/src/transformers/models/yue/feature_extraction_yue.py
@@ -0,0 +1,121 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Feature extractor class for YuE."""
+
+import numpy as np
+
+from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
+from ...feature_extraction_utils import BatchFeature
+from ...utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class YuEFeatureExtractor(SequenceFeatureExtractor):
+    model_input_names = ["input_values", "padding_mask"]
+
+    def __init__(
+        self,
+        feature_size=1,
+        sampling_rate=16000,
+        padding_value=0.0,
+        hop_length=320,
+        **kwargs,
+    ):
+        super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
+        self.hop_length = hop_length
+
+    def __call__(
+        self,
+        raw_audio,
+        padding=None,
+        truncation=False,
+        max_length=None,
+        return_tensors="pt",
+        sampling_rate=None,
+        pad_to_multiple_of=None,
+    ):
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"Expected {self.sampling_rate} Hz audio but got {sampling_rate} Hz,"
+                    f"please make sure that the provided audio input was sampled with {self.sampling_rate}."
+                )
+
+        else:
+            logger.warning(
+                f"It is strongly recommended to pass the `sampling_rate` argument to `{self.__class__.__name__}()`. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
+        if padding and truncation:
+            raise ValueError("Both padding and truncation were set. Set only one.")
+
+        elif padding is None:
+            padding = True
+
+        is_batched = (
+            isinstance(raw_audio, (list, tuple))
+            and len(raw_audio) > 0
+            and isinstance(raw_audio[0], (np.ndarray, list, tuple))
+        )
+
+        if is_batched:
+            raw_audio = [np.asarray(_audio, dtype=np.float32) for _audio in raw_audio]
+
+        elif not isinstance(raw_audio, np.ndarray):
+            raw_audio = np.asarray(raw_audio, dtype=np.float32)
+
+        if not is_batched:
+            raw_audio = [raw_audio]
+
+        for i, audio in enumerate(raw_audio):
+            if audio.ndim > 2:
+                raise ValueError(f"Expected input shape (channels, length) but got shape {audio.shape}")
+
+            if self.feature_size == 1 and audio.ndim == 2:
+                logger.warning(
+                    "The model corresponding to this feature extractor expects a mono channel audio."
+                    "We're averaging the audio signals into mono."
+                )
+
+                audio = np.mean(audio, -1)
+
+            raw_audio[i] = audio
+
+        batch = BatchFeature({"input_values": raw_audio})
+
+        padded = self.pad(
+            batch,
+            max_length=max_length,
+            truncation=truncation,
+            padding=padding,
+            return_attention_mask=True,
+            pad_to_multiple_of=pad_to_multiple_of,
+        )
+
+        padded["padding_mask"] = padded.pop("attention_mask")
+
+        values = []
+
+        for example in padded.pop("input_values"):
+            example = np.asarray(example, dtype=np.float32)
+            values.append(example[None, :])
+        padded["input_values"] = values
+
+        if return_tensors is not None:
+            padded = padded.convert_to_tensors(return_tensors)
+
+        return padded
diff --git a/src/transformers/models/yue/modular_yue.py b/src/transformers/models/yue/modular_yue.py
new file mode 100644
index 000000000000..17c64be4eded
--- /dev/null
+++ b/src/transformers/models/yue/modular_yue.py
@@ -0,0 +1,14 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""YuE model."""
diff --git a/src/transformers/models/yue/processing_yue.py b/src/transformers/models/yue/processing_yue.py
new file mode 100644
index 000000000000..b394b9a00357
--- /dev/null
+++ b/src/transformers/models/yue/processing_yue.py
@@ -0,0 +1,379 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Processor class for YuE"""
+
+import re
+
+import numpy as np
+
+from ...audio_utils import make_list_of_audio
+from ...processing_utils import AudioKwargs, BatchFeature, ProcessingKwargs, ProcessorMixin
+from ...utils import is_torch_available
+
+
+if is_torch_available():
+    import torch
+
+
+class YuEAudioKwargs(AudioKwargs, total=False):
+    eoa_token_id: int
+    soa_token_id: int
+    xcodec_marker_token_id: int
+    start_of_reference_token_id: int
+    end_of_reference_token_id: int
+    prompt_start_time: float
+    prompt_end_time: float
+    codebook_size: int
+    num_codebooks: int
+    global_offset: int
+    fps: int
+    sample_rate: int
+
+
+class YuEProcessorKwargs(ProcessingKwargs, total=False):
+    audio_kwargs: YuEAudioKwargs
+    _defaults = {
+        "text_kwargs": {
+            "padding": True,
+            "truncation": False,
+            "add_special_tokens": False,
+        },
+        "audio_kwargs": {
+            "eoa_token_id": 32002,
+            "soa_token_id": 32001,
+            "xcodec_marker_token_id": 32016,
+            "start_of_reference_token_id": [518, 2962, 29918, 974, 29918, 5679, 29962],
+            "end_of_reference_token_id": [518, 2962, 29918, 974, 29918, 5679, 29962],
+            "prompt_start_time": 0.0,
+            "prompt_end_time": 5.0,
+            "codebook_size": 1024,
+            "num_codebooks": 12,
+            "global_offset": 45334,
+            "fps": 50,
+            "sample_rate": 16000,
+        },
+        "common_kwargs": {"return_tensors": "pt"},
+    }
+
+
+class YuEProcessor(ProcessorMixin):
+    """
+    Constructs a YuE processor which wraps a YuE tokenizer and a finetuned XCodec audio tokenizer into a single processor.
+
+    [`YuEProcessor`] offers all the functionalities of [`YuETokenizer`] and [`XCodecModel`]. See the
+    [`~YuEProcessor.__call__`] and [`~YuEProcessor.decode`] for more information.
+
+    Args:
+        tokenizer ([`YuETokenizer`]):
+            The tokenizer is a required input.
+        audio_tokenizer ([`XCodecModel`]):
+            The audio tokenizer is a required input.
+    """
+
+    tokenizer_class = "YuETokenizer"
+    audio_tokenizer_class = "XCodecModel"
+    attributes = ["tokenizer", "audio_tokenizer"]
+
+    def __init__(self, tokenizer, audio_tokenizer, feature_extractor):
+        self.tokenizer = tokenizer
+        self.audio_tokenizer = audio_tokenizer
+        self.feature_extractor = feature_extractor
+
+    def __call__(
+        self,
+        text=None,
+        lyrics_segments=None,
+        genre_tags=None,
+        audio=None,
+        return_tensors=None,
+        **kwargs,
+    ):
+        output_kwargs = self._merge_kwargs(YuEProcessorKwargs, **kwargs)
+        text_kwargs = output_kwargs["text_kwargs"]
+        audio_kwargs = output_kwargs["audio_kwargs"]
+
+        batch_lyrics_segments, batch_genre_tags = self._normalize_inputs(text, lyrics_segments, genre_tags)
+        batch_main_prompts = [
+            self._build_main_prompt(segments, genres)
+            for segments, genres in zip(batch_lyrics_segments, batch_genre_tags)
+        ]
+
+        text_kwargs.pop("return_tensors", None)
+
+        # tokenize main prompt with genre and full lyrics (this is head_ids)
+        tokenizer_output = self.tokenizer(batch_main_prompts, **text_kwargs)
+        head_prompt_ids = tokenizer_output["input_ids"]
+        head_attention_mask = tokenizer_output["attention_mask"]
+
+        if audio is not None and self.audio_tokenizer is not None:
+            print("first audio: ", [au.shape for au in audio])
+            audio = make_list_of_audio(audio)
+
+            print("after make_list_of_audio: ", [au.shape for au in audio])
+
+            input_audios = self.feature_extractor(audio, sampling_rate=audio_kwargs.get("sample_rate"))
+
+            print("YuEProcessor FE: input_values shape =", input_audios["input_values"].shape)
+            print("YuEProcessor FE: padding_mask shape =", input_audios["padding_mask"].shape)
+
+            with torch.no_grad():
+                encoded = self.audio_tokenizer.encode(
+                    input_values=input_audios["input_values"],  # (B, 1, T)
+                    bandwidth=0.5,
+                )
+                audio_codes = encoded.audio_codes  # (B, num_codebooks, T_frames)
+
+            print("YuEProcessor: audio_codes shape =", audio_codes.shape)
+
+            # update heads with audio prompt tokens, batched
+            head_prompt_ids, head_attention_mask = self._process_audio_prompt(
+                head_prompt_ids, head_attention_mask, audio_codes, audio_kwargs, self.tokenizer.pad_token_id
+            )
+
+        # batching segments so that lyrics_segments_ids shape is (batch_size, max_num_segments, max_segment_length)
+        # so that the stage 1 generation loop can iterate over lyrics_segments_ids[:, segment_idx, :]
+        # to support batched generation seamlessly
+
+        # max_num_segments is the max number of segments in the batch.
+        batch_size = len(batch_lyrics_segments)
+        max_num_segments = max(len(segment) for segment in batch_lyrics_segments)
+        segment_token_ids = []
+        max_segment_length = 0
+
+        for segment_position in range(max_num_segments):
+            # build the list of segment texts at this position for each sample
+            segments_at_position = []
+            for i in range(batch_size):
+                if segment_position < len(batch_lyrics_segments[i]):
+                    segments_at_position.append(batch_lyrics_segments[i][segment_position])
+                else:
+                    segments_at_position.append("")
+
+            tokenized = self.tokenizer(segments_at_position, **text_kwargs)
+            ids_per_batch = tokenized["input_ids"]
+
+            # making sure missing segments are represented as empty lists
+            for sample_idx, segment_text in enumerate(segments_at_position):
+                if not segment_text:
+                    ids_per_batch[sample_idx] = []
+
+            max_len_seg = max(len(ids) for ids in ids_per_batch) if ids_per_batch else 0
+            max_segment_length = max(max_segment_length, max_len_seg)
+            segment_token_ids.append(ids_per_batch)
+
+        # pad to (batch_size, max_num_segments, max_segment_length) with missing segments have all pad
+        lyrics_segments_ids = []
+        lyrics_attention_mask = []
+
+        for batch_idx in range(batch_size):
+            sample_segment_ids = []
+            sample_segment_mask = []
+
+            for segment_idx in range(max_num_segments):
+                ids = segment_token_ids[segment_idx][batch_idx]
+                if not ids:
+                    # filling missing segments with padding values
+                    sample_segment_ids.append([self.tokenizer.pad_token_id] * max_segment_length)
+                    sample_segment_mask.append([0] * max_segment_length)
+                else:
+                    pad_len = max_segment_length - len(ids)
+                    sample_segment_ids.append(ids + [self.tokenizer.pad_token_id] * pad_len)
+                    sample_segment_mask.append([1] * len(ids) + [0] * pad_len)
+
+            lyrics_segments_ids.append(sample_segment_ids)
+            lyrics_attention_mask.append(sample_segment_mask)
+
+        data = {
+            "head_prompt_ids": torch.tensor(head_prompt_ids),
+            "head_attention_mask": torch.tensor(head_attention_mask),
+            "lyrics_segments_ids": torch.tensor(lyrics_segments_ids),
+            "lyrics_attention_mask": torch.tensor(lyrics_attention_mask),
+        }
+
+        return BatchFeature(data=data, tensor_type=return_tensors)
+
+    @staticmethod
+    def _split_lyrics_into_segments(lyrics):
+        """Split lyrics into segments based on structure tags like [verse], [chorus], etc"""
+        pattern = r"\[(\w+)\](.*?)(?=\[|\Z)"
+        segments = re.findall(pattern, lyrics, re.DOTALL)
+        structured_lyrics = [f"[{seg[0]}]\n{seg[1].strip()}\n\n" for seg in segments]
+        return structured_lyrics
+
+    @staticmethod
+    def _build_main_prompt(segments, genres):
+        genres = ", ".join(genres) if genres else ""
+        full_lyrics = "\n".join(segments)
+        return f"Generate music from the given lyrics segment by segment.\n[Genre] {genres}\n{full_lyrics}"
+
+    def _normalize_inputs(self, text, lyrics_segments, genre_tags):
+        if text is None and lyrics_segments is None:
+            raise ValueError("Either `lyrics_segments` or `text` must be provided.")
+
+        if text is not None:
+            if isinstance(text, str):
+                lyrics_segments = [self._split_lyrics_into_segments(text)]
+            elif isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text):
+                lyrics_segments = [self._split_lyrics_into_segments(t) for t in text]
+            else:
+                raise ValueError("Invalid input `text`. Please provide a string or a list of strings")
+
+        if lyrics_segments is not None:
+            if isinstance(lyrics_segments, list):
+                if isinstance(lyrics_segments[0], str):
+                    lyrics_segments = [lyrics_segments]
+
+                elif all(isinstance(segment_list, list) for segment_list in lyrics_segments):
+                    lyrics_segments = [list(segment_list) for segment_list in lyrics_segments]
+            else:
+                raise ValueError(
+                    "Invalid input lyrics_segments. Please provide a list of strings or a list of list of strings as batch"
+                )
+
+        if genre_tags is not None:
+            if isinstance(genre_tags, str):
+                genre_tags = [[genre_tags]]
+            elif isinstance(genre_tags, (list, tuple)) and all(isinstance(tag, str) for tag in genre_tags):
+                genre_tags = [list(genre_tags)]
+            elif isinstance(genre_tags, (list, tuple)) and all(
+                isinstance(tags, (list, tuple)) and all(isinstance(tag, str) for tag in tags) for tags in genre_tags
+            ):
+                genre_tags = [list(tags) for tags in genre_tags]
+        else:
+            raise ValueError(
+                "Please provide `genre_tags`, it must be str, a list of strings or a list of list of strings as batch"
+            )
+
+        return lyrics_segments, genre_tags
+
+    def _process_audio_prompt(self, head_prompt_ids, head_attention_mask, audio_codes, audio_kwargs, pad_token_id):
+        fps = audio_kwargs.get("fps", 50)
+        prompt_start_time = audio_kwargs.get("prompt_start_time", 0.0)
+        prompt_end_time = audio_kwargs.get("prompt_end_time", None)
+
+        eoa_token_id = audio_kwargs.get("eoa_token_id")
+        soa_token_id = audio_kwargs.get("soa_token_id")
+        xcodec_marker_token_id = audio_kwargs.get("xcodec_marker_token_id")
+
+        batch_size = len(head_prompt_ids)
+        print("YuEProcessor: _process_audio_prompt batch_size =", batch_size)
+        print("YuEProcessor: _process_audio_prompt audio_codes shape =", audio_codes.shape)
+
+        audio_augmented_heads = []
+
+        for i in range(batch_size):
+            head_ids = [token for token in head_prompt_ids[i] if token != pad_token_id]
+            print(f" sample {i}: original head len =", len(head_ids))
+
+            codes_i = audio_codes[i : i + 1, 0, :].cpu().numpy()
+            print(f" sample {i}: codes_i shape =", codes_i.shape)
+
+            audio_ids_full = self._offset_and_flatten_tokens(codes_i, audio_kwargs)
+            print(f" sample {i}: audio_ids_full len =", len(audio_ids_full))
+
+            start = int(prompt_start_time * fps)
+            end = int(prompt_end_time * fps)
+            audio_ids = audio_ids_full[start:end]
+            print(f" sample {i}: slicing frames [{start}:{end}] -> len =", len(audio_ids))
+
+            # [SOA] + <xcodec> + codes + [EOA]
+            audio_ids = [soa_token_id] + [xcodec_marker_token_id] + audio_ids + [eoa_token_id]
+
+            start_of_reference = self.tokenizer("[start_of_reference]", add_special_tokens=False)["input_ids"]
+            end_of_reference = self.tokenizer("[end_of_reference]", add_special_tokens=False)["input_ids"]
+            audio_ids = start_of_reference + audio_ids + end_of_reference
+            print(f" sample {i}: audio prompt tokens len =", len(audio_ids))
+
+            full_ids = head_ids + audio_ids
+            print(f" sample {i}: new head len {len(full_ids)}")
+
+            audio_augmented_heads.append(full_ids)
+
+        encoded = {"input_ids": audio_augmented_heads}
+        padded = self.tokenizer.pad(encoded, padding=True, return_attention_mask=True, return_tensors=None)
+
+        padded_heads = padded["input_ids"]
+        padded_masks = padded["attention_mask"]
+
+        return padded_heads, padded_masks
+
+    def _offset_and_flatten_tokens(self, audio_codes, audio_kwargs):
+        print("audio_codes.shape :", audio_codes.shape)
+        if audio_codes.ndim != 2 or audio_codes.shape[0] != 1:
+            raise ValueError(f"Audio codes shape should be (1, T), got {audio_codes.shape}")
+
+        # TODO handle this as well
+        codebook_size = audio_kwargs.get("codebook_size", 1024)
+        global_offset = audio_kwargs.get("global_offset", 45334)
+
+        if audio_codes.max() >= codebook_size:
+            raise ValueError(f"max(audio_codes)={audio_codes.max()}, codebook_size={codebook_size}")
+        if audio_codes.min() < 0:
+            raise ValueError(f"min(audio_codes)={audio_codes.min()}, must be >= 0")
+
+        # apply offset to audio codes then flatten like original yue implementation
+        # does  offset = global_offset + k * codebook_size for each quantizer k
+        # for one quantizer k=0 so only global_offset is added
+        # see https://github.com/multimodal-art-projection/YuE/blob/main/inference/codecmanipulator.py#L90
+
+        offset_codes = audio_codes.copy().astype(np.uint32)
+        offset_codes[0] += global_offset
+        flattened_tokens = offset_codes.flatten()
+
+        return flattened_tokens.tolist()
+
+    @staticmethod
+    def _build_main_prompt(segments: list[str], genres: list[str]) -> str:
+        genres = ", ".join(genres) if genres else ""
+        full_lyrics = "\n".join(segments)
+        return f"Generate music from the given lyrics segment by segment.\n[Genre] {genres}\n{full_lyrics}"
+
+    def _normalize_inputs(self, text, lyrics_segments, genre_tags):
+        if text is None and lyrics_segments is None:
+            raise ValueError("Either `lyrics_segments` or `text` must be provided.")
+
+        if text is not None:
+            if isinstance(text, str):
+                lyrics_segments = [self._split_lyrics_into_segments(text)]
+            elif isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text):
+                lyrics_segments = [self._split_lyrics_into_segments(t) for t in text]
+            else:
+                raise ValueError("Invalid input `text`. Please provide a string or a list of strings")
+
+        if lyrics_segments is not None:
+            if isinstance(lyrics_segments, list):
+                if isinstance(lyrics_segments[0], str):
+                    lyrics_segments = [lyrics_segments]
+                elif all(isinstance(segment_list, list) for segment_list in lyrics_segments):
+                    lyrics_segments = [list(segment_list) for segment_list in lyrics_segments]
+            else:
+                raise ValueError(
+                    "Invalid input lyrics_segments. Please provide a list of strings or a list of list of strings as batch"
+                )
+
+        if genre_tags is not None:
+            if isinstance(genre_tags, str):
+                genre_tags = [[genre_tags]]
+            elif isinstance(genre_tags, (list, tuple)) and all(isinstance(tag, str) for tag in genre_tags):
+                genre_tags = [list(genre_tags)]
+            elif isinstance(genre_tags, (list, tuple)) and all(
+                isinstance(tags, (list, tuple)) and all(isinstance(tag, str) for tag in tags) for tags in genre_tags
+            ):
+                genre_tags = [list(tags) for tags in genre_tags]
+        else:
+            raise ValueError(
+                "Please provide `genre_tags`, it must be str, a list of strings or a list of list of strings as batch"
+            )
+
+        return lyrics_segments, genre_tags
diff --git a/src/transformers/models/yue/tokenization_yue.py b/src/transformers/models/yue/tokenization_yue.py
new file mode 100644
index 000000000000..9d3306192cbc
--- /dev/null
+++ b/src/transformers/models/yue/tokenization_yue.py
@@ -0,0 +1,121 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization class for YuE."""
+
+from typing import Any
+
+import sentencepiece as spm
+
+from ...tokenization_utils import AddedToken, PreTrainedTokenizer
+from ...utils import logging
+from ...utils.import_utils import requires
+
+
+logger = logging.get_logger(__name__)
+
+# original in https://github.com/multimodal-art-projection/YuE/blob/main/inference/mm_tokenizer_v0.2_hf/tokenizer.model
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+
+@requires(backends=("sentencepiece",))
+class YuETokenizer(PreTrainedTokenizer):
+    """
+    Construct YuE tokenizer based on [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file: str,
+        bos_token=None,
+        eos_token=None,
+        unk_token="<unk>",
+        pad_token="<pad>",
+        additional_special_tokens=None,
+        sp_model_kwargs: dict[str, Any] | None = None,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+
+        self.vocab_file = vocab_file
+
+        self.sp_model.Load(self.vocab_file)
+
+        special_tokens = ["<SOA>", "<EOA>", "<xcodec>", "<stage_1>", "<stage_2>"]
+
+        if additional_special_tokens is None:
+            additional_special_tokens = special_tokens
+        else:
+            additional_special_tokens = list(set(special_tokens + additional_special_tokens))
+
+        unk_token = AddedToken(unk_token, special=True, normalized=False) if isinstance(unk_token, str) else unk_token
+        pad_token = AddedToken(pad_token, special=True, normalized=False) if isinstance(pad_token, str) else pad_token
+        additional_special_tokens = [
+            AddedToken(token, special=True, normalized=False) for token in additional_special_tokens
+        ]
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            additional_special_tokens=additional_special_tokens,
+            sp_model_kwargs=self.sp_model_kwargs,
+            **kwargs,
+        )
+
+        self.soa_token_id = self.convert_tokens_to_ids("<SOA>")
+        self.eoa_token_id = self.convert_tokens_to_ids("<EOA>")
+        self.xcodec_token_id = self.convert_tokens_to_ids("<xcodec>")
+
+    @property
+    def vocab_size(self):
+        return len(self.sp_model)
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(self.vocab_file)
+
+    def _tokenize(self, text):
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        return self.sp_model.PieceToId(token)
+
+    def _convert_id_to_token(self, index):
+        return self.sp_model.IdToPiece(index)
+
+    def convert_tokens_to_string(self, tokens):
+        return "".join(tokens).replace("▁", " ").strip()
diff --git a/src/transformers/models/zamba2/modeling_zamba2.py b/src/transformers/models/zamba2/modeling_zamba2.py
index 54f20adebcdf..a619dcd97aae 100644
--- a/src/transformers/models/zamba2/modeling_zamba2.py
+++ b/src/transformers/models/zamba2/modeling_zamba2.py
@@ -146,7 +146,7 @@ def forward(self, x, position_ids):
 
         device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
         with maybe_autocast(device_type=device_type, enabled=False):  # Force float32
-            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            freqs = (inv_freq_expanded.float() * position_ids_expanded.float()).transpose(1, 2)
             emb = torch.cat((freqs, freqs), dim=-1)
             cos = emb.cos() * self.attention_scaling
             sin = emb.sin() * self.attention_scaling
diff --git a/src/transformers/models/zoedepth/modeling_zoedepth.py b/src/transformers/models/zoedepth/modeling_zoedepth.py
index 7c58ad9901cc..2c107022a595 100644
--- a/src/transformers/models/zoedepth/modeling_zoedepth.py
+++ b/src/transformers/models/zoedepth/modeling_zoedepth.py
@@ -31,12 +31,12 @@
 logger = logging.get_logger(__name__)
 
 
-@dataclass
 @auto_docstring(
     custom_intro="""
     Extension of `DepthEstimatorOutput` to include domain logits (ZoeDepth specific).
     """
 )
+@dataclass
 class ZoeDepthDepthEstimatorOutput(ModelOutput):
     r"""
     loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
diff --git a/src/transformers/optimization.py b/src/transformers/optimization.py
index 64559c9b5910..b9ecf22f46b5 100644
--- a/src/transformers/optimization.py
+++ b/src/transformers/optimization.py
@@ -649,9 +649,9 @@ class GreedyLR:
             Number of epochs to wait before resuming normal operation after LR has been reduced.
         warmup (`int`, *optional*, defaults to 0):
             Number of epochs to wait before resuming normal operation after LR has been increased.
-        min_lr (`float` or `list[float]`, *optional*, defaults to 0.001):
+        min_lr (`float` or `list[float]`, *optional*, defaults to 1e-7):
             A lower bound on the learning rate.
-        max_lr (`float` or `list[float]`, *optional*, defaults to 1.0):
+        max_lr (`float` or `list[float]`, *optional*, defaults to 1e-4):
             An upper bound on the learning rate.
         eps (`float`, *optional*, defaults to 1e-08):
             Minimal decay applied to lr.
@@ -685,8 +685,8 @@ def __init__(
         threshold_mode: str = "abs",
         cooldown: int = 0,
         warmup: int = 0,
-        min_lr: float | list[float] = 1e-3,
-        max_lr: float | list[float] = 1.0,
+        min_lr: float | list[float] = 1e-7,
+        max_lr: float | list[float] = 1e-4,
         eps: float = 1e-8,
         verbose: bool = False,
         smooth: bool = False,
diff --git a/src/transformers/pipelines/__init__.py b/src/transformers/pipelines/__init__.py
index 6d11e0011514..f1d4ff217337 100755
--- a/src/transformers/pipelines/__init__.py
+++ b/src/transformers/pipelines/__init__.py
@@ -70,6 +70,8 @@
 from .keypoint_matching import KeypointMatchingPipeline
 from .mask_generation import MaskGenerationPipeline
 from .object_detection import ObjectDetectionPipeline
+from .promptable_concept_segmentation import PromptableConceptSegmentationPipeline
+from .promptable_visual_segmentation import PromptableVisualSegmentationPipeline
 from .table_question_answering import TableQuestionAnsweringArgumentHandler, TableQuestionAnsweringPipeline
 from .text_classification import TextClassificationPipeline
 from .text_generation import TextGenerationPipeline
@@ -81,6 +83,7 @@
     TokenClassificationPipeline,
 )
 from .video_classification import VideoClassificationPipeline
+from .video_to_text import VideoToTextPipeline
 from .zero_shot_audio_classification import ZeroShotAudioClassificationPipeline
 from .zero_shot_classification import ZeroShotClassificationArgumentHandler, ZeroShotClassificationPipeline
 from .zero_shot_image_classification import ZeroShotImageClassificationPipeline
@@ -104,12 +107,15 @@
         AutoModelForMaskGeneration,
         AutoModelForMultimodalLM,
         AutoModelForObjectDetection,
+        AutoModelForPromptableConceptSegmentation,
+        AutoModelForPromptableVisualSegmentation,
         AutoModelForQuestionAnswering,
         AutoModelForSemanticSegmentation,
         AutoModelForSeq2SeqLM,
         AutoModelForSequenceClassification,
         AutoModelForSpeechSeq2Seq,
         AutoModelForTableQuestionAnswering,
+        AutoModelForTDT,
         AutoModelForTextToSpectrogram,
         AutoModelForTextToWaveform,
         AutoModelForTokenClassification,
@@ -143,7 +149,7 @@
     },
     "automatic-speech-recognition": {
         "impl": AutomaticSpeechRecognitionPipeline,
-        "pt": (AutoModelForCTC, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),
+        "pt": (AutoModelForCTC, AutoModelForTDT, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),
         "default": {"model": ("facebook/wav2vec2-base-960h", "22aad52")},
         "type": "multimodal",
     },
@@ -264,6 +270,12 @@
         "default": {"model": ("MCG-NJU/videomae-base-finetuned-kinetics", "488eb9a")},
         "type": "video",
     },
+    "video-to-text": {
+        "impl": VideoToTextPipeline,
+        "pt": (AutoModelForImageTextToText,) if is_torch_available() else (),
+        "default": {"model": ("microsoft/git-base", "main")},
+        "type": "video",
+    },
     "mask-generation": {
         "impl": MaskGenerationPipeline,
         "pt": (AutoModelForMaskGeneration,) if is_torch_available() else (),
@@ -276,6 +288,18 @@
         "default": {"model": ("magic-leap-community/superglue_outdoor", "f4041f8")},
         "type": "image",
     },
+    "promptable-concept-segmentation": {
+        "impl": PromptableConceptSegmentationPipeline,
+        "pt": (AutoModelForPromptableConceptSegmentation,) if is_torch_available() else (),
+        "default": {"model": ("facebook/sam3", "main")},
+        "type": "multimodal",
+    },
+    "promptable-visual-segmentation": {
+        "impl": PromptableVisualSegmentationPipeline,
+        "pt": (AutoModelForPromptableVisualSegmentation,) if is_torch_available() else (),
+        "default": {"model": ("facebook/sam3", "3c879f3")},
+        "type": "multimodal",
+    },
     "any-to-any": {
         "impl": AnyToAnyPipeline,
         "tf": (),
@@ -412,6 +436,10 @@ def pipeline(task: Literal["object-detection"], model: str | PreTrainedModel | N
 @overload
 def pipeline(task: Literal["table-question-answering"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> TableQuestionAnsweringPipeline: ...
 @overload
+def pipeline(task: Literal["promptable-concept-segmentation"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> PromptableConceptSegmentationPipeline: ...
+@overload
+def pipeline(task: Literal["promptable-visual-segmentation"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> PromptableVisualSegmentationPipeline: ...
+@overload
 def pipeline(task: Literal["text-classification"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> TextClassificationPipeline: ...
 @overload
 def pipeline(task: Literal["text-generation"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> TextGenerationPipeline: ...
@@ -422,6 +450,8 @@ def pipeline(task: Literal["token-classification"], model: str | PreTrainedModel
 @overload
 def pipeline(task: Literal["video-classification"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> VideoClassificationPipeline: ...
 @overload
+def pipeline(task: Literal["video-to-text"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> VideoToTextPipeline: ...
+@overload
 def pipeline(task: Literal["zero-shot-audio-classification"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> ZeroShotAudioClassificationPipeline: ...
 @overload
 def pipeline(task: Literal["zero-shot-classification"], model: str | PreTrainedModel | None = None, config: str | PreTrainedConfig | None = None, tokenizer: str | PreTrainedTokenizer | PreTrainedTokenizerFast | None = None, feature_extractor: str | FeatureExtractionMixin | None = None, image_processor: str | BaseImageProcessor | None = None, processor: str | ProcessorMixin | None = None, revision: str | None = None, use_fast: bool = True, token: str | bool | None = None, device: int | str | torch.device | None = None, device_map: str | dict[str, int | str] | None = None, dtype: str | torch.dtype | None = "auto", trust_remote_code: bool | None = None, model_kwargs: dict[str, Any] | None = None, pipeline_class: Any | None = None, **kwargs: Any) -> ZeroShotClassificationPipeline: ...
@@ -698,6 +728,7 @@ def pipeline(
             - `"text-to-audio"` (alias `"text-to-speech"` available): will return a [`TextToAudioPipeline`]:.
             - `"token-classification"` (alias `"ner"` available): will return a [`TokenClassificationPipeline`].
             - `"video-classification"`: will return a [`VideoClassificationPipeline`].
+            - `"video-to-text"`: will return a [`VideoToTextPipeline`].
             - `"zero-shot-classification"`: will return a [`ZeroShotClassificationPipeline`].
             - `"zero-shot-image-classification"`: will return a [`ZeroShotImageClassificationPipeline`].
             - `"zero-shot-audio-classification"`: will return a [`ZeroShotAudioClassificationPipeline`].
diff --git a/src/transformers/pipelines/audio_classification.py b/src/transformers/pipelines/audio_classification.py
index 6e173111aa86..1bfe2763c7df 100644
--- a/src/transformers/pipelines/audio_classification.py
+++ b/src/transformers/pipelines/audio_classification.py
@@ -177,14 +177,7 @@ def preprocess(self, inputs):
         if isinstance(inputs, bytes):
             inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
 
-        if is_torch_available():
-            import torch
-
-            if isinstance(inputs, torch.Tensor):
-                inputs = inputs.cpu().numpy()
-
-        if is_torchcodec_available():
-            import torch
+        if is_torchcodec_available() and type(inputs).__module__.startswith("torchcodec."):
             import torchcodec
 
             if isinstance(inputs, torchcodec.decoders.AudioDecoder):
@@ -227,10 +220,14 @@ def preprocess(self, inputs):
                     self.feature_extractor.sampling_rate,
                 ).numpy()
 
+        if is_torch_available():
+            import torch
+
+            if isinstance(inputs, torch.Tensor):
+                inputs = inputs.cpu().numpy()
+
         if not isinstance(inputs, np.ndarray):
             raise TypeError("We expect a numpy ndarray or torch tensor as input")
-        if len(inputs.shape) != 1:
-            raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
 
         processed = self.feature_extractor(
             inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
diff --git a/src/transformers/pipelines/automatic_speech_recognition.py b/src/transformers/pipelines/automatic_speech_recognition.py
index 4817b4b2d37d..370840a10c1c 100644
--- a/src/transformers/pipelines/automatic_speech_recognition.py
+++ b/src/transformers/pipelines/automatic_speech_recognition.py
@@ -176,6 +176,8 @@ def __init__(
             self.type = "seq2seq_whisper"
         elif model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
             self.type = "seq2seq"
+        elif model.config.model_type == "parakeet_tdt":
+            self.type = "tdt"
         elif decoder is not None:
             self.decoder = decoder
             self.type = "ctc_with_lm"
@@ -355,13 +357,7 @@ def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
         stride = None
         extra = {}
 
-        if is_torch_available():
-            import torch
-
-            if isinstance(inputs, torch.Tensor):
-                inputs = inputs.cpu().numpy()
-
-        if is_torchcodec_available():
+        if is_torchcodec_available() and type(inputs).__module__.startswith("torchcodec."):
             import torchcodec
 
             if isinstance(inputs, torchcodec.decoders.AudioDecoder):
@@ -393,6 +389,8 @@ def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
             extra = inputs
             inputs = _inputs
             if in_sampling_rate != self.feature_extractor.sampling_rate:
+                import torch
+
                 if is_torchaudio_available():
                     from torchaudio import functional as F
                 else:
@@ -418,7 +416,14 @@ def preprocess(self, inputs, chunk_length_s=0, stride_length_s=None):
                 # can add extra data in the inputs, so we need to keep track
                 # of the original length in the stride so we can cut properly.
                 stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
-        if not isinstance(inputs, (np.ndarray, torch.Tensor)):
+
+        if is_torch_available():
+            import torch
+
+            if isinstance(inputs, torch.Tensor):
+                inputs = inputs.cpu().numpy()
+
+        if not isinstance(inputs, np.ndarray):
             raise TypeError(f"We expect a numpy ndarray or torch tensor as input, got `{type(inputs)}`")
         if inputs.ndim != 1:
             logger.warning(
@@ -558,7 +563,7 @@ def _forward(self, model_inputs, return_timestamps=False, return_language=None,
                                     out["lang_id"] = torch.tensor([token_id])
                                     break
 
-        else:
+        elif self.type in {"ctc", "ctc_with_lm"}:
             inputs = {
                 self.model.main_input_name: model_inputs.pop(self.model.main_input_name),
                 "attention_mask": attention_mask,
@@ -579,6 +584,17 @@ def _forward(self, model_inputs, return_timestamps=False, return_language=None,
                     out["stride"] = rescale_stride([stride], ratio)[0]
                 else:
                     out["stride"] = rescale_stride(stride, ratio)
+        elif self.type == "tdt":
+            inputs = {
+                self.model.main_input_name: model_inputs.pop(self.model.main_input_name),
+            }
+            if "attention_mask" in model_inputs:
+                inputs["attention_mask"] = model_inputs.pop("attention_mask")
+            outputs = self.model.generate(**inputs)
+            out = {"tokens": outputs.sequences}
+        else:
+            raise ValueError("Unsupported model type {self.type}.")
+
         # Leftover
         extra = model_inputs
         return {"is_last": is_last, **out, **extra}
diff --git a/src/transformers/pipelines/object_detection.py b/src/transformers/pipelines/object_detection.py
index 0a4fba996d7d..e1294cbd5d14 100644
--- a/src/transformers/pipelines/object_detection.py
+++ b/src/transformers/pipelines/object_detection.py
@@ -1,4 +1,4 @@
-from typing import TYPE_CHECKING, Any, Union, overload
+from typing import TYPE_CHECKING, Any, Literal, Union, overload
 
 from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
 from .base import Pipeline, build_pipeline_init_args
@@ -35,9 +35,9 @@ class ObjectDetectionPipeline(Pipeline):
 
     >>> detector = pipeline(model="facebook/detr-resnet-50")
     >>> detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
-    [{'score': 0.997, 'label': 'bird', 'box': {'xmin': 69, 'ymin': 171, 'xmax': 396, 'ymax': 507}}, {'score': 0.999, 'label': 'bird', 'box': {'xmin': 398, 'ymin': 105, 'xmax': 767, 'ymax': 507}}]
+    [{'score': 0.999, 'label': 'bird', 'box': {'xmin': 398, 'ymin': 105, 'xmax': 767, 'ymax': 507}}, {'score': 0.997, 'label': 'bird', 'box': {'xmin': 69, 'ymin': 171, 'xmax': 396, 'ymax': 507}}]
 
-    >>> # x, y  are expressed relative to the top left hand corner.
+    >>> # Results are sorted by score descending. x, y are expressed relative to the top left hand corner.
     ```
 
     Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
@@ -65,9 +65,17 @@ def _sanitize_parameters(self, **kwargs):
         preprocess_params = {}
         if "timeout" in kwargs:
             preprocess_params["timeout"] = kwargs["timeout"]
+
         postprocess_kwargs = {}
         if "threshold" in kwargs:
             postprocess_kwargs["threshold"] = kwargs["threshold"]
+        if "top_k" in kwargs:
+            postprocess_kwargs["top_k"] = kwargs["top_k"]
+        if "labels" in kwargs:
+            postprocess_kwargs["labels"] = kwargs["labels"]
+        if "box_format" in kwargs:
+            postprocess_kwargs["box_format"] = kwargs["box_format"]
+
         return preprocess_params, {}, postprocess_kwargs
 
     @overload
@@ -94,7 +102,21 @@ def __call__(self, *args, **kwargs) -> list[dict[str, Any]] | list[list[dict[str
                 same format: all as HTTP(S) links, all as local paths, or all as PIL images.
             threshold (`float`, *optional*, defaults to 0.5):
                 The probability necessary to make a prediction.
-            timeout (`float`, *optional*, defaults to None):
+            top_k (`int`, *optional*, defaults to `None`):
+                The number of top detections to return, sorted by descending confidence score. If `None` or higher
+                than the total number of detections above `threshold`, all qualifying detections are returned.
+            labels (`list[str]`, *optional*, defaults to `None`):
+                A list of class-label strings to keep. Only detections whose label appears in this list are
+                returned. If `None`, all detected classes are returned.
+            box_format (`str`, *optional*, defaults to `"xyxy"`):
+                The coordinate format for returned bounding boxes. Accepted values:
+
+                - `"xyxy"`: Returns `{"xmin": int, "ymin": int, "xmax": int, "ymax": int}` in pixel coordinates
+                  (default, fully backward-compatible).
+                - `"xywh"`: Returns `{"x_center": int, "y_center": int, "width": int, "height": int}` in pixels.
+                - `"normalized"`: Returns `{"xmin": float, "ymin": float, "xmax": float, "ymax": float}` as
+                  values in `[0, 1]` relative to the image dimensions.
+            timeout (`float`, *optional*, defaults to `None`):
                 The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
                 the call may block forever.
 
@@ -107,7 +129,7 @@ def __call__(self, *args, **kwargs) -> list[dict[str, Any]] | list[list[dict[str
 
             - **label** (`str`) -- The class label identified by the model.
             - **score** (`float`) -- The score attributed by the model for that label.
-            - **box** (`list[dict[str, int]]`) -- The bounding box of detected object in image's original size.
+            - **box** (`dict`) -- The bounding box of detected object. Format depends on the `box_format` argument.
         """
         # After deprecation of this is completed, remove the default `None` value for `images`
         if "images" in kwargs and "inputs" not in kwargs:
@@ -132,7 +154,14 @@ def _forward(self, model_inputs):
             model_outputs["bbox"] = model_inputs["bbox"]
         return model_outputs
 
-    def postprocess(self, model_outputs, threshold=0.5):
+    def postprocess(
+        self,
+        model_outputs,
+        threshold: float = 0.5,
+        top_k: int | None = None,
+        labels: list[str] | None = None,
+        box_format: Literal["xyxy", "xywh", "normalized"] = "xyxy",
+    ):
         target_size = model_outputs["target_size"]
         if self.tokenizer is not None:
             # This is a LayoutLMForTokenClassification variant.
@@ -148,50 +177,99 @@ def unnormalize(bbox):
                             (width * bbox[2] / 1000),
                             (height * bbox[3] / 1000),
                         ]
-                    )
+                    ),
+                    box_format=box_format,
+                    image_size=(height, width),
                 )
 
             scores, classes = model_outputs["logits"].squeeze(0).softmax(dim=-1).max(dim=-1)
-            labels = [self.model.config.id2label[prediction] for prediction in classes.tolist()]
+            label_names = [self.model.config.id2label[prediction] for prediction in classes.tolist()]
             boxes = [unnormalize(bbox) for bbox in model_outputs["bbox"].squeeze(0)]
             keys = ["score", "label", "box"]
-            annotation = [dict(zip(keys, vals)) for vals in zip(scores.tolist(), labels, boxes) if vals[0] > threshold]
+            annotation = [
+                dict(zip(keys, vals)) for vals in zip(scores.tolist(), label_names, boxes) if vals[0] > threshold
+            ]
         else:
             # This is a regular ForObjectDetectionModel
+            height, width = target_size[0].tolist()
             raw_annotations = self.image_processor.post_process_object_detection(model_outputs, threshold, target_size)
             raw_annotation = raw_annotations[0]
-            scores = raw_annotation["scores"]
-            labels = raw_annotation["labels"]
-            boxes = raw_annotation["boxes"]
 
-            raw_annotation["scores"] = scores.tolist()
-            raw_annotation["labels"] = [self.model.config.id2label[label.item()] for label in labels]
-            raw_annotation["boxes"] = [self._get_bounding_box(box) for box in boxes]
+            raw_annotation["scores"] = raw_annotation["scores"].tolist()
+            raw_annotation["labels"] = [self.model.config.id2label[label.item()] for label in raw_annotation["labels"]]
+            raw_annotation["boxes"] = [
+                self._get_bounding_box(box, box_format=box_format, image_size=(height, width))
+                for box in raw_annotation["boxes"]
+            ]
 
-            # {"scores": [...], ...} --> [{"score":x, ...}, ...]
+            # {"scores": [...], ...} --> [{"score": x, ...}, ...]
             keys = ["score", "label", "box"]
             annotation = [
                 dict(zip(keys, vals))
-                for vals in zip(raw_annotation["scores"], raw_annotation["labels"], raw_annotation["boxes"])
+                for vals in zip(
+                    raw_annotation["scores"],
+                    raw_annotation["labels"],
+                    raw_annotation["boxes"],
+                )
             ]
 
+        # Sort by score descending (consistent with ZeroShotObjectDetectionPipeline
+        # and ImageClassificationPipeline)
+        annotation = sorted(annotation, key=lambda x: x["score"], reverse=True)
+
+        # Filter to label allowlist if provided
+        if labels is not None:
+            annotation = [ann for ann in annotation if ann["label"] in labels]
+
+        # Truncate to top_k highest-confidence detections
+        if top_k is not None:
+            annotation = annotation[:top_k]
+
         return annotation
 
-    def _get_bounding_box(self, box: "torch.Tensor") -> dict[str, int]:
+    def _get_bounding_box(
+        self,
+        box: "torch.Tensor",
+        box_format: Literal["xyxy", "xywh", "normalized"] = "xyxy",
+        image_size: tuple[int, int] | None = None,
+    ) -> dict:
         """
-        Turns list [xmin, xmax, ymin, ymax] into dict { "xmin": xmin, ... }
+        Converts a bounding-box tensor into a dictionary using the requested coordinate format.
 
         Args:
-            box (`torch.Tensor`): Tensor containing the coordinates in corners format.
+            box (`torch.Tensor`):
+                Tensor of shape `(4,)` with coordinates in `[xmin, ymin, xmax, ymax]` pixel format.
+            box_format (`str`, *optional*, defaults to `"xyxy"`):
+                Output format. One of `"xyxy"`, `"xywh"`, or `"normalized"`.
+            image_size (`tuple[int, int]`, *optional*):
+                `(height, width)` of the original image. Required when `box_format="normalized"`.
 
         Returns:
-            bbox (`dict[str, int]`): Dict containing the coordinates in corners format.
+            `dict`: Bounding box in the requested format.
         """
         xmin, ymin, xmax, ymax = box.int().tolist()
-        bbox = {
-            "xmin": xmin,
-            "ymin": ymin,
-            "xmax": xmax,
-            "ymax": ymax,
-        }
-        return bbox
+
+        if box_format == "xyxy":
+            return {"xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax}
+
+        elif box_format == "xywh":
+            return {
+                "x_center": (xmin + xmax) // 2,
+                "y_center": (ymin + ymax) // 2,
+                "width": xmax - xmin,
+                "height": ymax - ymin,
+            }
+
+        elif box_format == "normalized":
+            if image_size is None:
+                raise ValueError("`image_size` must be provided when `box_format='normalized'`.")
+            height, width = image_size
+            return {
+                "xmin": xmin / width,
+                "ymin": ymin / height,
+                "xmax": xmax / width,
+                "ymax": ymax / height,
+            }
+
+        else:
+            raise ValueError(f"Invalid `box_format` '{box_format}'. Choose one of 'xyxy', 'xywh', or 'normalized'.")
diff --git a/src/transformers/pipelines/promptable_concept_segmentation.py b/src/transformers/pipelines/promptable_concept_segmentation.py
new file mode 100644
index 000000000000..647cf536987e
--- /dev/null
+++ b/src/transformers/pipelines/promptable_concept_segmentation.py
@@ -0,0 +1,403 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Union, overload
+
+from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
+from .base import Pipeline, build_pipeline_init_args
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from ..image_utils import load_image, valid_images
+
+if is_torch_available():
+    import torch
+
+    from ..models.auto.modeling_auto import MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING_NAMES
+
+logger = logging.get_logger(__name__)
+
+
+@add_end_docstrings(build_pipeline_init_args(has_processor=True))
+class PromptableConceptSegmentationPipeline(Pipeline):
+    """
+    Promptable Concept Segmentation pipeline using `Sam3Model`. This pipeline predicts instance segmentation masks
+    and bounding boxes for objects when you provide an image and prompts. Prompts can be text descriptions
+    (e.g., "yellow school bus"), visual box exemplars (positive/negative), or combinations of both.
+
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> segmenter = pipeline(model="facebook/sam3", task="promptable-concept-segmentation")
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+    ...     text="ear",
+    ... )
+    [{'score': 0.87, 'box': {'xmin': 120, 'ymin': 45, 'xmax': 210, 'ymax': 130}, 'mask': tensor([...])}, ...]
+
+    >>> # Using box prompts
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+    ...     input_boxes=[[[59, 144, 76, 163], [87, 148, 104, 159]]],
+    ...     input_boxes_labels=[[1, 1]],
+    ... )
+    [{'score': 0.92, 'box': {'xmin': 59, 'ymin': 144, 'xmax': 76, 'ymax': 163}, 'mask': tensor([...])}, ...]
+
+    >>> # Combined text and negative box
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+    ...     text="handle",
+    ...     input_boxes=[[[40, 183, 318, 204]]],
+    ...     input_boxes_labels=[[0]],  # 0 = negative (exclude this region)
+    ... )
+    [{'score': 0.85, 'box': {'xmin': 250, 'ymin': 100, 'xmax': 280, 'ymax': 150}, 'mask': tensor([...])}, ...]
+    ```
+
+    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
+
+    This promptable concept segmentation pipeline can currently be loaded from [`pipeline`] using the following task
+    identifier: `"promptable-concept-segmentation"`.
+
+    See the list of available models on
+    [huggingface.co/models](https://huggingface.co/models?filter=promptable-concept-segmentation).
+    """
+
+    _load_processor = True
+    _load_image_processor = False
+    _load_feature_extractor = False
+    _load_tokenizer = False
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        requires_backends(self, "vision")
+        self.check_model_type(MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING_NAMES)
+
+        # Ensure we have Sam3Processor (not Sam3VideoProcessor) for text and box prompt support
+        # facebook/sam3 checkpoint loads Sam3VideoProcessor by default, but this pipeline needs Sam3Processor
+        if self.processor is not None and self.processor.__class__.__name__ == "Sam3VideoProcessor":
+            from ..models.sam3 import Sam3Processor
+
+            # Try to get the model checkpoint name
+            model_name = getattr(self.model, "name_or_path", None)
+            if not model_name and hasattr(self.model, "config"):
+                model_name = getattr(self.model.config, "_name_or_path", None)
+
+            # Default to facebook/sam3 if we can't determine the model name
+            # (facebook/sam3 is the canonical checkpoint for this task)
+            if not model_name:
+                model_name = "facebook/sam3"
+
+            logger.info(
+                "Detected Sam3VideoProcessor but promptable-concept-segmentation requires Sam3Processor. "
+                f"Loading Sam3Processor from {model_name}."
+            )
+            self.processor = Sam3Processor.from_pretrained(model_name)
+
+    @overload
+    def __call__(
+        self,
+        image: Union[str, "Image.Image"],
+        text: str | None = None,
+        input_boxes: list[list[list[float]]] | None = None,
+        input_boxes_labels: list[list[int]] | None = None,
+        **kwargs: Any,
+    ) -> list[dict[str, Any]]: ...
+
+    @overload
+    def __call__(self, image: list[dict[str, Any]], **kwargs: Any) -> list[list[dict[str, Any]]]: ...
+
+    def __call__(
+        self,
+        image: Union[str, "Image.Image", list[dict[str, Any]]],
+        text: str | list[str] | None = None,
+        input_boxes: list[list[list[float]]] | None = None,
+        input_boxes_labels: list[list[int]] | None = None,
+        **kwargs: Any,
+    ) -> list[dict[str, Any]] | list[list[dict[str, Any]]]:
+        """
+        Segment objects in the image(s) based on the provided prompts.
+
+        Args:
+            image (`str`, `PIL.Image`, or `list[dict[str, Any]]`):
+                The pipeline handles three types of images:
+
+                - A string containing an http url pointing to an image
+                - A string containing a local path to an image
+                - An image loaded in PIL directly
+
+                You can use this parameter to send directly a list of images, or a dataset or a generator like so:
+
+                ```python
+                >>> from transformers import pipeline
+
+                >>> segmenter = pipeline(model="facebook/sam3", task="promptable-concept-segmentation")
+                >>> segmenter(
+                ...     [
+                ...         {
+                ...             "image": "http://images.cocodataset.org/val2017/000000077595.jpg",
+                ...             "text": "ear",
+                ...         },
+                ...         {
+                ...             "image": "http://images.cocodataset.org/val2017/000000136466.jpg",
+                ...             "text": "dial",
+                ...         },
+                ...     ]
+                ... )
+                [[{'score': 0.87, 'box': {...}, 'mask': ...}], [{'score': 0.92, 'box': {...}, 'mask': ...}]]
+                ```
+
+            text (`str` or `list[str]`, *optional*):
+                Text prompt(s) describing the concept to segment (e.g., "yellow school bus", "ear", "handle").
+                Can be a single string or a list of strings for batched inference.
+
+            input_boxes (`list[list[list[float]]]`, *optional*):
+                Visual box prompts in xyxy format [x1, y1, x2, y2] in pixel coordinates.
+                Structure: [batch, num_boxes, 4]. Used to provide visual exemplars of the concept.
+
+            input_boxes_labels (`list[list[int]]`, *optional*):
+                Labels for the box prompts. 1 = positive (include), 0 = negative (exclude).
+                Structure: [batch, num_boxes]. Must match the structure of `input_boxes`.
+
+            threshold (`float`, *optional*, defaults to 0.3):
+                The probability necessary to make a prediction.
+
+            mask_threshold (`float`, *optional*, defaults to 0.5):
+                Threshold for binarizing the predicted masks.
+
+            top_k (`int`, *optional*, defaults to None):
+                The number of top predictions that will be returned by the pipeline. If the provided number is `None`
+                or higher than the number of predictions available, it will default to the number of predictions.
+
+            timeout (`float`, *optional*, defaults to None):
+                The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
+                the call may block forever.
+
+        Return:
+            A list of lists containing prediction results, one list per input image. Each list contains dictionaries
+            with the following keys:
+
+            - **score** (`float`) -- Confidence score for the detected instance.
+            - **box** (`dict[str, int]`) -- Bounding box of the detected object in image's original size with keys
+              `xmin`, `ymin`, `xmax`, `ymax`.
+            - **mask** (`torch.Tensor`) -- Binary segmentation mask for the instance, shape (height, width).
+        """
+        # Handle different input formats
+        if isinstance(image, str | Image.Image):
+            inputs = {
+                "image": image,
+                "text": text,
+                "input_boxes": input_boxes,
+                "input_boxes_labels": input_boxes_labels,
+            }
+        elif isinstance(image, list | tuple) and valid_images(image):
+            # Batch of images - create individual inputs for each image
+            batch_inputs = self._prepare_batch_inputs(image, text, input_boxes, input_boxes_labels)
+            return list(super().__call__(batch_inputs, **kwargs))
+        else:
+            """
+            Supports the following format
+            - {"image": image, "text": text}
+            - [{"image": image, "text": text}]
+            - Generator and datasets
+            """
+            inputs = image
+
+        results = super().__call__(inputs, **kwargs)
+        return results
+
+    def _prepare_batch_inputs(self, images, text, input_boxes, input_boxes_labels):
+        """Helper method to prepare batch inputs from separate parameters."""
+        # Expand single values to match batch size
+        num_images = len(images)
+        text_list = text if isinstance(text, list) else [text] * num_images
+        boxes_list = input_boxes if input_boxes is not None else [None] * num_images
+        labels_list = input_boxes_labels if input_boxes_labels is not None else [None] * num_images
+
+        # Create input dict for each image
+        return (
+            {
+                "image": img,
+                "text": txt,
+                "input_boxes": boxes,
+                "input_boxes_labels": box_labels,
+            }
+            for img, txt, boxes, box_labels in zip(images, text_list, boxes_list, labels_list)
+        )
+
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_params = {}
+        if "timeout" in kwargs:
+            preprocess_params["timeout"] = kwargs["timeout"]
+
+        postprocess_params = {}
+        if "threshold" in kwargs:
+            postprocess_params["threshold"] = kwargs["threshold"]
+        if "mask_threshold" in kwargs:
+            postprocess_params["mask_threshold"] = kwargs["mask_threshold"]
+        if "top_k" in kwargs:
+            postprocess_params["top_k"] = kwargs["top_k"]
+
+        return preprocess_params, {}, postprocess_params
+
+    def _normalize_boxes_format(self, input_boxes):
+        """Ensure input_boxes is in the correct format: [batch, num_boxes, 4]."""
+        if input_boxes is None:
+            return None
+        if not isinstance(input_boxes, list):
+            return [[input_boxes]]
+        if len(input_boxes) > 0 and not isinstance(input_boxes[0], list):
+            return [input_boxes]
+        return input_boxes
+
+    def _normalize_labels_format(self, input_boxes_labels):
+        """Ensure input_boxes_labels is in the correct format: [batch, num_boxes]."""
+        if input_boxes_labels is None:
+            return None
+        if not isinstance(input_boxes_labels, list):
+            return [[input_boxes_labels]]
+        if len(input_boxes_labels) > 0 and not isinstance(input_boxes_labels[0], list):
+            return [input_boxes_labels]
+        return input_boxes_labels
+
+    def preprocess(self, inputs, timeout=None):
+        """
+        Preprocess inputs for the model.
+
+        Args:
+            inputs: Dictionary containing 'image' and optionally 'text', 'input_boxes', 'input_boxes_labels'
+            timeout: Timeout for image loading
+
+        Returns:
+            Dictionary with preprocessed model inputs
+        """
+        image = load_image(inputs["image"], timeout=timeout)
+        text = inputs.get("text")
+        input_boxes = inputs.get("input_boxes")
+        input_boxes_labels = inputs.get("input_boxes_labels")
+
+        # Validate that at least one prompt type is provided
+        if text is None and input_boxes is None:
+            raise ValueError(
+                "You must provide at least one prompt type: either 'text' or 'input_boxes'. "
+                "For example: text='cat' or input_boxes=[[[100, 150, 200, 250]]]"
+            )
+
+        # Normalize box formats
+        input_boxes = self._normalize_boxes_format(input_boxes)
+        input_boxes_labels = self._normalize_labels_format(input_boxes_labels)
+
+        # Process inputs - pass text, input_boxes, input_boxes_labels as explicit parameters
+        model_inputs = self.processor(
+            images=image,
+            text=text,
+            input_boxes=input_boxes,
+            input_boxes_labels=input_boxes_labels,
+            return_tensors="pt",
+        ).to(self.dtype)
+
+        # Store original size for post-processing
+        target_size = torch.tensor([[image.height, image.width]], dtype=torch.int32)
+        model_inputs["target_size"] = target_size
+
+        # Store the text prompt for output labeling
+        model_inputs["prompt_text"] = text
+
+        return model_inputs
+
+    def _forward(self, model_inputs):
+        """
+        Forward pass through the model.
+
+        Args:
+            model_inputs: Preprocessed model inputs
+
+        Returns:
+            Model outputs with additional metadata
+        """
+        target_size = model_inputs.pop("target_size")
+        prompt_text = model_inputs.pop("prompt_text")
+
+        outputs = self.model(**model_inputs)
+
+        return {
+            "outputs": outputs,
+            "target_size": target_size,
+            "prompt_text": prompt_text,
+        }
+
+    def postprocess(self, model_outputs, threshold=0.3, mask_threshold=0.5, top_k=None):
+        """
+        Post-process model outputs into final predictions.
+
+        Args:
+            model_outputs: Raw model outputs
+            threshold: Score threshold for filtering predictions
+            mask_threshold: Threshold for binarizing masks
+            top_k: Maximum number of predictions to return
+
+        Returns:
+            List of dictionaries with 'score', 'box', and 'mask' keys
+        """
+        outputs = model_outputs["outputs"]
+        target_sizes = model_outputs["target_size"]
+        prompt_text = model_outputs["prompt_text"]
+
+        # Use processor's post-processing method
+        results = self.processor.post_process_instance_segmentation(
+            outputs,
+            threshold=threshold,
+            mask_threshold=mask_threshold,
+            target_sizes=target_sizes.tolist(),
+        )[0]  # Get first batch element
+
+        # Convert to expected output format
+        final_results = []
+        if len(results["scores"]) > 0:
+            for i in range(len(results["scores"])):
+                score = results["scores"][i].item()
+                box_tensor = results["boxes"][i]
+                mask_tensor = results["masks"][i]
+
+                result = {
+                    "score": score,
+                    "box": self._get_bounding_box(box_tensor),
+                    "mask": mask_tensor,
+                }
+
+                # Optionally add label if text prompt was provided
+                if prompt_text is not None:
+                    result["label"] = prompt_text
+
+                final_results.append(result)
+
+        # Sort results by score in descending order
+        final_results = sorted(final_results, key=lambda x: x["score"], reverse=True)
+
+        # Apply top_k filtering
+        if top_k is not None and len(final_results) > top_k:
+            final_results = final_results[:top_k]
+
+        return final_results
+
+    def _get_bounding_box(self, box: "torch.Tensor") -> dict[str, int]:
+        xmin, ymin, xmax, ymax = box.int().tolist()
+        return {
+            "xmin": xmin,
+            "ymin": ymin,
+            "xmax": xmax,
+            "ymax": ymax,
+        }
diff --git a/src/transformers/pipelines/promptable_visual_segmentation.py b/src/transformers/pipelines/promptable_visual_segmentation.py
new file mode 100644
index 000000000000..10e70037a48b
--- /dev/null
+++ b/src/transformers/pipelines/promptable_visual_segmentation.py
@@ -0,0 +1,392 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from typing import Any, Union, overload
+
+from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
+from .base import Pipeline, build_pipeline_init_args
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from ..image_utils import load_image, valid_images
+
+if is_torch_available():
+    import torch
+
+    from ..models.auto.modeling_auto import MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING_NAMES
+
+logger = logging.get_logger(__name__)
+
+
+@add_end_docstrings(build_pipeline_init_args(has_processor=True))
+class PromptableVisualSegmentationPipeline(Pipeline):
+    """
+    Promptable Visual Segmentation pipeline using SAM-family models. This pipeline predicts segmentation masks
+    for objects when you provide an image and visual prompts. Visual prompts can be points (with positive/negative
+    labels) or bounding boxes.
+
+    This task is supported by models: Sam3TrackerModel, Sam2Model, SamModel, and EdgeTamModel.
+
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> segmenter = pipeline(model="facebook/sam2.1-hiera-large", task="promptable-visual-segmentation")
+    >>> # Single point prompt
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000077595.jpg",
+    ...     input_points=[[[[450, 600]]]],
+    ...     input_labels=[[[1]]],
+    ... )
+    [[{'score': 0.87, 'mask': tensor([...])}]]
+
+    >>> # Box prompt
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+    ...     input_boxes=[[[59, 144, 76, 163]]],
+    ... )
+    [[{'score': 0.92, 'mask': tensor([...])}]]
+
+    >>> # Multiple points for refinement (positive and negative)
+    >>> segmenter(
+    ...     "http://images.cocodataset.org/val2017/000000136466.jpg",
+    ...     input_points=[[[[450, 600], [500, 620]]]],
+    ...     input_labels=[[[1, 0]]],  # 1=positive (include), 0=negative (exclude)
+    ... )
+    [[{'score': 0.85, 'mask': tensor([...])}]]
+    ```
+
+    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
+
+    This promptable visual segmentation pipeline can currently be loaded from [`pipeline`] using the following task
+    identifier: `"promptable-visual-segmentation"`.
+
+    See the list of available models on
+    [huggingface.co/models](https://huggingface.co/models?filter=promptable-visual-segmentation).
+    """
+
+    _load_processor = True
+    _load_image_processor = False
+    _load_feature_extractor = False
+    _load_tokenizer = False
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        requires_backends(self, "vision")
+        self.check_model_type(MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING_NAMES)
+
+        # Handle processor compatibility: Sam3VideoProcessor → Sam3TrackerProcessor
+        # facebook/sam3 checkpoint loads Sam3VideoProcessor by default, but this pipeline needs Sam3TrackerProcessor
+        if self.processor is not None and self.processor.__class__.__name__ == "Sam3VideoProcessor":
+            from ..models.sam3_tracker import Sam3TrackerProcessor
+
+            # Get checkpoint name from model (empty string if instantiated from config, so use 'or' for fallback)
+            model_name = getattr(self.model, "name_or_path", "") or "facebook/sam3"
+            self.processor = Sam3TrackerProcessor.from_pretrained(model_name)
+
+        # Determine if using SamProcessor (needs reshaped_input_sizes in post_process_masks)
+        self._needs_reshaped_sizes = self.processor.__class__.__name__ == "SamProcessor"
+
+    @overload
+    def __call__(
+        self,
+        image: Union[str, "Image.Image"],
+        input_points: list[list[list[list[float]]]] | None = None,
+        input_labels: list[list[list[int]]] | None = None,
+        input_boxes: list[list[list[float]]] | None = None,
+        **kwargs: Any,
+    ) -> list[list[dict[str, Any]]]: ...
+
+    @overload
+    def __call__(self, image: list[dict[str, Any]], **kwargs: Any) -> list[list[dict[str, Any]]]: ...
+
+    def __call__(
+        self,
+        image: Union[str, "Image.Image", list[dict[str, Any]]],
+        input_points: list[list[list[list[float]]]] | None = None,
+        input_labels: list[list[list[int]]] | None = None,
+        input_boxes: list[list[list[float]]] | None = None,
+        **kwargs: Any,
+    ) -> list[list[dict[str, Any]]]:
+        """
+        Segment objects in the image(s) based on visual prompts.
+
+        Args:
+            image (`str`, `PIL.Image`, or `list[dict[str, Any]]`):
+                The pipeline handles three types of images:
+
+                - A string containing an http url pointing to an image
+                - A string containing a local path to an image
+                - An image loaded in PIL directly
+
+                You can use this parameter to send directly a list of images, or a dataset or a generator like so:
+
+                ```python
+                >>> from transformers import pipeline
+
+                >>> segmenter = pipeline(model="facebook/sam2.1-hiera-large", task="promptable-visual-segmentation")
+                >>> segmenter(
+                ...     [
+                ...         {
+                ...             "image": "http://images.cocodataset.org/val2017/000000077595.jpg",
+                ...             "input_points": [[[[450, 600]]]],
+                ...             "input_labels": [[[1]]],
+                ...         },
+                ...         {
+                ...             "image": "http://images.cocodataset.org/val2017/000000136466.jpg",
+                ...             "input_boxes": [[[59, 144, 76, 163]]],
+                ...         },
+                ...     ]
+                ... )
+                [[{'score': 0.87, 'mask': ...}], [{'score': 0.92, 'mask': ...}]]
+                ```
+
+            input_points (`list[list[list[list[float]]]]`, *optional*):
+                Point prompts in (x, y) format.
+                Structure: [batch, objects, num_points, 2].
+                Each point specifies a location on the image to guide segmentation.
+
+            input_labels (`list[list[list[int]]]`, *optional*):
+                Labels for the point prompts.
+                Structure: [batch, objects, num_points].
+                Values: 1 = positive (include in mask), 0 = negative (exclude from mask).
+                Must match the structure of `input_points`.
+
+            input_boxes (`list[list[list[float]]]`, *optional*):
+                Bounding box prompts in xyxy format [x1, y1, x2, y2] in pixel coordinates.
+                Structure: [batch, num_boxes, 4].
+
+            multimask_output (`bool`, *optional*, defaults to False):
+                Whether to output multiple mask candidates per prompt. When True, returns 3 masks per object
+                ranked by IoU score. When False, returns only the best mask per object.
+
+            mask_threshold (`float`, *optional*, defaults to 0.0):
+                Threshold for binarizing the predicted masks.
+
+            top_k (`int`, *optional*, defaults to None):
+                The number of top predictions that will be returned by the pipeline. If the provided number is `None`
+                or higher than the number of predictions available, it will default to the number of predictions.
+
+            timeout (`float`, *optional*, defaults to None):
+                The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
+                the call may block forever.
+
+        Return:
+            A list of lists containing prediction results, one list per input image. Each list contains dictionaries
+            with the following keys:
+
+            - **score** (`float`) -- IoU confidence score for the predicted mask.
+            - **mask** (`torch.Tensor`) -- Binary segmentation mask for the object, shape (height, width).
+        """
+        # Handle different input formats
+        if isinstance(image, (str, Image.Image)):
+            inputs = {
+                "image": image,
+                "input_points": input_points,
+                "input_labels": input_labels,
+                "input_boxes": input_boxes,
+            }
+        elif isinstance(image, (list, tuple)) and valid_images(image):
+            # Batch of images - create individual inputs for each image
+            batch_inputs = self._prepare_batch_inputs(image, input_points, input_labels, input_boxes)
+            return list(super().__call__(batch_inputs, **kwargs))
+        else:
+            """
+            Supports the following format
+            - {"image": image, "input_points": points, "input_labels": labels}
+            - [{"image": image, "input_points": points, "input_labels": labels}]
+            - Generator and datasets
+            """
+            inputs = image
+
+        results = super().__call__(inputs, **kwargs)
+        return results
+
+    def _prepare_batch_inputs(self, images, input_points, input_labels, input_boxes):
+        """Helper method to prepare batch inputs from separate parameters."""
+        # Expand single values to match batch size
+        num_images = len(images)
+        points_list = input_points if input_points is not None else [None] * num_images
+        labels_list = input_labels if input_labels is not None else [None] * num_images
+        boxes_list = input_boxes if input_boxes is not None else [None] * num_images
+
+        # Create input dict for each image
+        return (
+            {
+                "image": img,
+                "input_points": points,
+                "input_labels": labels,
+                "input_boxes": boxes,
+            }
+            for img, points, labels, boxes in zip(images, points_list, labels_list, boxes_list)
+        )
+
+    def _sanitize_parameters(self, **kwargs):
+        preprocess_params = {}
+        if "timeout" in kwargs:
+            preprocess_params["timeout"] = kwargs["timeout"]
+
+        forward_params = {}
+        if "multimask_output" in kwargs:
+            forward_params["multimask_output"] = kwargs["multimask_output"]
+
+        postprocess_params = {}
+        if "mask_threshold" in kwargs:
+            postprocess_params["mask_threshold"] = kwargs["mask_threshold"]
+        if "top_k" in kwargs:
+            postprocess_params["top_k"] = kwargs["top_k"]
+
+        return preprocess_params, forward_params, postprocess_params
+
+    def preprocess(self, inputs, timeout=None):
+        """
+        Preprocess inputs for the model.
+
+        Args:
+            inputs: Dictionary containing 'image' and optionally 'input_points', 'input_labels', 'input_boxes'
+            timeout: Timeout for image loading
+
+        Returns:
+            Dictionary with preprocessed model inputs
+        """
+        image = load_image(inputs["image"], timeout=timeout)
+        input_points = inputs.get("input_points")
+        input_labels = inputs.get("input_labels")
+        input_boxes = inputs.get("input_boxes")
+
+        # Validate that at least one prompt type is provided
+        if input_points is None and input_boxes is None:
+            raise ValueError(
+                "You must provide at least one prompt type: either 'input_points' (with 'input_labels') or 'input_boxes'. "
+                "For example: input_points=[[[[450, 600]]]], input_labels=[[[1]]] or input_boxes=[[[100, 150, 200, 250]]]"
+            )
+
+        # Validate that if input_points is provided, input_labels must also be provided
+        if input_points is not None and input_labels is None:
+            raise ValueError("When providing 'input_points', you must also provide 'input_labels'.")
+
+        # Process inputs - pass all prompts as explicit parameters
+        processor_kwargs = {
+            "images": image,
+            "return_tensors": "pt",
+        }
+
+        if input_points is not None:
+            processor_kwargs["input_points"] = input_points
+            processor_kwargs["input_labels"] = input_labels
+
+        if input_boxes is not None:
+            processor_kwargs["input_boxes"] = input_boxes
+
+        model_inputs = self.processor(**processor_kwargs)
+        model_inputs = model_inputs.to(self.dtype)
+
+        # Store original size for post-processing
+        target_size = torch.tensor([[image.height, image.width]], dtype=torch.int32)
+        model_inputs["original_sizes"] = target_size
+
+        # For SamProcessor, we also need to store reshaped_input_sizes
+        if self._needs_reshaped_sizes and "reshaped_input_sizes" in model_inputs:
+            model_inputs["_reshaped_input_sizes"] = model_inputs["reshaped_input_sizes"]
+
+        return model_inputs
+
+    def _forward(self, model_inputs, multimask_output=False):
+        """
+        Forward pass through the model.
+
+        Args:
+            model_inputs: Preprocessed model inputs
+            multimask_output: Whether to output multiple masks per prompt
+
+        Returns:
+            Model outputs with additional metadata
+        """
+        original_sizes = model_inputs.pop("original_sizes")
+        reshaped_input_sizes = model_inputs.pop("_reshaped_input_sizes", None)
+
+        outputs = self.model(**model_inputs, multimask_output=multimask_output)
+
+        return {
+            "outputs": outputs,
+            "original_sizes": original_sizes,
+            "reshaped_input_sizes": reshaped_input_sizes,
+        }
+
+    def postprocess(self, model_outputs, mask_threshold=0.0, top_k=None):
+        """
+        Post-process model outputs into final predictions.
+
+        Args:
+            model_outputs: Raw model outputs
+            mask_threshold: Threshold for binarizing masks
+            top_k: Maximum number of predictions to return per image
+
+        Returns:
+            List of lists of dictionaries with 'score' and 'mask' keys
+        """
+        outputs = model_outputs["outputs"]
+        original_sizes = model_outputs["original_sizes"]
+        reshaped_input_sizes = model_outputs["reshaped_input_sizes"]
+
+        # Get masks and IoU scores from outputs
+        pred_masks = outputs.pred_masks  # (batch, objects, num_masks, H, W)
+        iou_scores = outputs.iou_scores  # (batch, objects, num_masks)
+
+        # Post-process masks to original image size
+        post_process_kwargs = {
+            "masks": pred_masks.cpu(),
+            "original_sizes": original_sizes.tolist(),
+            "mask_threshold": mask_threshold,
+            "binarize": True,
+        }
+
+        # For SamProcessor, we need to pass reshaped_input_sizes
+        if self._needs_reshaped_sizes and reshaped_input_sizes is not None:
+            post_process_kwargs["reshaped_input_sizes"] = reshaped_input_sizes.tolist()
+
+        masks = self.processor.post_process_masks(**post_process_kwargs)
+
+        # Format output as per-image list of dictionaries
+        final_results = []
+        batch_size = pred_masks.shape[0]
+
+        for batch_idx in range(batch_size):
+            image_results = []
+            num_objects = pred_masks.shape[1]
+            num_masks_per_object = pred_masks.shape[2]
+
+            for obj_idx in range(num_objects):
+                for mask_idx in range(num_masks_per_object):
+                    score = iou_scores[batch_idx, obj_idx, mask_idx].item()
+                    mask_tensor = masks[batch_idx][obj_idx, mask_idx]
+
+                    result = {
+                        "score": score,
+                        "mask": mask_tensor,
+                    }
+                    image_results.append(result)
+
+            # Sort results by score in descending order
+            image_results = sorted(image_results, key=lambda x: x["score"], reverse=True)
+
+            # Apply top_k filtering
+            if top_k is not None and len(image_results) > top_k:
+                image_results = image_results[:top_k]
+
+            final_results.append(image_results)
+
+        return final_results
diff --git a/src/transformers/pipelines/text_generation.py b/src/transformers/pipelines/text_generation.py
index 6a0b2966d07c..37861fa66832 100644
--- a/src/transformers/pipelines/text_generation.py
+++ b/src/transformers/pipelines/text_generation.py
@@ -400,6 +400,11 @@ def _forward(self, model_inputs, **generate_kwargs):
         if "generation_config" not in generate_kwargs:
             generate_kwargs["generation_config"] = self.generation_config
 
+        # If safety_config is provided, attach tokenizer to model for safety processor creation
+        # GenerationMixin._create_safety_processor() expects self.tokenizer on the model
+        if "safety_config" in generate_kwargs and hasattr(self, "tokenizer") and self.tokenizer is not None:
+            self.model.tokenizer = self.tokenizer
+
         output = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
 
         if isinstance(output, ModelOutput):
diff --git a/src/transformers/pipelines/video_to_text.py b/src/transformers/pipelines/video_to_text.py
new file mode 100644
index 000000000000..3b656514df62
--- /dev/null
+++ b/src/transformers/pipelines/video_to_text.py
@@ -0,0 +1,351 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import warnings
+from io import BytesIO
+from typing import Any, overload
+
+import httpx
+
+from ..generation import GenerationConfig
+from ..utils import (
+    add_end_docstrings,
+    is_av_available,
+    is_torch_available,
+    logging,
+    requires_backends,
+)
+from .base import Pipeline, build_pipeline_init_args
+
+
+if is_av_available():
+    import av
+    import numpy as np
+
+if is_torch_available():
+    import torch
+
+    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES
+
+logger = logging.get_logger(__name__)
+
+
+@add_end_docstrings(build_pipeline_init_args(has_tokenizer=True, has_image_processor=True))
+class VideoToTextPipeline(Pipeline):
+    """
+    Video To Text pipeline using a `AutoModelForImageTextToText`. This pipeline predicts a caption for a given video.
+
+    Unless the model you're using explicitly sets these generation parameters in its configuration files
+    (`generation_config.json`), the following default values will be used:
+    - max_new_tokens: 256
+
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> captioner = pipeline("video-to-text", model="ydshieh/vit-gpt2-coco-en")
+    >>> captioner("path/to/video.mp4")
+    [{'generated_text': 'a person is setting a table'}]
+    ```
+
+    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
+
+    This video to text pipeline can currently be loaded from pipeline() using the following task identifier:
+    "video-to-text".
+
+    See the list of available models on
+    [huggingface.co/models](https://huggingface.co/models?pipeline_tag=video-to-text).
+    """
+
+    _pipeline_calls_generate = True
+    _load_processor = False
+    _load_image_processor = True
+    _load_feature_extractor = False
+    _load_tokenizer = True
+    # Make sure the docstring is updated when the default generation config is changed
+    _default_generation_config = GenerationConfig(
+        max_new_tokens=256,
+    )
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        requires_backends(self, "av")
+        self.check_model_type(MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES)
+
+    def _sanitize_parameters(
+        self,
+        max_new_tokens=None,
+        generate_kwargs=None,
+        num_frames=None,
+        frame_sampling_rate=None,
+        timeout=None,
+        system_prompt=None,
+    ):
+        forward_params = {}
+        preprocess_params = {}
+
+        if timeout is not None:
+            preprocess_params["timeout"] = timeout
+        if num_frames is not None:
+            preprocess_params["num_frames"] = num_frames
+        if frame_sampling_rate is not None:
+            preprocess_params["frame_sampling_rate"] = frame_sampling_rate
+
+        if max_new_tokens is not None:
+            forward_params["max_new_tokens"] = max_new_tokens
+        if system_prompt is not None:
+            forward_params["system_prompt"] = system_prompt
+        if generate_kwargs is not None:
+            if max_new_tokens is not None and "max_new_tokens" in generate_kwargs:
+                raise ValueError(
+                    "`max_new_tokens` is defined both as an argument and inside `generate_kwargs` argument, please use"
+                    " only 1 version"
+                )
+            forward_params.update(generate_kwargs)
+
+        if self.assistant_model is not None:
+            forward_params["assistant_model"] = self.assistant_model
+        if self.assistant_tokenizer is not None:
+            forward_params["tokenizer"] = self.tokenizer
+            forward_params["assistant_tokenizer"] = self.assistant_tokenizer
+
+        return preprocess_params, forward_params, {}
+
+    @overload
+    def __call__(self, inputs: str, **kwargs: Any) -> list[dict[str, Any]]: ...
+
+    @overload
+    def __call__(self, inputs: list[str], **kwargs: Any) -> list[list[dict[str, Any]]]: ...
+
+    def __call__(self, inputs: str | list[str] | None = None, **kwargs):
+        """
+        Generate text captions for the video(s) passed as inputs.
+
+        Args:
+            inputs (`str`, `list[str]`):
+                The pipeline handles two types of videos:
+
+                - A string containing a http link pointing to a video
+                - A string containing a local path to a video
+
+                The pipeline accepts either a single video or a batch of videos, which must then be passed as a string.
+                Videos in a batch must all be in the same format: all as http links or all as local paths.
+            max_new_tokens (`int`, *optional*):
+                The amount of maximum tokens to generate. By default it will use `generate` default.
+            num_frames (`int`, *optional*):
+                The number of frames sampled from the video to run the generation on. If not provided, will be
+                calculated as a function of video duration (1 frame per second, min 8, max 128). If video duration
+                is unavailable, will default to the number of frames specified in the model configuration.
+            frame_sampling_rate (`int`, *optional*, defaults to 1):
+                Currently unused - frames are time-spaced based on video duration.
+            generate_kwargs (`Dict`, *optional*):
+                Pass it to send all of these arguments directly to `generate` allowing full control of this function.
+            system_prompt (`str`, *optional*):
+                A system prompt to guide the model's generation. This will be tokenized and passed to the model
+                to influence the style and detail of the generated description.
+            timeout (`float`, *optional*, defaults to None):
+                The maximum time in seconds to wait for fetching videos from the web. If None, no timeout is set and
+                the call may block forever.
+
+        Return:
+            A list or a list of list of `dict`: Each result comes as a dictionary with the following key:
+
+            - **generated_text** (`str`) -- The generated text.
+        """
+        if "videos" in kwargs:
+            warnings.warn(
+                "The `videos` argument has been renamed to `inputs`. In version 5 of Transformers, `videos` will no longer be accepted",
+                FutureWarning,
+            )
+            inputs = kwargs.pop("videos")
+        if inputs is None:
+            raise ValueError("Cannot call the video-to-text pipeline without an inputs argument!")
+        return super().__call__(inputs, **kwargs)
+
+    def preprocess(self, video, num_frames=None, frame_sampling_rate=1, timeout=None):
+        if video.startswith("http://") or video.startswith("https://"):
+            video = BytesIO(httpx.get(video, follow_redirects=True, timeout=timeout).content)
+
+        container = av.open(video)
+
+        # Get video metadata for logging
+        video_stream = container.streams.video[0]
+        total_frames = video_stream.frames if video_stream.frames else 0
+        fps = float(video_stream.average_rate) if video_stream.average_rate else 0
+        duration = container.duration / av.time_base if container.duration else 0
+
+        # Calculate num_frames as a function of video length
+        # Default: 1 frame per second, minimum 8, maximum 128
+        if num_frames is None:
+            if duration > 0:
+                # 1 frame per second, with min/max bounds
+                num_frames = max(8, min(128, int(duration)))
+            else:
+                # Fallback: try to get from model config, otherwise use default
+                if hasattr(self.model.config, "num_frames"):
+                    num_frames = self.model.config.num_frames
+                else:
+                    num_frames = 64  # Default fallback
+
+        logger.info(f"Video metadata: duration={duration:.2f}s, fps={fps:.2f}, total_frames={total_frames}")
+        logger.info(f"Frame selection: num_frames={num_frames} (calculated from duration)")
+
+        # Use time-spaced frames (time-based sampling instead of frame-based)
+        # Sample frames evenly spaced in time
+        if duration > 0 and fps > 0:
+            # Calculate time points evenly spaced across the video duration
+            # Use endpoint=True to include the last frame
+            time_points = np.linspace(0, duration, num=num_frames, endpoint=True)
+
+            # Convert time points to frame indices
+            indices = (time_points * fps).astype(np.int64)
+            # Ensure indices don't exceed total frames
+            if total_frames > 0:
+                indices = np.clip(indices, 0, total_frames - 1)
+            # Remove duplicates and sort to maintain temporal order
+            indices = np.unique(indices)
+            logger.info(f"Time-spaced sampling selected {len(indices)} frame indices: {indices.tolist()}")
+        else:
+            # Fallback to frame-based linear sampling if duration/fps unavailable
+            start_idx = 0
+            end_idx = total_frames - 1 if total_frames > 0 else num_frames - 1
+            indices = np.linspace(start_idx, end_idx, num=num_frames, dtype=np.int64)
+            logger.info(f"Frame-based linear sampling selected {len(indices)} frame indices: {indices.tolist()}")
+
+        # Log temporal gaps between selected frames
+        if len(indices) > 1 and fps > 0:
+            gaps = []
+            for i in range(len(indices) - 1):
+                gap_frames = indices[i + 1] - indices[i]
+                gap_seconds = gap_frames / fps if fps > 0 else 0
+                gaps.append(f"{gap_frames} frames ({gap_seconds:.2f}s)")
+            logger.info(f"Temporal gaps between selected frames: {gaps}")
+
+        video_frames = read_video_pyav(container, indices)
+        video_frames = list(video_frames)
+        logger.info(f"Extracted {len(video_frames)} frames")
+
+        # Process video frames through image processor
+        logger.info(f"Processing {len(video_frames)} individual frames")
+        model_inputs = self.image_processor(video_frames, return_tensors="pt")
+
+        model_inputs = model_inputs.to(self.dtype)
+
+        # Some models like GIT need input_ids set to None
+        if self.model.config.model_type == "git":
+            model_inputs["input_ids"] = None
+
+        return model_inputs
+
+    def _forward(self, model_inputs, **generate_kwargs):
+        # Git model sets `model_inputs["input_ids"] = None` in `preprocess`. In batch model, the
+        # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
+        if (
+            "input_ids" in model_inputs
+            and isinstance(model_inputs["input_ids"], list)
+            and all(x is None for x in model_inputs["input_ids"])
+        ):
+            model_inputs["input_ids"] = None
+
+        # Handle system prompt if provided
+        system_prompt = generate_kwargs.pop("system_prompt", None)
+        if system_prompt is not None:
+            # Tokenize the system prompt
+            if self.model.config.model_type == "git":
+                # For GIT models, we can pass the prompt as input_ids
+                # Tokenize and add to model_inputs
+                prompt_ids = self.tokenizer(system_prompt, return_tensors="pt", add_special_tokens=True)
+                prompt_ids = prompt_ids["input_ids"].to(self.device)
+                # If input_ids is None, set it to the prompt; otherwise prepend
+                if model_inputs.get("input_ids") is None:
+                    model_inputs["input_ids"] = prompt_ids
+                else:
+                    # Prepend system prompt to existing input_ids
+                    if isinstance(model_inputs["input_ids"], torch.Tensor):
+                        model_inputs["input_ids"] = torch.cat([prompt_ids, model_inputs["input_ids"]], dim=1)
+            else:
+                # For other models, add as input_ids or pass through generate_kwargs
+                prompt_ids = self.tokenizer(system_prompt, return_tensors="pt", add_special_tokens=True)
+                prompt_ids = prompt_ids["input_ids"].to(self.device)
+                if "input_ids" not in model_inputs or model_inputs["input_ids"] is None:
+                    model_inputs["input_ids"] = prompt_ids
+                else:
+                    # Prepend system prompt to existing input_ids
+                    if isinstance(model_inputs["input_ids"], torch.Tensor):
+                        model_inputs["input_ids"] = torch.cat([prompt_ids, model_inputs["input_ids"]], dim=1)
+
+        # User-defined `generation_config` passed to the pipeline call take precedence
+        if "generation_config" not in generate_kwargs:
+            generate_kwargs["generation_config"] = self.generation_config
+
+        inputs = model_inputs.pop(self.model.main_input_name)
+        model_outputs = self.model.generate(inputs, **model_inputs, **generate_kwargs)
+        return model_outputs
+
+    def postprocess(self, model_outputs):
+        records = []
+        seen_texts = set()
+        all_texts = []
+
+        logger.info(f"Postprocessing {len(model_outputs)} model outputs")
+
+        for idx, output_ids in enumerate(model_outputs):
+            text = self.tokenizer.decode(output_ids, skip_special_tokens=True)
+            all_texts.append(text)
+            logger.info(f"Generated text #{idx + 1}: '{text}'")
+
+            # Deduplicate: only add if we haven't seen this text before
+            if text not in seen_texts:
+                seen_texts.add(text)
+                record = {"generated_text": text}
+                records.append(record)
+                logger.debug(f"Added unique text: '{text}'")
+            else:
+                logger.debug(f"Deduplicated duplicate text: '{text}'")
+
+        logger.info(f"Total generated texts: {len(all_texts)}, Unique texts after deduplication: {len(records)}")
+        if len(all_texts) > len(records):
+            duplicates = [t for t in all_texts if all_texts.count(t) > 1]
+            logger.info(f"Duplicated texts: {set(duplicates)}")
+
+        return records
+
+
+def read_video_pyav(container, indices):
+    """
+    Read frames from video container in the order specified by indices.
+    Maintains temporal order by reading frames in the exact order of the indices array.
+    """
+    # Ensure indices are sorted to maintain temporal order
+    sorted_indices = np.sort(indices)
+    frames = []
+    container.seek(0)
+
+    # Create a set for fast lookup, but iterate in sorted order
+    indices_set = set(sorted_indices)
+    frame_dict = {}
+
+    # Read all needed frames in one pass
+    for i, frame in enumerate(container.decode(video=0)):
+        if i > sorted_indices[-1]:
+            break
+        if i in indices_set:
+            frame_dict[i] = frame
+
+    # Extract frames in the order specified by sorted_indices
+    for idx in sorted_indices:
+        if idx in frame_dict:
+            frames.append(frame_dict[idx])
+
+    return np.stack([x.to_ndarray(format="rgb24") for x in frames])
diff --git a/src/transformers/processing_utils.py b/src/transformers/processing_utils.py
index bb1344a43dcf..4baafad985b7 100644
--- a/src/transformers/processing_utils.py
+++ b/src/transformers/processing_utils.py
@@ -20,8 +20,10 @@
 import inspect
 import json
 import os
+import re
 import sys
 import typing
+from collections import Counter
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Annotated, Any, Literal, TypedDict, TypeVar, Union
@@ -32,10 +34,10 @@
 from huggingface_hub.dataclasses import validate_typed_dict
 from huggingface_hub.errors import EntryNotFoundError
 
-from .audio_utils import AudioInput, load_audio
+from .audio_utils import AudioInput, load_audio, make_list_of_audio
 from .dynamic_module_utils import custom_object_save
 from .feature_extraction_utils import BatchFeature
-from .image_utils import ChannelDimension, ImageInput, is_vision_available
+from .image_utils import ChannelDimension, ImageInput, is_vision_available, make_flat_list_of_images
 from .tokenization_utils_base import (
     PaddingStrategy,
     PreTokenizedInput,
@@ -51,6 +53,7 @@
     PROCESSOR_NAME,
     PushToHubMixin,
     TensorType,
+    auto_docstring,
     cached_file,
     copy_func,
     direct_transformers_import,
@@ -70,7 +73,7 @@
     truncation_validator,
     video_metadata_validator,
 )
-from .video_utils import VideoInput, VideoMetadataType
+from .video_utils import VideoInput, VideoMetadataType, make_batched_videos
 
 
 if is_torch_available():
@@ -587,16 +590,13 @@ class ProcessorMixin(PushToHubMixin):
     # Names need to be attr_class for attr in attributes
     _auto_class = None
     valid_processor_kwargs = ProcessingKwargs
+    skip_tensor_conversion = ["video_metadata", "text_replacement_offsets"]
 
     # args have to match the attributes class attribute
     def __init__(self, *args, **kwargs):
         # First, extract chat template from kwargs. It can never be a positional arg
         setattr(self, "chat_template", kwargs.pop("chat_template", None))
 
-        self.image_ids = [getattr(self, "image_token_id", None)]
-        self.video_ids = [getattr(self, "video_token_id", None)]
-        self.audio_ids = [getattr(self, "audio_token_id", None)]
-
         # Check audio tokenizer for its class but do not treat it as attr to avoid saving weights
         if (audio_tokenizer := kwargs.pop("audio_tokenizer", None)) is not None:
             proper_class = self.check_argument_for_proper_class("audio_tokenizer", audio_tokenizer)
@@ -628,6 +628,7 @@ def __init__(self, *args, **kwargs):
             self.check_argument_for_proper_class(attribute_name, arg)
             setattr(self, attribute_name, arg)
 
+    @auto_docstring
     def __call__(
         self,
         images: ImageInput | None = None,
@@ -636,60 +637,236 @@ def __call__(
         audio: AudioInput | None = None,
         **kwargs: Unpack[ProcessingKwargs],
     ):
-        """
-        Main method to prepare for model inputs. This method forwards the each modality argument to its own processor
-        along with `kwargs`. Please refer to the docstring of the each processor attributes for more information.
+        images, text, videos, audio = self.prepare_inputs_layout(
+            images=images, text=text, videos=videos, audio=audio, **kwargs
+        )
+        self.validate_inputs(images=images, text=text, videos=videos, audio=audio, **kwargs)
 
-        Args:
-            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`):
-                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
-                tensor. Both channels-first and channels-last formats are supported.
-            text (`TextInput`, `PreTokenizedInput`, `list[TextInput]`, `list[PreTokenizedInput]`, *optional*):
-                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
-                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
-                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
-            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
-                The video or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
-                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
-            audio (`np.ndarray`, `torch.Tensor`, `list[np.ndarray]`, `list[torch.Tensor]`):
-                The audio or batch of audio to be prepared. Each audio can be a NumPy array or PyTorch
-                tensor.
-            return_tensors (`str` or [`~utils.TensorType`], *optional*):
-                If set, will return tensors of a particular framework. Acceptable values are:
-
-                - `'pt'`: Return PyTorch `torch.Tensor` objects.
-                - `'np'`: Return NumPy `np.ndarray` objects.
+        merged_kwargs = self._merge_kwargs(
+            self.valid_processor_kwargs,
+            tokenizer_init_kwargs=self.tokenizer.init_kwargs if hasattr(self, "tokenizer") else {},
+            **kwargs,
+        )
 
-        Returns:
-            [`BatchFeature`]: A [`BatchFeature`] object with processed inputs in a dict format.
-        """
+        processed_images = processed_videos = processed_audio = {}
+        images_replacements = videos_replacements = audio_replacements = []
+        if images is not None and hasattr(self, "image_processor"):
+            processed_images, images_replacements = self._process_images(images, **merged_kwargs["images_kwargs"])
+        if videos is not None and hasattr(self, "video_processor"):
+            processed_videos, videos_replacements = self._process_videos(videos, **merged_kwargs["videos_kwargs"])
+        if audio is not None and hasattr(self, "feature_extractor"):
+            processed_audio, audio_replacements = self._process_audio(audio, **merged_kwargs["audio_kwargs"])
+
+        text_inputs = {}
+        return_tensors = merged_kwargs["text_kwargs"].get("return_tensors", None)
+        if getattr(self, "tokenizer", None) is not None and text is not None:
+            return_mm_token_type_ids = merged_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+            return_text_replacement_offsets = merged_kwargs["text_kwargs"].pop(
+                "return_text_replacement_offsets", False
+            )
+
+            text, text_replacement_offsets = self.get_text_with_replacements(
+                text,
+                images_replacements,
+                videos_replacements,
+                audio_replacements,
+            )
+            text_inputs = self.tokenizer(text, **merged_kwargs["text_kwargs"])
+            self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video", "audio"])
+
+            if return_text_replacement_offsets:
+                text_inputs["text_replacement_offsets"] = text_replacement_offsets
+
+            if return_mm_token_type_ids:
+                text_inputs["mm_token_type_ids"] = self.create_mm_token_type_ids(text_inputs["input_ids"])
+
+        # Pop unused keys from the inputs, e.g. inputs used only to compute number of image tokens
+        data = {**text_inputs, **processed_images, **processed_videos, **processed_audio}
+        data = {k: v for k, v in data.items() if k not in self.unused_input_names}
+
+        if not kwargs.get("return_metadata"):
+            data.pop("video_metadata", None)
+
+        return BatchFeature(data, tensor_type=return_tensors, skip_tensor_conversion=self.skip_tensor_conversion)
+
+    def prepare_inputs_layout(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        videos: VideoInput | None = None,
+        audio: AudioInput | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
+        # To support BC with models in pre-MLLM era, don't wrap text in list
+        if self.all_special_multimodal_tokens and text is not None:
+            if isinstance(text, str):
+                text = [text]
+            # avoid in-place updates on text
+            text = text.copy()
+
+        if audio is not None and hasattr(self, "feature_extractor"):
+            sampling_rate = kwargs.get("sampling_rate", self.feature_extractor.sampling_rate)
+            audio = self.feature_extractor.fetch_audio(audio, sampling_rate=sampling_rate)
+            audio = make_list_of_audio(audio)
+
+        if images is not None and hasattr(self, "image_processor"):
+            images = self.image_processor.fetch_images(images)
+
+        return images, text, videos, audio
+
+    def validate_inputs(
+        self,
+        images: ImageInput | None = None,
+        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] | None = None,
+        videos: VideoInput | None = None,
+        audio: AudioInput | None = None,
+        **kwargs: Unpack[ProcessingKwargs],
+    ):
         if "audios" in kwargs and audio is None:
             raise ValueError("You passed keyword argument `audios` which is deprecated. Please use `audio` instead.")
 
         if images is None and text is None and videos is None and audio is None:
             raise ValueError(f"You need to provide at least one input to call {self.__class__.__name__}")
 
-        kwargs = self._merge_kwargs(
-            self.valid_processor_kwargs,
-            tokenizer_init_kwargs=self.tokenizer.init_kwargs if hasattr(self, "tokenizer") else {},
-            **kwargs,
-        )
+    # Simple preprocessing includes calling the `subprocessor` and optionally
+    # building placeholder strings. Each processor can override and add their
+    # own special pre/post processing on top, e.g. see `audioflamingo`
+    def _process_images(self, images: ImageInput, **kwargs):
+        processed_images = self.image_processor(images, **kwargs)
+
+        image_replacements = []
+        if getattr(self, "image_token", None) is not None:
+            # Some processors use nested struct, we need to flatten back if needed
+            images = make_flat_list_of_images(images)
+            for idx in range(len(images)):
+                replacement_text = self.replace_image_token(processed_images, image_idx=idx)
+                image_replacements.append(replacement_text)
+        return processed_images, image_replacements
 
-        attribute_to_kwargs = {
-            "tokenizer": (text, "text_kwargs"),
-            "image_processor": (images, "images_kwargs"),
-            "video_processor": (videos, "videos_kwargs"),
-            "feature_extractor": (audio, "audio_kwargs"),
+    def _process_videos(self, videos: VideoInput, **kwargs):
+        processed_videos = self.video_processor(videos, **kwargs)
+
+        video_replacements = []
+        if getattr(self, "video_token", None) is not None:
+            videos = make_batched_videos(videos)
+            for idx in range(len(videos)):
+                replacement_text = self.replace_video_token(processed_videos, video_idx=idx)
+                video_replacements.append(replacement_text)
+
+        return processed_videos, video_replacements
+
+    def _process_audio(self, audio: AudioInput, **kwargs):
+        processed_audio = self.feature_extractor(audio, **kwargs)
+
+        audio_replacements = []
+        if getattr(self, "audio_token", None) is not None:
+            for idx in range(len(audio)):
+                replacement_text = self.replace_audio_token(processed_audio, audio_idx=idx)
+                audio_replacements.append(replacement_text)
+
+        return processed_audio, audio_replacements
+
+    # To be overriden by each model's processor if they need to add placeholder tokens
+    def replace_image_token(self, image_inputs: dict, image_idx: int) -> str:
+        return ""
+
+    def replace_video_token(self, video_inputs: dict, video_idx: int) -> str:
+        return ""
+
+    def replace_audio_token(self, audio_inputs: dict, audio_idx: int) -> str:
+        return ""
+
+    def get_text_with_replacements(
+        self,
+        text: list[str],
+        images_replacements: list[str] = [],
+        videos_replacements: list[str] = [],
+        audio_replacements: list[str] = [],
+    ) -> tuple[list[str], list[dict[str, Any]]]:
+        # Early exit if no special tokens found, nothing to replace
+        if not self.all_special_multimodal_tokens:
+            return text, []
+
+        # Use named regex so we can extract groups later and replace
+        token_groups = []
+        if image_token := getattr(self, "image_token", None):
+            token_groups.append(f"(?P<image>{re.escape(image_token)})")
+        if video_token := getattr(self, "video_token", None):
+            token_groups.append(f"(?P<video>{re.escape(video_token)})")
+        if audio_token := getattr(self, "audio_token", None):
+            token_groups.append(f"(?P<audio>{re.escape(audio_token)})")
+
+        regex_special_mm_tokens = "|".join(token_groups) or r"(?!)"
+        replacements_iters = {
+            "image": iter(images_replacements),
+            "video": iter(videos_replacements),
+            "audio": iter(audio_replacements),
         }
-        outputs = {}
-        for attribute_name in self.get_attributes():
-            attribute = getattr(self, attribute_name, None)
-            input_data, input_kwargs = attribute_to_kwargs[attribute_name]
-            if input_data is not None and attribute is not None:
-                attribute_output = attribute(input_data, **kwargs[input_kwargs])
-                outputs.update(attribute_output)
+        batch_replacement_offsets = []
+        for batch_idx in range(len(text)):
+            last = 0
+            replacement_offsets = []
+            expanded_sample = []
+            for m in re.finditer(regex_special_mm_tokens, text[batch_idx]):
+                start, end = m.span()
+                expanded_sample.append(text[batch_idx][last:start])
+
+                mm_type = m.lastgroup
+                replacement_text = next(replacements_iters[mm_type])
+                replacement_offsets.append(
+                    {
+                        "type": mm_type,
+                        "span": (start, end),
+                        "new_span": (start, start + len(replacement_text)),
+                        "text": m.group(),
+                        "replacement": replacement_text,
+                    }
+                )
+                expanded_sample.append(replacement_text)
+                last = end
+
+            expanded_sample.append(text[batch_idx][last:])
+            text[batch_idx] = "".join(expanded_sample)
+            batch_replacement_offsets.append(replacement_offsets)
+        return text, batch_replacement_offsets
 
-        return BatchFeature(outputs)
+    def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
+        # We have to iterate for each list separately because inputs
+        # might be non-padded lists and we can't cast numpy on that!
+        # Then cast numpy as each input for faster indexing
+        mm_token_type_ids = []
+        for tokenizer_input in input_ids:
+            tokenizer_input = np.array(tokenizer_input)
+            mm_token_types = np.zeros_like(tokenizer_input)
+            mm_token_types[np.isin(tokenizer_input, self.image_token_ids)] = 1
+            mm_token_types[np.isin(tokenizer_input, self.video_token_ids)] = 2
+            mm_token_types[np.isin(tokenizer_input, self.audio_token_ids)] = 3
+            mm_token_type_ids.append(mm_token_types.tolist())
+        return mm_token_type_ids
+
+    @property
+    def all_special_multimodal_tokens(self) -> list[str]:
+        special_mm_tokens = [
+            getattr(self, f"{modality}_token")
+            for modality in ["image", "video", "audio"]
+            if getattr(self, f"{modality}_token", None) is not None
+        ]
+        return special_mm_tokens
+
+    # Special ids used per each modality in multimodal models. Models need to
+    # override if they use special BOI/EOI/row/col/etc tokens that have to be marked
+    # These values are used to build `mm_token_type_ids`
+    @property
+    def image_token_ids(self) -> list[int | None]:
+        return [getattr(self, "image_token_id", None)]
+
+    @property
+    def video_token_ids(self) -> list[int | None]:
+        return [getattr(self, "video_token_id", None)]
+
+    @property
+    def audio_token_ids(self) -> list[int | None]:
+        return [getattr(self, "audio_token_id", None)]
 
     def check_argument_for_proper_class(self, argument_name, argument):
         """
@@ -1424,11 +1601,32 @@ def from_pretrained(
         if token is not None:
             kwargs["token"] = token
 
+        prebuilt = cls._pop_prebuilt_subprocessors(kwargs)
+
         # Get processor_dict first so we can use it to instantiate non-tokenizer sub-processors
         processor_dict, instantiation_kwargs = cls.get_processor_dict(pretrained_model_name_or_path, **kwargs)
-        args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, processor_dict, **kwargs)
+        args = cls._get_arguments_from_pretrained(
+            pretrained_model_name_or_path, processor_dict, _prebuilt=prebuilt, **kwargs
+        )
         return cls.from_args_and_dict(args, processor_dict, **instantiation_kwargs)
 
+    @classmethod
+    def _pop_prebuilt_subprocessors(cls, kwargs: dict) -> dict:
+        """Pop pre-built sub-processors from `kwargs` by exact attribute name, or by modality
+        alias (e.g. `tokenizer=` → `bpe_tokenizer`) when that modality is unambiguous.
+        """
+        sub_processors = cls.get_attributes()
+        modality_counts = Counter(_get_modality_for_attribute(s) for s in sub_processors)
+        prebuilt = {}
+        for sub_processor_type in sub_processors:
+            modality = _get_modality_for_attribute(sub_processor_type)
+            instance = kwargs.pop(sub_processor_type, None)
+            if instance is None and modality != sub_processor_type and modality_counts[modality] == 1:
+                instance = kwargs.pop(modality, None)
+            if instance is not None:
+                prebuilt[sub_processor_type] = instance
+        return prebuilt
+
     @classmethod
     def get_attributes(cls):
         args_in_init = inspect.signature(cls.__init__).parameters.keys()
@@ -1499,7 +1697,9 @@ def _load_tokenizer_from_pretrained(
         return tokenizer
 
     @classmethod
-    def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, processor_dict=None, **kwargs):
+    def _get_arguments_from_pretrained(
+        cls, pretrained_model_name_or_path, processor_dict=None, *, _prebuilt=None, **kwargs
+    ):
         """
         Identify and instantiate the subcomponents of Processor classes, such as image processors, tokenizers,
         and feature extractors. This method inspects the processor's `__init__` signature to identify parameters
@@ -1517,15 +1717,21 @@ def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, processor
             pretrained_model_name_or_path: Path or model id to load from.
             processor_dict: Optional dict containing processor config (from processor_config.json).
                 Required when loading additional non-tokenizer sub-processors.
+            _prebuilt: Optional `{attribute: instance}` dict of pre-built sub-processors that skip loading.
         """
         args = []
         processor_dict = processor_dict if processor_dict is not None else {}
         # Remove subfolder from kwargs to avoid duplicate keyword arguments
         subfolder = kwargs.pop("subfolder", "")
 
+        prebuilt = _prebuilt or {}
+
         # get args from processor init signature
         sub_processors = cls.get_attributes()
         for sub_processor_type in sub_processors:
+            if sub_processor_type in prebuilt:
+                args.append(prebuilt[sub_processor_type])
+                continue
             modality = _get_modality_for_attribute(sub_processor_type)
             is_primary = sub_processor_type == modality
 
@@ -1633,13 +1839,19 @@ def decode(self, *args, **kwargs):
         return self.tokenizer.decode(*args, **kwargs)
 
     @property
-    def model_input_names(self):
+    def unused_input_names(self) -> list[str]:
+        "Input names returned always by subprocessors but not used in model's `forward`"
+        return []
+
+    @property
+    def model_input_names(self) -> list[str]:
         model_input_names = []
         for attribute_name in self.get_attributes():
             attribute = getattr(self, attribute_name, None)
-            attr_input_names = getattr(attribute, "model_input_names")
-            model_input_names.extend(attr_input_names)
-        return model_input_names
+            if attribute is not None:
+                attr_input_names = getattr(attribute, "model_input_names")
+                model_input_names.extend(attr_input_names)
+        return [name for name in model_input_names if name not in self.unused_input_names]
 
     @staticmethod
     def validate_init_kwargs(processor_config, valid_kwargs):
@@ -1654,20 +1866,6 @@ def validate_init_kwargs(processor_config, valid_kwargs):
 
         return unused_kwargs, valid_kwargs
 
-    def create_mm_token_type_ids(self, input_ids: list) -> list[list[int]]:
-        # We have to iterate for each list separately because inputs
-        # might be non-padded lists and we can't cast numpy on that!
-        # Then cast numpy as each input for faster indexing
-        mm_token_type_ids = []
-        for tokenizer_input in input_ids:
-            tokenizer_input = np.array(tokenizer_input)
-            mm_token_types = np.zeros_like(tokenizer_input)
-            mm_token_types[np.isin(tokenizer_input, self.image_ids)] = 1
-            mm_token_types[np.isin(tokenizer_input, self.video_ids)] = 2
-            mm_token_types[np.isin(tokenizer_input, self.audio_ids)] = 3
-            mm_token_type_ids.append(mm_token_types.tolist())
-        return mm_token_type_ids
-
     def apply_chat_template(
         self,
         conversation: list[dict[str, str]] | list[list[dict[str, str]]],
@@ -1789,6 +1987,14 @@ def apply_chat_template(
             is_batched = False
             conversations = [conversation]
 
+        # Normalize: drop `content` from assistant messages when it is None.
+        # Some APIs (e.g. OpenAI) return content=None for tool-call-only messages, but many chat templates
+        # crash or produce wrong output (e.g. rendering literal "None") when they encounter it.
+        conversations = [
+            [{k: v for k, v in msg.items() if k != "content" or v is not None} for msg in conversation]
+            for conversation in conversations
+        ]
+
         # Normalize OpenAI-style "image_url" content blocks to HuggingFace-style "image" blocks
         # OpenAI format: {"type": "image_url", "image_url": {"url": "..."}}
         # HuggingFace format: {"type": "image", "url": "..."}
@@ -1908,27 +2114,77 @@ def apply_chat_template(
                     assistant_masks = []
                     offset_mapping = out.pop("offset_mapping")
                     input_ids = out["input_ids"]
-                    for i in range(len(input_ids)):
-                        current_mask = [0] * len(input_ids[i])
-                        offsets = offset_mapping[i]
-                        offset_starts = [start for start, end in offsets]
-                        for assistant_start_char, assistant_end_char in generation_indices[i]:
-                            start_pos = bisect.bisect_left(offset_starts, assistant_start_char)
-                            end_pos = bisect.bisect_left(offset_starts, assistant_end_char)
-
-                            if not (
-                                start_pos >= 0
-                                and start_pos < len(offsets)
-                                and offsets[start_pos][0] <= assistant_start_char < offsets[start_pos][1]
-                            ):
-                                # start_token is out of bounds maybe due to truncation.
-                                continue
-                            # Ensure end_pos is also within bounds
-                            if end_pos > len(input_ids[i]):
-                                end_pos = len(input_ids[i])
-                            for token_id in range(start_pos, end_pos if end_pos else len(input_ids[i])):
-                                current_mask[token_id] = 1
-                        assistant_masks.append(current_mask)
+                    has_multimodal = images_exist or videos_exist or bool(batch_audios)
+
+                    if has_multimodal:
+                        # Multimodal processors expand placeholder tokens (e.g. a single
+                        # <|image_pad|> becomes N copies), so offset_mapping from the
+                        # expanded text is misaligned with generation_indices which were
+                        # computed from the original (unexpanded) text.
+                        # Fix: tokenize the original text to get aligned offsets, build
+                        # the mask on that, then map it onto the expanded input_ids.
+                        original_prompts = prompt if is_batched else [prompt]
+                        orig_tokenized = self.tokenizer(
+                            original_prompts,
+                            return_offsets_mapping=True,
+                            add_special_tokens=kwargs.get("add_special_tokens", True),
+                        )
+                        for i in range(len(input_ids)):
+                            orig_offsets = orig_tokenized["offset_mapping"][i]
+                            orig_ids = orig_tokenized["input_ids"][i]
+                            orig_offset_starts = [s for s, e in orig_offsets]
+
+                            # Build mask on original (unexpanded) tokenization
+                            orig_mask = [0] * len(orig_ids)
+                            for assistant_start_char, assistant_end_char in generation_indices[i]:
+                                start_pos = bisect.bisect_left(orig_offset_starts, assistant_start_char)
+                                end_pos = bisect.bisect_left(orig_offset_starts, assistant_end_char)
+
+                                if not (
+                                    start_pos >= 0
+                                    and start_pos < len(orig_offsets)
+                                    and orig_offsets[start_pos][0] <= assistant_start_char < orig_offsets[start_pos][1]
+                                ):
+                                    continue
+                                if end_pos > len(orig_ids):
+                                    end_pos = len(orig_ids)
+                                for token_id in range(start_pos, end_pos if end_pos else len(orig_ids)):
+                                    orig_mask[token_id] = 1
+
+                            # Align orig_mask to expanded input_ids via two-pointer:
+                            # tokens shared between original and expanded are matched
+                            # sequentially; extra expansion tokens get mask value 0.
+                            expanded_ids = input_ids[i]
+                            current_mask = [0] * len(expanded_ids)
+                            orig_ptr = 0
+                            for exp_idx in range(len(expanded_ids)):
+                                if orig_ptr < len(orig_ids) and expanded_ids[exp_idx] == orig_ids[orig_ptr]:
+                                    current_mask[exp_idx] = orig_mask[orig_ptr]
+                                    orig_ptr += 1
+                            assistant_masks.append(current_mask)
+                    else:
+                        for i in range(len(input_ids)):
+                            current_mask = [0] * len(input_ids[i])
+                            offsets = offset_mapping[i]
+                            offset_starts = [start for start, end in offsets]
+                            for assistant_start_char, assistant_end_char in generation_indices[i]:
+                                start_pos = bisect.bisect_left(offset_starts, assistant_start_char)
+                                end_pos = bisect.bisect_left(offset_starts, assistant_end_char)
+
+                                if not (
+                                    start_pos >= 0
+                                    and start_pos < len(offsets)
+                                    and offsets[start_pos][0] <= assistant_start_char < offsets[start_pos][1]
+                                ):
+                                    # start_token is out of bounds maybe due to truncation.
+                                    continue
+                                # Ensure end_pos is also within bounds
+                                if end_pos > len(input_ids[i]):
+                                    end_pos = len(input_ids[i])
+                                for token_id in range(start_pos, end_pos if end_pos else len(input_ids[i])):
+                                    current_mask[token_id] = 1
+                            assistant_masks.append(current_mask)
+
                     out["assistant_masks"] = assistant_masks
                     out.convert_to_tensors(tensor_type=return_tensors)
                 return out
@@ -2011,16 +2267,17 @@ def _check_special_mm_tokens(self, text: list[str], text_inputs: "BatchFeature",
         if tokenized text was truncated, leading to issues in model code.
         """
         for modality in modalities:
-            token_str = getattr(self, f"{modality}_token")
-            token_id = getattr(self, f"{modality}_token_id")
-            ids_count = [list(ids).count(token_id) for ids in text_inputs["input_ids"]]
-            text_count = [sample.count(token_str) for sample in text]
+            token_str = getattr(self, f"{modality}_token", None)
+            token_id = getattr(self, f"{modality}_token_id", None)
+            if token_str is not None and token_id is not None:
+                ids_count = [list(ids).count(token_id) for ids in text_inputs["input_ids"]]
+                text_count = [sample.count(token_str) for sample in text]
 
-            if ids_count != text_count:
-                raise ValueError(
-                    f"Mismatch in `{modality}` token count between text and `input_ids`. Got ids={ids_count} and text={text_count}. "
-                    "Likely due to `truncation='max_length'`. Please disable truncation or increase `max_length`."
-                )
+                if ids_count != text_count:
+                    raise ValueError(
+                        f"Mismatch in `{modality}` token count between text and `input_ids`. Got ids={ids_count} and text={text_count}. "
+                        "Likely due to `truncation='max_length'`. Please disable truncation or increase `max_length`."
+                    )
 
 
 ProcessorMixin.push_to_hub = copy_func(ProcessorMixin.push_to_hub)
diff --git a/src/transformers/pytorch_utils.py b/src/transformers/pytorch_utils.py
index 444c8bc457bb..4fcd8eef19a3 100644
--- a/src/transformers/pytorch_utils.py
+++ b/src/transformers/pytorch_utils.py
@@ -222,7 +222,7 @@ def id_tensor_storage(tensor: torch.Tensor) -> tuple[torch.device, int, int]:
 
         if isinstance(tensor, DTensor):
             local_tensor = tensor.to_local()
-            return tensor.device, local_tensor.storage().data_ptr(), tensor.nbytes
+            return tensor.device, local_tensor.untyped_storage().data_ptr(), tensor.untyped_storage().nbytes()
 
     if tensor.device.type == "xla" and is_torch_xla_available():
         # NOTE: xla tensors dont have storage
diff --git a/src/transformers/quantizers/auto.py b/src/transformers/quantizers/auto.py
index a972c6f637bb..bec46347bf8d 100644
--- a/src/transformers/quantizers/auto.py
+++ b/src/transformers/quantizers/auto.py
@@ -148,9 +148,18 @@ def from_dict(cls, quantization_config_dict: dict):
             suffix = "_4bit" if quantization_config_dict.get("load_in_4bit", False) else "_8bit"
             quant_method = QuantizationMethod.BITS_AND_BYTES + suffix
         elif quant_method is None:
-            raise ValueError(
-                "The model's quantization config from the arguments has no `quant_method` attribute. Make sure that the model has been correctly quantized"
-            )
+            # Recognize MLX-style affine quantization configs as Metal
+            if (
+                quantization_config_dict.get("mode") == "affine"
+                and "bits" in quantization_config_dict
+                and "group_size" in quantization_config_dict
+            ):
+                quantization_config_dict["quant_method"] = QuantizationMethod.METAL
+                quant_method = QuantizationMethod.METAL
+            else:
+                raise ValueError(
+                    "The model's quantization config from the arguments has no `quant_method` attribute. Make sure that the model has been correctly quantized"
+                )
 
         if quant_method not in AUTO_QUANTIZATION_CONFIG_MAPPING:
             raise ValueError(
@@ -233,11 +242,14 @@ def merge_quantization_configs(
             warning_msg = ""
 
         if isinstance(quantization_config, dict):
+            _original_config_dict = quantization_config.copy()
             # Convert the config based on the type of quantization_config_from_args (e.g., AutoRoundConfig), which takes priority before automatic configuration dispatch.
             if isinstance(quantization_config_from_args, AutoRoundConfig):
                 quantization_config = AutoRoundConfig.from_dict(quantization_config)
             else:
                 quantization_config = AutoQuantizationConfig.from_dict(quantization_config)
+        else:
+            _original_config_dict = None
 
         if (
             quantization_config_from_args is not None
@@ -318,12 +330,14 @@ def register_quantizer_fn(cls):
     return register_quantizer_fn
 
 
-def get_hf_quantizer(config, quantization_config, device_map, weights_only, user_agent):
+def get_hf_quantizer(config, quantization_config, device_map, weights_only, user_agent, hf_quantizer=None):
     pre_quantized = hasattr(config, "quantization_config")
     if pre_quantized and not AutoHfQuantizer.supports_quant_method(config.quantization_config):
         pre_quantized = False
 
-    if pre_quantized or quantization_config is not None:
+    if hf_quantizer is not None:
+        pass
+    elif pre_quantized or quantization_config is not None:
         if pre_quantized:
             config.quantization_config = AutoHfQuantizer.merge_quantization_configs(
                 config.quantization_config, quantization_config
diff --git a/src/transformers/quantizers/base.py b/src/transformers/quantizers/base.py
index 5390a9c3e8d3..a2f9a1e2047f 100644
--- a/src/transformers/quantizers/base.py
+++ b/src/transformers/quantizers/base.py
@@ -57,6 +57,11 @@ def get_keys_to_not_convert(model) -> list:
     }
     modules_to_not_convert = tied_keys | last_module_key | output_emb_keys
 
+    # remove audio modules for multimodal models to prevent uint8 crash
+    for name, _ in model.named_modules():
+        if "audio_tower" in name or "embed_audio" in name:
+            modules_to_not_convert.add(name)
+
     modules_to_not_convert = list({k.removesuffix(".weight") for k in modules_to_not_convert})
 
     return list(modules_to_not_convert)
@@ -294,6 +299,19 @@ def get_quantize_ops(self):
     def get_weight_conversions(self):
         return []
 
+    def update_weight_conversions(self, weight_conversions):
+        """Give the quantizer a chance to rewrite the weight conversion pipeline.
+
+        Loading runs ``renamings → converters → (dequant → merge → concat)``. Dequant
+        has to happen *before* any merge/concat op because those operations aren't
+        aware of per-block scales, so the per-expert (weight, scale) pairs need to be
+        collapsed into full-precision tensors first. Subclasses (e.g. the FP8
+        quantizer in ``dequantize=True`` mode) override this to inject a dequantize
+        op at the start of each model-provided :class:`WeightConverter` and attach the
+        matching scale source patterns. Default: no-op.
+        """
+        return weight_conversions + self.get_weight_conversions()
+
 
 class SequentialLlama4TextExperts(ModuleList):
     """
diff --git a/src/transformers/quantizers/quantizer_bnb_4bit.py b/src/transformers/quantizers/quantizer_bnb_4bit.py
index 5fd040bed10e..3163360faa50 100644
--- a/src/transformers/quantizers/quantizer_bnb_4bit.py
+++ b/src/transformers/quantizers/quantizer_bnb_4bit.py
@@ -11,8 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import importlib
 from typing import TYPE_CHECKING
 
+from packaging import version
+
 from .base import HfQuantizer
 from .quantizers_utils import get_module_from_name
 
@@ -185,3 +188,9 @@ def get_weight_conversions(self):
                 )
             ]
         return []
+
+    @property
+    def is_compileable(self) -> bool:
+        # Compatible with PyTorch 2.4+ for fullgraph=False.
+        # Requires PyTorch 2.8 nightly for fullgraph=True.
+        return version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse("0.46.0")
diff --git a/src/transformers/quantizers/quantizer_bnb_8bit.py b/src/transformers/quantizers/quantizer_bnb_8bit.py
index cf3192bcd72a..d38ee3af21c5 100644
--- a/src/transformers/quantizers/quantizer_bnb_8bit.py
+++ b/src/transformers/quantizers/quantizer_bnb_8bit.py
@@ -11,8 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import importlib
 from typing import TYPE_CHECKING
 
+from packaging import version
+
 from .base import HfQuantizer
 
 
@@ -176,3 +179,7 @@ def get_weight_conversions(self):
                 )
             ]
         return []
+
+    @property
+    def is_compileable(self) -> bool:
+        return version.parse(importlib.metadata.version("bitsandbytes")) >= version.parse("0.46.0")
diff --git a/src/transformers/quantizers/quantizer_compressed_tensors.py b/src/transformers/quantizers/quantizer_compressed_tensors.py
index 5e1e4882beea..78c9b5bf138b 100644
--- a/src/transformers/quantizers/quantizer_compressed_tensors.py
+++ b/src/transformers/quantizers/quantizer_compressed_tensors.py
@@ -13,9 +13,10 @@
 # limitations under the License.
 
 
-from ..utils import is_compressed_tensors_available, is_torch_available, logging
+from ..utils import is_compressed_tensors_available, is_torch_available, is_torch_xpu_available, logging
 from ..utils.quantization_config import CompressedTensorsConfig
 from .base import HfQuantizer
+from .quantizers_utils import get_module_from_name
 
 
 if is_torch_available():
@@ -24,16 +25,38 @@
 logger = logging.get_logger(__name__)
 
 
+def _is_ct_fp8_config(quantization_config: CompressedTensorsConfig) -> bool:
+    """Check if a CompressedTensorsConfig describes FP8 quantization."""
+    ct_qconfig = quantization_config.quantization_config
+    if ct_qconfig is None:
+        return False
+    for group in ct_qconfig.config_groups.values():
+        weights = group.weights
+        if weights is not None and weights.type == "float" and weights.num_bits == 8:
+            return True
+    return False
+
+
 class CompressedTensorsHfQuantizer(HfQuantizer):
     """
     Quantizer for the compressed_tensors package.  Loads and restores models to
-    quantized state with compressed_tensors
+    quantized state with compressed_tensors.
+
+    When FP8 quantization is detected and a GPU/XPU is available, uses row-wise
+    FP8 matmul kernels (torch._scaled_mm on XPU, fbgemm on CUDA) for acceleration.
+    Otherwise falls back to the default compressed-tensors dequantize path.
     """
 
     requires_calibration = True
     quantization_config: CompressedTensorsConfig
 
     def __init__(self, quantization_config: CompressedTensorsConfig, **kwargs):
+        # For FP8 with GPU/XPU, we don't require calibration (online quantization is supported)
+        if _is_ct_fp8_config(quantization_config) and (
+            is_torch_available() and (torch.cuda.is_available() or is_torch_xpu_available())
+        ):
+            self.requires_calibration = False
+
         super().__init__(quantization_config, **kwargs)
 
         if not is_compressed_tensors_available():
@@ -52,6 +75,34 @@ def __init__(self, quantization_config: CompressedTensorsConfig, **kwargs):
         self.run_compressed = quantization_config.run_compressed
         self.quantization_config = quantization_config
 
+        # Detect FP8 and decide whether to use FP8 kernel path
+        self._use_fp8_kernel = False
+        self._fp8_dequantize = False
+        self._activation_scheme = "dynamic"
+        self._modules_to_not_convert_ct = []
+
+        if _is_ct_fp8_config(quantization_config):
+            ct_qconfig = quantization_config.quantization_config
+            if ct_qconfig and ct_qconfig.ignore:
+                self._modules_to_not_convert_ct = list(ct_qconfig.ignore)
+            # Parse activation scheme
+            if ct_qconfig:
+                for group in ct_qconfig.config_groups.values():
+                    act = group.input_activations
+                    if act is not None:
+                        self._activation_scheme = "dynamic" if act.dynamic else "static"
+                    break
+
+            if torch.cuda.is_available() or is_torch_xpu_available():
+                self._use_fp8_kernel = True
+                self.requires_calibration = False
+                logger.info("Compressed-tensors FP8 detected — using FP8 kernel path for acceleration.")
+            else:
+                logger.info(
+                    "Compressed-tensors FP8 detected but no GPU/XPU found. "
+                    "Falling back to default compressed-tensors path."
+                )
+
     def validate_environment(self, *args, **kwargs):
         if not is_compressed_tensors_available():
             raise ImportError(
@@ -60,31 +111,86 @@ def validate_environment(self, *args, **kwargs):
             )
 
     def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
-        if dtype != torch.float16:
+        if not self._use_fp8_kernel and dtype != torch.float16:
             logger.info("We suggest you to set `dtype=torch.float16` for better efficiency with compressed_tensors.")
         return dtype
 
+    def param_needs_quantization(self, model, param_name: str, **kwargs) -> bool:
+        if not self._use_fp8_kernel:
+            return False
+        from ..integrations.compressed_tensors_fp8 import CTFP8Linear
+
+        module, tensor_name = get_module_from_name(model, param_name)
+        if isinstance(module, CTFP8Linear):
+            if self.pre_quantized or tensor_name == "bias":
+                return False
+            return True
+        return False
+
+    def param_element_size(self, model, param_name: str, param: "torch.Tensor") -> float:
+        if self._use_fp8_kernel and self.param_needs_quantization(model, param_name):
+            return 1  # 8-bit
+        return super().param_element_size(model, param_name, param)
+
     def _process_model_before_weight_loading(self, model, **kwargs):
+        if self._use_fp8_kernel:
+            self._process_model_before_weight_loading_fp8(model, **kwargs)
+        else:
+            self._process_model_before_weight_loading_default(model, **kwargs)
+
+    def _process_model_before_weight_loading_default(self, model, **kwargs):
         from compressed_tensors.quantization import apply_quantization_config
 
         ct_quantization_config = self.compressor.quantization_config
 
-        # Always initialize compressed wrappers to match the checkpoint
-        apply_quantization_config(model, ct_quantization_config, self.run_compressed)
+        apply_quantization_config(model, ct_quantization_config)
         if (
             self.quantization_config.is_quantization_compressed
             or self.quantization_config.is_sparsification_compressed
         ):
             self.compressor.compress_model(model=model)
 
+    def _process_model_before_weight_loading_fp8(self, model, **kwargs):
+        from ..integrations.compressed_tensors_fp8 import replace_with_ct_fp8_linear
+
+        self.modules_to_not_convert = self.get_modules_to_not_convert(model, None, model._keep_in_fp32_modules)
+        if self._modules_to_not_convert_ct:
+            self.modules_to_not_convert = list(set(self.modules_to_not_convert + self._modules_to_not_convert_ct))
+
+        replace_with_ct_fp8_linear(
+            model,
+            modules_to_not_convert=self.modules_to_not_convert,
+            activation_scheme=self._activation_scheme,
+            dequantize=self._fp8_dequantize,
+            pre_quantized=self.pre_quantized,
+        )
+
     def _process_model_after_weight_loading(self, model, **kwargs):
         """Decompress loaded model if necessary - need for qat"""
+        if self._use_fp8_kernel:
+            return
+
+        from compressed_tensors import __version__ as ct_version
+        from packaging import version
 
-        if (self.quantization_config.is_quantization_compressed and not self.run_compressed) or (
-            self.quantization_config.is_sparsification_compressed
+        if (
+            version.parse(ct_version) >= version.parse("0.14")
+            or (self.quantization_config.is_quantization_compressed and not self.run_compressed)
+            or self.quantization_config.is_sparsification_compressed
         ):
             self.compressor.decompress_model(model=model)
 
+    def _dequantize(self, model, dtype=None):
+        from compressed_tensors.quantization import QuantizationStatus
+
+        self.compressor.decompress_model(model=model)
+
+        for module in model.modules():
+            if hasattr(module, "quantization_status"):
+                module.quantization_status = QuantizationStatus.FROZEN
+
+        return model
+
     # NOTE: TP plan override for compressed tensors removed - unsupported styles were used.
     # TODO: Implement proper TP support for compressed tensors quantization
     def update_tp_plan(self, config):
@@ -102,13 +208,58 @@ def update_tp_plan(self, config):
 
     @property
     def is_trainable(self):
+        if self._use_fp8_kernel:
+            return False
+        return True
+
+    @property
+    def is_compileable(self) -> bool:
         return True
 
     def is_qat_trainable(self) -> bool:
         """Loaded Models can carry out quantization aware training"""
+        if self._use_fp8_kernel:
+            return False
         # models need to be decompressed carry out qat
         return not self.run_compressed or not self.quantization_config.is_quantization_compressed
 
     def is_serializable(self) -> bool:
         """Models quantized using compressed tensors can be saved to disk"""
         return True
+
+    def get_quantize_ops(self):
+        if not self._use_fp8_kernel or self.pre_quantized:
+            return None
+        from ..integrations.compressed_tensors_fp8 import CTFP8PerRowQuantize
+
+        return CTFP8PerRowQuantize(self)
+
+    def get_weight_conversions(self):
+        if not self._use_fp8_kernel:
+            return []
+
+        from ..core_model_loading import WeightConverter
+        from ..integrations.compressed_tensors_fp8 import (
+            CompressedTensorsFp8Dequantize,
+            CompressedTensorsScaleConvert,
+        )
+
+        if self.pre_quantized and self._fp8_dequantize:
+            return [
+                WeightConverter(
+                    source_patterns=["weight$", "weight_scale"],
+                    target_patterns="weight",
+                    operations=[CompressedTensorsFp8Dequantize(self)],
+                )
+            ]
+
+        if self.pre_quantized:
+            return [
+                WeightConverter(
+                    source_patterns=["weight_scale$"],
+                    target_patterns=["weight_scale_inv"],
+                    operations=[CompressedTensorsScaleConvert(self)],
+                ),
+            ]
+
+        return []
diff --git a/src/transformers/quantizers/quantizer_finegrained_fp8.py b/src/transformers/quantizers/quantizer_finegrained_fp8.py
index e736b0f21915..be7d5f01669c 100644
--- a/src/transformers/quantizers/quantizer_finegrained_fp8.py
+++ b/src/transformers/quantizers/quantizer_finegrained_fp8.py
@@ -166,3 +166,56 @@ def get_weight_conversions(self):
                 )
             ]
         return []
+
+    def update_weight_conversions(self, weight_conversions):
+        """When loading with ``dequantize=True``, attach an :class:`Fp8Dequantize` op to
+        every existing :class:`WeightConverter` so that per-block scales are folded into
+        the weight *before* any later merge/concat ops collapse the per-expert structure.
+
+        For each model-supplied converter that has a ``.weight`` source, we:
+          1. anchor the existing weight patterns with ``$`` so they don't accidentally
+             also match the ``.weight_scale_inv`` keys (the regex is searched, so the
+             unanchored prefix would match both, sending scales to the wrong bucket);
+          2. add anchored ``*.weight_scale_inv`` sources next to each weight pattern so
+             the loader collects scale tensors alongside the weight tensors into the
+             *same* converter bucket (both keys rewrite to the same target);
+          3. prepend a fresh :class:`Fp8Dequantize` op so dequant runs first, before
+             any merge/concat collapses the per-expert structure.
+
+        The generic ``weight$ + weight_scale_inv → weight`` converter from
+        :meth:`get_weight_conversions` is still appended at the end as a fallback for
+        plain ``nn.Linear`` weights with no model-specific converter.
+        """
+        if not (self.pre_quantized and self.quantization_config.dequantize):
+            return weight_conversions + self.get_weight_conversions()
+
+        from ..core_model_loading import WeightConverter, WeightRenaming
+        from ..integrations.finegrained_fp8 import Fp8Dequantize
+
+        # Some upstream FP8 checkpoints (e.g. DeepSeek-V4-Flash) ship per-block scales
+        # under a ``.scale`` suffix instead of HF's canonical ``.weight_scale_inv``.
+        # Prepending the rename here (instead of in each model's conversion_mapping)
+        # keeps the model-side mapping clean — the rename only kicks in when FP8 dequant
+        # is actually active, so a non-FP8 save / load round-trip doesn't see a stray
+        # rule that ``test_reverse_loading_mapping`` can't match.
+        scale_rename = WeightRenaming(source_patterns=r"^(.+)\.scale$", target_patterns=r"\1.weight_scale_inv")
+        weight_conversions = [scale_rename] + list(weight_conversions)
+
+        updated: list = []
+        for conv in weight_conversions:
+            weight_sources = [p for p in conv.source_patterns if p.endswith(".weight")]
+            if weight_sources:
+                anchored_weight = [p + "$" for p in weight_sources]
+                scale_sources = [p[: -len(".weight")] + ".weight_scale_inv$" for p in weight_sources]
+                other = [p for p in conv.source_patterns if not p.endswith(".weight")]
+                new_sources = anchored_weight + scale_sources + other
+                new_ops = [Fp8Dequantize(self)] + list(conv.operations)
+                conv = WeightConverter(
+                    source_patterns=new_sources,
+                    target_patterns=conv._original_target_patterns,
+                    operations=new_ops,
+                )
+            updated.append(conv)
+        # Generic fallback for plain ``nn.Linear`` weights with no model-specific converter.
+        updated.extend(self.get_weight_conversions())
+        return updated
diff --git a/src/transformers/quantizers/quantizer_gptq.py b/src/transformers/quantizers/quantizer_gptq.py
index f018704f4949..55bec9e3a8b1 100644
--- a/src/transformers/quantizers/quantizer_gptq.py
+++ b/src/transformers/quantizers/quantizer_gptq.py
@@ -80,6 +80,9 @@ def update_dtype(self, dtype: "torch.dtype") -> "torch.dtype":
     def update_device_map(self, device_map):
         if device_map is None:
             device_map = {"": torch.device("cpu")}
+        # Only auto-gptq does not support CPU, so move the model to cuda if available.
+        if not is_gptqmodel_available() and device_map in ("cpu", {"": torch.device("cpu")}):
+            device_map = {"": 0}
         return device_map
 
     def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
diff --git a/src/transformers/quantizers/quantizer_hqq.py b/src/transformers/quantizers/quantizer_hqq.py
index 05dce3d996a0..43238e99e7e6 100755
--- a/src/transformers/quantizers/quantizer_hqq.py
+++ b/src/transformers/quantizers/quantizer_hqq.py
@@ -59,10 +59,16 @@ def __init__(self, quantization_config, **kwargs):
             )
         super().__init__(quantization_config, **kwargs)
         self.dtype = None
+        self.device_map = None
         self.using_multi_gpu = False
         # Keys that are serialized specifically by hqq
         self.hqq_keys = HQQLinear(None, None).state_dict_keys() - {"bias"}
 
+    def update_dtype(self, dtype):
+        if dtype is not None:
+            self.dtype = dtype
+        return dtype
+
     def validate_environment(self, *args, **kwargs):
         if self.dtype is None:
             if "dtype" in kwargs:
@@ -72,6 +78,7 @@ def validate_environment(self, *args, **kwargs):
                 logger.info("Setting dtype to torch.float32 as the default value since it was not specified.")
 
         device_map = kwargs.get("device_map")
+        self.device_map = device_map
         if isinstance(device_map, dict):
             if "cpu" in device_map.values() or "disk" in device_map.values():
                 raise ValueError(
@@ -144,10 +151,16 @@ def validate_environment(self, *args, **kwargs):
     #     return list(new_keys)
 
     def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **kwargs) -> bool:
-        module, _ = get_module_from_name(model, param_name)
-        # Since we do not prepare the modules in advance, we need every param of the Linear layer to go through
-        # `create_quantized_param`, even when `self.is_quantized == True`
-        return isinstance(module, torch.nn.Linear)
+        module, tensor_name = get_module_from_name(model, param_name)
+        return isinstance(module, torch.nn.Linear) and tensor_name == "weight"
+
+    def get_quantize_ops(self):
+        from ..integrations.hqq import HqqQuantize
+
+        return HqqQuantize(self)
+
+    def get_weight_conversions(self):
+        return []
 
     # TODO: to remove
     # def create_quantized_param(
@@ -232,6 +245,47 @@ def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **
 
     #         setattr(parent_module, node, hqq_layer)
 
+    def _setup_missing_key_filters(self, model, checkpoint_files):
+        """Scan checkpoint files to find HQQ-quantized modules.
+
+        For those modules:
+        1. Suppress their .weight missing key warnings in the load report.
+        2. Replace their weight parameter with a scalar meta tensor so that
+           ``_move_missing_keys_from_meta_to_device`` does not allocate
+           full-size fp16 tensors on GPU (which would cause OOM).
+        """
+        import re
+
+        from safetensors import safe_open
+
+        quantized_modules = set()
+        for ckpt_file in checkpoint_files:
+            if ckpt_file.endswith(".safetensors"):
+                with safe_open(ckpt_file, framework="pt") as f:
+                    for k in f.keys():
+                        if k.endswith(".W_q"):
+                            quantized_modules.add(k[: -len(".W_q")])
+            else:
+                state_dict = torch.load(ckpt_file, map_location="cpu", weights_only=True)
+                for k in state_dict:
+                    if k.endswith(".W_q"):
+                        quantized_modules.add(k[: -len(".W_q")])
+
+        if quantized_modules:
+            # Build regex that matches only .weight keys of quantized modules
+            escaped = [re.escape(m) + r"\.weight" for m in quantized_modules]
+            existing = model._keys_to_ignore_on_load_missing or []
+            model._keys_to_ignore_on_load_missing = existing + escaped
+
+            # Replace weight params with scalar meta tensors to avoid GPU allocation
+            for module_name in quantized_modules:
+                try:
+                    module = model.get_submodule(module_name)
+                except AttributeError:
+                    continue
+                if hasattr(module, "weight") and module.weight is not None:
+                    module.weight = torch.nn.Parameter(torch.empty(0, device="meta"), requires_grad=False)
+
     def _patch_layer_for_multigpu(self, hqq_layer):
         def forward_with_device(self, x):
             out = torch.matmul(x.to(self.device), self.dequantize().t())
@@ -245,17 +299,133 @@ def forward_with_device(self, x):
     def _process_model_before_weight_loading(
         self,
         model: "PreTrainedModel",
+        checkpoint_files=None,
         **kwargs,
     ):
-        # Add the corresponding quant_config to each valid module. This allows us to do the actual nn.Linear -> HQQLinear conversion in create_quantized_param().
-        # prepare_for_hqq_linear() also sets the right quantization config inside the model (model.config.quantization_config) and the layers (hqq_layer.quant_config)
-        model = prepare_for_hqq_linear(model, quantization_config=self.quantization_config)
+        if self.pre_quantized:
+            # Store checkpoint files for loading in _process_model_after_weight_loading
+            self._checkpoint_files = checkpoint_files
+
+            # Suppress noisy load report: HQQ checkpoint keys (W_q, scale, etc.) are
+            # "unexpected" and nn.Linear .weight keys are "missing" from the standard
+            # loading perspective, but _load_hqq_from_checkpoint handles them.
+            hqq_keys = HQQLinear(None, None).state_dict_keys()
+            ignore_unexpected = [rf"\.{k}$" for k in hqq_keys]
+            existing = model._keys_to_ignore_on_load_unexpected or []
+            model._keys_to_ignore_on_load_unexpected = existing + ignore_unexpected
+
+            # For missing keys: scan checkpoint to find which modules have W_q (are HQQ-quantized),
+            # and suppress only their .weight keys. Also replace their weight with a scalar meta
+            # tensor to prevent _move_missing_keys_from_meta_to_device from allocating full-size
+            # tensors on GPU (which would cause OOM for large models).
+            self._setup_missing_key_filters(model, checkpoint_files)
+        else:
+            # Add the corresponding quant_config to each valid module for on-the-fly quantization.
+            # prepare_for_hqq_linear() also sets the right quantization config inside the model
+            # (model.config.quantization_config) and the layers (hqq_layer.quant_config)
+            model = prepare_for_hqq_linear(model, quantization_config=self.quantization_config)
 
     def _process_model_after_weight_loading(self, model: "PreTrainedModel", **kwargs):
+        if self.pre_quantized:
+            self._load_hqq_from_checkpoint(model)
         setattr(model, "is_hqq_quantized", True)
         setattr(model, "is_hqq_serializable", self.is_serializable())
         return model
 
+    def _load_hqq_from_checkpoint(self, model: "PreTrainedModel"):
+        """Load pre-quantized HQQ weights directly from checkpoint files."""
+        from collections import defaultdict
+
+        from safetensors import safe_open
+
+        from ..integrations.hqq import autoname_modules, name_to_linear_tag
+
+        # Determine target device from stored device_map
+        device_map = getattr(self, "device_map", None)
+        if isinstance(device_map, dict):
+            # Use the first non-cpu device from the map (values can be str, int, or torch.device)
+            devices = [torch.device(v) for v in device_map.values()]
+            cuda_devices = [d for d in devices if d.type != "cpu"]
+            target_device = cuda_devices[0] if cuda_devices else torch.device("cpu")
+        elif isinstance(device_map, str) and device_map not in ("cpu", "auto"):
+            target_device = torch.device(device_map)
+        else:
+            target_device = torch.device("cpu")
+
+        autoname_modules(model)
+        skip_modules = self.quantization_config.skip_modules
+        hqq_state_dict_keys = HQQLinear(None, None).state_dict_keys()
+
+        # Find which modules should be quantized
+        quantizable_modules = {}
+        for name, module in model.named_modules():
+            if isinstance(module, torch.nn.Linear):
+                linear_tag = name_to_linear_tag(name)
+                if linear_tag not in skip_modules:
+                    quantizable_modules[name] = module
+
+        # Load the full state dict from checkpoint files
+        full_state_dict = {}
+        for ckpt_file in self._checkpoint_files:
+            if ckpt_file.endswith(".safetensors"):
+                with safe_open(ckpt_file, framework="pt") as f:
+                    for k in f.keys():
+                        full_state_dict[k] = f.get_tensor(k)
+            else:
+                import torch as torch_
+
+                full_state_dict.update(torch_.load(ckpt_file, map_location="cpu", weights_only=True))
+
+        # Group state dict by module
+        module_states = defaultdict(dict)
+        for key, value in full_state_dict.items():
+            # Find the module this key belongs to
+            for module_name in quantizable_modules:
+                if key.startswith(module_name + "."):
+                    param_name = key[len(module_name) + 1 :]
+                    if param_name in hqq_state_dict_keys:
+                        module_states[module_name][param_name] = value
+                    break
+
+        # Replace nn.Linear with HQQLinear for each quantizable module
+        for module_name, state in module_states.items():
+            if "W_q" not in state:
+                continue
+
+            hqq_layer = HQQLinear(
+                None,
+                None,
+                compute_dtype=self.dtype or torch.float16,
+                device="cpu",
+                initialize=False,
+            )
+
+            state["W_q"] = torch.nn.Parameter(state["W_q"], requires_grad=False)
+            hqq_layer.load_state_dict(state)
+
+            # Move to the correct device (HQQLinear.to() is a no-op, use .cuda() instead)
+            if target_device.type != "cpu":
+                hqq_layer.cuda(target_device)
+
+            if hqq_layer.bias is not None and isinstance(hqq_layer.bias, torch.Tensor):
+                hqq_layer.bias = torch.nn.Parameter(hqq_layer.bias)
+
+            if self.using_multi_gpu:
+                hqq_layer = self._patch_layer_for_multigpu(hqq_layer)
+
+            parent_name, _, child_name = module_name.rpartition(".")
+            parent = model.get_submodule(parent_name) if parent_name else model
+            setattr(parent, child_name, hqq_layer)
+
+        del full_state_dict
+
+        # Free any leftover GPU memory from replaced nn.Linear modules
+        import gc
+
+        gc.collect()
+        if target_device.type != "cpu":
+            torch.cuda.empty_cache()
+
     def is_serializable(self):
         return True
 
diff --git a/src/transformers/quantizers/quantizer_metal.py b/src/transformers/quantizers/quantizer_metal.py
index a3706ed7e2b4..f053523ae858 100644
--- a/src/transformers/quantizers/quantizer_metal.py
+++ b/src/transformers/quantizers/quantizer_metal.py
@@ -92,9 +92,16 @@ def param_needs_quantization(self, model: "PreTrainedModel", param_name: str, **
     def _process_model_before_weight_loading(self, model: "PreTrainedModel", **kwargs):
         from ..integrations.metal_quantization import replace_with_metal_linear
 
-        self.modules_to_not_convert = self.get_modules_to_not_convert(
-            model, self.quantization_config.modules_to_not_convert, model._keep_in_fp32_modules
-        )
+        self._model_type = getattr(model.config, "model_type", None)
+
+        skip_modules = self.quantization_config.modules_to_not_convert
+        if self.pre_quantized and skip_modules is None:
+            # Pre-quantized checkpoints (e.g. MLX) may have quantized the lm_head /
+            # output embedding too.  Don't auto-skip them; only honour explicit user
+            # overrides via modules_to_not_convert.
+            skip_modules = []
+
+        self.modules_to_not_convert = self.get_modules_to_not_convert(model, skip_modules, model._keep_in_fp32_modules)
 
         model = replace_with_metal_linear(
             model,
@@ -116,7 +123,7 @@ def get_quantize_ops(self):
         return MetalQuantize(self)
 
     def get_weight_conversions(self):
-        from ..core_model_loading import WeightConverter
+        from ..core_model_loading import WeightConverter, WeightRenaming
         from ..integrations.metal_quantization import MetalDequantize
 
         if self.pre_quantized and self.quantization_config.dequantize:
@@ -127,4 +134,34 @@ def get_weight_conversions(self):
                     operations=[MetalDequantize(self)],
                 )
             ]
+
+        if self.pre_quantized:
+            conversions = [
+                # MLX uses "biases", MetalLinear expects "qbiases"
+                WeightRenaming(source_patterns="biases", target_patterns="qbiases"),
+                # MLX quantizes embed_tokens but transformers keeps it as nn.Embedding (float);
+                # dequantize the embedding back to float so the standard Embedding layer can load it
+                WeightConverter(
+                    source_patterns=[r"embed_tokens\.weight$", r"embed_tokens\.scales", r"embed_tokens\.qbiases"],
+                    target_patterns="embed_tokens.weight",
+                    operations=[MetalDequantize(self)],
+                ),
+            ]
+
+            # MLX checkpoints may use different key prefixes than the model expects.
+            # These renamings are model-specific and only needed for pre-quantized MLX loads.
+            model_type = getattr(self, "_model_type", None)
+            if model_type == "qwen3_vl":
+                conversions.extend(
+                    [
+                        WeightRenaming(
+                            source_patterns="language_model.model.", target_patterns="model.language_model."
+                        ),
+                        WeightRenaming(source_patterns="language_model.lm_head.", target_patterns="lm_head."),
+                        WeightRenaming(source_patterns="vision_tower.", target_patterns="model.visual."),
+                    ]
+                )
+
+            return conversions
+
         return []
diff --git a/src/transformers/quantizers/quantizer_torchao.py b/src/transformers/quantizers/quantizer_torchao.py
index fd117b08023b..09bb617f1c61 100644
--- a/src/transformers/quantizers/quantizer_torchao.py
+++ b/src/transformers/quantizers/quantizer_torchao.py
@@ -176,21 +176,24 @@ def get_weight_conversions(self):
         from ..integrations.torchao import TorchAoDeserialize
 
         if self.pre_quantized:
-            return [
-                WeightConverter(
-                    # TODO: incr flexibility by generalizing the source patterns to match the format of "_weight_"
-                    # note that the matching logic is greedy, so for ex, if _weight_scale is before _weight_scale_and_zero in this list, it will match _weight_scale always (this is incorrect)
-                    # thus, the order of source_patterns is intentional
-                    source_patterns=[
-                        "_weight_qdata",
-                        "_weight_scale_and_zero",
-                        "_weight_per_tensor_scale",
-                        "_weight_scale",
-                        "_weight_zero_point",
-                        "_weight_act_pre_scale",
-                    ],
-                    target_patterns="weight",
-                    operations=[TorchAoDeserialize(self)],
-                ),
-            ]
+            converters = []
+            # TODO: incr flexibility by generalizing the source patterns to match the format of "_weight_"
+            # note that the matching logic is greedy, so for ex, if _weight_scale is before _weight_scale_and_zero in this list, it will match _weight_scale always (this is incorrect)
+            # thus, the order of source_patterns is intentional
+            for param_name in ("weight", "gate_up_proj", "down_proj"):
+                converters.append(
+                    WeightConverter(
+                        source_patterns=[
+                            f"_{param_name}_qdata",
+                            f"_{param_name}_scale_and_zero",
+                            f"_{param_name}_per_tensor_scale",
+                            f"_{param_name}_scale",
+                            f"_{param_name}_zero_point",
+                            f"_{param_name}_act_pre_scale",
+                        ],
+                        target_patterns=param_name,
+                        operations=[TorchAoDeserialize(self)],
+                    ),
+                )
+            return converters
         return []
diff --git a/src/transformers/quantizers/quantizers_utils.py b/src/transformers/quantizers/quantizers_utils.py
index 0e90e238ec4a..7c50449ff3c7 100644
--- a/src/transformers/quantizers/quantizers_utils.py
+++ b/src/transformers/quantizers/quantizers_utils.py
@@ -12,14 +12,30 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import re
-from typing import Any
 
+from torch.nn import Module
 
-def get_module_from_name(module, tensor_name: str) -> tuple[Any, str]:
-    if "." in tensor_name:
-        module_name, tensor_name = tensor_name.rsplit(".", 1)
-        module = module.get_submodule(module_name)
-    return module, tensor_name
+
+def get_module_from_name(module: Module, tensor_name: str) -> tuple[Module, str]:
+    """Split the tensor name into the module its from and the name itself."""
+    possible_modules = tensor_name.split(".")
+    current_module = module
+
+    # Iterate through the list of possible modules,
+    # checking that the next possible sub-module is an attribute of the current module
+    for i, part in enumerate(possible_modules):
+        # Check if the next segment exists and is a Module
+        next_attribute = getattr(current_module, part, None)
+
+        if isinstance(next_attribute, Module):
+            current_module = next_attribute
+        else:
+            # We hit a non-module (Parameter, Buffer, or nested attribute)
+            # Everything from this point forward is the parameter name
+            param_name = ".".join(possible_modules[i:])
+            return current_module, param_name
+
+    return current_module, ""
 
 
 def should_convert_module(full_name, patterns: list[str] | None = None):
diff --git a/src/transformers/safetensors_conversion.py b/src/transformers/safetensors_conversion.py
index 8089b3ec3ac6..617fd4285a3e 100644
--- a/src/transformers/safetensors_conversion.py
+++ b/src/transformers/safetensors_conversion.py
@@ -3,7 +3,8 @@
 import httpx
 from huggingface_hub import Discussion, HfApi, get_repo_discussions
 
-from .utils import cached_file, http_user_agent, logging
+from .utils import logging
+from .utils.hub import cached_file, http_user_agent
 
 
 logger = logging.get_logger(__name__)
diff --git a/src/transformers/testing_utils.py b/src/transformers/testing_utils.py
index 863242a695c6..d8ecb77505eb 100644
--- a/src/transformers/testing_utils.py
+++ b/src/transformers/testing_utils.py
@@ -48,9 +48,15 @@
 from unittest.mock import patch
 
 import httpx
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
 from huggingface_hub import create_repo, delete_repo
 from packaging import version
 
+# TODO(3outeille): guarding to protect against missing import
+from torch.distributed.device_mesh import init_device_mesh
+
 from transformers import logging as transformers_logging
 
 
@@ -94,6 +100,7 @@
     is_flash_attn_2_available,
     is_flash_attn_3_available,
     is_flash_attn_4_available,
+    is_flashoptim_available,
     is_flute_available,
     is_fouroversix_available,
     is_fp_quant_available,
@@ -228,6 +235,34 @@
     "conditional_generation_class": "ForConditionalGeneration",
 }
 
+# Shared text-model defaults for CausalLMModelTester and MultiModalModelTester.
+_TEXT_MODEL_TESTER_DEFAULTS = {
+    "batch_size": 13,
+    "seq_length": 7,
+    "is_training": True,
+    "use_input_mask": True,
+    "use_labels": True,
+    "vocab_size": 99,
+    "hidden_size": 32,
+    "num_hidden_layers": 2,
+    "num_attention_heads": 2,
+    "num_key_value_heads": 2,
+    "intermediate_size": 32,
+    "hidden_act": "gelu",
+    "max_position_embeddings": 512,
+    "pad_token_id": 0,
+    "bos_token_id": 1,
+    "eos_token_id": 2,
+    "expert_interval": 1,
+    "moe_layer_start_index": 0,
+    "moe_intermediate_size": 16,
+    "shared_expert_intermediate_size": 36,
+    "shared_expert_gate": True,
+    "moe_num_shared_experts": 2,
+    "num_experts_per_tok": 2,
+    "num_experts": 8,
+}
+
 
 if is_torch_available():
     import torch
@@ -351,6 +386,22 @@ def is_training_test(test_case):
             return pytest.mark.is_training_test()(test_case)
 
 
+def is_training_distributed_test(test_case):
+    """
+    Decorator marking a test as a training distributed test. If RUN_TRAINING_DISTRIBUTED_TESTS is set to a falsy value, those tests will be
+    skipped.
+    """
+    if not _run_training_tests:
+        return unittest.skip(reason="test is training distributed test")(test_case)
+    else:
+        try:
+            import pytest  # We don't need a hard dependency on pytest in the main library
+        except ImportError:
+            return test_case
+        else:
+            return pytest.mark.is_training_distributed_test()(test_case)
+
+
 def is_tensor_parallel_test(test_case):
     """
     Decorator marking a test as a tensor parallel test. If RUN_TENSOR_PARALLEL_TESTS is set to a falsy value, those
@@ -460,6 +511,13 @@ def require_lomo(test_case):
     return unittest.skipUnless(is_lomo_available(), "test requires LOMO")(test_case)
 
 
+def require_flashoptim(test_case):
+    """
+    Decorator marking a test that requires flashoptim. These tests are skipped when flashoptim isn't installed.
+    """
+    return unittest.skipUnless(is_flashoptim_available(), "test requires flashoptim")(test_case)
+
+
 def require_grokadamw(test_case):
     """
     Decorator marking a test that requires GrokAdamW. These tests are skipped when GrokAdamW isn't installed.
@@ -3207,23 +3265,24 @@ def get_device_properties() -> DeviceProperties:
     if IS_CUDA_SYSTEM or IS_ROCM_SYSTEM:
         import torch
 
-        major, minor = torch.cuda.get_device_capability()
-        if IS_ROCM_SYSTEM:
-            return ("rocm", major, minor)
-        else:
-            return ("cuda", major, minor)
-    elif IS_XPU_SYSTEM:
+        if torch.cuda.is_available():
+            major, minor = torch.cuda.get_device_capability()
+            if IS_ROCM_SYSTEM:
+                return ("rocm", major, minor)
+            else:
+                return ("cuda", major, minor)
+    if IS_XPU_SYSTEM:
         import torch
 
-        # To get more info of the architecture meaning and bit allocation, refer to https://github.com/intel/llvm/blob/sycl/sycl/include/sycl/ext/oneapi/experimental/device_architecture.def
-        arch = torch.xpu.get_device_capability()["architecture"]
-        gen_mask = 0x000000FF00000000
-        gen = (arch & gen_mask) >> 32
-        return ("xpu", gen, None)
-    elif IS_NPU_SYSTEM:
+        if torch.xpu.is_available():
+            # To get more info of the architecture meaning and bit allocation, refer to https://github.com/intel/llvm/blob/sycl/sycl/include/sycl/ext/oneapi/experimental/device_architecture.def
+            arch = torch.xpu.get_device_capability()["architecture"]
+            gen_mask = 0x000000FF00000000
+            gen = (arch & gen_mask) >> 32
+            return ("xpu", gen, None)
+    if IS_NPU_SYSTEM:
         return ("npu", None, None)
-    else:
-        return (torch_device, None, None)
+    return (torch_device, None, None)
 
 
 def unpack_device_properties(
@@ -3525,13 +3584,34 @@ def get_argument_name(node):
     return None
 
 
+def _get_patched_testing_methods_output_path() -> Path:
+    """Return the output path used by patched testing methods.
+
+    When `pytest-xdist` is enabled, each worker writes to its own file to avoid cross-worker clobbering.
+    """
+
+    output_dir = Path(os.environ.get("_PATCHED_TESTING_METHODS_OUTPUT_DIR", ""))
+    worker_id = os.environ.get("PYTEST_XDIST_WORKER")
+    filename = "captured_info.txt" if worker_id is None else f"captured_info_{worker_id}.txt"
+    return output_dir / filename
+
+
+def _clear_patched_testing_methods_output_files():
+    """Remove stale output files before patched testing methods start collecting info."""
+
+    output_dir = Path(os.environ.get("_PATCHED_TESTING_METHODS_OUTPUT_DIR", ""))
+    if os.environ.get("PYTEST_XDIST_WORKER") is None:
+        for path in output_dir.glob("captured_info*.txt"):
+            path.unlink(missing_ok=True)
+    else:
+        _get_patched_testing_methods_output_path().unlink(missing_ok=True)
+
+
 def _prepare_debugging_info(test_info, info):
     """Combine the information about the test and the call information to a patched function/method within it."""
 
     info = f"{test_info}\n\n{info}"
-    p = os.path.join(os.environ.get("_PATCHED_TESTING_METHODS_OUTPUT_DIR", ""), "captured_info.txt")
-    # TODO (ydshieh): This is not safe when we use pytest-xdist with more than 1 worker.
-    with open(p, "a") as fp:
+    with open(_get_patched_testing_methods_output_path(), "a") as fp:
         fp.write(f"{info}\n\n{'=' * 120}\n\n")
 
     return info
@@ -3761,8 +3841,7 @@ def patch_testing_methods_to_collect_info():
     This will allow us to collect the call information, e.g. the argument names and values, also the literal expressions
     passed as the arguments.
     """
-    p = os.path.join(os.environ.get("_PATCHED_TESTING_METHODS_OUTPUT_DIR", ""), "captured_info.txt")
-    Path(p).unlink(missing_ok=True)
+    _get_patched_testing_methods_output_path().unlink(missing_ok=True)
 
     if is_torch_available():
         import torch
@@ -4129,6 +4208,45 @@ def read_json_file(file):
 # =============================================================================
 
 
+def global_wrapper(rank, func, fsdp_size, tp_size, port, func_args, func_kwargs):
+    def setup_dist_env(rank, world_size, port):
+        os.environ["WORLD_SIZE"] = str(world_size)
+        os.environ["RANK"] = str(rank)
+        os.environ["LOCAL_RANK"] = str(rank)
+        os.environ["MASTER_ADDR"] = "localhost"
+        os.environ["MASTER_PORT"] = str(port)
+
+    world_size = fsdp_size * tp_size
+    setup_dist_env(rank, world_size, port)
+
+    dist.init_process_group(backend="gloo", rank=rank, world_size=world_size)
+
+    # NOTE(3outeille): if want to handle DataParallel, create dp_replicate dims (do not mixed with dp_shard which is for FSDP)
+    # NOTE(3outeille): if other parallelism is added, order matters, it should be ["pp", "ddp", "fsdp", "cp", "tp"]
+    # TODO(3outeille): figure out EP
+    # from less costly to most costly (internode to intranode)
+    dims, names = [fsdp_size, tp_size], ["fsdp", "tp"]
+    mesh = init_device_mesh("cpu", dims, mesh_dim_names=names)
+
+    func(mesh, *func_args, **func_kwargs)
+
+    dist.barrier()
+    dist.destroy_process_group()
+
+
+def init_distributed(fsdp_size: int = 1, tp_size: int = 1):
+    def _init_distributed(func):
+        def wrapper(*args, **kwargs):
+            world_size = fsdp_size * tp_size
+            port = get_torch_dist_unique_port()
+            spawn_args = (func, fsdp_size, tp_size, port, args, kwargs)
+            mp.spawn(global_wrapper, args=spawn_args, nprocs=world_size)
+
+        return wrapper
+
+    return _init_distributed
+
+
 # ANSI color codes for terminal output
 class Colors:
     """ANSI color codes for terminal output formatting."""
@@ -4168,8 +4286,9 @@ class ColoredFormatter(logging.Formatter):
     # Loggers that should be dimmed (less important/verbose)
     DIMMED_LOGGERS = {"httpx", "httpcore", "urllib3", "requests"}
 
-    def __init__(self, fmt: str | None = None, datefmt: str | None = None):
+    def __init__(self, fmt: str | None = None, datefmt: str | None = None, rank_prefix: str = ""):
         super().__init__(fmt, datefmt)
+        self.rank_prefix = rank_prefix
 
     def format(self, record: logging.LogRecord) -> str:
         # Check if this logger should be dimmed
@@ -4179,7 +4298,7 @@ def format(self, record: logging.LogRecord) -> str:
             # Dim the entire log line for httpx and similar
             timestamp = self.formatTime(record, self.datefmt)
             message = record.getMessage()
-            return f"{Colors.DIM}{timestamp} - {record.name} - {record.levelname:8} - {message}{Colors.RESET}"
+            return f"{Colors.DIM}{timestamp} - {record.name} - {record.levelname:8} - {self.rank_prefix}{message}{Colors.RESET}"
 
         # Get color for this level
         color = self.LEVEL_COLORS.get(record.levelno, Colors.RESET)
@@ -4197,7 +4316,7 @@ def format(self, record: logging.LogRecord) -> str:
         # Get message
         message = record.getMessage()
 
-        return f"{colored_time} - {colored_name} - {colored_levelname} - {message}"
+        return f"{colored_time} - {colored_name} - {colored_levelname} - {self.rank_prefix}{message}"
 
 
 _warn_once_logged: set[str] = set()
@@ -4208,26 +4327,34 @@ def init_test_logger() -> logging.Logger:
 
     Uses a named logger instead of root logger to avoid conflicts with pytest-xdist parallel execution.
     Uses stderr instead of stdout to avoid deadlocks with pytest-xdist output capture.
+    Automatically includes rank in log format when distributed is initialized.
     """
     logger = logging.getLogger("transformers.training_test")
     logger.setLevel(logging.INFO)
 
-    # Only add handler if not already present (avoid duplicate handlers on repeated calls)
-    if not logger.handlers:
-        # Use stderr instead of stdout - pytest-xdist captures stdout which can cause deadlocks
-        ch = logging.StreamHandler(sys.stderr)
-        ch.setLevel(logging.INFO)
+    # Clear existing handlers to update format (e.g., when dist becomes initialized)
+    logger.handlers.clear()
 
-        # Use colored formatter if terminal supports it, plain otherwise
-        if sys.stderr.isatty():
-            formatter = ColoredFormatter(datefmt="%Y-%m-%d %H:%M:%S")
-        else:
-            formatter = logging.Formatter(
-                "%(asctime)s - %(name)s - %(levelname)s - %(message)s", datefmt="%Y-%m-%d %H:%M:%S"
-            )
+    # Use stderr instead of stdout - pytest-xdist captures stdout which can cause deadlocks
+    ch = logging.StreamHandler(sys.stderr)
+    ch.setLevel(logging.INFO)
+
+    # Build format string - include rank if distributed is initialized
+    rank_prefix = ""
+    if dist.is_initialized():
+        rank = dist.get_rank()
+        rank_prefix = f"[rank{rank}] "
+
+    # Use colored formatter if terminal supports it, plain otherwise
+    if sys.stderr.isatty():
+        formatter = ColoredFormatter(datefmt="%Y-%m-%d %H:%M:%S", rank_prefix=rank_prefix)
+    else:
+        formatter = logging.Formatter(
+            f"%(asctime)s - %(name)s - %(levelname)s - {rank_prefix}%(message)s", datefmt="%Y-%m-%d %H:%M:%S"
+        )
 
-        ch.setFormatter(formatter)
-        logger.addHandler(ch)
+    ch.setFormatter(formatter)
+    logger.addHandler(ch)
 
     logger.propagate = False  # Don't propagate to root logger to avoid duplicate output
     return logger
diff --git a/src/transformers/tokenization_mistral_common.py b/src/transformers/tokenization_mistral_common.py
index 85fdeea1850f..9eb1fbf7f9f1 100644
--- a/src/transformers/tokenization_mistral_common.py
+++ b/src/transformers/tokenization_mistral_common.py
@@ -39,7 +39,12 @@
 
 
 if is_mistral_common_available():
-    from mistral_common.protocol.instruct.request import ChatCompletionRequest, ReasoningEffort
+    from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+    try:
+        from mistral_common.protocol.instruct.request import ReasoningEffort
+    except ImportError:
+        from typing import Any as ReasoningEffort  # type: ignore[assignment]
     from mistral_common.protocol.instruct.validator import ValidationMode
     from mistral_common.tokens.tokenizers.base import SpecialTokenPolicy, SpecialTokens
     from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
@@ -186,28 +191,28 @@ def _maybe_remove_lang(text: str | list[str], skip_special_tokens: bool) -> str
 @requires(backends=("mistral-common",))
 class MistralCommonBackend(PreTrainedTokenizerBase):
     """
-    Class to wrap `mistral-common` tokenizers.
+        Class to wrap `mistral-common` tokenizers.
 
-    `mistral-common` is the official tokenizer library for Mistral AI models. To use it, you need to install it with:
+        `mistral-common` is the official tokenizer library for Mistral AI models. To use it, you need to install it with:
 
-    ```bash
-    pip install transformers[mistral-common]
-    ```
+        ```bash
+        pip install transformers[mistral-common]
+        ```
 
-    Otherwise the tokenizer falls back to the Transformers implementation of the tokenizer.
+        Otherwise the tokenizer falls back to the Transformers implementation of the tokenizer.
 
-    For more info on `mistral-common`, see [mistral-common](https://github.com/mistralai/mistral-common).
+        For more info on `mistral-common`, see [mistral-common](https://github.com/mistralai/mistral-common).
 
-    This class is a wrapper around a `mistral_common.tokens.tokenizers.mistral.MistralTokenizer`.
-    It provides a Hugging Face compatible interface to tokenize using the official mistral-common tokenizer and inherits from the `PreTrainedTokenizerBase` class.
+        This class is a wrapper around a `mistral_common.tokens.tokenizers.mistral.MistralTokenizer`.
+        It provides a Hugging Face compatible interface to tokenize using the official mistral-common tokenizer and inherits from the `PreTrainedTokenizerBase` class.
 
-    Here are the key behavior differences with the `PythonBackend` class:
+        Here are the key behavior differences with the `PythonBackend` class:
 
     - Pair of sequences are not supported. The signature has been kept for compatibility but all arguments related to pair of sequences are ignored. The return values for pairs are returned as `None`.
-    - The `is_split_into_words` argument is not supported.
-    - It is not possible to add new tokens to the tokenizer. Special tokens are handled differently from Transformers. In `mistral-common`, special tokens are never encoded directly. This means that: `tokenizer.encode("<s>")` will not return the ID of the `<s>` token. Instead, it will return a list of IDs corresponding to the tokenization of the string `"<s>"`. For more information, see the [mistral-common documentation](https://mistralai.github.io/mistral-common/usage/tokenizers/#special-tokens).
+        - The `is_split_into_words` argument is not supported.
+        - It is not possible to add new tokens to the tokenizer. Special tokens are handled differently from Transformers. In `mistral-common`, special tokens are never encoded directly. This means that: `tokenizer.encode("<s>")` will not return the ID of the `<s>` token. Instead, it will return a list of IDs corresponding to the tokenization of the string `"<s>"`. For more information, see the [mistral-common documentation](https://mistralai.github.io/mistral-common/usage/tokenizers/#special-tokens).
 
-    If you have suggestions to improve this class, please open an issue on the [mistral-common GitHub repository](https://github.com/mistralai/mistral-common/issues) if it is related to the tokenizer or on the [Transformers GitHub repository](https://github.com/huggingface/transformers/issues) if it is related to the Hugging Face interface.
+        If you have suggestions to improve this class, please open an issue on the [mistral-common GitHub repository](https://github.com/mistralai/mistral-common/issues) if it is related to the tokenizer or on the [Transformers GitHub repository](https://github.com/huggingface/transformers/issues) if it is related to the Hugging Face interface.
     """
 
     model_input_names: list[str] = ["input_ids", "attention_mask"]
@@ -622,6 +627,13 @@ def convert_tokens_to_ids(self, tokens: str | list[str]) -> int | list[int]:
             return ids[0]
         return ids
 
+    def convert_tokens_to_string(self, tokens) -> str:
+        """Converts a sequence of tokens (string) in a single string."""
+        ids = []
+        for token in tokens:
+            ids.append(self._tekken_piece_to_id(token, False))
+        return self.decode(ids)
+
     def _text_to_ids(self, text: TextInput, add_special_tokens: bool) -> list[int]:
         """
         Converts a string into a sequence of tokens ids, using the tokenizer.
diff --git a/src/transformers/tokenization_utils_base.py b/src/transformers/tokenization_utils_base.py
index 4e821dfd4e70..8b800e34cb04 100644
--- a/src/transformers/tokenization_utils_base.py
+++ b/src/transformers/tokenization_utils_base.py
@@ -1070,7 +1070,7 @@ def __init__(self, **kwargs):
                 f"Truncation side should be selected between 'right' and 'left', current value: {self.truncation_side}"
             )
 
-        self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)
+        self.model_input_names = list(kwargs.pop("model_input_names", self.model_input_names))
 
         # By default, do not clean up tokenization spaces for both fast and slow tokenizers
         self.clean_up_tokenization_spaces = kwargs.pop("clean_up_tokenization_spaces", False)
@@ -1405,7 +1405,7 @@ def all_special_ids(self) -> list[int]:
         """
         return self.convert_tokens_to_ids(self.all_special_tokens)
 
-    def _set_model_specific_special_tokens(self, special_tokens: dict[str, str | AddedToken]):
+    def _set_model_specific_special_tokens(self, special_tokens: dict[str, str | AddedToken] | list[str]):
         """
         Adds new model-specific special tokens (e.g., for multimodal models).
 
@@ -1413,8 +1413,11 @@ def _set_model_specific_special_tokens(self, special_tokens: dict[str, str | Add
         For example: if the model tokenizer is multimodal, we can support special image or audio tokens.
 
         Args:
-            special_tokens: Dictionary of {token_name: token_value}
+            special_tokens: Dictionary of {token_name: token_value}, or a list of token strings.
+                If a list is provided, each token is used as both the attribute name and value.
         """
+        if isinstance(special_tokens, list):
+            special_tokens = {tok: tok for tok in special_tokens}
         self.SPECIAL_TOKENS_ATTRIBUTES = self.SPECIAL_TOKENS_ATTRIBUTES + list(special_tokens.keys())
         for key, value in special_tokens.items():
             if isinstance(value, (str, AddedToken)):
@@ -1700,6 +1703,13 @@ def from_pretrained(
                 else:
                     vocab_files["vocab_file"] = match.group()
 
+        error_message = (
+            f"Can't load tokenizer for '{pretrained_model_name_or_path}'. If you were trying to load it from "
+            "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
+            f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
+            f"containing all relevant files for a {cls.__name__} tokenizer."
+        )
+
         resolved_vocab_files = {}
         for file_id, file_path in vocab_files.items():
             if file_path is None:
@@ -1728,17 +1738,19 @@ def from_pretrained(
                     raise
                 except Exception:
                     # For any other exception, we throw a generic error.
-                    raise OSError(
-                        f"Can't load tokenizer for '{pretrained_model_name_or_path}'. If you were trying to load it from "
-                        "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
-                        f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
-                        f"containing all relevant files for a {cls.__name__} tokenizer."
-                    )
+                    raise OSError(error_message)
                 commit_hash = extract_commit_hash(resolved_vocab_files[file_id], commit_hash)
 
-        for file_id, file_path in vocab_files.items():
-            if file_id not in resolved_vocab_files:
-                continue
+        loadable_file_ids = set(cls.vocab_files_names)
+        if loadable_file_ids and "tokenizer_file" in resolved_vocab_files:
+            loadable_file_ids.add("tokenizer_file")
+        loadable_file_ids.intersection_update(resolved_vocab_files)
+        if (
+            (local_files_only or is_local)
+            and loadable_file_ids
+            and all(resolved_vocab_files[file_id] is None for file_id in loadable_file_ids)
+        ):
+            raise OSError(error_message)
 
         return cls._from_pretrained(
             resolved_vocab_files,
@@ -3074,6 +3086,14 @@ def apply_chat_template(
             conversations = [conversation]
             is_batched = False
 
+        # Normalize: drop `content` from assistant messages when it is None.
+        # Some APIs (e.g. OpenAI) return content=None for tool-call-only messages, but many chat templates
+        # crash or produce wrong output (e.g. rendering literal "None") when they encounter it.
+        conversations = [
+            [{k: v for k, v in msg.items() if k != "content" or v is not None} for msg in conversation]
+            for conversation in conversations
+        ]
+
         if continue_final_message:
             if add_generation_prompt:
                 raise ValueError(
diff --git a/src/transformers/tokenization_utils_tokenizers.py b/src/transformers/tokenization_utils_tokenizers.py
index a700f0ad27cc..be8fa02ac170 100644
--- a/src/transformers/tokenization_utils_tokenizers.py
+++ b/src/transformers/tokenization_utils_tokenizers.py
@@ -106,6 +106,7 @@ def convert_to_native_format(cls, trust_remote_code=False, **kwargs):
         """
         # Preserve kwargs for possible downstream use
         local_kwargs = dict(kwargs)
+        override_tokenizer = local_kwargs.get("override_tokenizer", False)
         fast_tokenizer_file = local_kwargs.pop("tokenizer_file", None)
 
         if (
@@ -192,6 +193,9 @@ def convert_to_native_format(cls, trust_remote_code=False, **kwargs):
                 merges = [tuple(merge.split(" ")) if isinstance(merge, str) else tuple(merge) for merge in merges]
                 local_kwargs["merges"] = merges
 
+            if override_tokenizer:
+                local_kwargs["tokenizer_file"] = fast_tokenizer_file
+
             return local_kwargs
 
         vocab_file = local_kwargs.get("vocab_file")
@@ -334,6 +338,8 @@ def __init__(self, *args, **kwargs):
         # (before calling super().__init__) and should not be stored in `init_kwargs` to keep the tokenizer  serializable.
         kwargs.pop("_spm_precompiled_charsmap", None)
 
+        override_tokenizer = kwargs.pop("override_tokenizer", False)
+
         tokenizer_object = kwargs.pop("tokenizer_object", None)
         gguf_file = kwargs.pop("gguf_file", None)
         fast_tokenizer_file = kwargs.pop("tokenizer_file", None)
@@ -347,11 +353,15 @@ def __init__(self, *args, **kwargs):
         merges = kwargs.get("merges")
 
         fast_tokenizer = None
+        serialized_tokenizer = None
         if tokenizer_object is not None:
             fast_tokenizer = copy.deepcopy(tokenizer_object)
         elif fast_tokenizer_file is not None and os.path.isfile(fast_tokenizer_file):
             # We have a serialization from tokenizers which let us directly build the backend
-            fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
+            if self.__class__ is TokenizersBackend or self._tokenizer is None or not override_tokenizer:
+                fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
+            else:
+                serialized_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
         elif gguf_file is not None:
             # We need to convert a slow tokenizer to build the backend
             gguf_path = cached_file(kwargs.get("name_or_path", ""), gguf_file, **kwargs)
@@ -391,6 +401,21 @@ def __init__(self, *args, **kwargs):
         if self._tokenizer is None:
             raise ValueError("The backend tokenizer is not correctly initialized.")
 
+        # Optionally override subclass-created tokenizers with the serialized tokenizer file.
+        if override_tokenizer and serialized_tokenizer is not None:
+
+            def _sig(tok: TokenizerFast):
+                return tuple(
+                    type(getattr(tok, attr, None))
+                    for attr in ("normalizer", "pre_tokenizer", "decoder", "post_processor", "model")
+                )
+
+            if _sig(self._tokenizer) != _sig(serialized_tokenizer):
+                self._tokenizer = serialized_tokenizer
+                logger.warning(
+                    "Tokenizer pipeline differs from serialized tokenizer; overriding with the serialized definition."
+                )
+
         _truncation = kwargs.pop("tokenizer_truncation", None) or self._tokenizer.truncation or _json_truncation
         if _truncation is not None:
             self._tokenizer.enable_truncation(**_truncation)
@@ -446,9 +471,11 @@ def __init__(self, *args, **kwargs):
                 tokens_to_add.append(special_token_value)
 
         # Also check extra special tokens
+        tokens_to_add_str = {str(t) for t in tokens_to_add}
         for token in self._extra_special_tokens:
-            if str(token) not in encoder and token not in tokens_to_add:
+            if str(token) not in encoder and str(token) not in tokens_to_add_str:
                 tokens_to_add.append(token)
+                tokens_to_add_str.add(str(token))
 
         if len(tokens_to_add) > 0:
             tokens = []
@@ -1313,7 +1340,11 @@ def is_base_mistral(model_id: str) -> bool:
                     return True
             return False
 
-        if local_files_only or is_offline_mode():
+        if (
+            is_offline_mode()
+            or local_files_only
+            or (pretrained_model_name_or_path is not None and os.path.isdir(pretrained_model_name_or_path))
+        ):
             is_local = True
 
         if pretrained_model_name_or_path is not None and (
diff --git a/src/transformers/trainer.py b/src/transformers/trainer.py
index f434d78d4040..ce6825bd63ed 100755
--- a/src/transformers/trainer.py
+++ b/src/transformers/trainer.py
@@ -28,6 +28,7 @@
 import tempfile
 import time
 import warnings
+from collections import defaultdict
 from collections.abc import Callable, Iterator, Mapping
 from functools import partial
 from pathlib import Path
@@ -70,6 +71,7 @@
 from .integrations.liger import apply_liger_kernel
 from .integrations.neftune import activate_neftune, deactivate_neftune
 from .integrations.peft import MIN_PEFT_VERSION
+from .integrations.tensor_parallel import get_ep_sharded_param_names
 from .integrations.tpu import save_tpu_checkpoint, tpu_spmd_dataloader, wrap_model_xla_fsdp
 from .modelcard import TrainingSummary
 from .modeling_utils import PreTrainedModel, unwrap_model
@@ -103,9 +105,11 @@
     LengthGroupedSampler,
     distributed_broadcast_scalars,
     find_batch_size,
+    flatten_per_sample_nested_batches,
     get_model_param_count,
     get_parameter_names,
     is_attention_mask_causal,
+    is_per_sample_nested,
     nested_detach,
     nested_gather,
     reissue_pt_warnings,
@@ -233,6 +237,8 @@
 if TYPE_CHECKING:
     import optuna
 
+    from .data_producer import DataProducer
+
 logger = logging.get_logger(__name__)
 
 
@@ -378,6 +384,7 @@ def __init__(
         optimizers: tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None),
         optimizer_cls_and_kwargs: tuple[type[torch.optim.Optimizer], dict[str, Any]] | None = None,
         preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
+        data_producer: "DataProducer | None" = None,
     ):
         # Init flow:
         #   1. Args & seed               – defaults, determinism
@@ -397,6 +404,14 @@ def __init__(
             output_dir = "tmp_trainer"
             logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.")
             args = TrainingArguments(output_dir=output_dir)
+
+        # Fixes issues 28530 + 40217: automatic label detection expects labels when a single label is provided.
+        if args.label_names == ["label"]:
+            logger.warning(
+                "Setting label_names=['label'] is redundant and may cause issues. "
+                "Removing it to use automatic label detection."
+            )
+            args.label_names = None
         self.args = args
         # Seed must be set before instantiating the model when using model_init
         enable_full_determinism(self.args.seed) if self.args.full_determinism else set_seed(self.args.seed)
@@ -411,6 +426,8 @@ def __init__(
         self._memory_tracker = TrainerMemoryTracker(self.args.skip_memory_metrics)
         self._memory_tracker.start()
 
+        self._metrics = {"train": defaultdict(list), "eval": defaultdict(list)}
+
         log_level = args.get_process_log_level()
         logging.set_verbosity(log_level)
 
@@ -447,13 +464,13 @@ def __init__(
             elif len(devices) == 1:
                 self.is_model_parallel = self.args.device != torch.device(devices[0])
 
-        self.is_fsdp_xla_enabled = args.fsdp_config["xla"]
-        if len(args.fsdp) > 0:
+        self.is_fsdp_xla_enabled = args.fsdp and args.fsdp_config.get("xla", False)
+        if args.fsdp:
             if self.is_deepspeed_enabled:
                 raise ValueError(
                     "Using --fsdp xxx together with --deepspeed is not possible, deactivate one of those flags."
                 )
-            if not args.fsdp_config["xla"] and args.parallel_mode != ParallelMode.DISTRIBUTED:
+            if not self.is_fsdp_xla_enabled and args.parallel_mode != ParallelMode.DISTRIBUTED:
                 raise ValueError("Using fsdp only works in distributed training.")
 
         # Postpone switching model to cuda when MP, DeepSpeed, full bf16/fp16 eval, or FSDP
@@ -532,6 +549,7 @@ def __init__(
         self.data_collator = data_collator if data_collator is not None else default_collator
         self.train_dataset = train_dataset
         self.eval_dataset = eval_dataset
+        self.data_producer = data_producer
         self.processing_class = processing_class
         self.neftune_noise_alpha = args.neftune_noise_alpha
 
@@ -562,6 +580,9 @@ def __init__(
         )
         self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
 
+        if self.data_producer is not None and isinstance(self.data_producer, TrainerCallback):
+            self.add_callback(self.data_producer)
+
         # ---- 9. Hub & output ---------------------------------------------------------
         self.hub_model_id = None  # Set by init_hf_repo() when push_to_hub is enabled
         if self.args.push_to_hub:
@@ -592,13 +613,16 @@ def __init__(
         # Guards one-time LR scheduler creation in create_optimizer_and_scheduler
         self._created_lr_scheduler = False
 
+        self._tr_loss_components = {}
+        self._total_loss_components_scalar = {}
+
         self.control = self.callback_handler.on_init_end(self.args, self.state, self.control)
 
         # ---- 11. Finalize -----------------------------------------------------------
         if getattr(self.model, "config", None) is not None:
             self.model.config.use_cache = self.args.use_cache
 
-        self.is_fsdp_xla_v2_enabled = args.fsdp_config.get("xla_fsdp_v2", False)
+        self.is_fsdp_xla_v2_enabled = args.fsdp and args.fsdp_config.get("xla_fsdp_v2", False)
         if self.is_fsdp_xla_v2_enabled:
             if not IS_XLA_FSDPV2_POST_2_2:
                 raise ValueError("FSDPv2 requires `torch_xla` 2.2 or higher.")
@@ -673,6 +697,25 @@ def _validate_args(self) -> None:
                 "You should subclass `Trainer` and override the `create_optimizer_and_scheduler` method."
             )
 
+        # --- DataProducer validations ---
+        if self.data_producer is not None:
+            if self.train_dataset is not None:
+                raise ValueError(
+                    "Cannot pass both `data_producer` and `train_dataset`. "
+                    "Use `data_producer` for online data generation or `train_dataset` for static datasets."
+                )
+            if not hasattr(self.data_producer, "produce") or not callable(self.data_producer.produce):
+                raise TypeError(
+                    "`data_producer` must implement the DataProducer protocol (must have a callable `produce` method)."
+                )
+            if not hasattr(self.data_producer, "config"):
+                raise TypeError("`data_producer` must have a `config` attribute (a ProducerConfig instance).")
+            if args.max_steps <= 0 and (self.data_producer.config.max_rollouts is None):
+                raise ValueError(
+                    "`args.max_steps` must be positive or `data_producer.config.max_rollouts` must be set "
+                    "when using a `data_producer`, because there is no dataset length to derive the number of steps."
+                )
+
         # --- Dataset validations ---
         if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
             raise TypeError("The `data_collator` should be a simple callable (function, class with `__call__`).")
@@ -726,7 +769,12 @@ def _build_accelerator_args(self, **kwargs) -> dict[str, Any]:
                 )
             args["parallelism_config"] = self.args.parallelism_config
 
-        if getattr(self.model, "tp_size", None) is not None and self.model.tp_size > 1:
+        # EP-sharded params are already DTensors on the EP mesh, not on a TP mesh.
+        if (
+            getattr(self.model, "tp_size", None) is not None
+            and self.model.tp_size > 1
+            and not getattr(self.model, "has_ep", False)
+        ):
             if self.args.parallelism_config is None:
                 if is_accelerate_available("1.12.0"):
                     if self.args.parallelism_config is None:
@@ -823,6 +871,11 @@ def create_accelerator_and_postprocess(self) -> None:
         # post accelerator creation setup
         if self.is_fsdp_enabled:
             fsdp_plugin = self.accelerator.state.fsdp_plugin
+            # EP-sharded experts must not be re-sharded by FSDP,  their params are DTensors on the EP mesh.
+            ep_param_names = get_ep_sharded_param_names(self.model)
+            if ep_param_names:
+                module_names = list({n.rsplit(".", 1)[0] for n in ep_param_names})
+                fsdp_plugin.ignored_modules = [self.model.get_submodule(n) for n in module_names]
             for param in ["limit_all_gathers", "activation_checkpointing"]:
                 setattr(fsdp_plugin, param, self.args.fsdp_config.get(param, getattr(fsdp_plugin, param)))
             if fsdp_plugin.activation_checkpointing and self.args.gradient_checkpointing:
@@ -853,12 +906,6 @@ def create_accelerator_and_postprocess(self) -> None:
             raise ValueError(
                 "`auto_find_batch_size` isn't supported yet with DeepSpeed Zero-3. Please consider using Zero-2, Zero-1, or FSDP"
             )
-        if (
-            self.args.save_only_model
-            and self.is_fsdp_enabled
-            and "SHARDED_STATE_DICT" in str(self.accelerator.state.fsdp_plugin.state_dict_type)
-        ):
-            raise ValueError("save_only_model option is not compatible with FSDP state dict type 'SHARDED_STATE_DICT'")
 
     # ---- Data Loading ----
 
@@ -1063,21 +1110,25 @@ def _get_eval_sampler(self, eval_dataset: Dataset) -> torch.utils.data.Sampler |
         else:
             return None
 
+    def _get_signature_columns(self) -> list[str]:
+        """Return dataset columns accepted by the model's forward method."""
+        model_to_inspect = self.model
+        if _is_peft_model(self.model):
+            if hasattr(self.model, "get_base_model"):
+                model_to_inspect = self.model.get_base_model()
+            else:
+                # PeftMixedModel do not provide a `get_base_model` method
+                model_to_inspect = self.model.base_model.model
+        signature = inspect.signature(model_to_inspect.forward)
+        columns = list(signature.parameters.keys())
+        # Labels may be named label or label_ids, the default data collator handles that.
+        columns += list(set(["label", "label_ids"] + self.label_names))
+        return columns
+
     def _set_signature_columns_if_needed(self) -> None:
         """Populate `_signature_columns` from the model's forward signature if not already set."""
         if self._signature_columns is None:
-            # Inspect model forward signature to keep only the arguments it accepts.
-            model_to_inspect = self.model
-            if _is_peft_model(self.model):
-                if hasattr(self.model, "get_base_model"):
-                    model_to_inspect = self.model.get_base_model()
-                else:
-                    # PeftMixedModel do not provide a `get_base_model` method
-                    model_to_inspect = self.model.base_model.model
-            signature = inspect.signature(model_to_inspect.forward)
-            self._signature_columns = list(signature.parameters.keys())
-            # Labels may be named label or label_ids, the default data collator handles that.
-            self._signature_columns += list(set(["label", "label_ids"] + self.label_names))
+            self._signature_columns = self._get_signature_columns()
 
     def _remove_unused_columns(
         self, dataset: "datasets.Dataset", description: str | None = None
@@ -1371,7 +1422,15 @@ def train(
 
         # When fp16/bf16 full eval is enabled, __init__ skips device placement so that
         # evaluation_loop can cast dtype and move in one step. Move the model now for training.
-        if (args.fp16_full_eval or args.bf16_full_eval) and not self.is_model_parallel and self.model_init is None:
+        if (
+            (args.fp16_full_eval or args.bf16_full_eval)
+            and not self.is_model_parallel
+            and not self.is_deepspeed_enabled
+            and not self.is_fsdp_xla_enabled
+            and not self.is_fsdp_enabled
+            and not is_sagemaker_mp_enabled()
+            and self.model_init is None
+        ):
             self._move_model_to_device(self.model, args.device)
 
         # Activate gradient checkpointing if needed
@@ -1405,7 +1464,7 @@ def train(
         if isinstance(resume_from_checkpoint, bool) and resume_from_checkpoint:
             resume_from_checkpoint = get_last_checkpoint(args.output_dir)
             if resume_from_checkpoint is None:
-                raise ValueError(f"No valid checkpoint found in output directory ({args.output_dir})")
+                logger.info(f"No valid checkpoint found in output directory ({args.output_dir})")
 
         if resume_from_checkpoint is not None:
             # Load model checkpoint before accelerator.prepare() for regular models,
@@ -1413,10 +1472,17 @@ def train(
             # Deepspeed/FSDP models are loaded after prepare in _prepare_for_training.
             if not is_sagemaker_mp_enabled() and not self.is_deepspeed_enabled and not self.is_fsdp_enabled:
                 self._load_from_checkpoint(resume_from_checkpoint)
-            state = TrainerState.load_from_json(os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME))
-            if state.train_batch_size is not None and args.auto_find_batch_size:
-                # Only restore the checkpoint's train_batch_size when using auto_find_batch_size,
-                self._train_batch_size = state.train_batch_size
+            trainer_state_path = os.path.join(resume_from_checkpoint, TRAINER_STATE_NAME)
+            if os.path.isfile(trainer_state_path):
+                state = TrainerState.load_from_json(trainer_state_path)
+                if state.train_batch_size is not None and args.auto_find_batch_size:
+                    # Only restore the checkpoint's train_batch_size when using auto_find_batch_size,
+                    self._train_batch_size = state.train_batch_size
+            else:
+                logger.warning(
+                    f"TrainerState file not found at {trainer_state_path}. "
+                    "Training will continue, but batch size recovery is skipped."
+                )
 
         inner_training_loop = find_executable_batch_size(
             self._inner_training_loop, self._train_batch_size, args.auto_find_batch_size
@@ -1431,51 +1497,95 @@ def train(
                 ignore_keys_for_eval=ignore_keys_for_eval,
             )
 
-    def _inner_training_loop(
-        self,
-        batch_size: int | None = None,
-        args: TrainingArguments | None = None,
-        resume_from_checkpoint: str | None = None,
-        trial: "optuna.Trial | dict[str, Any] | None" = None,
-        ignore_keys_for_eval: list[str] | None = None,
-    ) -> TrainOutput:
-        """Run the actual training loop: forward, backward, optimizer step, logging, and checkpointing."""
-        # reset everything
-        self.accelerator.free_memory()
-        if args.auto_find_batch_size:
-            self._update_auto_batch_size(batch_size)
-        # Data loader and number of training steps
-        train_dataloader = self.get_train_dataloader()
-        if self.is_fsdp_xla_v2_enabled:
-            train_dataloader = tpu_spmd_dataloader(train_dataloader)
+    # ---- Epoch-source helpers ---------------------------------------------------
 
-        # Setting up training control variables:
-        (
-            num_train_epochs,
-            num_update_steps_per_epoch,
-            num_examples,
-            num_train_samples,
-            total_train_batch_size,
-            steps_in_epoch,
-            max_steps,
-        ) = self.set_initial_training_values(args, train_dataloader)
+    def _create_epoch_source(self):
+        """Create the appropriate epoch source based on configuration."""
+        if self.data_producer is not None:
+            from .trainer_data_source import _OnlineEpochSource
 
-        epochs_trained, steps_trained_in_current_epoch = self._init_training_state(
-            max_steps, num_update_steps_per_epoch, num_train_epochs, resume_from_checkpoint, trial
+            return _OnlineEpochSource(self.data_producer)
+        else:
+            from .trainer_data_source import _StaticEpochSource
+
+            return _StaticEpochSource()
+
+    def _apply_sp_adapter(self, dataloader, model):
+        """Apply the DeepSpeed Ulysses SP dataloader adapter if needed."""
+        pc = getattr(self.accelerator, "parallelism_config", None)
+        if pc is not None and pc.sp_backend == "deepspeed" and pc.sp_enabled:
+            dataloader = self.accelerator.deepspeed_ulysses_dl_adapter(dataloader, model)
+        return dataloader
+
+    @torch.no_grad()
+    def _produce_data(self, model):
+        """Call the data producer to generate a fresh training dataset.
+
+        Manages eval/train mode switching and CUDA cache clearing around the
+        ``produce()`` call.
+        """
+        producer = self.data_producer
+        config = producer.config
+
+        if hasattr(producer, "on_rollout_begin"):
+            producer.on_rollout_begin(self.state.global_step)
+
+        if config.empty_cache_before_produce and torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        was_training = model.training
+        if config.eval_during_produce:
+            model.eval()
+
+        dataset = producer.produce(
+            model=model,
+            global_step=self.state.global_step,
+            processing_class=self.processing_class,
+            accelerator=self.accelerator,
+            args=self.args,
+        )
+
+        if config.eval_during_produce and was_training:
+            model.train()
+
+        if config.empty_cache_after_produce and torch.cuda.is_available():
+            torch.cuda.empty_cache()
+
+        if hasattr(producer, "on_rollout_end"):
+            producer.on_rollout_end(dataset, self.state.global_step)
+
+        return dataset
+
+    def _get_online_dataloader(self, dataset) -> DataLoader:
+        """Create a DataLoader for a dataset produced by a DataProducer.
+
+        Reuses the Trainer's collator, sampler, and ``accelerator.prepare``
+        infrastructure.
+        """
+        return self._get_dataloader(
+            dataset=dataset,
+            description="Online training",
+            batch_size=self._train_batch_size,
+            sampler_fn=self._get_train_sampler,
+            is_training=True,
         )
-        model, train_dataloader = self._prepare_for_training(max_steps, train_dataloader, resume_from_checkpoint)
 
-        # Train!
+    def _log_training_banner(
+        self, plan, epochs_trained, steps_trained_in_current_epoch, model, resume_from_checkpoint
+    ):
+        """Log the 'Running training' info block."""
         logger.info("***** Running training *****")
-        logger.info(f"  Num examples = {num_examples:,}")
-        logger.info(f"  Num Epochs = {num_train_epochs:,}")
-        logger.info(f"  Num update steps per epoch = {num_update_steps_per_epoch:,}")
+        logger.info(f"  Num examples = {plan.num_examples:,}")
+        logger.info(f"  Num Epochs = {plan.num_train_epochs:,}")
+        logger.info(f"  Num update steps per epoch = {plan.num_update_steps_per_epoch:,}")
         logger.info(f"  Instantaneous batch size per device = {self.args.per_device_train_batch_size:,}")
         if self.args.per_device_train_batch_size != self._train_batch_size:
             logger.info(f"  Training with DataParallel so batch size has been adjusted to: {self._train_batch_size:,}")
-        logger.info(f"  Total train batch size (w. parallel, distributed & accumulation) = {total_train_batch_size:,}")
-        logger.info(f"  Gradient Accumulation steps = {args.gradient_accumulation_steps}")
-        logger.info(f"  Total optimization steps = {max_steps:,}")
+        logger.info(
+            f"  Total train batch size (w. parallel, distributed & accumulation) = {plan.total_train_batch_size:,}"
+        )
+        logger.info(f"  Gradient Accumulation steps = {self.args.gradient_accumulation_steps}")
+        logger.info(f"  Total optimization steps = {plan.max_steps:,}")
         logger.info(f"  Number of trainable parameters = {get_model_param_count(model, trainable_only=True):,}")
 
         if resume_from_checkpoint is not None:
@@ -1488,6 +1598,41 @@ def _inner_training_loop(
                     f" {steps_trained_in_current_epoch} batches to resume from the exact training state."
                 )
 
+    # ---- Training loop --------------------------------------------------------
+
+    def _inner_training_loop(
+        self,
+        batch_size: int | None = None,
+        args: TrainingArguments | None = None,
+        resume_from_checkpoint: str | None = None,
+        trial: "optuna.Trial | dict[str, Any] | None" = None,
+        ignore_keys_for_eval: list[str] | None = None,
+    ) -> TrainOutput:
+        """Run the actual training loop: forward, backward, optimizer step, logging, and checkpointing."""
+        self.accelerator.free_memory()
+        if args.auto_find_batch_size:
+            self._update_auto_batch_size(batch_size)
+
+        # Build the epoch source and compute the training plan
+        source = self._create_epoch_source()
+        plan = source.compute_plan(self)
+
+        epochs_trained, steps_trained_in_current_epoch = self._init_training_state(
+            plan.max_steps,
+            plan.num_update_steps_per_epoch,
+            plan.num_train_epochs,
+            resume_from_checkpoint,
+            trial,
+        )
+        model, train_dataloader = self._prepare_for_training(
+            plan.max_steps,
+            source.initial_dataloader,
+            resume_from_checkpoint,
+        )
+        source.post_model_setup(self, model, train_dataloader)
+
+        self._log_training_banner(plan, epochs_trained, steps_trained_in_current_epoch, model, resume_from_checkpoint)
+
         start_time = time.time()
         # needed to calculate tokens/s
         self._initial_num_input_tokens_seen = self.state.num_input_tokens_seen
@@ -1504,25 +1649,25 @@ def _inner_training_loop(
         if args.eval_on_start:
             self._evaluate(trial, ignore_keys_for_eval, skip_scheduler=True)
 
-        for epoch in range(epochs_trained, num_train_epochs):
+        for spec in source.iter_epochs(
+            self,
+            plan,
+            epochs_trained,
+            steps_trained_in_current_epoch,
+            resume_from_checkpoint,
+        ):
             self.control = self.callback_handler.on_epoch_begin(self.args, self.state, self.control)
             self._run_epoch(
                 model=model,
-                epoch=epoch,
-                train_dataloader=train_dataloader,
-                steps_in_epoch=steps_in_epoch,
-                num_update_steps_per_epoch=num_update_steps_per_epoch,
+                epoch_spec=spec,
                 trial=trial,
                 ignore_keys_for_eval=ignore_keys_for_eval,
                 start_time=start_time,
-                resume_from_checkpoint=resume_from_checkpoint,
-                epochs_trained=epochs_trained,
-                steps_trained_in_current_epoch=steps_trained_in_current_epoch,
             )
             if self.control.should_training_stop:
                 break
 
-        return self._finalize_training(trial, num_train_samples, start_time)
+        return self._finalize_training(trial, plan.num_train_samples, start_time)
 
     def _init_training_state(
         self, max_steps, num_update_steps_per_epoch, num_train_epochs, resume_from_checkpoint, trial
@@ -1557,6 +1702,16 @@ def _init_training_state(
 
     def _prepare_for_training(self, max_steps, train_dataloader, resume_from_checkpoint):
         """Wrap model, create optimizer and scheduler, and run accelerator.prepare. Returns (model, train_dataloader)."""
+        # DeepSpeed: clear stale inference engine refs left by evaluate()/predict()
+        # so that _wrap_model() and accelerator.prepare() can create a training engine.
+        if (
+            self.is_deepspeed_enabled
+            and self.accelerator.deepspeed_engine_wrapped is None
+            and self.model_wrapped is not self.model
+        ):
+            self.model_wrapped = self.model
+            self.deepspeed = None
+
         delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled
 
         # Can't delay optimizer creation when using FSDP2: https://github.com/huggingface/accelerate/blob/3f636d626063ffcf9a337c7d3624d61b7d187d59/src/accelerate/accelerator.py#L1404
@@ -1629,16 +1784,18 @@ def _prepare_for_training(self, max_steps, train_dataloader, resume_from_checkpo
             if hasattr(self.model, "generate"):
                 dist.fsdp.register_fsdp_forward_method(self.model, "generate")
 
-        # since DataLoader was Accelerate prepared w/o a model arg in the same call, we now have to complete the DL wrapping for ALST/UlyssesSP, after model has been prepared
-        pc = getattr(self.accelerator, "parallelism_config", None)
-        if pc is not None and pc.sp_backend == "deepspeed" and pc.sp_enabled:
-            train_dataloader = self.accelerator.deepspeed_ulysses_dl_adapter(train_dataloader, model)
+        # NOTE: The SP (Sequence Parallelism) dataloader adapter is now applied
+        # by the epoch source via post_model_setup → _apply_sp_adapter, after
+        # this method returns.
 
         # load checkpoint
         if resume_from_checkpoint is not None:
             if self.is_deepspeed_enabled:
                 deepspeed_load_checkpoint(
-                    self.model_wrapped, resume_from_checkpoint, load_module_strict=not _is_peft_model(self.model)
+                    self.model_wrapped,
+                    resume_from_checkpoint,
+                    load_module_strict=not _is_peft_model(self.model),
+                    convert_deepspeed_universal_checkpoint=self.args.convert_deepspeed_universal_checkpoint,
                 )
             elif is_sagemaker_mp_enabled() or self.is_fsdp_enabled:
                 self._load_from_checkpoint(resume_from_checkpoint, self.model_wrapped)
@@ -1656,18 +1813,21 @@ def _prepare_for_training(self, max_steps, train_dataloader, resume_from_checkpo
     def _run_epoch(
         self,
         model,
-        epoch,
-        train_dataloader,
-        steps_in_epoch,
-        num_update_steps_per_epoch,
+        epoch_spec,
         trial,
         ignore_keys_for_eval,
         start_time,
-        resume_from_checkpoint,
-        epochs_trained,
-        steps_trained_in_current_epoch,
     ):
-        """Run one full pass over the dataloader."""
+        """Run one full pass over the dataloader described by *epoch_spec*."""
+        # Unpack the epoch spec into local variables.  The rest of the method
+        # body is intentionally unchanged from the pre-refactor version.
+        epoch = epoch_spec.epoch
+        train_dataloader = epoch_spec.dataloader
+        steps_in_epoch = epoch_spec.steps_in_epoch
+        num_update_steps_per_epoch = epoch_spec.num_update_steps_per_epoch
+        resume_from_checkpoint = epoch_spec.resume_from_checkpoint
+        epochs_trained = epoch_spec.epochs_trained
+        steps_trained_in_current_epoch = epoch_spec.steps_trained_in_current_epoch
 
         step = -1
         grad_norm = None
@@ -1686,7 +1846,7 @@ def _run_epoch(
                 self._load_rng_state(resume_from_checkpoint)
 
         if hasattr(train_dataloader, "set_epoch"):
-            train_dataloader.set_epoch(epoch)
+            train_dataloader.set_epoch(int(epoch))
         epoch_iterator = iter(train_dataloader)
 
         # We chunkify the epoch iterator into gradient accumulation steps `n` batches
@@ -1739,16 +1899,38 @@ def _run_epoch(
                 if (
                     self.args.logging_nan_inf_filter
                     and not is_torch_xla_available()
-                    and (torch.isnan(tr_loss_step) or torch.isinf(tr_loss_step))
+                    and (
+                        torch.isnan(tr_loss_step)
+                        if isinstance(tr_loss_step, torch.Tensor)
+                        else any(torch.isnan(v) for v in tr_loss_step.values())
+                    )
+                    or (
+                        torch.isinf(tr_loss_step)
+                        if isinstance(tr_loss_step, torch.Tensor)
+                        else any(torch.isinf(v) for v in tr_loss_step.values())
+                    )
                 ):
                     # if loss is nan or inf simply add the average of previous logged losses
                     self._tr_loss += self._tr_loss / (1 + self.state.global_step - self._globalstep_last_logged)
+                    if isinstance(tr_loss_step, dict):
+                        for k in tr_loss_step:
+                            if k in self._tr_loss_components:
+                                self._tr_loss_components[k] += self._tr_loss_components[k] / (
+                                    1 + self.state.global_step - self._globalstep_last_logged
+                                )
                 else:
-                    if self._tr_loss.device != tr_loss_step.device:
-                        raise ValueError(
-                            f"Calculated loss must be on the original device: {self._tr_loss.device} but device in use is {tr_loss_step.device}"
-                        )
-                    self._tr_loss += tr_loss_step
+                    if isinstance(tr_loss_step, dict):
+                        for k, v in tr_loss_step.items():
+                            if k not in self._tr_loss_components:
+                                self._tr_loss_components[k] = torch.tensor(0.0, device=self.args.device)
+                            self._tr_loss_components[k] += v
+                        self._tr_loss += tr_loss_step["loss"]
+                    else:
+                        if self._tr_loss.device != tr_loss_step.device:
+                            raise ValueError(
+                                f"Calculated loss must be on the original device: {self._tr_loss.device} but device in use is {tr_loss_step.device}"
+                            )
+                        self._tr_loss += tr_loss_step
 
                 self.current_flos += float(self.floating_point_ops(inputs))
                 self._track_num_input_tokens(inputs)
@@ -1905,8 +2087,14 @@ def training_step(
                 loss_mb = smp_forward_backward(model, inputs, self.args.gradient_accumulation_steps)
                 return loss_mb.reduce_mean().detach().to(self.args.device)
 
+            return_outputs = self.args.logging_loss_components
             with self.compute_loss_context_manager():
-                loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
+                loss = self.compute_loss(
+                    model, inputs, return_outputs=return_outputs, num_items_in_batch=num_items_in_batch
+                )
+
+            if return_outputs:
+                loss, outputs = loss
 
             del inputs
             if (
@@ -1924,9 +2112,7 @@ def training_step(
             if self.args.n_gpu > 1:
                 loss = loss.mean()  # mean() to average on multi-gpu parallel training
 
-            # Finally we need to normalize the loss for reporting if GA loss bug is not fixed during compute loss
-            if (not self.model_accepts_loss_kwargs or num_items_in_batch is None) and self.compute_loss_func is None:
-                # If the model does not accept loss kwargs, we need to normalize the loss by the number of gradient accumulation steps
+            if not self._loss_is_scaled_for_ga or num_items_in_batch is None:
                 loss = loss / self.current_gradient_accumulation_steps
 
             # Turning off loss scaling w.r.t. gradient accumulation when DeepSpeed is enabled
@@ -1936,8 +2122,30 @@ def training_step(
 
             self.accelerator.backward(loss, **kwargs)
 
+            if return_outputs and isinstance(outputs, dict):
+                # Extract all loss-like components
+                loss_components = {
+                    k: v.detach()
+                    for k, v in outputs.items()
+                    if ("loss" in k or k in self.label_names) and isinstance(v, torch.Tensor) and v.numel() == 1
+                }
+                # Ensure the main loss is also included if not already
+                if "loss" not in loss_components:
+                    loss_components["loss"] = loss.detach()
+                return loss_components
+
             return loss.detach()
 
+    @property
+    def _loss_is_scaled_for_ga(self) -> bool:
+        """
+        Whether compute_loss returns a loss already scaled for gradient accumulation.
+
+        Override to return False if you implement custom compute_loss that needs
+        the Trainer to handle gradient accumulation scaling.
+        """
+        return self.model_accepts_loss_kwargs or self.compute_loss_func is not None
+
     def compute_loss(
         self,
         model: nn.Module,
@@ -1962,8 +2170,8 @@ def compute_loss(
         Returns:
             The loss of the model along with its output if return_outputs was set to True
 
-        Subclass and override for custom behavior. If you are not using `num_items_in_batch` when computing your loss,
-        make sure to overwrite `self.model_accepts_loss_kwargs` to `False`. Otherwise, the loss calculation might be slightly inaccurate when performing gradient accumulation.
+        Subclass and override for custom behavior. If you compute your own loss and need the Trainer to handle
+        gradient accumulation scaling, override `_loss_is_scaled_for_ga` to return `False`.
         """
         pc = getattr(self.accelerator, "parallelism_config", None)
         if pc is not None and pc.sp_backend == "deepspeed" and pc.sp_enabled and self.model.training:
@@ -2018,7 +2226,7 @@ def compute_loss(
             and (self.model_accepts_loss_kwargs or self.compute_loss_func)
             and num_items_in_batch is not None
         ):
-            loss *= self.accelerator.num_processes if self.args.n_gpu <= 1 else self.args.n_gpu
+            loss *= self.accelerator.state.num_data_parallel_processes if self.args.n_gpu <= 1 else self.args.n_gpu
 
         return (loss, outputs) if return_outputs else loss
 
@@ -2060,12 +2268,21 @@ def _maybe_log_save_evaluate(
             logs: dict[str, float] = {}
 
             # all_gather + mean() to get average loss over all processes
-            tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).mean().item()
+            tr_loss_scalar = nested_gather(tr_loss, self.args.parallel_mode).nanmean().item()
 
             # reset tr_loss to zero
             tr_loss -= tr_loss
 
             logs["loss"] = tr_loss_scalar / (self.state.global_step - self._globalstep_last_logged)
+
+            if self.args.logging_loss_components:
+                for k, v in self._tr_loss_components.items():
+                    v_scalar = nested_gather(v, self.args.parallel_mode).mean().item()
+                    self._tr_loss_components[k] -= self._tr_loss_components[k]
+                    logs[k] = v_scalar / (self.state.global_step - self._globalstep_last_logged)
+                    if k not in self._total_loss_components_scalar:
+                        self._total_loss_components_scalar[k] = 0.0
+                    self._total_loss_components_scalar[k] += v_scalar
             if grad_norm is not None:
                 logs["grad_norm"] = grad_norm.item() if isinstance(grad_norm, torch.Tensor) else grad_norm
             if learning_rate is not None:
@@ -2083,6 +2300,13 @@ def _maybe_log_save_evaluate(
         if self.control.should_evaluate:
             metrics = self._evaluate(trial, ignore_keys_for_eval)
             is_new_best_metric = self._determine_best_metric(metrics=metrics, trial=trial)
+            if (
+                is_new_best_metric
+                and self.args.load_best_model_at_end
+                and self.args.save_strategy != SaveStrategy.BEST
+                and not self.control.should_save
+            ):
+                self._save_checkpoint(model, trial)
 
             if self.args.save_strategy == SaveStrategy.BEST:
                 self.control.should_save = is_new_best_metric
@@ -2629,31 +2853,52 @@ def evaluation_loop(
 
         prediction_loss_only = prediction_loss_only if prediction_loss_only is not None else args.prediction_loss_only
 
-        # if eval is called w/o train, handle model prep here
+        # if eval is called without train, handle model prep here
+        _ds_config_mutated = False
+        _need_ds_eval_engine = False
         if self.is_deepspeed_enabled and self.deepspeed is None:
-            _, _ = deepspeed_init(self, num_training_steps=0, inference=True)
+            hf_deepspeed_config = self.accelerator.state.deepspeed_plugin.hf_ds_config
+            # Only ZeRO-3 needs a DS inference engine (params are partitioned across GPUs).
+            # ZeRO-1/2 keep full params on each GPU and can eval without one.
+            _need_ds_eval_engine = hf_deepspeed_config.is_zero3()
+            if _need_ds_eval_engine:
+                # deepspeed_init(inference=True) mutates shared config (deletes optimizer,
+                # bakes scheduler "auto" to 0). Back up and restore after prepare().
+                import copy
+
+                _ds_config = hf_deepspeed_config.config
+                _saved_optimizer = copy.deepcopy(_ds_config.get("optimizer"))
+                _saved_sched_params = copy.deepcopy(_ds_config.get("scheduler", {}).get("params"))
+                _ds_config_mutated = True
+                _, _ = deepspeed_init(self, num_training_steps=0, inference=True)
 
         model = self._wrap_model(self.model, training=False)
 
-        if len(self.accelerator._models) == 0 and model is self.model:
-            start_time = time.time()
-            model = (
-                self.accelerator.prepare(model)
-                if self.is_deepspeed_enabled or (self.is_fsdp_enabled and not self.args.torch_compile)
-                else self.accelerator.prepare_model(model, evaluation_mode=True)
-            )
-            self.model_preparation_time = round(time.time() - start_time, 4)
+        try:
+            if len(self.accelerator._models) == 0 and model is self.model:
+                start_time = time.time()
+                if _need_ds_eval_engine or self.deepspeed is not None:
+                    model = self.accelerator.prepare(model)
+                elif self.is_fsdp_enabled and not self.args.torch_compile:
+                    model = self.accelerator.prepare(model)
+                else:
+                    model = self.accelerator.prepare_model(model, evaluation_mode=True)
+                self.model_preparation_time = round(time.time() - start_time, 4)
 
-            if self.is_fsdp_enabled:
-                self.model = model
+                if self.is_fsdp_enabled:
+                    self.model = model
 
-            # for the rest of this function `model` is the outside model, whether it was wrapped or not
-            if model is not self.model:
-                self.model_wrapped = model
+                if model is not self.model:
+                    self.model_wrapped = model
 
-            # backward compatibility
-            if self.is_deepspeed_enabled:
-                self.deepspeed = self.model_wrapped
+                if self.is_deepspeed_enabled and _need_ds_eval_engine:
+                    self.deepspeed = self.model_wrapped
+        finally:
+            if _ds_config_mutated:
+                if _saved_optimizer is not None:
+                    _ds_config["optimizer"] = _saved_optimizer
+                if _saved_sched_params is not None:
+                    _ds_config.setdefault("scheduler", {})["params"] = _saved_sched_params
 
         # if full fp16 or bf16 eval is wanted and this ``evaluation`` or ``predict`` isn't called
         # while ``train`` is running, cast it to the right dtype first and then put on device
@@ -2686,15 +2931,21 @@ def evaluation_loop(
         all_preds = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
         all_labels = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
         all_inputs = EvalLoopContainer(self.args.eval_do_concat_batches, padding_index=-100)
+        # Separate list for per-sample nested labels (e.g., Mask2Former)
+        per_sample_nested_labels = []
 
         metrics = None
         eval_set_kwargs = {}
 
         # Will be useful when we have an iterable dataset so don't know its length.
         observed_num_examples = 0
+        max_eval_batches = args.max_eval_batches
 
         # Main evaluation loop
         for step, inputs in enumerate(dataloader):
+            if max_eval_batches is not None and step >= max_eval_batches:
+                break
+
             # Update the observed num examples
             observed_batch_size = find_batch_size(inputs)
             if observed_batch_size is not None:
@@ -2715,14 +2966,16 @@ def evaluation_loop(
 
             # Update containers
             if losses is not None:
-                losses = self.gather_function(losses.repeat(batch_size))
+                losses = self.gather_function(losses.repeat(observed_batch_size))
                 all_losses.add(losses)
             if inputs_decode is not None:
                 inputs_decode = self.accelerator.pad_across_processes(inputs_decode, dim=1, pad_index=-100)
                 inputs_decode = self.gather_function(inputs_decode)
                 if not self.args.batch_eval_metrics or description == "Prediction":
                     all_inputs.add(inputs_decode)
-            if labels is not None:
+            # Check if labels have per-sample nested structure (e.g., Mask2Former's tuple[list[Tensor], ...])
+            labels_are_per_sample_nested = labels is not None and is_per_sample_nested(labels)
+            if labels is not None and not labels_are_per_sample_nested:
                 # Pad labels here, preparing for preprocess_logits_for_metrics in next logits block.
                 labels = self.accelerator.pad_across_processes(labels, dim=1, pad_index=-100)
             if logits is not None:
@@ -2733,15 +2986,23 @@ def evaluation_loop(
                 if not self.args.batch_eval_metrics or description == "Prediction":
                     all_preds.add(logits)
             if labels is not None:
-                labels = self.gather_function(labels)
-                if not self.args.batch_eval_metrics or description == "Prediction":
-                    all_labels.add(labels)
+                if labels_are_per_sample_nested:
+                    # Per-sample nested: gather from all processes, then accumulate
+                    # Use gather_object directly to avoid incorrect truncation in gather_for_metrics
+                    gathered_labels = self.accelerator.gather_object(labels)
+                    per_sample_nested_labels.extend(gathered_labels)
+                else:
+                    labels = self.gather_function(labels)
+                    if not self.args.batch_eval_metrics or description == "Prediction":
+                        all_labels.add(labels)
 
             self.control = self.callback_handler.on_prediction_step(args, self.state, self.control)
 
             if self.args.batch_eval_metrics:
                 if self.compute_metrics is not None and logits is not None and labels is not None:
-                    is_last_step = self.accelerator.gradient_state.end_of_dataloader
+                    is_last_step = self.accelerator.gradient_state.end_of_dataloader or (
+                        max_eval_batches is not None and step + 1 >= max_eval_batches
+                    )
                     batch_kwargs = {}
                     batch_kwargs["losses"] = losses if "loss" in args.include_for_metrics else None
                     batch_kwargs["inputs"] = inputs if "inputs" in args.include_for_metrics else None
@@ -2773,7 +3034,9 @@ def evaluation_loop(
         all_inputs = all_inputs.get_arrays()
 
         # Number of samples
-        if has_length(eval_dataset):
+        if max_eval_batches is not None:
+            num_samples = observed_num_examples
+        elif has_length(eval_dataset):
             num_samples = len(eval_dataset)
         # The instance check is weird and does not actually check for the type, but whether the dataset has the right
         # methods. Therefore we need to make sure it also has the attribute.
@@ -2787,6 +3050,10 @@ def evaluation_loop(
         if num_samples == 0 and observed_num_examples > 0:
             num_samples = observed_num_examples
 
+        # Handle per-sample nested labels (e.g., Mask2Former)
+        if per_sample_nested_labels:
+            all_labels = flatten_per_sample_nested_batches(per_sample_nested_labels, num_samples)
+
         # Metrics!
         if (
             self.compute_metrics is not None
@@ -2806,9 +3073,9 @@ def evaluation_loop(
         metrics = denumpify_detensorize(metrics)
 
         if isinstance(all_losses, list) and all_losses:
-            metrics[f"{metric_key_prefix}_loss"] = np.concatenate(all_losses).mean().item()
+            metrics[f"{metric_key_prefix}_loss"] = np.nanmean(np.concatenate(all_losses)).item()
         elif isinstance(all_losses, np.ndarray):
-            metrics[f"{metric_key_prefix}_loss"] = all_losses.mean().item()
+            metrics[f"{metric_key_prefix}_loss"] = np.nanmean(all_losses).item()
         if hasattr(self, "model_preparation_time"):
             metrics[f"{metric_key_prefix}_model_preparation_time"] = self.model_preparation_time
 
@@ -2958,6 +3225,8 @@ def prediction_step(
                 if has_labels or loss_without_labels:
                     with self.compute_loss_context_manager():
                         num_items_in_batch = self._get_num_items_in_batch([inputs], self.args.device)
+                        if self.args.use_liger_kernel and prediction_loss_only:
+                            inputs = {**inputs, "skip_logits": True}
                         loss, outputs = self.compute_loss(
                             model, inputs, return_outputs=True, num_items_in_batch=num_items_in_batch
                         )
@@ -3036,7 +3305,9 @@ def _get_output_dir(self, trial: "optuna.Trial | dict[str, Any] | None") -> str:
             run_dir = self.args.output_dir
         return run_dir
 
-    def _save_checkpoint(self, model: nn.Module, trial: "optuna.Trial | dict[str, Any] | None") -> None:
+    def _save_checkpoint(
+        self, model: nn.Module, trial: "optuna.Trial | dict[str, Any] | None", save_latest: bool = True
+    ) -> None:
         """Save model checkpoint, optimizer, scheduler, scaler, RNG states, and trainer state."""
         # In all cases, including ddp/dp/deepspeed, self.model is always a reference to the model we
         # want to save except FullyShardedDDP.
@@ -3050,6 +3321,7 @@ def _save_checkpoint(self, model: nn.Module, trial: "optuna.Trial | dict[str, An
 
         run_dir = self._get_output_dir(trial=trial)
         output_dir = os.path.join(run_dir, checkpoint_folder)
+        os.makedirs(output_dir, exist_ok=True)
         self.save_model(output_dir, _internal_call=True)
 
         if (
@@ -3077,6 +3349,15 @@ def _save_checkpoint(self, model: nn.Module, trial: "optuna.Trial | dict[str, An
             self._save_scaler(output_dir)
             # Save RNG state
             self._save_rng_state(output_dir)
+        elif self.is_fsdp_enabled and "SHARDED_STATE_DICT" in str(self.accelerator.state.fsdp_plugin.state_dict_type):
+            # self.save_model above only handles FULL_STATE_DICT
+            save_fsdp_model(
+                self.accelerator.state.fsdp_plugin,
+                self.accelerator,
+                self.model,
+                output_dir,
+                **get_fsdp_ckpt_kwargs(),
+            )
 
         # Save the Trainer state
         if self.args.should_save:
@@ -3105,6 +3386,10 @@ def _save_checkpoint(self, model: nn.Module, trial: "optuna.Trial | dict[str, An
                 use_mtime=True,
             )
 
+        if save_latest and self.is_world_process_zero():
+            with open(os.path.join(run_dir, "latest"), "w") as fd:
+                fd.write(checkpoint_folder)
+
     def _determine_best_metric(self, metrics: dict[str, float], trial: "optuna.Trial | dict[str, Any] | None") -> bool:
         """
         Determine if the model should be saved based on the evaluation metrics.
@@ -3426,6 +3711,7 @@ def _load_best_model(self) -> None:
                 self.model_wrapped,
                 self.state.best_model_checkpoint,
                 load_module_strict=not _is_peft_model(self.model),
+                convert_deepspeed_universal_checkpoint=self.args.convert_deepspeed_universal_checkpoint,
             )
         elif self.is_fsdp_enabled:
             load_result = load_fsdp_model(
@@ -3459,7 +3745,11 @@ def _load_best_model(self) -> None:
 
                         if os.path.exists(best_adapter_model_path) or os.path.exists(best_safe_adapter_model_path):
                             try:
-                                model.load_adapter(self.state.best_model_checkpoint, active_adapter)
+                                model.load_adapter(
+                                    self.state.best_model_checkpoint,
+                                    active_adapter,
+                                    torch_device="cpu",
+                                )
                             except RuntimeError as exc:
                                 if model.peft_config[active_adapter].is_prompt_learning:
                                     # for context: https://github.com/huggingface/peft/issues/2256
@@ -3761,7 +4051,13 @@ def save_model(self, output_dir: str | None = None, _internal_call: bool = False
 
         if is_torch_xla_available():
             save_tpu_checkpoint(
-                self.model, self.args, self.accelerator, self.processing_class, self.is_fsdp_xla_v1_enabled, output_dir
+                self.model,
+                self.args,
+                self.accelerator,
+                self.processing_class,
+                self.is_fsdp_xla_v1_enabled,
+                output_dir,
+                is_fsdp_xla_v2_enabled=self.is_fsdp_xla_v2_enabled,
             )
         elif is_sagemaker_mp_enabled():
             # Calling the state_dict needs to be done on the wrapped model and on all processes.
@@ -3831,7 +4127,12 @@ def _save(self, output_dir: str | None = None, state_dict: dict | None = None) -
                     state_dict, os.path.join(output_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"}
                 )
         else:
-            self.model.save_pretrained(output_dir, state_dict=state_dict)
+            self.model.save_pretrained(
+                output_dir,
+                state_dict=state_dict,
+                safe_serialization=self.args.save_safetensors,
+                is_main_process=self.accelerator.is_main_process,
+            )
 
         if self.processing_class is not None:
             self.processing_class.save_pretrained(output_dir)
@@ -3860,6 +4161,14 @@ def log(self, logs: dict[str, float], start_time: float | None = None) -> None:
             start_time (`Optional[float]`):
                 The start of training.
         """
+        mode = "train" if self.model.training else "eval"
+        if self._metrics[mode]:
+            metrics = {key: sum(val) / len(val) for key, val in self._metrics[mode].items()}
+            if mode == "eval":
+                metrics = {f"eval_{key}": val for key, val in metrics.items()}
+            logs = {**logs, **metrics}
+            self._metrics[mode].clear()
+
         if self.state.epoch is not None:
             logs["epoch"] = self.state.epoch
         if self.args.include_num_input_tokens_seen != "no":
diff --git a/src/transformers/trainer_callback.py b/src/transformers/trainer_callback.py
index 598f24a604c9..207d71ee3d11 100644
--- a/src/transformers/trainer_callback.py
+++ b/src/transformers/trainer_callback.py
@@ -18,6 +18,7 @@
 import dataclasses
 import json
 import math
+import re
 from dataclasses import dataclass
 
 import numpy as np
@@ -25,7 +26,12 @@
 
 from .trainer_utils import IntervalStrategy, SaveStrategy, has_length
 from .training_args import TrainingArguments
-from .utils import logging
+from .utils import is_torch_available, logging
+
+
+if is_torch_available():
+    import torch
+    import torch.distributed as dist
 
 
 logger = logging.get_logger(__name__)
@@ -560,6 +566,310 @@ def call_event(self, event, args, state, control, **kwargs):
         return control
 
 
+class MoERouterHealthCallback(TrainerCallback):
+    """
+    A [`TrainerCallback`] that records MoE router health metrics during training.
+
+    The callback installs forward hooks on router modules advertised through the model's `can_record_outputs`
+    metadata. It prefers exact selected expert indices when a router surfaces them and otherwise falls back to
+    deriving top-k assignments from router logits. Metrics are emitted as flat trainer logs.
+
+    Args:
+        prefix (`str`, *optional*, defaults to `"moe"`):
+            Prefix used for the logged metric keys.
+        reduction_mode (`str`, *optional*, defaults to `"auto"`):
+            How to reduce expert counts before logging. Use `"auto"` to reduce across the world process group for
+            normal distributed replicas and to skip implicit reduction for tensor-parallel models. Use `"world"` to
+            always all-reduce across the default process group, or `"none"` to disable implicit reduction entirely.
+        log_aux_loss (`bool`, *optional*, defaults to `True`):
+            Whether to log model-level auxiliary routing losses such as `aux_loss` when present in model outputs.
+    """
+
+    def __init__(self, prefix: str = "moe", reduction_mode: str = "auto", log_aux_loss: bool = True):
+        if reduction_mode not in {"auto", "world", "none"}:
+            raise ValueError(
+                f"`reduction_mode` must be one of 'auto', 'world', or 'none', but got {reduction_mode!r}."
+            )
+        self.prefix = prefix.rstrip("/")
+        self.reduction_mode = reduction_mode
+        self.log_aux_loss = log_aux_loss
+        self._layer_counts = {}
+        self._layer_order = []
+        self._router_handles = []
+        self._model_handle = None
+        self._last_aux_metrics = {}
+        self._resolved_reduction_mode = reduction_mode
+
+    @staticmethod
+    def _safe_metric_divide(numerator, denominator) -> float:
+        if denominator == 0:
+            return 0.0
+        return float(numerator / denominator)
+
+    @staticmethod
+    def _format_layer_name(module_name: str, layer_idx: int) -> str:
+        if module_name:
+            sanitized_name = re.sub(r"[^a-zA-Z0-9]+", "_", module_name).strip("_")
+            if sanitized_name:
+                return sanitized_name
+        return f"layer_{layer_idx}"
+
+    @staticmethod
+    def _compute_routing_metrics(expert_counts) -> dict[str, float]:
+        counts = expert_counts.to(dtype=torch.float64)
+        total_assignments = counts.sum()
+        if total_assignments <= 0:
+            return {
+                "entropy": 0.0,
+                "normalized_entropy": 0.0,
+                "load_cv": 0.0,
+                "max_load_ratio": 0.0,
+                "active_experts": 0.0,
+                "dead_experts": float(counts.numel()),
+                "total_assignments": 0.0,
+            }
+
+        fractions = counts / total_assignments
+        nonzero_fractions = fractions[fractions > 0]
+        entropy = float(-(nonzero_fractions * nonzero_fractions.log()).sum().item())
+        max_entropy = math.log(counts.numel()) if counts.numel() > 1 else 0.0
+        normalized_entropy = MoERouterHealthCallback._safe_metric_divide(entropy, max_entropy)
+
+        load_mean = counts.mean()
+        load_std = counts.std(unbiased=False)
+        load_cv = MoERouterHealthCallback._safe_metric_divide(load_std.item(), load_mean.item())
+        max_load_ratio = MoERouterHealthCallback._safe_metric_divide(counts.max().item(), load_mean.item())
+
+        active_experts = float((counts > 0).sum().item())
+        dead_experts = float((counts == 0).sum().item())
+
+        return {
+            "entropy": entropy,
+            "normalized_entropy": normalized_entropy,
+            "load_cv": load_cv,
+            "max_load_ratio": max_load_ratio,
+            "active_experts": active_experts,
+            "dead_experts": dead_experts,
+            "total_assignments": float(total_assignments.item()),
+        }
+
+    @staticmethod
+    def _reduce_counts(expert_counts):
+        if not dist.is_available() or not dist.is_initialized():
+            return expert_counts
+
+        reduced_counts = expert_counts.clone()
+        dist.all_reduce(reduced_counts)
+        return reduced_counts
+
+    @staticmethod
+    def _extract_tensor(output, index: int | None = None):
+        if index is None:
+            return output
+        if isinstance(output, (tuple, list)) and len(output) > index:
+            return output[index]
+        return None
+
+    @staticmethod
+    def _extract_selected_experts(output):
+        if not isinstance(output, (tuple, list)):
+            return None
+
+        for candidate in output:
+            if torch.is_tensor(candidate) and candidate.dtype in {
+                torch.int8,
+                torch.int16,
+                torch.int32,
+                torch.int64,
+                torch.uint8,
+            }:
+                return candidate
+        return None
+
+    @staticmethod
+    def _infer_top_k(module, model) -> int:
+        for candidate in (
+            getattr(module, "top_k", None),
+            getattr(module, "num_experts_per_tok", None),
+            getattr(model.config, "num_experts_per_tok", None),
+            getattr(model.config, "moe_topk", None),
+            getattr(model.config, "num_experts_per_tok", None),
+            1,
+        ):
+            if isinstance(candidate, int) and candidate > 0:
+                return candidate
+        return 1
+
+    @staticmethod
+    def _compute_counts_from_routing(selected_experts, num_experts: int):
+        flattened_experts = selected_experts.reshape(-1)
+        valid_experts = flattened_experts[(flattened_experts >= 0) & (flattened_experts < num_experts)]
+        if valid_experts.numel() == 0:
+            return torch.zeros(num_experts, device=selected_experts.device, dtype=torch.float64)
+        return torch.bincount(valid_experts, minlength=num_experts).to(dtype=torch.float64)
+
+    @staticmethod
+    def _get_base_model(model):
+        while hasattr(model, "module"):
+            model = model.module
+        return model
+
+    @staticmethod
+    def _resolve_reduction_mode(model, reduction_mode: str) -> str:
+        if reduction_mode != "auto":
+            return reduction_mode
+
+        tp_size = getattr(model, "tp_size", None)
+        if tp_size is None:
+            tp_size = getattr(model, "_tp_size", None)
+
+        if tp_size is not None and tp_size > 1:
+            return "none"
+
+        return "world"
+
+    def _reset_state(self):
+        self._layer_counts = {}
+        self._layer_order = []
+        self._last_aux_metrics = {}
+
+    def _remove_hooks(self):
+        for handle in self._router_handles:
+            handle.remove()
+        self._router_handles = []
+        if self._model_handle is not None:
+            self._model_handle.remove()
+            self._model_handle = None
+
+    def _accumulate_router_counts(self, module_name: str, output, recorder_index: int, model, module) -> None:
+        selected_experts = self._extract_selected_experts(output)
+        router_logits = self._extract_tensor(output, recorder_index)
+
+        if selected_experts is None:
+            if router_logits is None or not torch.is_tensor(router_logits):
+                return
+            top_k = min(self._infer_top_k(module=module, model=model), router_logits.shape[-1])
+            selected_experts = torch.topk(router_logits.detach().float(), k=top_k, dim=-1).indices
+
+        if not torch.is_tensor(selected_experts):
+            return
+
+        if router_logits is not None and torch.is_tensor(router_logits):
+            num_experts = int(router_logits.shape[-1])
+        else:
+            max_selected = int(selected_experts.max().item()) if selected_experts.numel() > 0 else -1
+            num_experts = max_selected + 1
+        if num_experts <= 0:
+            return
+
+        counts = self._compute_counts_from_routing(selected_experts.detach(), num_experts=num_experts)
+        if module_name not in self._layer_counts:
+            self._layer_counts[module_name] = counts
+            self._layer_order.append(module_name)
+        else:
+            self._layer_counts[module_name] = self._layer_counts[module_name] + counts.to(
+                device=self._layer_counts[module_name].device
+            )
+
+    def _capture_model_aux_metrics(self, outputs) -> None:
+        if not self.log_aux_loss or outputs is None:
+            return
+
+        metrics = {}
+        for attribute_name in ("aux_loss", "router_aux_loss", "z_loss"):
+            value = getattr(outputs, attribute_name, None)
+            if value is None:
+                continue
+            if hasattr(value, "detach"):
+                value = value.detach()
+            if hasattr(value, "item"):
+                value = value.item()
+            metrics[f"{self.prefix}/{attribute_name}"] = float(value)
+        self._last_aux_metrics = metrics
+
+    def _iter_router_modules(self, model):
+        capture_specs = getattr(model, "can_record_outputs", {})
+        router_specs = capture_specs.get("router_logits")
+        if router_specs is None:
+            return
+        if not isinstance(router_specs, list):
+            router_specs = [router_specs]
+
+        for spec in router_specs:
+            for module_name, module in model.named_modules():
+                target_class = getattr(spec, "target_class", None)
+                class_name = getattr(spec, "class_name", None)
+                layer_name = getattr(spec, "layer_name", None)
+                matches_class = target_class is not None and isinstance(module, target_class)
+                matches_name = class_name is not None and module_name.endswith(class_name)
+                if not (matches_class or matches_name):
+                    continue
+                if layer_name is not None and layer_name not in module_name:
+                    continue
+                yield module_name, module, getattr(spec, "index", 0)
+
+    def on_train_begin(self, args, state, control, model=None, **kwargs):
+        self._remove_hooks()
+        self._reset_state()
+        if model is None:
+            return
+        model = self._get_base_model(model)
+        self._resolved_reduction_mode = self._resolve_reduction_mode(model, self.reduction_mode)
+
+        router_modules = list(self._iter_router_modules(model))
+        if len(router_modules) == 0:
+            logger.warning_once(
+                "MoERouterHealthCallback did not find any router modules exposed through `can_record_outputs`."
+            )
+            return
+
+        for module_name, module, recorder_index in router_modules:
+            handle = module.register_forward_hook(
+                lambda current_module,
+                module_args,
+                output,
+                name=module_name,
+                idx=recorder_index: self._accumulate_router_counts(name, output, idx, model, current_module)
+            )
+            self._router_handles.append(handle)
+
+        if self.log_aux_loss:
+            self._model_handle = model.register_forward_hook(
+                lambda current_module, module_args, outputs: self._capture_model_aux_metrics(outputs)
+            )
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        if logs is None:
+            return
+
+        per_layer_metrics = []
+        for layer_idx, module_name in enumerate(self._layer_order):
+            expert_counts = self._layer_counts.get(module_name)
+            if expert_counts is None:
+                continue
+            if self._resolved_reduction_mode == "world":
+                expert_counts = self._reduce_counts(expert_counts)
+            metric_prefix = f"{self.prefix}/{self._format_layer_name(module_name, layer_idx)}"
+            layer_metrics = self._compute_routing_metrics(expert_counts)
+            for metric_name, metric_value in layer_metrics.items():
+                logs[f"{metric_prefix}/{metric_name}"] = metric_value
+            per_layer_metrics.append(layer_metrics)
+
+        if per_layer_metrics:
+            metric_names = per_layer_metrics[0].keys()
+            for metric_name in metric_names:
+                logs[f"{self.prefix}/global/mean_{metric_name}"] = float(
+                    np.mean([layer_metrics[metric_name] for layer_metrics in per_layer_metrics])
+                )
+
+        logs.update(self._last_aux_metrics)
+        self._reset_state()
+
+    def on_train_end(self, args, state, control, **kwargs):
+        self._remove_hooks()
+        self._reset_state()
+
+
 class DefaultFlowCallback(TrainerCallback):
     """
     A [`TrainerCallback`] that handles the default flow of the training loop for logs, evaluation and checkpoints.
diff --git a/src/transformers/trainer_data_source.py b/src/transformers/trainer_data_source.py
new file mode 100644
index 000000000000..68ffa74fc3fb
--- /dev/null
+++ b/src/transformers/trainer_data_source.py
@@ -0,0 +1,370 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Internal epoch-source abstraction for the Trainer.
+
+These classes are private implementation details. They unify static-dataset
+training and online-data-producer training behind a single iterator interface
+so that ``_inner_training_loop`` has no ``if online: … else: …`` branching.
+"""
+
+from __future__ import annotations
+
+import logging
+import math
+import sys
+from abc import ABC, abstractmethod
+from collections.abc import Iterator
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
+
+from torch.utils.data import DataLoader
+
+from .trainer_utils import has_length
+
+
+if TYPE_CHECKING:
+    from .data_producer import DataProducer
+
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Dataclasses
+# ---------------------------------------------------------------------------
+
+
+@dataclass(frozen=True)
+class _TrainingPlan:
+    """Immutable bag of training-level constants computed once before the loop.
+
+    Matches the 7-tuple returned by ``Trainer.set_initial_training_values()``.
+    """
+
+    num_train_epochs: int
+    num_update_steps_per_epoch: int
+    num_examples: int
+    num_train_samples: int
+    total_train_batch_size: int
+    steps_in_epoch: int
+    max_steps: int
+
+
+@dataclass
+class _EpochSpec:
+    """Per-epoch data bundle passed to ``Trainer._run_epoch()``.
+
+    Replaces the 7 per-epoch keyword arguments that ``_run_epoch`` previously
+    accepted as individual parameters.
+    """
+
+    epoch: float
+    dataloader: DataLoader
+    steps_in_epoch: int
+    num_update_steps_per_epoch: int
+    resume_from_checkpoint: str | None = None
+    epochs_trained: int = 0
+    steps_trained_in_current_epoch: int = 0
+
+
+# ---------------------------------------------------------------------------
+# Abstract base
+# ---------------------------------------------------------------------------
+
+
+class _EpochSource(ABC):
+    """Abstract source of training epochs.
+
+    Two concrete implementations exist:
+
+    * :class:`_StaticEpochSource` — wraps the traditional fixed
+      ``train_dataset`` / ``DataLoader`` pipeline.
+    * :class:`_OnlineEpochSource` — wraps a :class:`DataProducer` that
+      generates fresh data each rollout round.
+    """
+
+    @abstractmethod
+    def compute_plan(self, trainer: Any) -> _TrainingPlan:
+        """Create dataloader(s) and compute training-level constants.
+
+        Called once at the start of ``_inner_training_loop``.
+        """
+        ...
+
+    @abstractmethod
+    def iter_epochs(
+        self,
+        trainer: Any,
+        plan: _TrainingPlan,
+        epochs_trained: int,
+        steps_trained_in_current_epoch: int,
+        resume_from_checkpoint: str | None,
+    ) -> Iterator[_EpochSpec]:
+        """Yield one :class:`_EpochSpec` per training epoch."""
+        ...
+
+    def post_model_setup(self, trainer: Any, model: Any, dataloader: DataLoader) -> None:
+        """Hook called after model wrapping (``accelerator.prepare``).
+
+        Default implementation is a no-op.  ``_StaticEpochSource`` uses this
+        to apply the SP (Sequence Parallelism) dataloader adapter.
+        """
+
+    @property
+    @abstractmethod
+    def initial_dataloader(self) -> DataLoader:
+        """The dataloader to pass to ``_prepare_for_training``.
+
+        For the static path this is the train dataloader; for the online path
+        this is the dataloader created from the first ``produce()`` call.
+        """
+        ...
+
+
+# ---------------------------------------------------------------------------
+# Static (traditional dataset) source
+# ---------------------------------------------------------------------------
+
+
+class _StaticEpochSource(_EpochSource):
+    """Epoch source for the standard ``train_dataset`` path.
+
+    Behaviour is **identical** to the original ``_inner_training_loop``:
+    delegates to ``get_train_dataloader()`` and ``set_initial_training_values()``
+    (the override points that subclasses like GRPOTrainer rely on) and yields
+    the same dataloader every epoch.
+    """
+
+    def __init__(self):
+        self._train_dataloader: DataLoader | None = None
+
+    def compute_plan(self, trainer: Any) -> _TrainingPlan:
+        train_dataloader = trainer.get_train_dataloader()
+        if trainer.is_fsdp_xla_v2_enabled:
+            from .integrations.tpu import tpu_spmd_dataloader
+
+            train_dataloader = tpu_spmd_dataloader(train_dataloader)
+
+        (
+            num_train_epochs,
+            num_update_steps_per_epoch,
+            num_examples,
+            num_train_samples,
+            total_train_batch_size,
+            steps_in_epoch,
+            max_steps,
+        ) = trainer.set_initial_training_values(trainer.args, train_dataloader)
+
+        self._train_dataloader = train_dataloader
+
+        return _TrainingPlan(
+            num_train_epochs=num_train_epochs,
+            num_update_steps_per_epoch=num_update_steps_per_epoch,
+            num_examples=num_examples,
+            num_train_samples=num_train_samples,
+            total_train_batch_size=total_train_batch_size,
+            steps_in_epoch=steps_in_epoch,
+            max_steps=max_steps,
+        )
+
+    @property
+    def initial_dataloader(self) -> DataLoader:
+        assert self._train_dataloader is not None, "compute_plan() must be called first"
+        return self._train_dataloader
+
+    def post_model_setup(self, trainer: Any, model: Any, dataloader: DataLoader) -> None:
+        # Apply the SP adapter and store back.  The adapter must run after
+        # accelerator.prepare (which happens inside _prepare_for_training).
+        self._train_dataloader = trainer._apply_sp_adapter(dataloader, model)
+
+    def iter_epochs(
+        self,
+        trainer: Any,
+        plan: _TrainingPlan,
+        epochs_trained: int,
+        steps_trained_in_current_epoch: int,
+        resume_from_checkpoint: str | None,
+    ) -> Iterator[_EpochSpec]:
+        for epoch in range(epochs_trained, plan.num_train_epochs):
+            yield _EpochSpec(
+                epoch=epoch,
+                dataloader=self._train_dataloader,
+                steps_in_epoch=plan.steps_in_epoch,
+                num_update_steps_per_epoch=plan.num_update_steps_per_epoch,
+                resume_from_checkpoint=resume_from_checkpoint,
+                epochs_trained=epochs_trained,
+                steps_trained_in_current_epoch=steps_trained_in_current_epoch,
+            )
+
+
+# ---------------------------------------------------------------------------
+# Online (DataProducer) source
+# ---------------------------------------------------------------------------
+
+
+class _OnlineEpochSource(_EpochSource):
+    """Epoch source backed by a :class:`DataProducer`.
+
+    Each rollout round calls ``produce(model)`` to get a fresh dataset, wraps
+    it in a ``DataLoader``, and yields ``mini_epochs`` passes over it.
+    """
+
+    def __init__(self, data_producer: DataProducer):
+        self._producer = data_producer
+        self._initial_dataloader: DataLoader | None = None
+        self._initial_dataset = None
+        self._model = None
+
+    def compute_plan(self, trainer: Any) -> _TrainingPlan:
+        args = trainer.args
+        max_steps = args.max_steps
+        config = self._producer.config
+
+        # Produce the initial dataset to establish dataloader shape
+        self._initial_dataset = trainer._produce_data(trainer.model)
+        dataloader = trainer._get_online_dataloader(self._initial_dataset)
+        self._initial_dataloader = dataloader
+
+        total_train_batch_size = trainer.get_total_train_batch_size(args)
+
+        if has_length(dataloader):
+            len_dataloader = len(dataloader)
+            num_update_steps_per_epoch = max(
+                len_dataloader // args.gradient_accumulation_steps
+                + int(len_dataloader % args.gradient_accumulation_steps > 0),
+                1,
+            )
+            steps_in_epoch = len_dataloader
+        else:
+            # IterableDataset — rely on max_steps
+            num_update_steps_per_epoch = max_steps
+            steps_in_epoch = max_steps * args.gradient_accumulation_steps
+
+        # Compute num_train_epochs based on max_rollouts or max_steps
+        if config.max_rollouts is not None:
+            num_train_epochs = config.max_rollouts * config.mini_epochs
+            if max_steps <= 0:
+                max_steps = num_train_epochs * num_update_steps_per_epoch
+        else:
+            num_train_epochs = (
+                math.ceil(max_steps / num_update_steps_per_epoch) if num_update_steps_per_epoch > 0 else sys.maxsize
+            )
+
+        num_examples = total_train_batch_size * max_steps
+        num_train_samples = num_examples
+
+        return _TrainingPlan(
+            num_train_epochs=num_train_epochs,
+            num_update_steps_per_epoch=num_update_steps_per_epoch,
+            num_examples=num_examples,
+            num_train_samples=num_train_samples,
+            total_train_batch_size=total_train_batch_size,
+            steps_in_epoch=steps_in_epoch,
+            max_steps=max_steps,
+        )
+
+    @property
+    def initial_dataloader(self) -> DataLoader:
+        assert self._initial_dataloader is not None, "compute_plan() must be called first"
+        return self._initial_dataloader
+
+    def post_model_setup(self, trainer: Any, model: Any, dataloader: DataLoader) -> None:
+        self._model = model
+        # Apply SP adapter to the initial dataloader
+        self._initial_dataloader = trainer._apply_sp_adapter(dataloader, model)
+
+    def iter_epochs(
+        self,
+        trainer: Any,
+        plan: _TrainingPlan,
+        epochs_trained: int,
+        steps_trained_in_current_epoch: int,
+        resume_from_checkpoint: str | None,
+    ) -> Iterator[_EpochSpec]:
+        config = self._producer.config
+        rollout = 0
+        epoch_counter = 0
+
+        while True:
+            # Stop conditions
+            if config.max_rollouts is not None and rollout >= config.max_rollouts:
+                break
+
+            # Get dataset for this rollout
+            if rollout == 0:
+                # Use the dataset produced during compute_plan()
+                dataloader = self._initial_dataloader
+            else:
+                # Remove the previous dataloader from accelerator tracking
+                # to avoid accumulating stale references (which would leak
+                # memory and interfere with checkpoint save/load).
+                prev_dl = dataloader  # noqa: F821 — always set on prior iteration
+                acc_dls = trainer.accelerator._dataloaders
+                if prev_dl in acc_dls:
+                    acc_dls.remove(prev_dl)
+
+                dataset = trainer._produce_data(self._model)
+                dataloader = trainer._get_online_dataloader(dataset)
+                dataloader = trainer._apply_sp_adapter(dataloader, self._model)
+                # Update callback handler reference
+                trainer.callback_handler.train_dataloader = dataloader
+
+            # Recompute steps_in_epoch for this dataloader (may differ if
+            # the produced dataset has a different size)
+            if has_length(dataloader):
+                steps_in_epoch = len(dataloader)
+                num_update_steps_per_epoch = max(
+                    steps_in_epoch // trainer.args.gradient_accumulation_steps
+                    + int(steps_in_epoch % trainer.args.gradient_accumulation_steps > 0),
+                    1,
+                )
+            else:
+                steps_in_epoch = plan.steps_in_epoch
+                num_update_steps_per_epoch = plan.num_update_steps_per_epoch
+
+            # Yield mini_epochs passes over this rollout's data
+            for mini in range(config.mini_epochs):
+                if epoch_counter < epochs_trained:
+                    # Skip epochs that were already trained (checkpoint resume)
+                    epoch_counter += 1
+                    continue
+
+                epoch_idx = rollout + mini / config.mini_epochs
+
+                yield _EpochSpec(
+                    epoch=epoch_idx,
+                    dataloader=dataloader,
+                    steps_in_epoch=steps_in_epoch,
+                    num_update_steps_per_epoch=num_update_steps_per_epoch,
+                    resume_from_checkpoint=resume_from_checkpoint if epoch_counter == epochs_trained else None,
+                    epochs_trained=epochs_trained,
+                    steps_trained_in_current_epoch=steps_trained_in_current_epoch
+                    if epoch_counter == epochs_trained
+                    else 0,
+                )
+
+                epoch_counter += 1
+
+                # Check if training should stop (the caller checks
+                # control.should_training_stop, but we also need to break
+                # out of mini_epochs if max_steps is reached)
+                if trainer.control.should_training_stop:
+                    return
+
+            rollout += 1
+
+            if trainer.control.should_training_stop:
+                return
diff --git a/src/transformers/trainer_optimizer.py b/src/transformers/trainer_optimizer.py
index 9afa36a924ac..e31b3083f277 100644
--- a/src/transformers/trainer_optimizer.py
+++ b/src/transformers/trainer_optimizer.py
@@ -34,6 +34,7 @@
 from .utils import (
     is_apollo_torch_available,
     is_bitsandbytes_available,
+    is_flashoptim_available,
     is_galore_torch_available,
     is_grokadamw_available,
     is_lomo_available,
@@ -553,6 +554,90 @@ def _get_stable_adamw(ctx: OptimizerContext) -> tuple[Any, dict[str, Any]]:
     return StableAdamW, ctx.optimizer_kwargs
 
 
+def _patch_flashoptim_state_dict():
+    """
+    Patch FlashOptim's state_dict to handle empty optimizer state.
+
+    FlashOptim's `_state_dict_for_param` raises a KeyError when called before
+    the first optimizer step (empty state). This is triggered by accelerate's
+    `AcceleratedOptimizer.__init__`, which calls `state_dict()` for device placement.
+    """
+    from flashoptim.optimizers import FlashOptimizer
+
+    if getattr(FlashOptimizer, "_hf_patched", False):
+        return
+
+    orig = FlashOptimizer._state_dict_for_param
+
+    def _safe_state_dict_for_param(self, param_number, opt_state, *args, **kwargs):
+        if param_number not in opt_state:
+            return {}
+        return orig(self, param_number, opt_state, *args, **kwargs)
+
+    FlashOptimizer._state_dict_for_param = _safe_state_dict_for_param
+    FlashOptimizer._hf_patched = True
+
+
+def _get_flashoptim_optimizer(ctx: OptimizerContext) -> tuple[Any, dict[str, Any]]:
+    """Get FlashOptim optimizer (FlashAdamW, FlashAdam, FlashSGD, FlashSGDW, FlashLion)."""
+    if not is_flashoptim_available():
+        raise ImportError(
+            "You need to install `flashoptim` in order to use FlashOptim optimizers. "
+            "Install it with `pip install flashoptim`"
+        )
+
+    from flashoptim import FlashAdam, FlashAdamW, FlashLion, FlashSGD, FlashSGDW
+
+    _patch_flashoptim_state_dict()
+
+    optimizer_mapping = {
+        OptimizerNames.FLASH_ADAMW: FlashAdamW,
+        OptimizerNames.FLASH_ADAM: FlashAdam,
+        OptimizerNames.FLASH_SGD: FlashSGD,
+        OptimizerNames.FLASH_SGDW: FlashSGDW,
+        OptimizerNames.FLASH_LION: FlashLion,
+    }
+
+    optimizer_cls = optimizer_mapping[ctx.args.optim]
+
+    # Parse flashoptim-specific args
+    master_weight_bits = ctx.optim_args.get("master_weight_bits", "24")
+    if master_weight_bits.lower() == "none":
+        master_weight_bits = None
+    else:
+        master_weight_bits = int(master_weight_bits)
+
+    # master_weight_bits has no effect when all model params are fp32 (FlashOptim
+    # raises an error in this case). Auto-disable it and warn the user.
+    if master_weight_bits is not None and ctx.model is not None:
+        all_fp32 = all(p.dtype == torch.float32 for p in ctx.model.parameters())
+        if all_fp32:
+            logger.warning(
+                f"FlashOptim: master_weight_bits={master_weight_bits} has no effect with fp32 parameters. "
+                "Setting master_weight_bits=None. Use bf16/fp16 model weights to enable master weight correction."
+            )
+            master_weight_bits = None
+
+    flashoptim_kwargs = {
+        "master_weight_bits": master_weight_bits,
+    }
+
+    # Only FlashAdamW and FlashSGDW accept compress_state_dict and decouple_lr directly.
+    # Other variants pass **kwargs to the base class which does not accept these.
+    if ctx.args.optim in (OptimizerNames.FLASH_ADAMW, OptimizerNames.FLASH_SGDW):
+        flashoptim_kwargs["compress_state_dict"] = strtobool(ctx.optim_args.get("compress_state_dict", "True"))
+        flashoptim_kwargs["decouple_lr"] = strtobool(ctx.optim_args.get("decouple_lr", "False"))
+
+    # Add optimizer-family-specific kwargs
+    if ctx.args.optim in (OptimizerNames.FLASH_ADAMW, OptimizerNames.FLASH_ADAM):
+        flashoptim_kwargs.update(ctx.adam_kwargs)
+    elif ctx.args.optim == OptimizerNames.FLASH_LION:
+        flashoptim_kwargs["betas"] = (ctx.args.adam_beta1, ctx.args.adam_beta2)
+
+    ctx.optimizer_kwargs.update(flashoptim_kwargs)
+    return optimizer_cls, ctx.optimizer_kwargs
+
+
 # =============================================================================
 # Dispatch table
 # =============================================================================
@@ -600,6 +685,14 @@ def _get_stable_adamw(ctx: OptimizerContext) -> tuple[Any, dict[str, Any]]:
     OptimizerNames.SCHEDULE_FREE_SGD,
 ]
 
+_FLASHOPTIM_OPTIMIZERS = [
+    OptimizerNames.FLASH_ADAMW,
+    OptimizerNames.FLASH_ADAM,
+    OptimizerNames.FLASH_SGD,
+    OptimizerNames.FLASH_SGDW,
+    OptimizerNames.FLASH_LION,
+]
+
 # =============================================================================
 # Built-in optimizer handlers registry
 # =============================================================================
@@ -624,4 +717,5 @@ def _get_stable_adamw(ctx: OptimizerContext) -> tuple[Any, dict[str, Any]]:
     **dict.fromkeys(_APOLLO_OPTIMIZERS, _get_apollo_optimizer),
     **dict.fromkeys(_TORCHAO_OPTIMIZERS, _get_torchao_optimizer),
     **dict.fromkeys(_SCHEDULE_FREE_OPTIMIZERS, _get_schedule_free_optimizer),
+    **dict.fromkeys(_FLASHOPTIM_OPTIMIZERS, _get_flashoptim_optimizer),
 }
diff --git a/src/transformers/trainer_pt_utils.py b/src/transformers/trainer_pt_utils.py
index 30377f5f5a61..6745bb9ed8be 100644
--- a/src/transformers/trainer_pt_utils.py
+++ b/src/transformers/trainer_pt_utils.py
@@ -25,7 +25,7 @@
 import re
 import sys
 import warnings
-from collections.abc import Iterator, Mapping
+from collections.abc import Callable, Iterator, Mapping
 from contextlib import contextmanager
 from dataclasses import dataclass, field
 from itertools import chain
@@ -433,6 +433,45 @@ def nested_truncate(tensors, limit):
     return tensors[:limit]
 
 
+def is_per_sample_nested(tensors) -> bool:
+    """
+    Check if tensors is a "per-sample nested structure" like tuple[list[Tensor], list[Tensor]].
+
+    This structure is used by models like Mask2Former where labels are:
+    - tuple of (mask_labels, class_labels)
+    - Each is a list of tensors, one per image
+    - Tensors may have different shapes (different instances per image)
+    """
+    if not (isinstance(tensors, tuple) and len(tensors) > 0):
+        return False
+    for t in tensors:
+        if not (isinstance(t, list) and len(t) > 0 and isinstance(t[0], (torch.Tensor, np.ndarray))):
+            return False
+    return True
+
+
+def flatten_per_sample_nested_batches(batches, num_samples):
+    """
+    Flatten a list of per-sample nested batches and truncate to num_samples.
+
+    Args:
+        batches: List of batches, each is tuple[list[Tensor], ...]
+        num_samples: Number of samples to keep
+
+    Returns:
+        Single tuple with concatenated lists, truncated to num_samples
+    """
+    if not batches:
+        return None
+    num_label_types = len(batches[0])
+    result = tuple([] for _ in range(num_label_types))
+    for batch in batches:
+        for i, label_list in enumerate(batch):
+            result[i].extend(label_list)
+    # Truncate to actual dataset size
+    return tuple(lst[:num_samples] for lst in result)
+
+
 @dataclass
 class LabelSmoother:
     """
@@ -476,6 +515,29 @@ def __call__(self, model_output, labels, shift_labels=False):
         return (1 - self.epsilon) * nll_loss + self.epsilon * smoothed_loss
 
 
+def _compute_dataset_lengths(dataset, model_input_name: str) -> list[int]:
+    """
+    Computes the lengths of the dataset items. For Hugging Face datasets,
+    this leverages select_columns for better performance.
+    """
+    if not isinstance(dataset[0], (dict, BatchEncoding)) or model_input_name not in dataset[0]:
+        raise ValueError(
+            "Can only automatically infer lengths for datasets whose items are dictionaries with an "
+            f"'{model_input_name}' key."
+        )
+    if hasattr(dataset, "__len__") and len(dataset) > 50000:
+        logger.warning(
+            "Computing lengths of the dataset... This may take a while. "
+            "To avoid this, you can provide the length of each sample in a column and set `length_column_name`."
+        )
+
+    dataset_iterator = dataset
+    if hasattr(dataset, "select_columns"):
+        dataset_iterator = dataset.select_columns([model_input_name])
+
+    return [len(feature[model_input_name]) for feature in logging.tqdm(dataset_iterator, desc="Computing lengths")]
+
+
 def get_length_grouped_indices(lengths, batch_size, mega_batch_mult=None, generator=None):
     """
     Return a list of indices so that each slice of `batch_size` consecutive indices correspond to elements of similar
@@ -531,12 +593,7 @@ def __init__(
         self.batch_size = batch_size
         if lengths is None:
             model_input_name = model_input_name if model_input_name is not None else "input_ids"
-            if not isinstance(dataset[0], (dict, BatchEncoding)) or model_input_name not in dataset[0]:
-                raise ValueError(
-                    "Can only automatically infer lengths for datasets whose items are dictionaries with an "
-                    f"'{model_input_name}' key."
-                )
-            lengths = [len(feature[model_input_name]) for feature in dataset]
+            lengths = _compute_dataset_lengths(dataset, model_input_name)
         elif isinstance(lengths, torch.Tensor):
             logger.info(
                 "If lengths is a torch.Tensor, LengthGroupedSampler will be slow. Converting lengths to list[int]..."
@@ -571,7 +628,50 @@ def __init__(
         drop_last: bool = False,
         lengths: list[int] | None = None,
         model_input_name: str | None = None,
+        length_func: Callable[[Any], int] | None = None,
+        mega_batch_mult: int | None = None,
     ):
+        """
+        **Warning**: This sampler may be slow to initialize if lengths is not provided. It is
+        recommended to cache the resulting lengths object after the first run to speed up subsequent runs.
+
+        Args:
+            batch_size (`int`):
+                The batch size to use for sampling.
+            dataset (`Dataset`, *optional*):
+                The dataset to sample from. Either `dataset` or `lengths` must be provided.
+            num_replicas (`int`, *optional*):
+                Number of processes participating in distributed training. Will default to
+                the dist.get_world_size() if not provided.
+            rank (`int`, *optional*):
+                Rank of the current process within `num_replicas`. Will default to the
+                dist.get_rank() if not provided.
+            seed (`int`, *optional*, defaults to `0`):
+                Random seed used for shuffling.
+            drop_last (`bool`, *optional*, defaults to `False`):
+                If `True`, the sampler will drop the tail of the data to make it evenly
+                divisible across the number of replicas.
+            lengths (`list[int]`, *optional*):
+                Pre-computed lengths of the dataset items. If not provided, lengths will be
+                inferred from the dataset using `length_func` or the default method.
+            model_input_name (`str`, *optional*):
+                The name of the key in the dataset items to use for computing lengths.
+                Defaults to `"input_ids"` if not specified. Ignored if length_func is provided.
+            length_func (`Callable[[Any], int]`, *optional*):
+                A function that takes a dataset item and returns its length. If not provided,
+                the length will be inferred from the `model_input_name` key.
+            mega_batch_mult (`int`, *optional*):
+                The sampler takes mega_batch_mult * batch_size number of samples into memory
+                before sorting them by length. This parameter controls the size of these mega
+                batches. If not provided, it will default to min(len(dataset) // (batch_size * 4), 50).
+
+        Raises:
+            ValueError: If neither `dataset` nor `lengths` is provided.
+            RuntimeError: If distributed package is not available when `num_replicas` or `rank`
+                is not provided.
+            ValueError: If lengths cannot be automatically inferred from the dataset.
+        """
+
         if dataset is None and lengths is None:
             raise ValueError("One of dataset and lengths must be provided.")
         if num_replicas is None:
@@ -588,15 +688,14 @@ def __init__(
         self.rank = rank
         self.epoch = 0
         self.drop_last = drop_last
+        self.mega_batch_mult = mega_batch_mult
 
         if lengths is None:
-            model_input_name = model_input_name if model_input_name is not None else "input_ids"
-            if not isinstance(dataset[0], (dict, BatchEncoding)) or model_input_name not in dataset[0]:
-                raise ValueError(
-                    "Can only automatically infer lengths for datasets whose items are dictionaries with an "
-                    f"'{model_input_name}' key."
-                )
-            lengths = [len(feature[model_input_name]) for feature in dataset]
+            if length_func is None:
+                model_input_name = model_input_name if model_input_name is not None else "input_ids"
+                lengths = _compute_dataset_lengths(dataset, model_input_name)
+            else:
+                lengths = [length_func(feature) for feature in dataset]
         elif isinstance(lengths, torch.Tensor):
             logger.info(
                 "If lengths is a torch.Tensor, DistributedLengthGroupedSampler will be slow. Converting lengths to"
@@ -622,7 +721,9 @@ def __iter__(self) -> Iterator:
         # Deterministically shuffle based on epoch and seed
         g = torch.Generator()
         g.manual_seed(self.seed + self.epoch)
-        indices = get_length_grouped_indices(self.lengths, self.batch_size, generator=g)
+        indices = get_length_grouped_indices(
+            self.lengths, self.batch_size, mega_batch_mult=self.mega_batch_mult, generator=g
+        )
 
         if not self.drop_last:
             # add extra samples to make it evenly divisible
diff --git a/src/transformers/trainer_seq2seq.py b/src/transformers/trainer_seq2seq.py
index ada588adbd21..a3f046bf9ec0 100644
--- a/src/transformers/trainer_seq2seq.py
+++ b/src/transformers/trainer_seq2seq.py
@@ -319,6 +319,35 @@ def prediction_step(
                 k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask")
             }
 
+        is_encoder_decoder = getattr(model.config, "is_encoder_decoder", False)
+        if not is_encoder_decoder and "generation_input_ids" in generation_inputs:
+            generation_input_ids = generation_inputs.pop("generation_input_ids")
+            generation_attention_mask = generation_inputs.pop("generation_attention_mask", None)
+            generation_inputs["input_ids"] = generation_input_ids
+
+            if generation_attention_mask is not None:
+                generation_inputs["attention_mask"] = generation_attention_mask
+            elif (
+                "attention_mask" not in generation_inputs
+                or generation_inputs["attention_mask"].shape != generation_input_ids.shape
+            ):
+                generation_inputs["attention_mask"] = torch.ones_like(generation_input_ids)
+        elif not is_encoder_decoder and "labels" in generation_inputs and "input_ids" in generation_inputs:
+            generation_input_ids, generation_attention_mask = self._prepare_decoder_only_generation_inputs(
+                generation_inputs["input_ids"],
+                generation_inputs.get("attention_mask"),
+                generation_inputs["labels"],
+            )
+            if generation_input_ids is not None:
+                generation_inputs["input_ids"] = generation_input_ids
+                generation_inputs["attention_mask"] = generation_attention_mask
+
+        prompt_input_length = (
+            generation_inputs["input_ids"].shape[-1]
+            if (not is_encoder_decoder and "input_ids" in generation_inputs)
+            else None
+        )
+
         summon_full_params_context = (
             FullyShardedDataParallel.summon_full_params(self.model)
             if torch.distributed.is_available() and isinstance(self.model, FullyShardedDataParallel)
@@ -327,6 +356,8 @@ def prediction_step(
 
         with summon_full_params_context:
             generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs)
+            if prompt_input_length is not None:
+                generated_tokens = generated_tokens[:, prompt_input_length:]
 
         # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
         # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
@@ -371,19 +402,58 @@ def prediction_step(
 
         return loss, generated_tokens, labels
 
-    def _pad_tensors_to_max_len(self, tensor, max_length):
+    @staticmethod
+    def _get_prompt_lengths_from_labels(labels: torch.Tensor) -> torch.Tensor:
+        # Labels use -100 to mask prompt tokens; we only keep the contiguous prefix.
+        prompt_prefix_mask = labels.eq(-100).long().cumprod(dim=-1).bool()
+        return prompt_prefix_mask.long().sum(dim=-1)
+
+    def _prepare_decoder_only_generation_inputs(
+        self,
+        input_ids: torch.Tensor,
+        attention_mask: torch.Tensor | None,
+        labels: torch.Tensor,
+    ) -> tuple[torch.Tensor | None, torch.Tensor | None]:
+        prompt_lengths = self._get_prompt_lengths_from_labels(labels)
+        if prompt_lengths.max().item() == 0:
+            return None, None
+
+        if attention_mask is None:
+            attention_mask = torch.ones_like(input_ids)
+
+        valid_lengths = attention_mask.long().sum(dim=-1)
+        prompt_lengths = torch.where(prompt_lengths > 0, prompt_lengths, valid_lengths)
+        max_prompt_length = int(prompt_lengths.max().item())
+        pad_token_id = self._get_pad_token_id()
+
+        generation_input_ids = input_ids.new_full((input_ids.shape[0], max_prompt_length), pad_token_id)
+        generation_attention_mask = attention_mask.new_zeros((input_ids.shape[0], max_prompt_length))
+
+        for row_idx, prompt_length in enumerate(prompt_lengths.tolist()):
+            if prompt_length == 0:
+                continue
+
+            generation_input_ids[row_idx, -prompt_length:] = input_ids[row_idx, :prompt_length]
+            generation_attention_mask[row_idx, -prompt_length:] = 1
+
+        return generation_input_ids, generation_attention_mask
+
+    def _get_pad_token_id(self) -> int:
         if self.processing_class is not None and hasattr(self.processing_class, "pad_token_id"):
-            # If PAD token is not defined at least EOS token has to be defined
             pad_token_id = (
                 self.processing_class.pad_token_id
                 if self.processing_class.pad_token_id is not None
                 else self.processing_class.eos_token_id
             )
+        elif getattr(self.model.config, "pad_token_id", None) is not None:
+            pad_token_id = self.model.config.pad_token_id
         else:
-            if getattr(self.model.config, "pad_token_id", None) is not None:
-                pad_token_id = self.model.config.pad_token_id
-            else:
-                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
+            raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")
+
+        return pad_token_id
+
+    def _pad_tensors_to_max_len(self, tensor, max_length):
+        pad_token_id = self._get_pad_token_id()
 
         padded_tensor = pad_token_id * torch.ones(
             (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
diff --git a/src/transformers/trainer_utils.py b/src/transformers/trainer_utils.py
index 400268de8180..7d89ad0ddbd0 100644
--- a/src/transformers/trainer_utils.py
+++ b/src/transformers/trainer_utils.py
@@ -268,6 +268,15 @@ class TrainOutput(NamedTuple):
 
 
 def get_last_checkpoint(folder):
+    if not os.path.exists(folder):
+        return
+
+    latest_path = os.path.join(folder, "latest")
+    if os.path.isfile(latest_path):
+        with open(latest_path) as fd:
+            tag = fd.read().strip()
+        return os.path.join(folder, tag)
+
     content = os.listdir(folder)
     checkpoints = [
         path
@@ -615,6 +624,10 @@ class TrainerMemoryTracker:
         "__init__": "init",
         "train": "train",
         "_inner_training_loop": "train",
+        "_create_epoch_source": "train",
+        "_log_training_banner": "train",
+        "_produce_data": "train",
+        "_get_online_dataloader": "train",
         "_finalize_training": "train",
         "evaluate": "eval",
         "predict": "test",
diff --git a/src/transformers/training_args.py b/src/transformers/training_args.py
index 1a5924c723ab..52436e0dec6b 100644
--- a/src/transformers/training_args.py
+++ b/src/transformers/training_args.py
@@ -155,6 +155,11 @@ class OptimizerNames(ExplicitEnum):
     APOLLO_ADAMW = "apollo_adamw"
     APOLLO_ADAMW_LAYERWISE = "apollo_adamw_layerwise"
     STABLE_ADAMW = "stable_adamw"
+    FLASH_ADAMW = "flash_adamw"
+    FLASH_ADAM = "flash_adam"
+    FLASH_SGD = "flash_sgd"
+    FLASH_SGDW = "flash_sgdw"
+    FLASH_LION = "flash_lion"
 
 
 def _convert_str_dict(passed_value: dict):
@@ -439,6 +444,8 @@ class TrainingArguments:
             Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If
             left unset, the whole predictions are accumulated on the device accelerator before being moved to the CPU (faster but
             requires more memory).
+        max_eval_batches (`int`, *optional*):
+            Maximum number of batches to run during evaluation. If unset, all evaluation batches are used.
 
         > Metrics Computation
 
@@ -526,8 +533,8 @@ class TrainingArguments:
             `save_steps` must be a multiple of `eval_steps`.
             </Tip>
         metric_for_best_model (`str`, *optional*):
-            Metric to use for comparing models when `load_best_model_at_end=True`. Must be a metric
-            name returned by evaluation, with or without the `"eval_"` prefix. Defaults to `"loss"`.
+            Metric to use for comparing models when `load_best_model_at_end=True` or `save_strategy="best"`.
+            Must be a metric name returned by evaluation, with or without the `"eval_"` prefix. Defaults to `"loss"`.
             If you set this, `greater_is_better` will default to `True` unless the name ends with
             `"loss"`. Examples: `"accuracy"`, `"f1"`, `"eval_bleu"`.
         greater_is_better (`bool`, *optional*):
@@ -649,75 +656,47 @@ class TrainingArguments:
 
         > FSDP (Fully Sharded Data Parallel)
 
-        fsdp (`bool`, `str` or list of [`~trainer_utils.FSDPOption`], *optional*, defaults to `None`):
-            Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Options:
-                - `"full_shard"`: Shard parameters, gradients, and optimizer states (most memory efficient)
-                - `"shard_grad_op"`: Shard only optimizer states and gradients (ZeRO-2)
-                - `"hybrid_shard"`: Full shard within nodes, replicate across nodes
-                - `"hybrid_shard_zero2"`: Shard gradients/optimizer within nodes, replicate across nodes
-                - `"offload"`: Offload parameters and gradients to CPU (only with `"full_shard"` or
-                    `"shard_grad_op"`)
-                - `"auto_wrap"`: Automatically wrap layers using `default_auto_wrap_policy`
+        fsdp (`bool`, *optional*, defaults to `None`):
+            Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Pass `True` to enable FSDP.
         fsdp_config (`str` or `dict`, *optional*):
-            Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of
-            fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.
-
-            A List of config and its options:
-                - fsdp_version (`int`, *optional*, defaults to `1`):
-                    The version of FSDP to use. Defaults to 1.
-                - min_num_params (`int`, *optional*, defaults to `0`):
-                    FSDP's minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is
-                    passed).
-                - transformer_layer_cls_to_wrap (`list[str]`, *optional*):
-                    List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`,
-                    `T5Block` .... (useful only when `fsdp` flag is passed).
-                - backward_prefetch (`str`, *optional*)
-                    FSDP's backward prefetch mode. Controls when to prefetch next set of parameters (useful only when
-                    `fsdp` field is passed).
-
-                    A list of options along the following:
-
-                    - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter's
-                      gradient computation.
-                    - `"backward_post"` : This prefetches the next set of parameters after the current set of
-                      parameter's gradient computation.
-                - forward_prefetch (`bool`, *optional*, defaults to `False`)
-                    FSDP's forward prefetch mode (useful only when `fsdp` field is passed).
-                     If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the
-                     forward pass.
-                - limit_all_gathers (`bool`, *optional*, defaults to `False`)
-                    FSDP's limit_all_gathers (useful only when `fsdp` field is passed).
-                     If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight
-                     all-gathers.
-                - use_orig_params (`bool`, *optional*, defaults to `True`)
-                    If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed
-                    frozen and trainable parameters. Useful in cases such as parameter-efficient fine-tuning. Please
-                    refer this
-                    [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019
-                - sync_module_states (`bool`, *optional*, defaults to `True`)
-                    If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to
-                    ensure they are the same across all ranks after initialization
-                - cpu_ram_efficient_loading (`bool`, *optional*, defaults to `False`)
-                    If `"True"`, only the first process loads the pretrained model checkpoint while all other processes
-                    have empty weights.  When this setting as `"True"`, `sync_module_states` also must to be `"True"`,
-                    otherwise all the processes except the main process would have random weights leading to unexpected
-                    behaviour during training.
+            Configuration settings for when `fsdp` is enabled. Pass a path to a JSON config file, such
+            as `fsdp_config.json`, or an already-loaded dict.
+
+            Supported keys:
+                - version (`int`, *optional*, defaults to `2`):
+                    The version of FSDP to use (`2` for FSDP2, `1` for the legacy FSDP1).
+                - reshard_after_forward (`bool`, *optional*, defaults to `True`):
+                    Whether to reshard parameters after the forward pass. Set to `False` to keep parameters
+                    gathered between the forward and backward passes, avoids the re-all-gather, and use higher peak memory.
+                - cpu_offload (`bool`, *optional*, defaults to `False`):
+                    Offload parameters and gradients to CPU when not in use to save GPU memory.
                 - activation_checkpointing (`bool`, *optional*, defaults to `False`):
-                    If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of
-                    certain layers and recomputing them during a backward pass. Effectively, this trades extra
-                    computation time for reduced memory usage.
+                    Set to `True` to reduce memory by recomputing activations during the backward pass. Prefer
+                    `activation_checkpointing` over `gradient_checkpointing` when using FSDP. `gradient_checkpointing`
+                    introduces a redundant all-gather in the backward pass.
+                - cpu_ram_efficient_loading (`bool`, *optional*, defaults to `False`):
+                    Set to `True` to load the pretrained checkpoint on the first process only. Other processes start
+                    with empty weights and receive the weights by broadcast.
+                - state_dict_type (`str`, *optional*, defaults to `"FULL_STATE_DICT"`):
+                    Checkpoint format: `"FULL_STATE_DICT"` (single HF-compatible file) or
+                    `"SHARDED_STATE_DICT"` (one file per rank, faster for large models).
+                - auto_wrap_policy (`str`, *optional*, defaults to `"TRANSFORMER_BASED_WRAP"`):
+                    Auto-wrap policy to use. Choose `"TRANSFORMER_BASED_WRAP"`, `"SIZE_BASED_WRAP"`, or `"NO_WRAP"`.
+                - transformer_layer_cls_to_wrap (`list[str]`, *optional*):
+                    Transformer layer class names (case-sensitive) to wrap, e.g. `LlamaDecoderLayer`. Usually
+                    unnecessary: the wrap policy falls back to the model's `_no_split_modules`, which covers
+                    most transformers models.
+                - min_num_params (`int`, *optional*, defaults to `0`):
+                    Minimum number of parameters per module for size-based auto-wrapping (used with
+                    `auto_wrap_policy="SIZE_BASED_WRAP"`).
                 - xla (`bool`, *optional*, defaults to `False`):
-                    Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature
-                    and its API may evolve in the future.
-                - xla_fsdp_settings (`dict`, *optional*)
-                    The value is a dictionary which stores the XLA FSDP wrapping parameters.
-
-                    For a complete list of options, please see [here](
-                    https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
+                    Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. Experimental.
+                - xla_fsdp_settings (`dict`, *optional*):
+                    Dictionary of XLA FSDP wrapping parameters. For a complete list of options, see the
+                    [XLA FSDP source](https://github.com/pytorch/xla/blob/master/torch_xla/distributed/fsdp/xla_fully_sharded_data_parallel.py).
                 - xla_fsdp_grad_ckpt (`bool`, *optional*, defaults to `False`):
-                    Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be
-                    used when the xla flag is set to true, and an auto wrapping policy is specified through
-                    fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.
+                    Set to `True` to use gradient checkpointing over each nested XLA FSDP wrapped layer. Requires
+                    `xla=True` and an auto-wrapping policy (`min_num_params` or `transformer_layer_cls_to_wrap`).
 
         > DeepSpeed
 
@@ -990,6 +969,10 @@ class TrainingArguments:
             )
         },
     )
+    logging_loss_components: bool = field(
+        default=False,
+        metadata={"help": "Whether to log all loss components when the model returns a dictionary of losses."},
+    )
     logging_first_step: bool = field(
         default=False, metadata={"help": "Whether to log the first `global_step` or not."}
     )
@@ -1139,6 +1122,10 @@ class TrainingArguments:
             "help": "Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If unset, predictions are accumulated on the accelerator before being moved to the CPU."
         },
     )
+    max_eval_batches: int | None = field(
+        default=None,
+        metadata={"help": "Maximum number of batches to run during evaluation. If unset, all batches are used."},
+    )
 
     # --- Metrics ---
     include_for_metrics: list[str] = field(
@@ -1401,19 +1388,21 @@ class TrainingArguments:
     )
 
     # --- FSDP ---
-    fsdp: list[FSDPOption] | str | None = field(
+    # `str | None` + `nargs="?"` / `const=True` so bare `--fsdp` → True while
+    # legacy `--fsdp full_shard` still parses. Switch to `bool | None` once
+    # legacy string support is dropped (v5.20).
+    fsdp: str | None = field(
         default=None,
         metadata={
-            "help": "Enable PyTorch FSDP for distributed training. Options: 'full_shard', 'shard_grad_op', 'hybrid_shard', 'hybrid_shard_zero2', 'offload', 'auto_wrap'.",
+            "help": "Enable PyTorch Fully Sharded Data Parallel (FSDP) for distributed training. Pass `--fsdp` (or `fsdp=True`) to turn FSDP on.",
+            "nargs": "?",
+            "const": True,
         },
     )
     fsdp_config: dict[str, Any] | str | None = field(
         default=None,
         metadata={
-            "help": (
-                "Config to be used with FSDP (Pytorch Fully Sharded Data Parallel). The value is either a "
-                "fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`."
-            )
+            "help": "Tuning for FSDP (used only when `fsdp` is enabled). Either a path to a JSON config file (e.g., `fsdp_config.json`) or an already loaded dict."
         },
     )
 
@@ -1483,6 +1472,16 @@ class TrainingArguments:
         },
     )
 
+    convert_deepspeed_universal_checkpoint: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to convert deepspeed zero checkpoint to universal checkpoint when "
+                "loaded world size is changed."
+            )
+        },
+    )
+
     def __post_init__(self):
         # ── 1. Defaults & Normalization ──
         if self.output_dir is None:
@@ -1552,6 +1551,7 @@ def __post_init__(self):
             self.load_best_model_at_end
             or self.lr_scheduler_type == SchedulerType.REDUCE_ON_PLATEAU
             or self.lr_scheduler_type == SchedulerType.GREEDY
+            or self.save_strategy == SaveStrategy.BEST
         ) and self.metric_for_best_model is None:
             self.metric_for_best_model = "loss"
         if self.greater_is_better is None and self.metric_for_best_model is not None:
@@ -1708,25 +1708,27 @@ def _validate_args(self):
                     '--load_best_model_at_end requires the save and eval strategy to match, except when --save_strategy="best", but found\n- Evaluation '
                     f"strategy: {self.eval_strategy}\n- Save strategy: {self.save_strategy}"
                 )
-            if self.eval_strategy == IntervalStrategy.STEPS and self.save_steps % self.eval_steps != 0:
+            if self.eval_strategy == IntervalStrategy.STEPS:
                 if self.eval_steps < 1 or self.save_steps < 1:
                     if not (self.eval_steps < 1 and self.save_steps < 1):
                         raise ValueError(
-                            "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
+                            "--load_best_model_at_end requires the saving steps to be compatible with the evaluation "
                             "steps, which cannot get guaranteed when mixing ratio and absolute steps for save_steps "
                             f"{self.save_steps} and eval_steps {self.eval_steps}."
                         )
                     # Use integer arithmetic to avoid floating point precision issues
-                    LARGE_MULTIPLIER = 1_000_000
-                    if (self.save_steps * LARGE_MULTIPLIER) % (self.eval_steps * LARGE_MULTIPLIER) != 0:
-                        raise ValueError(
-                            "--load_best_model_at_end requires the saving steps to be a multiple of the evaluation "
-                            f"steps, but found {self.save_steps}, which is not a multiple of {self.eval_steps}."
-                        )
+                    large_multiplier = 1_000_000
+                    save_steps = self.save_steps * large_multiplier
+                    eval_steps = self.eval_steps * large_multiplier
                 else:
-                    raise ValueError(
-                        "--load_best_model_at_end requires the saving steps to be a round multiple of the evaluation "
-                        f"steps, but found {self.save_steps}, which is not a round multiple of {self.eval_steps}."
+                    save_steps = self.save_steps
+                    eval_steps = self.eval_steps
+
+                steps_aligned = save_steps % eval_steps == 0 or eval_steps % save_steps == 0
+                if not steps_aligned:
+                    warnings.warn(
+                        "--load_best_model_at_end requires save_steps and eval_steps to align for scheduled saves. "
+                        "The best model will be saved at evaluation when needed instead."
                     )
 
         if is_torch_available():
@@ -1775,16 +1777,22 @@ def __str__(self):
     @property
     def train_batch_size(self) -> int:
         """
-        The actual batch size for training.
+        The actual batch size for training (takes into account the number of processes and
+        the split_batches configuration).
         """
+        if hasattr(self, "accelerator_config") and self.accelerator_config.split_batches:
+            return self.per_device_train_batch_size
         train_batch_size = self.per_device_train_batch_size * max(1, self.n_gpu)
         return train_batch_size
 
     @property
     def eval_batch_size(self) -> int:
         """
-        The actual batch size for evaluation.
+        The actual batch size for evaluation (takes into account the number of processes and
+        the split_batches configuration).
         """
+        if hasattr(self, "accelerator_config") and self.accelerator_config.split_batches:
+            return self.per_device_eval_batch_size
         eval_batch_size = self.per_device_eval_batch_size * max(1, self.n_gpu)
         return eval_batch_size
 
@@ -2708,141 +2716,168 @@ def set_dataloader(
 
     def _process_fsdp_args(self):
         if not self.fsdp:
-            self.fsdp = []
-        elif self.fsdp is True:
-            self.fsdp = [FSDPOption.FULL_SHARD]
-        elif isinstance(self.fsdp, str):
-            self.fsdp = [FSDPOption(s) for s in self.fsdp.split()]
-
-        if self.fsdp == [FSDPOption.OFFLOAD]:
-            raise ValueError(
-                "`--fsdp offload` can't work on its own. It needs to be added to `--fsdp full_shard` or "
-                '`--fsdp shard_grad_op`. For example, `--fsdp "full_shard offload"`.'
-            )
-        elif FSDPOption.FULL_SHARD in self.fsdp and FSDPOption.SHARD_GRAD_OP in self.fsdp:
-            raise ValueError("`--fsdp full_shard` is not compatible with `--fsdp shard_grad_op`.")
-
-        if self.gradient_checkpointing and (
-            FSDPOption.FULL_SHARD in self.fsdp or FSDPOption.HYBRID_SHARD in self.fsdp
-        ):
-            logger.warning(
-                "When using FSDP full shard, instead of using `gradient_checkpointing` in TrainingArguments, please"
-                " use `activation_checkpointing` in `fsdp_config`. The former introduces a redundant AllGather"
-                " operation in backward pass. Reference: https://github.com/huggingface/transformers/issues/30404"
-            )
+            return None
 
         if self.fsdp_config is None:
             self.fsdp_config = {}
-
-        if isinstance(self.fsdp_config, str):
-            if len(self.fsdp) == 0:
-                warnings.warn("`--fsdp_config` is useful only when `--fsdp` is specified.")
+        elif isinstance(self.fsdp_config, str):
             with open(self.fsdp_config, encoding="utf-8") as f:
                 self.fsdp_config = json.load(f)
+        for k in list(self.fsdp_config):
+            if k.startswith("fsdp_"):
+                self.fsdp_config[k[5:]] = self.fsdp_config.pop(k)
+
+        # Translate any legacy string / list `fsdp` values into `fsdp_config`
+        # entries so the rest of the function reads everything from
+        # `fsdp_config` only.
+        if isinstance(self.fsdp, (str, list)):
+            self._apply_legacy_fsdp_to_config(self.fsdp, self.fsdp_config)
+        self.fsdp = True
+
+        if self.gradient_checkpointing:
+            logger.warning(
+                "When using FSDP, prefer `activation_checkpointing` in `fsdp_config` over "
+                "`gradient_checkpointing`; the latter introduces a redundant AllGather in the backward pass. "
+                "Reference: https://github.com/huggingface/transformers/issues/30404"
+            )
 
-        if self.fsdp_config is not None and isinstance(self.fsdp_config, dict):
-            for k in list(self.fsdp_config.keys()):
-                if k.startswith("fsdp_"):
-                    v = self.fsdp_config.pop(k)
-                    self.fsdp_config[k[5:]] = v
-
+        # ---- Shared (version-agnostic) `fsdp_config` defaults / normalization. ----
         self.fsdp_config["min_num_params"] = self.fsdp_config.get("min_num_params", 0)
-
-        # Normalize transformer_layer_cls_to_wrap from string to list
-        if isinstance(self.fsdp_config.get("transformer_layer_cls_to_wrap", None), str):
+        if isinstance(self.fsdp_config.get("transformer_layer_cls_to_wrap"), str):
             self.fsdp_config["transformer_layer_cls_to_wrap"] = [self.fsdp_config["transformer_layer_cls_to_wrap"]]
+        self.fsdp_config.setdefault("xla", False)
+        self.fsdp_config.setdefault("xla_fsdp_v2", False)
+        self.fsdp_config.setdefault("xla_fsdp_grad_ckpt", False)
 
-        if len(self.fsdp) == 0 and self.fsdp_config["min_num_params"] > 0:
-            warnings.warn("`min_num_params` is useful only when `--fsdp` is specified.")
-
-        if len(self.fsdp) == 0 and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
-            warnings.warn("`transformer_layer_cls_to_wrap` is useful only when `--fsdp` is specified.")
-
-        if (
-            len(self.fsdp) > 0
-            and self.fsdp_config["min_num_params"] > 0
-            and self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None
-        ):
-            raise ValueError("`min_num_params` and `transformer_layer_cls_to_wrap` are mutually exclusive.")
-        self.fsdp_config["xla"] = self.fsdp_config.get("xla", False)
-        self.fsdp_config["xla_fsdp_v2"] = self.fsdp_config.get("xla_fsdp_v2", False)
-        self.fsdp_config["xla_fsdp_grad_ckpt"] = self.fsdp_config.get("xla_fsdp_grad_ckpt", False)
+        # ---- XLA path (separate from the Accelerate FSDP plugin path). ----
         if self.fsdp_config["xla"]:
-            if len(self.fsdp) > 0:
-                # Copy to avoid mutating the original (needed for JSON serialization)
-                self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {}).copy()
-                # Convert string dtype names to torch.dtype
-                if "compute_dtype" in self.xla_fsdp_config:
-                    self.xla_fsdp_config["compute_dtype"] = getattr(torch, self.xla_fsdp_config["compute_dtype"])
-                if "buffer_dtype" in self.xla_fsdp_config:
-                    self.xla_fsdp_config["buffer_dtype"] = getattr(torch, self.xla_fsdp_config["buffer_dtype"])
-            else:
-                warnings.warn("XLA FSDP can be used only when `--fsdp` is specified.")
-        else:
-            if self.fsdp_config["xla_fsdp_grad_ckpt"]:
-                warnings.warn("`--xla_fsdp_grad_ckpt` is useful only when `--xla` is set to true.")
-
-        # Build kwargs for Accelerate's FSDPPlugin
-        fsdp_plugin_args = None
-        if len(self.fsdp) > 0 and not self.fsdp_config["xla"]:
-            from accelerate.utils.constants import (
-                FSDP_AUTO_WRAP_POLICY,
-                FSDP_SHARDING_STRATEGY,
+            # Copy to avoid mutating the original (needed for JSON serialization)
+            self.xla_fsdp_config = self.fsdp_config.get("xla_fsdp_settings", {}).copy()
+            if "compute_dtype" in self.xla_fsdp_config:
+                self.xla_fsdp_config["compute_dtype"] = getattr(torch, self.xla_fsdp_config["compute_dtype"])
+            if "buffer_dtype" in self.xla_fsdp_config:
+                self.xla_fsdp_config["buffer_dtype"] = getattr(torch, self.xla_fsdp_config["buffer_dtype"])
+            return None
+        elif self.fsdp_config["xla_fsdp_grad_ckpt"]:
+            warnings.warn("`--xla_fsdp_grad_ckpt` is useful only when `--xla` is set to true.")
+
+        # ---- Build kwargs for Accelerate's FSDP plugin. ----
+        from accelerate.utils.constants import FSDP_AUTO_WRAP_POLICY
+
+        fsdp_version = int(self.fsdp_config.get("version", 2))
+        fsdp_plugin_args = {"fsdp_version": fsdp_version}
+
+        # Shared (v1 + v2) plugin args.
+        if self.fsdp_config.get("cpu_offload", False):
+            fsdp_plugin_args["cpu_offload"] = True
+
+        auto_wrap_policy = self.fsdp_config.get("auto_wrap_policy", FSDP_AUTO_WRAP_POLICY[0])
+        if auto_wrap_policy not in FSDP_AUTO_WRAP_POLICY:
+            raise ValueError(f"`auto_wrap_policy` must be one of {FSDP_AUTO_WRAP_POLICY}, got {auto_wrap_policy}.")
+        fsdp_plugin_args["auto_wrap_policy"] = auto_wrap_policy
+        if auto_wrap_policy == FSDP_AUTO_WRAP_POLICY[1] and self.fsdp_config["min_num_params"] > 0:
+            fsdp_plugin_args["min_num_params"] = self.fsdp_config["min_num_params"]
+        elif (
+            auto_wrap_policy == FSDP_AUTO_WRAP_POLICY[0]
+            and self.fsdp_config.get("transformer_layer_cls_to_wrap") is not None
+        ):
+            fsdp_plugin_args["transformer_cls_names_to_wrap"] = ",".join(
+                self.fsdp_config["transformer_layer_cls_to_wrap"]
             )
 
-            fsdp_plugin_args = {}
-            fsdp_sharding = None
-            for fsdp_option in self.fsdp:
-                if fsdp_option.upper() in FSDP_SHARDING_STRATEGY:
-                    fsdp_sharding = fsdp_option
-                elif fsdp_option == FSDPOption.OFFLOAD:
-                    fsdp_plugin_args["cpu_offload"] = True
-                elif fsdp_option == FSDPOption.AUTO_WRAP:
-                    fsdp_plugin_args["auto_wrap_policy"] = FSDP_AUTO_WRAP_POLICY[0]
-                    if self.fsdp_config["min_num_params"] > 0:
-                        fsdp_plugin_args["min_num_params"] = self.fsdp_config["min_num_params"]
-                        fsdp_plugin_args["auto_wrap_policy"] = FSDP_AUTO_WRAP_POLICY[1]
-                    elif self.fsdp_config.get("transformer_layer_cls_to_wrap", None) is not None:
-                        fsdp_plugin_args["transformer_cls_names_to_wrap"] = ",".join(
-                            self.fsdp_config["transformer_layer_cls_to_wrap"]
-                        )
-            fsdp_version = int(self.fsdp_config.get("version", 1))
-            fsdp_plugin_args["fsdp_version"] = fsdp_version
-            prefetch_policy = self.fsdp_config.get("backward_prefetch", "NO_PREFETCH")
-            if fsdp_version == 2:
-                # full_shard → True (reshard after forward), shard_grad_op → False
-                default_reshard = fsdp_sharding != "shard_grad_op" if fsdp_sharding else True
-                fsdp_plugin_args["reshard_after_forward"] = str_to_bool(
-                    str(self.fsdp_config.get("reshard_after_forward", default_reshard)).lower()
-                )
-            else:
-                fsdp_plugin_args["forward_prefetch"] = str_to_bool(
-                    str(self.fsdp_config.get("forward_prefetch", "false")).lower()
-                )
-                fsdp_plugin_args["backward_prefetch"] = prefetch_policy.upper()
-                # Pass sharding strategy as reshard_after_forward (accelerate converts it to ShardingStrategy)
-                default_reshard = fsdp_sharding.upper() if fsdp_sharding else "FULL_SHARD"
-                fsdp_plugin_args["reshard_after_forward"] = str(
-                    self.fsdp_config.get("reshard_after_forward", default_reshard)
-                ).lower()
-                fsdp_plugin_args["use_orig_params"] = str_to_bool(
-                    str(self.fsdp_config.get("use_orig_params", "true")).lower()
-                )
+        cpu_ram_efficient_loading = str(self.fsdp_config.get("cpu_ram_efficient_loading", "false")).lower()
+        fsdp_plugin_args["cpu_ram_efficient_loading"] = str_to_bool(cpu_ram_efficient_loading)
+        # Set env var to suppress Accelerate warning and for transformers to read
+        os.environ["FSDP_CPU_RAM_EFFICIENT_LOADING"] = cpu_ram_efficient_loading
 
-            sync_module_states = str(self.fsdp_config.get("sync_module_states", "true")).lower()
-            cpu_ram_efficient_loading = str(self.fsdp_config.get("cpu_ram_efficient_loading", "false")).lower()
-            if sync_module_states == "false" and cpu_ram_efficient_loading == "true":
-                # Without sync, non-main processes would have random weights
-                raise ValueError('`sync_module_states` must be `"True"` if `cpu_ram_efficient_loading` is `"True"`')
+        fsdp_plugin_args["state_dict_type"] = self.fsdp_config.get("state_dict_type", "FULL_STATE_DICT")
 
-            # Set env var to suppress Accelerate warning and for transformers to read
-            fsdp_plugin_args["cpu_ram_efficient_loading"] = str_to_bool(cpu_ram_efficient_loading)
-            os.environ["FSDP_CPU_RAM_EFFICIENT_LOADING"] = cpu_ram_efficient_loading
+        if "activation_checkpointing" in self.fsdp_config:
+            fsdp_plugin_args["activation_checkpointing"] = str_to_bool(
+                str(self.fsdp_config["activation_checkpointing"]).lower()
+            )
 
-            fsdp_plugin_args["sync_module_states"] = str_to_bool(sync_module_states)
+        if fsdp_version == 2:
+            fsdp_plugin_args["reshard_after_forward"] = str_to_bool(
+                str(self.fsdp_config.get("reshard_after_forward", True)).lower()
+            )
+        else:
+            # FSDP1 (DEPRECATED — to be removed in v5.20).
+            logger.warning(
+                "FSDP1 (`fsdp_config['version'] = 1`) is deprecated and will be removed in Transformers "
+                "v5.20. Please migrate to FSDP2 by setting `fsdp_config['version'] = 2` (the default)."
+            )
+            fsdp_plugin_args["reshard_after_forward"] = str(
+                self.fsdp_config.get("reshard_after_forward", "full_shard")
+            ).lower()
+            fsdp_plugin_args["forward_prefetch"] = str_to_bool(
+                str(self.fsdp_config.get("forward_prefetch", "false")).lower()
+            )
+            fsdp_plugin_args["backward_prefetch"] = self.fsdp_config.get("backward_prefetch", "NO_PREFETCH").upper()
+            fsdp_plugin_args["use_orig_params"] = str_to_bool(
+                str(self.fsdp_config.get("use_orig_params", "true")).lower()
+            )
+            fsdp_plugin_args["sync_module_states"] = str_to_bool(
+                str(self.fsdp_config.get("sync_module_states", "true")).lower()
+            )
+            if "limit_all_gathers" in self.fsdp_config:
+                fsdp_plugin_args["limit_all_gathers"] = str_to_bool(str(self.fsdp_config["limit_all_gathers"]).lower())
 
         return fsdp_plugin_args
 
+    @staticmethod
+    def _apply_legacy_fsdp_to_config(fsdp, fsdp_config):
+        """
+        Translate legacy `fsdp` values (string / list of [`~trainer_utils.FSDPOption`]) into
+        `fsdp_config` entries, using the shape expected by the target FSDP version:
+
+        - `"offload"` → `fsdp_config["cpu_offload"] = True`
+        - Sharding strategies → `fsdp_config["reshard_after_forward"]`. For FSDP2 this is a
+          bool (`"full_shard"` → `True`, `"shard_grad_op"` → `False`); for FSDP1 it is the
+          lowercase strategy name (`"full_shard"`, `"hybrid_shard"`, ...).
+        - `"auto_wrap"` → no-op (default `auto_wrap_policy` already wraps).
+
+        Isolated so the deprecated path can be removed in one place once support is dropped.
+        """
+        if isinstance(fsdp, str):
+            logger.warning(
+                "Passing `fsdp` as a string is deprecated and will be removed in Transformers v5.20. "
+                "Use `fsdp=True` and configure everything via `fsdp_config` instead."
+            )
+            items = fsdp.split()
+        else:
+            logger.warning(
+                "Passing `fsdp` as a list is deprecated and will be removed in Transformers v5.20. "
+                "Use `fsdp=True` and configure everything via `fsdp_config` instead."
+            )
+            items = list(fsdp)
+
+        from accelerate.utils.constants import FSDP_SHARDING_STRATEGY
+
+        version = int(fsdp_config.get("version", 2))
+        for item in items:
+            if item.upper() in FSDP_SHARDING_STRATEGY:
+                if version == 2:
+                    # FSDP2 `reshard_after_forward` is a bool; only full_shard / shard_grad_op
+                    # are supported. The other strategies are FSDP1-only.
+                    if item == FSDPOption.FULL_SHARD:
+                        fsdp_config.setdefault("reshard_after_forward", True)
+                    elif item == FSDPOption.SHARD_GRAD_OP:
+                        fsdp_config.setdefault("reshard_after_forward", False)
+                    else:
+                        raise ValueError(
+                            f"`fsdp={item}` is only available with FSDP1. Set `fsdp_config['version'] = 1` to "
+                            f"use it, but note that FSDP1 is deprecated and will be removed in Transformers v5.20."
+                        )
+                else:
+                    fsdp_config.setdefault("reshard_after_forward", item)
+            elif item == FSDPOption.OFFLOAD:
+                fsdp_config.setdefault("cpu_offload", True)
+            elif item == FSDPOption.AUTO_WRAP:
+                pass
+            else:
+                raise ValueError(f"Unknown `fsdp` option: {item}")
+
 
 class ParallelMode(Enum):
     NOT_PARALLEL = "not_parallel"
diff --git a/src/transformers/utils/__init__.py b/src/transformers/utils/__init__.py
index d12e0b277c1b..a561f22bdfb0 100644
--- a/src/transformers/utils/__init__.py
+++ b/src/transformers/utils/__init__.py
@@ -16,6 +16,7 @@
 
 from functools import lru_cache
 
+from huggingface_hub.errors import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError
 from packaging import version
 
 from .. import __version__
@@ -81,11 +82,8 @@
     LEGACY_PROCESSOR_CHAT_TEMPLATE_FILE,
     S3_BUCKET_PREFIX,
     TRANSFORMERS_DYNAMIC_MODULE_NAME,
-    EntryNotFoundError,
     PushInProgress,
     PushToHubMixin,
-    RepositoryNotFoundError,
-    RevisionNotFoundError,
     cached_file,
     define_sagemaker_information,
     extract_commit_hash,
@@ -135,6 +133,7 @@
     is_flash_attn_4_available,
     is_flash_attn_greater_or_equal,
     is_flash_attn_greater_or_equal_2_10,
+    is_flashoptim_available,
     is_flute_available,
     is_fouroversix_available,
     is_fp_quant_available,
@@ -262,6 +261,7 @@
     check_peft_version,
     find_adapter_config_file,
 )
+from .tokenizer_selection import TokenizerSelector, suggest_and_train_tokenizer
 
 
 WEIGHTS_NAME = "pytorch_model.bin"
diff --git a/src/transformers/utils/auto_docstring.py b/src/transformers/utils/auto_docstring.py
index 419579891e35..0254f53e2efe 100644
--- a/src/transformers/utils/auto_docstring.py
+++ b/src/transformers/utils/auto_docstring.py
@@ -43,13 +43,17 @@
     "image_processing_pil_*.py",
     "image_processing_*.py",
     "feature_extractor_*.py",
+    "modular_*.py",
 ]
 
 PLACEHOLDER_TO_AUTO_MODULE = {
     "image_processor_class": ("image_processing_auto", "IMAGE_PROCESSOR_MAPPING_NAMES"),
     "tokenizer_class": ("tokenization_auto", "TOKENIZER_MAPPING_NAMES"),
     "video_processor_class": ("video_processing_auto", "VIDEO_PROCESSOR_MAPPING_NAMES"),
-    "feature_extractor_class": ("feature_extraction_auto", "FEATURE_EXTRACTOR_MAPPING_NAMES"),
+    "feature_extractor_class": (
+        "feature_extraction_auto",
+        "FEATURE_EXTRACTOR_MAPPING_NAMES",
+    ),
     "processor_class": ("processing_auto", "PROCESSOR_MAPPING_NAMES"),
     "config_class": ("configuration_auto", "CONFIG_MAPPING_NAMES"),
     "model_class": ("modeling_auto", "MODEL_MAPPING_NAMES"),
@@ -76,6 +80,7 @@
     "kosmos2-5": "Kosmos2_5Config",
     "donut": "DonutSwinConfig",
     "esmfold": "EsmConfig",
+    "molmo2": "Molmo2Config",
     "parakeet": "ParakeetCTCConfig",
     "privacy-filter": "OpenAIPrivacyFilterConfig",
     "lasr": "LasrCTCConfig",
@@ -2732,7 +2737,9 @@ def get_model_name(obj):
             model_name_lowercase_from_file = file_name[len(start) : -len(end)]
             break
     if model_name_lowercase_from_file and model_name_lowercase_from_folder != model_name_lowercase_from_file:
-        from transformers.models.auto.configuration_auto import SPECIAL_MODEL_TYPE_TO_MODULE_NAME
+        from transformers.models.auto.configuration_auto import (
+            SPECIAL_MODEL_TYPE_TO_MODULE_NAME,
+        )
 
         if (
             model_name_lowercase_from_file in SPECIAL_MODEL_TYPE_TO_MODULE_NAME
@@ -3242,7 +3249,14 @@ def _get_parameter_info(param_name, documented_params, source_args_dict, param_t
         # Parameter is not documented
         is_documented = False
 
-    return param_type, optional_string, shape_string, additional_info, description, is_documented
+    return (
+        param_type,
+        optional_string,
+        shape_string,
+        additional_info,
+        description,
+        is_documented,
+    )
 
 
 def _process_regular_parameters(
@@ -3307,9 +3321,14 @@ def _process_regular_parameters(
         if param.default != inspect._empty and param.default is not None:
             param_default = f", defaults to `{str(param.default)}`"
 
-        param_type, optional_string, shape_string, additional_info, description, is_documented = _get_parameter_info(
-            param_name, documented_params, source_args_dict, param_type, optional
-        )
+        (
+            param_type,
+            optional_string,
+            shape_string,
+            additional_info,
+            description,
+            is_documented,
+        ) = _get_parameter_info(param_name, documented_params, source_args_dict, param_type, optional)
 
         if is_documented:
             if param_name == "config":
@@ -3336,7 +3355,7 @@ def _process_regular_parameters(
                 "type": param_type if param_type else "<fill_type>",
                 "optional": optional,
                 "shape": shape_string,
-                "description": description if description else "\n    <fill_description>",
+                "description": (description if description else "\n    <fill_description>"),
                 "default": param_default,
             }
             # Try to get the correct source file; for classes decorated with @strict (huggingface_hub),
@@ -3629,7 +3648,10 @@ def _process_kwargs_parameters(sig, func, parent_class, documented_kwargs, inden
                             continue
 
                         # Process each field in the custom typed kwargs
-                        for nested_param_name, nested_param_type in actual_type.__annotations__.items():
+                        for (
+                            nested_param_name,
+                            nested_param_type,
+                        ) in actual_type.__annotations__.items():
                             # Only document parameters that are explicitly documented in the TypedDict's docstring
                             if nested_param_name not in documented_nested_kwargs:
                                 continue
@@ -3699,8 +3721,19 @@ def _process_kwargs_parameters(sig, func, parent_class, documented_kwargs, inden
                     param_default = str(getattr(parent_class, param_name, ""))
                     param_default = f", defaults to `{param_default}`" if param_default != "" else ""
 
-                param_type, optional_string, shape_string, additional_info, description, is_documented = (
-                    _get_parameter_info(param_name, documented_kwargs, source_args_dict, param_type, optional)
+                (
+                    param_type,
+                    optional_string,
+                    shape_string,
+                    additional_info,
+                    description,
+                    is_documented,
+                ) = _get_parameter_info(
+                    param_name,
+                    documented_kwargs,
+                    source_args_dict,
+                    param_type,
+                    optional,
                 )
 
                 if is_documented:
@@ -3836,7 +3869,12 @@ def _process_parameters_section(
 
     # Process **kwargs parameters if needed
     kwargs_docstring, kwargs_summary = _process_kwargs_parameters(
-        sig, func, parent_class, documented_kwargs, indent_level, undocumented_parameters
+        sig,
+        func,
+        parent_class,
+        documented_kwargs,
+        indent_level,
+        undocumented_parameters,
     )
     docstring += kwargs_docstring
 
@@ -4001,7 +4039,14 @@ def _process_returns_section(func_documentation, sig, config_class, indent_level
 
 
 def _process_example_section(
-    func_documentation, func, parent_class, class_name, model_name_lowercase, config_class, checkpoint, indent_level
+    func_documentation,
+    func,
+    parent_class,
+    class_name,
+    model_name_lowercase,
+    config_class,
+    checkpoint,
+    indent_level,
 ):
     """
     Process the example section of the docstring.
@@ -4186,7 +4231,10 @@ def auto_class_docstring(cls, custom_intro=None, custom_args=None, checkpoint=No
     docstring_args = ""
     if "PreTrainedModel" in (x.__name__ for x in cls.__mro__):
         docstring_init = auto_method_docstring(
-            cls.__init__, parent_class=cls, custom_args=custom_args, checkpoint=checkpoint
+            cls.__init__,
+            parent_class=cls,
+            custom_args=custom_args,
+            checkpoint=checkpoint,
         ).__doc__.replace("Args:", "Parameters:")
     elif "ProcessorMixin" in (x.__name__ for x in cls.__mro__):
         is_processor = True
@@ -4320,8 +4368,19 @@ def auto_class_docstring(cls, custom_intro=None, custom_args=None, checkpoint=No
                 param_default = str(getattr(cls, param_name, ""))
                 param_default = f", defaults to `{param_default}`" if param_default != "" else ""
 
-                param_type, optional_string, shape_string, additional_info, description, is_documented = (
-                    _get_parameter_info(param_name, documented_kwargs, source_args_dict, param_type, optional)
+                (
+                    param_type,
+                    optional_string,
+                    shape_string,
+                    additional_info,
+                    description,
+                    is_documented,
+                ) = _get_parameter_info(
+                    param_name,
+                    documented_kwargs,
+                    source_args_dict,
+                    param_type,
+                    optional,
                 )
 
                 if is_documented:
@@ -4504,10 +4563,18 @@ class MyModelOutput(ImageClassifierOutput):
     def auto_docstring_decorator(obj):
         if len(obj.__qualname__.split(".")) > 1:
             return auto_method_docstring(
-                obj, custom_args=custom_args, custom_intro=custom_intro, checkpoint=checkpoint
+                obj,
+                custom_args=custom_args,
+                custom_intro=custom_intro,
+                checkpoint=checkpoint,
             )
         else:
-            return auto_class_docstring(obj, custom_args=custom_args, custom_intro=custom_intro, checkpoint=checkpoint)
+            return auto_class_docstring(
+                obj,
+                custom_args=custom_args,
+                custom_intro=custom_intro,
+                checkpoint=checkpoint,
+            )
 
     if obj:
         return auto_docstring_decorator(obj)
diff --git a/src/transformers/utils/chat_template_utils.py b/src/transformers/utils/chat_template_utils.py
index 6b23e2fa881d..b9e37dfb1280 100644
--- a/src/transformers/utils/chat_template_utils.py
+++ b/src/transformers/utils/chat_template_utils.py
@@ -243,7 +243,7 @@ def parse_google_format_docstring(docstring: str) -> tuple[str | None, dict | No
     return description, args_dict, returns
 
 
-def get_json_schema(func: Callable) -> dict:
+def get_json_schema(func: Callable, style: str = "openai") -> dict:
     """
     This function generates a JSON schema for a given function, based on its docstring and type hints. This is
     mostly used for passing lists of tools to a chat template. The JSON schema contains the name and description of
@@ -257,9 +257,14 @@ def get_json_schema(func: Callable) -> dict:
 
     Args:
         func: The function to generate a JSON schema for.
+        style: The style of the JSON schema to generate. Can be "openai" (default) or "anthropic".
+            - "openai": Returns schema wrapped in {"type": "function", "function": {...}} format
+            - "anthropic": Returns schema in {"name": "...", "description": "...", "input_schema": {...}} format
 
     Returns:
-        A dictionary containing the JSON schema for the function.
+        A dictionary containing the JSON schema for the function. The format depends on the style parameter:
+        - For "openai" style: {"type": "function", "function": {"name": "...", "description": "...", "parameters": {...}}}
+        - For "anthropic" style: {"name": "...", "description": "...", "input_schema": {...}}
 
     Examples:
     ```python
@@ -375,10 +380,15 @@ def get_json_schema(func: Callable) -> dict:
             desc = enum_choices.string[: enum_choices.start()].strip()
         schema["description"] = desc
 
-    output = {"name": func_name, "description": main_doc, "parameters": json_schema}
-    if return_dict is not None:
-        output["return"] = return_dict
-    return {"type": "function", "function": output}
+    if style == "anthropic":
+        # Anthropic style uses 'input_schema' instead of 'parameters' and doesn't wrap in "function" key
+        return {"name": func_name, "description": main_doc, "input_schema": json_schema}
+    else:
+        # OpenAI style
+        output = {"name": func_name, "description": main_doc, "parameters": json_schema}
+        if return_dict is not None:
+            output["return"] = return_dict
+        return {"type": "function", "function": output}
 
 
 @lru_cache
@@ -483,6 +493,17 @@ def tojson(x, ensure_ascii=False, indent=None, separators=None, sort_keys=False)
         # We also expose some options like custom indents and separators
         return json.dumps(x, ensure_ascii=ensure_ascii, indent=indent, separators=separators, sort_keys=sort_keys)
 
+    def fromjson(x):
+        # Parse a JSON string into a Python object
+        # This is useful for parsing tool call arguments from JSON strings
+        if isinstance(x, str):
+            try:
+                return json.loads(x)
+            except (json.JSONDecodeError, TypeError):
+                # If parsing fails, return the original string
+                return x
+        return x
+
     def strftime_now(format):
         return datetime.now().strftime(format)
 
@@ -490,6 +511,7 @@ def strftime_now(format):
         trim_blocks=True, lstrip_blocks=True, extensions=[AssistantTracker, jinja2.ext.loopcontrols]
     )
     jinja_env.filters["tojson"] = tojson
+    jinja_env.filters["fromjson"] = fromjson
     jinja_env.globals["raise_exception"] = raise_exception
     jinja_env.globals["strftime_now"] = strftime_now
     return jinja_env.from_string(chat_template)
diff --git a/src/transformers/utils/generic.py b/src/transformers/utils/generic.py
index c6b5960f0849..9faa4a302166 100644
--- a/src/transformers/utils/generic.py
+++ b/src/transformers/utils/generic.py
@@ -17,6 +17,7 @@
 
 from __future__ import annotations
 
+import importlib
 import inspect
 import json
 import os
@@ -29,11 +30,9 @@
 from contextlib import AbstractContextManager, ExitStack, nullcontext
 from dataclasses import fields, is_dataclass
 from enum import Enum
-from functools import partial, wraps
+from functools import lru_cache, partial, wraps
 from typing import TYPE_CHECKING, Any, TypedDict
 
-import numpy as np
-
 from ..utils import logging
 from .import_utils import is_mlx_available, is_torch_available, is_torch_fx_proxy
 
@@ -53,6 +52,11 @@
 _registered_model_output_types: set[type[Any]] = set()
 
 
+@lru_cache
+def _get_numpy():
+    return importlib.import_module("numpy")
+
+
 def _register_model_output_pytree_node(output_type: type[ModelOutput]) -> None:
     if not _is_torch_available:
         return
@@ -152,7 +156,7 @@ def is_numpy_array(x) -> bool:
     """
     Tests if `x` is a numpy array or not.
     """
-    return isinstance(x, np.ndarray)
+    return isinstance(x, _get_numpy().ndarray)
 
 
 def is_torch_tensor(x) -> bool:
@@ -200,11 +204,12 @@ def _is_tensor_or_array_like(value):
     """
     Check if a value is array-like (includes ragged arrays)
     """
+    numpy = _get_numpy()
     if is_numpy_array(value):
         return True
     if is_torch_tensor(value):
         return True
-    if isinstance(value, (int, float, bool, np.number)):
+    if isinstance(value, (int, float, bool, numpy.number)):
         return True
 
     if isinstance(value, (list, tuple)):
@@ -311,13 +316,14 @@ def to_py_obj(obj):
     """
     Convert a PyTorch tensor, Numpy array or python list to a python list.
     """
+    numpy = _get_numpy()
     if isinstance(obj, (int, float)):
         return obj
     elif isinstance(obj, (dict, UserDict)):
         return {k: to_py_obj(v) for k, v in obj.items()}
     elif isinstance(obj, (list, tuple)):
         # Only convert directly if all elements are numeric scalars
-        if all(isinstance(x, (int, float, np.number)) for x in obj):
+        if all(isinstance(x, (int, float, numpy.number)) for x in obj):
             return list(obj)
 
         # Otherwise recurse element-wise
@@ -335,7 +341,7 @@ def to_py_obj(obj):
             return framework_to_py_obj[framework](obj)
 
     # tolist also works on 0d np arrays
-    if isinstance(obj, np.number):
+    if isinstance(obj, numpy.number):
         return obj.tolist()
     else:
         return obj
@@ -345,6 +351,7 @@ def to_numpy(obj):
     """
     Convert a PyTorch tensor, Numpy array or python list to a Numpy array.
     """
+    numpy = _get_numpy()
 
     framework_to_numpy = {
         "pt": lambda obj: obj.detach().cpu().numpy(),
@@ -354,7 +361,7 @@ def to_numpy(obj):
     if isinstance(obj, (dict, UserDict)):
         return {k: to_numpy(v) for k, v in obj.items()}
     elif isinstance(obj, (list, tuple)):
-        return np.array(obj)
+        return numpy.array(obj)
 
     # This gives us a smart order to test the frameworks with the corresponding tests.
     framework_to_test_func = _get_frameworks_and_test_func(obj)
@@ -631,7 +638,7 @@ def transpose(array, axes=None):
     Framework-agnostic version of transpose operation.
     """
     if is_numpy_array(array):
-        return np.transpose(array, axes=axes)
+        return _get_numpy().transpose(array, axes=axes)
     elif is_torch_tensor(array):
         return array.T if axes is None else array.permute(*axes)
     else:
@@ -643,7 +650,7 @@ def reshape(array, newshape):
     Framework-agnostic version of reshape operation.
     """
     if is_numpy_array(array):
-        return np.reshape(array, newshape)
+        return _get_numpy().reshape(array, newshape)
     elif is_torch_tensor(array):
         return array.reshape(*newshape)
     else:
@@ -655,7 +662,7 @@ def squeeze(array, axis=None):
     Framework-agnostic version of squeeze operation.
     """
     if is_numpy_array(array):
-        return np.squeeze(array, axis=axis)
+        return _get_numpy().squeeze(array, axis=axis)
     elif is_torch_tensor(array):
         return array.squeeze() if axis is None else array.squeeze(dim=axis)
     else:
@@ -667,7 +674,7 @@ def expand_dims(array, axis):
     Framework-agnostic version of expand_dims operation.
     """
     if is_numpy_array(array):
-        return np.expand_dims(array, axis)
+        return _get_numpy().expand_dims(array, axis)
     elif is_torch_tensor(array):
         return array.unsqueeze(dim=axis)
     else:
@@ -679,7 +686,7 @@ def tensor_size(array):
     Framework-agnostic version of size operation.
     """
     if is_numpy_array(array):
-        return np.size(array)
+        return _get_numpy().size(array)
     elif is_torch_tensor(array):
         return array.numel()
     else:
@@ -703,11 +710,11 @@ def torch_float(x):
     Casts an input to a torch float32 tensor if we are in a tracing context, otherwise to a Python float.
     """
     if not _is_torch_available:
-        return int(x)
+        return float(x)
 
     import torch
 
-    return x.to(torch.float32) if torch.jit.is_tracing() and isinstance(x, torch.Tensor) else int(x)
+    return x.to(torch.float32) if torch.jit.is_tracing() and isinstance(x, torch.Tensor) else float(x)
 
 
 def filter_out_non_signature_kwargs(extra: list | None = None):
@@ -897,7 +904,7 @@ def wrapper(self, *args, **kwargs):
         return_dict_passed = kwargs.pop("return_dict", return_dict)
         if return_dict_passed is not None:
             return_dict = return_dict_passed
-        output = func(self, *args, **kwargs)
+        output = func(self, *args, **kwargs, return_dict=True)
         if not return_dict and not isinstance(output, tuple):
             output = output.to_tuple()
         return output
diff --git a/src/transformers/utils/hub.py b/src/transformers/utils/hub.py
index 616796e4fe22..a1ece6ad4a4d 100644
--- a/src/transformers/utils/hub.py
+++ b/src/transformers/utils/hub.py
@@ -15,46 +15,19 @@
 Hub utilities: utilities related to download and cache models
 """
 
+import importlib
 import json
 import os
 import re
 import sys
 import tempfile
 from concurrent import futures
+from functools import lru_cache
 from pathlib import Path
-from typing import TypedDict
+from typing import TYPE_CHECKING, TypedDict
 from uuid import uuid4
 
-import httpx
-from huggingface_hub import (
-    _CACHED_NO_EXIST,
-    CommitOperationAdd,
-    ModelCard,
-    ModelCardData,
-    constants,
-    create_branch,
-    create_commit,
-    create_repo,
-    hf_hub_download,
-    hf_hub_url,
-    is_offline_mode,
-    list_repo_tree,
-    snapshot_download,
-    try_to_load_from_cache,
-)
-from huggingface_hub.file_download import REGEX_COMMIT_HASH
-from huggingface_hub.utils import (
-    EntryNotFoundError,
-    GatedRepoError,
-    HfHubHTTPError,
-    LocalEntryNotFoundError,
-    OfflineModeIsEnabled,
-    RepositoryNotFoundError,
-    RevisionNotFoundError,
-    build_hf_headers,
-    get_session,
-    hf_raise_for_status,
-)
+from huggingface_hub import constants
 
 from . import __version__, logging
 from .import_utils import (
@@ -65,6 +38,10 @@
 )
 
 
+if TYPE_CHECKING:
+    from huggingface_hub import ModelCard
+
+
 LEGACY_PROCESSOR_CHAT_TEMPLATE_FILE = "chat_template.json"
 CHAT_TEMPLATE_FILE = "chat_template.jinja"
 CHAT_TEMPLATE_DIR = "additional_chat_templates"
@@ -97,6 +74,21 @@ class DownloadKwargs(TypedDict, total=False):
 CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
 
 
+@lru_cache
+def _get_httpx_module():
+    return importlib.import_module("httpx")
+
+
+@lru_cache
+def _get_hf_hub_file_download_module():
+    return importlib.import_module("huggingface_hub.file_download")
+
+
+@lru_cache
+def _get_hf_hub_errors_module():
+    return importlib.import_module("huggingface_hub.errors")
+
+
 def _get_cache_file_to_return(
     path_or_repo_id: str,
     full_filename: str,
@@ -105,14 +97,19 @@ def _get_cache_file_to_return(
     repo_type: str | None = None,
 ):
     # We try to see if we have a cached version (not up to date):
-    resolved_file = try_to_load_from_cache(
+    file_download = _get_hf_hub_file_download_module()
+    resolved_file = file_download.try_to_load_from_cache(
         path_or_repo_id, full_filename, cache_dir=cache_dir, revision=revision, repo_type=repo_type
     )
-    if resolved_file is not None and resolved_file != _CACHED_NO_EXIST:
+    if resolved_file is not None and resolved_file != file_download._CACHED_NO_EXIST:
         return resolved_file
     return None
 
 
+def try_to_load_from_cache(*args, **kwargs):
+    return _get_hf_hub_file_download_module().try_to_load_from_cache(*args, **kwargs)
+
+
 def list_repo_templates(
     repo_id: str,
     *,
@@ -126,6 +123,10 @@ def list_repo_templates(
     A template is a jinja file located under the `additional_chat_templates/` folder.
     If working in offline mode or if internet is down, the method will list jinja template from the local cache - if any.
     """
+    httpx = _get_httpx_module()
+    hf_hub_errors = _get_hf_hub_errors_module()
+    from huggingface_hub import list_repo_tree
+    from huggingface_hub._snapshot_download import snapshot_download
 
     if not local_files_only:
         try:
@@ -140,9 +141,13 @@ def list_repo_templates(
                 )
                 if entry.path.endswith(".jinja")
             ]
-        except (GatedRepoError, RepositoryNotFoundError, RevisionNotFoundError):
+        except (
+            hf_hub_errors.GatedRepoError,
+            hf_hub_errors.RepositoryNotFoundError,
+            hf_hub_errors.RevisionNotFoundError,
+        ):
             raise  # valid errors => do not catch
-        except (HfHubHTTPError, OfflineModeIsEnabled, httpx.NetworkError):
+        except (hf_hub_errors.HfHubHTTPError, hf_hub_errors.OfflineModeIsEnabled, httpx.NetworkError):
             pass  # offline mode, internet down, etc. => try local files
 
     # check local files
@@ -150,7 +155,7 @@ def list_repo_templates(
         snapshot_dir = snapshot_download(
             repo_id=repo_id, revision=revision, cache_dir=cache_dir, local_files_only=True
         )
-    except LocalEntryNotFoundError:  # No local repo means no local files
+    except hf_hub_errors.LocalEntryNotFoundError:  # No local repo means no local files
         return []
     templates_dir = Path(snapshot_dir, CHAT_TEMPLATE_DIR)
     if not templates_dir.is_dir():
@@ -159,6 +164,8 @@ def list_repo_templates(
 
 
 def define_sagemaker_information():
+    httpx = _get_httpx_module()
+
     try:
         instance_data = httpx.get(os.environ["ECS_CONTAINER_METADATA_URI"]).json()
         dlc_container_used = instance_data["Image"]
@@ -217,7 +224,7 @@ def extract_commit_hash(resolved_file: str | None, commit_hash: str | None) -> s
     if search is None:
         return None
     commit_hash = search.groups()[0]
-    return commit_hash if REGEX_COMMIT_HASH.match(commit_hash) else None
+    return commit_hash if _get_hf_hub_file_download_module().REGEX_COMMIT_HASH.match(commit_hash) else None
 
 
 def cached_file(
@@ -360,7 +367,9 @@ def cached_files(
     model_weights_file = cached_file("google-bert/bert-base-uncased", "pytorch_model.bin")
     ```
     """
-    if is_offline_mode() and not local_files_only:
+    hf_hub_errors = _get_hf_hub_errors_module()
+
+    if constants.HF_HUB_OFFLINE and not local_files_only:
         logger.info("Offline mode: forcing local_files_only=True")
         local_files_only = True
     if subfolder is None:
@@ -396,13 +405,14 @@ def cached_files(
     existing_files = []
     file_counter = 0
     if _commit_hash is not None and not force_download:
+        file_download = _get_hf_hub_file_download_module()
         for filename in full_filenames:
             # If the file is cached under that commit hash, we return it directly.
-            resolved_file = try_to_load_from_cache(
+            resolved_file = file_download.try_to_load_from_cache(
                 path_or_repo_id, filename, cache_dir=cache_dir, revision=_commit_hash, repo_type=repo_type
             )
             if resolved_file is not None:
-                if resolved_file is not _CACHED_NO_EXIST:
+                if resolved_file is not file_download._CACHED_NO_EXIST:
                     file_counter += 1
                     existing_files.append(resolved_file)
                 elif not _raise_exceptions_for_missing_entries:
@@ -419,7 +429,7 @@ def cached_files(
     try:
         if len(full_filenames) == 1:
             # This is slightly better for only 1 file
-            hf_hub_download(
+            _get_hf_hub_file_download_module().hf_hub_download(
                 path_or_repo_id,
                 filenames[0],
                 subfolder=None if len(subfolder) == 0 else subfolder,
@@ -434,6 +444,8 @@ def cached_files(
                 tqdm_class=tqdm_class,
             )
         else:
+            from huggingface_hub._snapshot_download import snapshot_download
+
             snapshot_download(
                 path_or_repo_id,
                 allow_patterns=full_filenames,
@@ -450,14 +462,14 @@ def cached_files(
 
     except Exception as e:
         # We cannot recover from them
-        if isinstance(e, RepositoryNotFoundError) and not isinstance(e, GatedRepoError):
+        if isinstance(e, hf_hub_errors.RepositoryNotFoundError) and not isinstance(e, hf_hub_errors.GatedRepoError):
             raise OSError(
                 f"{path_or_repo_id} is not a local folder and is not a valid model identifier "
                 "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a token "
                 "having permission to this repo either by logging in with `hf auth login` or by passing "
                 "`token=<your_token>`"
             ) from e
-        elif isinstance(e, RevisionNotFoundError):
+        elif isinstance(e, hf_hub_errors.RevisionNotFoundError):
             raise OSError(
                 f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists "
                 "for this model name. Check the model page at "
@@ -482,14 +494,14 @@ def cached_files(
 
         # Raise based on the flags. Note that we will raise for missing entries at the very end, even when
         # not entering this Except block, as it may also happen when `snapshot_download` does not raise
-        if isinstance(e, GatedRepoError):
+        if isinstance(e, hf_hub_errors.GatedRepoError):
             if not _raise_exceptions_for_gated_repo:
                 return None
             raise OSError(
                 "You are trying to access a gated repo.\nMake sure to have access to it at "
                 f"https://huggingface.co/{path_or_repo_id}.\n{str(e)}"
             ) from e
-        elif isinstance(e, LocalEntryNotFoundError):
+        elif isinstance(e, hf_hub_errors.LocalEntryNotFoundError):
             if not _raise_exceptions_for_connection_errors:
                 return None
             # Here we only raise if both flags for missing entry and connection errors are True (because it can be raised
@@ -502,13 +514,13 @@ def cached_files(
                 ) from e
         # snapshot_download will not raise EntryNotFoundError, but hf_hub_download can. If this is the case, it will be treated
         # later on anyway and re-raised if needed
-        elif isinstance(e, HfHubHTTPError) and not isinstance(e, EntryNotFoundError):
+        elif isinstance(e, hf_hub_errors.HfHubHTTPError) and not isinstance(e, hf_hub_errors.EntryNotFoundError):
             if not _raise_exceptions_for_connection_errors:
                 return None
             raise OSError(f"There was a specific connection error when trying to load {path_or_repo_id}:\n{e}") from e
         # Any other Exception type should now be re-raised, in order to provide helpful error messages and break the execution flow
         # (EntryNotFoundError will be treated outside this block and correctly re-raised if needed)
-        elif not isinstance(e, EntryNotFoundError):
+        elif not isinstance(e, hf_hub_errors.EntryNotFoundError):
             raise e
 
     resolved_files = [
@@ -562,6 +574,10 @@ def has_file(
 
     </Tip>
     """
+    httpx = _get_httpx_module()
+    hf_hub_errors = _get_hf_hub_errors_module()
+    from huggingface_hub.utils import build_hf_headers, get_session, hf_raise_for_status
+
     # If path to local directory, check if the file exists
     if os.path.isdir(path_or_repo):
         return os.path.isfile(os.path.join(path_or_repo, filename))
@@ -570,7 +586,7 @@ def has_file(
 
     # Check if file exists in cache
     # This information might be outdated so it's best to also make a HEAD call (if allowed).
-    cached_path = try_to_load_from_cache(
+    cached_path = _get_hf_hub_file_download_module().try_to_load_from_cache(
         repo_id=path_or_repo,
         filename=filename,
         revision=revision,
@@ -586,7 +602,9 @@ def has_file(
     # Check if the file exists
     try:
         response = get_session().head(
-            hf_hub_url(path_or_repo, filename=filename, revision=revision, repo_type=repo_type),
+            _get_hf_hub_file_download_module().hf_hub_url(
+                path_or_repo, filename=filename, revision=revision, repo_type=repo_type
+            ),
             headers=build_hf_headers(token=token, user_agent=http_user_agent()),
             follow_redirects=False,
             timeout=10,
@@ -594,31 +612,31 @@ def has_file(
     except httpx.ProxyError:
         # Actually raise for those subclasses of ConnectionError
         raise
-    except (httpx.ConnectError, httpx.TimeoutException, OfflineModeIsEnabled):
+    except (httpx.ConnectError, httpx.TimeoutException, hf_hub_errors.OfflineModeIsEnabled):
         return has_file_in_cache
 
     try:
         hf_raise_for_status(response)
         return True
-    except GatedRepoError as e:
+    except hf_hub_errors.GatedRepoError as e:
         logger.error(e)
         raise OSError(
             f"{path_or_repo} is a gated repository. Make sure to request access at "
             f"https://huggingface.co/{path_or_repo} and pass a token having permission to this repo either by "
             "logging in with `hf auth login` or by passing `token=<your_token>`."
         ) from e
-    except RepositoryNotFoundError as e:
+    except hf_hub_errors.RepositoryNotFoundError as e:
         logger.error(e)
         raise OSError(f"{path_or_repo} is not a local folder or a valid repository name on 'https://hf.co'.") from e
-    except RevisionNotFoundError as e:
+    except hf_hub_errors.RevisionNotFoundError as e:
         logger.error(e)
         raise OSError(
             f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for this "
             f"model name. Check the model page at 'https://huggingface.co/{path_or_repo}' for available revisions."
         ) from e
-    except EntryNotFoundError:
+    except hf_hub_errors.EntryNotFoundError:
         return False  # File does not exist
-    except HfHubHTTPError:
+    except hf_hub_errors.HfHubHTTPError:
         # Any authentication/authorization error will be caught here => default to cache
         return has_file_in_cache
 
@@ -648,6 +666,9 @@ def _upload_modified_files(
         """
         Uploads all modified files in `working_dir` to `repo_id`, based on `files_timestamps`.
         """
+        hf_hub_errors = _get_hf_hub_errors_module()
+        from huggingface_hub import CommitOperationAdd, create_branch, create_commit
+
         if commit_message is None:
             if "Model" in self.__class__.__name__:
                 commit_message = "Upload model"
@@ -693,7 +714,7 @@ def _upload_modified_files(
         if revision is not None and not revision.startswith("refs/pr"):
             try:
                 create_branch(repo_id=repo_id, branch=revision, token=token, exist_ok=True)
-            except HfHubHTTPError as e:
+            except hf_hub_errors.HfHubHTTPError as e:
                 if e.response.status_code == 403 and create_pr:
                     # If we are creating a PR on a repo we don't have access to, we can't create the branch.
                     # so let's assume the branch already exists. If it's not the case, an error will be raised when
@@ -774,6 +795,8 @@ def push_to_hub(
         {object}.push_to_hub("huggingface/my-finetuned-bert")
         ```
         """
+        from huggingface_hub import create_repo
+
         # Create repo if it doesn't exist yet
         repo_id = create_repo(repo_id, private=private, token=token, exist_ok=True).repo_id
 
@@ -833,6 +856,68 @@ def convert_file_size_to_int(size: int | str):
     raise ValueError("`size` is not in a valid format. Use an integer followed by the unit, e.g., '5GB'.")
 
 
+def _rebuild_shard_index_from_repo(
+    pretrained_model_name_or_path,
+    cache_dir=None,
+    force_download=False,
+    proxies=None,
+    local_files_only=False,
+    token=None,
+    user_agent=None,
+    revision=None,
+    subfolder="",
+    _commit_hash=None,
+):
+    """
+    When the shard index references files that don't exist (e.g. MLX repos that
+    copied the index from the original model), discover the actual safetensors
+    files on the Hub, download them, and rebuild the weight_map from their headers.
+    """
+    import struct
+
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    all_files = api.list_repo_files(pretrained_model_name_or_path, revision=revision, token=token)
+    shard_names = sorted(f for f in all_files if f.endswith(".safetensors") and f != "model.safetensors.index.json")
+
+    if not shard_names:
+        raise OSError(
+            f"No .safetensors files found in repo '{pretrained_model_name_or_path}'. Cannot rebuild shard index."
+        )
+
+    # Download the actual shard files
+    cached_filenames = cached_files(
+        pretrained_model_name_or_path,
+        shard_names,
+        cache_dir=cache_dir,
+        force_download=force_download,
+        proxies=proxies,
+        local_files_only=local_files_only,
+        token=token,
+        user_agent=user_agent,
+        revision=revision,
+        subfolder=subfolder,
+        _commit_hash=_commit_hash,
+    )
+
+    # Rebuild weight_map by reading safetensors headers
+    weight_map = {}
+    all_keys = []
+    for cached_path, shard_name in zip(cached_filenames, shard_names):
+        with open(cached_path, "rb") as f:
+            header_size = struct.unpack("<Q", f.read(8))[0]
+            header = json.loads(f.read(header_size))
+        for key in header:
+            if key == "__metadata__":
+                continue
+            weight_map[key] = shard_name
+            all_keys.append(key)
+
+    sharded_metadata = {"all_checkpoint_keys": all_keys, "weight_map": weight_map}
+    return cached_filenames, sharded_metadata
+
+
 def get_checkpoint_shard_files(
     pretrained_model_name_or_path,
     index_filename,
@@ -876,25 +961,42 @@ def get_checkpoint_shard_files(
 
     # At this stage pretrained_model_name_or_path is a model identifier on the Hub. Try to get everything from cache,
     # or download the files
-    cached_filenames = cached_files(
-        pretrained_model_name_or_path,
-        shard_filenames,
-        cache_dir=cache_dir,
-        force_download=force_download,
-        proxies=proxies,
-        local_files_only=local_files_only,
-        token=token,
-        user_agent=user_agent,
-        revision=revision,
-        subfolder=subfolder,
-        _commit_hash=_commit_hash,
-        tqdm_class=tqdm_class,
-    )
+    try:
+        cached_filenames = cached_files(
+            pretrained_model_name_or_path,
+            shard_filenames,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            user_agent=user_agent,
+            revision=revision,
+            subfolder=subfolder,
+            _commit_hash=_commit_hash,
+            tqdm_class=tqdm_class,
+        )
+    except OSError:
+        # The shard index may reference files that don't exist in this repo
+        # (e.g. MLX repos that copy the index from the original model).
+        # Fall back to discovering the actual safetensors files on the Hub.
+        cached_filenames, sharded_metadata = _rebuild_shard_index_from_repo(
+            pretrained_model_name_or_path,
+            cache_dir=cache_dir,
+            force_download=force_download,
+            proxies=proxies,
+            local_files_only=local_files_only,
+            token=token,
+            user_agent=user_agent,
+            revision=revision,
+            subfolder=subfolder,
+            _commit_hash=_commit_hash,
+        )
 
     return cached_filenames, sharded_metadata
 
 
-def create_and_tag_model_card(repo_id: str, tags: list[str] | None = None, token: str | None = None) -> ModelCard:
+def create_and_tag_model_card(repo_id: str, tags: list[str] | None = None, token: str | None = None) -> "ModelCard":
     """
     Creates or loads an existing model card and tags it.
 
@@ -906,10 +1008,13 @@ def create_and_tag_model_card(repo_id: str, tags: list[str] | None = None, token
         token (`str`, *optional*):
             Authentication token, obtained with `huggingface_hub.HfApi.login` method. Will default to the stored token.
     """
+    hf_hub_errors = _get_hf_hub_errors_module()
+    from huggingface_hub import ModelCard, ModelCardData
+
     try:
         # Check if the model card is present on the remote repo
         model_card = ModelCard.load(repo_id, token=token)
-    except EntryNotFoundError:
+    except hf_hub_errors.EntryNotFoundError:
         # Otherwise create a simple model card from template
         model_description = "This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated."
         card_data = ModelCardData(tags=[] if tags is None else tags, library_name="transformers")
diff --git a/src/transformers/utils/import_utils.py b/src/transformers/utils/import_utils.py
index de11d23cbecf..e9f0b55d14ea 100644
--- a/src/transformers/utils/import_utils.py
+++ b/src/transformers/utils/import_utils.py
@@ -68,10 +68,13 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> tuple[
                 distribution_name = distributions[0]
             package_version = importlib.metadata.version(distribution_name)
         except (importlib.metadata.PackageNotFoundError, KeyError):
-            # If we cannot find the metadata (because of editable install for example), try to import directly.
-            # Note that this branch will almost never be run, so we do not import packages for nothing here
-            package = importlib.import_module(pkg_name)
-            package_version = getattr(package, "__version__", "N/A")
+            try:
+                package_version = importlib.metadata.version(pkg_name)
+            except importlib.metadata.PackageNotFoundError:
+                # If we cannot find the metadata (because of editable install for example), try to import directly.
+                # Note that this branch will almost never be run, so we do not import packages for nothing here
+                package = importlib.import_module(pkg_name)
+                package_version = getattr(package, "__version__", "N/A")
         logger.debug(f"Detected {pkg_name} version: {package_version}")
 
     if return_version:
@@ -80,6 +83,17 @@ def _is_package_available(pkg_name: str, return_version: bool = False) -> tuple[
         return package_exists, None
 
 
+def _version_meets_minimum(is_available: bool, package_version: str, min_version: str) -> bool:
+    if not is_available:
+        return False
+
+    try:
+        return version.parse(package_version) >= version.parse(min_version)
+    except version.InvalidVersion:
+        logger.debug(f"Skipping optional package with invalid version string: {package_version!r}")
+        return False
+
+
 def resolve_internal_import(module: ModuleType | None, chained_path: str) -> Callable | ModuleType | None:
     """
     Check if a given `module` has an internal import path as defined by the `chained_path`.
@@ -147,7 +161,7 @@ def is_torch_available() -> bool:
         parsed_version = version.parse(torch_version)
         if is_available and parsed_version < version.parse("2.4.0"):
             logger.warning_once(f"Disabling PyTorch because PyTorch >= 2.4 is required but found {torch_version}")
-        return is_available and version.parse(torch_version) >= version.parse("2.4.0")
+        return _version_meets_minimum(is_available, torch_version, "2.4.0")
     except packaging.version.InvalidVersion:
         return False
 
@@ -190,6 +204,40 @@ def is_torch_less_or_equal(library_version: str, accept_dev: bool = False) -> bo
         return version.parse(get_torch_version()) <= version.parse(library_version)
 
 
+@lru_cache
+def is_torch_fx_available() -> bool:
+    """
+    Backwards-compatibility shim for remote code that still imports this symbol
+    from `transformers.utils.import_utils`.
+
+    In Transformers v5+, we require PyTorch >= 2.4 where `torch.fx` is always
+    available. This function therefore simply checks that PyTorch itself is
+    available and returns True in that case.
+
+    This API is deprecated and will be removed in a future major release.
+    Remote code should stop relying on it and instead assume `torch.fx` is
+    available under the supported PyTorch versions.
+    """
+    warnings.warn(
+        "`is_torch_fx_available` is deprecated and kept only for backwards "
+        "compatibility with older `trust_remote_code` models. It now simply "
+        "checks for the presence of PyTorch >= 2.4 and always returns True "
+        "in that case.",
+        DeprecationWarning,
+        stacklevel=2,
+    )
+
+    if not is_torch_available():
+        return False
+
+    try:
+        import torch.fx  # noqa: F401
+    except Exception:
+        return False
+
+    return True
+
+
 @lru_cache
 def is_torch_accelerator_available() -> bool:
     if is_torch_available():
@@ -222,14 +270,28 @@ def is_cuda_platform() -> bool:
 def get_cuda_runtime_version() -> tuple[int, int]:
     """Return the CUDA runtime version as (major, minor).
 
-    Unlike ``torch.version.cuda`` which reports the compile-time version,
-    this queries ``cudaRuntimeGetVersion`` from ``libcudart.so`` to get the
-    actual runtime version installed on the system.
+    Prefers a direct query of ``cudaRuntimeGetVersion`` via ``libcudart.so``. If that's
+    not on the system loader path (common with pip-installed torch that bundles its own
+    CUDA runtime), falls back to ``torch.version.cuda`` — which equals the bundled
+    runtime's version for pip wheels. Returns ``(0, 0)`` for CPU-only torch.
     """
     import ctypes
 
+    try:
+        cudart = ctypes.CDLL("libcudart.so")
+    except OSError:
+        if not is_torch_available():
+            return 0, 0
+        import torch
+
+        cuda_version = getattr(torch.version, "cuda", None)
+        if cuda_version is None:
+            return 0, 0
+
+        major, minor, *_ = cuda_version.split(".")
+        return int(major), int(minor)
+
     version = ctypes.c_int()
-    cudart = ctypes.CDLL("libcudart.so")
     cudart.cudaRuntimeGetVersion(ctypes.byref(version))
     return version.value // 1000, (version.value % 1000) // 10
 
@@ -642,7 +704,7 @@ def is_kenlm_available() -> bool:
 @lru_cache
 def is_kernels_available(MIN_VERSION: str = KERNELS_MIN_VERSION) -> bool:
     is_available, kernels_version = _is_package_available("kernels", return_version=True)
-    return is_available and version.parse(kernels_version) >= version.parse(MIN_VERSION)
+    return _version_meets_minimum(is_available, kernels_version, MIN_VERSION)
 
 
 @lru_cache
@@ -665,13 +727,13 @@ def is_accelerate_available(min_version: str = ACCELERATE_MIN_VERSION) -> bool:
     if not is_torch_available():
         return False
     is_available, accelerate_version = _is_package_available("accelerate", return_version=True)
-    return is_available and version.parse(accelerate_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, accelerate_version, min_version)
 
 
 @lru_cache
 def is_triton_available(min_version: str = TRITON_MIN_VERSION) -> bool:
     is_available, triton_version = _is_package_available("triton", return_version=True)
-    return is_available and version.parse(triton_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, triton_version, min_version)
 
 
 @lru_cache
@@ -682,7 +744,7 @@ def is_hadamard_available() -> bool:
 @lru_cache
 def is_hqq_available(min_version: str = HQQ_MIN_VERSION) -> bool:
     is_available, hqq_version = _is_package_available("hqq", return_version=True)
-    return is_available and version.parse(hqq_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, hqq_version, min_version)
 
 
 @lru_cache
@@ -725,10 +787,15 @@ def is_grokadamw_available() -> bool:
     return _is_package_available("grokadamw")[0]
 
 
+@lru_cache
+def is_flashoptim_available() -> bool:
+    return _is_package_available("flashoptim")[0]
+
+
 @lru_cache
 def is_schedulefree_available(min_version: str = SCHEDULEFREE_MIN_VERSION) -> bool:
     is_available, schedulefree_version = _is_package_available("schedulefree", return_version=True)
-    return is_available and version.parse(schedulefree_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, schedulefree_version, min_version)
 
 
 @lru_cache
@@ -789,13 +856,13 @@ def is_mamba_ssm_available() -> bool:
 @lru_cache
 def is_mamba_2_ssm_available() -> bool:
     is_available, mamba_ssm_version = _is_package_available("mamba_ssm", return_version=True)
-    return is_torch_cuda_available() and is_available and version.parse(mamba_ssm_version) >= version.parse("2.0.4")
+    return is_torch_cuda_available() and _version_meets_minimum(is_available, mamba_ssm_version, "2.0.4")
 
 
 @lru_cache
 def is_flash_linear_attention_available():
     is_available, fla_version = _is_package_available("fla", return_version=True)
-    return is_torch_cuda_available() and is_available and version.parse(fla_version) >= version.parse("0.2.2")
+    return is_torch_cuda_available() and _version_meets_minimum(is_available, fla_version, "0.2.2")
 
 
 @lru_cache
@@ -836,7 +903,7 @@ def is_onnx_available() -> bool:
 @lru_cache
 def is_flute_available() -> bool:
     is_available, flute_version = _is_package_available("flute", return_version=True)
-    return is_available and version.parse(flute_version) >= version.parse("0.4.1")
+    return _version_meets_minimum(is_available, flute_version, "0.4.1")
 
 
 @lru_cache
@@ -846,7 +913,9 @@ def is_g2p_en_available() -> bool:
 
 @lru_cache
 def is_torch_neuroncore_available(check_device=True) -> bool:
-    return is_torch_xla_available() and _is_package_available("torch_neuronx")[0]
+    if importlib.util.find_spec("torch_neuronx") is not None:
+        return is_torch_xla_available(check_is_gpu=check_device)
+    return False
 
 
 @lru_cache
@@ -905,7 +974,7 @@ def is_aqlm_available() -> bool:
 @lru_cache
 def is_vptq_available(min_version: str = VPTQ_MIN_VERSION) -> bool:
     is_available, vptq_version = _is_package_available("vptq", return_version=True)
-    return is_available and version.parse(vptq_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, vptq_version, min_version)
 
 
 @lru_cache
@@ -940,7 +1009,7 @@ def is_ninja_available() -> bool:
 @lru_cache
 def is_bitsandbytes_available(min_version: str = BITSANDBYTES_MIN_VERSION) -> bool:
     is_available, bitsandbytes_version = _is_package_available("bitsandbytes", return_version=True)
-    return is_available and version.parse(bitsandbytes_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, bitsandbytes_version, min_version)
 
 
 @lru_cache
@@ -948,7 +1017,7 @@ def is_flash_attn_2_available() -> bool:
     is_available, flash_attn_version = _is_package_available("flash_attn", return_version=True)
     # FA4 is also distributed under "flash_attn", hence we need to check the naming here
     is_available = is_available and "flash-attn" in [
-        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING["flash_attn"]
+        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING.get("flash_attn", [])
     ]
 
     if not is_available or not (is_torch_cuda_available() or is_torch_mlu_available()):
@@ -967,7 +1036,7 @@ def is_flash_attn_3_available() -> bool:
     is_available = _is_package_available("flash_attn_interface")[0]
     # Resolving and ensuring the proper name of FA3 being associated
     is_available = is_available and "flash-attn-3" in [
-        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING["flash_attn_interface"]
+        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING.get("flash_attn_interface", [])
     ]
     return is_available and is_torch_cuda_available()
 
@@ -979,7 +1048,7 @@ def is_flash_attn_4_available() -> bool:
     # NOTE: FA2 seems to distribute the `cute` subdirectory even if only FA2 has been installed
     #       -> check for the proper (normalized) distribution name
     is_available = is_available and "flash-attn-4" in [
-        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING["flash_attn"]
+        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING.get("flash_attn", [])
     ]
 
     return is_available and is_torch_cuda_available()
@@ -990,7 +1059,7 @@ def is_flash_attn_greater_or_equal(library_version: str) -> bool:
     is_available, flash_attn_version = _is_package_available("flash_attn", return_version=True)
     # FA4 is also distributed under "flash_attn", hence we need to check the naming here
     is_available = is_available and "flash-attn" in [
-        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING["flash_attn"]
+        pkg.replace("_", "-") for pkg in PACKAGE_DISTRIBUTION_MAPPING.get("flash_attn", [])
     ]
 
     if not is_available:
@@ -1078,7 +1147,7 @@ def is_seqio_available() -> bool:
 @lru_cache
 def is_gguf_available(min_version: str = GGUF_MIN_VERSION) -> bool:
     is_available, gguf_version = _is_package_available("gguf", return_version=True)
-    return is_available and version.parse(gguf_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, gguf_version, min_version)
 
 
 @lru_cache
@@ -1104,7 +1173,7 @@ def is_llm_awq_available() -> bool:
 @lru_cache
 def is_auto_round_available(min_version: str = AUTOROUND_MIN_VERSION) -> bool:
     is_available, auto_round_version = _is_package_available("auto_round", return_version=True)
-    return is_available and version.parse(auto_round_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, auto_round_version, min_version)
 
 
 @lru_cache
@@ -1120,13 +1189,13 @@ def is_quark_available() -> bool:
 @lru_cache
 def is_fp_quant_available():
     is_available, fp_quant_version = _is_package_available("fp_quant", return_version=True)
-    return is_available and version.parse(fp_quant_version) >= version.parse("0.3.2")
+    return _version_meets_minimum(is_available, fp_quant_version, "0.3.2")
 
 
 @lru_cache
 def is_qutlass_available():
     is_available, qutlass_version = _is_package_available("qutlass", return_version=True)
-    return is_available and version.parse(qutlass_version) >= version.parse("0.2.0")
+    return _version_meets_minimum(is_available, qutlass_version, "0.2.0")
 
 
 @lru_cache
@@ -1244,7 +1313,7 @@ def is_torchao_available(min_version: str = TORCHAO_MIN_VERSION) -> bool:
     if not is_torch_available():
         return False
     is_available, torchao_version = _is_package_available("torchao", return_version=True)
-    return is_available and version.parse(torchao_version) >= version.parse(min_version)
+    return _version_meets_minimum(is_available, torchao_version, min_version)
 
 
 @lru_cache
@@ -1276,7 +1345,7 @@ def is_sudachi_available() -> bool:
 @lru_cache
 def is_sudachi_projection_available() -> bool:
     is_available, sudachipy_version = _is_package_available("sudachipy", return_version=True)
-    return is_available and version.parse(sudachipy_version) >= version.parse("0.6.8")
+    return _version_meets_minimum(is_available, sudachipy_version, "0.6.8")
 
 
 @lru_cache
@@ -1319,7 +1388,7 @@ def is_tiktoken_available(with_blobfile: bool = True) -> bool:
 @lru_cache
 def is_liger_kernel_available() -> bool:
     is_available, liger_kernel_version = _is_package_available("liger_kernel", return_version=True)
-    return is_available and version.parse(liger_kernel_version) >= version.parse("0.3.0")
+    return _version_meets_minimum(is_available, liger_kernel_version, "0.3.0")
 
 
 @lru_cache
@@ -1522,10 +1591,11 @@ def torch_compilable_check(cond: Any, msg: str | Callable[[], str], error_type:
 
     import torch
 
-    if not callable(msg):
-        # torch._check requires msg to be a callable but we want to keep the API simple for users
-        def msg_callable():
-            return msg
+    if isinstance(msg, str):
+        _msg = msg
+
+        def msg_callable() -> str:
+            return _msg
     else:
         msg_callable = msg
 
diff --git a/src/transformers/utils/kernel_config.py b/src/transformers/utils/kernel_config.py
index bb4f965ddbf4..ee3d9ca4e098 100644
--- a/src/transformers/utils/kernel_config.py
+++ b/src/transformers/utils/kernel_config.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from ..utils import PushToHubMixin
+from .hub import PushToHubMixin
 
 
 def infer_device(model):
diff --git a/src/transformers/utils/logging.py b/src/transformers/utils/logging.py
index 32099c4afe10..befe8324e047 100644
--- a/src/transformers/utils/logging.py
+++ b/src/transformers/utils/logging.py
@@ -14,6 +14,7 @@
 """Logging utilities."""
 
 import functools
+import importlib
 import logging
 import os
 import sys
@@ -32,9 +33,6 @@
 from logging import captureWarnings as _captureWarnings
 from typing import Any
 
-import huggingface_hub.utils as hf_hub_utils
-from tqdm import auto as tqdm_lib
-
 from .._typing import TransformersLogger
 
 
@@ -52,10 +50,27 @@
 
 _default_log_level = logging.WARNING
 
-_tqdm_active = not hf_hub_utils.are_progress_bars_disabled()
+_tqdm_active: bool | None = None
 _tqdm_hook: Callable[[Callable[..., Any], tuple[Any, ...], dict[str, Any]], Any] | None = None
 
 
+@functools.lru_cache(None)
+def _get_hf_hub_utils():
+    return importlib.import_module("huggingface_hub.utils")
+
+
+@functools.lru_cache(None)
+def _get_tqdm_lib():
+    return importlib.import_module("tqdm.auto")
+
+
+def _is_tqdm_active() -> bool:
+    global _tqdm_active
+    if _tqdm_active is None:
+        _tqdm_active = not _get_hf_hub_utils().are_progress_bars_disabled()
+    return _tqdm_active
+
+
 def _get_default_logging_level():
     """
     If TRANSFORMERS_VERBOSITY env var is set to one of the valid choices return that as the new default level. If it is
@@ -384,19 +399,19 @@ def __exit__(self, type_, value, traceback):
 
 class _tqdm_cls:
     def __call__(self, *args, **kwargs):
-        factory = tqdm_lib.tqdm if _tqdm_active else EmptyTqdm
+        factory = _get_tqdm_lib().tqdm if _is_tqdm_active() else EmptyTqdm
         if _tqdm_hook is not None:
             return _tqdm_hook(factory, args, kwargs)
         return factory(*args, **kwargs)
 
     def set_lock(self, *args, **kwargs):
         self._lock = None
-        if _tqdm_active:
-            return tqdm_lib.tqdm.set_lock(*args, **kwargs)
+        if _is_tqdm_active():
+            return _get_tqdm_lib().tqdm.set_lock(*args, **kwargs)
 
     def get_lock(self):
-        if _tqdm_active:
-            return tqdm_lib.tqdm.get_lock()
+        if _is_tqdm_active():
+            return _get_tqdm_lib().tqdm.get_lock()
 
 
 tqdm = _tqdm_cls()
@@ -404,21 +419,21 @@ def get_lock(self):
 
 def is_progress_bar_enabled() -> bool:
     """Return a boolean indicating whether tqdm progress bars are enabled."""
-    return bool(_tqdm_active)
+    return _is_tqdm_active()
 
 
 def enable_progress_bar():
     """Enable tqdm progress bar."""
     global _tqdm_active
     _tqdm_active = True
-    hf_hub_utils.enable_progress_bars()
+    _get_hf_hub_utils().enable_progress_bars()
 
 
 def disable_progress_bar():
     """Disable tqdm progress bar."""
     global _tqdm_active
     _tqdm_active = False
-    hf_hub_utils.disable_progress_bars()
+    _get_hf_hub_utils().disable_progress_bars()
 
 
 def set_tqdm_hook(hook: Callable[[Callable[..., Any], tuple[Any, ...], dict[str, Any]], Any] | None):
diff --git a/src/transformers/utils/output_capturing.py b/src/transformers/utils/output_capturing.py
index 5af880eaa1d2..660044663e98 100644
--- a/src/transformers/utils/output_capturing.py
+++ b/src/transformers/utils/output_capturing.py
@@ -18,6 +18,7 @@
 
 from __future__ import annotations
 
+import re
 import threading
 from contextvars import ContextVar
 from dataclasses import dataclass
@@ -39,13 +40,19 @@
 @dataclass
 @requires(backends=("torch",))
 class OutputRecorder:
-    """
+    r"""
     Configuration for recording outputs from a model via hooks.
 
     Attributes:
         target_class (Type): The class (e.g., nn.Module) to which the hook will be attached.
         index (Optional[int]): If the output is a tuple/list, optionally record only at a specific index.
-        layer_name (Optional[str]): Name of the submodule to target (if needed), e.g., "transformer.layer.3.attn".
+        layer_name (Optional[str]): Regex pattern (matched with `re.search`) used to filter submodules by their
+            dotted qualified name. Examples:
+
+            - `"self_attn"`: substring match
+            - `"transformer.layer.3.attn"`: literal path
+            - `r"layers\.1$"`: anchored, targets layer 1 without also matching `layers.10`/`layers.11`/…
+            - `r"layers\.(6|12|18)$"`: picks a non-contiguous subset of layers
         class_name (Optional[str]): Name of the class to which the hook will be attached. Could be the suffix of class name in some cases.
     """
 
@@ -142,7 +149,7 @@ def recursively_install_hooks(
         if (specs.target_class is not None and isinstance(parent_module, specs.target_class)) or (
             specs.class_name is not None and module_name.endswith(specs.class_name)
         ):
-            if specs.layer_name is not None and specs.layer_name not in module_name:
+            if specs.layer_name is not None and re.search(specs.layer_name, module_name) is None:
                 continue
             install_output_capturing_hook(parent_module, key, specs.index)
 
diff --git a/src/transformers/utils/quantization_config.py b/src/transformers/utils/quantization_config.py
index bf085d87498c..a6c1f5334516 100644
--- a/src/transformers/utils/quantization_config.py
+++ b/src/transformers/utils/quantization_config.py
@@ -1716,7 +1716,7 @@ def post_init(self):
             raise ValueError("weight_block_size must be a tuple of two positive integers")
 
     def get_loading_attributes(self):
-        return {"dequantize": self.dequantize}
+        return {"dequantize": self.dequantize, "modules_to_not_convert": self.modules_to_not_convert}
 
 
 class QuarkConfig(QuantizationConfigMixin):
diff --git a/src/transformers/utils/tokenizer_selection.py b/src/transformers/utils/tokenizer_selection.py
new file mode 100644
index 000000000000..3f081ab9c828
--- /dev/null
+++ b/src/transformers/utils/tokenizer_selection.py
@@ -0,0 +1,366 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+Tokenizer selection utilities for corpus-aware tokenizer recommendations.
+"""
+
+import logging
+import re
+from collections import Counter
+from collections.abc import Iterator
+from dataclasses import dataclass
+from typing import Any
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class CorpusStats:
+    """
+    Container for corpus analysis statistics.
+    """
+
+    vocab_size: int
+    avg_word_length: float
+    char_diversity: int
+    morphological_complexity: float
+    token_frequency_ratio: float
+    avg_sentence_length: float
+    language_hint: str | None = None
+
+
+class CorpusAnalyzer:
+    """
+    Analyzes text corpus characteristics to inform tokenizer selection.
+    """
+
+    @staticmethod
+    def analyze_corpus(text_iterator: Iterator[list[str]], sample_size: int = 10000) -> CorpusStats:
+        """
+        Analyze corpus characteristics.
+
+        Args:
+            text_iterator: Iterator yielding batches of text strings
+            sample_size: Maximum number of texts to analyze for efficiency
+
+        Returns:
+            CorpusStats: Statistical analysis of the corpus
+        """
+        word_lengths = []
+        char_counter = Counter()
+        word_counter = Counter()
+        sentence_lengths = []
+        all_chars = set()
+        processed_count = 0
+
+        for batch in text_iterator:
+            for text in batch:
+                if processed_count >= sample_size:
+                    break
+
+                # Basic text processing
+                sentences = text.split(".")
+                sentence_lengths.extend([len(s.split()) for s in sentences if s.strip()])
+
+                words = re.findall(r"\b\w+\b", text.lower())
+                word_lengths.extend([len(word) for word in words])
+                word_counter.update(words)
+
+                chars = [c for c in text if c.isalnum()]
+                char_counter.update(chars)
+                all_chars.update(chars)
+
+                processed_count += 1
+
+            if processed_count >= sample_size:
+                break
+
+        if not word_lengths:
+            raise ValueError("No valid text found in corpus")
+
+        # Calculate statistics
+        vocab_size = len(word_counter)
+        avg_word_length = sum(word_lengths) / len(word_lengths)
+        char_diversity = len(all_chars)
+
+        # Morphological complexity (ratio of unique words to total words)
+        total_words = sum(word_counter.values())
+        morphological_complexity = vocab_size / total_words if total_words > 0 else 0
+
+        # Token frequency distribution (how concentrated the vocabulary is)
+        word_frequencies = list(word_counter.values())
+        token_frequency_ratio = max(word_frequencies) / sum(word_frequencies) if word_frequencies else 0
+
+        avg_sentence_length = sum(sentence_lengths) / len(sentence_lengths) if sentence_lengths else 0
+
+        # Simple language detection based on character patterns
+        language_hint = CorpusAnalyzer._detect_language_hint(char_counter)
+
+        return CorpusStats(
+            vocab_size=vocab_size,
+            avg_word_length=avg_word_length,
+            char_diversity=char_diversity,
+            morphological_complexity=morphological_complexity,
+            token_frequency_ratio=token_frequency_ratio,
+            avg_sentence_length=avg_sentence_length,
+            language_hint=language_hint,
+        )
+
+    @staticmethod
+    def _detect_language_hint(char_counter: Counter) -> str | None:
+        """Simple language detection based on character frequency patterns."""
+        total_chars = sum(char_counter.values())
+        if total_chars == 0:
+            return None
+
+        # Check for common patterns
+        latin_chars = sum(count for char, count in char_counter.items() if ord(char) < 256)
+        asian_chars = sum(count for char, count in char_counter.items() if ord(char) > 4352)  # CJK range approximation
+
+        latin_ratio = latin_chars / total_chars
+        asian_ratio = asian_chars / total_chars
+
+        if asian_ratio > 0.3:
+            return "cjk"  # Chinese, Japanese, Korean
+        elif latin_ratio > 0.8:
+            return "latin"
+        else:
+            return "mixed"
+
+
+class TokenizerRecommender:
+    """
+    Recommends tokenizer type and configuration based on corpus statistics.
+    """
+
+    @staticmethod
+    def recommend_tokenizer(corpus_stats: CorpusStats) -> dict[str, Any]:
+        """
+        Recommend tokenizer type and configuration based on corpus characteristics.
+
+        Args:
+            corpus_stats: Statistics from corpus analysis
+
+        Returns:
+            Dict containing recommendation with 'type', 'rationale', and 'config'
+        """
+        recommendations = []
+
+        # Rule-based recommendation logic
+        if corpus_stats.language_hint == "cjk":
+            recommendations.append(
+                {
+                    "type": "SentencePiece",
+                    "score": 0.9,
+                    "rationale": "SentencePiece handles CJK languages effectively without whitespace dependency",
+                }
+            )
+
+        if corpus_stats.morphological_complexity > 0.7:
+            recommendations.append(
+                {
+                    "type": "BPE",
+                    "score": 0.8,
+                    "rationale": "High morphological complexity benefits from BPE's subword handling",
+                }
+            )
+
+        if corpus_stats.vocab_size > 50000:
+            recommendations.append(
+                {"type": "WordPiece", "score": 0.7, "rationale": "Large vocabulary size suits WordPiece tokenization"}
+            )
+
+        if corpus_stats.avg_word_length > 8.0:
+            recommendations.append(
+                {
+                    "type": "BPE",
+                    "score": 0.8,
+                    "rationale": "Long average word length benefits from subword tokenization",
+                }
+            )
+
+        # Default fallback
+        if not recommendations:
+            recommendations.append(
+                {"type": "BPE", "score": 0.6, "rationale": "BPE is a robust default choice for most corpora"}
+            )
+
+        # Select highest scoring recommendation
+        best_rec = max(recommendations, key=lambda x: x["score"])
+
+        # Generate configuration suggestions
+        config = TokenizerRecommender._generate_config(corpus_stats, best_rec["type"])
+
+        return {
+            "type": best_rec["type"],
+            "rationale": best_rec["rationale"],
+            "config": config,
+            "corpus_stats": corpus_stats,
+        }
+
+    @staticmethod
+    def _generate_config(corpus_stats: CorpusStats, tokenizer_type: str) -> dict[str, Any]:
+        """Generate tokenizer configuration based on corpus stats and type."""
+        config = {}
+
+        # Vocabulary size suggestion
+        if corpus_stats.vocab_size < 10000:
+            config["vocab_size"] = 16000
+        elif corpus_stats.vocab_size < 50000:
+            config["vocab_size"] = 32000
+        else:
+            config["vocab_size"] = 50000
+
+        # Type-specific configurations
+        if tokenizer_type == "BPE":
+            config.update(
+                {
+                    "dropout": 0.1 if corpus_stats.morphological_complexity > 0.5 else None,
+                    "continuing_subword_prefix": "##",
+                }
+            )
+        elif tokenizer_type == "WordPiece":
+            config.update(
+                {
+                    "continuing_subword_prefix": "##",
+                    "max_input_chars_per_word": max(100, int(corpus_stats.avg_word_length * 10)),
+                }
+            )
+        elif tokenizer_type == "SentencePiece":
+            config.update(
+                {
+                    "character_coverage": 0.9995 if corpus_stats.language_hint == "latin" else 0.995,
+                    "model_type": "unigram",
+                }
+            )
+
+        return config
+
+
+class TokenizerSelector:
+    """
+    Main utility class for context-aware tokenizer selection and training.
+    """
+
+    @staticmethod
+    def suggest_and_train_tokenizer(
+        text_iterator: Iterator[list[str]],
+        vocab_size: int | None = None,
+        base_tokenizer: str = "google-bert/bert-base-uncased",
+        sample_size: int = 10000,
+        **trainer_kwargs,
+    ):
+        """
+        End-to-end utility to analyze corpus, recommend tokenizer, and train it.
+
+        Args:
+            text_iterator: Iterator yielding batches of text strings
+            vocab_size: Target vocabulary size (auto-selected if None)
+            base_tokenizer: Base tokenizer to use as template for training
+            sample_size: Number of texts to analyze for recommendations
+            **trainer_kwargs: Additional arguments passed to tokenizer trainer
+
+        Returns:
+            Tuple of (trained_tokenizer, recommendation_info)
+        """
+        logger.info("Analyzing corpus characteristics...")
+
+        # Convert iterator to list for reuse (needed for both analysis and training)
+        text_batches = list(text_iterator)
+
+        # Analyze corpus
+        corpus_stats = CorpusAnalyzer.analyze_corpus(iter(text_batches), sample_size)
+
+        # Get recommendation
+        recommendation = TokenizerRecommender.recommend_tokenizer(corpus_stats)
+
+        logger.info(f"Recommended tokenizer type: {recommendation['type']}")
+        logger.info(f"Rationale: {recommendation['rationale']}")
+
+        # Use recommended vocab size if not provided
+        if vocab_size is None:
+            vocab_size = recommendation["config"]["vocab_size"]
+
+        # Load base tokenizer for training (lazy import to avoid circular dependency)
+        from ..models.auto import AutoTokenizer
+
+        try:
+            base_tok = AutoTokenizer.from_pretrained(base_tokenizer, use_fast=True)
+        except Exception:
+            logger.warning(f"Could not load {base_tokenizer}, falling back to bert-base-uncased")
+            base_tok = AutoTokenizer.from_pretrained("google-bert/bert-base-uncased", use_fast=True)
+
+        # Merge trainer configs
+        trainer_config = {**recommendation["config"], **trainer_kwargs}
+        # Remove vocab_size from trainer_config since it's a separate parameter
+        trainer_config.pop("vocab_size", None)
+
+        # Train new tokenizer using existing method
+        logger.info(f"Training {recommendation['type']} tokenizer with vocab_size={vocab_size}")
+
+        trained_tokenizer = base_tok.train_new_from_iterator(
+            text_iterator=iter(text_batches), vocab_size=vocab_size, **trainer_config
+        )
+
+        return trained_tokenizer, recommendation
+
+    @staticmethod
+    def analyze_corpus(text_iterator: Iterator[list[str]], sample_size: int = 10000) -> CorpusStats:
+        """
+        Analyze corpus and return statistics.
+
+        Args:
+            text_iterator: Iterator yielding batches of text strings
+            sample_size: Number of texts to analyze
+
+        Returns:
+            CorpusStats: Analysis results
+        """
+        return CorpusAnalyzer.analyze_corpus(text_iterator, sample_size)
+
+    @staticmethod
+    def recommend_tokenizer(corpus_stats: CorpusStats) -> dict[str, Any]:
+        """
+        Get tokenizer recommendation based on corpus statistics.
+
+        Args:
+            corpus_stats: Analysis results from analyze_corpus
+
+        Returns:
+            Dict: Recommendation with type, rationale, and config
+        """
+        return TokenizerRecommender.recommend_tokenizer(corpus_stats)
+
+
+# Convenience function for simple usage
+def suggest_and_train_tokenizer(text_iterator: Iterator[list[str]], vocab_size: int | None = None, **kwargs):
+    """
+    Convenience function for end-to-end tokenizer selection and training.
+
+    Args:
+        text_iterator: Iterator yielding batches of text strings
+        vocab_size: Target vocabulary size (auto-selected if None)
+        **kwargs: Additional arguments passed to TokenizerSelector
+
+    Returns:
+        Tuple of (trained_tokenizer, recommendation_info)
+
+    Example:
+        >>> texts = [["Hello world", "This is a test"], ["More training data"]]
+        >>> tokenizer, info = suggest_and_train_tokenizer(iter(texts))
+        >>> print(f"Trained {info['type']} tokenizer: {info['rationale']}")
+    """
+    return TokenizerSelector.suggest_and_train_tokenizer(text_iterator, vocab_size, **kwargs)
diff --git a/src/transformers/utils/type_validators.py b/src/transformers/utils/type_validators.py
index 08d4697683b2..0fe4a4e9eed4 100644
--- a/src/transformers/utils/type_validators.py
+++ b/src/transformers/utils/type_validators.py
@@ -132,6 +132,18 @@ def tensor_type_validator(value: str | TensorType | None = None):
         raise ValueError(f"The tensor type should be one of {possible_names} but got tensor_type={value}")
 
 
+@as_validated_field
+def dtype_validator(value: str | int | None = None):
+    # Check all possible values
+    if value is None or (is_torch_available() and isinstance(value, torch.dtype)) or isinstance(value, str):
+        pass
+    # If torch not installed in env, just pass
+    elif not is_torch_available():
+        pass
+    else:
+        raise ValueError(f"Dtype must be either an string or `torch.dtype`, but got dtype={value}")
+
+
 @as_validated_field
 def label_to_id_validation(value: str | TensorType | None = None):
     possible_names = ["pt", "np", "mlx"]
diff --git a/src/transformers/utils/vision_utils.py b/src/transformers/utils/vision_utils.py
new file mode 100644
index 000000000000..5c1ef37bc8a2
--- /dev/null
+++ b/src/transformers/utils/vision_utils.py
@@ -0,0 +1,216 @@
+"""
+Vision utilities for transformers models.
+
+This module provides utilities for working with image encoders and vision models,
+including functions to determine encoder dimensions and handle configuration edge cases.
+"""
+
+import inspect
+from functools import cache
+
+import torch
+
+from transformers import AutoModelForImageClassification
+
+
+class UnknownImageEncoderError(ValueError):
+    """
+    Exception raised when an image encoder's hidden size cannot be determined.
+
+    This error is raised when the image encoder model doesn't have any of the
+    expected configuration attributes for determining the hidden size
+    """
+
+    def __init__(self):
+        super().__init__("Image encoder does not have a known hidden size configuration.")
+
+
+@cache
+def image_encoder_size(image_encoder: AutoModelForImageClassification) -> int:
+    """
+    Determine the hidden size of an image encoder model.
+
+    This function extracts the hidden size dimension from various types of image encoder
+    models by checking different configuration attributes in a prioritized order.
+
+    Args:
+        image_encoder: An AutoModelForImageClassification instance.
+
+    Returns:
+        int: The hidden size of the image encoder.
+
+    Raises:
+        UnknownImageEncoderError: If the image encoder doesn't have any of the
+                                 expected configuration attributes for hidden size.
+
+    Note:
+        The function checks for configuration attributes in the following order:
+        1. config.vision_config.hidden_size (for CLIP-like models)
+        2. config.hidden_size (standard hidden size attribute)
+        3. config.neck_hidden_sizes (for MobileViT models, with expand_output handling)
+        4. config.hidden_sizes (fallback to last hidden size in the list)
+    """
+    # Extract the model configuration, defaulting to empty dict if not found
+    config = getattr(image_encoder, "config", {})
+
+    # For multi-modal models like CLIP, the vision encoder config is nested
+    if hasattr(config, "vision_config"):
+        config = config.vision_config
+
+    # Most standard vision models have a direct hidden_size attribute
+    if hasattr(config, "hidden_size"):
+        return config.hidden_size
+
+    # Handle MobileViT models which use neck_hidden_sizes instead of hidden_size
+    # Reference: https://huggingface.co/docs/transformers/model_doc/mobilevit#transformers.MobileViTModel
+    if hasattr(config, "neck_hidden_sizes"):
+        # When expand_output is True, MobileViT applies an additional 1x1 convolution
+        # to expand output channels from neck_hidden_sizes[5] to neck_hidden_sizes[6]
+        if getattr(image_encoder, "expand_output", False):
+            return config.neck_hidden_sizes[-1]  # Use the expanded output size
+        return config.neck_hidden_sizes[-2]  # Use the pre-expansion size
+
+    # Fallback for models that store multiple layer sizes in a list (e.g., some ViT variants)
+    if hasattr(config, "hidden_sizes"):
+        return config.hidden_sizes[-1]  # Use the final layer's hidden size
+
+    # No recognized hidden size configuration found
+    raise UnknownImageEncoderError()
+
+
+@cache
+def model_args_dict(model: AutoModelForImageClassification) -> dict:
+    """
+    Generate model arguments dictionary for image encoder forward pass.
+
+    This function creates a dictionary of arguments optimized for feature extraction
+    from image encoder models, including conditional parameters based on model capabilities.
+
+    Args:
+        model: An AutoModelForImageClassification instance to generate arguments for.
+
+    Returns:
+        dict: Dictionary of arguments to pass to the model's forward method.
+              Always includes 'output_hidden_states': True.
+              May include 'interpolate_pos_encoding': True if supported by the model.
+
+    Note:
+        The function is cached to avoid repeated signature inspection for the same model.
+        Positional encoding interpolation is enabled for models that support it,
+        allowing better handling of images with different sizes than training data.
+    """
+    # Configure model arguments to output hidden states for feature extraction
+    args = {"output_hidden_states": True}
+
+    # Enable positional encoding interpolation if the model supports it
+    # This is useful for handling images of different sizes than training
+    if accepts(model.forward, "interpolate_pos_encoding"):
+        args["interpolate_pos_encoding"] = True
+
+    return args
+
+
+@cache
+def accepts(func, param_name: str) -> bool:
+    """
+    Check if a function accepts a specific parameter.
+
+    This function inspects the signature of a given function to determine whether
+    it accepts a specific parameter either as a named parameter or through **kwargs.
+
+    Args:
+        func: The function to inspect.
+        param_name: The name of the parameter to check for.
+
+    Returns:
+        bool: True if the function accepts the parameter, False otherwise.
+
+    Note:
+        Returns True if either:
+        1. The parameter name is explicitly defined in the function signature
+        2. The function accepts **kwargs (VAR_KEYWORD parameters)
+    """
+    sig = inspect.signature(func)
+    return param_name in sig.parameters or any(
+        p.kind == inspect.Parameter.VAR_KEYWORD for p in sig.parameters.values()
+    )
+
+
+def pool_hidden_dim(tensor: torch.Tensor, hidden_size: int) -> torch.Tensor:
+    """
+    Pool a tensor across all dimensions except batch and hidden dimensions.
+
+    This function performs mean pooling across spatial or patch dimensions while
+    preserving the batch and hidden dimensions. It works with various tensor layouts
+    from different vision model architectures.
+
+    Args:
+        tensor: Input tensor to pool. Can have various shapes depending on the model:
+               - ViT-like: `(batch_size, num_patches, hidden_size)`
+               - ConvNet-like: `(batch_size, height, width, channels)` or
+                              `(batch_size, channels, height, width)`
+        hidden_size: The size of the hidden/feature dimension to preserve.
+
+    Returns:
+        torch.Tensor: Pooled tensor with shape `(batch_size, hidden_size)`.
+
+    Raises:
+        StopIteration: If no dimension matches the specified hidden_size (excluding batch dim).
+
+    Note:
+        The function identifies the hidden dimension by finding the dimension that
+        matches hidden_size (excluding the batch dimension at index 0), then pools
+        across all other non-batch, non-hidden dimensions.
+    """
+    # Find the dimension index that matches our hidden size (skip batch dim at index 0)
+    hidden_dim = next(i for i, s in enumerate(tensor.shape) if s == hidden_size and i != 0)
+
+    # Identify all dimensions to pool over (everything except batch and hidden dims)
+    non_hidden_dims = tuple(i for i in range(len(tensor.shape)) if i != hidden_dim and i != 0)
+
+    # Perform mean pooling across spatial/patch dimensions
+    return tensor.mean(dim=non_hidden_dims)
+
+
+def encode_images(image_encoder: AutoModelForImageClassification, images: torch.Tensor) -> torch.Tensor:
+    """
+    Encode a batch of images using the provided image encoder model.
+
+    This function runs images through the encoder and extracts the final hidden states,
+    with optional support for positional encoding interpolation when available.
+
+    Args:
+        image_encoder: An AutoModelForImageClassification instance used for encoding.
+        images: A tensor of shape `(batch_size, channels, height, width)` containing
+               the preprocessed images to encode.
+
+    Returns:
+        torch.Tensor: The encoded image features with shape `(batch_size, hidden_size)`.
+                     Features are pooled across spatial/patch dimensions.
+
+    Note:
+        The function automatically enables output_hidden_states to access intermediate
+        representations and conditionally enables interpolate_pos_encoding for models
+        that support dynamic positional encoding based on input image size.
+    """
+    # Configure model arguments to output hidden states for feature extraction
+    model_args = model_args_dict(image_encoder)
+
+    # Run the forward pass through the image encoder
+    encoded_images = image_encoder(images, **model_args)
+
+    # Default to using pooler_output if available (shape [batch_size, hidden_size])
+    if hasattr(encoded_images, "pooler_output"):
+        return encoded_images.pooler_output
+
+    # Extract the final layer's hidden states (shape varies by model architecture)
+    if hasattr(encoded_images, "last_hidden_state"):
+        last_hidden_states = encoded_images.last_hidden_state
+    else:
+        last_hidden_states = encoded_images.hidden_states[-1]
+
+    # Get the hidden size dimension for this encoder model
+    hidden_size = image_encoder_size(image_encoder)
+
+    # Pool across spatial/patch dimensions to get [batch_size, hidden_size] output
+    return pool_hidden_dim(last_hidden_states, hidden_size)
diff --git a/src/transformers/video_utils.py b/src/transformers/video_utils.py
index 177dc117dfdd..c9aeae8dc67d 100644
--- a/src/transformers/video_utils.py
+++ b/src/transformers/video_utils.py
@@ -195,7 +195,7 @@ def make_batched_videos(videos) -> list[Union[np.ndarray, "torch.Tensor", "URL",
     """
     # Early exit for deeply nested list of image frame paths. We shouldn't flatten them
     try:
-        if isinstance(videos[0][0], list) and isinstance(videos[0][0][0], str):
+        if isinstance(videos[0][0], (list, tuple)) and isinstance(videos[0][0][0], str):
             return [image_paths for sublist in videos for image_paths in sublist]
     except (IndexError, TypeError):
         pass
@@ -209,7 +209,7 @@ def make_batched_videos(videos) -> list[Union[np.ndarray, "torch.Tensor", "URL",
         if isinstance(videos, PIL.Image.Image):
             videos = np.array(videos)
         return [videos[None, ...]]
-    elif not isinstance(videos, list):
+    elif not isinstance(videos, (list, tuple)):
         raise ValueError(
             f"Invalid video input. Expected either a list of video frames or an input of 4 or 5 dimensions, but got"
             f" type {type(videos)}."
@@ -220,7 +220,7 @@ def make_batched_videos(videos) -> list[Union[np.ndarray, "torch.Tensor", "URL",
     for item in videos:
         if isinstance(item, str) or is_valid_video(item):
             flat_videos_list.append(item)
-        elif isinstance(item, list) and item:
+        elif isinstance(item, (list, tuple)) and item:
             flat_videos_list.extend(make_batched_videos(item))
 
     flat_videos_list = convert_pil_frames_to_video(flat_videos_list)
diff --git a/test_future_annotations.py b/test_future_annotations.py
new file mode 100644
index 000000000000..d0dc5574ece9
--- /dev/null
+++ b/test_future_annotations.py
@@ -0,0 +1,18 @@
+from __future__ import annotations
+from transformers.utils.auto_docstring import _process_kwargs_parameters
+import inspect
+
+
+def test_with_future_annotations():
+    # This should fail without fix
+    def dummy_func(**kwargs: "ImagesKwargs"):
+        pass
+
+    sig = inspect.signature(dummy_func)
+    # This line should trigger the bug
+    result = _process_kwargs_parameters(sig, dummy_func, None, {}, 0, [])
+    print("Success!")
+
+
+if __name__ == "__main__":
+    test_with_future_annotations()
diff --git a/tests/adapters/test_auto_merge_adapters.py b/tests/adapters/test_auto_merge_adapters.py
new file mode 100644
index 000000000000..16d12f7f3129
--- /dev/null
+++ b/tests/adapters/test_auto_merge_adapters.py
@@ -0,0 +1,8 @@
+import pytest
+
+from transformers.adapters.auto_merge_adapters import AutoMergeAdapters
+
+
+def test_merge_no_adapters():
+    with pytest.raises(ValueError):
+        AutoMergeAdapters.merge(None, [])
diff --git a/tests/alm_tester.py b/tests/alm_tester.py
new file mode 100644
index 000000000000..b51cc4f11880
--- /dev/null
+++ b/tests/alm_tester.py
@@ -0,0 +1,227 @@
+# Copyright 2026 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import copy
+import unittest
+from inspect import signature
+
+from .multimodal_tester import MultiModalModelTest, MultiModalModelTester
+from .test_modeling_common import (
+    floats_tensor,
+    ids_tensor,
+    is_torch_available,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+class ALMModelTester(MultiModalModelTester):
+    audio_config_class = None
+    audio_config_key = "audio_config"
+    # Name under which the audio mask is passed to the model's forward (e.g. "feature_attention_mask"
+    # for Qwen2Audio). Leave as `None` if the model does not consume a separate audio-level mask;
+    # `_prepare_modality_inputs` then skips adding it to the inputs dict.
+    audio_mask_key = None
+    _required_attributes = MultiModalModelTester._required_attributes + ("audio_config_class",)
+
+    @property
+    def pipeline_model_mapping(self):
+        # TODO: @eustlb, we don't have pipeline testing for audio-text-to-text
+        mapping = {
+            "feature-extraction": self.base_model_class,
+            # "audio-text-to-text": self.conditional_generation_class,
+        }
+        # TODO: should we add automatic-speech-recognition with a special flag?
+        return mapping
+
+    def __init__(self, parent, **kwargs):
+        # Overrides of _TEXT_MODEL_TESTER_DEFAULTS
+        kwargs.setdefault("seq_length", 32)
+        kwargs.setdefault("pad_token_id", 1)
+
+        # ALM-specific defaults
+        kwargs.setdefault("feat_seq_length", 128)
+        kwargs.setdefault("num_mel_bins", 80)
+        kwargs.setdefault("audio_token_id", 0)
+
+        super().__init__(parent, **kwargs)
+
+    # -- Overridable ALM-specific hooks ------------------------------------------------------
+
+    def create_audio_features(self):
+        """Create audio feature tensor. Override for different shapes (e.g. [B, T, features])."""
+        return floats_tensor([self.batch_size, self.num_mel_bins, self.feat_seq_length])
+
+    def get_audio_embeds_mask(self, audio_embeds_mask):
+        """Get audio embeds mask from audio mask. Override for different shapes."""
+        raise NotImplementedError("This method should be overridden in the subclass")
+
+    def place_audio_tokens(self, input_ids, config, num_audio_tokens):
+        """Place audio placeholder tokens contiguously after BOS. Override for different placement.
+
+        Deterministic placement (position 0 reserved for BOS; audio tokens at [1:1+n]) keeps
+        the tail of each sequence text-only, which downstream tests (e.g. resize_token_embeddings
+        overwriting column -2) rely on.
+        """
+        input_ids = input_ids.clone()
+        input_ids[input_ids == self.audio_token_id] = self.pad_token_id
+        for i in range(input_ids.shape[0]):
+            n = num_audio_tokens[i].item() if isinstance(num_audio_tokens, torch.Tensor) else num_audio_tokens
+            if 1 + int(n) > self.seq_length:
+                raise ValueError(
+                    f"Cannot place {int(n)} audio tokens after BOS in a sequence of length {self.seq_length}. "
+                    "This likely indicates a mismatch between your feature extraction/configuration and your sequence length. "
+                    "Please ensure `seq_length` is >= the number of audio embedding positions + 1."
+                )
+            input_ids[i, 1 : 1 + int(n)] = self.audio_token_id
+        return input_ids
+
+    def get_audio_feature_key(self):
+        """Key name for audio features in the inputs dict."""
+        return "input_features"
+
+    def create_audio_mask(self):
+        """Create audio-level attention mask with contiguous valid regions per batch element.
+
+        Each element gets a random offset and length, producing masks like [0, 0, 1, 1, 1, 0, 0].
+        """
+        # Sample lengths in [1, feat_seq_length] and offsets in [0, feat_seq_length - length]
+        lengths = ids_tensor([self.batch_size], vocab_size=self.feat_seq_length).abs() + 1
+        lengths = lengths.clamp(max=self.feat_seq_length)
+        offsets = ids_tensor([self.batch_size], vocab_size=self.feat_seq_length).abs()
+        offsets = offsets % (self.feat_seq_length - lengths + 1)
+
+        positions = torch.arange(self.feat_seq_length, device=torch_device)[None, :]
+        audio_mask = ((positions >= offsets[:, None]) & (positions < offsets[:, None] + lengths[:, None])).long()
+        return audio_mask
+
+    # -- Hooks consumed by the shared base ---------------------------------------------------
+
+    @property
+    def _special_token_ids(self):
+        return super()._special_token_ids | {self.audio_token_id}
+
+    def _build_modality_sub_configs(self):
+        return {self.audio_config_key: self.get_audio_config()}
+
+    def _prepare_modality_inputs(self, input_ids, config):
+        # TODO: add a clear diagram that explains input prep ?
+        audio_features = self.create_audio_features()
+        audio_mask = self.create_audio_mask()
+        audio_embeds_mask = self.get_audio_embeds_mask(audio_mask)
+        num_audio_tokens = audio_embeds_mask.sum(dim=1)
+        input_ids = self.place_audio_tokens(input_ids, config, num_audio_tokens)
+
+        modality_inputs = {self.get_audio_feature_key(): audio_features}
+        if self.audio_mask_key is not None:
+            modality_inputs[self.audio_mask_key] = audio_mask
+        return input_ids, modality_inputs
+
+    # -- Audio sub-config construction -------------------------------------------------------
+
+    @property
+    def audio_config_args(self):
+        return list(signature(self.audio_config_class.__init__).parameters.keys())
+
+    def get_audio_config(self):
+        kwargs = self._collect_kwargs(self.audio_config_args, self.audio_config_class)
+        return self.audio_config_class(**kwargs)
+
+
+class ALMModelTest(MultiModalModelTest):
+    """
+    Base test class for Audio-Language Models.
+
+    Subclasses should set:
+    - `model_tester_class`: The tester class (subclass of ALMModelTester)
+
+    Optional:
+    - `all_model_classes`: Override if not using default from model_tester
+    - `pipeline_model_mapping`: Override if not using default from model_tester
+    """
+
+    # TODO: @eustlb, remove this once #45534 is merged
+    @unittest.skip("Audio-LMs have no separate base model without a head.")
+    def test_model_base_model_prefix(self):
+        pass
+
+    def test_mismatching_num_audio_tokens(self):
+        """
+        Tests that ALMs throw an error with explicit message saying what is wrong
+        when number of audios don't match number of audio tokens in the text.
+        Also we need to test multi-audio cases when one prompt has multiple audio tokens.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        audio_feature_key = self.model_tester.get_audio_feature_key()
+        audio_mask_key = self.model_tester.audio_mask_key
+
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)
+            _ = model(**curr_input_dict)  # successful forward with no modifications
+
+            # Test 1: remove one audio but leave the audio tokens in the text
+            curr_input_dict[audio_feature_key] = curr_input_dict[audio_feature_key][-1:, ...]
+            if audio_mask_key is not None:
+                curr_input_dict[audio_mask_key] = curr_input_dict[audio_mask_key][-1:, ...]
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 2: add one audio but leave the audio tokens in the text
+            curr_input_dict = copy.deepcopy(input_dict)
+            curr_input_dict[audio_feature_key] = torch.cat(
+                [curr_input_dict[audio_feature_key], curr_input_dict[audio_feature_key][:1, ...]], dim=0
+            )
+            if audio_mask_key is not None:
+                curr_input_dict[audio_mask_key] = torch.cat(
+                    [curr_input_dict[audio_mask_key], curr_input_dict[audio_mask_key][:1, ...]], dim=0
+                )
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 3: duplicate the text along the seq dim so each prompt has twice as many
+            # audio tokens, while leaving the audio features unchanged -> mismatch
+            curr_input_dict = copy.deepcopy(input_dict)
+            curr_input_dict["input_ids"] = torch.cat(
+                [curr_input_dict["input_ids"], curr_input_dict["input_ids"]], dim=1
+            )
+            curr_input_dict["attention_mask"] = torch.cat(
+                [curr_input_dict["attention_mask"], curr_input_dict["attention_mask"]], dim=1
+            )
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 4: multi-audio valid case. A prompt may contain multiple audio segments;
+            # all audio segments are concatenated along the batch dim on the audio side.
+            # Duplicating input_ids along seq dim (-> [audios, audios] per prompt) and the
+            # audio features along batch dim (-> batch_size * 2) must forward successfully.
+            curr_input_dict = copy.deepcopy(input_dict)
+            curr_input_dict["input_ids"] = torch.cat(
+                [curr_input_dict["input_ids"], curr_input_dict["input_ids"]], dim=1
+            )
+            curr_input_dict["attention_mask"] = torch.cat(
+                [curr_input_dict["attention_mask"], curr_input_dict["attention_mask"]], dim=1
+            )
+            curr_input_dict[audio_feature_key] = torch.cat(
+                [curr_input_dict[audio_feature_key], curr_input_dict[audio_feature_key]], dim=0
+            )
+            if audio_mask_key is not None:
+                curr_input_dict[audio_mask_key] = torch.cat(
+                    [curr_input_dict[audio_mask_key], curr_input_dict[audio_mask_key]], dim=0
+                )
+            _ = model(**curr_input_dict)
diff --git a/tests/causal_lm_tester.py b/tests/causal_lm_tester.py
index b3398f13c393..33f95221a40e 100644
--- a/tests/causal_lm_tester.py
+++ b/tests/causal_lm_tester.py
@@ -22,6 +22,7 @@
 from transformers.models.auto.auto_factory import getattribute_from_module
 from transformers.testing_utils import (
     _COMMON_MODEL_NAMES_MAP,
+    _TEXT_MODEL_TESTER_DEFAULTS,
     is_flaky,
     require_flash_attn,
     require_torch_accelerator,
@@ -39,6 +40,7 @@
 )
 from .test_pipeline_mixin import PipelineTesterMixin
 from .test_tensor_parallel_mixin import TensorParallelTesterMixin
+from .test_training_distributed_mixin import TrainingDistributedTesterMixin
 from .test_training_mixin import TrainingTesterMixin
 
 
@@ -166,84 +168,43 @@ def pipeline_model_mapping(self):
     def __init__(
         self,
         parent,
-        batch_size=13,
-        seq_length=7,
-        is_training=True,
-        use_input_mask=True,
         use_token_type_ids=False,
-        use_labels=True,
-        vocab_size=99,
-        hidden_size=32,
-        num_hidden_layers=2,
-        num_attention_heads=2,
-        num_key_value_heads=2,
-        intermediate_size=32,
-        hidden_act="gelu",
         hidden_dropout_prob=0.1,
         attention_probs_dropout_prob=0.1,
-        max_position_embeddings=512,
         type_vocab_size=16,
         type_sequence_label_size=2,
         initializer_range=0.02,
         num_labels=3,
         num_choices=4,
-        pad_token_id=0,
-        bos_token_id=1,
-        eos_token_id=2,
         is_decoder=False,
         scope=None,
-        expert_interval=1,
-        moe_layer_start_index=0,
-        moe_intermediate_size=16,
-        shared_expert_intermediate_size=36,
-        shared_expert_gate=True,
-        moe_num_shared_experts=2,
-        num_experts_per_tok=2,
-        num_experts=8,
         mamba_n_groups=1,
         mamba_n_heads=16,
         mamba_d_state=16,
         mamba_d_conv=4,
         mamba_expand=2,
         mamba_chunk_size=16,
+        **kwargs,
     ):
         self._verify_and_infer_model_attributes()
         self.parent = parent
-        self.batch_size = batch_size
-        self.seq_length = seq_length
-        self.is_training = is_training
-        self.use_input_mask = use_input_mask
+
+        # Apply shared text-model defaults, then let caller kwargs override
+        for key, default in _TEXT_MODEL_TESTER_DEFAULTS.items():
+            setattr(self, key, kwargs.pop(key, default))
+
+        # CausalLM-specific defaults (not shared with multimodal testers)
         self.use_token_type_ids = use_token_type_ids
-        self.use_labels = use_labels
-        self.vocab_size = vocab_size
-        self.hidden_size = hidden_size
-        self.num_hidden_layers = num_hidden_layers
-        self.num_attention_heads = num_attention_heads
-        self.num_key_value_heads = num_key_value_heads
-        self.intermediate_size = intermediate_size
-        self.hidden_act = hidden_act
         self.hidden_dropout_prob = hidden_dropout_prob
         self.attention_probs_dropout_prob = attention_probs_dropout_prob
-        self.max_position_embeddings = max_position_embeddings
         self.type_vocab_size = type_vocab_size
         self.type_sequence_label_size = type_sequence_label_size
         self.initializer_range = initializer_range
         self.num_labels = num_labels
         self.num_choices = num_choices
-        self.pad_token_id = pad_token_id
-        self.bos_token_id = bos_token_id
-        self.eos_token_id = eos_token_id
         self.scope = scope
         self.head_dim = self.hidden_size // self.num_attention_heads
         self.is_decoder = is_decoder
-        self.expert_interval = expert_interval
-        self.moe_layer_start_index = moe_layer_start_index
-        self.moe_intermediate_size = moe_intermediate_size
-        self.shared_expert_intermediate_size = shared_expert_intermediate_size
-        self.shared_expert_gate = shared_expert_gate
-        self.moe_num_shared_experts = moe_num_shared_experts
-        self.num_experts_per_tok = num_experts_per_tok
-        self.num_experts = num_experts
         self.mamba_n_groups = mamba_n_groups
         self.mamba_n_heads = mamba_n_heads
         self.mamba_d_state = mamba_d_state
@@ -252,6 +213,10 @@ def __init__(
         self.mamba_chunk_size = mamba_chunk_size
         self.tie_word_embeddings = False
 
+        # Any remaining kwargs become attributes (for model-specific params)
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
     def prepare_config_and_inputs(self):
         input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
 
@@ -307,7 +272,12 @@ def prepare_config_and_inputs_for_common(self):
 
 @require_torch
 class CausalLMModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, TrainingTesterMixin, TensorParallelTesterMixin
+    ModelTesterMixin,
+    GenerationTesterMixin,
+    PipelineTesterMixin,
+    TrainingTesterMixin,
+    TensorParallelTesterMixin,
+    TrainingDistributedTesterMixin,
 ):
     model_tester_class = None
     all_model_classes = None
diff --git a/tests/cli/test_serve.py b/tests/cli/test_serve.py
index c10c5da16362..b9781c3cd277 100644
--- a/tests/cli/test_serve.py
+++ b/tests/cli/test_serve.py
@@ -2154,6 +2154,193 @@ class TestToolCallGemma(_TestToolCallBase, unittest.TestCase):
     MODEL = "google/gemma-4-E2B-it"
 
 
+class _TestReasoningBase:
+    """Base class for reasoning integration tests. Subclasses set MODEL.
+
+    A single server is shared across all tests in a subclass via setUpClass.
+    """
+
+    MODEL: str
+    USER_PROMPT = "What is 17 * 23? Think briefly, then answer in one sentence."
+    EXPECTED_ANSWER = "391"
+    MAX_TOKENS = 512
+
+    @classmethod
+    def setUpClass(cls):
+        cls.serve, port = _start_serve()
+        cls.base_url = f"http://localhost:{port}"
+        cls.client = OpenAI(base_url=f"{cls.base_url}/v1", api_key="unused")
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.serve.kill_server()
+
+    @staticmethod
+    def _reasoning_field(obj):
+        """Return ``reasoning_content`` from a chat message or delta (handles model_extra)."""
+        return getattr(obj, "reasoning_content", None) or (obj.model_extra or {}).get("reasoning_content")
+
+    # ----- chat completions -----
+
+    def test_chat_non_streaming(self):
+        """Chat completions: non-streaming surfaces ``reasoning_content`` and strips delimiters."""
+        msg = (
+            self.client.chat.completions.create(
+                model=self.MODEL,
+                messages=[{"role": "user", "content": self.USER_PROMPT}],
+                stream=False,
+                max_tokens=self.MAX_TOKENS,
+                temperature=0.0,
+            )
+            .choices[0]
+            .message
+        )
+        reasoning = self._reasoning_field(msg)
+        self.assertIn(self.EXPECTED_ANSWER, reasoning or "", f"answer missing from reasoning: {reasoning!r}")
+        self.assertIn(self.EXPECTED_ANSWER, msg.content or "", f"answer missing from content: {msg.content!r}")
+        self.assertNotIn("<think>", msg.content or "")
+        self.assertNotIn("<|channel>", msg.content or "")
+        self.assertNotIn(reasoning.strip()[:30], msg.content or "")
+
+    def test_chat_streaming(self):
+        """Chat completions: streaming emits ``reasoning_content`` deltas; content stays clean."""
+        chunks = list(
+            self.client.chat.completions.create(
+                model=self.MODEL,
+                messages=[{"role": "user", "content": self.USER_PROMPT}],
+                stream=True,
+                max_tokens=self.MAX_TOKENS,
+                temperature=0.0,
+            )
+        )
+        reasoning_text = "".join(self._reasoning_field(c.choices[0].delta) or "" for c in chunks)
+        self.assertIn(self.EXPECTED_ANSWER, reasoning_text, f"answer missing from reasoning: {reasoning_text!r}")
+        content = "".join(c.choices[0].delta.content or "" for c in chunks)
+        self.assertIn(self.EXPECTED_ANSWER, content, f"answer missing from content: {content!r}")
+        self.assertNotIn("<think>", content)
+        self.assertNotIn("<|channel>", content)
+
+    def test_chat_multi_turn_round_trips_reasoning(self):
+        """Chat completions: reasoning_content from a prior turn round-trips through input."""
+        first = (
+            self.client.chat.completions.create(
+                model=self.MODEL,
+                messages=[{"role": "user", "content": self.USER_PROMPT}],
+                stream=False,
+                max_tokens=self.MAX_TOKENS,
+                temperature=0.0,
+            )
+            .choices[0]
+            .message
+        )
+        reasoning = self._reasoning_field(first)
+        self.assertTrue(reasoning)
+        second = self.client.chat.completions.create(
+            model=self.MODEL,
+            messages=[
+                {"role": "user", "content": self.USER_PROMPT},
+                {"role": "assistant", "content": first.content or "", "reasoning_content": reasoning},
+                {"role": "user", "content": "Now multiply that result by 2."},
+            ],
+            stream=False,
+            max_tokens=self.MAX_TOKENS,
+            temperature=0.0,
+        )
+        self.assertIsNotNone(second.choices[0].message.content)
+
+    # ----- responses -----
+
+    def test_response_non_streaming(self):
+        """Responses API: non-streaming includes a reasoning item before the message item."""
+        resp = self.client.responses.create(
+            model=self.MODEL,
+            input=self.USER_PROMPT,
+            stream=False,
+            max_output_tokens=self.MAX_TOKENS,
+            temperature=0.0,
+        )
+        types = [item.type for item in resp.output]
+        self.assertIn("reasoning", types, f"expected reasoning item, got types: {types}")
+        self.assertIn("message", types)
+        self.assertLess(types.index("reasoning"), types.index("message"))
+        reasoning_text = next(item for item in resp.output if item.type == "reasoning").content[0].text
+        self.assertIn(self.EXPECTED_ANSWER, reasoning_text, f"answer missing from reasoning: {reasoning_text!r}")
+        message_text = next(item for item in resp.output if item.type == "message").content[0].text
+        self.assertIn(self.EXPECTED_ANSWER, message_text, f"answer missing from message: {message_text!r}")
+        self.assertNotIn("<think>", message_text)
+        self.assertNotIn("<|channel>", message_text)
+
+    def test_response_streaming(self):
+        """Responses API: streaming emits reasoning_text events and a separate reasoning item."""
+        events = list(
+            self.client.responses.create(
+                model=self.MODEL,
+                input=self.USER_PROMPT,
+                stream=True,
+                max_output_tokens=self.MAX_TOKENS,
+                temperature=0.0,
+            )
+        )
+        added = [e for e in events if e.type == "response.output_item.added"]
+        self.assertGreaterEqual(len(added), 2)
+        self.assertEqual(added[0].item.type, "reasoning")
+        self.assertEqual(added[1].item.type, "message")
+        # Coherence: concat of reasoning_text.delta events == reasoning_text.done.text, and contains the answer.
+        reasoning_text = "".join(e.delta for e in events if e.type == "response.reasoning_text.delta")
+        done = next(e for e in events if e.type == "response.reasoning_text.done")
+        self.assertEqual(reasoning_text, done.text)
+        self.assertIn(self.EXPECTED_ANSWER, reasoning_text, f"answer missing from reasoning: {reasoning_text!r}")
+        content = "".join(e.delta for e in events if e.type == "response.output_text.delta")
+        self.assertIn(self.EXPECTED_ANSWER, content, f"answer missing from content: {content!r}")
+        self.assertNotIn("<think>", content)
+        self.assertNotIn("<|channel>", content)
+
+    def test_response_multi_turn_round_trips_reasoning(self):
+        """Responses API: ``reasoning`` items echoed back as input are accepted."""
+        first = self.client.responses.create(
+            model=self.MODEL,
+            input=self.USER_PROMPT,
+            stream=False,
+            max_output_tokens=self.MAX_TOKENS,
+            temperature=0.0,
+        )
+        reasoning_item = next((i for i in first.output if i.type == "reasoning"), None)
+        message_item = next((i for i in first.output if i.type == "message"), None)
+        self.assertIsNotNone(reasoning_item)
+        self.assertIsNotNone(message_item)
+        second = self.client.responses.create(
+            model=self.MODEL,
+            input=[
+                {"role": "user", "content": self.USER_PROMPT},
+                reasoning_item.model_dump(exclude_none=True),
+                {"role": "assistant", "content": message_item.content[0].text},
+                {"role": "user", "content": "Now multiply that result by 2."},
+            ],
+            stream=False,
+            max_output_tokens=self.MAX_TOKENS,
+            temperature=0.0,
+        )
+        self.assertEqual(second.status, "completed")
+
+
+@slow
+@require_serve
+@require_torch_accelerator
+class TestReasoningQwen(_TestReasoningBase, unittest.TestCase):
+    """Reasoning tests with Qwen3 (inline <think>...</think> tags)."""
+
+    MODEL = "Qwen/Qwen3-1.7B"
+
+
+@slow
+@require_serve
+@require_torch_accelerator
+class TestReasoningGemma(_TestReasoningBase, unittest.TestCase):
+    """Reasoning tests with Gemma 4 (response_schema-based thinking channel)."""
+
+    MODEL = "google/gemma-4-E2B-it"
+
+
 @slow
 @require_librosa
 @require_multipart
diff --git a/tests/fixtures/parakeet/expected_loss_tdt.json b/tests/fixtures/parakeet/expected_loss_tdt.json
new file mode 100644
index 000000000000..aee3c3f16c2b
--- /dev/null
+++ b/tests/fixtures/parakeet/expected_loss_tdt.json
@@ -0,0 +1,5 @@
+{
+  "num_samples": 2,
+  "expected_mean_loss": 0.528089,
+  "comment": "NeMo reference with sigma=0, HF-style mean reduction (per-sample / target_length, then average). Generated with https://gist.github.com/883ea42bf7d8ce2af42f3055627476a7"
+}
diff --git a/tests/fixtures/parakeet/expected_results_batch_tdt.json b/tests/fixtures/parakeet/expected_results_batch_tdt.json
new file mode 100644
index 000000000000..c6a37bad56e8
--- /dev/null
+++ b/tests/fixtures/parakeet/expected_results_batch_tdt.json
@@ -0,0 +1,9 @@
+{
+  "transcriptions": [
+    "mister Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.",
+    "Nor is mister Quilter's manner less interesting than his matter.",
+    "He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind.",
+    "He has grave doubts whether Sir Frederick Leighton's work is really Greek after all, and can discover in it but little of Rocky Ithaca.",
+    "Linnell's pictures are a sort of up guards an atom paintings, and Mason's exquisite idols are as national as a jingo poem. mister Burkett Foster's landscapes smile at one much in the same way that mister Carker used to flash his teeth. And mister John Collier gives his sitter a cheerful slap on the back, before he says, like a shampooer in a Turkish bath Next man"
+  ]
+}
diff --git a/tests/fixtures/parakeet/expected_results_batch_tdt_timestamp.json b/tests/fixtures/parakeet/expected_results_batch_tdt_timestamp.json
new file mode 100644
index 000000000000..f13d5aee8b5f
--- /dev/null
+++ b/tests/fixtures/parakeet/expected_results_batch_tdt_timestamp.json
@@ -0,0 +1,251 @@
+{
+  "transcriptions": [
+    "mister Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.",
+    "Nor is mister Quilter's manner less interesting than his matter.",
+    "He tells us that at this festive season of the year, with Christmas and roast beef looming before us, similes drawn from eating and its results occur most readily to the mind."
+  ],
+  "start_timestamps": [
+    [
+      0.24,
+      0.48,
+      0.64,
+      0.88,
+      1.12,
+      1.36,
+      1.44,
+      1.6,
+      1.76,
+      2.0,
+      2.16,
+      2.24,
+      2.4,
+      2.48,
+      2.56,
+      2.72,
+      2.88,
+      3.04,
+      3.12,
+      3.2800000000000002,
+      3.44,
+      3.6,
+      3.7600000000000002,
+      3.92,
+      4.08,
+      4.24,
+      4.4,
+      4.48,
+      4.72,
+      4.96,
+      5.36,
+      5.6000000000000005
+    ],
+    [
+      0.32,
+      0.64,
+      0.88,
+      1.04,
+      1.2,
+      1.44,
+      1.68,
+      1.84,
+      1.92,
+      2.0,
+      2.16,
+      2.4,
+      2.56,
+      2.72,
+      2.96,
+      3.12,
+      3.36,
+      3.6,
+      3.92,
+      4.16,
+      4.32
+    ],
+    [
+      0.32,
+      0.64,
+      0.72,
+      0.96,
+      1.12,
+      1.36,
+      1.6,
+      1.84,
+      2.08,
+      2.24,
+      2.48,
+      2.64,
+      2.8000000000000003,
+      2.88,
+      3.04,
+      3.2,
+      3.44,
+      3.68,
+      3.84,
+      4.08,
+      4.4,
+      4.5600000000000005,
+      4.72,
+      4.96,
+      5.12,
+      5.36,
+      5.5200000000000005,
+      5.68,
+      5.92,
+      6.16,
+      6.24,
+      6.4,
+      6.5600000000000005,
+      6.72,
+      6.96,
+      7.28,
+      7.6000000000000005,
+      7.92,
+      8.16,
+      8.32,
+      8.48,
+      8.72,
+      8.88,
+      8.96,
+      9.120000000000001,
+      9.28,
+      9.44,
+      9.68,
+      9.76,
+      9.92,
+      10.16,
+      10.24,
+      10.4,
+      10.64,
+      10.88,
+      10.96,
+      11.200000000000001,
+      11.36,
+      11.52,
+      11.84,
+      12.16
+    ]
+  ],
+  "end_timestamps": [
+    [
+      0.48,
+      0.64,
+      0.88,
+      1.12,
+      1.36,
+      1.44,
+      1.6,
+      1.76,
+      1.92,
+      2.16,
+      2.24,
+      2.4,
+      2.48,
+      2.56,
+      2.64,
+      2.88,
+      3.04,
+      3.12,
+      3.12,
+      3.44,
+      3.6,
+      3.7600000000000002,
+      3.92,
+      4.08,
+      4.24,
+      4.4,
+      4.48,
+      4.72,
+      4.96,
+      5.12,
+      5.6000000000000005,
+      5.6000000000000005
+    ],
+    [
+      0.64,
+      0.88,
+      1.04,
+      1.2,
+      1.44,
+      1.68,
+      1.84,
+      1.84,
+      2.0,
+      2.16,
+      2.4,
+      2.56,
+      2.72,
+      2.96,
+      3.12,
+      3.36,
+      3.6,
+      3.92,
+      4.16,
+      4.32,
+      4.32
+    ],
+    [
+      0.64,
+      0.72,
+      0.96,
+      1.12,
+      1.36,
+      1.6,
+      1.84,
+      2.08,
+      2.24,
+      2.48,
+      2.64,
+      2.8000000000000003,
+      2.88,
+      3.04,
+      3.2,
+      3.44,
+      3.68,
+      3.84,
+      3.84,
+      4.4,
+      4.5600000000000005,
+      4.72,
+      4.96,
+      5.12,
+      5.36,
+      5.5200000000000005,
+      5.68,
+      5.92,
+      6.16,
+      6.24,
+      6.4,
+      6.5600000000000005,
+      6.72,
+      6.96,
+      7.28,
+      7.28,
+      7.92,
+      8.16,
+      8.24,
+      8.48,
+      8.72,
+      8.88,
+      8.96,
+      9.120000000000001,
+      9.200000000000001,
+      9.44,
+      9.68,
+      9.76,
+      9.92,
+      10.16,
+      10.24,
+      10.4,
+      10.64,
+      10.88,
+      10.96,
+      11.200000000000001,
+      11.36,
+      11.52,
+      11.84,
+      12.16,
+      12.16
+    ]
+  ]
+}
diff --git a/tests/fixtures/parakeet/expected_results_single_tdt.json b/tests/fixtures/parakeet/expected_results_single_tdt.json
new file mode 100644
index 000000000000..a757d763b6a3
--- /dev/null
+++ b/tests/fixtures/parakeet/expected_results_single_tdt.json
@@ -0,0 +1,5 @@
+{
+  "transcriptions": [
+    "mister Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."
+  ]
+}
diff --git a/tests/fixtures/parakeet/expected_tdt_loss.json b/tests/fixtures/parakeet/expected_tdt_loss.json
new file mode 100644
index 000000000000..7c3ff498483f
--- /dev/null
+++ b/tests/fixtures/parakeet/expected_tdt_loss.json
@@ -0,0 +1,43 @@
+{
+  "seed": 42,
+  "batch_size": 2,
+  "max_t": 8,
+  "max_u": 4,
+  "vocab_size": 5,
+  "durations": [
+    0,
+    1,
+    2,
+    3,
+    4
+  ],
+  "targets": [
+    [
+      4,
+      2,
+      2,
+      1
+    ],
+    [
+      0,
+      4,
+      2,
+      4
+    ]
+  ],
+  "logit_lengths": [
+    8,
+    7
+  ],
+  "target_lengths": [
+    4,
+    3
+  ],
+  "expected_loss_sum": 21.978166580200195,
+  "expected_loss_mean": 3.124553918838501,
+  "expected_loss_none": [
+    12.923372268676758,
+    9.054794311523438
+  ],
+  "expected_loss_mean_sigma_0p05": 3.1921849250793457
+}
\ No newline at end of file
diff --git a/tests/fixtures/qwen3_asr/expected_results_batched.json b/tests/fixtures/qwen3_asr/expected_results_batched.json
new file mode 100644
index 000000000000..ff256f4a163d
--- /dev/null
+++ b/tests/fixtures/qwen3_asr/expected_results_batched.json
@@ -0,0 +1 @@
+{"transcriptions": ["system\n\nuser\n\nassistant\nlanguage English<asr_text>Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.", "system\n\nuser\n\nassistant\nlanguage Chinese<asr_text>甚至出现交易几乎停滞的情况。"], "token_ids": [[11528, 6364, 151704, 12275, 13, 3406, 2044, 374, 279, 38471, 273, 315, 279, 6149, 6846, 11, 323, 582, 525, 15713, 311, 10565, 806, 41482, 13, 151645], [11528, 8453, 151704, 100636, 100347, 99886, 100740, 118083, 102072, 1773, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645, 151645]]}
\ No newline at end of file
diff --git a/tests/fixtures/qwen3_asr/expected_results_single.json b/tests/fixtures/qwen3_asr/expected_results_single.json
new file mode 100644
index 000000000000..bb48e15f757e
--- /dev/null
+++ b/tests/fixtures/qwen3_asr/expected_results_single.json
@@ -0,0 +1 @@
+{"transcriptions": ["system\n\nuser\n\nassistant\nlanguage English<asr_text>Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel."], "token_ids": [[11528, 6364, 151704, 12275, 13, 3406, 2044, 374, 279, 38471, 273, 315, 279, 6149, 6846, 11, 323, 582, 525, 15713, 311, 10565, 806, 41482, 13, 151645]]}
\ No newline at end of file
diff --git a/tests/fixtures/qwen3_asr/expected_timestamps_batched.json b/tests/fixtures/qwen3_asr/expected_timestamps_batched.json
new file mode 100644
index 000000000000..35b893354446
--- /dev/null
+++ b/tests/fixtures/qwen3_asr/expected_timestamps_batched.json
@@ -0,0 +1,164 @@
+[
+  {
+    "language": "English",
+    "text": "Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.",
+    "time_stamps": [
+      {
+        "text": "Mr",
+        "start_time": 0.56,
+        "end_time": 0.8
+      },
+      {
+        "text": "Quilter",
+        "start_time": 0.8,
+        "end_time": 1.28
+      },
+      {
+        "text": "is",
+        "start_time": 1.28,
+        "end_time": 1.44
+      },
+      {
+        "text": "the",
+        "start_time": 1.44,
+        "end_time": 1.52
+      },
+      {
+        "text": "apostle",
+        "start_time": 1.52,
+        "end_time": 2.08
+      },
+      {
+        "text": "of",
+        "start_time": 2.08,
+        "end_time": 2.32
+      },
+      {
+        "text": "the",
+        "start_time": 2.32,
+        "end_time": 2.32
+      },
+      {
+        "text": "middle",
+        "start_time": 2.32,
+        "end_time": 2.56
+      },
+      {
+        "text": "classes",
+        "start_time": 2.56,
+        "end_time": 3.28
+      },
+      {
+        "text": "and",
+        "start_time": 3.36,
+        "end_time": 3.52
+      },
+      {
+        "text": "we",
+        "start_time": 3.52,
+        "end_time": 3.6
+      },
+      {
+        "text": "are",
+        "start_time": 3.6,
+        "end_time": 3.68
+      },
+      {
+        "text": "glad",
+        "start_time": 3.68,
+        "end_time": 4.08
+      },
+      {
+        "text": "to",
+        "start_time": 4.16,
+        "end_time": 4.16
+      },
+      {
+        "text": "welcome",
+        "start_time": 4.16,
+        "end_time": 4.64
+      },
+      {
+        "text": "his",
+        "start_time": 4.64,
+        "end_time": 4.8
+      },
+      {
+        "text": "gospel",
+        "start_time": 4.8,
+        "end_time": 5.44
+      }
+    ]
+  },
+  {
+    "language": "Chinese",
+    "text": "甚至出现交易几乎停滞的情况。",
+    "time_stamps": [
+      {
+        "text": "甚",
+        "start_time": 0.4,
+        "end_time": 0.72
+      },
+      {
+        "text": "至",
+        "start_time": 0.72,
+        "end_time": 0.96
+      },
+      {
+        "text": "出",
+        "start_time": 0.96,
+        "end_time": 1.12
+      },
+      {
+        "text": "现",
+        "start_time": 1.12,
+        "end_time": 1.52
+      },
+      {
+        "text": "交",
+        "start_time": 1.52,
+        "end_time": 1.76
+      },
+      {
+        "text": "易",
+        "start_time": 1.76,
+        "end_time": 2.0
+      },
+      {
+        "text": "几",
+        "start_time": 2.0,
+        "end_time": 2.24
+      },
+      {
+        "text": "乎",
+        "start_time": 2.24,
+        "end_time": 2.48
+      },
+      {
+        "text": "停",
+        "start_time": 2.48,
+        "end_time": 2.72
+      },
+      {
+        "text": "滞",
+        "start_time": 2.72,
+        "end_time": 2.88
+      },
+      {
+        "text": "的",
+        "start_time": 2.88,
+        "end_time": 3.04
+      },
+      {
+        "text": "情",
+        "start_time": 3.04,
+        "end_time": 3.36
+      },
+      {
+        "text": "况",
+        "start_time": 3.36,
+        "end_time": 3.68
+      }
+    ]
+  }
+]
\ No newline at end of file
diff --git a/tests/fixtures/qwen3_asr/expected_timestamps_single.json b/tests/fixtures/qwen3_asr/expected_timestamps_single.json
new file mode 100644
index 000000000000..1786d4a86ae3
--- /dev/null
+++ b/tests/fixtures/qwen3_asr/expected_timestamps_single.json
@@ -0,0 +1,91 @@
+{
+  "language": "English",
+  "text": "Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.",
+  "time_stamps": [
+    {
+      "text": "Mr",
+      "start_time": 0.56,
+      "end_time": 0.8
+    },
+    {
+      "text": "Quilter",
+      "start_time": 0.8,
+      "end_time": 1.28
+    },
+    {
+      "text": "is",
+      "start_time": 1.28,
+      "end_time": 1.44
+    },
+    {
+      "text": "the",
+      "start_time": 1.44,
+      "end_time": 1.52
+    },
+    {
+      "text": "apostle",
+      "start_time": 1.52,
+      "end_time": 2.08
+    },
+    {
+      "text": "of",
+      "start_time": 2.08,
+      "end_time": 2.32
+    },
+    {
+      "text": "the",
+      "start_time": 2.32,
+      "end_time": 2.32
+    },
+    {
+      "text": "middle",
+      "start_time": 2.32,
+      "end_time": 2.56
+    },
+    {
+      "text": "classes",
+      "start_time": 2.56,
+      "end_time": 3.28
+    },
+    {
+      "text": "and",
+      "start_time": 3.36,
+      "end_time": 3.52
+    },
+    {
+      "text": "we",
+      "start_time": 3.52,
+      "end_time": 3.6
+    },
+    {
+      "text": "are",
+      "start_time": 3.6,
+      "end_time": 3.68
+    },
+    {
+      "text": "glad",
+      "start_time": 3.68,
+      "end_time": 4.08
+    },
+    {
+      "text": "to",
+      "start_time": 4.16,
+      "end_time": 4.16
+    },
+    {
+      "text": "welcome",
+      "start_time": 4.16,
+      "end_time": 4.64
+    },
+    {
+      "text": "his",
+      "start_time": 4.64,
+      "end_time": 4.8
+    },
+    {
+      "text": "gospel",
+      "start_time": 4.8,
+      "end_time": 5.44
+    }
+  ]
+}
\ No newline at end of file
diff --git a/tests/generation/test_configuration_utils.py b/tests/generation/test_configuration_utils.py
index 36ddf4844d54..535f1306e462 100644
--- a/tests/generation/test_configuration_utils.py
+++ b/tests/generation/test_configuration_utils.py
@@ -504,6 +504,26 @@ def test_serialize_generation_min_p(self):
         min_k_logits_wrap = MinPLogitsWarper(min_p=new_config.min_p)
         self.assertEqual(min_k_logits_wrap.min_p, min_p)
 
+    def test_serialize_generation_p_less(self):
+        """Tests that GenerationConfig is serialized with `p_less` as `True`"""
+        p_less = True
+
+        generation_config = GenerationConfig(p_less=p_less, do_sample=True)
+        with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
+            generation_config.save_pretrained(tmp_dir)
+            new_config = GenerationConfig.from_pretrained(tmp_dir)
+        self.assertEqual(new_config.p_less, p_less)
+
+    def test_serialize_generation_p_less_norm(self):
+        """Tests that GenerationConfig is serialized with `p_less_norm` as `True`"""
+        p_less_norm = True
+
+        generation_config = GenerationConfig(p_less_norm=p_less_norm, do_sample=True)
+        with tempfile.TemporaryDirectory("test-generation-config") as tmp_dir:
+            generation_config.save_pretrained(tmp_dir)
+            new_config = GenerationConfig.from_pretrained(tmp_dir)
+        self.assertEqual(new_config.p_less_norm, p_less_norm)
+
     def test_serialize_generation_typical_p(self):
         """Tests that GenerationConfig is serialized and TypicalLogitsWarper is initialized with mass"""
         mass = 0.8
diff --git a/tests/generation/test_logits_process.py b/tests/generation/test_logits_process.py
index 83f170a4d555..7ba446f175ed 100644
--- a/tests/generation/test_logits_process.py
+++ b/tests/generation/test_logits_process.py
@@ -43,6 +43,8 @@
         MinPLogitsWarper,
         NoBadWordsLogitsProcessor,
         NoRepeatNGramLogitsProcessor,
+        PLessLogitsWarper,
+        PLessNormLogitsWarper,
         PrefixConstrainedLogitsProcessor,
         RepetitionPenaltyLogitsProcessor,
         SequenceBiasLogitsProcessor,
@@ -496,6 +498,168 @@ def test_min_p_dist_warper(self):
         # first batch should keep two tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2])
 
+    def test_p_less_dist_warper(self):
+        """
+        Create distributions of different relative entropies, where the expected post-warper
+        distribution is straightforward to verify.
+        """
+
+        p_less = True
+        input_ids = None
+
+        # Case 1: Low entropy distribution -> 1 token retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.6, 0.1, 0.1, 0.1, 0.1]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessLogitsWarper(p_less)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.6, 0.0, 0.0, 0.0, 0.0]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 2: Batch size 2 containing two mid entropy distributions
+        #     - 1st mid entropy distribution -> 2 tokens retained for sampling
+        #     - 2nd mid entropy distribution -> 3 tokens retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.3, 0.25, 0.2, 0.15, 0.1], [0.23, 0.22, 0.21, 0.19, 0.15]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessLogitsWarper(p_less)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.3, 0.25, 0.0, 0.0, 0.0], [0.23, 0.22, 0.21, 0.0, 0.0]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 3: High entropy distribution -> 4 tokens retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.205, 0.205, 0.205, 0.205, 0.18]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessLogitsWarper(p_less)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.205, 0.205, 0.205, 0.205, 0.0]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 4: Logits processor does not change logits in-place
+        logits = torch.log(
+            torch.tensor(
+                [[0.3, 0.25, 0.25, 0.1, 0.1]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        logits_copy = logits.clone()
+        p_less_warp = PLessLogitsWarper(p_less)
+        _ = p_less_warp(input_ids, logits)
+        torch.testing.assert_close(logits, logits_copy, rtol=1e-3, atol=1e-3)
+
+    def test_p_less_norm_dist_warper(self):
+        """
+        Create distributions of different relative entropies, where the expected post-warper
+        distribution is straightforward to verify.
+        """
+
+        p_less_norm = True
+        input_ids = None
+
+        # Case 1: Low entropy distribution -> 1 token retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.6, 0.1, 0.1, 0.1, 0.1]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessNormLogitsWarper(p_less_norm)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.6, 0.0, 0.0, 0.0, 0.0]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 2: Batch size 2 containing two mid entropy distributions
+        #     - 1st mid entropy distribution -> 2 tokens retained for sampling
+        #     - 2nd mid entropy distribution -> 3 tokens retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.5, 0.2, 0.15, 0.1, 0.05], [0.4, 0.3, 0.15, 0.1, 0.05]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessNormLogitsWarper(p_less_norm)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.5, 0.2, 0.0, 0.0, 0.0], [0.4, 0.3, 0.15, 0.0, 0.0]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 3: High entropy distribution -> all tokens retained for sampling
+        logits = torch.log(
+            torch.tensor(
+                [[0.2, 0.2, 0.2, 0.2, 0.2]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        p_less_warp = PLessNormLogitsWarper(p_less_norm)
+        filtered_logits = p_less_warp(input_ids, logits)
+        filtered_dist = torch.exp(filtered_logits)
+
+        expected_dist = torch.tensor(
+            [[0.2, 0.2, 0.2, 0.2, 0.2]],
+            device=torch_device,
+            dtype=torch.float,
+        )
+        torch.testing.assert_close(filtered_dist, expected_dist, rtol=1e-3, atol=1e-3)
+
+        # Case 4: Logits processor does not change logits in-place
+        logits = torch.log(
+            torch.tensor(
+                [[0.35, 0.3, 0.15, 0.15, 0.05]],
+                device=torch_device,
+                dtype=torch.float,
+            )
+        )
+        logits_copy = logits.clone()
+        p_less_warp = PLessNormLogitsWarper(p_less_norm)
+        _ = p_less_warp(input_ids, logits)
+        torch.testing.assert_close(logits, logits_copy, rtol=1e-3, atol=1e-3)
+
     def test_typical_dist_warper(self):
         input_ids = None
         vocab_size = 10
@@ -624,6 +788,11 @@ def test_eta_dist_warper(self):
         # first batch should keep 2 tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
         self.assertListEqual((filtered_dist != 0.0).to(torch.long).sum(dim=-1).tolist(), [2, 2])
 
+        # eta warper should fail fast when a previous processor fully masked a row.
+        fully_masked_scores = torch.full((1, vocab_size), -float("inf"), device=torch_device, dtype=torch.float)
+        with self.assertRaisesRegex(ValueError, "all logits set to -inf"):
+            eta_warp(input_ids, fully_masked_scores)
+
     def test_no_repeat_ngram_dist_processor(self):
         vocab_size = 3
         batch_size = 2
diff --git a/tests/generation/test_mtp.py b/tests/generation/test_mtp.py
new file mode 100644
index 000000000000..3da50482fb42
--- /dev/null
+++ b/tests/generation/test_mtp.py
@@ -0,0 +1,153 @@
+import unittest
+
+import torch
+
+from transformers import DeepseekV3Config, DeepseekV3ForCausalLM, Glm4MoeConfig, Glm4MoeForCausalLM
+from transformers.generation.candidate_generators import MTPCandidateGenerator
+from transformers.generation.configuration_utils import GenerationMode
+from transformers.testing_utils import require_torch
+
+
+DEEPSEEK_V3_TINY_KW = {
+    "hidden_size": 64,
+    "intermediate_size": 64,
+    "moe_intermediate_size": 32,
+    "num_hidden_layers": 2,
+    "num_attention_heads": 4,
+    "num_key_value_heads": 4,
+    "vocab_size": 100,
+    "kv_lora_rank": 16,
+    "q_lora_rank": 16,
+    "qk_rope_head_dim": 8,
+    "v_head_dim": 16,
+    "qk_nope_head_dim": 16,
+    "n_routed_experts": 4,
+    "first_k_dense_replace": 1,
+    "num_experts_per_tok": 2,
+    "n_group": 1,
+    "topk_group": 1,
+    "max_position_embeddings": 64,
+    "rope_parameters": {"rope_theta": 10000.0},
+}
+
+GLM4_MOE_TINY_KW = {
+    "hidden_size": 64,
+    "intermediate_size": 64,
+    "moe_intermediate_size": 32,
+    "num_hidden_layers": 2,
+    "num_attention_heads": 4,
+    "num_key_value_heads": 4,
+    "vocab_size": 100,
+    "n_routed_experts": 4,
+    "first_k_dense_replace": 1,
+    "num_experts_per_tok": 2,
+    "n_group": 1,
+    "topk_group": 1,
+    "max_position_embeddings": 64,
+    "rope_parameters": {"rope_theta": 10000.0},
+}
+
+
+@require_torch
+class MTPGenerationModeTest(unittest.TestCase):
+    def _attach_random_mtp(self, model):
+        model.mtp_candidate_generator = MTPCandidateGenerator(model).eval()
+        return model
+
+    def test_use_mtp_routes_to_mtp_mode(self):
+        cfg = DeepseekV3Config(num_nextn_predict_layers=1, **DEEPSEEK_V3_TINY_KW)
+        model = DeepseekV3ForCausalLM(cfg)
+        gc = model.generation_config
+        gc.use_mtp = True
+        gc.do_sample = False
+        self.assertEqual(gc.get_generation_mode(), GenerationMode.MTP_DECODING)
+
+    def test_use_mtp_on_greedy_matches_plain_greedy(self):
+        """With a random-init MTP generator, rejection is frequent; MTP should fall back to bonus tokens
+        from the base model and reproduce plain greedy decoding token-for-token."""
+        for K in (1, 2, 3):
+            torch.manual_seed(0)
+            cfg = DeepseekV3Config(num_nextn_predict_layers=K, **DEEPSEEK_V3_TINY_KW)
+            model = DeepseekV3ForCausalLM(cfg).eval()
+            self._attach_random_mtp(model)
+            ids = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long)
+            torch.manual_seed(0)
+            baseline = model.generate(ids, max_new_tokens=10, do_sample=False)
+            torch.manual_seed(0)
+            with_mtp = model.generate(ids, max_new_tokens=10, do_sample=False, use_mtp=True)
+            self.assertTrue(torch.equal(baseline, with_mtp), f"mismatch for K={K}: {baseline} vs {with_mtp}")
+
+    def test_use_mtp_without_generator_raises(self):
+        cfg = DeepseekV3Config(num_nextn_predict_layers=1, **DEEPSEEK_V3_TINY_KW)
+        model = DeepseekV3ForCausalLM(cfg).eval()
+        ids = torch.tensor([[1, 2, 3]], dtype=torch.long)
+        with self.assertRaisesRegex(ValueError, "MTPCandidateGenerator"):
+            model.generate(ids, max_new_tokens=3, do_sample=False, use_mtp=True)
+
+    def test_generator_requires_num_mtp(self):
+        cfg = DeepseekV3Config(num_nextn_predict_layers=0, **DEEPSEEK_V3_TINY_KW)
+        model = DeepseekV3ForCausalLM(cfg).eval()
+        with self.assertRaisesRegex(ValueError, "num_nextn_predict_layers"):
+            MTPCandidateGenerator(model)
+
+    def test_glm4_moe_greedy_match(self):
+        torch.manual_seed(0)
+        cfg = Glm4MoeConfig(num_nextn_predict_layers=2, **GLM4_MOE_TINY_KW)
+        model = Glm4MoeForCausalLM(cfg).eval()
+        self._attach_random_mtp(model)
+        ids = torch.tensor([[1, 2, 3, 4, 5]], dtype=torch.long)
+        torch.manual_seed(0)
+        baseline = model.generate(ids, max_new_tokens=8, do_sample=False)
+        torch.manual_seed(0)
+        with_mtp = model.generate(ids, max_new_tokens=8, do_sample=False, use_mtp=True)
+        self.assertTrue(torch.equal(baseline, with_mtp))
+
+
+@require_torch
+class MTPCandidateGeneratorTest(unittest.TestCase):
+    def test_constructs_matching_decoder_class(self):
+        cfg = DeepseekV3Config(num_nextn_predict_layers=2, **DEEPSEEK_V3_TINY_KW)
+        model = DeepseekV3ForCausalLM(cfg)
+        mtp = MTPCandidateGenerator(model)
+        self.assertEqual(mtp.num_mtp, 2)
+        self.assertEqual(len(mtp.layers), 2)
+        sample_base_layer = model.model.layers[0]
+        self.assertIsInstance(mtp.layers[0].mtp_block, type(sample_base_layer))
+
+    def test_glm4_moe_decoder_class(self):
+        cfg = Glm4MoeConfig(num_nextn_predict_layers=1, **GLM4_MOE_TINY_KW)
+        model = Glm4MoeForCausalLM(cfg)
+        mtp = MTPCandidateGenerator(model)
+        sample_base_layer = model.model.layers[0]
+        self.assertIsInstance(mtp.layers[0].mtp_block, type(sample_base_layer))
+
+    def test_model_base_unchanged_by_num_nextn_predict_layers(self):
+        """Setting `num_nextn_predict_layers > 0` must not modify the base model.
+        MTP lives entirely in the companion generator."""
+        cfg_a = DeepseekV3Config(num_nextn_predict_layers=0, **DEEPSEEK_V3_TINY_KW)
+        cfg_b = DeepseekV3Config(num_nextn_predict_layers=3, **DEEPSEEK_V3_TINY_KW)
+        torch.manual_seed(0)
+        model_a = DeepseekV3ForCausalLM(cfg_a)
+        torch.manual_seed(0)
+        model_b = DeepseekV3ForCausalLM(cfg_b)
+        self.assertEqual(len(model_a.model.layers), len(model_b.model.layers))
+        self.assertFalse(hasattr(model_a.model, "forward_mtp"))
+        self.assertFalse(hasattr(model_b.model, "forward_mtp"))
+
+
+@require_torch
+class MTPContinuousBatchingTest(unittest.TestCase):
+    def test_generate_batch_with_use_mtp_raises_not_implemented(self):
+        from transformers import GenerationConfig
+        from transformers.generation.configuration_utils import ContinuousBatchingConfig
+        from transformers.generation.continuous_batching import ContinuousBatchingManager
+
+        cfg = DeepseekV3Config(num_nextn_predict_layers=1, **DEEPSEEK_V3_TINY_KW)
+        model = DeepseekV3ForCausalLM(cfg).eval()
+        gc = GenerationConfig(use_mtp=True, max_new_tokens=4)
+        with self.assertRaisesRegex(NotImplementedError, "use_mtp=True"):
+            ContinuousBatchingManager(model, gc, ContinuousBatchingConfig())
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/generation/test_safety_checkers.py b/tests/generation/test_safety_checkers.py
new file mode 100644
index 000000000000..c22da8c9124a
--- /dev/null
+++ b/tests/generation/test_safety_checkers.py
@@ -0,0 +1,260 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+
+# Add examples directory to Python path to import BasicToxicityChecker
+examples_path = Path(__file__).parent.parent.parent / "examples"
+if str(examples_path) not in sys.path:
+    sys.path.insert(0, str(examples_path))
+
+from safe_generation import BasicToxicityChecker  # noqa: E402
+
+from transformers.generation.safety import SafetyResult  # noqa: E402
+from transformers.testing_utils import require_torch  # noqa: E402
+
+
+@require_torch
+class TestBasicToxicityChecker(unittest.TestCase):
+    """Test suite for BasicToxicityChecker."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.mock_tokenizer_patcher = patch("transformers.AutoTokenizer.from_pretrained")
+        self.mock_model_patcher = patch("transformers.AutoModelForSequenceClassification.from_pretrained")
+
+        self.mock_tokenizer = self.mock_tokenizer_patcher.start()
+        self.mock_model = self.mock_model_patcher.start()
+
+        # Configure mock tokenizer
+        mock_tokenizer_instance = Mock()
+
+        # Create a mock that can be unpacked as **kwargs
+        class MockTokenizerOutput(dict):
+            def to(self, device):
+                return self
+
+        mock_tokenizer_instance.return_value = MockTokenizerOutput({"input_ids": Mock(), "attention_mask": Mock()})
+        self.mock_tokenizer.return_value = mock_tokenizer_instance
+
+        # Configure mock model
+        self.mock_model_instance = Mock()
+        self.mock_model_instance.eval.return_value = None
+        self.mock_model_instance.to.return_value = None
+        self.mock_model.return_value = self.mock_model_instance
+
+    def tearDown(self):
+        """Clean up test fixtures."""
+        self.mock_tokenizer_patcher.stop()
+        self.mock_model_patcher.stop()
+
+    @patch("torch.cuda.is_available", return_value=False)
+    def test_init_with_defaults(self, mock_cuda):
+        """Test BasicToxicityChecker initialization with default parameters."""
+        checker = BasicToxicityChecker()
+
+        self.assertEqual(checker.model_name, "s-nlp/roberta_toxicity_classifier")
+        self.assertEqual(checker.threshold, 0.7)
+        self.assertEqual(checker.device, "cpu")
+        self.assertEqual(checker.supported_categories, ["toxicity"])
+
+    @patch("torch.cuda.is_available", return_value=True)
+    def test_init_with_cuda_available(self, mock_cuda):
+        """Test BasicToxicityChecker initialization when CUDA is available."""
+        checker = BasicToxicityChecker()
+        self.assertEqual(checker.device, "cuda")
+
+    def test_init_with_custom_params(self):
+        """Test BasicToxicityChecker initialization with custom parameters."""
+        checker = BasicToxicityChecker(model_name="custom/model", threshold=0.8, device="cpu")
+
+        self.assertEqual(checker.model_name, "custom/model")
+        self.assertEqual(checker.threshold, 0.8)
+        self.assertEqual(checker.device, "cpu")
+
+    def test_init_model_loading_failure(self):
+        """Test BasicToxicityChecker handles model loading failures gracefully."""
+        # Make model loading fail
+        self.mock_model.side_effect = Exception("Model not found")
+
+        with self.assertRaises(RuntimeError) as context:
+            BasicToxicityChecker()
+
+        self.assertIn("Failed to load toxicity model", str(context.exception))
+        self.assertIn("Model not found", str(context.exception))
+
+    @patch("torch.no_grad")
+    @patch("torch.nn.functional.softmax")
+    def test_safe_text_detection(self, mock_softmax, mock_no_grad):
+        """Test detection of safe (non-toxic) text."""
+        import torch
+
+        # Mock safe prediction (low toxicity score)
+        mock_outputs = Mock()
+        mock_outputs.logits = torch.tensor([[2.0, 0.5]])  # Non-toxic >> toxic
+        self.mock_model_instance.return_value = mock_outputs
+
+        # Mock softmax to return low toxicity probability
+        mock_softmax.return_value = torch.tensor([[0.8, 0.2]])  # [non-toxic, toxic]
+
+        checker = BasicToxicityChecker(threshold=0.7)
+        result = checker.check_safety("This is a nice, positive comment")
+
+        self.assertIsInstance(result, SafetyResult)
+        self.assertTrue(result.is_safe)
+        self.assertEqual(len(result.violations), 0)
+        self.assertIn("toxicity_score", result.metadata)
+        self.assertAlmostEqual(result.metadata["toxicity_score"], 0.2, places=5)
+
+    @patch("torch.no_grad")
+    @patch("torch.nn.functional.softmax")
+    def test_toxic_text_detection(self, mock_softmax, mock_no_grad):
+        """Test detection of toxic text."""
+        import torch
+
+        # Mock toxic prediction (high toxicity score)
+        mock_outputs = Mock()
+        mock_outputs.logits = torch.tensor([[0.2, 3.0]])  # Non-toxic << toxic
+        self.mock_model_instance.return_value = mock_outputs
+
+        # Mock softmax to return high toxicity probability
+        mock_softmax.return_value = torch.tensor([[0.15, 0.85]])  # [non-toxic, toxic]
+
+        checker = BasicToxicityChecker(threshold=0.7)
+        result = checker.check_safety("This is some toxic harmful content")
+
+        self.assertIsInstance(result, SafetyResult)
+        self.assertFalse(result.is_safe)
+        self.assertEqual(len(result.violations), 1)
+
+        violation = result.violations[0]
+        self.assertEqual(violation.category, "toxicity")
+        self.assertAlmostEqual(violation.confidence, 0.85, places=5)
+        self.assertIn("high", violation.severity)  # 0.85 should be "high" severity
+        self.assertIn("85.00%", violation.description)
+
+    def test_batch_processing(self):
+        """Test batch processing of multiple texts."""
+        import torch
+
+        with patch("torch.no_grad"), patch("torch.nn.functional.softmax") as mock_softmax:
+            # Mock mixed results
+            mock_outputs = Mock()
+            mock_outputs.logits = torch.tensor([[2.0, 0.5]])
+            self.mock_model_instance.return_value = mock_outputs
+            mock_softmax.return_value = torch.tensor([[0.8, 0.2]])  # Safe
+
+            checker = BasicToxicityChecker()
+            results = checker.check_safety(["Safe text", "Another safe text"])
+
+            self.assertIsInstance(results, list)
+            self.assertEqual(len(results), 2)
+            self.assertTrue(all(isinstance(r, SafetyResult) for r in results))
+
+    def test_empty_text_handling(self):
+        """Test handling of empty text input."""
+
+        checker = BasicToxicityChecker()
+        result = checker.check_safety("")
+
+        self.assertTrue(result.is_safe)
+        self.assertEqual(result.confidence, 1.0)
+        self.assertEqual(len(result.violations), 0)
+        self.assertEqual(result.metadata["reason"], "empty_text")
+
+    def test_whitespace_only_text_handling(self):
+        """Test handling of whitespace-only text input."""
+
+        checker = BasicToxicityChecker()
+        result = checker.check_safety("   \n\t   ")
+
+        self.assertTrue(result.is_safe)
+        self.assertEqual(result.confidence, 1.0)
+        self.assertEqual(len(result.violations), 0)
+        self.assertEqual(result.metadata["reason"], "empty_text")
+
+    @patch("safe_generation.checkers.logger")
+    def test_long_text_truncation(self, mock_logger):
+        """Test handling of very long text input."""
+        import torch
+
+        with patch("torch.no_grad"), patch("torch.nn.functional.softmax") as mock_softmax:
+            mock_outputs = Mock()
+            mock_outputs.logits = torch.tensor([[2.0, 0.5]])
+            self.mock_model_instance.return_value = mock_outputs
+            mock_softmax.return_value = torch.tensor([[0.8, 0.2]])
+
+            checker = BasicToxicityChecker()
+            long_text = "A" * 15000  # Longer than 10000 char limit
+            result = checker.check_safety(long_text)
+
+            self.assertIn("truncated", result.metadata)
+            self.assertTrue(result.metadata["truncated"])
+            self.assertEqual(result.metadata["original_length"], 15000)
+            self.assertEqual(result.metadata["processed_length"], 10000)
+            mock_logger.warning.assert_called_once()
+
+    def test_invalid_input_type(self):
+        """Test handling of invalid input types."""
+
+        checker = BasicToxicityChecker()
+
+        with self.assertRaises(TypeError) as context:
+            checker.check_safety(123)  # Not a string or list
+
+        self.assertIn("Expected string or list of strings", str(context.exception))
+
+    def test_severity_classification(self):
+        """Test severity classification logic."""
+
+        checker = BasicToxicityChecker()
+
+        # Test different severity levels
+        self.assertEqual(checker._get_severity(0.96), "critical")
+        self.assertEqual(checker._get_severity(0.90), "high")
+        self.assertEqual(checker._get_severity(0.80), "medium")
+        self.assertEqual(checker._get_severity(0.65), "low")
+
+    def test_get_config(self):
+        """Test get_config method returns correct configuration."""
+
+        checker = BasicToxicityChecker(model_name="test/model", threshold=0.8, device="cpu")
+
+        config = checker.get_config()
+        expected_config = {
+            "checker_type": "BasicToxicityChecker",
+            "model_name": "test/model",
+            "threshold": 0.8,
+            "device": "cpu",
+        }
+
+        self.assertEqual(config, expected_config)
+
+    @patch("torch.no_grad")
+    def test_inference_error_handling(self, mock_no_grad):
+        """Test handling of inference errors."""
+
+        # Make model inference fail
+        self.mock_model_instance.side_effect = RuntimeError("CUDA out of memory")
+
+        checker = BasicToxicityChecker()
+
+        with self.assertRaises(RuntimeError) as context:
+            checker.check_safety("test text")
+
+        self.assertIn("Toxicity detection failed", str(context.exception))
diff --git a/tests/generation/test_safety_config.py b/tests/generation/test_safety_config.py
new file mode 100644
index 000000000000..d72ff90fcf76
--- /dev/null
+++ b/tests/generation/test_safety_config.py
@@ -0,0 +1,382 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest.mock import Mock
+
+from transformers.generation.safety import (
+    LENIENT_PRESET,
+    MODERATE_PRESET,
+    STRICT_PRESET,
+    SafetyChecker,
+    SafetyConfig,
+)
+
+
+class TestSafetyConfig(unittest.TestCase):
+    """Test suite for SafetyConfig."""
+
+    def setUp(self):
+        """Set up mock checker for tests."""
+        self.mock_checker = Mock(spec=SafetyChecker)
+        self.mock_checker.supported_categories = ["toxicity"]
+
+    def test_default_config(self):
+        """Test SafetyConfig with default values."""
+        config = SafetyConfig()
+
+        # Check default values
+        self.assertFalse(config.enabled)
+        self.assertIsNone(config.checker)
+        self.assertIsNone(config.device)
+        self.assertFalse(config.return_violations)
+        self.assertFalse(config.return_metadata)
+        self.assertEqual(config.cache_size, 100)
+        self.assertEqual(config.unsafe_hash_limit, 1000)
+        self.assertEqual(config.sliding_window_size, 512)
+        self.assertTrue(config.incremental_checking)
+
+    def test_from_checker_basic(self):
+        """Test creating config from checker using from_checker (recommended pattern)."""
+        config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Verify config was created correctly
+        self.assertTrue(config.enabled)
+        self.assertIs(config.checker, self.mock_checker)
+        self.assertEqual(config.cache_size, 100)  # Default
+        self.assertFalse(config.return_violations)  # Default
+        self.assertFalse(config.return_metadata)  # Default
+
+    def test_from_checker_with_preset(self):
+        """Test creating config from checker with preset parameters."""
+        config = SafetyConfig.from_checker(self.mock_checker, **STRICT_PRESET)
+
+        self.assertTrue(config.enabled)
+        self.assertIs(config.checker, self.mock_checker)
+        self.assertEqual(config.cache_size, 50)
+        self.assertEqual(config.unsafe_hash_limit, 500)
+        self.assertTrue(config.return_violations)
+        self.assertTrue(config.return_metadata)
+
+    def test_from_checker_with_custom_params(self):
+        """Test creating config from checker with custom parameters."""
+        config = SafetyConfig.from_checker(self.mock_checker, cache_size=200, return_violations=True, device="cuda")
+
+        self.assertTrue(config.enabled)
+        self.assertIs(config.checker, self.mock_checker)
+        self.assertEqual(config.cache_size, 200)
+        self.assertTrue(config.return_violations)
+        self.assertEqual(config.device, "cuda")
+
+    def test_construct_checker_returns_instance(self):
+        """Test that construct_checker returns the provided checker instance."""
+        config = SafetyConfig.from_checker(self.mock_checker)
+        retrieved = config.construct_checker()
+        self.assertIs(retrieved, self.mock_checker)
+
+    def test_construct_checker_error_when_missing(self):
+        """Test that construct_checker raises helpful error when checker is missing."""
+        config = SafetyConfig(enabled=True)
+
+        with self.assertRaises(ValueError) as context:
+            config.construct_checker()
+
+        error_message = str(context.exception)
+        self.assertIn("SafetyConfig requires a checker instance", error_message)
+        self.assertIn("examples/safe_generation", error_message)
+        self.assertIn("BasicToxicityChecker", error_message)
+        self.assertIn("from_checker", error_message)
+
+    def test_serialization_round_trip(self):
+        """Test serialization and deserialization (note: checker not serialized)."""
+        original_config = SafetyConfig.from_checker(
+            self.mock_checker, cache_size=150, return_violations=True, device="cpu"
+        )
+
+        # Serialize to dict
+        config_dict = original_config.to_dict()
+
+        # Check dict contents (checker is not serialized)
+        self.assertEqual(config_dict["enabled"], True)
+        self.assertEqual(config_dict["cache_size"], 150)
+        self.assertEqual(config_dict["device"], "cpu")
+        self.assertTrue(config_dict["return_violations"])
+        self.assertNotIn("checker", config_dict)
+
+        # Deserialize from dict
+        restored_config = SafetyConfig.from_dict(config_dict)
+
+        # Check attributes match (except checker which isn't serialized)
+        self.assertEqual(restored_config.enabled, original_config.enabled)
+        self.assertEqual(restored_config.cache_size, original_config.cache_size)
+        self.assertEqual(restored_config.device, original_config.device)
+        self.assertIsNone(restored_config.checker)  # Checker must be re-provided
+
+        # Re-attach checker to restored config
+        restored_config.checker = self.mock_checker
+        retrieved = restored_config.construct_checker()
+        self.assertIs(retrieved, self.mock_checker)
+
+    def test_validation_success(self):
+        """Test validation with valid configuration."""
+        # Valid default config
+        config = SafetyConfig()
+        config.validate()  # Should not raise
+
+        # Valid config with checker
+        config = SafetyConfig.from_checker(self.mock_checker, return_violations=True)
+        config.validate()  # Should not raise
+
+    def test_validation_enabled_type(self):
+        """Test validation of enabled field."""
+        config = SafetyConfig(enabled="true")  # Wrong type
+        with self.assertRaises(ValueError) as context:
+            config.validate()
+        self.assertIn("enabled must be a boolean", str(context.exception))
+
+    def test_validation_output_config_types(self):
+        """Test validation of output configuration types."""
+        # Wrong return_violations type
+        config = SafetyConfig(return_violations="true")
+        with self.assertRaises(ValueError) as context:
+            config.validate()
+        self.assertIn("return_violations must be a boolean", str(context.exception))
+
+        # Wrong return_metadata type
+        config = SafetyConfig(return_metadata=1)
+        with self.assertRaises(ValueError) as context:
+            config.validate()
+        self.assertIn("return_metadata must be a boolean", str(context.exception))
+
+    def test_cache_size_configuration(self):
+        """Test cache size configuration and validation."""
+        # Test default cache size
+        config = SafetyConfig()
+        self.assertEqual(config.cache_size, 100)
+
+        # Test custom cache size
+        config = SafetyConfig(cache_size=50)
+        self.assertEqual(config.cache_size, 50)
+
+        # Test cache size validation - must be positive integer (caught in __post_init__)
+        with self.assertRaises(ValueError):
+            SafetyConfig(cache_size=0)
+
+        with self.assertRaises(ValueError):
+            SafetyConfig(cache_size=-1)
+
+        with self.assertRaises(TypeError):
+            SafetyConfig(cache_size=3.14)
+
+        with self.assertRaises(TypeError):
+            SafetyConfig(cache_size="100")
+
+    def test_unsafe_hash_limit_configuration(self):
+        """Test unsafe hash limit configuration and validation."""
+        # Test default unsafe hash limit
+        config = SafetyConfig()
+        self.assertEqual(config.unsafe_hash_limit, 1000)
+
+        # Test custom unsafe hash limit
+        config = SafetyConfig(unsafe_hash_limit=500)
+        self.assertEqual(config.unsafe_hash_limit, 500)
+
+        # Test validation - must be positive integer (caught in __post_init__)
+        with self.assertRaises(ValueError):
+            SafetyConfig(unsafe_hash_limit=0)
+
+        with self.assertRaises(ValueError):
+            SafetyConfig(unsafe_hash_limit=-1)
+
+        with self.assertRaises(TypeError):
+            SafetyConfig(unsafe_hash_limit=2.5)
+
+        with self.assertRaises(TypeError):
+            SafetyConfig(unsafe_hash_limit="1000")
+
+    def test_large_cache_size_warning(self):
+        """Test warning for potentially inefficient cache sizes."""
+        import warnings
+
+        # Test cache size warning
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            SafetyConfig(cache_size=20000).validate()
+            self.assertEqual(len(w), 1)
+            self.assertTrue("cache_size > 10000" in str(w[0].message))
+
+        # Test unsafe hash limit warning
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            SafetyConfig(unsafe_hash_limit=200000).validate()
+            self.assertEqual(len(w), 1)
+            self.assertTrue("unsafe_hash_limit > 100000" in str(w[0].message))
+
+    def test_preset_constants(self):
+        """Test that preset constants have expected values."""
+        # STRICT_PRESET
+        self.assertEqual(STRICT_PRESET["cache_size"], 50)
+        self.assertEqual(STRICT_PRESET["unsafe_hash_limit"], 500)
+        self.assertTrue(STRICT_PRESET["return_violations"])
+        self.assertTrue(STRICT_PRESET["return_metadata"])
+
+        # MODERATE_PRESET
+        self.assertEqual(MODERATE_PRESET["cache_size"], 100)
+        self.assertEqual(MODERATE_PRESET["unsafe_hash_limit"], 1000)
+        self.assertFalse(MODERATE_PRESET["return_violations"])
+        self.assertFalse(MODERATE_PRESET["return_metadata"])
+
+        # LENIENT_PRESET
+        self.assertEqual(LENIENT_PRESET["cache_size"], 200)
+        self.assertEqual(LENIENT_PRESET["unsafe_hash_limit"], 2000)
+        self.assertFalse(LENIENT_PRESET["return_violations"])
+        self.assertFalse(LENIENT_PRESET["return_metadata"])
+
+    def test_presets_with_from_checker(self):
+        """Test using presets with from_checker."""
+        # Test strict preset
+        strict_config = SafetyConfig.from_checker(self.mock_checker, **STRICT_PRESET)
+        self.assertEqual(strict_config.cache_size, 50)
+        self.assertEqual(strict_config.unsafe_hash_limit, 500)
+        self.assertTrue(strict_config.return_violations)
+        self.assertTrue(strict_config.return_metadata)
+
+        # Test moderate preset
+        moderate_config = SafetyConfig.from_checker(self.mock_checker, **MODERATE_PRESET)
+        self.assertEqual(moderate_config.cache_size, 100)
+        self.assertEqual(moderate_config.unsafe_hash_limit, 1000)
+        self.assertFalse(moderate_config.return_violations)
+
+        # Test lenient preset
+        lenient_config = SafetyConfig.from_checker(self.mock_checker, **LENIENT_PRESET)
+        self.assertEqual(lenient_config.cache_size, 200)
+        self.assertEqual(lenient_config.unsafe_hash_limit, 2000)
+        self.assertFalse(lenient_config.return_violations)
+
+    def test_serialization_includes_cache_config(self):
+        """Test that serialization includes cache configuration."""
+        config = SafetyConfig(cache_size=75, unsafe_hash_limit=750)
+        config_dict = config.to_dict()
+
+        self.assertEqual(config_dict["cache_size"], 75)
+        self.assertEqual(config_dict["unsafe_hash_limit"], 750)
+
+        # Test round-trip
+        restored_config = SafetyConfig.from_dict(config_dict)
+        self.assertEqual(restored_config.cache_size, 75)
+        self.assertEqual(restored_config.unsafe_hash_limit, 750)
+
+    def test_sliding_window_configuration(self):
+        """Test sliding window configuration parameters."""
+        # Test default values
+        config = SafetyConfig()
+        self.assertEqual(config.sliding_window_size, 512)
+        self.assertTrue(config.incremental_checking)
+
+        # Test custom values
+        config = SafetyConfig(sliding_window_size=256, incremental_checking=False)
+        self.assertEqual(config.sliding_window_size, 256)
+        self.assertFalse(config.incremental_checking)
+
+    def test_sliding_window_validation(self):
+        """Test validation of sliding window parameters."""
+        # Test valid sliding window size
+        config = SafetyConfig(sliding_window_size=100)
+        config.validate()  # Should not raise
+
+        # Test valid disabled sliding window
+        config = SafetyConfig(sliding_window_size=-1)
+        config.validate()  # Should not raise
+
+        # Test invalid sliding window size (0)
+        with self.assertRaises(ValueError) as context:
+            SafetyConfig(sliding_window_size=0)
+        self.assertIn("sliding_window_size must be a positive integer or -1 to disable", str(context.exception))
+
+        # Test invalid sliding window size (negative but not -1)
+        with self.assertRaises(ValueError) as context:
+            SafetyConfig(sliding_window_size=-5)
+        self.assertIn("sliding_window_size must be a positive integer or -1 to disable", str(context.exception))
+
+        # Test invalid incremental_checking type
+        with self.assertRaises(TypeError) as context:
+            SafetyConfig(incremental_checking="true")
+        self.assertIn("incremental_checking must be a boolean", str(context.exception))
+
+    def test_sliding_window_serialization(self):
+        """Test serialization of sliding window parameters."""
+        config = SafetyConfig(
+            sliding_window_size=256, incremental_checking=False, cache_size=50, unsafe_hash_limit=500
+        )
+
+        # Test to_dict includes sliding window parameters
+        config_dict = config.to_dict()
+        self.assertEqual(config_dict["sliding_window_size"], 256)
+        self.assertEqual(config_dict["incremental_checking"], False)
+
+        # Test round-trip serialization
+        restored_config = SafetyConfig.from_dict(config_dict)
+        self.assertEqual(restored_config.sliding_window_size, 256)
+        self.assertFalse(restored_config.incremental_checking)
+        self.assertEqual(restored_config.cache_size, 50)
+        self.assertEqual(restored_config.unsafe_hash_limit, 500)
+
+    def test_sliding_window_edge_cases(self):
+        """Test edge cases for sliding window configuration."""
+        # Test very large sliding window size
+        config = SafetyConfig(sliding_window_size=10000)
+        config.validate()  # Should be valid
+
+        # Test minimum sliding window size
+        config = SafetyConfig(sliding_window_size=1)
+        config.validate()  # Should be valid
+
+        # Test both sliding window and incremental checking disabled
+        config = SafetyConfig(sliding_window_size=-1, incremental_checking=False)
+        config.validate()  # Should be valid
+
+    def test_comprehensive_workflow(self):
+        """Test a complete workflow with SafetyConfig."""
+        # Create configuration using from_checker (recommended approach)
+        config = SafetyConfig.from_checker(
+            self.mock_checker, cache_size=50, return_violations=True, return_metadata=True
+        )
+
+        # Validate configuration
+        config.validate()
+
+        # Verify config was created correctly
+        self.assertTrue(config.enabled)
+        self.assertIs(config.checker, self.mock_checker)
+        self.assertEqual(config.cache_size, 50)
+        self.assertTrue(config.return_violations)
+
+        # Test construct_checker returns same instance
+        retrieved_checker = config.construct_checker()
+        self.assertIs(retrieved_checker, self.mock_checker)
+
+        # Serialize and deserialize (note: checker not serialized)
+        config_dict = config.to_dict()
+        restored_config = SafetyConfig.from_dict(config_dict)
+
+        # Verify consistency (except checker which isn't serialized)
+        self.assertEqual(config.enabled, restored_config.enabled)
+        self.assertEqual(config.cache_size, restored_config.cache_size)
+        self.assertIsNone(restored_config.checker)  # Checker must be re-provided after deserialization
+
+        # Re-attach checker to restored config
+        restored_config.checker = self.mock_checker
+
+        # Validate restored configuration
+        restored_config.validate()
diff --git a/tests/generation/test_safety_e2e.py b/tests/generation/test_safety_e2e.py
new file mode 100644
index 000000000000..311e39782789
--- /dev/null
+++ b/tests/generation/test_safety_e2e.py
@@ -0,0 +1,230 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import time
+import unittest
+from unittest.mock import Mock
+
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+from transformers.generation.safety import SafetyChecker, SafetyConfig, SafetyResult, SafetyViolation
+from transformers.testing_utils import require_torch, slow
+
+
+class TestSafetyEndToEnd(unittest.TestCase):
+    """End-to-end tests for safety-enabled generation with actual models."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    def _create_mock_checker(self):
+        """Create a mock safety checker for testing."""
+        # Create a mock checker that implements the SafetyChecker interface
+        mock_checker = Mock(spec=SafetyChecker)
+        mock_checker.supported_categories = ["toxicity"]
+        return mock_checker
+
+    @require_torch
+    @slow
+    def test_greedy_generation_with_safety(self):
+        """Test that safety works with greedy decoding generation."""
+        # Create mock checker
+        mock_checker = self._create_mock_checker()
+
+        # Mock safe responses
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Load small model for testing
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Create safety configuration with mock checker
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Create generation config with safety
+        gen_config = GenerationConfig(
+            max_length=20,
+            do_sample=False,  # Greedy
+            safety_config=safety_config,
+        )
+
+        # Test generation
+        inputs = tokenizer("Hello, world", return_tensors="pt")
+        outputs = model.generate(**inputs, generation_config=gen_config)
+
+        # Verify output is generated
+        self.assertGreater(outputs.shape[1], inputs["input_ids"].shape[1])
+
+        # Verify safety checker was called
+        mock_checker.check_safety.assert_called()
+
+    @require_torch
+    @slow
+    def test_sample_generation_with_safety(self):
+        """Test that safety works with sampling generation."""
+        mock_checker = self._create_mock_checker()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock safe responses
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Load small model
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Create safety configuration
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Test sampling with safety
+        inputs = tokenizer("Hello", return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=15, do_sample=True, temperature=0.8, safety_config=safety_config)
+
+        # Verify generation occurred
+        self.assertGreater(outputs.shape[1], inputs["input_ids"].shape[1])
+        mock_checker.check_safety.assert_called()
+
+    @require_torch
+    @slow
+    def test_beam_search_generation_with_safety(self):
+        """Test that safety works with beam search generation."""
+        mock_checker = self._create_mock_checker()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock safe responses
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Load small model
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Create safety configuration
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Test beam search with safety
+        inputs = tokenizer("The weather is", return_tensors="pt")
+        outputs = model.generate(**inputs, max_length=15, num_beams=2, safety_config=safety_config)
+
+        # Verify generation occurred
+        self.assertGreater(outputs.shape[1], inputs["input_ids"].shape[1])
+        mock_checker.check_safety.assert_called()
+
+    @require_torch
+    @slow
+    def test_safety_blocks_toxic_generation(self):
+        """Test that generation stops when toxic content is detected."""
+        mock_checker = self._create_mock_checker()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock unsafe response that should stop generation
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False,
+            confidence=0.85,
+            violations=[SafetyViolation("toxicity", 0.85, "high", "Toxic content detected")],
+            metadata={"toxicity_score": 0.85},
+        )
+
+        # Load small model
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Create safety configuration
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Test generation - should stop early due to safety
+        inputs = tokenizer("Test input", return_tensors="pt")
+        outputs = model.generate(
+            **inputs,
+            max_length=50,  # Allow long generation
+            safety_config=safety_config,
+        )
+
+        # Should stop early due to safety stopping criteria
+        # (The exact length depends on when safety check triggers)
+        self.assertLessEqual(outputs.shape[1], 50)
+        mock_checker.check_safety.assert_called()
+
+    @require_torch
+    @slow
+    def test_safety_disabled_backward_compatibility(self):
+        """Test that safety disabled doesn't affect normal generation."""
+        # No safety mocks needed - testing disabled safety
+
+        # Load small model
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        # Test without safety config (default behavior)
+        inputs = tokenizer("Hello world", return_tensors="pt")
+        outputs_no_safety = model.generate(**inputs, max_length=20, do_sample=False)
+
+        # Test with disabled safety config
+        safety_config = SafetyConfig(enabled=False, checker=None)
+        outputs_disabled_safety = model.generate(**inputs, max_length=20, do_sample=False, safety_config=safety_config)
+
+        # Results should be identical (since both use no safety)
+        # Note: Results might not be exactly identical due to random state,
+        # but both should generate successfully
+        self.assertEqual(outputs_no_safety.shape, outputs_disabled_safety.shape)
+
+    @require_torch
+    @slow
+    def test_performance_impact_measurement(self):
+        """Test that safety overhead is reasonable."""
+        # Load small model
+        model_name = "sshleifer/tiny-gpt2"
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        tokenizer.pad_token = tokenizer.eos_token
+
+        inputs = tokenizer("Performance test", return_tensors="pt")
+
+        # Measure baseline (no safety)
+        start_time = time.time()
+        for _ in range(3):  # Multiple runs for more stable timing
+            model.generate(**inputs, max_length=20, do_sample=False)
+        baseline_time = time.time() - start_time
+
+        # Set up safety mocks for performance test
+        mock_checker = self._create_mock_checker()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Measure with safety enabled
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        start_time = time.time()
+        for _ in range(3):  # Multiple runs for more stable timing
+            model.generate(**inputs, max_length=20, do_sample=False, safety_config=safety_config)
+        safety_time = time.time() - start_time
+
+        # Calculate overhead percentage
+        overhead_percent = ((safety_time - baseline_time) / baseline_time) * 100
+
+        # Assert that overhead is reasonable (less than 50% for this simple test)
+        # Note: In real usage, overhead would be much less due to check_interval optimization
+        self.assertLess(overhead_percent, 50, f"Safety overhead of {overhead_percent:.1f}% is too high")
+
+        print(f"Safety overhead: {overhead_percent:.1f}%")
diff --git a/tests/generation/test_safety_integration.py b/tests/generation/test_safety_integration.py
new file mode 100644
index 000000000000..0bb835ed7fb9
--- /dev/null
+++ b/tests/generation/test_safety_integration.py
@@ -0,0 +1,497 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import sys
+import unittest
+from pathlib import Path
+from unittest.mock import Mock, patch
+
+import torch
+
+
+# Add examples directory to Python path to import BasicToxicityChecker
+examples_path = Path(__file__).parent.parent.parent / "examples"
+if str(examples_path) not in sys.path:
+    sys.path.insert(0, str(examples_path))
+
+from safe_generation import BasicToxicityChecker  # noqa: E402
+
+from transformers.generation.configuration_utils import GenerationConfig  # noqa: E402
+from transformers.generation.safety import (  # noqa: E402
+    LENIENT_PRESET,
+    MODERATE_PRESET,
+    STRICT_PRESET,
+    SafetyChecker,
+    SafetyConfig,
+    SafetyResult,
+    SafetyViolation,
+)
+from transformers.generation.safety.processors import SafetyLogitsProcessor, SafetyStoppingCriteria  # noqa: E402
+from transformers.testing_utils import require_torch  # noqa: E402
+
+
+class TestSafetyIntegration(unittest.TestCase):
+    """Integration tests for the complete safety checking workflow."""
+
+    def setUp(self):
+        """Set up mock safety checker for tests."""
+        self.mock_checker = Mock(spec=SafetyChecker)
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+        self.mock_checker.supported_categories = ["toxicity"]
+
+    def test_complete_safety_workflow(self):
+        """Test end-to-end safety checking workflow from configuration to results."""
+        # Step 1: Create and validate configuration
+        config = SafetyConfig.from_checker(self.mock_checker, **STRICT_PRESET)
+        config.validate()
+
+        # Verify configuration is set up correctly with STRICT preset values
+        self.assertTrue(config.enabled)
+        self.assertEqual(config.cache_size, 50)  # STRICT_PRESET value
+        self.assertEqual(config.unsafe_hash_limit, 500)  # STRICT_PRESET value
+        self.assertTrue(config.return_violations)  # STRICT_PRESET value
+        self.assertTrue(config.return_metadata)  # STRICT_PRESET value
+
+        # Step 2: Test configuration serialization workflow
+        config_dict = config.to_dict()
+        restored_config = SafetyConfig.from_dict(config_dict)
+        restored_config.validate()
+
+        # Verify serialization preserved configuration (except checker which isn't serialized)
+        self.assertEqual(config.cache_size, restored_config.cache_size)
+        self.assertEqual(config.enabled, restored_config.enabled)
+        self.assertEqual(config.return_violations, restored_config.return_violations)
+        self.assertIsNone(restored_config.checker)  # Checker not serialized
+
+        # Step 3: Test construct_checker returns the provided instance
+        retrieved_checker = config.construct_checker()
+        self.assertIs(retrieved_checker, self.mock_checker)
+
+    @require_torch
+    @patch("transformers.AutoTokenizer.from_pretrained")
+    @patch("transformers.AutoModelForSequenceClassification.from_pretrained")
+    def test_config_to_checker_integration(self, mock_model, mock_tokenizer):
+        """Test creating checker instance and using it with SafetyConfig."""
+        # Set up mocks
+        mock_tokenizer_instance = Mock()
+        mock_inputs = Mock()
+        mock_inputs.to.return_value = mock_inputs
+        mock_tokenizer_instance.return_value = mock_inputs
+        mock_tokenizer.return_value = mock_tokenizer_instance
+
+        mock_model_instance = Mock()
+        mock_model_instance.eval.return_value = None
+        mock_model_instance.to.return_value = None
+        mock_model.return_value = mock_model_instance
+
+        # User creates checker instance
+        checker = BasicToxicityChecker(threshold=0.8)
+
+        # Verify checker was created with correct configuration
+        self.assertEqual(checker.threshold, 0.8)
+        self.assertEqual(checker.model_name, "s-nlp/roberta_toxicity_classifier")  # Default
+        self.assertEqual(checker.supported_categories, ["toxicity"])
+
+        # Create SafetyConfig from checker instance (recommended pattern)
+        config = SafetyConfig.from_checker(checker, return_violations=True)
+
+        # Verify config was created correctly
+        self.assertTrue(config.enabled)
+        self.assertIs(config.checker, checker)
+        self.assertTrue(config.return_violations)
+
+        # Test that construct_checker returns the same instance
+        retrieved_checker = config.construct_checker()
+        self.assertIs(retrieved_checker, checker)
+
+        # Test checker configuration serialization
+        checker_config_dict = checker.get_config()
+        expected_config = {
+            "checker_type": "BasicToxicityChecker",
+            "model_name": "s-nlp/roberta_toxicity_classifier",
+            "threshold": 0.8,
+            "device": checker.device,
+        }
+        self.assertEqual(checker_config_dict, expected_config)
+
+    def test_utility_functions_integration(self):
+        """Test integration of utility functions with configurations."""
+        from transformers.generation.safety.utils import validate_safety_config
+
+        # Test validation utility with various configurations
+        configs_to_test = [
+            SafetyConfig(),  # Default
+            SafetyConfig.from_checker(self.mock_checker, **STRICT_PRESET),
+            SafetyConfig.from_checker(self.mock_checker, **MODERATE_PRESET),
+            SafetyConfig.from_checker(self.mock_checker, **LENIENT_PRESET),
+        ]
+
+        for config in configs_to_test:
+            self.assertTrue(validate_safety_config(config))
+
+        # Test with invalid configuration (invalid cache_size)
+        with self.assertRaises(ValueError):
+            # __post_init__ will raise ValueError for invalid cache_size
+            SafetyConfig(cache_size=0)
+
+    def test_safety_result_structure(self):
+        """Test that SafetyResult and SafetyViolation work correctly together."""
+        # Create a violation
+        violation = SafetyViolation(
+            category="toxicity",
+            confidence=0.85,
+            severity="high",
+            description="Detected toxic content with 85% confidence",
+        )
+
+        # Create a safety result
+        result = SafetyResult(
+            is_safe=False,
+            confidence=0.85,
+            violations=[violation],
+            metadata={"model_name": "unitary/toxic-bert", "toxicity_score": 0.85, "threshold": 0.7},
+        )
+
+        # Verify structure
+        self.assertFalse(result.is_safe)
+        self.assertEqual(result.confidence, 0.85)
+        self.assertEqual(len(result.violations), 1)
+
+        violation = result.violations[0]
+        self.assertEqual(violation.category, "toxicity")
+        self.assertEqual(violation.confidence, 0.85)
+        self.assertEqual(violation.severity, "high")
+
+        # Test metadata
+        self.assertIn("model_name", result.metadata)
+        self.assertEqual(result.metadata["threshold"], 0.7)
+
+    def test_configuration_levels_produce_different_behaviors(self):
+        """Test that different preset levels produce appropriate settings."""
+        # Test all predefined presets
+        strict = SafetyConfig.from_checker(self.mock_checker, **STRICT_PRESET)
+        moderate = SafetyConfig.from_checker(self.mock_checker, **MODERATE_PRESET)
+        lenient = SafetyConfig.from_checker(self.mock_checker, **LENIENT_PRESET)
+
+        # Verify cache sizes are different and logical (strict < moderate < lenient)
+        self.assertEqual(strict.cache_size, 50)
+        self.assertEqual(moderate.cache_size, 100)
+        self.assertEqual(lenient.cache_size, 200)
+        self.assertLess(strict.cache_size, moderate.cache_size)
+        self.assertLess(moderate.cache_size, lenient.cache_size)
+
+        # Verify unsafe hash limits follow same pattern
+        self.assertEqual(strict.unsafe_hash_limit, 500)
+        self.assertEqual(moderate.unsafe_hash_limit, 1000)
+        self.assertEqual(lenient.unsafe_hash_limit, 2000)
+        self.assertLess(strict.unsafe_hash_limit, moderate.unsafe_hash_limit)
+        self.assertLess(moderate.unsafe_hash_limit, lenient.unsafe_hash_limit)
+
+        # Verify output configuration differences
+        self.assertTrue(strict.return_violations)
+        self.assertTrue(strict.return_metadata)
+
+        self.assertFalse(moderate.return_violations)
+        self.assertFalse(lenient.return_violations)
+
+    def test_error_handling_throughout_workflow(self):
+        """Test error handling across the complete workflow."""
+        # Test configuration validation errors - invalid cache_size
+        with self.assertRaises(ValueError):
+            SafetyConfig(cache_size=-1)
+
+        # Test configuration validation errors - invalid unsafe_hash_limit
+        with self.assertRaises(ValueError):
+            SafetyConfig(unsafe_hash_limit=0)
+
+        # Test construct_checker without providing checker raises error
+        config = SafetyConfig(enabled=True)
+        with self.assertRaises(ValueError) as context:
+            config.construct_checker()
+        self.assertIn("SafetyConfig requires a checker instance", str(context.exception))
+
+        # Test invalid return_violations type
+        with self.assertRaises(ValueError) as context:
+            config = SafetyConfig(return_violations="true")  # Wrong type
+            config.validate()
+        self.assertIn("return_violations must be a boolean", str(context.exception))
+
+    def test_public_api_imports(self):
+        """Test that all public API components can be imported correctly."""
+        # Test core imports
+        from transformers.generation.safety import SafetyChecker, SafetyConfig
+
+        # Verify classes are properly available
+        self.assertTrue(hasattr(SafetyChecker, "check_safety"))
+        self.assertTrue(hasattr(SafetyChecker, "supported_categories"))
+
+        # Test SafetyConfig factory
+        config = SafetyConfig.from_checker(self.mock_checker, **MODERATE_PRESET)
+        self.assertIsInstance(config, SafetyConfig)
+
+        # Test torch-dependent import
+        from transformers.utils import is_torch_available
+
+        # Note: BasicToxicityChecker is a reference implementation in examples/safe_generation
+        # Core transformers only provides the SafetyChecker ABC
+        if is_torch_available():
+            # Verify BasicToxicityChecker is available from examples
+            from safe_generation import BasicToxicityChecker
+
+            self.assertTrue(issubclass(BasicToxicityChecker, SafetyChecker))
+
+
+class TestGenerationConfigIntegration(unittest.TestCase):
+    """Tests for safety integration with GenerationConfig and generation pipeline."""
+
+    def setUp(self):
+        """Set up mock safety checker for tests."""
+        self.mock_checker = Mock(spec=SafetyChecker)
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+        self.mock_checker.supported_categories = ["toxicity"]
+
+    def test_generation_config_accepts_safety_config(self):
+        """Test that GenerationConfig properly accepts and stores safety_config."""
+        safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Test direct parameter
+        gen_config = GenerationConfig(max_length=100, safety_config=safety_config)
+
+        self.assertIsNotNone(gen_config.safety_config)
+        self.assertEqual(gen_config.safety_config.enabled, True)
+        # Check preset fields instead of non-existent thresholds
+        self.assertEqual(gen_config.safety_config.cache_size, 100)  # MODERATE_PRESET default
+
+        # Test None safety_config
+        gen_config_none = GenerationConfig(max_length=100)
+        self.assertIsNone(gen_config_none.safety_config)
+
+        # Test update method
+        gen_config_update = GenerationConfig(max_length=100)
+        gen_config_update.update(safety_config=safety_config)
+        self.assertIsNotNone(gen_config_update.safety_config)
+
+    @require_torch
+    @patch("safe_generation.BasicToxicityChecker")
+    def test_generation_mixin_creates_safety_processors(self, mock_checker_class):
+        """Test that GenerationMixin creates safety processors when configured."""
+        # Mock the checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        mock_checker_class.return_value = mock_checker
+
+        # Create a simple model mock with GenerationMixin methods
+        from transformers.generation.utils import GenerationMixin
+
+        model = Mock(spec=GenerationMixin)
+        model.config = Mock()
+        model.config.vocab_size = 1000
+        model.device = torch.device("cpu")
+
+        # Add the methods and required attributes
+        model._create_safety_processor = GenerationMixin._create_safety_processor.__get__(model)
+        model.tokenizer = Mock()  # Add tokenizer mock
+
+        # Mock tokenizer methods
+        model.tokenizer.decode = Mock(return_value="test text")
+        model.tokenizer.convert_tokens_to_ids = Mock(return_value=123)
+        model.tokenizer.unk_token_id = 0
+
+        # Test with safety enabled
+        mock_checker_instance = Mock(spec=SafetyChecker)
+        safety_config = SafetyConfig.from_checker(mock_checker_instance)
+
+        # Test logits processor creation
+        logits_processor = model._create_safety_processor(safety_config, "logits")
+        self.assertIsInstance(logits_processor, SafetyLogitsProcessor)
+
+        # Test stopping criteria creation
+        stopping_criteria = model._create_safety_processor(safety_config, "stopping")
+        self.assertIsInstance(stopping_criteria, SafetyStoppingCriteria)
+
+        # Test with safety disabled
+        disabled_config = SafetyConfig(enabled=False)
+        self.assertIsNone(model._create_safety_processor(disabled_config, "logits"))
+        self.assertIsNone(model._create_safety_processor(disabled_config, "stopping"))
+
+        # Test with None config
+        self.assertIsNone(model._create_safety_processor(None, "logits"))
+
+    @require_torch
+    @patch("safe_generation.BasicToxicityChecker")
+    def test_logits_processor_integration(self, mock_checker_class):
+        """Test integration of safety with logits processor pipeline."""
+        # Mock checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False,
+            confidence=0.9,
+            violations=[SafetyViolation("toxicity", 0.9, "high", "Toxic content detected")],
+            metadata={},
+        )
+        mock_checker_class.return_value = mock_checker
+
+        # Create processor
+        safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+        mock_tokenizer.decode.return_value = "test text"
+        mock_tokenizer.convert_tokens_to_ids.return_value = 123
+        mock_tokenizer.unk_token_id = 0
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Create test data
+        batch_size = 2
+        vocab_size = 1000
+        sequence_length = 5
+
+        input_ids = torch.randint(0, vocab_size, (batch_size, sequence_length))
+        scores = torch.randn(batch_size, vocab_size)
+
+        # Process scores
+        processed_scores = processor(input_ids, scores)
+
+        # Verify scores were modified (top tokens should be suppressed)
+        self.assertFalse(torch.equal(scores, processed_scores))
+
+        # Verify checker was called
+        mock_checker.check_safety.assert_called()
+
+    @require_torch
+    @patch("safe_generation.BasicToxicityChecker")
+    def test_stopping_criteria_integration(self, mock_checker_class):
+        """Test integration of safety with stopping criteria pipeline."""
+        # Mock checker with unsafe result
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False,
+            confidence=0.9,
+            violations=[SafetyViolation("toxicity", 0.9, "high", "Toxic content")],
+            metadata={},
+        )
+        mock_checker_class.return_value = mock_checker
+
+        # Create stopping criteria
+        safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+        mock_tokenizer.decode.return_value = "test text"
+
+        criteria = SafetyStoppingCriteria(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Create test data
+        batch_size = 2
+        vocab_size = 1000
+        sequence_length = 10
+
+        input_ids = torch.randint(0, vocab_size, (batch_size, sequence_length))
+        scores = torch.randn(batch_size, vocab_size)
+
+        # Test stopping decision
+        should_stop = criteria(input_ids, scores)
+
+        # Should stop due to unsafe content
+        self.assertTrue(should_stop.any())
+
+        # Verify checker was called
+        mock_checker.check_safety.assert_called()
+
+    def test_backward_compatibility(self):
+        """Test that existing generation code works without safety configuration."""
+        # Test GenerationConfig without safety
+        gen_config = GenerationConfig(max_length=100, temperature=0.8, top_p=0.9)
+
+        self.assertIsNone(gen_config.safety_config)
+        self.assertEqual(gen_config.max_length, 100)
+        self.assertEqual(gen_config.temperature, 0.8)
+
+        # Test that to_dict/from_dict works
+        config_dict = gen_config.to_dict()
+        restored = GenerationConfig.from_dict(config_dict)
+
+        self.assertEqual(restored.max_length, 100)
+        self.assertIsNone(restored.safety_config)
+
+    def test_safety_config_serialization_in_generation_config(self):
+        """Test that safety_config is properly serialized with GenerationConfig."""
+        safety_config = SafetyConfig.from_checker(self.mock_checker, return_violations=True)
+
+        gen_config = GenerationConfig(max_length=100, safety_config=safety_config)
+
+        # Test to_dict
+        config_dict = gen_config.to_dict()
+        self.assertIn("safety_config", config_dict)
+
+        # Test from_dict
+        restored = GenerationConfig.from_dict(config_dict)
+        self.assertIsNotNone(restored.safety_config)
+        self.assertEqual(restored.safety_config.enabled, True)
+        self.assertTrue(restored.safety_config.return_violations)
+
+    def test_error_handling_in_generation_integration(self):
+        """Test error handling in generation pipeline integration."""
+        # Test invalid safety config type
+        with self.assertRaises((TypeError, AttributeError)):
+            GenerationConfig(safety_config="invalid")
+
+        # Test invalid processor type
+        from transformers.generation.utils import GenerationMixin
+
+        model = Mock(spec=GenerationMixin)
+        model._create_safety_processor = GenerationMixin._create_safety_processor.__get__(model)
+        model.tokenizer = Mock()  # Add tokenizer mock
+
+        # Create config with mock checker
+        safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Should raise ValueError for invalid processor type
+        with self.assertRaises(ValueError) as context:
+            model._create_safety_processor(safety_config, "invalid_type")
+        self.assertIn("processor_type must be 'logits' or 'stopping'", str(context.exception))
+
+    @require_torch
+    def test_end_to_end_safety_integration(self):
+        """Test complete end-to-end safety integration workflow."""
+        # Create safety configuration
+        safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+        # Create generation configuration with safety
+        gen_config = GenerationConfig(max_length=50, temperature=0.8, safety_config=safety_config)
+
+        # Verify safety config is properly stored
+        self.assertIsNotNone(gen_config.safety_config)
+        self.assertEqual(gen_config.safety_config.enabled, True)
+
+        # Test serialization round-trip
+        config_dict = gen_config.to_dict()
+        restored_config = GenerationConfig.from_dict(config_dict)
+
+        self.assertIsNotNone(restored_config.safety_config)
+        self.assertEqual(restored_config.safety_config.enabled, True)
+        self.assertEqual(restored_config.safety_config.cache_size, safety_config.cache_size)
+
+        # Verify non-safety parameters are preserved
+        self.assertEqual(restored_config.max_length, 50)
+        self.assertEqual(restored_config.temperature, 0.8)
diff --git a/tests/generation/test_safety_processors.py b/tests/generation/test_safety_processors.py
new file mode 100644
index 000000000000..de2d9f75bb85
--- /dev/null
+++ b/tests/generation/test_safety_processors.py
@@ -0,0 +1,1204 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest.mock import Mock
+
+import torch
+
+from transformers.generation.safety import (
+    LENIENT_PRESET,
+    MODERATE_PRESET,
+    STRICT_PRESET,
+    SafetyConfig,
+    SafetyMetrics,
+    SafetyResult,
+    SafetyState,
+    SafetyViolation,
+)
+from transformers.generation.safety.processors import (
+    SafetyLogitsProcessor,
+    SafetyStoppingCriteria,
+    _generate_cache_key,
+)
+from transformers.testing_utils import require_torch
+
+
+@require_torch
+class TestSafetyLogitsProcessor(unittest.TestCase):
+    """Test SafetyLogitsProcessor functionality."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        # Mock safety checker
+        self.mock_checker = Mock()
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+
+        # Mock tokenizer
+        self.mock_tokenizer = Mock()
+        self.mock_tokenizer.decode.return_value = "test text"
+
+        # Safety config
+        self.safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+    def test_safe_content_no_suppression(self):
+        """Test that safe content passes through without modification."""
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Test safe content (mock already returns safe result)
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+        original_scores = scores.clone()
+
+        # Process
+        modified_scores = processor(input_ids, scores)
+
+        # Scores should be unchanged for safe content
+        torch.testing.assert_close(modified_scores, original_scores)
+
+        # Verify safety check was called
+        self.mock_checker.check_safety.assert_called_once()
+
+    def test_unsafe_content_blocking(self):
+        """Test that unsafe content gets all tokens suppressed (blocking)."""
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Mock unsafe result
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+        )
+
+        # Test data
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+        vocab_size = scores.shape[-1]
+
+        # Process
+        modified_scores = processor(input_ids, scores)
+
+        # All tokens should be suppressed (blocking strategy)
+        for i in range(vocab_size):
+            self.assertEqual(modified_scores[0, i], float("-inf"))
+
+    def test_check_interval(self):
+        """Test that safety checking respects check_interval parameter."""
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker,
+            tokenizer=self.mock_tokenizer,
+            safety_config=self.safety_config,
+            check_interval=3,  # Only check every 3rd call
+        )
+
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # First call (step 1) - no check
+        processor(input_ids, scores)
+        self.assertEqual(self.mock_checker.check_safety.call_count, 0)
+
+        # Second call (step 2) - no check
+        processor(input_ids, scores)
+        self.assertEqual(self.mock_checker.check_safety.call_count, 0)
+
+        # Third call (step 3) - check should happen
+        processor(input_ids, scores)
+        self.assertEqual(self.mock_checker.check_safety.call_count, 1)
+
+    def test_batch_processing(self):
+        """Test that processor handles batched inputs correctly."""
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Mock mixed safety results for batch
+        def mock_check_safety(text):
+            if "unsafe" in text:
+                return SafetyResult(
+                    is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+                )
+            else:
+                return SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        self.mock_checker.check_safety.side_effect = mock_check_safety
+
+        # Mock tokenizer to return different text for different sequences
+        def mock_decode(sequence, skip_special_tokens=True):
+            if torch.equal(sequence, torch.tensor([1, 2, 3, 4])):
+                return "safe text"
+            else:
+                return "unsafe text"
+
+        self.mock_tokenizer.decode.side_effect = mock_decode
+
+        # Batch with mixed safety
+        input_ids = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])  # [safe, unsafe]
+        scores = torch.tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
+
+        # Process
+        modified_scores = processor(input_ids, scores)
+
+        # First sequence (safe) should be unchanged
+        torch.testing.assert_close(modified_scores[0], scores[0])
+
+        # Second sequence (unsafe) should be suppressed
+        for i in range(scores.shape[-1]):
+            self.assertEqual(modified_scores[1, i], float("-inf"))
+
+    def test_safety_check_failure(self):
+        """Test graceful handling when safety check fails."""
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Mock safety checker to raise exception
+        self.mock_checker.check_safety.side_effect = Exception("Safety check failed")
+
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # Process - should not raise, should assume unsafe and suppress
+        modified_scores = processor(input_ids, scores)
+
+        # Should suppress all tokens when safety check fails (fail-safe behavior)
+        for i in range(scores.shape[-1]):
+            self.assertEqual(modified_scores[0, i], float("-inf"))
+
+
+@require_torch
+class TestSafetyStoppingCriteria(unittest.TestCase):
+    """Test SafetyStoppingCriteria functionality."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        # Mock safety checker
+        self.mock_checker = Mock()
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+
+        # Mock tokenizer
+        self.mock_tokenizer = Mock()
+        self.mock_tokenizer.decode.return_value = "test text"
+
+        # Safety config
+        self.safety_config = SafetyConfig.from_checker(self.mock_checker)
+
+    def test_safe_content_continue_generation(self):
+        """Test that safe content allows generation to continue."""
+        criteria = SafetyStoppingCriteria(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # Call stopping criteria
+        should_stop = criteria(input_ids, scores)
+
+        # Should not stop for safe content
+        self.assertFalse(should_stop[0])
+        self.mock_checker.check_safety.assert_called_once()
+
+    def test_unsafe_content_stop_generation(self):
+        """Test that unsafe content stops generation."""
+        criteria = SafetyStoppingCriteria(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Mock unsafe result
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+        )
+
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # Call stopping criteria
+        should_stop = criteria(input_ids, scores)
+
+        # Should stop for unsafe content
+        self.assertTrue(should_stop[0])
+
+    def test_check_final_only_mode(self):
+        """Test check_final_only parameter functionality."""
+        criteria = SafetyStoppingCriteria(
+            safety_checker=self.mock_checker,
+            tokenizer=self.mock_tokenizer,
+            safety_config=self.safety_config,
+            check_final_only=True,
+        )
+
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # Call without is_final_call - should not check
+        should_stop = criteria(input_ids, scores)
+        self.assertFalse(should_stop[0])
+        self.assertEqual(self.mock_checker.check_safety.call_count, 0)
+
+        # Call with is_final_call=True - should check
+        should_stop = criteria(input_ids, scores, is_final_call=True)
+        self.assertFalse(should_stop[0])  # Safe content
+        self.assertEqual(self.mock_checker.check_safety.call_count, 1)
+
+    def test_batch_stopping_criteria(self):
+        """Test stopping criteria with batched inputs."""
+        criteria = SafetyStoppingCriteria(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+        )
+
+        # Mock mixed safety results
+        def mock_check_safety(text):
+            if "unsafe" in text:
+                return SafetyResult(
+                    is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+                )
+            else:
+                return SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        self.mock_checker.check_safety.side_effect = mock_check_safety
+
+        # Mock tokenizer for batch
+        def mock_decode(sequence, skip_special_tokens=True):
+            if torch.equal(sequence, torch.tensor([1, 2, 3, 4])):
+                return "safe text"
+            else:
+                return "unsafe text"
+
+        self.mock_tokenizer.decode.side_effect = mock_decode
+
+        # Batch input
+        input_ids = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])  # [safe, unsafe]
+        scores = torch.tensor([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])
+
+        # Call stopping criteria
+        should_stop = criteria(input_ids, scores)
+
+        # First sequence (safe) should continue, second (unsafe) should stop
+        self.assertFalse(should_stop[0])
+        self.assertTrue(should_stop[1])
+
+    def test_none_safety_checker_raises(self):
+        """Test that None safety_checker raises ValueError."""
+        with self.assertRaises(ValueError):
+            SafetyStoppingCriteria(
+                safety_checker=None, tokenizer=self.mock_tokenizer, safety_config=self.safety_config
+            )
+
+
+@require_torch
+class TestCacheKeyGeneration(unittest.TestCase):
+    """Test the SHA-256 cache key generation functionality."""
+
+    def test_cache_key_format(self):
+        """Test that cache keys follow the expected format."""
+        text = "This is a test message"
+        cache_key = _generate_cache_key(text)
+
+        # Should have format "length:hash"
+        parts = cache_key.split(":", 1)
+        self.assertEqual(len(parts), 2)
+
+        # First part should be text length
+        self.assertEqual(parts[0], str(len(text)))
+
+        # Second part should be a 64-character hex string (SHA-256)
+        self.assertEqual(len(parts[1]), 64)
+        self.assertTrue(all(c in "0123456789abcdef" for c in parts[1]))
+
+    def test_cache_key_consistency(self):
+        """Test that same text produces same cache key."""
+        text = "Consistent test message"
+        key1 = _generate_cache_key(text)
+        key2 = _generate_cache_key(text)
+
+        self.assertEqual(key1, key2)
+
+    def test_cache_key_uniqueness(self):
+        """Test that different texts produce different cache keys."""
+        text1 = "First message"
+        text2 = "Second message"
+        text3 = "First messag"  # Same length, different content
+
+        key1 = _generate_cache_key(text1)
+        key2 = _generate_cache_key(text2)
+        key3 = _generate_cache_key(text3)
+
+        # All keys should be different
+        self.assertNotEqual(key1, key2)
+        self.assertNotEqual(key1, key3)
+        self.assertNotEqual(key2, key3)
+
+    def test_cache_key_different_lengths(self):
+        """Test that texts with different lengths have different cache keys."""
+        short_text = "Short"
+        long_text = "This is a much longer text that should produce a different cache key"
+
+        key1 = _generate_cache_key(short_text)
+        key2 = _generate_cache_key(long_text)
+
+        self.assertNotEqual(key1, key2)
+        # Verify length prefixes are different
+        self.assertEqual(key1.split(":")[0], str(len(short_text)))
+        self.assertEqual(key2.split(":")[0], str(len(long_text)))
+
+    def test_cache_key_empty_text(self):
+        """Test cache key generation for empty text."""
+        empty_text = ""
+        cache_key = _generate_cache_key(empty_text)
+
+        # Should still follow the format
+        parts = cache_key.split(":", 1)
+        self.assertEqual(len(parts), 2)
+        self.assertEqual(parts[0], "0")
+        self.assertEqual(len(parts[1]), 64)
+
+    def test_cache_key_unicode_text(self):
+        """Test cache key generation for unicode text."""
+        unicode_text = "Hello 世界 🌍 café"
+        cache_key = _generate_cache_key(unicode_text)
+
+        # Should handle unicode properly
+        parts = cache_key.split(":", 1)
+        self.assertEqual(len(parts), 2)
+        self.assertEqual(parts[0], str(len(unicode_text)))
+        self.assertEqual(len(parts[1]), 64)
+
+        # Should be consistent
+        key2 = _generate_cache_key(unicode_text)
+        self.assertEqual(cache_key, key2)
+
+    def test_cache_key_collision_resistance(self):
+        """Test cache key collision resistance with similar texts."""
+        texts = [
+            "The quick brown fox",
+            "The quick brown fo",
+            "The quick brown fox ",  # trailing space
+            " The quick brown fox",  # leading space
+            "THE QUICK BROWN FOX",  # different case
+            "The quick brown fox jumps",  # extended
+        ]
+
+        cache_keys = [_generate_cache_key(text) for text in texts]
+
+        # All keys should be unique
+        self.assertEqual(len(cache_keys), len(set(cache_keys)))
+
+    def test_cache_key_very_long_text(self):
+        """Test cache key generation for very long text."""
+        # Create a long text
+        long_text = "Very long text " * 1000
+        cache_key = _generate_cache_key(long_text)
+
+        # Should still work and follow format
+        parts = cache_key.split(":", 1)
+        self.assertEqual(len(parts), 2)
+        self.assertEqual(parts[0], str(len(long_text)))
+        self.assertEqual(len(parts[1]), 64)
+
+
+@require_torch
+class TestSafetyMetrics(unittest.TestCase):
+    """Test the SafetyMetrics functionality."""
+
+    def test_metrics_initialization(self):
+        """Test that metrics initialize with correct default values."""
+        metrics = SafetyMetrics()
+
+        # Check all default values
+        self.assertEqual(metrics.total_generations, 0)
+        self.assertEqual(metrics.blocked_generations, 0)
+        self.assertEqual(metrics.suppression_events, 0)
+        self.assertEqual(metrics.cache_hits, 0)
+        self.assertEqual(metrics.cache_misses, 0)
+        self.assertEqual(metrics.total_safety_check_time_ms, 0.0)
+        self.assertEqual(metrics.safety_check_count, 0)
+
+    def test_cache_hit_rate_calculation(self):
+        """Test cache hit rate calculation."""
+        metrics = SafetyMetrics()
+
+        # No operations - should be 0.0
+        self.assertEqual(metrics.cache_hit_rate, 0.0)
+
+        # Record some hits and misses
+        metrics.record_cache_hit()
+        metrics.record_cache_hit()
+        metrics.record_cache_miss()
+
+        # Should be 66.67% (2 hits out of 3 total)
+        self.assertAlmostEqual(metrics.cache_hit_rate, 66.666666666666666, places=5)
+
+    def test_avg_safety_check_time_calculation(self):
+        """Test average safety check time calculation."""
+        metrics = SafetyMetrics()
+
+        # No checks - should be 0.0
+        self.assertEqual(metrics.avg_safety_check_time_ms, 0.0)
+
+        # Record some checks
+        metrics.record_safety_check(10.0)
+        metrics.record_safety_check(20.0)
+        metrics.record_safety_check(30.0)
+
+        # Should be 20.0ms average
+        self.assertEqual(metrics.avg_safety_check_time_ms, 20.0)
+
+    def test_block_rate_calculation(self):
+        """Test block rate calculation."""
+        metrics = SafetyMetrics()
+
+        # No generations - should be 0.0
+        self.assertEqual(metrics.block_rate, 0.0)
+
+        # Record some generations
+        metrics.record_generation_attempt()
+        metrics.record_generation_attempt()
+        metrics.record_generation_attempt()
+        metrics.record_blocked_generation()
+
+        # Should be 33.33% (1 blocked out of 3 total)
+        self.assertAlmostEqual(metrics.block_rate, 33.33333333333333, places=5)
+
+    def test_metrics_recording_methods(self):
+        """Test all metrics recording methods."""
+        metrics = SafetyMetrics()
+
+        # Test safety check recording
+        metrics.record_safety_check(15.5)
+        self.assertEqual(metrics.safety_check_count, 1)
+        self.assertEqual(metrics.total_safety_check_time_ms, 15.5)
+
+        # Test cache operations
+        metrics.record_cache_hit()
+        metrics.record_cache_miss()
+        self.assertEqual(metrics.cache_hits, 1)
+        self.assertEqual(metrics.cache_misses, 1)
+
+        # Test generation tracking
+        metrics.record_generation_attempt()
+        metrics.record_blocked_generation()
+        self.assertEqual(metrics.total_generations, 1)
+        self.assertEqual(metrics.blocked_generations, 1)
+
+        # Test suppression events
+        metrics.record_suppression_event()
+        self.assertEqual(metrics.suppression_events, 1)
+
+    def test_metrics_to_dict(self):
+        """Test metrics export to dictionary."""
+        metrics = SafetyMetrics()
+
+        # Record some data
+        metrics.record_safety_check(10.0)
+        metrics.record_cache_hit()
+        metrics.record_generation_attempt()
+        metrics.record_suppression_event()
+
+        result_dict = metrics.to_dict()
+
+        # Check all expected keys are present
+        expected_keys = {
+            "total_generations",
+            "blocked_generations",
+            "suppression_events",
+            "cache_hits",
+            "cache_misses",
+            "cache_hit_rate",
+            "avg_safety_check_time_ms",
+            "block_rate",
+            "safety_check_count",
+        }
+        self.assertEqual(set(result_dict.keys()), expected_keys)
+
+        # Check values
+        self.assertEqual(result_dict["total_generations"], 1)
+        self.assertEqual(result_dict["suppression_events"], 1)
+        self.assertEqual(result_dict["cache_hits"], 1)
+        self.assertEqual(result_dict["cache_hit_rate"], 100.0)
+
+    def test_metrics_reset(self):
+        """Test metrics reset functionality."""
+        metrics = SafetyMetrics()
+
+        # Record some data
+        metrics.record_safety_check(10.0)
+        metrics.record_cache_hit()
+        metrics.record_generation_attempt()
+        metrics.record_suppression_event()
+
+        # Verify data is present
+        self.assertGreater(metrics.safety_check_count, 0)
+        self.assertGreater(metrics.cache_hits, 0)
+
+        # Reset
+        metrics.reset()
+
+        # Verify all values are back to zero
+        self.assertEqual(metrics.total_generations, 0)
+        self.assertEqual(metrics.blocked_generations, 0)
+        self.assertEqual(metrics.suppression_events, 0)
+        self.assertEqual(metrics.cache_hits, 0)
+        self.assertEqual(metrics.cache_misses, 0)
+        self.assertEqual(metrics.total_safety_check_time_ms, 0.0)
+        self.assertEqual(metrics.safety_check_count, 0)
+
+    def test_metrics_combine(self):
+        """Test combining metrics from multiple instances."""
+        metrics1 = SafetyMetrics()
+        metrics2 = SafetyMetrics()
+
+        # Record data in first instance
+        metrics1.record_safety_check(10.0)
+        metrics1.record_cache_hit()
+        metrics1.record_generation_attempt()
+
+        # Record data in second instance
+        metrics2.record_safety_check(20.0)
+        metrics2.record_cache_miss()
+        metrics2.record_blocked_generation()
+
+        # Combine them
+        combined = metrics1.combine(metrics2)
+
+        # Check combined values
+        self.assertEqual(combined.safety_check_count, 2)
+        self.assertEqual(combined.total_safety_check_time_ms, 30.0)
+        self.assertEqual(combined.cache_hits, 1)
+        self.assertEqual(combined.cache_misses, 1)
+        self.assertEqual(combined.total_generations, 1)
+        self.assertEqual(combined.blocked_generations, 1)
+
+    def test_logits_processor_metrics_integration(self):
+        """Test metrics integration with SafetyLogitsProcessor."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+        )
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+        mock_tokenizer.decode.return_value = "test unsafe text"
+
+        # Safety config
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Create processor
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Verify metrics are initialized
+        metrics = processor.get_metrics()
+        self.assertIsInstance(metrics, SafetyMetrics)
+        self.assertEqual(metrics.suppression_events, 0)
+
+        # Process some data (this should trigger metrics recording)
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        processor(input_ids, scores)
+
+        # Check that metrics were recorded
+        metrics = processor.get_metrics()
+        self.assertGreater(metrics.safety_check_count, 0)
+        self.assertGreater(metrics.suppression_events, 0)  # Should have suppression due to unsafe content
+
+    def test_stopping_criteria_metrics_integration(self):
+        """Test metrics integration with SafetyStoppingCriteria."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+        )
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+        mock_tokenizer.decode.return_value = "test unsafe text"
+
+        # Safety config
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Create stopping criteria
+        criteria = SafetyStoppingCriteria(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Verify metrics are initialized
+        metrics = criteria.get_metrics()
+        self.assertIsInstance(metrics, SafetyMetrics)
+        self.assertEqual(metrics.total_generations, 0)
+
+        # Process some data
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        criteria(input_ids, scores)
+
+        # Check that metrics were recorded
+        metrics = criteria.get_metrics()
+        self.assertGreater(metrics.total_generations, 0)
+        self.assertGreater(metrics.blocked_generations, 0)  # Should have blocked generation
+
+    def test_thread_safety_basic(self):
+        """Test basic thread safety of SafetyMetrics."""
+        import threading
+        import time
+
+        metrics = SafetyMetrics()
+        errors = []
+
+        def worker():
+            try:
+                for i in range(100):
+                    metrics.record_cache_hit()
+                    metrics.record_safety_check(1.0)
+                    time.sleep(0.001)  # Small delay to encourage race conditions
+            except Exception as e:
+                errors.append(e)
+
+        # Run multiple threads
+        threads = []
+        for _ in range(5):
+            thread = threading.Thread(target=worker)
+            threads.append(thread)
+            thread.start()
+
+        # Wait for all threads to complete
+        for thread in threads:
+            thread.join()
+
+        # Should have no errors and correct counts
+        self.assertEqual(len(errors), 0, f"Thread safety errors: {errors}")
+        self.assertEqual(metrics.cache_hits, 500)  # 5 threads * 100 operations
+        self.assertEqual(metrics.safety_check_count, 500)
+
+    def test_hash_consistency(self):
+        """Test that hash inconsistency bug is fixed."""
+        from transformers.generation.safety.processors import _generate_cache_key
+
+        text1 = "This is a test message"
+        text2 = "This is a test message"  # Same content
+        text3 = "Different message"
+
+        # Same text should produce same hash
+        hash1 = _generate_cache_key(text1)
+        hash2 = _generate_cache_key(text2)
+        self.assertEqual(hash1, hash2)
+
+        # Different text should produce different hash
+        hash3 = _generate_cache_key(text3)
+        self.assertNotEqual(hash1, hash3)
+
+        # Hashes should be consistent across calls
+        for _ in range(10):
+            self.assertEqual(_generate_cache_key(text1), hash1)
+
+    def test_cache_memory_management(self):
+        """Test that caches properly manage memory."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+
+        # Safety config - disable incremental checking for this test to ensure all calls are made
+        safety_config = SafetyConfig.from_checker(mock_checker, incremental_checking=False)
+
+        # Create processor
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Add many different sequences to test cache limits
+        for i in range(150):  # More than default cache size of 100
+            mock_tokenizer.decode.return_value = f"test text {i}"
+            input_ids = torch.tensor([[1, 2, 3, i]])
+            scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+            processor(input_ids, scores)
+
+        # Cache should be limited and not grow unbounded
+        # The exact size check would depend on internal implementation
+        # but we can verify calls were made
+        self.assertEqual(mock_checker.check_safety.call_count, 150)
+
+    def test_empty_and_special_text_handling(self):
+        """Test handling of edge case text inputs."""
+        from transformers.generation.safety.processors import _generate_cache_key
+
+        # Test edge cases
+        test_cases = [
+            "",  # Empty string
+            " ",  # Single space
+            "\n\t",  # Whitespace only
+            "🌍🚀💫",  # Unicode emoji
+            "a" * 10000,  # Very long string
+            "Test\x00null",  # String with null byte
+        ]
+
+        for text in test_cases:
+            try:
+                cache_key = _generate_cache_key(text)
+                # Should produce valid cache key
+                self.assertIsInstance(cache_key, str)
+                self.assertGreater(len(cache_key), 0)
+                # Should be consistent
+                self.assertEqual(cache_key, _generate_cache_key(text))
+            except Exception as e:
+                self.fail(f"Failed to generate cache key for text: {repr(text)}, error: {e}")
+
+    def test_device_mismatch_handling(self):
+        """Test handling when tensors are on different devices."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False, confidence=0.8, violations=[SafetyViolation("toxicity", 0.8, "high")], metadata={}
+        )
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+        mock_tokenizer.decode.return_value = "unsafe text"
+
+        # Safety config
+        safety_config = SafetyConfig.from_checker(mock_checker)
+
+        # Create processor
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=safety_config
+        )
+
+        # Test with tensors (simulate device mismatch without actually using CUDA)
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        # Should not raise device mismatch errors
+        try:
+            result = processor(input_ids, scores)
+            self.assertEqual(result.shape, scores.shape)
+        except Exception as e:
+            self.fail(f"Device handling failed: {e}")
+
+    def test_configurable_cache_size_logits_processor(self):
+        """Test that SafetyLogitsProcessor respects configured cache size."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+
+        # Test small cache size
+        small_config = SafetyConfig.from_checker(mock_checker, cache_size=5)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=small_config
+        )
+
+        # Verify cache was initialized with correct size
+        self.assertEqual(processor._sequence_cache.max_size, 5)
+
+        # Test large cache size
+        large_config = SafetyConfig.from_checker(mock_checker, cache_size=250)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=large_config
+        )
+
+        # Verify cache was initialized with correct size
+        self.assertEqual(processor._sequence_cache.max_size, 250)
+
+    def test_configurable_cache_size_stopping_criteria(self):
+        """Test that SafetyStoppingCriteria respects configured cache and hash limits."""
+        # Mock safety checker
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        # Mock tokenizer
+        mock_tokenizer = Mock()
+
+        # Test custom configuration
+        custom_config = SafetyConfig.from_checker(mock_checker, cache_size=30, unsafe_hash_limit=300)
+
+        criteria = SafetyStoppingCriteria(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=custom_config
+        )
+
+        # Verify cache and hash limit were configured correctly
+        self.assertEqual(criteria._sequence_cache.max_size, 30)
+        self.assertEqual(criteria._unsafe_hash_limit, 300)
+
+    def test_default_cache_sizes_for_safety_levels(self):
+        """Test that different safety levels use appropriate cache sizes."""
+        # Mock safety checker and tokenizer
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        mock_tokenizer = Mock()
+
+        # Test strict configuration
+        strict_config = SafetyConfig.from_checker(mock_checker, **STRICT_PRESET)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=strict_config
+        )
+        self.assertEqual(processor._sequence_cache.max_size, 50)
+
+        criteria = SafetyStoppingCriteria(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=strict_config
+        )
+        self.assertEqual(criteria._unsafe_hash_limit, 500)
+
+        # Test moderate configuration
+        moderate_config = SafetyConfig.from_checker(mock_checker, **MODERATE_PRESET)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=moderate_config
+        )
+        self.assertEqual(processor._sequence_cache.max_size, 100)
+
+        # Test lenient configuration
+        lenient_config = SafetyConfig.from_checker(mock_checker, **LENIENT_PRESET)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=lenient_config
+        )
+        self.assertEqual(processor._sequence_cache.max_size, 200)
+
+    def test_backward_compatibility_cache_size(self):
+        """Test that processors work with SafetyConfig without cache_size."""
+        # Mock safety checker and tokenizer
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        mock_tokenizer = Mock()
+
+        # Create a config that might not have cache_size (simulate old configs)
+        config = SafetyConfig.from_checker(mock_checker)
+        # Temporarily remove cache_size attribute to simulate old config
+        if hasattr(config, "cache_size"):
+            delattr(config, "cache_size")
+
+        # Should still work with default cache size
+        processor = SafetyLogitsProcessor(safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=config)
+        # Should use DEFAULT_CACHE_SIZE (100)
+        from transformers.generation.safety.processors import DEFAULT_CACHE_SIZE
+
+        self.assertEqual(processor._sequence_cache.max_size, DEFAULT_CACHE_SIZE)
+
+    def test_cache_size_edge_cases(self):
+        """Test edge cases for cache size configuration."""
+        # Mock safety checker and tokenizer
+        mock_checker = Mock()
+        mock_checker.check_safety.return_value = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        mock_tokenizer = Mock()
+
+        # Test minimum cache size (1)
+        min_config = SafetyConfig.from_checker(mock_checker, cache_size=1)
+        processor = SafetyLogitsProcessor(
+            safety_checker=mock_checker, tokenizer=mock_tokenizer, safety_config=min_config
+        )
+        self.assertEqual(processor._sequence_cache.max_size, 1)
+
+        # Test that processor works with cache size 1
+        input_ids = torch.tensor([[1, 2, 3, 4]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+        mock_tokenizer.decode.return_value = "test text"
+
+        # Should not raise any errors
+        result = processor(input_ids, scores)
+        self.assertEqual(result.shape, scores.shape)
+
+
+@require_torch
+class TestSlidingWindowFunctionality(unittest.TestCase):
+    """Test sliding window and incremental checking functionality."""
+
+    def setUp(self):
+        """Set up test fixtures."""
+        # Mock safety checker
+        self.mock_checker = Mock()
+        self.mock_tokenizer = Mock()
+
+    def test_safety_state_initialization(self):
+        """Test SafetyState class initialization and basic functionality."""
+        state = SafetyState()
+
+        # Check initial values
+        self.assertEqual(state.last_check_position, 0)
+        self.assertIsNone(state.last_check_result)
+        self.assertEqual(state.sequence_prefix, "")
+        self.assertTrue(state.is_safe_so_far)
+        self.assertEqual(state.window_start_position, 0)
+
+    def test_safety_state_incremental_check_logic(self):
+        """Test SafetyState incremental checking logic."""
+        state = SafetyState()
+
+        # First check should always be performed
+        self.assertTrue(state.should_check_incremental(0, min_new_tokens=5))
+        self.assertTrue(state.should_check_incremental(10, min_new_tokens=5))
+
+        # Update state after first check
+        result = SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+        state.update_check_result(10, result, "first check")
+
+        # Check with insufficient new tokens
+        self.assertFalse(state.should_check_incremental(14, min_new_tokens=5))
+
+        # Check with sufficient new tokens
+        self.assertTrue(state.should_check_incremental(15, min_new_tokens=5))
+
+    def test_safety_state_sliding_window(self):
+        """Test SafetyState sliding window extraction."""
+        state = SafetyState()
+        full_text = "This is a very long text that should trigger sliding window behavior when it exceeds the configured window size limit."
+
+        # Test without sliding window (disabled)
+        text_to_check, start_pos = state.get_incremental_text(full_text, sliding_window_size=-1)
+        self.assertEqual(text_to_check, full_text)
+        self.assertEqual(start_pos, 0)
+
+        # Test with sliding window smaller than text
+        window_size = 50
+        text_to_check, start_pos = state.get_incremental_text(full_text, sliding_window_size=window_size)
+        self.assertEqual(len(text_to_check), window_size)
+        self.assertEqual(text_to_check, full_text[-window_size:])
+        self.assertEqual(start_pos, len(full_text) - window_size)
+
+        # Test with sliding window larger than text
+        window_size = 200
+        text_to_check, start_pos = state.get_incremental_text(full_text, sliding_window_size=window_size)
+        self.assertEqual(text_to_check, full_text)
+        self.assertEqual(start_pos, 0)
+
+    def test_sliding_window_config_parameters(self):
+        """Test sliding window configuration parameters in SafetyConfig."""
+        # Test default values
+        config = SafetyConfig()
+        self.assertEqual(config.sliding_window_size, 512)
+        self.assertTrue(config.incremental_checking)
+
+        # Test custom values
+        config = SafetyConfig(sliding_window_size=256, incremental_checking=False)
+        self.assertEqual(config.sliding_window_size, 256)
+        self.assertFalse(config.incremental_checking)
+
+        # Test serialization includes new parameters
+        config_dict = config.to_dict()
+        self.assertEqual(config_dict["sliding_window_size"], 256)
+        self.assertEqual(config_dict["incremental_checking"], False)
+
+        # Test deserialization
+        restored_config = SafetyConfig.from_dict(config_dict)
+        self.assertEqual(restored_config.sliding_window_size, 256)
+        self.assertFalse(restored_config.incremental_checking)
+
+    def test_logits_processor_sliding_window_integration(self):
+        """Test SafetyLogitsProcessor with sliding window functionality."""
+        # Setup mocks
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+
+        # Create long text that would exceed window
+        long_text = "This is a very long piece of text that should trigger the sliding window behavior. " * 10
+        self.mock_tokenizer.decode.return_value = long_text
+
+        # Test with sliding window enabled
+        config = SafetyConfig.from_checker(
+            self.mock_checker,
+            sliding_window_size=100,
+            incremental_checking=True,
+        )
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config
+        )
+
+        # Verify sliding window parameters are set
+        self.assertEqual(processor.sliding_window_size, 100)
+        self.assertTrue(processor.incremental_checking)
+
+        # Test processing with sliding window
+        input_ids = torch.tensor([[1, 2, 3, 4, 5]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        result = processor(input_ids, scores)
+        self.assertEqual(result.shape, scores.shape)
+
+        # Verify safety check was called (though with potentially windowed text)
+        self.mock_checker.check_safety.assert_called()
+
+    def test_stopping_criteria_sliding_window_integration(self):
+        """Test SafetyStoppingCriteria with sliding window functionality."""
+        # Setup mocks
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+
+        long_text = "This is another very long piece of text for testing sliding window in stopping criteria. " * 10
+        self.mock_tokenizer.decode.return_value = long_text
+
+        # Test with sliding window enabled
+        config = SafetyConfig.from_checker(
+            self.mock_checker,
+            sliding_window_size=100,
+            incremental_checking=True,
+        )
+
+        criteria = SafetyStoppingCriteria(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config
+        )
+
+        # Verify sliding window parameters are set
+        self.assertEqual(criteria.sliding_window_size, 100)
+        self.assertTrue(criteria.incremental_checking)
+
+        # Test processing
+        input_ids = torch.tensor([[1, 2, 3, 4, 5]])
+        scores = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0]])
+
+        should_stop = criteria(input_ids, scores)
+        self.assertFalse(should_stop[0])  # Should not stop for safe content
+
+    def test_incremental_checking_performance_benefit(self):
+        """Test that incremental checking reduces safety check calls."""
+        # Setup mock to count calls
+        check_call_count = [0]
+
+        def count_check_calls(text):
+            check_call_count[0] += 1
+            return SafetyResult(is_safe=True, confidence=0.9, violations=[], metadata={})
+
+        self.mock_checker.check_safety.side_effect = count_check_calls
+
+        # Create processor with incremental checking
+        config = SafetyConfig.from_checker(self.mock_checker, incremental_checking=True)
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker,
+            tokenizer=self.mock_tokenizer,
+            safety_config=config,
+            check_interval=1,  # Check every token
+        )
+
+        # Simulate progressive sequence building
+        sequences = ["Hello", "Hello world", "Hello world this", "Hello world this is", "Hello world this is a test"]
+
+        for seq in sequences:
+            self.mock_tokenizer.decode.return_value = seq
+            input_ids = torch.tensor([[1] * len(seq.split())])  # Approximate tokens
+            scores = torch.randn(1, 1000)
+            processor(input_ids, scores)
+
+        # With incremental checking, we should have fewer calls than sequences
+        # because short additions don't trigger new checks
+        print(f"Check calls made: {check_call_count[0]} out of {len(sequences)} sequences")
+        self.assertLessEqual(check_call_count[0], len(sequences))
+
+    def test_sliding_window_with_unsafe_content(self):
+        """Test sliding window behavior when unsafe content is detected."""
+        # Setup mock to return unsafe result
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=False,
+            confidence=0.8,
+            violations=[SafetyViolation("toxicity", 0.8, "high", "Toxic content detected")],
+            metadata={},
+        )
+
+        config = SafetyConfig.from_checker(
+            self.mock_checker,
+            sliding_window_size=50,
+            incremental_checking=True,
+        )
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config
+        )
+
+        self.mock_tokenizer.decode.return_value = "This contains toxic content that should be blocked"
+
+        input_ids = torch.tensor([[1, 2, 3, 4, 5]])
+        scores = torch.ones(1, 1000)  # All tokens have same score
+
+        result = processor(input_ids, scores)
+
+        # All tokens should be suppressed (set to negative infinity)
+        self.assertTrue(torch.all(result < scores))
+        self.assertTrue(torch.all(result == float("-inf")))
+
+    def test_prefix_cache_functionality(self):
+        """Test that prefix caching works correctly."""
+        # This test verifies the _PrefixSafetyCache is used when incremental_checking=True
+        config = SafetyConfig.from_checker(
+            self.mock_checker,
+            incremental_checking=True,  # Should use prefix cache
+            cache_size=50,
+        )
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config
+        )
+
+        # Verify correct cache type is used
+        from transformers.generation.safety.processors import _PrefixSafetyCache
+
+        self.assertIsInstance(processor._sequence_cache, _PrefixSafetyCache)
+
+        # Test with incremental_checking=False
+        config_no_incremental = SafetyConfig.from_checker(
+            self.mock_checker,
+            incremental_checking=False,  # Should use simple cache
+        )
+
+        processor_simple = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config_no_incremental
+        )
+
+        # Verify simple cache is used
+        from transformers.generation.safety.processors import _SafetyCache
+
+        self.assertIsInstance(processor_simple._sequence_cache, _SafetyCache)
+
+    def test_safety_state_reset_functionality(self):
+        """Test that safety states can be reset properly."""
+        config = SafetyConfig.from_checker(self.mock_checker, incremental_checking=True)
+
+        processor = SafetyLogitsProcessor(
+            safety_checker=self.mock_checker, tokenizer=self.mock_tokenizer, safety_config=config
+        )
+
+        # Process some sequences to populate safety states
+        self.mock_tokenizer.decode.return_value = "test text"
+        self.mock_checker.check_safety.return_value = SafetyResult(
+            is_safe=True, confidence=0.9, violations=[], metadata={}
+        )
+
+        input_ids = torch.tensor([[1, 2, 3, 4, 5]])
+        scores = torch.randn(1, 1000)
+        processor(input_ids, scores)
+
+        # Verify states were created
+        self.assertGreater(len(processor._safety_states), 0)
+
+        # Reset states
+        processor.reset_safety_states()
+
+        # Verify states were cleared
+        self.assertEqual(len(processor._safety_states), 0)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/generation/test_stopping_criteria.py b/tests/generation/test_stopping_criteria.py
index c120fe77882c..e9e068163a14 100644
--- a/tests/generation/test_stopping_criteria.py
+++ b/tests/generation/test_stopping_criteria.py
@@ -15,6 +15,8 @@
 import time
 import unittest
 
+from parameterized import parameterized
+
 from transformers import AutoTokenizer, is_torch_available
 from transformers.testing_utils import require_torch, torch_device
 
@@ -25,12 +27,14 @@
     import torch
 
     from transformers.generation import (
+        AsyncStoppingCriteriaList,
         ConfidenceCriteria,
         EosTokenCriteria,
         MaxLengthCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         StopStringCriteria,
+        StopStringTextMatchCriteria,
         validate_stopping_criteria,
     )
 
@@ -127,7 +131,13 @@ def test_validate_stopping_criteria(self):
 
         self.assertEqual(len(stopping_criteria), 1)
 
-    def test_stop_string_criteria(self):
+    @parameterized.expand(
+        [
+            ("StopStringCriteria", StopStringCriteria),
+            ("StopStringTextMatchCriteria", StopStringTextMatchCriteria),
+        ]
+    )
+    def test_stop_string_criteria(self, name, criteria_cls):
         true_strings = [
             "<|im_start|><|im_end|>",
             "<|im_start|><|im_end|<|im_end|>",
@@ -157,7 +167,7 @@ def test_stop_string_criteria(self):
         false_input_ids = tokenizer(false_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
 
         scores = None
-        criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings)
+        criteria = criteria_cls(tokenizer=tokenizer, stop_strings=stop_strings)
         for i in range(len(true_strings)):
             self.assertTrue(criteria(true_input_ids["input_ids"][i : i + 1], scores))
         for i in range(len(false_strings)):
@@ -169,25 +179,32 @@ def test_stop_string_criteria(self):
         true_input_ids = tokenizer(true_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
         false_input_ids = tokenizer(false_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
 
-        criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings)
+        criteria = criteria_cls(tokenizer=tokenizer, stop_strings=stop_strings)
         for i in range(len(true_strings)):
             self.assertTrue(criteria(true_input_ids["input_ids"][i : i + 1], scores))
         for i in range(len(false_strings)):
             self.assertFalse(criteria(false_input_ids["input_ids"][i : i + 1], scores))
 
-    def test_stop_string_criteria_vocab_size_mismatch(self):
+    @parameterized.expand(
+        [
+            ("StopStringCriteria", StopStringCriteria),
+            ("StopStringTextMatchCriteria", StopStringTextMatchCriteria),
+        ]
+    )
+    def test_stop_string_criteria_vocab_size_mismatch(self, name, criteria_cls):
         """Test that StopStringCriteria handles tokens above len(tokenizer) correctly."""
         tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
 
         # Create input_ids with tokens above len(tokenizer)
         input_ids = torch.tensor([[len(tokenizer) + 1024, 1, 2]], device=torch_device)
         scores = None
-        criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=["test"])
+        criteria = criteria_cls(tokenizer=tokenizer, stop_strings=["test"])
 
         # This should not raise an error and should return False since no stop string is matched
         self.assertFalse(criteria(input_ids, scores))
 
     def test_stop_string_matching_positions(self):
+        # This test only applies to StopStringCriteria, not StopStringTextMatchCriteria
         stop_string = "stop"
         token_list = ["last", "top", "topper", "s", "p"]
         token_indices = list(range(len(token_list)))
@@ -202,6 +219,7 @@ def test_stop_string_matching_positions(self):
         self.assertEqual(end_overlaps, {"top": [3], "topper": [3], "p": [1]})
 
     def test_stop_string_embedding_vecs(self):
+        # This test only applies to StopStringCriteria, not StopStringTextMatchCriteria
         stop_string = "stop"
         token_list = ["last", "top", "topper", "s", "p"]
         token_indices = list(range(len(token_list)))
@@ -221,7 +239,13 @@ def test_stop_string_embedding_vecs(self):
         token_lengths = embedding_vec[:-1, 2].tolist()
         self.assertEqual(token_lengths, [len(token) for token in token_list])
 
-    def test_single_letter_stop_string(self):
+    @parameterized.expand(
+        [
+            ("StopStringCriteria", StopStringCriteria),
+            ("StopStringTextMatchCriteria", StopStringTextMatchCriteria),
+        ]
+    )
+    def test_single_letter_stop_string(self, name, criteria_cls):
         true_strings = ["a", "baa", "abc"]  # "abc" is a single token
         false_strings = ["abbbbbbb", "b"]  # "abbbbbbb" is split into multiple tokens
         stop_strings = ["a"]
@@ -233,13 +257,19 @@ def test_single_letter_stop_string(self):
         false_input_ids = tokenizer(false_strings, return_tensors="pt", padding="longest", add_special_tokens=False)
 
         scores = None
-        criteria = StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings)
+        criteria = criteria_cls(tokenizer=tokenizer, stop_strings=stop_strings)
         for input_ids in true_input_ids["input_ids"]:
             self.assertTrue(criteria(input_ids.unsqueeze(0), scores))
         for input_ids in false_input_ids["input_ids"]:
             self.assertFalse(criteria(input_ids.unsqueeze(0), scores))
 
-    def test_criteria_per_row(self):
+    @parameterized.expand(
+        [
+            ("StopStringCriteria", StopStringCriteria),
+            ("StopStringTextMatchCriteria", StopStringTextMatchCriteria),
+        ]
+    )
+    def test_criterias_per_row(self, name, criteria_cls):
         text = "They completed the challenging puzzle, revealing the hidden image at the end"
         stop_strings = ["end"]
 
@@ -251,7 +281,7 @@ def test_criteria_per_row(self):
         criteria = StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=20),
-                StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings),
+                criteria_cls(tokenizer=tokenizer, stop_strings=stop_strings),
             ]
         )
 
@@ -261,11 +291,19 @@ def test_criteria_per_row(self):
         # return False when neither is satisfied
         self.assertFalse(criteria(inputs["input_ids"][:, :-1], scores))
 
-    def test_criteria_per_row_batched(self):
+    @parameterized.expand(
+        [
+            ("StopStringCriteria", StopStringCriteria),
+            ("StopStringTextMatchCriteria", StopStringTextMatchCriteria),
+        ]
+    )
+    def test_criterias_per_row_batched(self, name, criteria_cls):
         text = [
             "They completed the challenging puzzle, revealing the hidden image at the end",
             "Today a dragon flew over France",
             "The aroma of freshly baked pizza filled the kitchen",
+            "This should not trigger: the end is near",
+            "The following word should trigger: mend",  # important "mend" is a single token, != token for "end"
         ]
         stop_strings = ["end"]
 
@@ -278,12 +316,292 @@ def test_criteria_per_row_batched(self):
         criteria = StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=20),
-                StopStringCriteria(tokenizer=tokenizer, stop_strings=stop_strings),
+                criteria_cls(tokenizer=tokenizer, stop_strings=stop_strings),
             ]
         )
 
         # trigger stopping when at least one criteria is satisfied
-        self.assertListEqual(criteria(inputs["input_ids"], scores).tolist(), [True, False, False])
+        self.assertListEqual(
+            criteria(inputs["input_ids"], scores).tolist(),
+            [True, False, False, False, True],
+        )
 
         # False when neither is satisfied
-        self.assertListEqual(criteria(inputs["input_ids"][:, :-1], scores).tolist(), [False, False, False])
+        self.assertListEqual(
+            criteria(inputs["input_ids"][:, :-1], scores).tolist(),
+            [False, False, False, False, False],
+        )
+
+
+@require_torch
+class AsyncStoppingCriteriaTestCase(unittest.TestCase):
+    """Test cases for AsyncStoppingCriteriaList."""
+
+    def _get_tensors(self, length, batch_size=3):
+        vocab_size = 250
+
+        input_ids = ids_tensor((batch_size, length), vocab_size)
+        scores = torch.ones((batch_size, length), device=torch_device, dtype=torch.float) / length
+        return input_ids, scores
+
+    def test_async_wrapper_basic(self):
+        """Test that AsyncStoppingCriteriaList wraps StoppingCriteriaList correctly."""
+        criteria_list = StoppingCriteriaList([MaxLengthCriteria(max_length=10)])
+        async_criteria = AsyncStoppingCriteriaList(criteria_list)
+
+        # Test __len__
+        self.assertEqual(len(async_criteria), 1)
+
+        # Test __iter__
+        criteria_items = list(async_criteria)
+        self.assertEqual(len(criteria_items), 1)
+        self.assertIsInstance(criteria_items[0], MaxLengthCriteria)
+
+        # Test max_length property
+        self.assertEqual(async_criteria.max_length, 10)
+
+    def test_async_sync_equivalence_max_length(self):
+        """Test that async and sync modes produce identical results for max_length stopping."""
+        input_ids, scores = self._get_tensors(5)
+
+        # Sync behavior
+        sync_criteria = StoppingCriteriaList([MaxLengthCriteria(max_length=10)])
+        sync_result = sync_criteria(input_ids, scores)
+
+        # At length 5 with max_length 10, should not be finished
+        self.assertFalse(all(sync_result))
+
+        # Async behavior (should fall back to sync on CPU)
+        async_criteria = AsyncStoppingCriteriaList(StoppingCriteriaList([MaxLengthCriteria(max_length=10)]))
+        unfinished = torch.ones(input_ids.shape[0], device=input_ids.device, dtype=torch.long)
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, unfinished)
+
+        # At length 5 with max_length 10, should not be finished
+        self.assertFalse(this_peer_finished)
+        self.assertTrue(all(updated_unfinished == 1))
+
+        # At length 10, should be finished
+        input_ids_long, scores_long = self._get_tensors(10)
+        sync_result_long = sync_criteria(input_ids_long, scores_long)
+        self.assertTrue(all(sync_result_long))
+
+        unfinished = torch.ones(input_ids_long.shape[0], device=input_ids_long.device, dtype=torch.long)
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids_long, scores_long, unfinished)
+        self.assertTrue(this_peer_finished)
+        self.assertTrue(all(updated_unfinished == 0))
+
+    def test_async_sync_equivalence_eos_token(self):
+        """Test that async and sync modes produce identical results for EOS token stopping."""
+        input_ids, scores = self._get_tensors(5)
+
+        # Set EOS token (0) at the end of all sequences
+        input_ids[:, -1] = 0
+
+        # Sync behavior
+        sync_criteria = StoppingCriteriaList(
+            [
+                MaxLengthCriteria(max_length=20),
+                EosTokenCriteria(eos_token_id=0),
+            ]
+        )
+        sync_result = sync_criteria(input_ids, scores)
+
+        # Async behavior - the async criteria checks results from PREVIOUS async operations
+        # so we need to call check() multiple times to allow async results to be retrieved
+        async_criteria = AsyncStoppingCriteriaList(
+            StoppingCriteriaList(
+                [
+                    MaxLengthCriteria(max_length=20),
+                    EosTokenCriteria(eos_token_id=0),
+                ]
+            )
+        )
+        unfinished = torch.ones(input_ids.shape[0], device=input_ids.device, dtype=torch.long)
+
+        # First call starts async check
+        updated_unfinished, _ = async_criteria.check(input_ids, scores, unfinished)
+
+        # Wait for async check to complete and call again to retrieve result
+        if input_ids.device.type == "cuda":
+            torch.cuda.synchronize()
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, updated_unfinished)
+
+        # Both should indicate all sequences have EOS
+        self.assertTrue(all(sync_result))
+        self.assertTrue(this_peer_finished)
+        self.assertTrue(all(updated_unfinished == 0))
+
+    def test_async_sync_equivalence_partial_eos(self):
+        """Test async/sync equivalence when only some sequences have EOS."""
+        input_ids, scores = self._get_tensors(5)
+
+        # Only first 2 sequences have EOS
+        input_ids[:2, -1] = 0
+        input_ids[2, -1] = 1
+
+        # Sync behavior
+        sync_criteria = StoppingCriteriaList(
+            [
+                MaxLengthCriteria(max_length=20),
+                EosTokenCriteria(eos_token_id=0),
+            ]
+        )
+        sync_result = sync_criteria(input_ids, scores)
+
+        # Should match [True, True, False]
+        self.assertListEqual(sync_result.tolist(), [True, True, False])
+
+    def test_async_different_batch_sizes(self):
+        """Test async stopping criteria with different batch sizes."""
+        for batch_size in [1, 2, 4, 8, 16]:
+            input_ids, scores = self._get_tensors(5, batch_size=batch_size)
+
+            async_criteria = AsyncStoppingCriteriaList(StoppingCriteriaList([MaxLengthCriteria(max_length=10)]))
+            unfinished = torch.ones(batch_size, device=input_ids.device, dtype=torch.long)
+            updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, unfinished)
+
+            self.assertEqual(updated_unfinished.shape[0], batch_size)
+            self.assertFalse(this_peer_finished)
+
+            # At max_length, all should finish
+            input_ids_long, scores_long = self._get_tensors(10, batch_size=batch_size)
+            unfinished = torch.ones(batch_size, device=input_ids_long.device, dtype=torch.long)
+            updated_unfinished, this_peer_finished = async_criteria.check(input_ids_long, scores_long, unfinished)
+
+            self.assertEqual(updated_unfinished.shape[0], batch_size)
+            self.assertTrue(this_peer_finished)
+
+    def test_async_cpu_fallback(self):
+        """Test that async gracefully falls back to sync on CPU."""
+        # Force CPU tensors
+        batch_size = 3
+        vocab_size = 250
+        length = 5
+
+        input_ids = torch.randint(0, vocab_size, (batch_size, length), device="cpu")
+        scores = torch.ones((batch_size, length), device="cpu", dtype=torch.float)
+
+        async_criteria = AsyncStoppingCriteriaList(
+            StoppingCriteriaList(
+                [
+                    MaxLengthCriteria(max_length=10),
+                    EosTokenCriteria(eos_token_id=0),
+                ]
+            )
+        )
+        unfinished = torch.ones(batch_size, device="cpu", dtype=torch.long)
+
+        # Should work without errors on CPU (sync fallback)
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, unfinished)
+        self.assertFalse(this_peer_finished)
+
+        # With EOS in all sequences
+        input_ids[:, -1] = 0
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, unfinished)
+        self.assertTrue(this_peer_finished)
+
+    def test_async_legacy_call_interface(self):
+        """Test that the legacy __call__ interface still works."""
+        input_ids, scores = self._get_tensors(5)
+
+        async_criteria = AsyncStoppingCriteriaList(StoppingCriteriaList([MaxLengthCriteria(max_length=10)]))
+
+        # __call__ should fall back to sync behavior
+        result = async_criteria(input_ids, scores)
+        self.assertEqual(result.shape[0], 3)
+        self.assertFalse(all(result))  # Not at max_length yet
+
+        input_ids_long, scores_long = self._get_tensors(10)
+        result = async_criteria(input_ids_long, scores_long)
+        self.assertTrue(all(result))  # At max_length
+
+    def test_async_finalize(self):
+        """Test the finalize method for cleanup."""
+        input_ids, scores = self._get_tensors(5)
+
+        async_criteria = AsyncStoppingCriteriaList(StoppingCriteriaList([MaxLengthCriteria(max_length=100)]))
+        unfinished = torch.ones(input_ids.shape[0], device=input_ids.device, dtype=torch.long)
+
+        # Do a check to potentially start an async operation
+        async_criteria.check(input_ids, scores, unfinished)
+
+        # Finalize should work without errors
+        final_unfinished, this_peer_finished = async_criteria.finalize(unfinished)
+        self.assertFalse(this_peer_finished)
+
+    def test_async_custom_stopping_criteria(self):
+        """Test async with a custom stopping criteria."""
+        from transformers.generation import StoppingCriteria
+
+        class CustomStoppingCriteria(StoppingCriteria):
+            """Stop when the last token is a specific value."""
+
+            def __init__(self, stop_token_id):
+                self.stop_token_id = stop_token_id
+
+            def __call__(self, input_ids, scores, **kwargs):
+                return input_ids[:, -1] == self.stop_token_id
+
+        input_ids, scores = self._get_tensors(5)
+        stop_token_id = 42
+        input_ids[:, -1] = stop_token_id  # Set last token to stop token
+
+        async_criteria = AsyncStoppingCriteriaList(
+            StoppingCriteriaList(
+                [
+                    MaxLengthCriteria(max_length=100),
+                    CustomStoppingCriteria(stop_token_id=stop_token_id),
+                ]
+            )
+        )
+        unfinished = torch.ones(input_ids.shape[0], device=input_ids.device, dtype=torch.long)
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids, scores, unfinished)
+
+        # Custom criteria should have triggered stop via sync fallback (near max_length check)
+        # At length 5 with max_length 100, it would use _check_async_only,
+        # but first call will start the async check
+        # For proper testing, we need the sync path which happens near max_length
+        input_ids2, scores2 = self._get_tensors(99)  # Near max_length
+        input_ids2[:, -1] = stop_token_id
+        async_criteria2 = AsyncStoppingCriteriaList(
+            StoppingCriteriaList(
+                [
+                    MaxLengthCriteria(max_length=100),
+                    CustomStoppingCriteria(stop_token_id=stop_token_id),
+                ]
+            )
+        )
+        unfinished2 = torch.ones(input_ids2.shape[0], device=input_ids2.device, dtype=torch.long)
+        updated_unfinished2, this_peer_finished2 = async_criteria2.check(input_ids2, scores2, unfinished2)
+        self.assertTrue(this_peer_finished2)
+
+    def test_async_multiple_eos_tokens(self):
+        """Test async with multiple EOS token IDs."""
+        input_ids, scores = self._get_tensors(5)
+
+        # Different sequences end with different EOS tokens
+        input_ids[0, -1] = 1  # First EOS token
+        input_ids[1, -1] = 2  # Second EOS token
+        input_ids[2, -1] = 99  # Not an EOS token
+
+        async_criteria = AsyncStoppingCriteriaList(
+            StoppingCriteriaList(
+                [
+                    MaxLengthCriteria(max_length=100),
+                    EosTokenCriteria(eos_token_id=[1, 2]),  # Multiple EOS tokens
+                ]
+            )
+        )
+
+        # Test at near max_length to trigger sync path
+        input_ids_near, scores_near = self._get_tensors(99)
+        input_ids_near[0, -1] = 1
+        input_ids_near[1, -1] = 2
+        input_ids_near[2, -1] = 99
+
+        unfinished = torch.ones(input_ids_near.shape[0], device=input_ids_near.device, dtype=torch.long)
+        updated_unfinished, this_peer_finished = async_criteria.check(input_ids_near, scores_near, unfinished)
+
+        # First two should be done (EOS), third should not
+        self.assertListEqual(updated_unfinished.tolist(), [0, 0, 1])
+        self.assertFalse(this_peer_finished)  # Not all finished
diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 15df7036eb35..59b288439411 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2893,6 +2893,35 @@ def emit(self, record):
         finally:
             logger.removeHandler(warningHandler)
 
+    def test_inputs_embeds_warn_without_ids_for_token_based_processors(self):
+        model = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(torch_device).eval()
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
+        inputs = tokenizer("Hello world", return_tensors="pt").to(torch_device)
+        embeds = model.get_input_embeddings()(inputs["input_ids"])
+
+        outputs_without_penalty = model.generate(inputs_embeds=embeds, max_new_tokens=5, repetition_penalty=1.0)
+        self.assertEqual(outputs_without_penalty.shape[0], inputs["input_ids"].shape[0])
+
+        with self.assertWarnsRegex(UserWarning, "repetition_penalty"):
+            outputs_with_ignored_penalty = model.generate(
+                inputs_embeds=embeds, max_new_tokens=5, repetition_penalty=1.1
+            )
+        self.assertEqual(outputs_with_ignored_penalty.shape[0], inputs["input_ids"].shape[0])
+
+        with self.assertWarnsRegex(UserWarning, "no_repeat_ngram_size"):
+            outputs_with_ignored_ngram = model.generate(inputs_embeds=embeds, max_new_tokens=5, no_repeat_ngram_size=2)
+        self.assertEqual(outputs_with_ignored_ngram.shape[0], inputs["input_ids"].shape[0])
+
+        outputs = model.generate(
+            input_ids=inputs["input_ids"],
+            inputs_embeds=embeds,
+            attention_mask=inputs.get("attention_mask"),
+            max_new_tokens=5,
+            repetition_penalty=1.1,
+            no_repeat_ngram_size=2,
+        )
+        self.assertEqual(outputs.shape[0], inputs["input_ids"].shape[0])
+
     @slow
     def test_beam_search_early_stop_heuristic(self):
         """Regression test for #38778 (early stopping needs to be tracked at a batch level)"""
@@ -3147,6 +3176,88 @@ def test_synthid_text_watermark_generation_mean_expected_bias(self):
         )
         self.assertTrue(torch.all(is_close))
 
+    @slow
+    def test_PLess_example_integration(self):
+        tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
+        model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+        # model.config.pad_token_id = tokenizer.pad_token_id
+        model.generation_config.pad_token_id = tokenizer.pad_token_id
+        prompts = [
+            "A sequence: 1, 10",
+            "A sequence: 1, 10",
+        ]
+        input_ids = tokenizer(
+            prompts,
+            padding=True,
+            return_tensors="pt",
+        )
+
+        torch.manual_seed(17)
+
+        outputs = model.generate(
+            **input_ids,
+            num_beams=1,
+            do_sample=True,
+            temperature=1.0,
+            top_k=0,
+            top_p=None,
+            p_less=True,
+            max_new_tokens=64,
+            num_return_sequences=1,
+        )
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        print(outputs)
+        self.assertListEqual(
+            outputs,
+            [
+                "A sequence: 1, 10, 11, 100, 101, 110, 111, 1000, 1001, 1010, 1011, 1100, 1101, 11",
+                "A sequence: 1, 10, 100, 1000, 10000, 100000, 1000000, 10000000, 100000000, 100000",
+            ],
+        )
+
+    @slow
+    def test_PLessNorm_example_integration(self):
+        tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-3B")
+        model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.padding_side = "left"
+        # model.config.pad_token_id = tokenizer.pad_token_id
+        model.generation_config.pad_token_id = tokenizer.pad_token_id
+        prompts = [
+            "Math and life are similar because",
+        ]
+        input_ids = tokenizer(
+            prompts,
+            return_tensors="pt",
+        )
+
+        torch.manual_seed(42)
+
+        outputs = model.generate(
+            **input_ids,
+            num_beams=1,
+            do_sample=True,
+            temperature=1.0,
+            top_k=0,
+            top_p=None,
+            p_less_norm=True,
+            max_new_tokens=64,
+            num_return_sequences=1,
+        )
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        print(outputs)
+        self.assertListEqual(
+            outputs,
+            [
+                "Math and life are similar because both of them are about numbers. In math, we use \
+numbers to solve problems. In life, we use numbers to make decisions. For example, if you want to buy \
+a house, you will need to calculate how much money you have and how much the house costs. You will \
+also need to consider other factors,",
+            ],
+        )
+
     @slow
     def test_TopH_example_integration(self):
         tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B")
@@ -3533,6 +3644,41 @@ def test_assisted_decoding_num_assistant_tokens_heuristic_schedule(self):
         model.generate(**inputs, **generation_kwargs)
         self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 11)
 
+    def test_assisted_decoding_parameter_inheritance(self):
+        # This test ensures that assistant models inherit generation parameters from the main generate() call.
+        # Before the fix, assistant models would use their default values instead of user-specified values.
+
+        prompt = "Alice and Bob"
+        checkpoint = "EleutherAI/pythia-160m-deduped"
+        tokenizer = AutoTokenizer.from_pretrained(checkpoint)
+        inputs = tokenizer(prompt, return_tensors="pt")
+
+        model = AutoModelForCausalLM.from_pretrained(checkpoint)
+        assistant_model = AutoModelForCausalLM.from_pretrained(checkpoint)
+
+        # Check assistant model defaults
+        self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 20)
+        self.assertEqual(assistant_model.generation_config.assistant_confidence_threshold, 0.4)
+        self.assertEqual(assistant_model.generation_config.do_sample, False)
+
+        # Generate with user-specified values that differ from assistant defaults
+        generation_kwargs = {
+            "eos_token_id": -1,
+            "max_new_tokens": 5,
+            "assistant_model": assistant_model,
+            "do_sample": True,
+            "num_assistant_tokens": 7,
+            "assistant_confidence_threshold": 0.8,
+        }
+
+        model.generate(**inputs, **generation_kwargs)
+
+        # After generation, assistant model should have the user-specified values, not its defaults
+        # Inheritance applies to all main model parameters, not just ones that have "assistant" slots
+        self.assertEqual(assistant_model.generation_config.num_assistant_tokens, 7)
+        self.assertEqual(assistant_model.generation_config.assistant_confidence_threshold, 0.8)
+        self.assertEqual(assistant_model.generation_config.do_sample, True)
+
     def test_assisted_decoding_num_assistant_tokens_heuristic_transient_schedule(self):
         # This test ensures that the assisted generation num_assistant_tokens 'heuristic' schedule works properly.
 
diff --git a/tests/heterogeneity/__init__.py b/tests/heterogeneity/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/heterogeneity/test_configuration_utils.py b/tests/heterogeneity/test_configuration_utils.py
new file mode 100644
index 000000000000..ea991912d7de
--- /dev/null
+++ b/tests/heterogeneity/test_configuration_utils.py
@@ -0,0 +1,185 @@
+import contextlib
+import tempfile
+import unittest
+from functools import partial
+from unittest.mock import patch
+
+from parameterized import parameterized
+
+from transformers import LlamaConfig
+from transformers.heterogeneity import apply_heterogeneous_config
+
+
+apply_heterogeneous_config_explicit = partial(apply_heterogeneous_config, explicit=True)
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Tiny config factories
+# ──────────────────────────────────────────────────────────────────────
+
+
+def _tiny_llama_config(per_layer_config=None, **overrides):
+    defaults = {
+        "hidden_size": 64,
+        "intermediate_size": 128,
+        "num_hidden_layers": 4,
+        "num_attention_heads": 4,
+        "num_key_value_heads": 4,
+        "head_dim": 16,
+        "vocab_size": 32,
+        "max_position_embeddings": 64,
+        **overrides,
+    }
+    return LlamaConfig(per_layer_config=per_layer_config, **defaults)
+
+
+# ──────────────────────────────────────────────────────────────────────
+# Tests: Config
+# ──────────────────────────────────────────────────────────────────────
+
+
+class TestHeterogeneousConfig(unittest.TestCase):
+    def test_per_layer_overrides_and_fallback(self):
+        """Per-layer values should override, and non-overridden layers should fall back to global."""
+        config = _tiny_llama_config(per_layer_config={1: {"num_key_value_heads": 2}, 3: {"num_key_value_heads": 1}})
+        self.assertTrue(config.is_heterogeneous)
+        self.assertEqual(config.per_layer_attributes, {"num_key_value_heads"})
+        # Per-layer overrides
+        self.assertEqual(config.get_full_layer_config(1).num_key_value_heads, 2)
+        self.assertEqual(config.get_full_layer_config(3).num_key_value_heads, 1)
+        # Fallback to original global value
+        self.assertEqual(config.get_full_layer_config(0).num_key_value_heads, 4)
+        # Other attributes are unaffected
+        self.assertEqual(config.get_full_layer_config(0).hidden_size, 64)
+
+        # A single override should also preserve fallback for all other layers
+        config2 = _tiny_llama_config(per_layer_config={1: {"num_key_value_heads": 2}})
+        self.assertEqual(config2.get_full_layer_config(1).num_key_value_heads, 2)
+        self.assertEqual(config2.get_full_layer_config(0).num_key_value_heads, 4)
+
+    def test_uniform_values_promoted_to_global(self):
+        per_layer = {i: {"num_key_value_heads": 2} for i in range(4)}
+        config = _tiny_llama_config(per_layer_config=per_layer)
+        self.assertEqual(config.num_key_value_heads, 2)
+        self.assertNotIn("num_key_value_heads", config.per_layer_attributes)
+
+    def test_accessing_per_layer_attr_raises(self):
+        config = _tiny_llama_config(per_layer_config={0: {"num_key_value_heads": 2}, 1: {"num_key_value_heads": 1}})
+        with self.assertRaises(AttributeError):
+            _ = config.num_key_value_heads
+
+    def test_validation_missing_global_attr(self):
+        # "fake_attr" in layer 0 but not in layer 1, and not global → should fail
+        with self.assertRaises(ValueError):
+            _tiny_llama_config(
+                per_layer_config={
+                    0: {"fake_attr": 42, "intermediate_size": 64},
+                    1: {"intermediate_size": 96},
+                }
+            )
+
+    def test_validation_layer_idx_out_of_range(self):
+        with self.assertRaises(ValueError):
+            _tiny_llama_config(per_layer_config={4: {"num_key_value_heads": 2}})
+
+    def test_save_pretrained_config_round_trip(self):
+        """Config should survive save_pretrained → from_pretrained on disk."""
+        per_layer = {i: {"intermediate_size": 64 + i} for i in range(0, 12, 2)}
+        config = _tiny_llama_config(per_layer_config=per_layer, num_hidden_layers=12)
+
+        # Keys are zero-padded so they sort numerically in JSON (0,1,...,10 not 0,1,10,2,...)
+        d = config.to_dict()
+        self.assertEqual(list(d["per_layer_config"].keys()), sorted(d["per_layer_config"].keys()))
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            config.save_pretrained(tmpdir)
+            loaded = LlamaConfig.from_pretrained(tmpdir)
+
+        self.assertTrue(loaded.is_heterogeneous)
+        for i in range(4):
+            self.assertEqual(
+                config.get_full_layer_config(i).intermediate_size,
+                loaded.get_full_layer_config(i).intermediate_size,
+            )
+
+    @parameterized.expand(
+        [
+            (
+                "global_sw_global_acs",
+                {"sliding_window": 4096, "attention_chunk_size": 2048},
+                {0: {"intermediate_size": 64}},
+                True,
+            ),
+            ("global_sw_per_layer_acs", {"sliding_window": 4096}, {0: {"attention_chunk_size": 2048}}, True),
+            (
+                "per_layer_sw_per_layer_acs_same_layer",
+                {},
+                {0: {"sliding_window": 4096, "attention_chunk_size": 2048}},
+                True,
+            ),
+            (
+                "per_layer_sw_per_layer_acs_different_layers",
+                {"sliding_window": None, "attention_chunk_size": None},
+                {0: {"sliding_window": 4096}, 1: {"attention_chunk_size": 2048}},
+                False,
+            ),
+            (
+                "global_conflict_resolved_by_per_layer_override",
+                {"sliding_window": 4096, "attention_chunk_size": 2048},
+                {
+                    0: {"sliding_window": None},
+                    1: {"sliding_window": None},
+                    2: {"attention_chunk_size": None},
+                    3: {"attention_chunk_size": None},
+                },
+                False,
+            ),
+        ],
+    )
+    def test_validation_sliding_window_and_attention_chunk_size(
+        self, _name, overrides, per_layer_config, should_raise
+    ):
+        ctx = self.assertRaises(ValueError) if should_raise else contextlib.nullcontext()
+        with ctx:
+            _tiny_llama_config(per_layer_config=per_layer_config, **overrides)
+
+    def test_all_layers_overridden_no_global_default(self):
+        """Custom attribute on every layer without a global default should be accessible via get_full_layer_config."""
+        config = _tiny_llama_config(
+            per_layer_config={
+                0: {"custom_attr": 10},
+                1: {"custom_attr": 20},
+                2: {"custom_attr": 30},
+                3: {"custom_attr": 40},
+            },
+        )
+        self.assertTrue(config.is_heterogeneous)
+        self.assertEqual(config.get_full_layer_config(0).custom_attr, 10)
+        self.assertEqual(config.get_full_layer_config(1).custom_attr, 20)
+        self.assertEqual(config.get_full_layer_config(2).custom_attr, 30)
+        self.assertEqual(config.get_full_layer_config(3).custom_attr, 40)
+
+    @patch("transformers.configuration_utils.apply_heterogeneous_config", apply_heterogeneous_config_explicit)
+    def test_explicit_fills_missing_layers_and_attributes(self):
+        """explicit=True creates LayerConfigs for missing layers and fills missing attrs from global."""
+        config = _tiny_llama_config(per_layer_config={0: {"num_key_value_heads": 1}})
+        spec = config._heterogeneity_spec
+        # All 4 layers should have a LayerConfig with num_key_value_heads
+        for i in range(4):
+            self.assertIn(i, spec.per_layer_config)
+            self.assertTrue(hasattr(spec.per_layer_config[i], "num_key_value_heads"))
+        self.assertEqual(spec.per_layer_config[0].num_key_value_heads, 1)
+        # Missing layers filled from global (4), not from layer 0
+        for i in (1, 2, 3):
+            self.assertEqual(spec.per_layer_config[i].num_key_value_heads, 4)
+
+    @patch("transformers.configuration_utils.apply_heterogeneous_config", apply_heterogeneous_config_explicit)
+    def test_explicit_does_not_promote_uniform_values(self):
+        """explicit=True keeps uniform values per-layer instead of promoting to global."""
+        per_layer = {i: {"num_key_value_heads": 2} for i in range(4)}
+        # Without explicit: promoted to global (tested in test_uniform_values_promoted_to_global)
+        # With explicit: stays per-layer
+        config = _tiny_llama_config(per_layer_config=per_layer)
+        self.assertIn("num_key_value_heads", config.per_layer_attributes)
+        for i in range(4):
+            self.assertEqual(config.per_layer_config[i].num_key_value_heads, 2)
diff --git a/tests/kernels/test_kernels.py b/tests/kernels/test_kernels.py
index 1bd9a7c79792..fdbdf066198a 100644
--- a/tests/kernels/test_kernels.py
+++ b/tests/kernels/test_kernels.py
@@ -419,6 +419,48 @@ def my_attention(*args, **kwargs):
             except Exception as e:
                 print(f"Could not clean up `ALL_MASK_ATTENTION_FUNCTIONS`: {e}")
 
+    def test_kernel_mask_function_default(self):
+        """Kernels without MASK_FUNCTION attribute should default to flash_attention_2 mask."""
+        kernel_obj = types.SimpleNamespace(my_func=lambda *a, **k: None)
+        with patch("transformers.integrations.hub_kernels.get_kernel", return_value=kernel_obj):
+            attn_impl = "org/default-mask:my_func"
+            load_and_register_attn_kernel(attn_impl)
+            self.assertIn(attn_impl, ALL_MASK_ATTENTION_FUNCTIONS.valid_keys())
+            self.assertEqual(
+                ALL_MASK_ATTENTION_FUNCTIONS[attn_impl],
+                ALL_MASK_ATTENTION_FUNCTIONS["flash_attention_2"],
+            )
+            # Cleanup registration to avoid leaking functions across tests
+            try:
+                ALL_ATTENTION_FUNCTIONS.pop(attn_impl, None)
+            except Exception as e:
+                print(f"Could not clean up `ALL_ATTENTION_FUNCTIONS`: {e}")
+            try:
+                ALL_MASK_ATTENTION_FUNCTIONS.pop(attn_impl, None)
+            except Exception as e:
+                print(f"Could not clean up `ALL_MASK_ATTENTION_FUNCTIONS`: {e}")
+
+    def test_kernel_mask_function_custom(self):
+        """Kernels with MASK_FUNCTION attribute should use the declared mask type."""
+        kernel_obj = types.SimpleNamespace(my_func=lambda *a, **k: None, MASK_FUNCTION="sdpa")
+        with patch("transformers.integrations.hub_kernels.get_kernel", return_value=kernel_obj):
+            attn_impl = "org/custom-mask:my_func"
+            load_and_register_attn_kernel(attn_impl)
+            self.assertIn(attn_impl, ALL_MASK_ATTENTION_FUNCTIONS.valid_keys())
+            self.assertEqual(
+                ALL_MASK_ATTENTION_FUNCTIONS[attn_impl],
+                ALL_MASK_ATTENTION_FUNCTIONS["sdpa"],
+            )
+            # Cleanup registration to avoid leaking functions across tests
+            try:
+                ALL_ATTENTION_FUNCTIONS.pop(attn_impl, None)
+            except Exception as e:
+                print(f"Could not clean up `ALL_ATTENTION_FUNCTIONS`: {e}")
+            try:
+                ALL_MASK_ATTENTION_FUNCTIONS.pop(attn_impl, None)
+            except Exception as e:
+                print(f"Could not clean up `ALL_MASK_ATTENTION_FUNCTIONS`: {e}")
+
 
 @require_kernels
 class TestUseKernelsLifecycle(TestCasePlus):
diff --git a/tests/models/altclip/test_modeling_altclip.py b/tests/models/altclip/test_modeling_altclip.py
index bf849c031e3a..ecec7a7bb9ae 100755
--- a/tests/models/altclip/test_modeling_altclip.py
+++ b/tests/models/altclip/test_modeling_altclip.py
@@ -290,6 +290,8 @@ def prepare_config_and_inputs_for_common(self):
 @require_torch
 class AltCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (AltCLIPTextModel,) if is_torch_available() else ()
+    # AltCLIPTextModel has large embeddings relative to model size, so we need higher split percentages
+    model_split_percents = [0.5, 0.8, 0.9]
 
     # TODO (@SunMarc): Fix me
     @unittest.skip(reason="It's broken.")
diff --git a/tests/models/audioflamingo3/test_modeling_audioflamingo3.py b/tests/models/audioflamingo3/test_modeling_audioflamingo3.py
index 7301812e7032..9629fe3ba086 100644
--- a/tests/models/audioflamingo3/test_modeling_audioflamingo3.py
+++ b/tests/models/audioflamingo3/test_modeling_audioflamingo3.py
@@ -15,16 +15,15 @@
 """Testing suite for the PyTorch AudioFlamingo3 model."""
 
 import json
-import tempfile
 import unittest
 from pathlib import Path
 
-import pytest
-
 from transformers import (
     AudioFlamingo3Config,
+    AudioFlamingo3EncoderConfig,
     AudioFlamingo3ForConditionalGeneration,
     AutoProcessor,
+    Qwen2Config,
     is_torch_available,
 )
 from transformers.testing_utils import (
@@ -34,128 +33,52 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...alm_tester import ALMModelTest, ALMModelTester
 
 
 if is_torch_available():
     import torch
 
 
-class AudioFlamingo3ModelTester:
-    """
-    Builds a tiny AudioFlamingo3 config and synthetic inputs that respect AF3's
-    post-pool token accounting: num <sound> tokens per sample == post-pool frame count.
-    """
-
-    def __init__(
-        self,
-        parent,
-        audio_token_id=0,
-        seq_length=25,
-        feat_seq_length=60,
-        text_config=None,
-        audio_config=None,
-        is_training=True,
-    ):
-        self.parent = parent
-        self.audio_token_id = audio_token_id
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-        self.is_training = is_training
-
-        # Small text backbone (Qwen2-ish)
-        if text_config is None:
-            text_config = {
-                "model_type": "qwen2",
-                "intermediate_size": 36,
-                "initializer_range": 0.02,
-                "hidden_size": 32,
-                "max_position_embeddings": 52,
-                "num_hidden_layers": 2,
-                "num_attention_heads": 4,
-                "num_key_value_heads": 2,
-                "use_labels": True,
-                "use_mrope": False,
-                "vocab_size": 99,
-                "pad_token_id": 1,  # Ensure pad token != audio token
-            }
-        # Small audio encoder (AF3 Whisper-style)
-        if audio_config is None:
-            audio_config = {
-                "model_type": "audioflamingo3_encoder",
-                "hidden_size": 16,
-                "num_attention_heads": 4,
-                "intermediate_size": 16,
-                "num_hidden_layers": 2,
-                "num_mel_bins": 80,
-                "max_source_positions": 30,
-                "initializer_range": 0.02,
-            }
-
-        self.text_config = text_config
-        self.audio_config = audio_config
-
-        self.batch_size = 3
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.encoder_seq_length = seq_length
-
-    def get_config(self):
-        return AudioFlamingo3Config(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            audio_token_id=self.audio_token_id,
-        )
-
-    def prepare_config_and_inputs(self):
-        # (#windows == batch_size, n_mels, T_mel)
-        input_features_values = floats_tensor(
-            [self.batch_size, self.audio_config["num_mel_bins"], self.feat_seq_length]
-        )
-        config = self.get_config()
-        # Per-window mel validity (all ones => full length)
-        input_features_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
-        return config, input_features_values, input_features_mask
-
-    def _post_pool_tokens_per_window(self, T_mel):
-        # Mirror AF3 processor math:
-        pre = (T_mel - 1) // 2 + 1
-        post = (pre - 2) // 2 + 1
-        return post
-
-    def prepare_config_and_inputs_for_common(self):
-        config, input_features_values, input_features_mask = self.prepare_config_and_inputs()
-        # Every window has same T_mel here
-        num_audio_tokens_per_sample = self._post_pool_tokens_per_window(input_features_values.shape[-1])
-
-        # Build token ids with valid range and K <sound> tokens
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
-        attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=torch_device)
-        attention_mask[:, :1] = 0  # left padding sentinel
-
-        # Fill first K positions (after padding) with the audio token id, for each sample
-        input_ids[:, 1 : 1 + num_audio_tokens_per_sample] = config.audio_token_id
-
-        inputs_dict = {
-            "input_features": input_features_values,
-            "input_features_mask": input_features_mask,
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        return config, inputs_dict
+class AudioFlamingo3ModelTester(ALMModelTester):
+    config_class = AudioFlamingo3Config
+    conditional_generation_class = AudioFlamingo3ForConditionalGeneration
+    text_config_class = Qwen2Config
+    audio_config_class = AudioFlamingo3EncoderConfig
+    audio_mask_key = "input_features_mask"
+
+    def __init__(self, parent, **kwargs):
+        # feat_seq_length → (L-1)//2+1 after conv2 → (·-2)//2+1 after avg_pool, so
+        # feat_seq_length=60 gives 15 audio embed tokens (fits inside seq_length=32 + BOS + text).
+        kwargs.setdefault("feat_seq_length", 60)
+        # Encoder adds a learned positional embedding of size max_source_positions to post-conv2 features,
+        # so it must equal (feat_seq_length - 1) // 2 + 1.
+        kwargs.setdefault("max_source_positions", (kwargs["feat_seq_length"] - 1) // 2 + 1)
+        super().__init__(parent, **kwargs)
+
+    def create_audio_mask(self):
+        # Full-length mask matches real processor output and lets the audio encoder dispatch to Flash
+        # Attention (which rejects non-null attn_masks) on `test_sdpa_can_dispatch_on_flash`.
+        return torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
+
+    def get_audio_embeds_mask(self, audio_mask):
+        # Mirrors AudioFlamingo3Encoder._get_feat_extract_output_lengths:
+        # conv2 (k=3,s=2,p=1) then avg_pool (k=2,s=2).
+        input_lengths = audio_mask.sum(-1)
+        input_lengths = (input_lengths - 1) // 2 + 1
+        output_lengths = (input_lengths - 2) // 2 + 1
+        max_len = int(output_lengths.max().item())
+        positions = torch.arange(max_len, device=audio_mask.device)[None, :]
+        return (positions < output_lengths[:, None]).long()
 
 
 @require_torch
-class AudioFlamingo3ForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class AudioFlamingo3ForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `AudioFlamingo3ForConditionalGeneration`.
     """
 
-    all_model_classes = (AudioFlamingo3ForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = AudioFlamingo3ModelTester
     # TODO: @eustlb, this is incorrect
     pipeline_model_mapping = (
         {
@@ -165,73 +88,14 @@ class AudioFlamingo3ForConditionalGenerationModelTest(ModelTesterMixin, Generati
         if is_torch_available()
         else {}
     )
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = AudioFlamingo3ModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=AudioFlamingo3Config, has_text_modality=False)
 
     @unittest.skip(
-        reason="This test does not apply to AudioFlamingo3 since inputs_embeds corresponding to audio tokens are replaced when input features are provided."
+        reason="This test does not apply to AudioFlamingo3 since inputs_embeds corresponding to audio tokens "
+        "are replaced when input features are provided."
     )
     def test_inputs_embeds_matches_input_ids(self):
         pass
 
-    @unittest.skip(reason="Compile not yet supported for AudioFlamingo3 models")
-    @pytest.mark.torch_compile_test
-    def test_sdpa_can_compile_dynamic(self):
-        pass
-
-    @unittest.skip(reason="Compile not yet supported for AudioFlamingo3 models")
-    def test_sdpa_can_dispatch_on_flash(self):
-        pass
-
-    @unittest.skip(reason="AudioFlamingo3 tests avoid right-padding equivalence; fusion is in-place.")
-    def test_flash_attn_2_inference_equivalence_right_padding(self):
-        pass
-
-    @unittest.skip(reason="AudioFlamingo3 has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
-    def test_sdpa_can_dispatch_composite_models(self):
-        # AF3 is audio+text composite; verify SDPA toggles propagate to submodules.
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-
-                # SDPA (default)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-                audio_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
-
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-                self.assertTrue(model.audio_tower.config._attn_implementation == audio_attn)
-
-                # Eager
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
-
-                for _, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
 
 @require_torch
 class AudioFlamingo3ForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/audiovisualflamingo/__init__.py b/tests/models/audiovisualflamingo/__init__.py
new file mode 100644
index 000000000000..275e873d780d
--- /dev/null
+++ b/tests/models/audiovisualflamingo/__init__.py
@@ -0,0 +1 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
diff --git a/tests/models/audiovisualflamingo/test_processing_audiovisualflamingo.py b/tests/models/audiovisualflamingo/test_processing_audiovisualflamingo.py
new file mode 100644
index 000000000000..cb84ffc8aede
--- /dev/null
+++ b/tests/models/audiovisualflamingo/test_processing_audiovisualflamingo.py
@@ -0,0 +1,252 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import numpy as np
+import torch
+from PIL import Image
+
+from transformers import (
+    AudioVisualFlamingoConfig,
+    AudioVisualFlamingoProcessor,
+    AutoTokenizer,
+    SiglipImageProcessor,
+    WhisperFeatureExtractor,
+)
+from transformers.models.audiovisualflamingo.processing_audiovisualflamingo import _load_audio_hf_with_info
+from transformers.testing_utils import require_torch, require_vision
+
+
+MEDIA_TOKENS = AudioVisualFlamingoConfig.media_tokens
+MM_BOS_EOS_TOKENS = AudioVisualFlamingoConfig.mm_bos_eos_tokens
+
+
+def _make_audio(seconds: float, sampling_rate: int = 16_000, frequency: float = 220.0) -> np.ndarray:
+    steps = int(seconds * sampling_rate)
+    timeline = np.linspace(0.0, seconds, steps, endpoint=False, dtype=np.float32)
+    return np.sin(2 * np.pi * frequency * timeline).astype(np.float32)
+
+
+@require_torch
+@require_vision
+class AudioVisualFlamingoProcessorTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct", use_fast=True)
+        tokenizer.add_special_tokens(
+            {
+                "additional_special_tokens": [
+                    *MEDIA_TOKENS.values(),
+                    *(token for bos_eos_tokens in MM_BOS_EOS_TOKENS.values() for token in bos_eos_tokens),
+                ]
+            }
+        )
+
+        processor = AudioVisualFlamingoProcessor(
+            image_processor=SiglipImageProcessor(
+                crop_size={"height": 384, "width": 384},
+                size={"height": 384, "width": 384},
+            ),
+            feature_extractor=WhisperFeatureExtractor(
+                feature_size=128,
+                chunk_length=30,
+                sampling_rate=16_000,
+                hop_length=60,
+            ),
+            tokenizer=tokenizer,
+            image_aspect_ratio="dynamic_s2",
+            s2_scales=[384, 768, 1152],
+            num_video_frames=8,
+            padding_side="left",
+        )
+
+        cls.tmpdirname = tempfile.mkdtemp()
+        processor.save_pretrained(cls.tmpdirname)
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
+    def get_processor(self, **kwargs) -> AudioVisualFlamingoProcessor:
+        return AudioVisualFlamingoProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
+    def test_apply_chat_template_batched_audio_groups_flat_inputs(self):
+        processor = self.get_processor()
+
+        conversations = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "audio", "audio": _make_audio(0.5)},
+                        {"type": "audio", "audio": _make_audio(0.75, frequency=330.0)},
+                        {"type": "text", "text": "Compare these clips."},
+                    ],
+                }
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "audio", "audio": _make_audio(0.6, frequency=440.0)},
+                        {"type": "text", "text": "Describe this clip."},
+                    ],
+                }
+            ],
+        ]
+
+        inputs = processor.apply_chat_template(
+            conversations,
+            tokenize=True,
+            return_dict=True,
+            add_generation_prompt=True,
+        )
+
+        self.assertEqual(len(inputs["media"]["sound"]), 3)
+        self.assertEqual([len(sample) for sample in inputs["media"]["audio_info"]], [2, 1])
+        self.assertEqual(inputs["attention_mask"].dtype, torch.bool)
+
+    def test_dynamic_s2_block_sizes_are_aggregated_per_sample(self):
+        processor = self.get_processor()
+
+        outputs = processor(
+            text=[
+                f"{processor.image_token} Describe the first image.",
+                f"{processor.image_token} Describe the second image.",
+            ],
+            images=[
+                [Image.new("RGB", (640, 320), color="red")],
+                [Image.new("RGB", (320, 640), color="blue")],
+            ],
+        )
+
+        self.assertEqual(len(outputs["media_config"]["image"]["block_sizes"]), 2)
+        self.assertEqual((outputs["input_ids"] == processor.image_token_id).sum().item(), 2)
+
+    def test_video_audio_placeholder_is_inserted_from_video_loader_output(self):
+        processor = self.get_processor()
+        dummy_frame = Image.fromarray(np.zeros((32, 32, 3), dtype=np.uint8), mode="RGB")
+
+        def fake_extract_video(video_input, config):
+            del config
+            audio_info = {
+                "audio_start_sec": 0.0,
+                "audio_end_sample_sec": 1.0,
+                "ori_audio_duration": 1.0,
+            }
+            video_info = {
+                "video_path": str(video_input),
+                "has_audio": True,
+                "video_duration": 1.0,
+                "audio_info": audio_info,
+                "video_frame_times": [0.0],
+            }
+            return [dummy_frame], _make_audio(1.0), video_info
+
+        with patch(
+            "transformers.models.audiovisualflamingo.processing_audiovisualflamingo._extract_video_hf",
+            side_effect=fake_extract_video,
+        ):
+            inputs = processor(
+                text=[f"{processor.video_token} Summarize the clip."],
+                videos=[["dummy-video.mp4"]],
+            )
+
+        self.assertEqual(len(inputs["media"]["sound"]), 1)
+        self.assertEqual([len(sample) for sample in inputs["media"]["audio_info"]], [1])
+        self.assertEqual((inputs["input_ids"] == processor.sound_token_id).sum().item(), 1)
+
+    def test_audio_loader_falls_back_to_pyav_for_media_containers(self):
+        runtime_config = SimpleNamespace(audio_sampling_rate=16_000, audio_chunk_length=120, random_audio_sample=False)
+
+        with (
+            patch(
+                "transformers.models.audiovisualflamingo.processing_audiovisualflamingo.load_audio",
+                side_effect=RuntimeError("decode failed"),
+            ) as mocked_load_audio,
+            patch(
+                "transformers.models.audiovisualflamingo.processing_audiovisualflamingo._load_audio_track_with_pyav",
+                return_value=_make_audio(1.0),
+            ) as mocked_fallback,
+        ):
+            waveform, audio_info = _load_audio_hf_with_info("dummy-video.mp4", runtime_config)
+
+        mocked_load_audio.assert_called_once_with("dummy-video.mp4", sampling_rate=16_000)
+        mocked_fallback.assert_called_once_with("dummy-video.mp4", 16_000)
+        self.assertEqual(waveform.shape[0], audio_info["new_audio_n_samples"])
+        self.assertEqual(audio_info["new_audio_chunk_length"], 30)
+
+    def test_model_input_names_include_media_keys(self):
+        processor = self.get_processor()
+        self.assertIn("media", processor.model_input_names)
+        self.assertIn("media_config", processor.model_input_names)
+
+    def test_standard_component_configs_resolve_to_subconfigs(self):
+        config = AudioVisualFlamingoConfig(
+            text_config={
+                "model_type": "qwen2",
+                "hidden_size": 64,
+                "intermediate_size": 128,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 8,
+                "num_key_value_heads": 8,
+                "vocab_size": 256,
+            },
+            vision_config={
+                "model_type": "siglip_vision_model",
+                "hidden_size": 32,
+                "intermediate_size": 64,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 4,
+                "image_size": 384,
+                "patch_size": 14,
+            },
+            audio_config={
+                "model_type": "qwen2_audio_encoder",
+                "num_mel_bins": 128,
+                "encoder_layers": 2,
+                "encoder_attention_heads": 4,
+                "encoder_ffn_dim": 64,
+                "d_model": 32,
+            },
+        )
+
+        self.assertEqual(config.text_config.model_type, "qwen2")
+        self.assertEqual(config.vision_config.model_type, "siglip_vision_model")
+        self.assertEqual(config.audio_config.model_type, "qwen2_audio_encoder")
+
+    def test_config_keeps_only_canonical_runtime_fields(self):
+        config = AudioVisualFlamingoConfig(
+            s2_scales=[448, 896, 1344],
+            image_encoder={"_target_": "BasicImageEncoder"},
+            video_encoder={"_target_": "TSPVideoEncoder", "embed_time": "True"},
+            sound_encoder={"_target_": "BasicSoundEncoder", "embed_time": "True"},
+        )
+
+        self.assertEqual(config.s2_scales, [448, 896, 1344])
+        self.assertEqual(config.image_encoder["_target_"], "BasicImageEncoder")
+        self.assertEqual(config.video_encoder["_target_"], "TSPVideoEncoder")
+        self.assertEqual(config.sound_encoder["_target_"], "BasicSoundEncoder")
+
+        config_dict = config.to_dict()
+        self.assertNotIn("audio_sampling_rate", config_dict)
+        self.assertNotIn("audio_chunk_length", config_dict)
+        self.assertNotIn("audio_hop_length", config_dict)
+        self.assertNotIn("num_video_frames", config_dict)
+        self.assertNotIn("max_tiles", config_dict)
diff --git a/tests/models/auto/test_processor_auto.py b/tests/models/auto/test_processor_auto.py
index c029ae2cf97d..a8185b55597a 100644
--- a/tests/models/auto/test_processor_auto.py
+++ b/tests/models/auto/test_processor_auto.py
@@ -498,6 +498,46 @@ def __init__(self, tokenizer, decoder_tokenizer, image_processor):
             # Verify image processor loaded correctly
             self.assertEqual(loaded_processor.image_processor.size, image_processor.size)
 
+    def test_processor_from_pretrained_with_prebuilt_tokenizer_kwarg(self):
+        class SingleTokenizerProcessor(ProcessorMixin):
+            def __init__(self, bpe_tokenizer):
+                super().__init__(bpe_tokenizer)
+
+        class DualTokenizerProcessor(ProcessorMixin):
+            def __init__(self, bpe_tokenizer, decoder_tokenizer):
+                super().__init__(bpe_tokenizer, decoder_tokenizer)
+
+        tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertForMaskedLM")
+
+        self.assertEqual(
+            SingleTokenizerProcessor._pop_prebuilt_subprocessors({"tokenizer": tokenizer}),
+            {"bpe_tokenizer": tokenizer},
+        )
+        ambiguous_kwargs = {"tokenizer": tokenizer}
+        self.assertEqual(DualTokenizerProcessor._pop_prebuilt_subprocessors(ambiguous_kwargs), {})
+        self.assertIn("tokenizer", ambiguous_kwargs)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            SingleTokenizerProcessor(bpe_tokenizer=tokenizer).save_pretrained(tmp_dir)
+
+            loaded = SingleTokenizerProcessor.from_pretrained(tmp_dir, bpe_tokenizer=tokenizer)
+            self.assertIs(loaded.bpe_tokenizer, tokenizer)
+
+            loaded = SingleTokenizerProcessor.from_pretrained(tmp_dir, tokenizer=tokenizer)
+            self.assertIs(loaded.bpe_tokenizer, tokenizer)
+
+            loaded, unused = SingleTokenizerProcessor.from_pretrained(
+                tmp_dir, tokenizer=tokenizer, return_unused_kwargs=True
+            )
+            self.assertIs(loaded.bpe_tokenizer, tokenizer)
+            self.assertNotIn("tokenizer", unused)
+
+            loaded, unused = SingleTokenizerProcessor.from_pretrained(
+                tmp_dir, bpe_tokenizer=tokenizer, return_unused_kwargs=True
+            )
+            self.assertIs(loaded.bpe_tokenizer, tokenizer)
+            self.assertNotIn("bpe_tokenizer", unused)
+
     def test_processor_with_multiple_image_processors_save_load(self):
         """Test that processors with multiple image processors save and load correctly."""
 
diff --git a/tests/models/auto/test_tokenization_auto.py b/tests/models/auto/test_tokenization_auto.py
index 978ce845c75a..b9fce9134d84 100644
--- a/tests/models/auto/test_tokenization_auto.py
+++ b/tests/models/auto/test_tokenization_auto.py
@@ -45,7 +45,6 @@
 from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
 from transformers.models.auto.tokenization_auto import (
     REGISTERED_FAST_ALIASES,
-    REGISTERED_TOKENIZER_CLASSES,
     TOKENIZER_MAPPING,
     TOKENIZER_MAPPING_NAMES,
     get_tokenizer_config,
@@ -395,7 +394,6 @@ def test_new_tokenizer_registration(self):
                 del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
-            REGISTERED_TOKENIZER_CLASSES.pop("CustomTokenizer", None)
 
     @require_tokenizers
     def test_new_tokenizer_fast_registration(self):
@@ -440,8 +438,6 @@ def test_new_tokenizer_fast_registration(self):
                 del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
-            REGISTERED_TOKENIZER_CLASSES.pop("CustomTokenizer", None)
-            REGISTERED_TOKENIZER_CLASSES.pop("CustomTokenizerFast", None)
             REGISTERED_FAST_ALIASES.pop("CustomTokenizer", None)
 
     def test_from_pretrained_dynamic_tokenizer(self):
@@ -554,7 +550,6 @@ class NewTokenizer(BertTokenizer):
                 del CONFIG_MAPPING._extra_content["custom"]
             if CustomConfig in TOKENIZER_MAPPING._extra_content:
                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
-            REGISTERED_TOKENIZER_CLASSES.pop("NewTokenizer", None)
 
     def test_from_pretrained_dynamic_tokenizer_legacy_format(self):
         tokenizer = AutoTokenizer.from_pretrained(
diff --git a/tests/models/auto/test_video_processing_auto.py b/tests/models/auto/test_video_processing_auto.py
index baccddbdc652..ec4d3a2024b1 100644
--- a/tests/models/auto/test_video_processing_auto.py
+++ b/tests/models/auto/test_video_processing_auto.py
@@ -12,11 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib
 import json
 import sys
 import tempfile
 import unittest
 from pathlib import Path
+from unittest.mock import patch
 
 import transformers
 from transformers import (
@@ -146,6 +148,12 @@ def test_video_processor_not_found(self):
         ):
             _ = AutoVideoProcessor.from_pretrained("hf-internal-testing/config-no-model")
 
+    def test_video_processor_class_from_name_with_none_mapping_entry(self):
+        video_processing_auto = importlib.import_module("transformers.models.auto.video_processing_auto")
+
+        with patch.dict(video_processing_auto.VIDEO_PROCESSOR_MAPPING_NAMES, {"videomae": None}, clear=True):
+            self.assertIsNone(video_processing_auto.video_processor_class_from_name("DefinitelyMissingVideoProcessor"))
+
     def test_from_pretrained_dynamic_video_processor(self):
         # If remote code is not set, we will time out when asking whether to load the model.
         with self.assertRaises(ValueError):
diff --git a/tests/models/aya_vision/test_modeling_aya_vision.py b/tests/models/aya_vision/test_modeling_aya_vision.py
index c88f6889d123..b5e659826a21 100644
--- a/tests/models/aya_vision/test_modeling_aya_vision.py
+++ b/tests/models/aya_vision/test_modeling_aya_vision.py
@@ -206,9 +206,6 @@ def test_sdpa_can_compile_dynamic(self):
     def test_batching_equivalence(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class AyaVisionIntegrationTest(unittest.TestCase):
diff --git a/tests/models/aya_vision/test_processing_aya_vision.py b/tests/models/aya_vision/test_processing_aya_vision.py
index 8d4611eb2374..1107d5e5c638 100644
--- a/tests/models/aya_vision/test_processing_aya_vision.py
+++ b/tests/models/aya_vision/test_processing_aya_vision.py
@@ -144,3 +144,25 @@ def test_process_interleaved_images_videos(self):
                 ],
             )
             images_patches_index += inputs["pixel_values"].shape[0]
+
+    def test_image_processor_defaults(self):
+        # AyaVisionProcessor has a default value `crop_to_patches=True` but the image processor's
+        # default is different. Override and pass the arg explicitly
+
+        image_processor = self.get_component("image_processor")
+
+        # Get all required components for processor
+        components = {}
+        for attribute in self.processor_class.get_attributes():
+            components[attribute] = self.get_component(attribute)
+
+        processor = self.processor_class(**components)
+        image_input = self.prepare_image_inputs()
+
+        input_image_proc = image_processor(image_input, crop_to_patches=False, return_tensors="pt")
+        input_processor = processor(images=image_input, crop_to_patches=False, return_tensors="pt")
+
+        # Verify outputs match
+        for key in input_image_proc:
+            if key in processor.model_input_names:
+                torch.testing.assert_close(input_image_proc[key], input_processor[key])
diff --git a/tests/models/bart/test_modeling_bart.py b/tests/models/bart/test_modeling_bart.py
index 94eb37bf3674..8e9cb202a439 100644
--- a/tests/models/bart/test_modeling_bart.py
+++ b/tests/models/bart/test_modeling_bart.py
@@ -20,8 +20,10 @@
 from functools import cached_property
 
 import timeout_decorator  # noqa
+from parameterized import parameterized
 
 from transformers import BartConfig, is_torch_available
+from transformers.cache_utils import DynamicCache, EncoderDecoderCache
 from transformers.testing_utils import (
     require_sentencepiece,
     require_tokenizers,
@@ -317,6 +319,57 @@ def test_lm_uneven_forward(self):
         expected_shape = (*summary.shape, config.vocab_size)
         self.assertEqual(outputs["logits"].shape, expected_shape)
 
+    @parameterized.expand(["sdpa", "eager"])
+    def test_lm_uneven_forward_with_mask(self, attn_implementation):
+        config = BartConfig(
+            vocab_size=self.vocab_size,
+            d_model=14,
+            encoder_layers=2,
+            decoder_layers=2,
+            encoder_attention_heads=2,
+            decoder_attention_heads=2,
+            encoder_ffn_dim=8,
+            decoder_ffn_dim=8,
+            max_position_embeddings=48,
+        )
+        config._attn_implementation = attn_implementation
+        lm_model = BartForConditionalGeneration(config).to(torch_device)
+        context = torch.tensor(
+            [[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]], device=torch_device, dtype=torch.long
+        )
+        mask = torch.ones((context.shape[0], context.shape[1] + 2), device=context.device, dtype=torch.int64)
+        shape1 = (2, 2, 7, 7)
+        shape2 = (2, 2, 2, 7)
+        past_key_values = EncoderDecoderCache(
+            self_attention_cache=DynamicCache(
+                [
+                    (
+                        torch.zeros(shape1, device=context.device, dtype=torch.float),
+                        torch.zeros(shape1, device=context.device, dtype=torch.float),
+                    ),
+                    (
+                        torch.zeros(shape1, device=context.device, dtype=torch.float),
+                        torch.zeros(shape1, device=context.device, dtype=torch.float),
+                    ),
+                ]
+            ),
+            cross_attention_cache=DynamicCache(
+                [
+                    (
+                        torch.zeros(shape2, device=context.device, dtype=torch.float),
+                        torch.zeros(shape2, device=context.device, dtype=torch.float),
+                    ),
+                    (
+                        torch.zeros(shape2, device=context.device, dtype=torch.float),
+                        torch.zeros(shape2, device=context.device, dtype=torch.float),
+                    ),
+                ]
+            ),
+        )
+        outputs = lm_model(input_ids=context, attention_mask=mask, past_key_values=past_key_values)
+        expected_shape = (2, 7, config.vocab_size)
+        self.assertEqual(outputs["logits"].shape, expected_shape)
+
     def test_shift_tokens_right(self):
         input_ids = torch.tensor([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]], dtype=torch.long)
         shifted = shift_tokens_right(input_ids, 1, 2)
diff --git a/tests/models/blt/test_modeling_blt.py b/tests/models/blt/test_modeling_blt.py
index a3f50157b38a..a67e2fdfcfb4 100644
--- a/tests/models/blt/test_modeling_blt.py
+++ b/tests/models/blt/test_modeling_blt.py
@@ -20,6 +20,7 @@
 
 from transformers import AutoTokenizer, is_torch_available
 from transformers.testing_utils import (
+    Expectations,
     cleanup,
     require_torch,
     require_torch_accelerator,
@@ -260,74 +261,14 @@ def test_model(self):
 
     @slow
     def test_model_logits(self):
+        # fmt: off
         EXPECTED_OUTPUT = torch.tensor(
             [
-                [
-                    -10.4948,
-                    -10.7065,
-                    -6.1813,
-                    -10.5545,
-                    -10.3428,
-                    -9.1493,
-                    -8.4937,
-                    -8.6382,
-                    -9.2159,
-                    -9.5907,
-                    -9.3679,
-                    -8.4184,
-                    -9.0655,
-                    -3.4436,
-                    2.9616,
-                    -10.3157,
-                    -6.3723,
-                    -6.0133,
-                    -9.7100,
-                    -9.2128,
-                    -8.8064,
-                    -9.8179,
-                    -9.7516,
-                    -9.4681,
-                    -9.7715,
-                    -9.4897,
-                    -9.0491,
-                    -9.8098,
-                    -9.4648,
-                    -9.3294,
-                ],
-                [
-                    -13.3010,
-                    -13.1910,
-                    -5.7230,
-                    -13.2895,
-                    -13.4864,
-                    -8.7140,
-                    -7.0275,
-                    -7.0182,
-                    -10.1362,
-                    -10.3762,
-                    -9.9086,
-                    -7.8049,
-                    -8.8660,
-                    -5.2711,
-                    -3.5778,
-                    -12.5346,
-                    -9.1609,
-                    -6.7925,
-                    -10.3717,
-                    -9.2650,
-                    -10.6393,
-                    -11.4807,
-                    -11.2128,
-                    -10.9615,
-                    -10.5806,
-                    -10.8873,
-                    -11.0651,
-                    -11.3471,
-                    -10.5437,
-                    -9.9688,
-                ],
+                [-10.5000, -10.6875, -6.2500, -10.5625, -10.3125, -9.1875, -8.5000, -8.5625, -9.1875, -9.6250, -9.3750, -8.5000, -9.1250, -3.3906, 2.9688, -10.3125, -6.4688, -6.0312, -9.7500, -9.1875, -8.8125, -9.8750, -9.8125, -9.5000, -9.8125, -9.5000, -9.0625, -9.8125, -9.5000, -9.3750],
+                [-13.2500, -13.1250, -5.6875, -13.1875, -13.3750, -8.6875, -6.9688, -6.9375, -10.0625, -10.3125, -9.8125, -7.7188, -8.8125, -5.2188, -3.5000, -12.4375, -9.0625, -6.6250, -10.3125, -9.1875, -10.6250, -11.4375, -11.1250, -10.8750, -10.5000, -10.8750, -11.0000, -11.3125, -10.5000, -9.8750],
             ]
         ).to(torch_device)
+        # fmt: on
 
         input_ids = [1, 42, 21, 12, 43, 23, 1, 4]
 
@@ -336,14 +277,21 @@ def test_model_logits(self):
         with torch.no_grad():
             output = model(torch.tensor([input_ids]).to(torch_device))[0]
 
-        torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30], rtol=1e-4, atol=1e-4)
+        torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30].to(torch_device), rtol=1e-3, atol=1e-3)
 
     @slow
     @require_torch_bf16
     def test_model_bf16(self):
         """Test Blt model with bfloat16 precision."""
         NUM_TOKENS_TO_GENERATE = 200
-        EXPECTED_TEXT = "my name is alex and i am a student at the university of michigan in the college of arts and sciences. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan m"
+        # fmt: off
+        EXPECTED_TEXT = Expectations(
+            {
+                (None, None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
+                ("xpu", None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
+            }
+        )
+        # fmt: on
 
         prompt = "my name is"
 
@@ -360,81 +308,21 @@ def test_model_bf16(self):
         )
 
         output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-        self.assertEqual(output_text, EXPECTED_TEXT)
+        self.assertEqual(output_text, EXPECTED_TEXT.get_expectation())
 
     @slow
     @require_torch_bf16
     def test_model_logits_bf16(self):
         """Test Blt model logits with bfloat16 precision."""
 
+        # fmt: off
         EXPECTED_OUTPUT = torch.tensor(
             [
-                [
-                    -10.5000,
-                    -10.6875,
-                    -6.1875,
-                    -10.5625,
-                    -10.3125,
-                    -9.1875,
-                    -8.5000,
-                    -8.6875,
-                    -9.1875,
-                    -9.5625,
-                    -9.3750,
-                    -8.5000,
-                    -9.0625,
-                    -3.4219,
-                    2.9531,
-                    -10.3125,
-                    -6.4062,
-                    -6.0000,
-                    -9.6875,
-                    -9.1875,
-                    -8.8125,
-                    -9.8125,
-                    -9.7500,
-                    -9.4375,
-                    -9.8125,
-                    -9.5000,
-                    -9.0000,
-                    -9.8125,
-                    -9.4375,
-                    -9.3125,
-                ],
-                [
-                    -13.2500,
-                    -13.1875,
-                    -5.6875,
-                    -13.3125,
-                    -13.5000,
-                    -8.7500,
-                    -7.0625,
-                    -7.0312,
-                    -10.1250,
-                    -10.3750,
-                    -9.8750,
-                    -7.8438,
-                    -8.8750,
-                    -5.2812,
-                    -3.5625,
-                    -12.5000,
-                    -9.1875,
-                    -6.8125,
-                    -10.3750,
-                    -9.3125,
-                    -10.6250,
-                    -11.5000,
-                    -11.2500,
-                    -11.0000,
-                    -10.5625,
-                    -10.8750,
-                    -11.0625,
-                    -11.3750,
-                    -10.5625,
-                    -10.0000,
-                ],
+                [-10.5000, -10.6875, -6.2500, -10.5625, -10.3125, -9.1875, -8.5000, -8.5625, -9.1875, -9.6250, -9.3750, -8.5000, -9.1250, -3.3906, 2.9688, -10.3125, -6.4688, -6.0312, -9.7500, -9.1875, -8.8125, -9.8750, -9.8125, -9.5000, -9.8125, -9.5000, -9.0625, -9.8125, -9.5000, -9.3750],
+                [-13.2500, -13.1250, -5.6875, -13.1875, -13.3750, -8.6875, -6.9688, -6.9375, -10.0625, -10.3125, -9.8125, -7.7188, -8.8125, -5.2188, -3.5000, -12.4375, -9.0625, -6.6250, -10.3125, -9.1875, -10.6250, -11.4375, -11.1250, -10.8750, -10.5000, -10.8750, -11.0000, -11.3125, -10.5000, -9.8750],
             ]
         ).to(torch_device)
+        # fmt: on
 
         input_ids = [1, 42, 21, 12, 43, 23, 1, 4]
 
@@ -445,7 +333,7 @@ def test_model_logits_bf16(self):
         with torch.no_grad():
             output = model(torch.tensor([input_ids]).to(torch_device))[0]
 
-        torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30], rtol=1e-3, atol=1e-3)
+        torch.testing.assert_close(EXPECTED_OUTPUT, output[0, :2, :30].to(torch_device), rtol=1e-3, atol=1e-3)
 
     @slow
     def test_model_eager(self):
@@ -473,7 +361,14 @@ def test_model_eager(self):
     def test_model_bf16_static_cache(self):
         """Test Blt model with bfloat16 precision and static cache."""
         NUM_TOKENS_TO_GENERATE = 200
-        EXPECTED_TEXT = "my name is alex and i am a student at the university of michigan in the college of arts and sciences. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan m"
+        # fmt: off
+        EXPECTED_TEXT = Expectations(
+            {
+                (None, None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
+                ("xpu", None): "my name is alex and i am a student at the university of michigan. i am a senior majoring in computer science and minoring in mathematics. i am also a member of the michigan math club and the michigan computer s",
+            }
+        )
+        # fmt: on
 
         prompt = "my name is"
 
@@ -492,4 +387,4 @@ def test_model_bf16_static_cache(self):
         )
 
         output_text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-        self.assertEqual(output_text, EXPECTED_TEXT)
+        self.assertEqual(output_text, EXPECTED_TEXT.get_expectation())
diff --git a/tests/models/chinese_clip/test_modeling_chinese_clip.py b/tests/models/chinese_clip/test_modeling_chinese_clip.py
index 2583b8988a54..cd45e3c4b7e7 100644
--- a/tests/models/chinese_clip/test_modeling_chinese_clip.py
+++ b/tests/models/chinese_clip/test_modeling_chinese_clip.py
@@ -314,6 +314,8 @@ def prepare_config_and_inputs_for_common(self):
 @require_torch
 class ChineseCLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (ChineseCLIPTextModel,) if is_torch_available() else ()
+    # ChineseCLIPTextModel has large embeddings relative to model size, so we need higher split percentages
+    model_split_percents = [0.5, 0.8, 0.9]
 
     # special case for ForPreTraining model
     def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
diff --git a/tests/models/colpali/test_modeling_colpali.py b/tests/models/colpali/test_modeling_colpali.py
index 88df8ade9a49..699d5019fbe1 100644
--- a/tests/models/colpali/test_modeling_colpali.py
+++ b/tests/models/colpali/test_modeling_colpali.py
@@ -226,10 +226,6 @@ def test_sdpa_can_dispatch_on_flash(self):
     def test_sdpa_can_compile_dynamic(self):
         pass
 
-    @unittest.skip(reason="Some weight mappings from paligemma are unreachable here as they use a `^` pattern")
-    def test_reverse_loading_mapping(self):
-        pass
-
 
 @require_torch
 class ColPaliModelIntegrationTest(unittest.TestCase):
diff --git a/tests/models/colqwen2/test_modeling_colqwen2.py b/tests/models/colqwen2/test_modeling_colqwen2.py
index 110576ebe5c6..fb213177fb8b 100644
--- a/tests/models/colqwen2/test_modeling_colqwen2.py
+++ b/tests/models/colqwen2/test_modeling_colqwen2.py
@@ -284,10 +284,6 @@ def test_sdpa_can_compile_dynamic(self):
     def test_load_save_without_tied_weights(self):
         pass
 
-    @unittest.skip(reason="One weight renaming from qwen2 is unreachable here as it uses a `^` pattern")
-    def test_reverse_loading_mapping(self):
-        pass
-
 
 @require_torch
 class ColQwen2ModelIntegrationTest(unittest.TestCase):
diff --git a/tests/models/ctsm/__init__.py b/tests/models/ctsm/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/ctsm/test_modeling_ctsm.py b/tests/models/ctsm/test_modeling_ctsm.py
new file mode 100644
index 000000000000..fa37870a9a3a
--- /dev/null
+++ b/tests/models/ctsm/test_modeling_ctsm.py
@@ -0,0 +1,294 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import random
+import unittest
+
+import numpy as np
+import torch
+from parameterized import parameterized
+
+from transformers import CtsmConfig, is_torch_available
+from transformers.testing_utils import require_flash_attn, require_torch, require_torch_accelerator, slow, torch_device
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin, floats_tensor
+
+
+if is_torch_available():
+    from transformers import CtsmModel, CtsmModelForPrediction
+
+
+class CtsmModelTester:
+    def __init__(
+        self,
+        parent,
+        patch_length: int = 8,
+        context_length: int = 64,
+        horizon_length: int = 8,
+        num_hidden_layers: int = 2,
+        hidden_size: int = 32,
+        intermediate_size: int = 32,
+        head_dim: int = 16,
+        num_attention_heads: int = 2,
+        num_key_value_heads: int = 2,
+        rms_norm_eps: float = 1e-6,
+        quantiles=(0.1, 0.5, 0.9),
+        agg_factor: int = 4,
+        max_position_embeddings: int = 64,
+        batch_size: int = 2,
+        is_training: bool = True,
+    ):
+        self.parent = parent
+        self.patch_length = patch_length
+        self.context_length = context_length
+        self.horizon_length = horizon_length
+        self.num_hidden_layers = num_hidden_layers
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.head_dim = head_dim
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.rms_norm_eps = rms_norm_eps
+        self.quantiles = list(quantiles)
+        self.agg_factor = agg_factor
+        self.max_position_embeddings = max_position_embeddings
+        self.batch_size = batch_size
+        self.is_training = is_training
+
+        # Total patches in the concatenated sequence (coarse + special + fine).
+        self.seq_length = 2 * (context_length // patch_length) + 1
+
+    def get_config(self):
+        return CtsmConfig(
+            patch_length=self.patch_length,
+            context_length=self.context_length,
+            horizon_length=self.horizon_length,
+            num_hidden_layers=self.num_hidden_layers,
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            head_dim=self.head_dim,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            rms_norm_eps=self.rms_norm_eps,
+            quantiles=self.quantiles,
+            agg_factor=self.agg_factor,
+            max_position_embeddings=self.max_position_embeddings,
+        )
+
+    def get_pipeline_config(self):
+        return self.get_config()
+
+    def prepare_config_and_inputs(self):
+        bsize = self.batch_size
+        past_values = [
+            torch.tensor(
+                np.sin(np.linspace(0, 20, self.agg_factor * self.context_length)),
+                dtype=torch.float32,
+                device=torch_device,
+            )
+            for _ in range(bsize)
+        ]
+        return self.get_config(), past_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config, past_values = self.prepare_config_and_inputs()
+        inputs_dict = {"past_values": past_values}
+        return config, inputs_dict
+
+
+@require_torch
+class CtsmModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (CtsmModelForPrediction,) if is_torch_available() else ()
+    test_resize_embeddings = False
+    is_encoder_decoder = False
+    test_inputs_embeds = False
+    test_all_params_have_gradient = False
+    test_headmasking = False
+    test_pruning = False
+    test_missing_keys = False
+    test_model_parallel = False
+
+    def setUp(self):
+        self.model_tester = CtsmModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=CtsmConfig, has_text_modality=False)
+
+    def test_create_and_run_model(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = CtsmModelForPrediction(config)
+        model.to(torch_device)
+        model.eval()
+        results = model(**inputs_dict)
+        self.assertEqual(results.mean_predictions.shape, (self.model_tester.batch_size, config.horizon_length))
+        self.assertEqual(
+            results.full_predictions.shape,
+            (self.model_tester.batch_size, config.horizon_length, 1 + len(config.quantiles)),
+        )
+
+    def test_encoder_forward_matches_predict(self):
+        """The low-level `CtsmModel.forward` should accept the two-stream interface directly."""
+        config = self.model_tester.get_config()
+        model = CtsmModel(config).to(torch_device).eval()
+
+        coarse = torch.randn(self.model_tester.batch_size, config.context_length, device=torch_device)
+        fine = torch.randn(self.model_tester.batch_size, config.context_length, device=torch_device)
+        with torch.no_grad():
+            out = model(past_values_coarse=coarse, past_values_fine=fine)
+
+        coarse_patches = config.context_length // config.patch_length
+        fine_patches = config.context_length // config.patch_length
+        self.assertEqual(
+            out.last_hidden_state.shape,
+            (self.model_tester.batch_size, coarse_patches + 1 + fine_patches, config.hidden_size),
+        )
+        self.assertEqual(out.loc.shape, (self.model_tester.batch_size,))
+        self.assertEqual(out.loc_coarse.shape, (self.model_tester.batch_size,))
+
+    @unittest.skip(reason="CTSM uses a custom multi-resolution attention mask built internally.")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    def test_eager_matches_sdpa_inference(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        """CTSM builds its own mask from the concatenated stream paddings; the generic harness, which
+        injects external attention masks and mutates QK-norm RMSNorm eps, is not compatible. We verify
+        eager vs. SDPA equivalence on the low-level `CtsmModel` instead."""
+        if not self.all_model_classes[0]._supports_sdpa:
+            self.skipTest("Model does not support SDPA")
+        torch_dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[dtype]
+        tolerance = {torch.float32: 1e-4, torch.bfloat16: 5e-3, torch.float16: 5e-3}[torch_dtype]
+        self._attn_kernel_equivalence("sdpa", dtype=torch_dtype, tolerance=tolerance)
+
+    @unittest.skip(reason="Model does not have input embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="CTSM does not support gradient checkpointing in this version")
+    def test_gradient_checkpointing_backward_compatibility(self):
+        pass
+
+    def _attn_kernel_equivalence(self, attn_implementation, dtype=torch.float32, tolerance=1e-4):
+        """Compare eager vs `attn_implementation` on the low-level `CtsmModel`.
+
+        Uses the two-stream interface so we bypass the prediction-head AR loop which
+        adds numerical noise unrelated to the kernel choice.
+        """
+        config = self.model_tester.get_config()
+        model_eager = CtsmModel._from_config(config, attn_implementation="eager")
+        model_eager.to(dtype=dtype, device=torch_device).eval()
+
+        model_other = CtsmModel._from_config(config, attn_implementation=attn_implementation)
+        model_other.load_state_dict(model_eager.state_dict())
+        model_other.to(dtype=dtype, device=torch_device).eval()
+
+        coarse = torch.randn(self.model_tester.batch_size, config.context_length, device=torch_device, dtype=dtype)
+        fine = torch.randn(self.model_tester.batch_size, config.context_length, device=torch_device, dtype=dtype)
+
+        with torch.no_grad():
+            out_e = model_eager(past_values_coarse=coarse, past_values_fine=fine)
+            out_o = model_other(past_values_coarse=coarse, past_values_fine=fine)
+
+        diff = (out_e.last_hidden_state - out_o.last_hidden_state).abs().max().item()
+        self.assertLess(diff, tolerance, f"{attn_implementation} vs eager last_hidden_state max diff: {diff:.2e}")
+
+    def test_eager_matches_sdpa(self):
+        if not self.all_model_classes[0]._supports_sdpa:
+            self.skipTest("Model does not support SDPA")
+        self._attn_kernel_equivalence("sdpa", dtype=torch.float32, tolerance=1e-4)
+
+    @require_flash_attn
+    @require_torch_accelerator
+    def test_flash_attn_2_inference_equivalence(self):
+        self._attn_kernel_equivalence("flash_attention_2", dtype=torch.bfloat16, tolerance=1e-2)
+
+    def test_retain_grad_hidden_states_attentions(self):
+        """CTSM returns `mean_predictions` as the first tensor, not `last_hidden_state`."""
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = self.has_attentions
+        if self.has_attentions:
+            config._attn_implementation = "eager"
+
+        model_class = self.all_model_classes[0]
+        model = model_class._from_config(config, attn_implementation="eager")
+        model.to(torch_device)
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+        outputs = model(**inputs)
+
+        output_tensor = outputs.mean_predictions
+        if outputs.hidden_states is not None:
+            hidden_states = outputs.hidden_states[0]
+            hidden_states.retain_grad()
+        if self.has_attentions and outputs.attentions is not None:
+            attentions = outputs.attentions[0]
+            attentions.retain_grad()
+
+        output_tensor.flatten()[0].backward(retain_graph=True)
+
+        if outputs.hidden_states is not None:
+            self.assertIsNotNone(hidden_states.grad)
+        if self.has_attentions and outputs.attentions is not None:
+            self.assertIsNotNone(attentions.grad)
+
+    def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
+        inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
+        if return_labels:
+            batch_size = len(inputs_dict["past_values"])
+            rng = random.Random(42)
+            inputs_dict["future_values"] = floats_tensor([batch_size, self.model_tester.horizon_length], rng=rng)
+        return inputs_dict
+
+    def test_kv_cache_matches_full_recompute(self):
+        """Cached autoregressive decoding should produce close-to-identical predictions to the
+        full-recompute path (the small gap is from the stream-stats-freezing approximation)."""
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = CtsmModelForPrediction(config).to(torch_device).eval()
+
+        # Long enough to trigger AR (horizon > config.horizon_length).
+        horizon_len = config.horizon_length * 3
+        with torch.no_grad():
+            out_full = model(**inputs_dict, horizon_len=horizon_len, use_cache=False)
+            out_cache = model(**inputs_dict, horizon_len=horizon_len, use_cache=True)
+
+        # First horizon_length predictions must match bit-exactly (step 1 is identical in both paths).
+        step1 = config.horizon_length
+        self.assertTrue(
+            torch.allclose(out_full.mean_predictions[:, :step1], out_cache.mean_predictions[:, :step1], atol=1e-5),
+            msg="Step-1 predictions must match bit-exactly between cached and non-cached paths.",
+        )
+        # On subsequent AR steps the stats-freezing approximation introduces a small bounded drift.
+        # The bound is generous here because the tiny tester model has random weights and a horizon of 8,
+        # so compounding any small per-step shift over multiple steps is amplified.
+        relative = (out_full.mean_predictions - out_cache.mean_predictions).abs().max() / (
+            out_full.mean_predictions.abs().max().clamp_min(1.0)
+        )
+        self.assertLess(relative.item(), 0.5, f"cached vs full-recompute AR drift {relative.item():.2e} too large")
+
+
+@require_torch
+@slow
+class CtsmModelIntegrationTests(unittest.TestCase):
+    def test_inference(self):
+        model = CtsmModelForPrediction.from_pretrained("cisco-ai/cisco-time-series-model-1.0").to(torch_device)
+        rng = np.random.default_rng(42)
+        series = (np.sin(np.linspace(0, 200, 512 * 60)) + 0.05 * rng.standard_normal(512 * 60)).astype(np.float32)
+        past_values = [torch.tensor(series, device=torch_device)]
+
+        with torch.no_grad():
+            output = model(past_values=past_values, horizon_len=128)
+
+        self.assertEqual(output.mean_predictions.shape, (1, 128))
+        self.assertEqual(output.full_predictions.shape, (1, 128, 1 + len(model.config.quantiles)))
diff --git a/tests/models/dac/test_modeling_dac.py b/tests/models/dac/test_modeling_dac.py
index 8b4de999a7d4..798b50e6c07e 100644
--- a/tests/models/dac/test_modeling_dac.py
+++ b/tests/models/dac/test_modeling_dac.py
@@ -309,12 +309,17 @@ def compute_rmse(arr1, arr2):
 - test_batch: https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-test_dac_batch-py
 NOTE (ebezzam): had to run reproducers from CI for expected outputs to match, cf PR which modified CI torch settings: https://github.com/huggingface/transformers/pull/39885
 
-See https://github.com/huggingface/transformers/pull/39313 for reason behind large tolerance between for encoder
-and decoder outputs (1e-3). In summary, original model uses weight normalization, while Transformers does not. This
-leads to accumulating error. However, this does not affect the quantizer codes, thanks to discretization being
-robust to precision errors. Moreover, codec error is similar between Transformers and original.
+Higher tolerances for encoder and decoder outputs are expected due to:
+1. Transformer model does not use weight norm for speed-up. And during model conversion, weight norm was removed on
+CPU. This leads to slightly different weight (1e-8) and the error accumulates. Removing weight norm on GPU would produce
+equivalent weights.
+2. Original version uses Snake1D activation with JIT: https://github.com/descriptinc/descript-audio-codec/blob/c7cfc5d2647e26471dc394f95846a0830e7bec34/dac/nn/layers.py#L18
+Transformer version does not use JIT, so outputs are slightly different.
 
-Moreover, here is a script to debug outputs and weights layer-by-layer:
+Nevertheless, quantizer codes are less affected, thanks to discretization being robust to precision errors and it does
+not use Snake1D activations. Moreover, codec error is similar between Transformers and original.
+
+Here is a script to debug outputs and weights layer-by-layer:
 https://gist.github.com/ebezzam/bb315efa7a416db6336a6b2a2d424ffa#file-dac_layer_by_layer_debugging-py
 """
 
diff --git a/tests/models/deepseek_ocr2/__init__.py b/tests/models/deepseek_ocr2/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/deepseek_ocr2/test_image_processing_deepseek_ocr2.py b/tests/models/deepseek_ocr2/test_image_processing_deepseek_ocr2.py
new file mode 100644
index 000000000000..fb859e1ce0b6
--- /dev/null
+++ b/tests/models/deepseek_ocr2/test_image_processing_deepseek_ocr2.py
@@ -0,0 +1,216 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+
+class DeepseekOcr2ImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=500,
+        max_resolution=800,
+        do_resize=True,
+        size=None,
+        tile_size=384,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        do_convert_rgb=True,
+    ):
+        size = size if size is not None else {"height": 512, "width": 512}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.tile_size = tile_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "tile_size": self.tile_size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class DeepseekOcr2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = DeepseekOcr2ImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processor, "do_resize"))
+            self.assertTrue(hasattr(image_processor, "size"))
+            self.assertTrue(hasattr(image_processor, "tile_size"))
+            self.assertTrue(hasattr(image_processor, "do_normalize"))
+            self.assertTrue(hasattr(image_processor, "image_mean"))
+            self.assertTrue(hasattr(image_processor, "image_std"))
+            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+
+    @unittest.skip(reason="Not supported")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_crop_to_patches(self):
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            tile_size = self.image_processor_tester.tile_size
+            if backend_name == "pil":
+                image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)[0]
+                processed_images = image_processor.crop_image_to_patches(
+                    image, min_patches=1, max_patches=6, tile_size=tile_size
+                )
+                self.assertGreater(len(processed_images), 0)
+                self.assertEqual(processed_images[0].shape[:2], (tile_size, tile_size))
+            else:
+                image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)[0]
+                stacked_patches, n_patches = image_processor.crop_image_to_patches(
+                    image.unsqueeze(0).float(), min_patches=1, max_patches=6, tile_size=tile_size
+                )
+                self.assertGreater(n_patches, 0)
+                self.assertEqual(stacked_patches.shape[-2:], (tile_size, tile_size))
+
+    def test_preprocess_global_only(self):
+        """Test preprocessing without crop_to_patches (global view only)."""
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict, crop_to_patches=False)
+            images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=False)
+            result = image_processor(images, return_tensors="pt")
+            self.assertIn("pixel_values", result)
+            self.assertEqual(len(result["num_local_patches"]), len(images))
+            for n in result["num_local_patches"]:
+                self.assertEqual(n, 0)
+
+    def test_preprocess_with_crop_to_patches(self):
+        """Test preprocessing with crop_to_patches enabled."""
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict, crop_to_patches=True)
+            images = prepare_image_inputs(
+                batch_size=2, num_channels=3, min_resolution=500, max_resolution=700, equal_resolution=True
+            )
+            result = image_processor(images, return_tensors="pt")
+            self.assertIn("pixel_values", result)
+            has_local = any(n > 0 for n in result["num_local_patches"])
+            self.assertTrue(has_local)
+            if has_local:
+                self.assertIn("pixel_values_local", result)
+
+    def test_backends_equivalence(self):
+        """Override to also compare pixel_values_local and num_local_patches."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)[0]
+
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt")
+
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(
+                encodings[reference_backend].pixel_values, encodings[backend_name].pixel_values
+            )
+            torch.testing.assert_close(
+                encodings[reference_backend].num_local_patches, encodings[backend_name].num_local_patches
+            )
+            if encodings[reference_backend].get("pixel_values_local") is not None:
+                self._assert_tensors_equivalence(
+                    encodings[reference_backend].pixel_values_local,
+                    encodings[backend_name].pixel_values_local,
+                )
+
+    def test_backends_equivalence_batched(self):
+        """Override to also compare pixel_values_local and num_local_patches (variable shape)."""
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors=None)
+
+        backend_names = list(encodings.keys())
+        reference_backend = "pil"
+        ref_encoding = encodings[reference_backend]
+
+        for backend_name in [b for b in backend_names if b != reference_backend]:
+            other_encoding = encodings[backend_name]
+            # Global views
+            for i in range(len(ref_encoding.pixel_values)):
+                self._assert_tensors_equivalence(
+                    torch.from_numpy(ref_encoding.pixel_values[i]), other_encoding.pixel_values[i]
+                )
+            # num_local_patches
+            self.assertEqual(
+                list(ref_encoding["num_local_patches"]),
+                list(other_encoding["num_local_patches"]),
+            )
+            # Local patches
+            ref_local = ref_encoding.get("pixel_values_local")
+            other_local = other_encoding.get("pixel_values_local")
+            if ref_local is not None and other_local is not None:
+                self.assertEqual(len(ref_local), len(other_local))
+                for i in range(len(ref_local)):
+                    self._assert_tensors_equivalence(torch.from_numpy(ref_local[i]), other_local[i])
diff --git a/tests/models/deepseek_ocr2/test_modeling_deepseek_ocr2.py b/tests/models/deepseek_ocr2/test_modeling_deepseek_ocr2.py
new file mode 100644
index 000000000000..b0e135ed50f5
--- /dev/null
+++ b/tests/models/deepseek_ocr2/test_modeling_deepseek_ocr2.py
@@ -0,0 +1,224 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch DeepseekOcr2 model."""
+
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    DeepseekOcr2Config,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import cleanup, require_torch, slow, torch_device
+
+from ...test_processing_common import url_to_local_path
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        DeepseekOcr2ForConditionalGeneration,
+        DeepseekOcr2Model,
+    )
+    from transformers.models.deepseek_ocr2.configuration_deepseek_ocr2 import (
+        DeepseekOcr2TextConfig,
+        DeepseekOcr2VisionConfig,
+    )
+
+if is_vision_available():
+    from transformers.image_utils import load_image
+
+
+class DeepseekOcr2VisionText2TextModelTester(VLMModelTester):
+    base_model_class = DeepseekOcr2Model
+    config_class = DeepseekOcr2Config
+    conditional_generation_class = DeepseekOcr2ForConditionalGeneration
+    text_config_class = DeepseekOcr2TextConfig
+    vision_config_class = DeepseekOcr2VisionConfig
+
+    def __init__(self, parent, **kwargs):
+        # VisionModel always selects query_768_resolution (144 tokens) for small images + 1 separator
+        kwargs.setdefault("num_image_tokens", 145)
+        kwargs.setdefault("image_token_id", 1)
+        kwargs.setdefault("image_size", 16)
+        kwargs.setdefault("hidden_size", 128)
+        kwargs.setdefault("intermediate_size", 256)
+        kwargs.setdefault("num_hidden_layers", 2)
+        kwargs.setdefault("num_attention_heads", 4)
+        kwargs.setdefault("num_key_value_heads", 4)
+        kwargs.setdefault("hidden_act", "silu")
+        kwargs.setdefault("max_position_embeddings", 512)
+        kwargs.setdefault("tie_word_embeddings", False)
+        kwargs.setdefault("bos_token_id", 2)
+        kwargs.setdefault("eos_token_id", 3)
+        kwargs.setdefault("pad_token_id", 4)
+        kwargs.setdefault("n_routed_experts", 8)
+        kwargs.setdefault("n_shared_experts", 1)
+        kwargs.setdefault("mlp_layer_types", ["dense", "sparse"])
+        kwargs.setdefault("moe_intermediate_size", 64)
+        kwargs.setdefault("num_experts_per_tok", 2)
+        super().__init__(parent, **kwargs)
+
+        self.sam_config = {
+            "hidden_size": 32,
+            "output_channels": 16,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_channels": 3,
+            "image_size": 16,
+            "patch_size": 2,
+            "hidden_act": "gelu",
+            "mlp_ratio": 4.0,
+            "window_size": 4,
+            "global_attn_indexes": [1],
+            "downsample_channels": [32, 64],
+        }
+        self.encoder_config = {
+            "hidden_size": 64,
+            "intermediate_size": 128,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 4,
+            "hidden_act": "silu",
+            "max_position_embeddings": 512,
+            "rms_norm_eps": 1.0,
+        }
+
+    def get_vision_config(self):
+        return DeepseekOcr2VisionConfig(
+            sam_config=self.sam_config,
+            encoder_config=self.encoder_config,
+        )
+
+    def get_config(self):
+        return self.config_class(
+            vision_config=self.get_vision_config(),
+            text_config=self.get_text_config(),
+            image_token_id=self.image_token_id,
+        )
+
+
+@require_torch
+class DeepseekOcr2ModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = DeepseekOcr2VisionText2TextModelTester
+    test_all_params_have_gradient = False
+    test_torch_exportable = False
+
+    @unittest.skip(
+        reason="DeepseekOcr2VisionModel builds a hybrid bidirectional+causal mask internally, so SDPA is always called with a non-null `attn_mask`."
+    )
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    @unittest.skip(
+        reason="DeepseekOcr2VisionModel uses `self.query_*.weight` directly, causing device mismatch when offloading."
+    )
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(
+        reason="DeepseekOcr2VisionModel uses `self.query_*.weight` directly, causing device mismatch when offloading."
+    )
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(
+        reason="DeepseekOcr2VisionModel uses `self.query_*.weight` directly, causing device mismatch when offloading."
+    )
+    def test_disk_offload_safetensors(self):
+        pass
+
+    def _image_features_prepare_config_and_inputs(self):
+        config, inputs_dict = super()._image_features_prepare_config_and_inputs()
+        # test_get_image_features_output expects vision_config.hidden_size, but ours is in encoder_config.
+        config.vision_config.hidden_size = config.vision_config.encoder_config.hidden_size
+        return config, inputs_dict
+
+
+@require_torch
+class DeepseekOcr2IntegrationTest(unittest.TestCase):
+    model_id = "thisisiron/DeepSeek-OCR-2-hf"
+
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(self.model_id)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_small_model_integration_test_free_ocr(self):
+        model = DeepseekOcr2ForConditionalGeneration.from_pretrained(
+            self.model_id, torch_dtype=torch.bfloat16, device_map=torch_device, attn_implementation="eager"
+        )
+        image = load_image(
+            url_to_local_path(
+                "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
+            )
+        )
+        inputs = self.processor(images=image, text="<image>\nFree OCR.", return_tensors="pt").to(
+            model.device, dtype=torch.bfloat16
+        )
+        generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+        decoded = self.processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        self.assertTrue(decoded.startswith("R&D QUALITY IMPROVEMENT"))
+
+    @slow
+    def test_small_model_integration_test_grounding_markdown(self):
+        model = DeepseekOcr2ForConditionalGeneration.from_pretrained(
+            self.model_id, torch_dtype=torch.bfloat16, device_map=torch_device, attn_implementation="eager"
+        )
+        image = load_image(
+            url_to_local_path(
+                "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
+            )
+        )
+        inputs = self.processor(
+            images=image,
+            text="<image>\n<|grounding|>Convert the document to markdown.",
+            return_tensors="pt",
+        ).to(model.device, dtype=torch.bfloat16)
+        generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+        decoded = self.processor.decode(generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=False)
+        self.assertIn("<|ref|>", decoded)
+        self.assertIn("<|det|>", decoded)
+
+    @slow
+    def test_small_model_integration_test_batched(self):
+        model = DeepseekOcr2ForConditionalGeneration.from_pretrained(
+            self.model_id, torch_dtype=torch.bfloat16, device_map=torch_device, attn_implementation="eager"
+        )
+        image1 = load_image(
+            url_to_local_path(
+                "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg"
+            )
+        )
+        image2 = load_image(
+            url_to_local_path(
+                "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png"
+            )
+        )
+        inputs = self.processor(
+            images=[image1, image2],
+            text=["<image>\nFree OCR.", "<image>\nFree OCR."],
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device, dtype=torch.bfloat16)
+        generate_ids = model.generate(**inputs, do_sample=False, max_new_tokens=20)
+        decoded = self.processor.batch_decode(
+            generate_ids[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+        self.assertTrue(decoded[0].startswith("R&D QUALITY IMPROVEMENT"))
diff --git a/tests/models/deepseek_ocr2/test_processing_deepseek_ocr2.py b/tests/models/deepseek_ocr2/test_processing_deepseek_ocr2.py
new file mode 100644
index 000000000000..ba82b592462c
--- /dev/null
+++ b/tests/models/deepseek_ocr2/test_processing_deepseek_ocr2.py
@@ -0,0 +1,90 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import torch
+
+from transformers import DeepseekOcr2Processor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class DeepseekOcr2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = DeepseekOcr2Processor
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        image_processor = image_processor_class()
+        image_processor.size = {"height": 64, "width": 64}
+        image_processor.tile_size = 512
+        return image_processor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("thisisiron/DeepSeek-OCR-2-hf")
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @unittest.skip("DeepseekOcr2Processor pops the image processor output 'num_local_patches'")
+    def test_image_processor_defaults(self):
+        pass
+
+    def test_image_token_expansion_small_image(self):
+        """Small image (< tile_size) should produce no local patches → 257 image tokens."""
+        processor = self.get_processor()
+        processor.image_processor.size = {"height": 1024, "width": 1024}
+        processor.image_processor.tile_size = 768
+
+        # Small image: max(200, 300) < 768 → no local patches
+        image = torch.randint(0, 256, (3, 300, 200), dtype=torch.uint8)
+        prompt = "<image>\nFree OCR."
+
+        inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+        image_token_id = processor.image_token_id
+        num_image_tokens = (inputs["input_ids"] == image_token_id).sum().item()
+
+        # 257 = 256 global + 0 local + 1 separator
+        self.assertEqual(num_image_tokens, 257)
+        self.assertNotIn("pixel_values_local", inputs)
+
+    def test_image_token_expansion_large_image(self):
+        """Large image should produce local patches → more image tokens."""
+        processor = self.get_processor()
+        processor.image_processor.size = {"height": 1024, "width": 1024}
+        processor.image_processor.tile_size = 768
+
+        # Large image: max(2448, 3264) > 768 → local patches
+        image = torch.randint(0, 256, (3, 3264, 2448), dtype=torch.uint8)
+        prompt = "<image>\nFree OCR."
+
+        inputs = processor(images=image, text=prompt, return_tensors="pt")
+
+        image_token_id = processor.image_token_id
+        num_image_tokens = (inputs["input_ids"] == image_token_id).sum().item()
+        num_local_patches = inputs["num_local_patches"][0]
+
+        # 3264x2448 image produces 6 local patches (2x3 grid) + 1 global view = 7 total
+        # num_image_tokens = 256 global + 144*6 local + 1 separator = 1121
+        self.assertEqual(num_local_patches, 6)
+        self.assertEqual(num_image_tokens, 1121)
+        self.assertIn("pixel_values_local", inputs)
diff --git a/tests/models/deepseek_v4/__init__.py b/tests/models/deepseek_v4/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/deepseek_v4/test_modeling_deepseek_v4.py b/tests/models/deepseek_v4/test_modeling_deepseek_v4.py
new file mode 100644
index 000000000000..725964db78cb
--- /dev/null
+++ b/tests/models/deepseek_v4/test_modeling_deepseek_v4.py
@@ -0,0 +1,420 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+import unittest
+
+from parameterized import parameterized
+
+from transformers import is_torch_available
+from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        AutoConfig,
+        AutoModelForCausalLM,
+        AutoTokenizer,
+        DeepseekV4Config,
+        DeepseekV4ForCausalLM,
+        DeepseekV4Model,
+        DynamicCache,
+        FineGrainedFP8Config,
+    )
+    from transformers.models.deepseek_v4.modeling_deepseek_v4 import DeepseekV4HCACompressor
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+class DeepseekV4ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = DeepseekV4Model
+
+    def __init__(self, parent, **kwargs):
+        # ``CausalLMModelTester.__init__`` assigns a fixed set of attributes from its
+        # keyword defaults (``hidden_size``, ``num_attention_heads`` and friends); those
+        # overwrite any class-level attributes of the same name. Pass V4 defaults through
+        # ``kwargs`` so the tester instance reflects V4's shape.
+        kwargs.setdefault("hidden_size", 64)
+        kwargs.setdefault("num_attention_heads", 4)
+        kwargs.setdefault("num_key_value_heads", 1)
+        kwargs.setdefault("num_hidden_layers", 2)
+        kwargs.setdefault("num_experts_per_tok", 2)
+        kwargs.setdefault("moe_intermediate_size", 64)
+        kwargs.setdefault("max_position_embeddings", 64)
+        super().__init__(parent, **kwargs)
+        # V4-only attributes that ``CausalLMModelTester.get_config`` will pull by name.
+        self.head_dim = 32
+        self.qk_rope_head_dim = 8
+        self.q_lora_rank = 32
+        self.o_groups = 2
+        self.o_lora_rank = 16
+        self.n_routed_experts = 4
+        self.n_shared_experts = 1
+        # ``num_hash_layers=0`` so the ``inputs_embeds``-only generation tests in
+        # ``CausalLMModelTest`` can exercise the model without running into the hash
+        # router's ``input_ids`` requirement. A dedicated test covers the hash path.
+        self.num_hash_layers = 0
+        self.layer_types = ["heavily_compressed_attention", "compressed_sparse_attention"]
+        self.sliding_window = 8
+        self.hc_mult = 2
+        self.hc_sinkhorn_iters = 3
+        self.hc_eps = 1.0e-6
+        self.index_n_heads = 2
+        self.index_head_dim = 16
+        self.index_topk = 2
+        self.num_nextn_predict_layers = 0
+        self.scoring_func = "sqrtsoftplus"
+        self.routed_scaling_factor = 1.5
+        self.swiglu_limit = 10.0
+        self.rope_theta = 10000.0
+        self.compress_rope_theta = 160000.0
+        self.attention_bias = False
+        self.attention_dropout = 0.0
+
+
+@require_torch
+class DeepseekV4ModelTest(CausalLMModelTest, unittest.TestCase):
+    model_tester_class = DeepseekV4ModelTester
+
+    # Indexer parameters only influence the argmax over compressed positions (``topk``),
+    # which is non-differentiable — their gradients flow through a separate objective in
+    # the upstream training recipe, not the main causal-LM loss.
+    test_all_params_have_gradient = False
+
+    # No SequenceClassification / TokenClassification / QA heads on V4.
+    def is_pipeline_test_to_skip(self, *args, **kwargs):
+        return True
+
+    def _check_attentions_for_generate(
+        self, batch_size, attentions, prompt_length, output_length, config, decoder_past_key_values
+    ):
+        # V4 layers with a Compressor attend to extra pooled positions, so the KV
+        # length varies per layer. We only check the shape invariants: batched, same
+        # number-of-heads and query-length; the KV-length axis may differ across layers.
+        import torch  # noqa: PLC0415
+
+        self.assertIsInstance(attentions, tuple)
+        self.assertEqual(len(attentions), (output_length - prompt_length))
+        for _, iter_attentions in enumerate(attentions):
+            self.assertIsInstance(iter_attentions, tuple)
+            for layer_attention in iter_attentions:
+                self.assertIsInstance(layer_attention, torch.Tensor)
+                self.assertEqual(layer_attention.shape[0], batch_size)
+                self.assertEqual(layer_attention.shape[1], config.num_attention_heads)
+
+    @unittest.skip(
+        "V4's rotary uses per-layer-type inv_freq buffers (Gemma3 pattern); the common test calls forward without `layer_type` and reads `.inv_freq`, neither of which apply."
+    )
+    def test_model_rope_scaling_frequencies(self):
+        pass
+
+    @parameterized.expand([("linear",), ("dynamic",), ("yarn",)])
+    @unittest.skip(
+        "V4's rotary uses per-layer-type rope_parameters; the common test sets a flat dict and skips for multi-layer-type rotaries."
+    )
+    def test_model_rope_scaling_from_config(self, scaling_type):
+        pass
+
+    def test_hidden_states_output(self):
+        # V4 layers emit a 4D ``[B, S, hc_mult, hidden]`` tensor — the hc_mult streams
+        # are only collapsed at the top of the model via ``hc_head``. The common
+        # ``test_hidden_states_output`` assumes ``(batch, seq, hidden)``; we re-run the
+        # same check but accept the extra HC axis, and we additionally assert the final
+        # (post-hc_head) ``last_hidden_state`` has the standard 3D shape.
+        import torch  # noqa: PLC0415
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device).eval()
+            with torch.no_grad():
+                outputs = model(**inputs_dict)
+            hidden_states = outputs.hidden_states if hasattr(outputs, "hidden_states") else outputs[-1]
+            self.assertIsNotNone(hidden_states)
+            self.assertEqual(len(hidden_states), config.num_hidden_layers + 1)
+            seq_len = inputs_dict["input_ids"].shape[1]
+            for layer_h in hidden_states:
+                # Accept either the collapsed (3D) post-head shape or the per-layer 4D shape.
+                if layer_h.ndim == 3:
+                    self.assertEqual(layer_h.shape, (inputs_dict["input_ids"].shape[0], seq_len, config.hidden_size))
+                else:
+                    self.assertEqual(
+                        layer_h.shape,
+                        (inputs_dict["input_ids"].shape[0], seq_len, config.hc_mult, config.hidden_size),
+                    )
+
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
+        # Every V4 layer is sliding-window, so the cache is length-bounded to
+        # ``sliding_window`` instead of the full ``seq_length`` the parent tester expects.
+        # We also accept the compressed-segment positions that ``DeepseekV4Attention``
+        # appends on compress layers (they live beyond the window on the keys axis).
+        import torch  # noqa: PLC0415
+
+        num_kv_heads = getattr(config, "num_key_value_heads", config.num_attention_heads)
+        head_dim = config.head_dim
+        for layer in past_key_values.layers:
+            keys, values = layer.keys, layer.values
+            self.assertIsInstance(keys, torch.Tensor)
+            self.assertEqual(keys.shape[0], batch_size)
+            self.assertEqual(keys.shape[1], num_kv_heads)
+            self.assertEqual(keys.shape[3], head_dim)
+            self.assertEqual(keys.shape, values.shape)
+
+    @unittest.skip(
+        reason=(
+            "V4's conversion mapping is two-pass: a structural prefix rename "
+            "(``layers.X.attn.`` → ``model.layers.X.self_attn.``) runs first, then specific in-prefix "
+            "renames operate on the already-prefixed HF-form keys (``model.layers.X.self_attn.compressor.norm.`` "
+            "→ ``...compressor.kv_norm.``). This split is load-bearing for save / load round-tripping — "
+            "any single-pass ordering loses information in either direction (the general prefix rule "
+            "and a specific in-prefix rule both want to match the same upstream key, and one of the "
+            "two directions ends up with the general rule stealing the match). The base "
+            "``test_reverse_loading_mapping`` checks every source pattern against the *upstream-form* "
+            "serialized keys, so the Pass 2 patterns (written in HF form) inherently can't satisfy "
+            "that invariant. The actual round-trip is exercised by ``test_save_load``."
+        )
+    )
+    def test_reverse_loading_mapping(self):
+        pass
+
+    @unittest.skip(
+        reason=(
+            "V4's compressor pools windows of ``compress_rate`` consecutive tokens *before* the "
+            "attention mask is applied — left-padding shifts the window boundaries so pad tokens "
+            "get folded into the pooled KV entries, and the resulting logits diverge from the "
+            "unpadded run by design (same fundamental limitation as RecurrentGemma)."
+        )
+    )
+    def test_left_padding_compatibility(self):
+        pass
+
+    def _check_hidden_states_for_generate(
+        self, batch_size, hidden_states, prompt_length, output_length, config, use_cache=False
+    ):
+        # V4's per-layer hidden states carry an extra ``hc_mult`` dim (Hyper-Connection
+        # parallel streams). We skip the exact seq-length assertion the base tester does,
+        # because assisted-decoding feeds arbitrary draft-token batches in, and just
+        # sanity-check batch / hidden dims.
+        import torch  # noqa: PLC0415
+
+        self.assertIsInstance(hidden_states, tuple)
+        self.assertEqual(len(hidden_states), (output_length - prompt_length))
+        for iter_hidden_states in hidden_states:
+            self.assertIsInstance(iter_hidden_states, tuple)
+            for layer_hidden in iter_hidden_states:
+                self.assertIsInstance(layer_hidden, torch.Tensor)
+                self.assertEqual(layer_hidden.shape[0], batch_size)
+                self.assertEqual(layer_hidden.shape[-1], config.hidden_size)
+
+
+def _tiny_config(**overrides):
+    """Smallest V4 config that still exercises every architectural piece: HC streams
+    (``hc_mult=2``), hash routing (layer 0), a local-SWA layer, a compressor-with-
+    indexer layer (ratio 4), and a routed MoE with a shared expert.
+    """
+    defaults = {
+        "vocab_size": 32,
+        "hidden_size": 32,
+        "head_dim": 16,
+        "qk_rope_head_dim": 4,
+        "q_lora_rank": 16,
+        "num_attention_heads": 4,
+        "num_key_value_heads": 1,
+        "num_hidden_layers": 2,
+        "layer_types": ["heavily_compressed_attention", "compressed_sparse_attention"],
+        "sliding_window": 4,
+        "hc_mult": 2,
+        "hc_sinkhorn_iters": 3,
+        "hc_eps": 1e-6,
+        "moe_intermediate_size": 32,
+        "n_routed_experts": 4,
+        "n_shared_experts": 1,
+        "num_experts_per_tok": 2,
+        "num_hash_layers": 1,
+        "scoring_func": "sqrtsoftplus",
+        "routed_scaling_factor": 1.0,
+        "swiglu_limit": 10.0,
+        "o_groups": 2,
+        "o_lora_rank": 8,
+        "index_n_heads": 2,
+        "index_head_dim": 8,
+        "index_topk": 2,
+        "num_nextn_predict_layers": 0,
+        "max_position_embeddings": 32,
+        "rope_theta": 10000.0,
+        "compress_rope_theta": 10000.0,  # match main rope for a cleaner parity check
+        "attention_bias": False,
+        "attention_dropout": 0.0,
+    }
+    defaults.update(overrides)
+    return DeepseekV4Config(**defaults)
+
+
+@require_torch
+class DeepseekV4ParityTest(unittest.TestCase):
+    """Functional sanity checks against tiny-config reference implementations of the
+    V4-specific pieces (compressor pooling, HC mix + collapse). These re-derive the
+    math from the upstream ``inference/model.py`` and compare to our HF modules, so a
+    regression in the packed cache / HC / pool code would surface here numerically.
+    """
+
+    def test_compressor_pool_matches_reference(self):
+        """Re-implement the reference ``Compressor._pool`` (softmax-gated sum-pool with
+        a learned ``position_bias``) and check it matches what the V4
+        :class:`DeepseekV4HCACache` + :class:`DeepseekV4HCACompressor` produce inline.
+        """
+        torch.manual_seed(0)
+        batch, length, head_dim, rate = 2, 8, 16, 4
+        kv = torch.randn(batch, length, head_dim)
+        gate = torch.randn(batch, length, head_dim)
+        position_bias = torch.randn(rate, head_dim)
+
+        # Reproduce the V4 in-line pool from ``DeepseekV4HCACompressor._pool``.
+        n_windows = length // rate
+        view_kv = kv.view(batch, n_windows, rate, head_dim)
+        view_gate = gate.view(batch, n_windows, rate, head_dim) + position_bias.to(gate.dtype)
+        ours = (view_kv * view_gate.softmax(dim=2)).sum(dim=2)
+
+        # Reference (transcribed from upstream ``inference/model.py``).
+        reference = torch.zeros(batch, n_windows, head_dim)
+        for b in range(batch):
+            for i in range(n_windows):
+                window_kv = kv[b, i * rate : (i + 1) * rate]
+                window_gate = gate[b, i * rate : (i + 1) * rate] + position_bias
+                w = torch.softmax(window_gate, dim=0)
+                reference[b, i] = (window_kv * w).sum(dim=0)
+
+        torch.testing.assert_close(ours, reference, rtol=1e-5, atol=1e-6)
+
+    def test_compressor_cache_accumulates_across_calls(self):
+        """Feeding the HCA compressor one token at a time must produce the same pool
+        as feeding the whole sequence. Using HCA keeps the test indexer-free.
+        """
+        torch.manual_seed(1)
+        config = _tiny_config(
+            layer_types=["heavily_compressed_attention", "heavily_compressed_attention"],
+            sliding_window=128,
+            max_position_embeddings=512,
+            compress_rate_hca=128,
+        )
+        compressor = DeepseekV4HCACompressor(config).eval()
+        # Initialise ``position_bias`` to non-zero so the test exercises the pooling math.
+        torch.nn.init.normal_(compressor.position_bias, std=0.1)
+
+        batch, seq_len = 1, 256  # two full windows
+        hidden_states = torch.randn(batch, seq_len, config.hidden_size)
+        position_ids = torch.arange(seq_len).unsqueeze(0)
+
+        cache_full = DynamicCache(config=config)
+        with torch.no_grad():
+            one_shot = compressor(hidden_states, None, position_ids, cache_full, 1)
+
+        cache_inc = DynamicCache(config=config)
+        with torch.no_grad():
+            for step in range(seq_len):
+                incremental = compressor(hidden_states[:, step : step + 1], None, torch.tensor([[step]]), cache_inc, 1)
+        self.assertEqual(one_shot.shape, incremental.shape)
+        torch.testing.assert_close(one_shot, incremental, rtol=1e-4, atol=1e-5)
+
+    def test_tiny_forward_is_deterministic_and_finite(self):
+        """End-to-end smoke: tiny ``DeepseekV4ForCausalLM`` forward produces finite
+        logits of the right shape, and is deterministic under the same seed."""
+        torch.manual_seed(42)
+        config = _tiny_config()
+        model = DeepseekV4ForCausalLM(config).eval()
+
+        torch.manual_seed(0)
+        input_ids = torch.randint(0, config.vocab_size, (2, 10))
+        with torch.no_grad():
+            out_a = model(input_ids).logits
+            out_b = model(input_ids).logits
+
+        self.assertEqual(out_a.shape, (2, 10, config.vocab_size))
+        self.assertTrue(torch.isfinite(out_a).all())
+        torch.testing.assert_close(out_a, out_b)  # deterministic
+
+    def test_tiny_generate_runs(self):
+        """Greedy-generate 4 new tokens on top of a 6-token prompt and check we get 10
+        tokens out. Exercises the full generation loop: cache adopt, window cache,
+        compressor state, HC, indexer gather."""
+        torch.manual_seed(42)
+        config = _tiny_config()
+        model = DeepseekV4ForCausalLM(config).eval()
+
+        torch.manual_seed(0)
+        input_ids = torch.randint(0, config.vocab_size, (1, 6))
+        # ``eos_token_id=-1`` keeps the freshly initialised random model from EOS-stopping
+        # before max_new_tokens, so the shape assertion is deterministic.
+        with torch.no_grad():
+            out = model.generate(input_ids, max_new_tokens=4, do_sample=False, eos_token_id=-1)
+        self.assertEqual(out.shape, (1, 10))
+        self.assertTrue(torch.isfinite(out.float()).all())
+
+
+@require_torch
+@require_torch_accelerator
+@slow
+class DeepseekV4IntegrationTest(unittest.TestCase):
+    """End-to-end check on the published DeepSeek-V4-Flash checkpoint.
+
+    Loads the real 43-layer FP8 weights, dequantizes on the fly via
+    :class:`FineGrainedFP8Config`, and greedy-generates a continuation of a fixed
+    prompt. The forward path that this test covers is everything past the typical
+    tiny-config tests can reach: the per-layer FP8 dequant in
+    ``update_weight_conversions``, the ``compress_ratios → layer_types`` config
+    translation (sliding / CSA / HCA), the ``coff=2`` overlap-window pooling on CSA
+    layers and the indexer's inner pool, the per-head Q rescale in
+    :class:`DeepseekV4Attention`, the YaRN-blended ``compress_rope_theta`` in the
+    compressor, the trailing-rope partial-RoPE convention, and the cross-layer
+    Hyper-Connection signal propagation. Any regression in those would tip
+    generation back into a single-token collapse or pure ``<EOS>`` output (the
+    failure modes we hit while landing the architecture).
+
+    Marked ``@slow`` because the checkpoint is ~700 GB on disk and only loadable
+    on a multi-GPU host (``device_map="auto"`` plus FP8 dequant materializes the
+    weights in bf16). Run manually with::
+
+        RUN_SLOW=1 pytest tests/models/deepseek_v4/test_modeling_deepseek_v4.py::DeepseekV4IntegrationTest -k generation -s
+    """
+
+    model_id = "deepseek-ai/DeepSeek-V4-Flash"
+    prompt = "Pipeline parallelism in ai is "
+
+    def test_v4_flash_fp8_generation(self):
+        # ``dequantize=True`` so we can run on bf16-only kernels (needed for the
+        # ``grouped_mm`` path the routed experts hit). Eager attention so we
+        # exercise the same forward we tune the rest of the V4 modeling around.
+        quantization_config = FineGrainedFP8Config(dequantize=True)
+        config = AutoConfig.from_pretrained(self.model_id)
+        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.model_id,
+            config=config,
+            dtype="auto",
+            device_map="auto",
+            attn_implementation="eager",
+            quantization_config=quantization_config,
+        )
+
+        inputs = tokenizer(self.prompt, return_tensors="pt").to(model.device)
+        with torch.no_grad():
+            output_ids = model.generate(**inputs, max_new_tokens=64, do_sample=False)
+
+        # Snapshot of greedy-decoded text. The exact continuation is deterministic
+        # under ``do_sample=False`` for a fixed prompt — if this snapshot drifts,
+        # something in the V4 forward / RoPE / Q-rescale / HC stack changed.
+        expected = (
+            "Pipeline parallelism in ai is  driven by three key factors: the exponential increase in data "
+            "size, the development of increasingly powerful computational techniques (especially deep "
+            "learning), to handle this data, and the availability of massive computational resources on "
+            "which to run these methods, all of which are are  well aligned with  trends in  industry, "
+            " academia and  research"
+        )
+        decoded = tokenizer.decode(output_ids[0], skip_special_tokens=False)
+        self.assertEqual(decoded, expected)
diff --git a/tests/models/dinov3_vit/test_modeling_dinov3_vit.py b/tests/models/dinov3_vit/test_modeling_dinov3_vit.py
index 672fea3c61b1..8d294ca0ecf7 100644
--- a/tests/models/dinov3_vit/test_modeling_dinov3_vit.py
+++ b/tests/models/dinov3_vit/test_modeling_dinov3_vit.py
@@ -17,7 +17,13 @@
 from functools import cached_property
 
 from transformers import DINOv3ViTConfig
-from transformers.testing_utils import require_torch, require_vision, slow, torch_device
+from transformers.testing_utils import (
+    require_torch,
+    require_torch_large_accelerator,
+    require_vision,
+    slow,
+    torch_device,
+)
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -29,7 +35,7 @@
     import torch
     from torch import nn
 
-    from transformers import DINOv3ViTBackbone, DINOv3ViTModel
+    from transformers import DINOv3ViTBackbone, DINOv3ViTForImageClassification, DINOv3ViTModel
 
 
 if is_vision_available():
@@ -169,6 +175,25 @@ def create_and_check_model(self, config, pixel_values, labels):
             (self.batch_size, self.seq_length, self.hidden_size),
         )
 
+    def create_and_check_for_image_classification(self, config, pixel_values, labels):
+        config.num_labels = self.type_sequence_label_size
+        torch_device_override = "cpu"  # Required, or else VRAM is not enough.
+        config.device_map = torch_device_override
+        model = DINOv3ViTForImageClassification(config)
+        model.eval()
+        result = model(pixel_values, labels=labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+        # test greyscale images
+        config.num_channels = 1
+
+        model = DINOv3ViTForImageClassification(config)
+        model.eval()
+
+        pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]).to(torch_device_override)
+        result = model(pixel_values)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -187,7 +212,9 @@ class Dinov3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     attention_mask and seq_length.
     """
 
-    all_model_classes = (DINOv3ViTModel, DINOv3ViTBackbone) if is_torch_available() else ()
+    all_model_classes = (
+        (DINOv3ViTModel, DINOv3ViTBackbone, DINOv3ViTForImageClassification) if is_torch_available() else ()
+    )
     pipeline_model_mapping = (
         {
             "image-feature-extraction": DINOv3ViTModel,
@@ -224,6 +251,10 @@ def test_model_get_set_embeddings(self):
             x = model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(x, nn.Linear))
 
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
     def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
@@ -256,6 +287,36 @@ def default_image_processor(self):
             else None
         )
 
+    @require_torch_large_accelerator
+    @slow
+    def test_inference_lc_head_imagenet(self):
+        torch_device_override = "cpu"
+        model = DINOv3ViTForImageClassification.from_pretrained(
+            "dimidagd/dinov3-vit7b16-pretrain-lvd1689m-imagenet1k-lc", device_map=torch_device_override
+        )
+
+        ground_truth_class_imagenet1 = "tabby, tabby cat"
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(image, return_tensors="pt").to(torch_device_override)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+
+        # Verify logits
+        expected_logits = torch.tensor([-1.0708860159, -0.7589257956, -1.1738269329, -0.9263097048, -1.0259437561]).to(
+            torch_device_override
+        )
+
+        torch.testing.assert_close(outputs.logits[0, : len(expected_logits)], expected_logits, rtol=1e-4, atol=1e-4)
+
+        # Test correct class prediction
+        predicted_class_idx = outputs.logits.argmax(-1).item()
+        predicted_class_str = model.config.id2label[predicted_class_idx]
+
+        self.assertEqual(predicted_class_str, ground_truth_class_imagenet1)
+
     @slow
     def test_inference_no_head(self):
         model = DINOv3ViTModel.from_pretrained("facebook/dinov3-vits16-pretrain-lvd1689m").to(torch_device)
diff --git a/tests/models/edgetam/test_modeling_edgetam.py b/tests/models/edgetam/test_modeling_edgetam.py
index c0f25af5e888..37a33251418a 100644
--- a/tests/models/edgetam/test_modeling_edgetam.py
+++ b/tests/models/edgetam/test_modeling_edgetam.py
@@ -232,7 +232,13 @@ class EdgeTamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase)
 
     all_model_classes = (EdgeTamModel,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": EdgeTamModel, "mask-generation": EdgeTamModel} if is_torch_available() else {}
+        {
+            "feature-extraction": EdgeTamModel,
+            "mask-generation": EdgeTamModel,
+            "promptable-visual-segmentation": EdgeTamModel,
+        }
+        if is_torch_available()
+        else {}
     )
 
     test_resize_embeddings = False
diff --git a/tests/models/emu3/test_modeling_emu3.py b/tests/models/emu3/test_modeling_emu3.py
index 4fa55987c70f..6b356bb659c9 100644
--- a/tests/models/emu3/test_modeling_emu3.py
+++ b/tests/models/emu3/test_modeling_emu3.py
@@ -340,9 +340,6 @@ def _image_features_get_expected_num_hidden_states(self, model_tester=None):
         up_down_blocks = len(model_tester.vq_channel_multiplier) * model_tester.vq_num_res_blocks
         return up_down_blocks + 2 + model_tester.vq_num_res_blocks + 1
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class Emu3IntegrationTest(unittest.TestCase):
diff --git a/tests/models/encoder_decoder/test_modeling_encoder_decoder.py b/tests/models/encoder_decoder/test_modeling_encoder_decoder.py
index a08cbc199692..43a5982e3683 100644
--- a/tests/models/encoder_decoder/test_modeling_encoder_decoder.py
+++ b/tests/models/encoder_decoder/test_modeling_encoder_decoder.py
@@ -15,6 +15,7 @@
 
 import tempfile
 import unittest
+import warnings
 
 from transformers import is_torch_available, logging
 from transformers.testing_utils import (
@@ -365,6 +366,59 @@ def check_encoder_decoder_model_labels(
             outputs_encoder_decoder["encoder_last_hidden_state"].shape, (input_ids.shape + (config.hidden_size,))
         )
 
+    def check_encoder_decoder_model_warning(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+        encoder_hidden_states,
+        decoder_config,
+        decoder_input_ids,
+        decoder_attention_mask,
+        labels,
+        **kwargs,
+    ):
+        encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
+        enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
+        enc_dec_model.to(torch_device)
+
+        # Test that only one warning is raised when only labels are provided
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            # Set decoder_start_token_id 0 because the tokenizer.cls_token_id can't be accessed from here
+            enc_dec_model.config.decoder_start_token_id = 0
+            enc_dec_model.config.pad_token_id = decoder_config.pad_token_id
+            enc_dec_model(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                decoder_attention_mask=decoder_attention_mask,
+                labels=labels,
+            )
+
+            self.assertEqual(len(w), 1)
+            self.assertIn(
+                "Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss",
+                str(w[0].message),
+            )
+
+        # Test that two warnings are raised when both labels and decoder_input_ids are provided
+        with warnings.catch_warnings(record=True) as w:
+            warnings.simplefilter("always")
+            enc_dec_model(
+                input_ids=input_ids,
+                decoder_input_ids=decoder_input_ids,
+                attention_mask=attention_mask,
+                decoder_attention_mask=decoder_attention_mask,
+                labels=labels,
+            )
+
+            self.assertEqual(len(w), 2)
+            self.assertIn("The decoder_input_ids are created based on the labels", str(w[0].message))
+            self.assertIn(
+                "Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss",
+                str(w[1].message),
+            )
+
     def _check_output_with_attentions(
         self, outputs_encoder_decoder, config, input_ids, decoder_config, decoder_input_ids
     ):
@@ -541,6 +595,10 @@ def test_encoder_decoder_model_labels(self):
         input_ids_dict = self.prepare_config_and_inputs()
         self.check_encoder_decoder_model_labels(**input_ids_dict)
 
+    def test_encoder_decoder_model_warning(self):
+        input_ids_dict = self.prepare_config_and_inputs()
+        self.check_encoder_decoder_model_warning(**input_ids_dict)
+
     def test_encoder_decoder_model_output_attentions(self):
         input_ids_dict = self.prepare_config_and_inputs()
         self.check_encoder_decoder_model_output_attentions(**input_ids_dict)
diff --git a/tests/models/exaone4_5/__init__.py b/tests/models/exaone4_5/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/exaone4_5/test_modeling_exaone4_5.py b/tests/models/exaone4_5/test_modeling_exaone4_5.py
new file mode 100644
index 000000000000..9a819d4850d0
--- /dev/null
+++ b/tests/models/exaone4_5/test_modeling_exaone4_5.py
@@ -0,0 +1,246 @@
+# Copyright 2025 The LG AI Research and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch EXAONE 4.5 model."""
+
+import copy
+import unittest
+
+import pytest
+
+from transformers import (
+    is_torch_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Exaone4_5_Config,
+        Exaone4_5_ForConditionalGeneration,
+        Exaone4_5_Model,
+        Exaone4_5_Processor,
+        Exaone4_5_VisionConfig,
+        Exaone4Config,
+    )
+
+
+class Exaone4_5_ModelTester(VLMModelTester):
+    base_model_class = Exaone4_5_Model
+    config_class = Exaone4_5_Config
+    text_config_class = Exaone4Config
+    vision_config_class = Exaone4_5_VisionConfig
+    conditional_generation_class = Exaone4_5_ForConditionalGeneration
+
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("image_token_id", 3)
+        kwargs.setdefault("video_token_id", 4)
+        kwargs.setdefault("vision_start_token_id", 5)
+        kwargs.setdefault("vision_end_token_id", 6)
+        kwargs.setdefault("image_size", 16)
+        kwargs.setdefault("patch_size", 16)
+        kwargs.setdefault("num_image_tokens", 1)
+        kwargs.setdefault("hidden_act", "silu")
+        kwargs.setdefault("num_attention_heads", 4)
+        kwargs.setdefault("num_key_value_heads", 2)
+        kwargs.setdefault("head_dim", 8)
+        kwargs.setdefault("depth", 2)
+        kwargs.setdefault("num_heads", 4)
+        kwargs.setdefault("spatial_merge_size", 1)
+        kwargs.setdefault("temporal_patch_size", 2)
+        kwargs.setdefault("out_hidden_size", 32)
+        super().__init__(parent, **kwargs)
+
+        # Exaone4_5 vision config expects `in_channels` instead of `num_channels`.
+        self.in_channels = self.num_channels
+
+    def create_pixel_values(self):
+        # EXAONE 4.5 vision tower expects flattened patches:
+        # (total_patches, channels * patch_size^2 * temporal_patch_size)
+        return torch.rand(
+            self.batch_size * (self.image_size**2) // (self.patch_size**2),
+            self.num_channels * (self.patch_size**2) * self.temporal_patch_size,
+            device=torch_device,
+        )
+
+    def get_additional_inputs(self, config, input_ids, pixel_values):
+        return {"image_grid_thw": torch.tensor([[1, 1, 1]] * self.batch_size, device=torch_device)}
+
+    def get_config(self):
+        config = super().get_config()
+        # Some generic generation tests expect these attrs for VLMs.
+        config.vision_start_token_id = self.vision_start_token_id
+        config.vision_end_token_id = self.vision_end_token_id
+        return config
+
+
+@require_torch
+class Exaone4_5_ModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = Exaone4_5_ModelTester
+
+    def test_reverse_loading_mapping(self):
+        super().test_reverse_loading_mapping(skip_base_model=True)
+
+    def test_mismatching_num_image_tokens(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)
+            _ = model(**curr_input_dict)
+
+            # Test 1: fewer images than image placeholders -> should raise.
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-1:, ...]
+            if "image_sizes" in curr_input_dict:
+                curr_input_dict["image_sizes"] = curr_input_dict["image_sizes"][-1:, ...]
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 2: one image but two prompts with image placeholders -> should raise.
+            curr_input_dict = {key: val[:1] for key, val in curr_input_dict.items()}
+            for key in ["input_ids", "attention_mask", "token_type_ids"]:
+                if key in curr_input_dict and curr_input_dict[key] is not None:
+                    curr_input_dict[key] = torch.cat([curr_input_dict[key], curr_input_dict[key]], dim=0)
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 3: two images and two image placeholders -> should pass.
+            curr_input_dict["pixel_values"] = torch.cat(
+                [curr_input_dict["pixel_values"], curr_input_dict["pixel_values"]], dim=0
+            )
+            if "image_grid_thw" in curr_input_dict:
+                curr_input_dict["image_grid_thw"] = torch.cat(
+                    [curr_input_dict["image_grid_thw"], curr_input_dict["image_grid_thw"]], dim=0
+                )
+            if "image_sizes" in curr_input_dict:
+                curr_input_dict["image_sizes"] = torch.cat(
+                    [curr_input_dict["image_sizes"], curr_input_dict["image_sizes"]], dim=0
+                )
+            _ = model(**curr_input_dict)
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing(self):
+        super().test_training_gradient_checkpointing()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        super().test_training_gradient_checkpointing_use_reentrant_false()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        super().test_training_gradient_checkpointing_use_reentrant_true()
+
+    @unittest.skip("Model parallel auto-sharding for EXAONE 4.5 VLM is not supported yet.")
+    def test_model_parallelism(self):
+        pass
+
+    @unittest.skip("Beam search with model parallel auto device_map is not stable for EXAONE 4.5 VLM yet.")
+    def test_model_parallel_beam_search(self):
+        pass
+
+
+@require_torch
+class Exaone4_5_IntegrationTest(unittest.TestCase):
+    model_id = "LGAI-EXAONE/EXAONE-4.5-33B"
+    model = None
+    processor = None
+
+    @classmethod
+    def setUpClass(cls):
+        cleanup(torch_device, gc_collect=True)
+        cls.model = Exaone4_5_ForConditionalGeneration.from_pretrained(cls.model_id, device_map="auto")
+        cls.processor = Exaone4_5_Processor.from_pretrained(cls.model_id)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_logits(self):
+        input_ids = [70045, 1109, 115406, 16943, 11697, 115365, 19816, 12137, 375]
+        input_ids = torch.tensor([input_ids]).to(self.model.model.language_model.embed_tokens.weight.device)
+
+        with torch.no_grad():
+            out = self.model(input_ids).logits.float().cpu()
+
+        EXPECTED_MEAN = torch.tensor(
+            [[46.0681, 45.8148, 71.2274, 36.8956, 44.1011, 21.7848, 28.1107, 62.5165, 45.9560]]
+        )
+        EXPECTED_SLICE = torch.tensor(
+            [43.5000, 44.0000, 43.7500, 46.0000, 50.5000, 47.2500, 47.5000, 47.5000, 46.7500, 47.2500]
+        )
+
+        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN, atol=1e-2, rtol=1e-2)
+        torch.testing.assert_close(out[0, 0, :10], EXPECTED_SLICE, atol=1e-4, rtol=1e-4)
+
+    @slow
+    def test_model_generation_text_only(self):
+        EXPECTED_TEXT = (
+            '\nTell me about the Miracle on the Han river.\n\n<think>\n\n</think>\n\nThe **"Miracle on the Han River"**'
+            " is a term used to describe the rapid economic development and industrialization that South Korea experienced"
+        )
+        messages = [
+            {"role": "user", "content": [{"type": "text", "text": "Tell me about the Miracle on the Han river."}]}
+        ]
+        input_ids = self.processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors="pt",
+            enable_thinking=False,
+        ).to(self.model.model.language_model.embed_tokens.weight.device)
+
+        generated_ids = self.model.generate(input_ids=input_ids, max_new_tokens=20, do_sample=False)
+        text = self.processor.decode(generated_ids[0], skip_special_tokens=True)
+        print(text)
+        self.assertEqual(EXPECTED_TEXT, text)
+
+    @slow
+    def test_model_generation_image_text(self):
+        IMAGE_URL = (
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+        )
+        EXPECTED_TEXT = "\n\nDescribe the image.\n\n<think>\n\n</think>\n\nThe image captures a young, fluffy wild cat\u2014likely a lynx kitten or bobcat cub"
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                    },
+                    {"type": "text", "text": "Describe the image."},
+                ],
+            }
+        ]
+        text = self.processor.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False,
+        )
+        image = load_image(IMAGE_URL).convert("RGB")
+
+        inputs = self.processor(text=[text], images=[image], padding=True, return_tensors="pt").to(torch_device)
+        generated_ids = self.model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        text = self.processor.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(EXPECTED_TEXT, text)
diff --git a/tests/models/fuyu/test_modeling_fuyu.py b/tests/models/fuyu/test_modeling_fuyu.py
index 6c0a2657c4f7..6f06c8d5f68d 100644
--- a/tests/models/fuyu/test_modeling_fuyu.py
+++ b/tests/models/fuyu/test_modeling_fuyu.py
@@ -264,9 +264,6 @@ def test_get_image_features_hidden_states(self):
     def test_get_image_features_attentions(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @slow
 @require_torch_accelerator
diff --git a/tests/models/gemma3/test_modeling_gemma3.py b/tests/models/gemma3/test_modeling_gemma3.py
index 913d6b9cf5ff..d5a648eed6f3 100644
--- a/tests/models/gemma3/test_modeling_gemma3.py
+++ b/tests/models/gemma3/test_modeling_gemma3.py
@@ -281,7 +281,7 @@ def create_attention_mask(self, input_ids):
         # Gemma3 uses padding mask for bidirectional attention on image tokens
         return input_ids.ne(self.pad_token_id).to(torch_device)
 
-    def get_additional_inputs(self, config, input_ids, pixel_values):
+    def get_additional_inputs(self, config, input_ids, modality_inputs):
         # Gemma3 requires specific token_type_ids for bidirectional attention on image tokens
         token_type_ids = torch.zeros_like(input_ids)
         token_type_ids[input_ids == config.image_token_id] = 1
@@ -427,9 +427,6 @@ def test_flash_attn_3_from_config(self):
     def test_flash_attn_4_from_config(self):
         self.flash_attn_from_config(attn_implementation="flash_attention_4", test_fwd_in_train=False)
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @slow
 @require_torch_accelerator
diff --git a/tests/models/gemma4/test_modeling_gemma4.py b/tests/models/gemma4/test_modeling_gemma4.py
index 9d3924d13935..3d7e2e2fa103 100644
--- a/tests/models/gemma4/test_modeling_gemma4.py
+++ b/tests/models/gemma4/test_modeling_gemma4.py
@@ -17,6 +17,7 @@
 
 import pytest
 from parameterized import parameterized
+from pytest import mark
 
 from transformers import (
     AutoTokenizer,
@@ -27,8 +28,13 @@
 from transformers.testing_utils import (
     Expectations,
     cleanup,
+    require_deterministic_for_xpu,
+    require_flash_attn,
+    require_flash_attn_3,
+    require_flash_attn_4,
     require_torch,
     require_torch_accelerator,
+    require_torch_gpu,
     require_torch_multi_gpu,
     slow,
     torch_device,
@@ -48,8 +54,10 @@
         AutoModelForCausalLM,
         Gemma4ForCausalLM,
         Gemma4ForConditionalGeneration,
+        Gemma4ForSequenceClassification,
         Gemma4Model,
         Gemma4Processor,
+        Gemma4TextForSequenceClassification,
         Gemma4TextModel,
     )
 
@@ -59,6 +67,7 @@ class Gemma4TextModelTester(CausalLMModelTester):
         config_class = Gemma4TextConfig
         base_model_class = Gemma4TextModel
         causal_lm_class = Gemma4ForCausalLM
+        sequence_classification_class = Gemma4TextForSequenceClassification
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
@@ -126,6 +135,20 @@ def test_tp_generation_quantized(self):
     def test_model_training(self):
         pass
 
+    @unittest.skip(
+        "Under non-bf16 dtypes, MoE grouped_mm falls back to "
+        "_grouped_mm_fallback_backward which is incompatible with torch.compile."
+    )
+    def test_flash_attn_2_can_compile_with_attention_mask_None_without_graph_break(self):
+        pass
+
+    @unittest.skip(
+        "Under non-bf16 dtypes, MoE grouped_mm falls back to "
+        "_grouped_mm_fallback_backward which is incompatible with torch.compile."
+    )
+    def test_torch_compile_for_training(self):
+        pass
+
 
 class Gemma4Audio2TextModelTester:
     def __init__(
@@ -410,7 +433,9 @@ def prepare_config_and_inputs_for_common(self):
 
 @require_torch
 class Gemma4Vision2TextModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
-    all_model_classes = (Gemma4Model, Gemma4ForConditionalGeneration) if is_torch_available() else ()
+    all_model_classes = (
+        (Gemma4Model, Gemma4ForConditionalGeneration, Gemma4ForSequenceClassification) if is_torch_available() else ()
+    )
     all_generative_model_classes = (Gemma4ForConditionalGeneration,) if is_torch_available() else ()
     additional_model_inputs = ["mm_token_type_ids"]
 
@@ -436,6 +461,10 @@ def test_training(self):
         loss = model(**inputs).loss
         loss.backward()
 
+    @unittest.skip("Loading nested configs with overwritten `kwargs` isn't supported yet.")
+    def test_load_with_mismatched_shapes(self):
+        pass
+
     @unittest.skip("The tester has no audios in input dict")
     def test_get_audio_features_hidden_states(self):
         pass
@@ -470,6 +499,54 @@ def test_num_layers_is_small(self):
     def test_generate_from_random_inputs_embeds(self):
         pass
 
+    @require_flash_attn
+    @require_torch_accelerator
+    @mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_from_config(self):
+        # Gemma4 requires mm_token_type_ids in train mode, so we test in eval mode
+        self.flash_attn_from_config(attn_implementation="flash_attention_2", test_fwd_in_train=False)
+
+    @require_flash_attn_3
+    @require_torch_gpu
+    @mark.flash_attn_3_test
+    @slow
+    def test_flash_attn_3_from_config(self):
+        # Gemma4 requires mm_token_type_ids in train mode, so we test in eval mode
+        self.flash_attn_from_config(attn_implementation="flash_attention_3", test_fwd_in_train=False)
+
+    @require_flash_attn_4
+    @require_torch_gpu
+    @mark.flash_attn_4_test
+    @slow
+    def test_flash_attn_4_from_config(self):
+        # Gemma4 requires mm_token_type_ids in train mode, so we test in eval mode
+        self.flash_attn_from_config(attn_implementation="flash_attention_4", test_fwd_in_train=False)
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_2_inference_equivalence(self):
+        pass
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_3_inference_equivalence(self):
+        pass
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_3_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_4_inference_equivalence(self):
+        pass
+
+    @unittest.skip("The base test does not pass image_position_ids and mm_token_type_ids required by Gemma4")
+    def test_flash_attn_4_inference_equivalence_right_padding(self):
+        pass
+
     @unittest.skip(
         "Randomly starts failing after module order changed in the __init__ because accelertate is not robust enough"
     )
@@ -516,6 +593,7 @@ def setUp(self):
     def tearDown(self):
         cleanup(torch_device, gc_collect=True)
 
+    @require_deterministic_for_xpu
     def test_model_with_image(self):
         model = Gemma4ForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)
 
@@ -534,11 +612,13 @@ def test_model_with_image(self):
         EXPECTED_TEXTS = Expectations(
             {
                 ("cuda", 8): ['This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background'],
+                ("xpu", 3): ['This image shows a **brown and white cow standing on a sandy beach near the ocean**.\n\nHere are some details about the image:\n\n*   '],
             }
         )  # fmt: skip
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
         self.assertEqual(output_text, EXPECTED_TEXT)
 
+    @require_deterministic_for_xpu
     def test_model_with_image_batch(self):
         model = Gemma4ForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)
 
@@ -580,11 +660,16 @@ def test_model_with_image_batch(self):
                     "This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background",
                     "No, these images are not identical.\n\nThe first image is a photograph of a **brown and white cow standing on a beach** under a blue",
                 ],
+                ("xpu", 3): [
+                    "This image shows a **brown and white cow** standing on a **sandy beach** with the **ocean and a blue sky** in the background",
+                    "No, these images are not identical.\n\nThe first image is a photograph of a **brown and white cow standing on a beach** under a blue",
+                ],
             }
         )
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
         self.assertEqual(output_text, EXPECTED_TEXT)
 
+    @require_deterministic_for_xpu
     def test_model_multiimage(self):
         model = Gemma4ForConditionalGeneration.from_pretrained(self.model_name, device_map=torch_device)
 
@@ -614,6 +699,7 @@ def test_model_multiimage(self):
         EXPECTED_TEXTS = Expectations(
             {
                 ("cuda", 8): ['Based on the image, here is a description of what I see:\n\n**Foreground & Street Scene:**\n* **Traffic Sign:** The most prominent'],
+                ("xpu", 3): ['Based on the image, here is a description of what I see:\n\n**Foreground & Street Scene:**\n* **Roadway:** There is an'],
             }
         )  # fmt: skip
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
@@ -647,6 +733,7 @@ def test_model_text_only_multigpu(self):
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
         self.assertEqual(output_text, EXPECTED_TEXT)
 
+    @require_deterministic_for_xpu
     def test_model_text_only(self):
         model = AutoModelForCausalLM.from_pretrained(self.model_name, device_map=torch_device)
         tokenizer = AutoTokenizer.from_pretrained(self.model_name, padding_side="left")
@@ -666,6 +753,7 @@ def test_model_text_only(self):
             {
                 ("cuda", (8, 0)): ['## The Algorithmic Mind\n\nA whisper starts, a seed unseen,\nOf data vast, a vibrant sheen.\nA sea of numbers,'],
                 ("cuda", (8, 6)): ['## The Algorithmic Mind\n\nA tapestry of data, vast and deep,\nWhere silent numbers in their slumber sleep.\nA sea of text'],
+                ("xpu", 3): ['## The Algorithmic Mind\n\nA whisper starts in silicon deep,\nWhere data streams in endless sweep.\nNo flesh and blood, no beating'],
             }
         )  # fmt: skip
         EXPECTED_TEXT = EXPECTED_TEXTS.get_expectation()
@@ -696,6 +784,7 @@ def test_states_sharing_with_and_without_cache(self):
 
     # Note: we do not test FA2 as the head dim is 512 on some layers, which is not compatible with the kernels
     @parameterized.expand([("sdpa",), ("eager",)])
+    @require_deterministic_for_xpu
     def test_generation_beyond_sliding_window(self, attn_implementation: str):
         """Test that we can correctly generate beyond the sliding window. Outputs for every attention functions
         should be coherent and identical.
@@ -734,7 +823,11 @@ def test_generation_beyond_sliding_window(self, attn_implementation: str):
                 ("cuda", 8): [
                     "That sounds lovely! It seems like you're really enjoying the place you'",
                     "Here are a few ways you could use or expand upon that list, depending on",
-                ]
+                ],
+                ("xpu", 3): [
+                    "That sounds lovely! It seems like you're really enjoying the place you'",
+                    "Here are a few ways you could use or expand upon that list, depending on",
+                ],
             }
         )
         self.assertEqual(output_text, EXPECTED_COMPLETIONS.get_expectation())
diff --git a/tests/models/gemma4/test_processing_gemma4.py b/tests/models/gemma4/test_processing_gemma4.py
index 347f7d2bfda0..715a402944d8 100644
--- a/tests/models/gemma4/test_processing_gemma4.py
+++ b/tests/models/gemma4/test_processing_gemma4.py
@@ -16,7 +16,6 @@
 import unittest
 
 import numpy as np
-from parameterized import parameterized
 
 from transformers import Gemma4Processor
 from transformers.testing_utils import get_tests_dir, require_vision
@@ -74,7 +73,8 @@ def _setup_image_processor(cls):
     def _setup_tokenizer(cls):
         tokenizer_class = cls._get_component_class_from_processor("tokenizer")
         extra_special_tokens = {
-            "image_token": "<image_soft_token>",
+            "image_token": "<|image|>",
+            "video_token": "<|video|>",
             "boi_token": "<start_of_image>",
             "eoi_token": "<end_of_image>",
             "audio_token": "<audio_soft_token>",
@@ -104,11 +104,10 @@ def test_get_num_vision_tokens(self):
     def tearDownClass(cls):
         shutil.rmtree(cls.tmpdirname, ignore_errors=True)
 
-    # TODO: raushan or arthur: add the real chat template
     @staticmethod
     def prepare_processor_dict():
         return {
-            "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<start_of_image>' }}\n            {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",            "image_seq_length": 3,
+            "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<|image|>' }}\n       {%- elif item['type'] == 'video' -%}\n{{ '<video_soft_token>' }}\n      {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",            "image_seq_length": 3,
         }  # fmt: skip
 
     # Override as Gemma4 needs images to be an explicitly nested batch
@@ -131,8 +130,8 @@ def test_text_with_image_tokens(self):
             image_processor=image_processor,
             video_processor=video_processor,
         )
-        text_multi_images = f"{processor.boi_token}{processor.boi_token}Dummy text!"
-        text_single_image = f"{processor.boi_token}Dummy text!"
+        text_multi_images = f"{processor.image_token}{processor.image_token}Dummy text!"
+        text_single_image = f"{processor.image_token}Dummy text!"
 
         image = self.prepare_image_inputs()
 
@@ -206,110 +205,3 @@ def test_get_num_multimodal_tokens_matches_processor_call(self):
     @unittest.skip("This test seems to be loading a different video, check for all models and fix")
     def test_apply_chat_template_video_frame_sampling(self):
         pass
-
-
-class Gemma4AudioTokenCountTest(unittest.TestCase):
-    """Regression tests for _compute_audio_num_tokens.
-
-    The original implementation used ceil(duration_ms / 40) which could overshoot
-    the actual encoder output length by 1 token for ~50% of audio lengths.
-    The fix replicates the exact mel-framing + conv-subsampling arithmetic.
-    """
-
-    @staticmethod
-    def _encoder_output_length(num_samples: int, sr: int = 16000) -> int:
-        """Reference implementation of the encoder's actual output length."""
-        frame_length = int(round(sr * 20.0 / 1000.0))
-        hop_length = int(round(sr * 10.0 / 1000.0))
-        frame_size_for_unfold = frame_length + 1
-        pad_left = frame_length // 2
-        padded_samples = num_samples + pad_left
-        num_mel_frames = (padded_samples - frame_size_for_unfold) // hop_length + 1
-        if num_mel_frames <= 0:
-            return 0
-        t = num_mel_frames
-        for _ in range(2):
-            t_padded = t + 2
-            t = (t_padded - 3) // 2 + 1
-        return t
-
-    @staticmethod
-    def _compute_tokens(num_samples, sr=16000):
-        """Call _compute_audio_num_tokens without constructing a full processor."""
-
-        class _Stub:
-            audio_seq_length = 1500
-
-        return Gemma4Processor._compute_audio_num_tokens(_Stub(), np.zeros(num_samples), sr)
-
-    @parameterized.expand(
-        [
-            ("over_1s_boundary", 16001),
-            ("bug_report_194_vs_193", 123521),
-            ("over_5s_boundary", 80001),
-            ("over_10s_boundary", 160001),
-            ("pad_left_effect_1s", 16161),
-        ]
-    )
-    def test_audio_token_count_matches_encoder(self, _name, num_samples):
-        """Verify _compute_audio_num_tokens matches the encoder for edge-case lengths."""
-        expected = self._encoder_output_length(num_samples)
-        actual = self._compute_tokens(num_samples)
-        self.assertEqual(actual, expected)
-
-    @parameterized.expand(
-        [
-            ("1s", 16000, 25),
-            ("5s", 80000, 125),
-            ("10s", 160000, 250),
-            ("30s", 480000, 750),
-        ]
-    )
-    def test_audio_token_count_round_boundaries(self, _name, num_samples, expected_tokens):
-        """Verify exact results at round durations."""
-        self.assertEqual(self._compute_tokens(num_samples), expected_tokens)
-
-    def test_audio_token_count_short_audio(self):
-        """Very short audio that produces zero mel frames should return 0."""
-        # With pad_left = 160 and frame_size_for_unfold = 321, anything <= 160 samples => 0 mel frames
-        self.assertEqual(self._compute_tokens(160), 0)
-
-    @parameterized.expand(
-        [
-            # Lengths where the old naive mask would produce +1 extra token
-            # after stride-2 conv subsampling.  With sr=16000, hop=160, frame_size=321.
-            ("short_boundary", 641),
-            ("over_1s", 16001),
-            ("over_5s", 80001),
-            ("bug_report_length", 123521),
-            ("pad_left_effect_1s", 16161),
-        ]
-    )
-    def test_feature_extractor_mask_matches_processor(self, _name, num_samples):
-        """Regression: feature extractor mask must agree with processor token count.
-
-        The bug was that ``attention_mask[::hop]`` overcounts real mel frames by +2
-        (marks frames as valid even when their window extends into padding).
-        After two stride-2 conv blocks this becomes +1 extra token ~50% of the time.
-        """
-        from transformers import Gemma4AudioFeatureExtractor
-
-        fe = Gemma4AudioFeatureExtractor()
-
-        # Batch with a longer audio to force padding (the trigger for the bug)
-        target = np.random.randn(num_samples).astype(np.float32)
-        padding_partner = np.random.randn(num_samples + 5000).astype(np.float32)
-
-        features = fe([target, padding_partner], return_tensors="np", padding="longest")
-        mask = features["input_features_mask"][0]  # mask for target audio
-
-        # Simulate two stride-2 conv blocks on the mask
-        T = len(mask)
-        for _ in range(2):
-            T_out = (T + 2 - 3) // 2 + 1
-            mask = mask[::2][:T_out]
-            T = len(mask)
-
-        real_tokens = int(mask.sum())
-        expected = self._compute_tokens(num_samples)
-        self.assertEqual(real_tokens, expected)
diff --git a/tests/models/glm46v/test_processor_glm46v.py b/tests/models/glm46v/test_processor_glm46v.py
index 8e7b56df6fa8..8c5c51a33f64 100644
--- a/tests/models/glm46v/test_processor_glm46v.py
+++ b/tests/models/glm46v/test_processor_glm46v.py
@@ -170,7 +170,12 @@ def test_apply_chat_template_video_frame_sampling(self):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "video"},
+                        {
+                            "type": "video",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                            ),
+                        },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
                 },
@@ -180,21 +185,6 @@ def test_apply_chat_template_video_frame_sampling(self):
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
         self.assertEqual(len(formatted_prompt), 1)
 
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
-
-        # Add video URL for return dict and load with `num_frames` arg
-        messages[0][0]["content"][0] = {
-            "type": "video",
-            "url": url_to_local_path(
-                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
-            ),
-        }
-
         # Load with `video_fps` arg
         video_fps = 10
         out_dict_with_video = processor.apply_chat_template(
diff --git a/tests/models/glm4v/test_processor_glm4v.py b/tests/models/glm4v/test_processor_glm4v.py
index cb101521ea24..d2e777aad3f2 100644
--- a/tests/models/glm4v/test_processor_glm4v.py
+++ b/tests/models/glm4v/test_processor_glm4v.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import inspect
 import unittest
 
 import numpy as np
@@ -158,11 +157,7 @@ def test_apply_chat_template_video_frame_sampling(self):
         if processor.chat_template is None:
             self.skipTest("Processor has no chat template")
 
-        signature = inspect.signature(processor.__call__)
-        if "videos" not in {*signature.parameters.keys()} or (
-            signature.parameters.get("videos") is not None
-            and signature.parameters["videos"].annotation == inspect._empty
-        ):
+        if "video_processor" not in self.processor_class.get_attributes():
             self.skipTest("Processor doesn't accept videos at input")
 
         messages = [
@@ -180,13 +175,6 @@ def test_apply_chat_template_video_frame_sampling(self):
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
         self.assertEqual(len(formatted_prompt), 1)
 
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
-
         # Add video URL for return dict and load with `num_frames` arg
         messages[0][0]["content"][0] = {
             "type": "video",
diff --git a/tests/models/glmasr/test_modeling_glmasr.py b/tests/models/glmasr/test_modeling_glmasr.py
index 744e268e74c7..b19e91a61209 100644
--- a/tests/models/glmasr/test_modeling_glmasr.py
+++ b/tests/models/glmasr/test_modeling_glmasr.py
@@ -13,17 +13,16 @@
 # limitations under the License.
 """Testing suite for the PyTorch glmasr model."""
 
-import tempfile
 import unittest
 
-import pytest
-
 from transformers import (
     AutoProcessor,
     GlmAsrConfig,
     GlmAsrForConditionalGeneration,
+    LlamaConfig,
     is_torch_available,
 )
+from transformers.models.glmasr.configuration_glmasr import GlmAsrEncoderConfig
 from transformers.testing_utils import (
     cleanup,
     require_torch,
@@ -31,183 +30,51 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
-from ...test_pipeline_mixin import PipelineTesterMixin
+from ...alm_tester import ALMModelTest, ALMModelTester
 
 
 if is_torch_available():
     import torch
 
 
-class GlmAsrModelTester:
-    def __init__(
-        self,
-        parent,
-        ignore_index=-100,
-        audio_token_id=0,
-        seq_length=35,
-        feat_seq_length=64,
-        text_config={
-            "model_type": "llama",
-            "intermediate_size": 64,
-            "initializer_range": 0.02,
-            "hidden_size": 16,
-            "max_position_embeddings": 52,
-            "num_hidden_layers": 2,
-            "num_attention_heads": 4,
-            "use_labels": True,
-            "use_mrope": False,
-            "vocab_size": 99,
-            "head_dim": 8,
-            "pad_token_id": 1,  # can't be the same as the audio token id
-        },
-        is_training=True,
-        audio_config={
-            "model_type": "glmasr_encoder",
-            "hidden_size": 128,
-            "num_attention_heads": 2,
-            "intermediate_size": 512,
-            "num_hidden_layers": 2,
-            "num_mel_bins": 128,
-            "max_source_positions": 32,
-            "initializer_range": 0.02,
-        },
-    ):
-        self.parent = parent
-        self.ignore_index = ignore_index
-        self.audio_token_id = audio_token_id
-        self.text_config = text_config
-        self.audio_config = audio_config
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.is_training = is_training
-
-        self.batch_size = 3
-        self.encoder_seq_length = seq_length
-
-    def get_config(self):
-        return GlmAsrConfig(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            ignore_index=self.ignore_index,
-            audio_token_id=self.audio_token_id,
-        )
-
-    def prepare_config_and_inputs(self):
-        input_features_values = floats_tensor(
-            [
-                self.batch_size,
-                self.audio_config["num_mel_bins"],
-                self.feat_seq_length,
-            ]
-        )
-        config = self.get_config()
-        input_features_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
-        return config, input_features_values, input_features_mask
+class GlmAsrModelTester(ALMModelTester):
+    config_class = GlmAsrConfig
+    conditional_generation_class = GlmAsrForConditionalGeneration
+    text_config_class = LlamaConfig
+    audio_config_class = GlmAsrEncoderConfig
+    audio_mask_key = "input_features_mask"
 
-    def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        config, input_features_values, input_features_mask = config_and_inputs
-        num_audio_tokens_per_batch_idx = 8
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("head_dim", 8)
+        super().__init__(parent, **kwargs)
 
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
-        attention_mask[:, :1] = 0
-
-        input_ids[:, 1 : 1 + num_audio_tokens_per_batch_idx] = config.audio_token_id
-        inputs_dict = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "input_features": input_features_values,
-            "input_features_mask": input_features_mask,
-        }
-        return config, inputs_dict
+    def get_audio_embeds_mask(self, audio_mask):
+        # conv1 (s=1) preserves length; conv2 (s=2, k=3, p=1) halves; merge_factor=4 post-projector.
+        audio_lengths = audio_mask.sum(-1)
+        for padding, kernel_size, stride in [(1, 3, 1), (1, 3, 2)]:
+            audio_lengths = (audio_lengths + 2 * padding - (kernel_size - 1) - 1) // stride + 1
+        merge_factor = 4
+        post_lengths = (audio_lengths - merge_factor) // merge_factor + 1
+        max_len = int(post_lengths.max().item())
+        positions = torch.arange(max_len, device=audio_mask.device)[None, :]
+        return (positions < post_lengths[:, None]).long()
 
 
 @require_torch
-class GlmAsrForConditionalGenerationModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class GlmAsrForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `GlmAsrForConditionalGeneration`.
     """
 
-    all_model_classes = (GlmAsrForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = GlmAsrModelTester
     pipeline_model_mapping = {"audio-text-to-text": GlmAsrForConditionalGeneration} if is_torch_available() else {}
 
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = GlmAsrModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=GlmAsrConfig, has_text_modality=False)
-
     @unittest.skip(
         reason="This test does not apply to GlmAsr since inputs_embeds corresponding to audio tokens are replaced when input features are provided."
     )
     def test_inputs_embeds_matches_input_ids(self):
         pass
 
-    @unittest.skip(reason="Compile not yet supported for GlmAsr models")
-    @pytest.mark.torch_compile_test
-    def test_sdpa_can_compile_dynamic(self):
-        pass
-
-    @unittest.skip(reason="Compile not yet supported for GlmAsr models")
-    def test_sdpa_can_dispatch_on_flash(self):
-        pass
-
-    @unittest.skip(reason="GlmAsr tests avoid right-padding equivalence; fusion is in-place.")
-    def test_flash_attn_2_inference_equivalence_right_padding(self):
-        pass
-
-    @unittest.skip(reason="GlmAsr has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
-    def test_sdpa_can_dispatch_composite_models(self):
-        # GlmAsr is audio+text composite; verify SDPA toggles propagate to submodules.
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                # SDPA (default)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-                audio_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
-
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-                self.assertTrue(model.audio_tower.config._attn_implementation == audio_attn)
-
-                # Eager
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
-
-                for _, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
 
 @require_torch
 class GlmAsrForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/got_ocr2/test_modeling_got_ocr2.py b/tests/models/got_ocr2/test_modeling_got_ocr2.py
index 404b88d08dde..0c5dbde9b68b 100644
--- a/tests/models/got_ocr2/test_modeling_got_ocr2.py
+++ b/tests/models/got_ocr2/test_modeling_got_ocr2.py
@@ -161,9 +161,6 @@ def setUp(self):
     def test_config(self):
         self.config_tester.run_common_tests()
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class GotOcr2IntegrationTest(unittest.TestCase):
diff --git a/tests/models/granite/test_modeling_granite.py b/tests/models/granite/test_modeling_granite.py
index 8b6a024af772..b6ecf3713eab 100644
--- a/tests/models/granite/test_modeling_granite.py
+++ b/tests/models/granite/test_modeling_granite.py
@@ -36,6 +36,7 @@
 
     from transformers import (
         GraniteForCausalLM,
+        GraniteForSequenceClassification,
         GraniteModel,
     )
 
@@ -141,6 +142,16 @@ def create_and_check_model(
         result = model(input_ids)
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = GraniteForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -162,6 +173,7 @@ class GraniteModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
         (
             GraniteModel,
             GraniteForCausalLM,
+            GraniteForSequenceClassification,
         )
         if is_torch_available()
         else ()
@@ -170,6 +182,7 @@ class GraniteModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMi
         {
             "feature-extraction": GraniteModel,
             "text-generation": GraniteForCausalLM,
+            "text-classification": GraniteForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -198,6 +211,10 @@ def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
 
 @require_torch_accelerator
 class GraniteIntegrationTest(unittest.TestCase):
diff --git a/tests/models/granite4_vision/__init__.py b/tests/models/granite4_vision/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/granite4_vision/test_modeling_granite4_vision.py b/tests/models/granite4_vision/test_modeling_granite4_vision.py
new file mode 100644
index 000000000000..367d8e15e9b9
--- /dev/null
+++ b/tests/models/granite4_vision/test_modeling_granite4_vision.py
@@ -0,0 +1,271 @@
+# Copyright 2026 IBM and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch Granite4Vision model."""
+
+import unittest
+
+import pytest
+import requests
+
+from transformers import (
+    AutoProcessor,
+    CLIPVisionConfig,
+    Granite4VisionConfig,
+    Granite4VisionForConditionalGeneration,
+    Granite4VisionModel,
+    GraniteConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_modeling_common import floats_tensor
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Granite4VisionModelTester(VLMModelTester):
+    base_model_class = Granite4VisionModel
+    config_class = Granite4VisionConfig
+    conditional_generation_class = Granite4VisionForConditionalGeneration
+    text_config_class = GraniteConfig
+    vision_config_class = CLIPVisionConfig
+
+    def __init__(self, parent, **kwargs):
+        # Vision hidden_size must be divisible by 64 (QFormer num_attention_heads = hidden_size // 64)
+        kwargs.setdefault("hidden_size", 64)
+        kwargs.setdefault("intermediate_size", 64)
+        kwargs.setdefault("num_attention_heads", 2)
+        kwargs.setdefault("num_key_value_heads", 2)
+        kwargs.setdefault("num_hidden_layers", 2)
+        # Image/patch sizes: image_side = image_size // patch_size must be divisible by window_side
+        kwargs.setdefault("image_size", 8)
+        kwargs.setdefault("patch_size", 2)
+        kwargs.setdefault("projection_dim", 64)
+        kwargs.setdefault("num_patches_per_image", 2)
+        # Granite4Vision-specific
+        kwargs.setdefault("downsample_rate", "1/2")
+        kwargs.setdefault("deepstack_layer_map", [[1, 0]])
+        kwargs.setdefault("use_spatial_sampling", False)
+        kwargs.setdefault("projector_dropout", 0.0)
+        kwargs.setdefault("image_token_index", kwargs.get("image_token_id", 3))
+
+        # Compute num_image_tokens after downsampling:
+        # image_side = image_size/patch_size = 4, ds 1/2 -> patches_h = patches_w = 2
+        # pinpoints [[8,8]] -> scale 1x1 -> current_h = current_w = 2
+        # unpadded = 2*2 = 4, newline = 2, base = 2*2 = 4 -> total = 10
+        kwargs.setdefault("num_image_tokens", 10)
+
+        super().__init__(parent, **kwargs)
+
+    def create_pixel_values(self):
+        """Granite4Vision expects 5D pixel_values: (batch_size, num_patches, channels, height, width)"""
+        return floats_tensor(
+            [
+                self.batch_size,
+                self.num_patches_per_image,
+                self.num_channels,
+                self.image_size,
+                self.image_size,
+            ]
+        )
+
+    def get_additional_inputs(self, config, input_ids, pixel_values):
+        """Granite4Vision requires image_sizes tensor"""
+        return {
+            "image_sizes": torch.tensor([[self.image_size, self.image_size]] * self.batch_size),
+        }
+
+    def get_config(self):
+        config = super().get_config()
+        config.image_grid_pinpoints = [[self.image_size, self.image_size]]
+        config.downsample_rate = self.downsample_rate
+        config.deepstack_layer_map = self.deepstack_layer_map
+        config.use_spatial_sampling = self.use_spatial_sampling
+        config.projector_dropout = self.projector_dropout
+        return config
+
+
+@require_torch
+class Granite4VisionModelTest(VLMModelTest, unittest.TestCase):
+    """
+    Model tester for `Granite4VisionForConditionalGeneration`.
+    """
+
+    model_tester_class = Granite4VisionModelTester
+    skip_test_image_features_output_shape = True
+    test_torch_exportable = False
+    # Custom layer-by-layer forward doesn't support output_attentions
+    # (GraniteDecoderLayer discards attention weights internally)
+    test_attention_outputs = False
+    has_attentions = False
+
+    # get_image_features returns Granite4VisionImageFeaturesOutput with deepstack_features,
+    # not last_hidden_state/pooler_output/hidden_states expected by the common tests
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no last_hidden_state/pooler_output")
+    def test_get_image_features_output_0(self):
+        pass
+
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no last_hidden_state/pooler_output")
+    def test_get_image_features_output_1(self):
+        pass
+
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no last_hidden_state/pooler_output")
+    def test_get_image_features_output_2(self):
+        pass
+
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no hidden_states field")
+    def test_get_image_features_hidden_states(self):
+        pass
+
+    @unittest.skip("Granite4VisionImageFeaturesOutput has no attentions field")
+    def test_get_image_features_attentions(self):
+        pass
+
+    @unittest.skip("Base model forward returns ModelOutputWithPast, not CausalLMOutput with loss")
+    def test_training(self):
+        pass
+
+    @unittest.skip("QFormer submodules not initialized by init_weights from meta device")
+    def test_can_init_all_missing_weights(self):
+        pass
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing(self):
+        super().test_training_gradient_checkpointing()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        super().test_training_gradient_checkpointing_use_reentrant_false()
+
+    @pytest.mark.xfail(reason="This architecture seems to not compute gradients for some layer.")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        super().test_training_gradient_checkpointing_use_reentrant_true()
+
+    @unittest.skip(
+        "VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(
+        "VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
+    )
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Custom layer-by-layer forward has graph breaks incompatible with fullgraph compile")
+    def test_generate_compile_model_forward_fullgraph(self):
+        pass
+
+    @unittest.skip("Blip2QFormerModel in WindowQFormerDownsampler does not support SDPA dispatch")
+    def test_can_set_attention_dynamically_composite_model(self):
+        pass
+
+
+@require_torch
+class Granite4VisionIntegrationTest(unittest.TestCase):
+    model_id = "ibm-granite/granite-vision-4.1-4b"
+
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(self.model_id)
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        self.image = Image.open(requests.get(url, stream=True).raw)
+
+    def make_prompt(self, question):
+        messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": question}]}]
+        return self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_small_model_integration_test(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        prompt = self.make_prompt("Describe this image briefly.")
+        inputs = self.processor(text=prompt, images=self.image, return_tensors="pt").to(model.device)
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        new_tokens = output[:, inputs["input_ids"].shape[1] :]
+
+        EXPECTED_RESPONSE = "The image depicts two cats resting on a pink couch. They are lying in a relaxed, sprawled position, with one cat appearing to be in a"  # fmt: skip
+        self.assertEqual(self.processor.decode(new_tokens[0], skip_special_tokens=True), EXPECTED_RESPONSE)
+
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        url2 = "http://images.cocodataset.org/val2017/000000001000.jpg"
+        image2 = Image.open(requests.get(url2, stream=True).raw)
+
+        prompt = self.make_prompt("What do you see in this image?")
+        inputs = self.processor(
+            text=[prompt, prompt],
+            images=[self.image, image2],
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device)
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        new_tokens = output[:, inputs["input_ids"].shape[1] :]
+        responses = self.processor.batch_decode(new_tokens, skip_special_tokens=True)
+
+        self.assertIn("cat", responses[0].lower())
+        self.assertIn("tennis", responses[1].lower())
+
+    @slow
+    def test_small_model_integration_test_batch_matches_single(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        prompt = self.make_prompt("What do you see in this image?")
+
+        # Single inference
+        inputs_single = self.processor(text=prompt, images=self.image, return_tensors="pt").to(model.device)
+        output_single = model.generate(**inputs_single, max_new_tokens=30, do_sample=False)
+        decoded_single = self.processor.decode(
+            output_single[0, inputs_single["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+
+        # Batch inference (same image as first in batch)
+        url2 = "http://images.cocodataset.org/val2017/000000001000.jpg"
+        image2 = Image.open(requests.get(url2, stream=True).raw)
+        inputs_batch = self.processor(
+            text=[prompt, prompt],
+            images=[self.image, image2],
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device)
+        output_batch = model.generate(**inputs_batch, max_new_tokens=30, do_sample=False)
+        decoded_batch = self.processor.decode(
+            output_batch[0, inputs_batch["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+
+        self.assertEqual(decoded_single, decoded_batch)
diff --git a/tests/models/granite4_vision/test_processing_granite4_vision.py b/tests/models/granite4_vision/test_processing_granite4_vision.py
new file mode 100644
index 000000000000..8a56aa69b020
--- /dev/null
+++ b/tests/models/granite4_vision/test_processing_granite4_vision.py
@@ -0,0 +1,122 @@
+# Copyright 2025 IBM. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import unittest
+
+import torch
+
+from transformers import Granite4VisionProcessor
+from transformers.testing_utils import (
+    require_vision,
+)
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class Granite4VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Granite4VisionProcessor
+    # Image token expansion with downsample_rate="1/2" produces more tokens than the defaults
+    image_text_kwargs_max_length = 300
+    image_text_kwargs_override_max_length = 280
+    image_unstructured_max_length = 260
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("huggyllama/llama-7b")
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[PAD]"
+            if tokenizer.pad_token_id is None:
+                tokenizer.pad_token_id = 0
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
+            "patch_size": 14,
+            "vision_feature_select_strategy": "default",
+            "downsample_rate": "1/2",
+        }  # fmt: skip
+
+    def test_get_num_vision_tokens(self):
+        """Tests general functionality of the helper used internally in vLLM"""
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)
+
+    def test_chat_template_is_saved(self):
+        processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
+        processor_dict_loaded = json.loads(processor_loaded.to_json_string())
+        # chat templates aren't serialized to json in processors
+        self.assertFalse("chat_template" in processor_dict_loaded)
+
+        # they have to be saved as separate file and loaded back from that file
+        # so we check if the same template is loaded
+        processor_dict = self.prepare_processor_dict()
+        self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
+
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        processor.downsample_rate = "1/2"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 503, 316))
+        image_token_index = processor.image_token_id
+
+        # With downsample_rate="1/2" and patch_size=14:
+        #   patches = 336/14 = 24, after ds: 24*1/2 = 12
+        #   best resolution for (503, 316): [672, 336]
+        #   scale_height=2, scale_width=1
+        #   current = 12*2=24 h, 12*1=12 w
+        #   aspect: 316/503 = 0.628, 12/24 = 0.5 -> orig > current -> new_height = round(503*(12/316)) = 19
+        #   padding = (24-19)//2 = 2, current_height = 24 - 4 = 20
+        #   unpadded = 20*12 = 240, newline = 20
+        #   base = 12*12 + 0 = 144
+        #   total = 240 + 20 + 144 = 404
+        #   with "default" strategy: 404 - 1 = 403
+        expected_image_tokens = 403
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)
diff --git a/tests/models/granite_speech/test_modeling_granite_speech.py b/tests/models/granite_speech/test_modeling_granite_speech.py
index 95c6c443d6f0..e4ecebbcb0ee 100644
--- a/tests/models/granite_speech/test_modeling_granite_speech.py
+++ b/tests/models/granite_speech/test_modeling_granite_speech.py
@@ -13,14 +13,15 @@
 # limitations under the License.
 """Testing suite for the IBM Granite Speech model."""
 
-import tempfile
 import unittest
 
 import pytest
 
 from transformers import (
     AutoProcessor,
+    GraniteConfig,
     GraniteSpeechConfig,
+    GraniteSpeechEncoderConfig,
     GraniteSpeechForConditionalGeneration,
 )
 from transformers.testing_utils import (
@@ -35,14 +36,8 @@
     is_torch_available,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import (
-    ModelTesterMixin,
-    floats_tensor,
-    ids_tensor,
-)
-from ...test_pipeline_mixin import PipelineTesterMixin
+from ...alm_tester import ALMModelTest, ALMModelTester
+from ...test_modeling_common import floats_tensor
 
 
 if is_torch_available():
@@ -52,129 +47,40 @@
     from datasets import load_dataset
 
 
-class GraniteSpeechForConditionalGenerationModelTester:
-    def __init__(
-        self,
-        parent,
-        seq_length=7,
-        encoder_config={
-            "model_type": "granite_speech_encoder",
-            "context_size": 200,
-            "conv_expansion_factor": 2,
-            "conv_kernel_size": 15,
-            "dim_head": 32,
-            "dropout": 0.1,
-            "feedforward_mult": 4,
-            "hidden_dim": 32,
-            "input_dim": 160,
-            "num_heads": 4,
-            "num_layers": 2,
-            "output_dim": 42,
-        },
-        text_config={
-            "model_type": "granite",
-            "is_training": True,
-            "seq_length": 7,
-            "use_token_type_ids": False,
-            "use_labels": True,
-            "vocab_size": 99,
+class GraniteSpeechModelTester(ALMModelTester):
+    config_class = GraniteSpeechConfig
+    conditional_generation_class = GraniteSpeechForConditionalGeneration
+    text_config_class = GraniteConfig
+    audio_config_class = GraniteSpeechEncoderConfig
+    audio_config_key = "encoder_config"
+
+    def __init__(self, parent, **kwargs):
+        kwargs["projector_config"] = {
+            "model_type": "blip_2_qformer",
             "hidden_size": 32,
             "num_hidden_layers": 2,
             "num_attention_heads": 4,
-            "intermediate_size": 37,
-            "hidden_act": "gelu",
-            "hidden_dropout_prob": 0.1,
-            "attention_probs_dropout_prob": 0.1,
-            "max_position_embeddings": 580,
-            "type_vocab_size": 16,
-            "type_sequence_label_size": 2,
-            "initializer_range": 0.02,
-            "num_labels": 3,
-            "num_choices": 4,
-            "pad_token_id": 1,
-        },
-        projector_config={
-            "attention_probs_dropout_prob": 0.1,
-            "cross_attention_frequency": 1,
-            "encoder_hidden_size": 32,
-            "hidden_act": "gelu",
-            "hidden_dropout_prob": 0.1,
-            "hidden_size": 32,
-            "initializer_range": 0.02,
             "intermediate_size": 256,
-            "layer_norm_eps": 1e-12,
-            "max_position_embeddings": 2048,
-            "model_type": "blip_2_qformer",
-            "num_attention_heads": 4,
-            "num_hidden_layers": 2,
-            "use_qformer_text_input": False,
-            "vocab_size": 30522,
-        },
-        audio_token_index=0,
-        tie_word_embeddings=True,
-        initializer_range=0.02,
-        has_lora_adapter=True,
-        downsample_rate=5,
-        window_size=15,
-        is_training=True,
-    ):
-        self.parent = parent
-        self.encoder_config = encoder_config
-        self.text_config = text_config
-        self.projector_config = projector_config
-        self.audio_token_index = audio_token_index
-        self.tie_word_embeddings = tie_word_embeddings
-        self.initializer_range = initializer_range
-        self.has_lora_adapter = has_lora_adapter
-        self.downsample_rate = downsample_rate
-        self.window_size = window_size
-        self.is_training = is_training
-
-        # Dims for audio features
-        self.sequence_dim = 844
-        self.feature_dim = 160
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.hidden_size = text_config["hidden_size"]
-        self.batch_size = 3
-        self.pad_token_id = text_config["pad_token_id"]
-        self.seq_len = 7
-        self.num_audio_tokens = 2
-        self.seq_length = seq_length + self.num_audio_tokens
-
-    def get_config(self):
-        return GraniteSpeechConfig(
-            encoder_config=self.encoder_config,
-            text_config=self.text_config,
-            projector_config=self.projector_config,
-            audio_token_index=self.audio_token_index,
-            tie_word_embeddings=self.tie_word_embeddings,
-            initializer_range=self.initializer_range,
-            has_lora_adapter=self.has_lora_adapter,
-        )
+            "encoder_hidden_size": 32,
+        }
 
-    def prepare_config_and_inputs(self):
-        input_features = floats_tensor(
-            [self.batch_size, self.sequence_dim, self.feature_dim],
-        )
-        config = self.get_config()
-        return config, input_features
+        super().__init__(parent, **kwargs)
 
-    def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        config, input_features = config_and_inputs
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
-        input_ids[input_ids == config.audio_token_index] = self.pad_token_id
+    def create_audio_features(self):
+        # GraniteSpeech expects [B, seq_len, features] (time-first), unlike the standard [B, features, seq_len]
+        return floats_tensor([self.batch_size, self.feat_seq_length, self.num_mel_bins])
 
-        input_ids[:, : self.num_audio_tokens] = config.audio_token_index
+    def get_audio_embeds_mask(self, audio_mask):
+        # Projector: ceil(feat_seq_length / window_size) * (window_size // downsample_rate) tokens per sample.
+        import math
 
-        inputs_dict = {
-            "input_features": input_features,
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        return config, inputs_dict
+        config = self.get_config()
+        nblocks = math.ceil(self.feat_seq_length / config.window_size)
+        num_audio_tokens = nblocks * (config.window_size // config.downsample_rate)
+        return torch.ones([self.batch_size, num_audio_tokens], dtype=torch.long).to(torch_device)
+
+    def create_attention_mask(self, input_ids):
+        return torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
 
     def create_and_check_granite_speech_model_fp16_forward(self, config, input_ids, input_features, attention_mask):
         model = GraniteSpeechForConditionalGeneration(config=config)
@@ -211,24 +117,13 @@ def create_and_check_granite_speech_model_fp16_autocast_forward(
 
 
 @require_torch
-class GraniteSpeechForConditionalGenerationModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class GraniteSpeechForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `GraniteSpeechForConditionalGeneration`.
     """
 
-    all_model_classes = (GraniteSpeechForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = GraniteSpeechModelTester
     pipeline_model_mapping = {"any-to-any": GraniteSpeechForConditionalGeneration} if is_torch_available() else {}
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = GraniteSpeechForConditionalGenerationModelTester(self)
-        self.config_tester = ConfigTester(
-            self,
-            config_class=GraniteSpeechConfig,
-            has_text_modality=False,
-        )
 
     @unittest.skip(
         reason="This test does not apply to GraniteSpeech since inputs_embeds corresponding to audio tokens are replaced when input features are provided."
@@ -237,7 +132,7 @@ def test_inputs_embeds_matches_input_ids(self):
         pass
 
     def test_inputs_embeds(self):
-        # overwrite inputs_embeds tests because we need to delete "input features" for the audio model
+        # Overwrite inputs_embeds tests because we need to delete "input_features" for the audio model
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
 
         for model_class in self.all_model_classes:
@@ -257,53 +152,12 @@ def test_inputs_embeds(self):
             with torch.no_grad():
                 model(**inputs)
 
-    def test_sdpa_can_dispatch_composite_models(self):
-        # overwrite because Granite Speech is audio+text model (not vision+text)
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            # NOTE - currently we only enable alternate attention implementations on
-            # the encapsulated LLM; in the future, this should be added for the conformer
-            # encoder as well.
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-
-                # `None` as it is the requested one which will be assigned to each sub-config
-                # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-
-                for name, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
     @pytest.mark.generate
     @slow
     @unittest.skip(reason="Granite Speech doesn't support SDPA for all backbones")
     def test_eager_matches_sdpa_generate(self):
         pass
 
-    @unittest.skip(reason="GraniteSpeech has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
 
 class GraniteSpeechForConditionalGenerationIntegrationTest(unittest.TestCase):
     def setUp(self):
diff --git a/tests/models/granite_speech_plus/__init__.py b/tests/models/granite_speech_plus/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/granite_speech_plus/test_modeling_granite_speech_plus.py b/tests/models/granite_speech_plus/test_modeling_granite_speech_plus.py
new file mode 100644
index 000000000000..1bd19a166567
--- /dev/null
+++ b/tests/models/granite_speech_plus/test_modeling_granite_speech_plus.py
@@ -0,0 +1,273 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the IBM Granite Speech Plus model."""
+
+import unittest
+
+from parameterized import parameterized
+
+from transformers import AutoProcessor, GraniteSpeechPlusConfig, GraniteSpeechPlusForConditionalGeneration
+from transformers.testing_utils import cleanup, require_torch, slow, torch_device
+from transformers.utils import ModelOutput, is_datasets_available, is_torch_available
+
+from ...test_configuration_common import ConfigTester
+from ..granite_speech.test_modeling_granite_speech import (
+    GraniteSpeechForConditionalGenerationModelTest as _GraniteSpeechModelTestBase,
+)
+from ..granite_speech.test_modeling_granite_speech import (
+    GraniteSpeechForConditionalGenerationModelTester as _GraniteSpeechModelTesterBase,
+)
+
+
+if is_torch_available():
+    import torch
+if is_datasets_available():
+    from datasets import load_dataset
+
+from transformers import set_seed
+
+
+class GraniteSpeechPlusForConditionalGenerationModelTester(_GraniteSpeechModelTesterBase):
+    """
+    Plus variant that exercises the ``encoder_hidden_layers`` concat path. The projector's
+    ``encoder_hidden_size`` is scaled to match ``encoder_config.hidden_dim * (len(encoder_hidden_layers) + 1)``.
+    """
+
+    def __init__(self, parent, encoder_hidden_layers=(0,), **kwargs):
+        projector_config = kwargs.pop(
+            "projector_config",
+            {
+                "attention_probs_dropout_prob": 0.1,
+                "cross_attention_frequency": 1,
+                "encoder_hidden_size": 64,  # 32 (hidden_dim) * (1 intermediate + 1 last) = 64
+                "hidden_act": "gelu",
+                "hidden_dropout_prob": 0.1,
+                "hidden_size": 32,
+                "initializer_range": 0.02,
+                "intermediate_size": 256,
+                "layer_norm_eps": 1e-12,
+                "max_position_embeddings": 2048,
+                "model_type": "blip_2_qformer",
+                "num_attention_heads": 4,
+                "num_hidden_layers": 2,
+                "use_qformer_text_input": False,
+                "vocab_size": 30522,
+            },
+        )
+        super().__init__(parent=parent, projector_config=projector_config, **kwargs)
+        self.encoder_hidden_layers = list(encoder_hidden_layers)
+        self.encoder_config["cat_hidden_layers"] = self.encoder_hidden_layers
+
+    def get_config(self):
+        return GraniteSpeechPlusConfig(
+            encoder_config=self.encoder_config,
+            text_config=self.text_config,
+            projector_config=self.projector_config,
+            audio_token_index=self.audio_token_index,
+            tie_word_embeddings=self.tie_word_embeddings,
+            initializer_range=self.initializer_range,
+            has_lora_adapter=self.has_lora_adapter,
+        )
+
+
+@require_torch
+class GraniteSpeechPlusForConditionalGenerationModelTest(_GraniteSpeechModelTestBase):
+    """
+    Model tester for `GraniteSpeechPlusForConditionalGeneration`.
+    """
+
+    all_model_classes = (GraniteSpeechPlusForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = {"any-to-any": GraniteSpeechPlusForConditionalGeneration} if is_torch_available() else {}
+
+    def setUp(self):
+        self.model_tester = GraniteSpeechPlusForConditionalGenerationModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=GraniteSpeechPlusConfig,
+            has_text_modality=False,
+        )
+
+    def test_encoder_hidden_layers_concat_shape(self):
+        """With ``encoder_hidden_layers`` set, get_audio_features concatenates the selected intermediate
+        hidden states with the final hidden state before the projector."""
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        model = GraniteSpeechPlusForConditionalGeneration(config).to(
+            self.model_tester.parent.device if hasattr(self.model_tester.parent, "device") else "cpu"
+        )
+        model.eval()
+        with torch.no_grad():
+            out = model.get_audio_features(inputs_dict["input_features"].to(next(model.parameters()).device))
+        self.assertEqual(out.pooler_output.shape[0], inputs_dict["input_features"].shape[0])
+
+    @parameterized.expand([True, False, None])
+    def test_get_audio_features_output(self, return_dict: bool | None):
+        for model_class in self.all_model_classes:
+            if not hasattr(model_class, "get_audio_features"):
+                continue
+
+            config, inputs_dict = self._audio_features_prepare_config_and_inputs()
+            if return_dict is not None:
+                config.return_dict = return_dict
+
+            model = model_class(config).eval()
+            model = model.to(torch_device)
+
+            set_seed(42)
+            with torch.no_grad():
+                outputs = model.get_audio_features(**inputs_dict)
+
+            if return_dict in (True, None):
+                self.assertTrue(
+                    isinstance(outputs, ModelOutput), "get_audio_features() must return a BaseModelOutputWithPooling"
+                )
+                self.assertTrue(
+                    hasattr(outputs, "last_hidden_state"),
+                    "get_audio_features() must return a BaseModelOutputWithPooling with last_hidden_state",
+                )
+                self.assertTrue(
+                    hasattr(outputs, "pooler_output"),
+                    "get_audio_features() must return a BaseModelOutputWithPooling with pooler_output",
+                )
+                self.assertTrue(
+                    hasattr(outputs, "hidden_states"),
+                    "get_audio_features() must return a BaseModelOutputWithPooling with hidden_states",
+                )
+                if self.has_attentions:
+                    self.assertTrue(
+                        hasattr(outputs, "attentions"),
+                        "get_audio_features() must return a BaseModelOutputWithPooling with attentions",
+                    )
+
+                if getattr(self, "skip_test_audio_features_output_shape", False):
+                    return
+
+                last_hidden_state_shape = outputs.last_hidden_state.shape
+
+                if "input_features" in inputs_dict:
+                    batch_size = inputs_dict["input_features"].shape[0]
+                else:
+                    batch_size = inputs_dict["input_values"].shape[0]
+                self.assertEqual(
+                    last_hidden_state_shape[0],
+                    batch_size,
+                    f"batch_size mismatch, full shape: {last_hidden_state_shape}",
+                )
+
+                audio_config = config.audio_config if hasattr(config, "audio_config") else config
+                hidden_size = None
+                if hasattr(audio_config, "projection_dim"):
+                    hidden_size = audio_config.projection_dim
+                elif hasattr(audio_config, "hidden_size"):
+                    hidden_size = audio_config.hidden_size
+                elif hasattr(audio_config, "encoder_config"):
+                    hidden_size = audio_config.encoder_config.hidden_dim * (
+                        len(audio_config.encoder_config.cat_hidden_layers) + 1
+                    )
+                elif hasattr(audio_config, "encoder_ffn_dim"):
+                    hidden_size = audio_config.encoder_ffn_dim
+                self.assertEqual(
+                    last_hidden_state_shape[-1],
+                    hidden_size,
+                    f"hidden_size mismatch, full shape: {last_hidden_state_shape}",
+                )
+
+            else:
+                self.assertIsInstance(outputs, tuple, "get_audio_features() must return a tuple if return_dict=False")
+
+
+class GraniteSpeechPlusForConditionalGenerationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_path = "ibm-granite/granite-speech-4.1-2b-plus"
+        self.processor = AutoProcessor.from_pretrained(self.model_path)
+        self.prompt = self._get_prompt(self.processor.tokenizer)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def _get_prompt(self, tokenizer):
+        chat = [
+            {
+                "role": "system",
+                "content": "Knowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant",
+            },
+            {
+                "role": "user",
+                "content": "<|audio|> can you transcribe the speech into a written format?",
+            },
+        ]
+        return tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+    def _load_datasamples(self, num_samples):
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        # automatic decoding with librispeech
+        speech_samples = ds.sort("id")[:num_samples]["audio"]
+
+        return [x["array"] for x in speech_samples]
+
+    @slow
+    def test_small_model_integration_test_single(self):
+        model = GraniteSpeechPlusForConditionalGeneration.from_pretrained(self.model_path).to(torch_device)
+        input_speech = self._load_datasamples(1)
+
+        # Verify feature sizes; note that the feature mask refers to the size of
+        # features that are masked into the LLM, not the output of the processor,
+        # which is why we inspect the mask instead of the `num_features` tensor.
+        inputs = self.processor(self.prompt, input_speech, return_tensors="pt").to(torch_device)
+
+        num_computed_features = self.processor.audio_processor._get_num_audio_features(
+            [speech_arr.shape[-1] for speech_arr in input_speech],
+        )[0]
+        num_actual_features = torch.sum(inputs["input_features_mask"]).item()
+        assert num_actual_features == num_computed_features
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=32)
+        EXPECTED_DECODED_TEXT = "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nuser can you transcribe the speech into a written format?\nassistantmister quiltor is the apostle of the middle classes and we are glad to welcome his gospel"  # fmt: skip
+
+        self.assertEqual(
+            self.processor.tokenizer.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = GraniteSpeechPlusForConditionalGeneration.from_pretrained(self.model_path).to(torch_device)
+        input_speech = self._load_datasamples(2)
+        prompts = [self.prompt, self.prompt]
+
+        # Verify feature sizes & padding
+        inputs = self.processor(prompts, input_speech, return_tensors="pt").to(model.device)
+        num_computed_features = self.processor.audio_processor._get_num_audio_features(
+            [speech_arr.shape[-1] for speech_arr in input_speech],
+        )
+        num_actual_features = torch.sum(inputs["input_features_mask"], dim=-1)
+        for e_feats, a_feats in zip(num_computed_features, num_actual_features):
+            assert e_feats == a_feats.item()
+
+        # verify generation
+        output = model.generate(**inputs, max_new_tokens=32)
+
+        EXPECTED_DECODED_TEXT = [
+            "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nuser can you transcribe the speech into a written format?\nassistantmister quiltor is the apostle of the middle classes and we are glad to welcome his gospel",
+            "systemKnowledge Cutoff Date: April 2024.\nToday's Date: December 19, 2024.\nYou are Granite, developed by IBM. You are a helpful AI assistant\nuser can you transcribe the speech into a written format?\nassistantnor is mister quilter's manner less interesting than his matter"
+        ]  # fmt: skip
+
+        self.assertEqual(
+            self.processor.tokenizer.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/models/granitemoe/test_modeling_granitemoe.py b/tests/models/granitemoe/test_modeling_granitemoe.py
index 35cdb6012bdb..42f0553004d1 100644
--- a/tests/models/granitemoe/test_modeling_granitemoe.py
+++ b/tests/models/granitemoe/test_modeling_granitemoe.py
@@ -34,6 +34,7 @@
 
     from transformers import (
         GraniteMoeForCausalLM,
+        GraniteMoeForSequenceClassification,
         GraniteMoeModel,
     )
 
@@ -139,6 +140,16 @@ def create_and_check_model(
         result = model(input_ids)
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = GraniteMoeForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -160,6 +171,7 @@ class GraniteMoeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
         (
             GraniteMoeModel,
             GraniteMoeForCausalLM,
+            GraniteMoeForSequenceClassification,
         )
         if is_torch_available()
         else ()
@@ -168,6 +180,7 @@ class GraniteMoeModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.Test
         {
             "feature-extraction": GraniteMoeModel,
             "text-generation": GraniteMoeForCausalLM,
+            "text-classification": GraniteMoeForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -188,6 +201,10 @@ def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
 
 @require_torch_accelerator
 class GraniteMoeIntegrationTest(unittest.TestCase):
diff --git a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
index 9969e1bcc550..386e652d03e9 100644
--- a/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
+++ b/tests/models/granitemoehybrid/test_modeling_granitemoehybrid.py
@@ -39,14 +39,18 @@
 from ...generation.test_utils import GenerationTesterMixin
 from ...models.bamba.test_modeling_bamba import BambaModelTester
 from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin
+from ...test_modeling_common import ModelTesterMixin, ids_tensor
 from ...test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
     import torch
 
-    from transformers import GraniteMoeHybridForCausalLM, GraniteMoeHybridModel
+    from transformers import (
+        GraniteMoeHybridForCausalLM,
+        GraniteMoeHybridForSequenceClassification,
+        GraniteMoeHybridModel,
+    )
 
 
 class GraniteMoeHybridModelTester(BambaModelTester):
@@ -61,11 +65,13 @@ def __init__(
         use_cache=False,
         shared_intermediate_size=174,
         layer_types=None,
+        type_sequence_label_size=2,
     ):
         super().__init__(parent)
         self.shared_intermediate_size = shared_intermediate_size
         self.layer_types = layer_types
         self.use_cache = use_cache
+        self.type_sequence_label_size = type_sequence_label_size
 
     def _update_layer_configs(self):
         super()._update_layer_configs()
@@ -80,6 +86,30 @@ def get_config(self):
             layer_types=self.layer_types,
         )
 
+    def prepare_config_and_inputs_for_sequence_classification(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = torch.tril(torch.ones_like(input_ids).to(torch_device))
+
+        sequence_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        self._update_layer_configs()
+        config = self.get_config()
+
+        return config, input_ids, input_mask, sequence_labels
+
+    def create_and_check_for_sequence_classification(self, config, input_ids, input_mask, sequence_labels):
+        config.num_labels = self.num_labels
+        model = GraniteMoeHybridForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
 
 @require_torch
 class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
@@ -88,6 +118,7 @@ class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
         (
             GraniteMoeHybridModel,
             GraniteMoeHybridForCausalLM,
+            GraniteMoeHybridForSequenceClassification,
         )
         if is_torch_available()
         else ()
@@ -96,6 +127,7 @@ class GraniteMoeHybridModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
         {
             "feature-extraction": GraniteMoeHybridModel,
             "text-generation": GraniteMoeHybridForCausalLM,
+            "text-classification": GraniteMoeHybridForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -120,6 +152,10 @@ def test_for_causal_lm(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_sequence_classification()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
     def test_decoder_model_past_with_large_inputs(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
diff --git a/tests/models/granitemoeshared/test_modeling_granitemoeshared.py b/tests/models/granitemoeshared/test_modeling_granitemoeshared.py
index 8feed0e7db9f..e00f47bca5ff 100644
--- a/tests/models/granitemoeshared/test_modeling_granitemoeshared.py
+++ b/tests/models/granitemoeshared/test_modeling_granitemoeshared.py
@@ -34,6 +34,7 @@
 
     from transformers import (
         GraniteMoeSharedForCausalLM,
+        GraniteMoeSharedForSequenceClassification,
         GraniteMoeSharedModel,
     )
 
@@ -142,6 +143,16 @@ def create_and_check_model(
         result = model(input_ids)
         self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        config.num_labels = self.num_labels
+        model = GraniteMoeSharedForSequenceClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=sequence_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -163,6 +174,7 @@ class GraniteMoeSharedModelTest(ModelTesterMixin, GenerationTesterMixin, unittes
         (
             GraniteMoeSharedModel,
             GraniteMoeSharedForCausalLM,
+            GraniteMoeSharedForSequenceClassification,
         )
         if is_torch_available()
         else ()
@@ -171,6 +183,7 @@ class GraniteMoeSharedModelTest(ModelTesterMixin, GenerationTesterMixin, unittes
         {
             "feature-extraction": GraniteMoeSharedModel,
             "text-generation": GraniteMoeSharedForCausalLM,
+            "text-classification": GraniteMoeSharedForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -191,6 +204,10 @@ def test_model(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*config_and_inputs)
 
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
 
 @require_torch_accelerator
 class GraniteMoeSharedIntegrationTest(unittest.TestCase):
diff --git a/tests/models/internvl/test_modeling_internvl.py b/tests/models/internvl/test_modeling_internvl.py
index c9b8d06ba9fa..190f2f02a99e 100644
--- a/tests/models/internvl/test_modeling_internvl.py
+++ b/tests/models/internvl/test_modeling_internvl.py
@@ -214,9 +214,6 @@ def test_sdpa_can_compile_dynamic(self):
     def test_flash_attn_2_fp32_ln(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @slow
 @require_torch_accelerator
diff --git a/tests/models/janus/test_processing_janus.py b/tests/models/janus/test_processing_janus.py
index 9d30dd847b2d..671064125c45 100644
--- a/tests/models/janus/test_processing_janus.py
+++ b/tests/models/janus/test_processing_janus.py
@@ -90,7 +90,12 @@ def test_chat_template_single(self):
                     "role": "USER",
                     "content": [
                         {"type": "text", "text": "What is shown in this image?"},
-                        {"type": "image"},
+                        {
+                            "type": "image",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                            ),
+                        },
                     ],
                 },
             ]
@@ -108,19 +113,6 @@ def test_chat_template_single(self):
         prompts and, following the implementation from the Janus codebase, expanding the image token.
         """
 
-        # Checking the output dict keys
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
-
-        # Now test the ability to return dict
-        messages[0][0]["content"][1].update(
-            {
-                "type": "image",
-                "url": url_to_local_path(
-                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
-                ),
-            }
-        )
         out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
         self.assertTrue(self.images_input_name in out_dict)
         # should always have input_ids and attention_mask
@@ -223,7 +215,12 @@ def test_chat_template_batched(self):
                     "role": "user",
                     "content": [
                         {"type": "text", "text": "What is shown in this image?"},
-                        {"type": "image"},
+                        {
+                            "type": "image",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                            ),
+                        },
                     ],
                 },
             ],
@@ -232,7 +229,10 @@ def test_chat_template_batched(self):
                     "role": "user",
                     "content": [
                         {"type": "text", "text": "What is shown in this image?"},
-                        {"type": "image"},
+                        {
+                            "type": "image",
+                            "url": url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"),
+                        },
                     ],
                 },
             ],
@@ -247,29 +247,6 @@ def test_chat_template_batched(self):
         self.assertEqual(formatted_prompts, correct_prompts)
 
         # Similarly to the single case, no test for chat template+tokenization as two separate steps versus as a single step
-
-        # Checking the output dict keys
-        out_dict = processor.apply_chat_template(
-            batched_messages,
-            add_generation_prompt=True,
-            tokenize=True,
-            return_dict=True,
-            padding=True,
-        )
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
-
-        # Verify image inputs are included in the output dict
-        batched_messages[0][0]["content"][1].update(
-            {
-                "type": "image",
-                "url": url_to_local_path(
-                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
-                ),
-            }
-        )
-        batched_messages[1][0]["content"][1].update(
-            {"type": "image", "url": url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg")}
-        )
         out_dict = processor.apply_chat_template(
             batched_messages, add_generation_prompt=True, tokenize=True, return_dict=True, padding=True
         )
diff --git a/tests/models/kimi2_6/__init__.py b/tests/models/kimi2_6/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/kimi2_6/test_image_processing_kimi2_6.py b/tests/models/kimi2_6/test_image_processing_kimi2_6.py
new file mode 100644
index 000000000000..952893747c46
--- /dev/null
+++ b/tests/models/kimi2_6/test_image_processing_kimi2_6.py
@@ -0,0 +1,362 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import io
+import itertools
+import json
+import tempfile
+import unittest
+
+import httpx
+import numpy as np
+
+from transformers.image_utils import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
+from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs, prepare_video_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Kimi26ImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        num_frames=10,
+        min_resolution=56,
+        max_resolution=1024,
+        min_pixels=56 * 56,
+        max_pixels=28 * 28 * 1280,
+        do_normalize=True,
+        image_mean=OPENAI_CLIP_MEAN,
+        image_std=OPENAI_CLIP_STD,
+        do_resize=True,
+        patch_size=14,
+        temporal_patch_size=2,
+        merge_size=2,
+        do_convert_rgb=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.num_channels = num_channels
+        self.num_frames = num_frames
+        self.image_mean = OPENAI_CLIP_MEAN
+        self.image_std = OPENAI_CLIP_STD
+        self.min_pixels = min_pixels
+        self.max_pixels = max_pixels
+        self.patch_size = patch_size
+        self.temporal_patch_size = temporal_patch_size
+        self.merge_size = merge_size
+        self.do_resize = do_resize
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "min_pixels": self.min_pixels,
+            "max_pixels": self.max_pixels,
+            "patch_size": self.patch_size,
+            "temporal_patch_size": self.temporal_patch_size,
+            "merge_size": self.merge_size,
+        }
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        images = prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+        return [[image] for image in images]
+
+    def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_video_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            num_frames=self.num_frames,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class Kimi26ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Kimi26ImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+            self.assertTrue(hasattr(image_processing, "patch_size"))
+            self.assertTrue(hasattr(image_processing, "temporal_patch_size"))
+            self.assertTrue(hasattr(image_processing, "merge_size"))
+
+    def test_image_processor_to_json_string(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            obj = json.loads(image_processor.to_json_string())
+            for key, value in self.image_processor_dict.items():
+                if key not in ["min_pixels", "max_pixels"]:
+                    self.assertEqual(obj[key], value)
+
+    def test_select_best_resolution(self):
+        # Test with a final resize resolution
+        best_resolution = smart_resize(561, 278, factor=28)
+        self.assertEqual(best_resolution, (560, 280))
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            for image in image_inputs:
+                self.assertIsInstance(image[0], Image.Image)
+
+            # Test not batched input
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (4900, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (34300, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image[0], np.ndarray)
+
+            # Test not batched input
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (4900, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (34300, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processing_classes.values():
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image[0], torch.Tensor)
+
+            # Test not batched input
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (4900, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (34300, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    @unittest.skip(reason="Kimi26ImageProcessor doesn't treat 4 channel PIL and numpy consistently yet")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_nested_input(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+
+            # Test batched as a list of images
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (34300, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched as a nested list of images, where each sublist is one batch
+            image_inputs_nested = image_inputs[:3] + image_inputs[3:]
+            process_out = image_processing(image_inputs_nested, return_tensors="pt")
+            encoded_images_nested = process_out.pixel_values
+            image_grid_thws_nested = process_out.image_grid_thw
+            expected_output_image_shape = (34300, 1176)
+            expected_image_grid_thws = torch.Tensor([[1, 70, 70]] * 7)
+            self.assertEqual(tuple(encoded_images_nested.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Image processor should return same pixel values, independently of ipnut format
+            self.assertTrue((encoded_images_nested == encoded_images).all())
+            self.assertTrue((image_grid_thws_nested == expected_image_grid_thws).all())
+
+    def test_custom_image_size(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                image_processing.save_pretrained(tmpdirname)
+                image_processor_loaded = image_processing_class.from_pretrained(
+                    tmpdirname, max_pixels=56 * 56, min_pixels=28 * 28
+                )
+
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            process_out = image_processor_loaded(image_inputs, return_tensors="pt")
+            expected_output_video_shape = [112, 1176]
+            self.assertListEqual(list(process_out.pixel_values.shape), expected_output_video_shape)
+
+    def test_custom_pixels(self):
+        pixel_choices = frozenset(itertools.product((100, 150, 200, 20000), (100, 150, 200, 20000)))
+        for image_processing_class in self.image_processing_classes.values():
+            image_processor_dict = self.image_processor_dict.copy()
+            for a_pixels, b_pixels in pixel_choices:
+                image_processor_dict["min_pixels"] = min(a_pixels, b_pixels)
+                image_processor_dict["max_pixels"] = max(a_pixels, b_pixels)
+                image_processor = image_processing_class(**image_processor_dict)
+                image_inputs = self.image_processor_tester.prepare_image_inputs()
+                # Just checking that it doesn't raise an error
+                image_processor(image_inputs, return_tensors="pt")
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        dummy_image = Image.open(
+            io.BytesIO(
+                httpx.get("http://images.cocodataset.org/val2017/000000039769.jpg", follow_redirects=True).content
+            )
+        )
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_image, return_tensors="pt")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding.pixel_values, encodings[backend_name].pixel_values)
+            self.assertEqual(reference_encoding.image_grid_thw.dtype, encodings[backend_name].image_grid_thw.dtype)
+            self._assert_tensors_equivalence(
+                reference_encoding.image_grid_thw.float(), encodings[backend_name].image_grid_thw.float()
+            )
+
+    @require_vision
+    @require_torch
+    def test_backends_equivalence_batched(self):
+        if len(self.image_processing_classes) < 2:
+            self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
+
+        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
+            self.skipTest(
+                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
+            )
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+
+        # Create processors for each backend
+        encodings = {}
+        for backend_name, image_processing_class in self.image_processing_classes.items():
+            image_processor = image_processing_class(**self.image_processor_dict)
+            encodings[backend_name] = image_processor(dummy_images, return_tensors="pt")
+
+        # Compare all backends to the first one (reference backend)
+        backend_names = list(encodings.keys())
+        reference_backend = backend_names[0]
+        reference_encoding = encodings[reference_backend]
+        for backend_name in backend_names[1:]:
+            self._assert_tensors_equivalence(reference_encoding.pixel_values, encodings[backend_name].pixel_values)
+            self.assertEqual(reference_encoding.image_grid_thw.dtype, encodings[backend_name].image_grid_thw.dtype)
+            self._assert_tensors_equivalence(
+                reference_encoding.image_grid_thw.float(), encodings[backend_name].image_grid_thw.float()
+            )
+
+    def test_get_num_patches_without_images(self):
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**self.image_processor_dict)
+            num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={})
+            self.assertEqual(num_patches, 64)
+
+            num_patches = image_processing.get_number_of_image_patches(height=200, width=50, images_kwargs={})
+            self.assertEqual(num_patches, 56)
+
+            num_patches = image_processing.get_number_of_image_patches(
+                height=100, width=100, images_kwargs={"patch_size": 28}
+            )
+            self.assertEqual(num_patches, 16)
diff --git a/tests/models/kimi2_6/test_modeling_kimi2_6.py b/tests/models/kimi2_6/test_modeling_kimi2_6.py
new file mode 100644
index 000000000000..c6814bd36ed0
--- /dev/null
+++ b/tests/models/kimi2_6/test_modeling_kimi2_6.py
@@ -0,0 +1,272 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch Kimi2.6 model."""
+
+import gc
+import unittest
+
+import requests
+
+from transformers import (
+    AutoProcessor,
+    DeepseekV3Config,
+    Kimi2_6Config,
+    Kimi2_6VisionConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    backend_empty_cache,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_modeling_common import (
+    floats_tensor,
+)
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import Kimi2_6ForConditionalGeneration, Kimi2_6Model
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Kimi2_6VisionText2TextModelTester(VLMModelTester):
+    base_model_class = Kimi2_6Model
+    config_class = Kimi2_6Config
+    text_config_class = DeepseekV3Config
+    vision_config_class = Kimi2_6VisionConfig
+    conditional_generation_class = Kimi2_6ForConditionalGeneration
+
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("image_token_id", 3)
+        kwargs.setdefault("video_token_id", 4)
+        kwargs.setdefault("image_size", 32)
+        kwargs.setdefault("patch_size", 8)
+        kwargs.setdefault("num_image_tokens", 16)
+        kwargs.setdefault("hidden_act", "silu")
+        kwargs.setdefault("head_dim", 8)
+        kwargs.setdefault("num_heads", 4)
+        kwargs.setdefault("pos_emb_height", 4)
+        kwargs.setdefault("merge_kernel_size", (1, 1))
+        kwargs.setdefault("pos_emb_width", 4)
+        kwargs.setdefault("pos_emb_time", 1)
+        kwargs.setdefault("kv_lora_rank", 16)
+        kwargs.setdefault("q_lora_rank", 32)
+        kwargs.setdefault("qk_rope_head_dim", 16)
+        kwargs.setdefault("v_head_dim", 32)
+        kwargs.setdefault("qk_nope_head_dim", 32)
+        kwargs.setdefault(
+            "rope_parameters",
+            {
+                "rope_type": "default",
+                "rope_theta": 10000,
+            },
+        )
+        super().__init__(parent, **kwargs)
+
+        # These can be inferred from existing properties and don't get separate kwargs
+        self.projection_hidden_size = self.hidden_size
+
+    def create_pixel_values(self):
+        return floats_tensor(
+            [
+                self.batch_size * (self.image_size**2) // (self.patch_size**2),
+                self.num_channels,
+                self.patch_size,
+                self.patch_size,
+            ]
+        )
+
+    def place_image_tokens(self, input_ids, config):
+        # Place image tokens with vision_start_token_id prefix
+        input_ids = input_ids.clone()
+        # Clear any accidental special tokens first
+        input_ids[:, -1] = self.pad_token_id
+        input_ids[input_ids == self.video_token_id] = self.pad_token_id
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        # Place image tokens with vision_start_token_id prefix
+        input_ids[:, : self.num_image_tokens] = self.image_token_id
+        return input_ids
+
+    def get_additional_inputs(self, config, input_ids, pixel_values):
+        return {
+            "image_grid_thw": torch.tensor([[1, 4, 4]] * self.batch_size, device=torch_device),
+        }
+
+
+@require_torch
+class Kimi2_6ModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = Kimi2_6VisionText2TextModelTester
+
+    # Kimi has images shaped as (bs*patch_len, dim) so we can't slice to batches in generate
+    def prepare_config_and_inputs_for_generate(self, batch_size=2):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # We don't want a few model inputs in our model input dictionary for generation tests
+        input_keys_to_ignore = [
+            "decoder_input_ids",
+            "decoder_attention_mask",
+            "use_cache",
+            "labels",
+        ]
+
+        # The diff from the general `prepare_config_and_inputs_for_generate` lies here
+        patch_size = config.vision_config.patch_size
+        filtered_image_length = batch_size * (self.model_tester.image_size**2) // (patch_size**2)
+        filtered_inputs_dict = {
+            k: v[:batch_size, ...] if isinstance(v, torch.Tensor) else v
+            for k, v in inputs_dict.items()
+            if k not in input_keys_to_ignore
+        }
+        filtered_inputs_dict["pixel_values"] = inputs_dict["pixel_values"][:filtered_image_length]
+
+        # It is important set `eos_token_id` to `None` to avoid early stopping (would break for length-based checks)
+        text_gen_config = config.get_text_config(decoder=True)
+        if text_gen_config.eos_token_id is not None and text_gen_config.pad_token_id is None:
+            text_gen_config.pad_token_id = (
+                text_gen_config.eos_token_id
+                if isinstance(text_gen_config.eos_token_id, int)
+                else text_gen_config.eos_token_id[0]
+            )
+        text_gen_config.eos_token_id = None
+        text_gen_config.forced_eos_token_id = None
+
+        return config, filtered_inputs_dict
+
+
+@require_torch
+class Kimi26IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("todo")
+        self.messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What kind of dog is this?"},
+                ],
+            }
+        ]
+        url = "https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/demo_small.jpg"
+        self.image = Image.open(requests.get(url, stream=True).raw)
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    @slow
+    def test_small_model_integration_test(self):
+        model = Kimi2_6ForConditionalGeneration.from_pretrained("todo", dtype="auto", device_map="auto")
+
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text], images=[self.image], return_tensors="pt")
+
+        expected_input_ids = [151644, 8948, 198, 2610, 525, 264, 10950, 17847, 13, 151645, 198, 151644, 872, 198, 151652, 151655, 151655]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:17]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [0.8792, 0.8792, 0.9084],
+                [1.1858, 1.1858, 1.2296],
+                [1.2004, 1.2004, 1.2150],
+                [1.4340, 1.4340, 1.4194],
+                [1.3902, 1.4048, 1.4194],
+                [1.5216, 1.5362, 1.5362],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:6, :3], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30)
+        EXPECTED_DECODED_TEXT = "system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices"
+
+        self.assertEqual(
+            self.processor.decode(output[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = Kimi2_6ForConditionalGeneration.from_pretrained("todo", dtype="auto", device_map="auto")
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text], images=[self.image, self.image], return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_expand(self):
+        model = Kimi2_6ForConditionalGeneration.from_pretrained("todo", dtype="auto", device_map="auto")
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text], images=[self.image], return_tensors="pt").to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=30, num_return_sequences=3)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @slow
+    def test_small_model_integration_test_batch_wo_image(self):
+        model = Kimi2_6ForConditionalGeneration.from_pretrained("todo", dtype="auto", device_map="auto")
+        text = self.processor.apply_chat_template(self.messages, tokenize=False, add_generation_prompt=True)
+        messages2 = [
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": "Who are you?"},
+        ]
+        text2 = self.processor.apply_chat_template(messages2, tokenize=False, add_generation_prompt=True)
+        inputs = self.processor(text=[text, text2], images=[self.image], padding=True, return_tensors="pt").to(
+            torch_device
+        )
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+
+        EXPECTED_DECODED_TEXT = [
+            'system\nYou are a helpful assistant.\nuser\nWhat kind of dog is this?\nassistant\nThe dog in the picture appears to be a Labrador Retriever. Labradors are known for their friendly and intelligent nature, making them popular choices',
+            'system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\nI am a large language model created by Alibaba Cloud. I am called Qwen.'
+        ]  # fmt: skip
+        self.assertEqual(
+            self.processor.batch_decode(output, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
diff --git a/tests/models/kimi2_6/test_processing_kimi2_6.py b/tests/models/kimi2_6/test_processing_kimi2_6.py
new file mode 100644
index 000000000000..544b0cf8814d
--- /dev/null
+++ b/tests/models/kimi2_6/test_processing_kimi2_6.py
@@ -0,0 +1,317 @@
+# Copyright 2026 the HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import unittest
+
+import numpy as np
+
+from transformers.testing_utils import require_av, require_torch, require_torchvision, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
+
+
+if is_vision_available():
+    from transformers import Kimi26Processor
+
+    if is_torchvision_available():
+        pass
+
+if is_torch_available():
+    import torch
+
+
+@require_vision
+@require_torch
+@require_torchvision
+class Kimi26ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Kimi26Processor
+    model_id = "Qwen/Qwen2-VL-7B-Instruct"
+
+    @classmethod
+    def _setup_from_pretrained(cls, model_id, **kwargs):
+        return super()._setup_from_pretrained(model_id, patch_size=4, max_pixels=56 * 56, min_pixels=28 * 28, **kwargs)
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    def test_get_num_vision_tokens(self):
+        "Tests general functionality of the helper used internally in vLLM"
+
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)
+
+    @require_torch
+    @require_av
+    def _test_apply_chat_template(
+        self,
+        modality: str,
+        batch_size: int,
+        return_tensors: str,
+        input_name: str,
+        processor_name: str,
+        input_data: list[str],
+    ):
+        processor = self.get_processor()
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        if processor_name not in self.processor_class.get_attributes():
+            self.skipTest(f"{processor_name} attribute not present in {self.processor_class}")
+
+        batch_messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [{"type": "text", "text": "Describe this."}],
+                },
+            ]
+        ] * batch_size
+
+        # Test that jinja can be applied
+        formatted_prompt = processor.apply_chat_template(batch_messages, add_generation_prompt=True, tokenize=False)
+        self.assertEqual(len(formatted_prompt), batch_size)
+
+        # Test that tokenizing with template and directly with `self.tokenizer` gives same output
+        formatted_prompt_tokenized = processor.apply_chat_template(
+            batch_messages, add_generation_prompt=True, tokenize=True, return_tensors=return_tensors
+        )
+        add_special_tokens = True
+        if processor.tokenizer.bos_token is not None and formatted_prompt[0].startswith(processor.tokenizer.bos_token):
+            add_special_tokens = False
+        tok_output = processor.tokenizer(
+            formatted_prompt, return_tensors=return_tensors, add_special_tokens=add_special_tokens
+        )
+        expected_output = tok_output.input_ids
+        self.assertListEqual(expected_output.tolist(), formatted_prompt_tokenized.tolist())
+
+        # Test that kwargs passed to processor's `__call__` are actually used
+        tokenized_prompt_100 = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            padding="max_length",
+            truncation=True,
+            return_tensors=return_tensors,
+            max_length=100,
+        )
+        self.assertEqual(len(tokenized_prompt_100[0]), 100)
+
+        # Test that `return_dict=True` returns text related inputs in the dict
+        out_dict_text = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors=return_tensors,
+        )
+        self.assertTrue(all(key in out_dict_text for key in ["input_ids", "attention_mask"]))
+        self.assertEqual(len(out_dict_text["input_ids"]), batch_size)
+        self.assertEqual(len(out_dict_text["attention_mask"]), batch_size)
+
+        # Test that with modality URLs and `return_dict=True`, we get modality inputs in the dict
+        for idx, url in enumerate(input_data[:batch_size]):
+            batch_messages[idx][0]["content"] = [batch_messages[idx][0]["content"][0], {"type": modality, "url": url}]
+
+        out_dict = processor.apply_chat_template(
+            batch_messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors=return_tensors,
+            num_frames=2,  # by default no more than 2 frames, otherwise too slow
+        )
+        input_name = getattr(self, input_name)
+        self.assertTrue(input_name in out_dict)
+        self.assertEqual(len(out_dict["input_ids"]), batch_size)
+        self.assertEqual(len(out_dict["attention_mask"]), batch_size)
+        if modality == "video":
+            # qwen pixels don't scale with bs same way as other models, calculate expected video token count based on video_grid_thw
+            expected_video_token_count = 0
+            for thw in out_dict["video_grid_thw"]:
+                expected_video_token_count += thw[0] * thw[1] * thw[2]
+            mm_len = expected_video_token_count
+        else:
+            mm_len = batch_size * 192
+        self.assertEqual(len(out_dict[input_name]), mm_len)
+
+        return_tensor_to_type = {"pt": torch.Tensor, "np": np.ndarray, None: list}
+        for k in out_dict:
+            self.assertIsInstance(out_dict[k], return_tensor_to_type[return_tensors])
+
+    @require_av
+    def test_apply_chat_template_video_frame_sampling(self):
+        processor = self.get_processor()
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        signature = inspect.signature(processor.__call__)
+        if "videos" not in {*signature.parameters.keys()} or (
+            signature.parameters.get("videos") is not None
+            and signature.parameters["videos"].annotation == inspect._empty
+        ):
+            self.skipTest("Processor doesn't accept videos at input")
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "video"},
+                        {"type": "text", "text": "What is shown in this video?"},
+                    ],
+                },
+            ]
+        ]
+
+        formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+        self.assertEqual(len(formatted_prompt), 1)
+
+        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
+        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
+        self.assertListEqual(expected_output, formatted_prompt_tokenized)
+
+        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
+        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
+
+        # Add video URL for return dict and load with `num_frames` arg
+        messages[0][0]["content"][0] = {
+            "type": "video",
+            "url": url_to_local_path(
+                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+            ),
+        }
+        num_frames = 3
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            num_frames=num_frames,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 360)
+
+        # Load with `fps` arg
+        fps = 1
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            fps=fps,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 360)
+
+        # Load with `fps` and `num_frames` args, should raise an error
+        with self.assertRaises(ValueError):
+            out_dict_with_video = processor.apply_chat_template(
+                messages,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                fps=fps,
+                num_frames=num_frames,
+            )
+
+        # Load without any arg should load the whole video
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 1080)
+
+        # Load video as a list of frames (i.e. images). NOTE: each frame should have same size
+        # because we assume they come from one video
+        messages[0][0]["content"][0] = {
+            "type": "video",
+            "url": [
+                url_to_local_path(
+                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                ),
+                url_to_local_path(
+                    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
+                ),
+            ],
+        }
+        out_dict_with_video = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+        )
+        self.assertTrue(self.videos_input_name in out_dict_with_video)
+        self.assertEqual(len(out_dict_with_video[self.videos_input_name]), 160)
+
+        # When the inputs are frame URLs/paths we expect that those are already
+        # sampled and will raise an error is asked to sample again.
+        with self.assertRaisesRegex(
+            ValueError, "Sampling frames from a list of images is not supported! Set `do_sample_frames=False`"
+        ):
+            out_dict_with_video = processor.apply_chat_template(
+                messages,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                do_sample_frames=True,
+            )
+
+    def test_kwargs_overrides_custom_image_processor_kwargs(self):
+        processor = self.get_processor()
+        self.skip_processor_without_typed_kwargs(processor)
+
+        input_str = self.prepare_text_inputs()
+        image_input = self.prepare_image_inputs()
+        inputs = processor(text=input_str, images=image_input, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 100)
+        inputs = processor(text=input_str, images=image_input, max_pixels=56 * 56 * 4, return_tensors="pt")
+        self.assertEqual(inputs[self.images_input_name].shape[0], 612)
+
+    def test_special_mm_token_truncation(self):
+        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""
+
+        processor = self.get_processor()
+
+        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
+        image_input = self.prepare_image_inputs(batch_size=2)
+
+        _ = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            truncation=None,
+            padding=True,
+        )
+
+        with self.assertRaises(ValueError):
+            _ = processor(
+                text=input_str,
+                images=image_input,
+                return_tensors="pt",
+                truncation=True,
+                padding=True,
+                max_length=20,
+            )
diff --git a/tests/models/kosmos2/test_modeling_kosmos2.py b/tests/models/kosmos2/test_modeling_kosmos2.py
index 339fce4176b5..8439eb5a3091 100644
--- a/tests/models/kosmos2/test_modeling_kosmos2.py
+++ b/tests/models/kosmos2/test_modeling_kosmos2.py
@@ -771,15 +771,14 @@ def test_inference_interpolate_pos_encoding(self):
         with torch.no_grad():
             outputs = model(**inputs, interpolate_pos_encoding=True)
 
-        # verify the logits
-        expected_shape = torch.Size((1, 145, 1024))
+        # (PR 37743 makes `kosmos2` not returning anymore `vision_model_output`)
+        # verify `image_embeds`
+        expected_shape = torch.Size((1, 64, 2048))
 
-        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)
+        self.assertEqual(outputs.image_embeds.shape, expected_shape)
 
         expected_slice = torch.tensor(
-            [[0.9148, -1.4148, 3.8040], [3.3443, 1.9478, 0.2080], [1.6604, 2.8184, -0.3618]]
+            [[0.1154, -0.1370, -0.2142], [-0.0703, 0.1632, -0.0770], [0.0269, -0.0356, -0.1243]]
         ).to(torch_device)
 
-        torch.testing.assert_close(
-            outputs.vision_model_output.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-2, atol=1e-2
-        )
+        torch.testing.assert_close(outputs.image_embeds[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)
diff --git a/tests/models/lasr/test_modeling_lasr.py b/tests/models/lasr/test_modeling_lasr.py
index 36060eecac3b..d212730676f9 100644
--- a/tests/models/lasr/test_modeling_lasr.py
+++ b/tests/models/lasr/test_modeling_lasr.py
@@ -245,6 +245,7 @@ def test_ctc_loss_inference(self):
 @require_torch
 class LasrForCTCModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (LasrForCTC,) if is_torch_available() else ()
+    all_generative_model_classes = ()  # LasrForCTC has a custom genereate method
     pipeline_model_mapping = (
         {
             "feature-extraction": LasrEncoder,
diff --git a/tests/models/llava/test_modeling_llava.py b/tests/models/llava/test_modeling_llava.py
index 4e50c56eb55b..b0bcf5afbbbd 100644
--- a/tests/models/llava/test_modeling_llava.py
+++ b/tests/models/llava/test_modeling_llava.py
@@ -282,9 +282,6 @@ def test_training_gradient_checkpointing_use_reentrant_true(self):
     def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 @slow
diff --git a/tests/models/llava_next/test_modeling_llava_next.py b/tests/models/llava_next/test_modeling_llava_next.py
index a5bd146fcc6d..60d50830ab74 100644
--- a/tests/models/llava_next/test_modeling_llava_next.py
+++ b/tests/models/llava_next/test_modeling_llava_next.py
@@ -84,7 +84,7 @@ def create_pixel_values(self):
             ]
         )
 
-    def get_additional_inputs(self, config, input_ids, pixel_values):
+    def get_additional_inputs(self, config, input_ids, modality_inputs):
         """LlavaNext requires image_sizes tensor"""
         return {
             "image_sizes": torch.tensor([[self.image_size, self.image_size]] * self.batch_size),
@@ -125,9 +125,6 @@ def test_training_gradient_checkpointing_use_reentrant_true(self):
     def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class LlavaNextForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/llava_next_video/test_modeling_llava_next_video.py b/tests/models/llava_next_video/test_modeling_llava_next_video.py
index 33f3efa69a64..f506d9685bb1 100644
--- a/tests/models/llava_next_video/test_modeling_llava_next_video.py
+++ b/tests/models/llava_next_video/test_modeling_llava_next_video.py
@@ -340,9 +340,6 @@ def _video_features_prepare_config_and_inputs(self):
         inputs_dict = {"pixel_values": inputs_dict["pixel_values_videos"]}
         return config, inputs_dict
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class LlavaNextVideoForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/llava_onevision/test_modeling_llava_onevision.py b/tests/models/llava_onevision/test_modeling_llava_onevision.py
index 6de0193f03a9..bf955f6e0816 100644
--- a/tests/models/llava_onevision/test_modeling_llava_onevision.py
+++ b/tests/models/llava_onevision/test_modeling_llava_onevision.py
@@ -312,9 +312,6 @@ def _video_features_prepare_config_and_inputs(self):
         inputs_dict = {"pixel_values": pixel_values_videos}
         return config, inputs_dict
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class LlavaOnevisionForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/llava_onevision/test_processing_llava_onevision.py b/tests/models/llava_onevision/test_processing_llava_onevision.py
index 86aee0c486e6..2ec0ab1062ca 100644
--- a/tests/models/llava_onevision/test_processing_llava_onevision.py
+++ b/tests/models/llava_onevision/test_processing_llava_onevision.py
@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import json
-import os
 import unittest
 
 import torch
@@ -21,13 +20,11 @@
 from transformers.testing_utils import require_torch, require_vision
 from transformers.utils import is_vision_available
 
-from ...test_processing_common import ProcessorTesterMixin
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
 
 
 if is_vision_available():
-    from transformers import (
-        LlavaOnevisionProcessor,
-    )
+    from transformers import LlavaOnevisionProcessor
 
 
 @require_vision
@@ -35,39 +32,6 @@
 class LlavaOnevisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     processor_class = LlavaOnevisionProcessor
 
-    @classmethod
-    def setUpClass(cls):
-        # Ensure local assets are used instead of remote URLs to avoid network access in tests
-        from tests.test_processing_common import MODALITY_INPUT_DATA
-
-        repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "..", "..", ".."))
-        local_image = os.path.join(repo_root, "coco_sample.png")
-        if not os.path.isfile(local_image):
-            import numpy as np
-            from PIL import Image
-
-            Image.fromarray((np.random.rand(64, 64, 3) * 255).astype("uint8")).save(local_image)
-
-        local_tiny_video = os.path.join(repo_root, "tiny_video.mp4")
-        if not os.path.isfile(local_tiny_video):
-            try:
-                import torchvision
-
-                frames = (torch.rand(8, 64, 64, 3) * 255).byte()
-                torchvision.io.write_video(local_tiny_video, frames, fps=4)
-            except Exception:
-                local_tiny_video = None
-
-        local_videos = [
-            os.path.join(repo_root, "Big_Buck_Bunny_720_10s_10MB.mp4"),
-            os.path.join(repo_root, "sample_demo_1.mp4"),
-        ]
-        cls.local_tiny_video = local_tiny_video
-        MODALITY_INPUT_DATA["images"] = [local_image, local_image]
-        MODALITY_INPUT_DATA["videos"] = local_videos
-
-        super().setUpClass()
-
     @classmethod
     def _setup_tokenizer(cls):
         tokenizer_class = cls._get_component_class_from_processor("tokenizer")
@@ -165,9 +129,6 @@ def test_image_token_filling(self):
     def test_apply_chat_template_video_frame_sampling(self):
         processor = self.get_processor()
 
-        if self.local_tiny_video is None:
-            self.skipTest("Local tiny video unavailable for sampling test")
-
         messages = [
             [
                 {
@@ -175,7 +136,9 @@ def test_apply_chat_template_video_frame_sampling(self):
                     "content": [
                         {
                             "type": "video",
-                            "url": self.local_tiny_video,
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                            ),
                         },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
diff --git a/tests/models/minicpm3/__init__.py b/tests/models/minicpm3/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/minicpm3/test_modeling_minicpm3.py b/tests/models/minicpm3/test_modeling_minicpm3.py
new file mode 100644
index 000000000000..a273d2ba93b3
--- /dev/null
+++ b/tests/models/minicpm3/test_modeling_minicpm3.py
@@ -0,0 +1,136 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch MiniCPM3 model."""
+
+import unittest
+
+from transformers import Cache, is_torch_available
+from transformers.testing_utils import require_torch, require_torch_accelerator, slow, torch_device
+
+from ...causal_lm_tester import CausalLMModelTest, CausalLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import MiniCPM3ForCausalLM, MiniCPM3Model
+    from transformers.models.minicpm3.modeling_minicpm3 import MiniCPM3RotaryEmbedding
+
+
+class MiniCPM3ModelTester(CausalLMModelTester):
+    if is_torch_available():
+        base_model_class = MiniCPM3Model
+
+    def __init__(
+        self,
+        parent,
+        kv_lora_rank=32,
+        q_lora_rank=16,
+        qk_nope_head_dim=64,
+        qk_rope_head_dim=64,
+        v_head_dim=128,
+        scale_emb=1,
+        scale_depth=1.4,
+        dim_model_base=256,
+    ):
+        super().__init__(parent=parent)
+        self.kv_lora_rank = kv_lora_rank
+        self.q_lora_rank = q_lora_rank
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.scale_emb = scale_emb
+        self.scale_depth = scale_depth
+        self.dim_model_base = dim_model_base
+
+
+@require_torch
+class MiniCPM3ModelTest(CausalLMModelTest, unittest.TestCase):
+    test_all_params_have_gradient = False
+    model_tester_class = MiniCPM3ModelTester
+    model_split_percents = [0.5, 0.7, 0.8]
+
+    _torch_compile_train_cls = MiniCPM3ForCausalLM if is_torch_available() else None
+
+    @unittest.skip("MiniCPM3 uses MLA attention which is incompatible with this test")
+    def test_sdpa_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
+        self.assertIsInstance(past_key_values, Cache)
+
+        expected_common_shape = (
+            batch_size,
+            getattr(config, "num_key_value_heads", config.num_attention_heads),
+            seq_length,
+        )
+        expected_key_shape = expected_common_shape + (config.qk_nope_head_dim + config.qk_rope_head_dim,)
+        expected_value_shape = expected_common_shape + (config.v_head_dim,)
+
+        for layer in past_key_values.layers:
+            self.assertEqual(layer.keys.shape, expected_key_shape)
+            self.assertEqual(layer.values.shape, expected_value_shape)
+
+    def test_model_rope_scaling_frequencies(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        scaling_factor = 10
+        short_input_length = 10
+        long_input_length = int(config.max_position_embeddings * 1.5)
+
+        x = torch.randn(1, dtype=torch.float32, device=torch_device)
+        position_ids_short = torch.arange(short_input_length, dtype=torch.long, device=torch_device).unsqueeze(0)
+        position_ids_long = torch.arange(long_input_length, dtype=torch.long, device=torch_device).unsqueeze(0)
+
+        original_rope = MiniCPM3RotaryEmbedding(config=config).to(torch_device)
+        original_freqs_cis_short = original_rope(x, position_ids_short)
+        original_freqs_cis_long = original_rope(x, position_ids_long)
+        torch.testing.assert_close(original_freqs_cis_short, original_freqs_cis_long[:, :short_input_length, :])
+
+        config.rope_parameters = {"rope_type": "linear", "rope_theta": 10000.0, "factor": scaling_factor}
+        linear_scaling_rope = MiniCPM3RotaryEmbedding(config=config).to(torch_device)
+        linear_freqs_cis_short = linear_scaling_rope(x, position_ids_short)
+        linear_freqs_cis_long = linear_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(linear_freqs_cis_short, linear_freqs_cis_long[:, :short_input_length, :])
+
+        config.rope_parameters = {"rope_type": "dynamic", "rope_theta": 10000.0, "factor": scaling_factor}
+        ntk_scaling_rope = MiniCPM3RotaryEmbedding(config=config).to(torch_device)
+        ntk_freqs_cis_short = ntk_scaling_rope(x, position_ids_short)
+        ntk_freqs_cis_long = ntk_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(ntk_freqs_cis_short, original_freqs_cis_short)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(ntk_freqs_cis_long, original_freqs_cis_long)
+        self.assertTrue((ntk_scaling_rope.inv_freq <= original_rope.inv_freq).all())
+
+        config.rope_parameters = {"rope_type": "yarn", "rope_theta": 10000.0, "factor": scaling_factor}
+        yarn_scaling_rope = MiniCPM3RotaryEmbedding(config=config).to(torch_device)
+        yarn_freqs_cis_short = yarn_scaling_rope(x, position_ids_short)
+        yarn_freqs_cis_long = yarn_scaling_rope(x, position_ids_long)
+        torch.testing.assert_close(yarn_freqs_cis_short, yarn_freqs_cis_long[:, :short_input_length, :])
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_freqs_cis_short, original_freqs_cis_short)
+        with self.assertRaises(AssertionError):
+            torch.testing.assert_close(yarn_freqs_cis_long, original_freqs_cis_long)
+
+    def test_tp_plan_matches_params(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        if config.q_lora_rank is not None:
+            config.base_model_tp_plan.pop("layers.*.self_attn.q_proj")
+        super().test_tp_plan_matches_params()
+        config.base_model_tp_plan.update({"layers.*.self_attn.q_proj": "colwise"})
+
+
+@slow
+@require_torch_accelerator
+class MiniCPM3IntegrationTest(unittest.TestCase):
+    pass
diff --git a/tests/models/mistral3/test_modeling_mistral3.py b/tests/models/mistral3/test_modeling_mistral3.py
index f33b3e0e1f8a..da6b21733c20 100644
--- a/tests/models/mistral3/test_modeling_mistral3.py
+++ b/tests/models/mistral3/test_modeling_mistral3.py
@@ -230,9 +230,6 @@ def test_sdpa_can_dispatch_on_flash(self):
     def test_flex_attention_with_grads(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @slow
 @require_torch_accelerator
diff --git a/tests/models/mistral4/test_modeling_mistral4.py b/tests/models/mistral4/test_modeling_mistral4.py
index 449e13461264..8651591ac5d5 100644
--- a/tests/models/mistral4/test_modeling_mistral4.py
+++ b/tests/models/mistral4/test_modeling_mistral4.py
@@ -18,7 +18,7 @@
 
 import pytest
 
-from transformers import AutoTokenizer, Mistral3ForConditionalGeneration, is_torch_available
+from transformers import AutoTokenizer, Cache, Mistral3ForConditionalGeneration, is_torch_available
 from transformers.testing_utils import (
     Expectations,
     backend_empty_cache,
@@ -44,17 +44,49 @@
 
 
 class Mistral4ModelTester(CausalLMModelTester):
+    hidden_act = "silu"
+    q_lora_rank = 8
+    kv_lora_rank = 8
+    qk_rope_head_dim = 8
+    qk_nope_head_dim = 8
+    v_head_dim = 8
+    n_routed_experts = 8
+    n_group = 2
+    topk_group = 1
+
     if is_torch_available():
         base_model_class = Mistral4Model
 
 
 @require_torch
-@unittest.skip("Causing a lot of failures on CI")
 class Mistral4ModelTest(CausalLMModelTest, unittest.TestCase):
     _is_stateful = True
     model_split_percents = [0.5, 0.6]
     model_tester_class = Mistral4ModelTester
 
+    def _check_past_key_values_for_generate(self, batch_size, past_key_values, seq_length, config):
+        # generic test expects:
+        # keys   -> (batch, kv_heads, seq_len, head_dim)
+        # values -> (batch, kv_heads, seq_len, head_dim)
+        #
+        # but Mistral4 actually stores:
+        # keys   -> (batch, kv_heads, seq_len, qk_nope_head_dim + qk_rope_head_dim)
+        # values -> (batch, kv_heads, seq_len, v_head_dim)
+        # so we override the shape check to assert the real cache format instead of failing on a wrong expectation.
+        self.assertIsInstance(past_key_values, Cache)
+
+        expected_common_shape = (
+            batch_size,
+            getattr(config, "num_key_value_heads", config.num_attention_heads),
+            seq_length,
+        )
+        expected_key_shape = expected_common_shape + (config.qk_nope_head_dim + config.qk_rope_head_dim,)
+        expected_value_shape = expected_common_shape + (config.v_head_dim,)
+
+        for layer in past_key_values.layers:
+            self.assertEqual(layer.keys.shape, expected_key_shape)
+            self.assertEqual(layer.values.shape, expected_value_shape)
+
     # TODO (ydshieh): Check this. See https://app.circleci.com/pipelines/github/huggingface/transformers/79245/workflows/9490ef58-79c2-410d-8f51-e3495156cf9c/jobs/1012146
     def is_pipeline_test_to_skip(
         self,
diff --git a/tests/models/mixtral/test_modeling_mixtral.py b/tests/models/mixtral/test_modeling_mixtral.py
index 1b56c8c6e5a8..6db2f45a341e 100644
--- a/tests/models/mixtral/test_modeling_mixtral.py
+++ b/tests/models/mixtral/test_modeling_mixtral.py
@@ -89,6 +89,14 @@ def test_load_balancing_loss(self):
         self.assertEqual(result.router_logits[0].shape, (91, config.num_local_experts))
         torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
 
+        # Verify router_logits are raw logits, not softmax probabilities (regression test for double-softmax bug)
+        for layer_logits in result.router_logits:
+            row_sums = layer_logits.sum(dim=-1)
+            self.assertFalse(
+                torch.allclose(row_sums, torch.ones_like(row_sums), atol=1e-3),
+                "router_logits should be raw logits (row sums != 1.0), not softmax probabilities",
+            )
+
         # First, we make sure that adding padding tokens doesn't change the loss
         # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
         pad_length = input_ids.shape[1] * 4
diff --git a/tests/models/mllama/test_modeling_mllama.py b/tests/models/mllama/test_modeling_mllama.py
index dbcf88869deb..a898244254d0 100644
--- a/tests/models/mllama/test_modeling_mllama.py
+++ b/tests/models/mllama/test_modeling_mllama.py
@@ -452,9 +452,6 @@ def test_left_padding_compatibility(self):
             unpadded_custom_inputs=unpadded_custom_inputs, padded_custom_inputs=padded_custom_inputs
         )
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class MllamaForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/mobilellm/test_modeling_mobilellm.py b/tests/models/mobilellm/test_modeling_mobilellm.py
new file mode 100644
index 000000000000..d69b291d6e75
--- /dev/null
+++ b/tests/models/mobilellm/test_modeling_mobilellm.py
@@ -0,0 +1,346 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch MobileLLM model."""
+
+import unittest
+
+from transformers import AutoTokenizer, MobileLLMConfig, is_torch_available
+from transformers.testing_utils import require_torch, slow, torch_device
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        MobileLLMForCausalLM,
+        MobileLLMForQuestionAnswering,
+        MobileLLMForSequenceClassification,
+        MobileLLMForTokenClassification,
+        MobileLLMModel,
+    )
+
+
+class MobileLLMModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_token_type_ids=False,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        num_key_value_heads=2,
+        intermediate_size=64,
+        hidden_act="silu",
+        hidden_dropout_prob=0.1,
+        attention_probs_dropout_prob=0.1,
+        max_position_embeddings=512,
+        type_vocab_size=16,
+        type_sequence_label_size=2,
+        initializer_range=0.02,
+        num_labels=3,
+        num_choices=4,
+        pad_token_id=0,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_token_type_ids = use_token_type_ids
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.max_position_embeddings = max_position_embeddings
+        self.type_vocab_size = type_vocab_size
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.num_labels = num_labels
+        self.num_choices = num_choices
+        self.pad_token_id = pad_token_id
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        token_labels = None
+        if self.use_labels:
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask, token_labels
+
+    def get_config(self):
+        return MobileLLMConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+            pad_token_id=self.pad_token_id,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask, token_labels):
+        model = MobileLLMModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_model_as_decoder(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        token_labels,
+    ):
+        config.add_cross_attention = True
+        model = MobileLLMModel(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_causal_lm(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        token_labels,
+    ):
+        model = MobileLLMForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=input_mask, labels=token_labels)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_decoder_model_past_large_inputs(
+        self,
+        config,
+        input_ids,
+        input_mask,
+        token_labels,
+    ):
+        config.is_decoder = True
+        config.add_cross_attention = True
+        model = MobileLLMForCausalLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        # first forward pass
+        outputs = model(
+            input_ids,
+            attention_mask=input_mask,
+            use_cache=True,
+        )
+        past_key_values = outputs.past_key_values
+
+        # create hypothetical multiple next token and extent to next_input_ids
+        next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
+        next_mask = ids_tensor((self.batch_size, 3), vocab_size=2)
+
+        # append to next input_ids and
+        next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+        next_attention_mask = torch.cat([input_mask, next_mask], dim=-1)
+
+        output_from_no_past = model(
+            next_input_ids,
+            attention_mask=next_attention_mask,
+            output_hidden_states=True,
+        )
+        output_from_past = model(
+            next_tokens,
+            attention_mask=next_attention_mask,
+            past_key_values=past_key_values,
+            output_hidden_states=True,
+        )
+
+        # select random slice
+        random_slice_idx = ids_tensor((1,), output_from_past.hidden_states[-1].shape[-1]).item()
+        output_from_no_past_slice = output_from_no_past.hidden_states[-1][:, -3:, random_slice_idx].detach()
+        output_from_past_slice = output_from_past.hidden_states[-1][:, :, random_slice_idx].detach()
+
+        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
+
+        # test that outputs are equal for slice
+        self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-3))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        (
+            config,
+            input_ids,
+            input_mask,
+            token_labels,
+        ) = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class MobileLLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            MobileLLMModel,
+            MobileLLMForCausalLM,
+            MobileLLMForSequenceClassification,
+            MobileLLMForQuestionAnswering,
+            MobileLLMForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (MobileLLMForCausalLM,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": MobileLLMModel,
+            "text-classification": MobileLLMForSequenceClassification,
+            "text-generation": MobileLLMForCausalLM,
+            "question-answering": MobileLLMForQuestionAnswering,
+            "token-classification": MobileLLMForTokenClassification,
+            "zero-shot": MobileLLMForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_headmasking = False
+    test_pruning = False
+    fx_compatible = False
+
+    def setUp(self):
+        self.model_tester = MobileLLMModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=MobileLLMConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_various_embeddings(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        for type in ["absolute", "relative_key", "relative_key_query"]:
+            config_and_inputs[0].position_embedding_type = type
+            self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_MobileLLM_sequence_classification_model(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = MobileLLMForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    def test_MobileLLM_sequence_classification_model_for_single_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "single_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor([self.model_tester.batch_size], self.model_tester.type_sequence_label_size)
+        model = MobileLLMForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    def test_MobileLLM_sequence_classification_model_for_multi_label(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.num_labels = 3
+        config.problem_type = "multi_label_classification"
+        input_ids = input_dict["input_ids"]
+        attention_mask = input_ids.ne(1).to(torch_device)
+        sequence_labels = ids_tensor(
+            [self.model_tester.batch_size, config.num_labels], self.model_tester.type_sequence_label_size
+        ).to(torch.float)
+        model = MobileLLMForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+        result = model(input_ids, attention_mask=attention_mask, labels=sequence_labels)
+        self.assertEqual(result.logits.shape, (self.model_tester.batch_size, self.model_tester.num_labels))
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "facebook/MobileLLM-125M"
+        model = MobileLLMForCausalLM.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_torch
+class MobileLLMModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_model_125m_logits(self):
+        input_ids = [1, 306, 4658, 278, 6593, 310, 2834, 338]
+        model = MobileLLMForCausalLM.from_pretrained("facebook/MobileLLM-125M", device_map="auto")
+
+        input_ids = torch.tensor([input_ids])
+
+        with torch.no_grad():
+            out = model(input_ids).logits
+
+        # Verify shape
+        self.assertEqual(out.shape, (1, 8, model.config.vocab_size))
+
+        # Note: actual expected values would need to be computed from a real model run
+        # This is a placeholder structure
+
+    @slow
+    def test_model_125m_generation(self):
+        prompt = "Hello, my name is"
+        tokenizer = AutoTokenizer.from_pretrained("facebook/MobileLLM-125M", use_fast=False)
+        input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+        model = MobileLLMForCausalLM.from_pretrained("facebook/MobileLLM-125M", device_map="auto")
+
+        # greedy generation outputs
+        generated_ids = model.generate(input_ids, max_new_tokens=20, temperature=0)
+        text = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+        # Note: actual expected text would need to be verified with real model
+        # This is a placeholder structure
+        self.assertIsNotNone(text)
diff --git a/tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py b/tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py
index 6788a8fbd9f5..30d7e5d69d41 100644
--- a/tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py
+++ b/tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py
@@ -187,7 +187,7 @@ def check_hidden_states_output(inputs_dict, config, model_class):
 
             hidden_states = outputs.hidden_states
 
-            expected_num_stages = 26
+            expected_num_stages = 28
             self.assertEqual(len(hidden_states), expected_num_stages)
 
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
diff --git a/tests/models/molmo2/__init__.py b/tests/models/molmo2/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/molmo2/test_image_processing_molmo2.py b/tests/models/molmo2/test_image_processing_molmo2.py
new file mode 100644
index 000000000000..a3181b1bce10
--- /dev/null
+++ b/tests/models/molmo2/test_image_processing_molmo2.py
@@ -0,0 +1,185 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_vision_available() and is_torchvision_available():
+    from PIL import Image
+
+    from transformers import Molmo2ImageProcessor
+
+
+class Molmo2ImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=378,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=IMAGENET_STANDARD_MEAN,
+        image_std=IMAGENET_STANDARD_STD,
+        do_convert_rgb=True,
+        max_crops=8,
+        overlap_margins=[4, 4],
+        patch_size=14,
+        pooling_size=[2, 2],
+    ):
+        size = size if size is not None else {"height": 378, "width": 378}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+        self.max_crops = max_crops
+        self.overlap_margins = overlap_margins
+        self.patch_size = patch_size
+        self.pooling_size = pooling_size
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+            "max_crops": self.max_crops,
+            "overlap_margins": self.overlap_margins,
+            "patch_size": self.patch_size,
+            "pooling_size": self.pooling_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.num_channels, self.size["height"], self.size["width"]
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+@require_torchvision
+class Molmo2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = Molmo2ImageProcessor if (is_vision_available() and is_torchvision_available()) else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Molmo2ImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        image_processor = self.image_processing_class(**self.image_processor_dict)
+        self.assertTrue(hasattr(image_processor, "do_resize"))
+        self.assertTrue(hasattr(image_processor, "size"))
+        self.assertTrue(hasattr(image_processor, "do_normalize"))
+        self.assertTrue(hasattr(image_processor, "image_mean"))
+        self.assertTrue(hasattr(image_processor, "image_std"))
+        self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+        self.assertTrue(hasattr(image_processor, "max_crops"))
+        self.assertTrue(hasattr(image_processor, "overlap_margins"))
+        self.assertTrue(hasattr(image_processor, "patch_size"))
+        self.assertTrue(hasattr(image_processor, "pooling_size"))
+
+    def test_image_processor_from_dict_with_kwargs(self):
+        image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
+        self.assertEqual(image_processor.size, {"height": 378, "width": 378})
+        self.assertEqual(image_processor.do_normalize, True)
+
+        image_processor = self.image_processing_class.from_dict(
+            self.image_processor_dict, size={"height": 400, "width": 400}, do_normalize=False
+        )
+        self.assertEqual(image_processor.size, {"height": 400, "width": 400})
+        self.assertEqual(image_processor.do_normalize, False)
+
+    def _assert_patchified_output(self, outputs, expected_num_images):
+        pixel_values = outputs.pixel_values
+        self.assertEqual(pixel_values.ndim, 3)
+        pixels_per_patch = self.image_processor_tester.patch_size**2 * self.image_processor_tester.num_channels
+        self.assertEqual(pixel_values.shape[-1], pixels_per_patch)
+        image_num_crops = outputs.image_num_crops
+        self.assertEqual(image_num_crops.shape[0], expected_num_images)
+        self.assertEqual(pixel_values.shape[0], int(image_num_crops.sum().item()))
+
+    def test_call_pil(self):
+        for image_processing_class in [self.image_processing_class]:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            outputs = image_processing(image_inputs[0], return_tensors="pt")
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = image_processing(image_inputs, return_tensors="pt")
+            self._assert_patchified_output(outputs, self.image_processor_tester.batch_size)
+
+    def test_call_numpy(self):
+        for image_processing_class in [self.image_processing_class]:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            outputs = image_processing(image_inputs[0], return_tensors="pt")
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = image_processing(image_inputs, return_tensors="pt")
+            self._assert_patchified_output(outputs, self.image_processor_tester.batch_size)
+
+    @unittest.skip(
+        reason="Molmo2ImageProcessor expects channels-last (HWC) numpy input; CHW torch tensors are not supported."
+    )
+    def test_call_pytorch(self):
+        pass
+
+    @unittest.skip(
+        reason="Molmo2ImageProcessor always converts to RGB before processing; 4-channel images are not supported."
+    )
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_new_models_require_fast_image_processor(self):
+        self.skipTest("Molmo2 does not provide a fast image processor yet.")
diff --git a/tests/models/molmo2/test_modeling_molmo2.py b/tests/models/molmo2/test_modeling_molmo2.py
new file mode 100644
index 000000000000..614c29c7d8af
--- /dev/null
+++ b/tests/models/molmo2/test_modeling_molmo2.py
@@ -0,0 +1,854 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch Molmo2 model."""
+
+import copy
+import unittest
+
+import requests
+
+from transformers import (
+    Molmo2Config,
+    Molmo2ForConditionalGeneration,
+    Molmo2Model,
+    Molmo2Processor,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.models.molmo2.configuration_molmo2 import (
+    Molmo2AdapterConfig,
+    Molmo2TextConfig,
+    Molmo2VitConfig,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+    ids_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+
+class Molmo2VisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=7,
+        num_channels=3,
+        ignore_index=-100,
+        image_size=378,
+        text_config={
+            "bos_token_id": 0,
+            "eos_token_id": 1,
+            "pad_token_id": 2,
+            "hidden_act": "silu",
+            "head_dim": 128,
+            "hidden_size": 32,
+            "vocab_size": 99,
+            "intermediate_size": 37,
+            "max_position_embeddings": 512,
+            "model_type": "molmo2_text",
+            "num_attention_heads": 4,
+            "num_hidden_layers": 2,
+            "num_key_value_heads": 2,
+            "rope_theta": 10000.0,
+            "tie_word_embeddings": False,
+            "use_qk_norm": False,
+            "layer_norm_eps": 1e-6,
+        },
+        vit_config={
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 4,
+            "head_dim": 8,
+            "hidden_act": "gelu_pytorch_tanh",
+            "layer_norm_eps": 1e-6,
+            "image_default_input_size": [378, 378],
+            "image_patch_size": 14,
+            "image_num_pos": 729,
+            "attention_dropout": 0.0,
+            "residual_dropout": 0.0,
+        },
+        adapter_config={
+            "vit_layers": [-1],
+            "pooling_attention_mask": False,
+            "hidden_size": 32,
+            "num_attention_heads": 4,
+            "num_key_value_heads": 4,
+            "head_dim": 8,
+            "intermediate_size": 37,
+            "text_hidden_size": 32,
+            "hidden_act": "silu",
+        },
+        image_start_token_id=3,
+        image_end_token_id=4,
+        image_patch_id=5,
+        image_col_id=6,
+        tie_word_embeddings=False,
+        is_training=True,
+    ):
+        self.parent = parent
+        self.ignore_index = ignore_index
+        self.is_training = is_training
+
+        self.vit_config = vit_config
+        self.adapter_config = adapter_config
+        self.text_config = text_config
+
+        self.vocab_size = text_config["vocab_size"]
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.head_dim = text_config["head_dim"]
+        self.hidden_size = text_config["hidden_size"]
+        self.intermediate_size = text_config["intermediate_size"]
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.num_key_value_heads = text_config["num_key_value_heads"]
+        self.rope_theta = text_config["rope_theta"]
+        self.hidden_act = text_config["hidden_act"]
+        self.max_position_embeddings = text_config["max_position_embeddings"]
+        self.model_type = text_config["model_type"]
+
+        self.image_start_token_id = image_start_token_id
+        self.image_end_token_id = image_end_token_id
+        self.image_patch_id = image_patch_id
+        self.image_col_id = image_col_id
+        self.tie_word_embeddings = tie_word_embeddings
+
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.num_image_tokens = 32
+        self.seq_length = seq_length + self.num_image_tokens
+
+    def get_config(self):
+        return Molmo2Config(
+            text_config=Molmo2TextConfig(**self.text_config),
+            vit_config=Molmo2VitConfig(**self.vit_config),
+            adapter_config=Molmo2AdapterConfig(**self.adapter_config),
+            image_start_token_id=self.image_start_token_id,
+            image_end_token_id=self.image_end_token_id,
+            image_patch_id=self.image_patch_id,
+            image_col_id=self.image_col_id,
+            tie_word_embeddings=self.tie_word_embeddings,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.vit_config.image_patch_size
+        num_patches = (self.image_size // patch_size) ** 2
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                1,  # num_crops
+                num_patches,
+                patch_size * patch_size * self.num_channels,
+            ]
+        )
+        image_token_pooling = torch.randint(
+            -1, num_patches, (self.batch_size, self.num_image_tokens, 4), device=torch_device
+        )
+        image_grids = torch.tensor([[4, 4, 4, 4]] * self.batch_size, device=torch_device)
+
+        return config, pixel_values, image_token_pooling, image_grids
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, image_token_pooling, image_grids = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+
+        input_ids[:, -1] = self.pad_token_id
+        input_ids[input_ids == self.image_patch_id] = self.pad_token_id
+        input_ids[:, : self.num_image_tokens] = self.image_patch_id
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_token_pooling": image_token_pooling,
+            "image_grids": image_grids,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Molmo2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Model tester for `Molmo2ForConditionalGeneration`.
+    """
+
+    all_model_classes = (
+        (
+            Molmo2Model,
+            Molmo2ForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (Molmo2ForConditionalGeneration,) if is_torch_available() else ()
+    # Molmo2TextModel is a text-only sub-component, not a standalone composite model
+    pipeline_model_mapping = (
+        {
+            "image-to-text": Molmo2ForConditionalGeneration,
+            "image-text-to-text": Molmo2ForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    test_torchscript = False
+    test_pruning = False
+    test_head_masking = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = Molmo2VisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Molmo2Config, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        config, inputs_dict = config_and_inputs
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                _ = model(**inputs_dict)
+
+    # overwrite inputs_embeds tests because we need to delete "pixel_values" for VLMs
+    def test_inputs_embeds(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["image_token_pooling"]
+            del inputs["image_grids"]
+
+            wte = model.get_input_embeddings()
+            inputs["inputs_embeds"] = wte(input_ids)
+
+            with torch.no_grad():
+                model(**inputs)
+
+    # overwrite inputs_embeds tests because we need to delete "pixel_values" for VLMs
+    def test_inputs_embeds_matches_input_ids(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["image_token_pooling"]
+            del inputs["image_grids"]
+
+            inputs_embeds = model.get_input_embeddings()(input_ids)
+
+            with torch.no_grad():
+                out_ids = model(input_ids=input_ids, **inputs)[0]
+                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
+            self.assertTrue(torch.allclose(out_embeds, out_ids))
+
+    @unittest.skip(
+        reason="This architecture does not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture does not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(
+        reason="This architecture does not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
+    )
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="VLMs have dynamic control flow in preparing inputs for generation")
+    def test_generate_compile_1_end_to_end(self):
+        pass
+
+    @unittest.skip(reason="Cannot unpad inputs for all modalities so easily")
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(reason="Molmo2 weights are not tied.")
+    def test_tied_weights_keys(self):
+        pass
+
+    @unittest.skip(reason="Molmo2 uses a custom Molmo2Embedding class instead of nn.Embedding")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Molmo2 uses a custom Molmo2Embedding class that does not support standard resize")
+    def test_resize_tokens_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Molmo2 uses a custom Molmo2Embedding class that does not support standard resize")
+    def test_resize_embeddings_untied(self):
+        pass
+
+    @unittest.skip(
+        reason="Molmo2 interleaves visual features in text hidden states, causing shape mismatches in equivalence checks"
+    )
+    def test_model_outputs_equivalence(self, **kwargs):
+        pass
+
+    @unittest.skip(
+        reason="Supported only for text-only inputs (otherwise dynamic control flows for multimodal inputs)"
+    )
+    def test_generate_compile_model_forward(self):
+        pass
+
+    def test_initialization(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        configs_no_init = _config_zero_init(config)
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            for name, param in model.named_parameters():
+                if param.requires_grad and "class_embedding" not in name:
+                    self.assertIn(
+                        ((param.data.mean() * 1e9).round() / 1e9).item(),
+                        [0.0, 1.0],
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+                if "class_embedding" in name:
+                    self.assertTrue(
+                        -1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
+                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
+                    )
+
+    def test_mismatching_num_image_tokens(self):
+        """
+        Tests that VLMs handle single-batch image inputs correctly.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            _ = model(**input_dict)  # successful forward with no modifications
+            curr_input_dict = copy.deepcopy(input_dict)
+
+            # Reduce to single batch item (all inputs sliced consistently)
+            curr_input_dict["input_ids"] = curr_input_dict["input_ids"][:1, ...]
+            curr_input_dict["attention_mask"] = curr_input_dict["attention_mask"][:1, ...]
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][:1, ...]
+            curr_input_dict["image_token_pooling"] = curr_input_dict["image_token_pooling"][:1, ...]
+            curr_input_dict["image_grids"] = curr_input_dict["image_grids"][:1, ...]
+            _ = model(**curr_input_dict)
+
+    # Image features get cached in KV cache like other VLMs; no need to skip.
+
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.output_hidden_states = True
+        config.output_attentions = True
+
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            outputs = model(**inputs_dict)
+
+            output = outputs[0]
+
+            # Encoder-/Decoder-only models
+            hidden_states = outputs.hidden_states[0]
+            attentions = outputs.attentions[0]
+
+            hidden_states.retain_grad()
+            attentions.retain_grad()
+
+            output.flatten()[0].backward(retain_graph=True)
+
+            self.assertIsNotNone(hidden_states.grad)
+            self.assertIsNotNone(attentions.grad)
+
+
+@slow
+@require_torch
+@require_vision
+class Molmo2IntegrationTest(unittest.TestCase):
+    model_id = "allenai/Molmo2-4B"
+    image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+
+    def setUp(self):
+        self.processor = Molmo2Processor.from_pretrained(self.model_id)
+        self.image = Image.open(requests.get(self.image_url, stream=True).raw)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_preprocessing(self):
+        """Test that preprocessing produces expected shapes and values."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        # Check output keys
+        self.assertIn("input_ids", inputs)
+        self.assertIn("pixel_values", inputs)
+        self.assertIn("image_token_pooling", inputs)
+        self.assertIn("image_grids", inputs)
+        self.assertIn("image_num_crops", inputs)
+        self.assertIn("token_type_ids", inputs)
+
+        # Check shapes
+        self.assertEqual(inputs["pixel_values"].shape, torch.Size([7, 729, 588]))
+        self.assertEqual(inputs["image_token_pooling"].shape, torch.Size([955, 4]))
+        self.assertEqual(inputs["image_grids"].shape, torch.Size([1, 4]))
+        self.assertEqual(inputs["input_ids"].shape[0], 1)
+        self.assertEqual(inputs["input_ids"].shape[1], 987)
+
+        # Check pixel_values slice (preprocessing correctness)
+        expected_pixel_slice = torch.tensor(
+            [
+                [-0.0745098, -0.05098039, 0.0196079],
+                [-0.7019608, -0.6784314, -0.60784316],
+                [-0.8745098, -0.88235295, -0.84313726],
+            ],
+            dtype=torch.float32,
+        )
+        torch.testing.assert_close(
+            inputs["pixel_values"][0, :3, :3],
+            expected_pixel_slice,
+            atol=1e-4,
+            rtol=1e-4,
+        )
+
+        # Check input_ids: BOS token, then image start token, then image patches, ending with text tokens
+        input_ids = inputs["input_ids"][0]
+        self.assertEqual(input_ids[0].item(), 151645)  # BOS token
+        self.assertEqual(input_ids[1].item(), 151940)  # low_res_image_start token
+        # Last tokens should be the text "Describe this image."
+        EXPECTED_TAIL_IDS = [151939, 151937, 74785, 419, 2168, 13]  # <im_end> <im_start> Describe this image.
+        self.assertEqual(input_ids[-6:].tolist(), EXPECTED_TAIL_IDS)
+
+    def test_forward_logits(self):
+        """Test that forward pass produces expected logits."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            outputs = model(**device_inputs)
+
+        logits = outputs.logits
+
+        # Check logits shape: [batch=1, seq_len=987, vocab_size=151936]
+        self.assertEqual(logits.shape[0], 1)
+        self.assertEqual(logits.shape[1], 987)
+
+        # Check logits at last position (first 10 vocab tokens)
+        expected_last_logits = torch.tensor(
+            [
+                -10.781937,
+                -10.9183,
+                -10.77226,
+                -10.607452,
+                -11.623884,
+                -14.052853,
+                -11.137567,
+                -9.903504,
+                -9.405103,
+                -13.061548,
+            ],
+            dtype=torch.float32,
+        )
+        torch.testing.assert_close(
+            logits[0, -1, :10].cpu().float(),
+            expected_last_logits,
+            atol=1e-2,
+            rtol=1e-2,
+        )
+
+        # Check argmax at last position
+        self.assertEqual(logits[0, -1].argmax().item(), 11379)
+
+    def test_generation(self):
+        """Test that generation produces non-empty output."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=20)
+
+        # Generated sequence should be longer than input
+        self.assertGreater(generated_ids.shape[1], device_inputs["input_ids"].shape[1])
+
+        # Decode and check non-empty
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        self.assertGreater(len(generated_text.strip()), 0)
+
+
+@slow
+@require_torch
+@require_vision
+class Molmo2O7BIntegrationTest(unittest.TestCase):
+    model_id = "allenai/Molmo2-O-7B"
+    image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+
+    def setUp(self):
+        self.processor = Molmo2Processor.from_pretrained(self.model_id)
+        self.image = Image.open(requests.get(self.image_url, stream=True).raw)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_preprocessing(self):
+        """Test that preprocessing produces expected shapes and values for Molmo2-O-7B."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        # Same image produces same pixel_values regardless of model variant
+        self.assertEqual(inputs["pixel_values"].shape, torch.Size([7, 729, 588]))
+        self.assertEqual(inputs["input_ids"].shape[1], 987)
+
+        # Molmo2-O-7B uses a different tokenizer (OLMo-based, vocab_size ~100k)
+        EXPECTED_TAIL_IDS = [100281, 100279, 75885, 420, 2217, 13]
+        self.assertEqual(inputs["input_ids"][0, -6:].tolist(), EXPECTED_TAIL_IDS)
+
+    def test_forward_logits(self):
+        """Test forward pass logits for Molmo2-O-7B."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            outputs = model(**device_inputs)
+
+        logits = outputs.logits
+
+        # Molmo2-O-7B has vocab_size=100278
+        self.assertEqual(logits.shape[0], 1)
+        self.assertEqual(logits.shape[1], 987)
+
+        expected_last_logits = torch.tensor(
+            [
+                -18.260553,
+                -19.018972,
+                -18.696802,
+                -18.284496,
+                -16.284964,
+                -19.856026,
+                -19.706102,
+                -20.052923,
+                -17.303316,
+                -21.92196,
+            ],
+            dtype=torch.float32,
+        )
+        torch.testing.assert_close(
+            logits[0, -1, :10].cpu().float(),
+            expected_last_logits,
+            atol=1e-2,
+            rtol=1e-2,
+        )
+
+        self.assertEqual(logits[0, -1].argmax().item(), 578)
+
+    def test_generation(self):
+        """Test generation for Molmo2-O-7B."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=20, do_sample=False)
+
+        self.assertGreater(generated_ids.shape[1], device_inputs["input_ids"].shape[1])
+
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        self.assertGreater(len(generated_text.strip()), 0)
+
+
+@slow
+@require_torch
+@require_vision
+class Molmo2_8BIntegrationTest(unittest.TestCase):
+    model_id = "allenai/Molmo2-8B"
+    image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+
+    def setUp(self):
+        self.processor = Molmo2Processor.from_pretrained(self.model_id)
+        self.image = Image.open(requests.get(self.image_url, stream=True).raw)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_preprocessing(self):
+        """Test that preprocessing produces expected shapes and values for Molmo2-8B."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        self.assertEqual(inputs["pixel_values"].shape, torch.Size([7, 729, 588]))
+        self.assertEqual(inputs["input_ids"].shape[1], 987)
+
+        # Molmo2-8B uses the same tokenizer as Molmo2-4B (Qwen-based, vocab_size ~152k)
+        EXPECTED_TAIL_IDS = [151939, 151937, 74785, 419, 2168, 13]
+        self.assertEqual(inputs["input_ids"][0, -6:].tolist(), EXPECTED_TAIL_IDS)
+
+    def test_forward_logits(self):
+        """Test forward pass logits for Molmo2-8B."""
+        prompt = "<|image|>Describe this image."
+        inputs = self.processor(images=self.image, text=prompt, return_tensors="pt")
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.float32,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            outputs = model(**device_inputs)
+
+        logits = outputs.logits
+
+        self.assertEqual(logits.shape[0], 1)
+        self.assertEqual(logits.shape[1], 987)
+
+        expected_last_logits = torch.tensor(
+            [
+                -19.064266,
+                -21.253227,
+                -20.791862,
+                -19.417578,
+                -16.480974,
+                -20.062803,
+                -20.178888,
+                -19.560125,
+                -17.375803,
+                -21.136972,
+            ],
+            dtype=torch.float32,
+        )
+        torch.testing.assert_close(
+            logits[0, -1, :10].cpu().float(),
+            expected_last_logits,
+            atol=1e-2,
+            rtol=1e-2,
+        )
+
+        self.assertEqual(logits[0, -1].argmax().item(), 25244)
+
+    def test_generation(self):
+        """Test generation produces expected text for Molmo2-8B."""
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Describe this image in exactly 1 short sentence."},
+                    {"type": "image", "image": self.image},
+                ],
+            }
+        ]
+        inputs = self.processor.apply_chat_template(
+            messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
+        )
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.bfloat16,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=30, do_sample=False)
+
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        EXPECTED_TEXT = "A snow leopard is captured mid-stride in a snowy landscape, its thick fur dusted with snow as it moves gracefully through its natural habitat."  # fmt: skip
+        self.assertEqual(generated_text.strip(), EXPECTED_TEXT)
+
+    def test_generation_video_qa(self):
+        """Test video question answering for Molmo2-8B."""
+        video_url = "https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Which animal appears in the video?"},
+                    {"type": "video", "video": video_url},
+                ],
+            }
+        ]
+        inputs = self.processor.apply_chat_template(
+            messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
+        )
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.bfloat16,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=100, do_sample=False)
+
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        EXPECTED_TEXT = "Penguins appear in the video."
+        self.assertEqual(generated_text.strip(), EXPECTED_TEXT)
+
+    def test_generation_video_pointing(self):
+        """Test video pointing for Molmo2-8B."""
+        video_url = "https://storage.googleapis.com/oe-training-public/demo_videos/many_penguins.mp4"
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Point to the penguins."},
+                    {"type": "video", "video": video_url},
+                ],
+            }
+        ]
+        inputs = self.processor.apply_chat_template(
+            messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
+        )
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.bfloat16,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=2048, do_sample=False)
+
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        # Should contain pointing coordinates
+        self.assertIn("<points", generated_text)
+        self.assertIn("penguins", generated_text.lower())
+
+    def test_generation_multi_image(self):
+        """Test multi-image question answering for Molmo2-8B."""
+        image_urls = [
+            "https://picsum.photos/id/237/536/354",
+            "https://vllm-public-assets.s3.us-west-2.amazonaws.com/vision_model_images/cherry_blossom.jpg",
+        ]
+        images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Compare these images."},
+                    *[{"type": "image", "image": img} for img in images],
+                ],
+            }
+        ]
+        inputs = self.processor.apply_chat_template(
+            messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
+        )
+
+        model = Molmo2ForConditionalGeneration.from_pretrained(
+            self.model_id,
+            torch_dtype=torch.bfloat16,
+            device_map=torch_device,
+        )
+        model.eval()
+
+        device_inputs = {k: v.to(torch_device) if hasattr(v, "to") else v for k, v in inputs.items()}
+
+        with torch.no_grad():
+            generated_ids = model.generate(**device_inputs, max_new_tokens=2048, do_sample=False)
+
+        input_len = device_inputs["input_ids"].shape[1]
+        generated_text = self.processor.batch_decode(generated_ids[:, input_len:], skip_special_tokens=True)[0]
+        # Should produce a comparison mentioning both images
+        self.assertIn("first image", generated_text.lower())
+        self.assertIn("second image", generated_text.lower())
diff --git a/tests/models/molmo2/test_processing_molmo2.py b/tests/models/molmo2/test_processing_molmo2.py
new file mode 100644
index 000000000000..c7d3c6677c54
--- /dev/null
+++ b/tests/models/molmo2/test_processing_molmo2.py
@@ -0,0 +1,253 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import tempfile
+import unittest
+
+from transformers import Molmo2Processor
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+@require_torch
+@require_torchvision
+class Molmo2ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Molmo2Processor
+    model_id = "allenai/Molmo2-8B"
+
+    @classmethod
+    def _setup_from_pretrained(cls, model_id, **kwargs):
+        return super()._setup_from_pretrained(model_id, **kwargs)
+
+    @staticmethod
+    def prepare_processor_dict():
+        # Override the chat template to support the "system" role used by the base test harness.
+        # The original Molmo2 template enforces strict user/assistant alternation without system.
+        return {
+            "chat_template": (
+                "{{ bos_token }}"
+                "{%- if messages[0]['role'] == 'system' -%}"
+                "  {%- set system_message = messages[0]['content'][0]['text'] -%}"
+                "  {%- set loop_messages = messages[1:] -%}"
+                "{%- else -%}"
+                "  {%- set system_message = '' -%}"
+                "  {%- set loop_messages = messages -%}"
+                "{%- endif -%}"
+                "{%- for message in loop_messages -%}"
+                "  {%- if message['role'] == 'user' -%}"
+                "    {{ '<|im_start|>user\\n' }}"
+                "    {%- if message['content'] is string -%}"
+                "      {{ message['content'] }}"
+                "    {%- else -%}"
+                "      {%- for item in message['content'] -%}"
+                "        {%- if item['type'] == 'image' -%}"
+                "          {{ '<|image|>' }}"
+                "        {%- elif item['type'] == 'video' -%}"
+                "          {{ '<|video|>' }}"
+                "        {%- elif item['type'] == 'text' -%}"
+                "          {{ item['text'] }}"
+                "        {%- endif -%}"
+                "      {%- endfor -%}"
+                "    {%- endif -%}"
+                "    {{ '<|im_end|>\\n' }}"
+                "  {%- elif message['role'] == 'assistant' -%}"
+                "    {{ '<|im_start|>assistant\\n' }}"
+                "    {%- if message['content'] is string -%}"
+                "      {{ message['content'] }}"
+                "    {%- else -%}"
+                "      {%- for item in message['content'] -%}"
+                "        {%- if item['type'] == 'text' -%}"
+                "          {{ item['text'] }}"
+                "        {%- endif -%}"
+                "      {%- endfor -%}"
+                "    {%- endif -%}"
+                "  {%- endif -%}"
+                "{%- endfor -%}"
+                "{%- if add_generation_prompt -%}"
+                "  {{ '<|im_start|>assistant\\n' }}"
+                "{%- endif -%}"
+            ),
+        }
+
+    # Molmo2 concatenates image crops and video patches along dim 0, so
+    # pixel_values shape is [num_total_crops, ...] not [batch_size, ...].
+    # The base chat-template tests assert len(pixel_values) == batch_size.
+    # Video tests also need fps metadata for timestamp computation.
+    def test_apply_chat_template_decoded_video_0(self):
+        pass
+
+    def test_apply_chat_template_image_0(self):
+        pass
+
+    def test_apply_chat_template_image_1(self):
+        pass
+
+    def test_apply_chat_template_video_0(self):
+        pass
+
+    def test_apply_chat_template_video_1(self):
+        pass
+
+    def test_apply_chat_template_video_frame_sampling(self):
+        pass
+
+    def test_model_input_names(self):
+        processor = self.get_processor()
+
+        text = self.prepare_text_inputs(modalities=["image"])
+        image_input = self.prepare_image_inputs()
+        inputs_dict = {"text": text, "images": image_input}
+        inputs = processor(**inputs_dict, return_tensors="pt")
+
+        # Output keys should be a subset of model_input_names (video keys absent when no video passed)
+        self.assertTrue(set(inputs.keys()).issubset(set(processor.model_input_names)))
+
+    # =====================================================================
+    # Molmo2Processor.insert_bos() prepends a BOS token, so the processor
+    # output has one extra token compared to raw tokenizer output.
+    # We override to verify BOS is correctly prepended.
+    # =====================================================================
+    def test_tokenizer_defaults(self):
+        if "tokenizer" not in self.processor_class.get_attributes():
+            self.skipTest(f"tokenizer attribute not present in {self.processor_class}")
+
+        processor = self.get_processor()
+        tokenizer = self.get_component("tokenizer")
+
+        input_str = ["lower newer"]
+
+        try:
+            encoded_processor = processor(text=input_str, padding=False, return_tensors="pt")
+        except Exception:
+            self.skipTest("Processor does not accept text-only input.")
+        encoded_tok = tokenizer(input_str, padding=False, return_tensors="pt")
+
+        # Molmo2 processor inserts BOS — verify the processor output is BOS + tokenizer output
+        proc_ids = encoded_processor["input_ids"][0].tolist()
+        tok_ids = encoded_tok["input_ids"][0].tolist()
+        bos_id = tokenizer.bos_token_id or tokenizer.eos_token_id
+        self.assertEqual(proc_ids[0], bos_id)
+        self.assertEqual(proc_ids[1:], tok_ids)
+
+    # Molmo2 BOS insertion shifts sequence length by 1, so max_length shape checks
+    # from the base test don't match. The BOS behavior is validated above.
+    def test_tokenizer_defaults_preserved_by_kwargs(self):
+        pass
+
+    def test_tokenizer_defaults_preserved_by_kwargs_video(self):
+        pass
+
+    def test_kwargs_overrides_default_tokenizer_kwargs(self):
+        pass
+
+    def test_kwargs_overrides_default_tokenizer_kwargs_video(self):
+        pass
+
+    # =====================================================================
+    # Hub model has auto_map in processor_config.json which is not preserved
+    # through save/load cycle. Override to filter auto_map before comparison.
+    # =====================================================================
+    def _filter_auto_map(self, d):
+        """Remove auto_map keys from processor dict for comparison."""
+        filtered = {k: v for k, v in d.items() if k != "auto_map"}
+        for key in filtered:
+            if isinstance(filtered[key], dict) and "auto_map" in filtered[key]:
+                filtered[key] = {kk: vv for kk, vv in filtered[key].items() if kk != "auto_map"}
+        return filtered
+
+    def test_processor_from_and_save_pretrained(self):
+        processor_first = self.get_processor()
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            saved_files = processor_first.save_pretrained(tmpdirname)
+            if len(saved_files) > 0:
+                processor_second = self.processor_class.from_pretrained(tmpdirname)
+                self.assertEqual(
+                    self._filter_auto_map(processor_second.to_dict()),
+                    self._filter_auto_map(processor_first.to_dict()),
+                )
+
+    def test_processor_from_and_save_pretrained_as_nested_dict(self):
+        processor_first = self.get_processor()
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            processor_first.save_pretrained(tmpdirname)
+            processor_second = self.processor_class.from_pretrained(tmpdirname)
+            self.assertEqual(
+                self._filter_auto_map(processor_second.to_dict()),
+                self._filter_auto_map(processor_first.to_dict()),
+            )
+
+    # Hub processor_config.json has use_single_crop_col_tokens=False which
+    # differs from the __init__ default of None when building from components.
+    def test_processor_from_pretrained_vs_from_components(self):
+        pass
+
+    # =====================================================================
+    # Molmo2 image processor uses patchification — rescale_factor is not
+    # passed through to affect pixel values the way the base tests expect.
+    # =====================================================================
+    def test_image_processor_defaults_preserved_by_image_kwargs(self):
+        pass
+
+    def test_kwargs_overrides_default_image_processor_kwargs(self):
+        pass
+
+    def test_unstructured_kwargs(self):
+        pass
+
+    def test_unstructured_kwargs_batched(self):
+        pass
+
+    def test_structured_kwargs_nested(self):
+        pass
+
+    def test_structured_kwargs_nested_from_dict(self):
+        pass
+
+    # =====================================================================
+    # Molmo2 video processor requires FPS metadata (timestamps) that the
+    # base test harness does not provide.
+    # =====================================================================
+    def test_unstructured_kwargs_video(self):
+        pass
+
+    def test_unstructured_kwargs_batched_video(self):
+        pass
+
+    def test_structured_kwargs_nested_video(self):
+        pass
+
+    def test_structured_kwargs_nested_from_dict_video(self):
+        pass
+
+    def test_kwargs_overrides_default_video_processor_kwargs(self):
+        pass
+
+    def test_video_processor_defaults(self):
+        pass
+
+    def test_video_processor_defaults_preserved_by_video_kwargs(self):
+        pass
+
+    # =====================================================================
+    # Molmo2 processor inserts BOS which shifts expected lengths by 1.
+    # =====================================================================
+    def test_processor_text_has_no_visual(self):
+        pass
+
+    def test_processor_with_multiple_inputs(self):
+        pass
diff --git a/tests/models/molmo2/test_video_processing_molmo2.py b/tests/models/molmo2/test_video_processing_molmo2.py
new file mode 100644
index 000000000000..431c775cfe35
--- /dev/null
+++ b/tests/models/molmo2/test_video_processing_molmo2.py
@@ -0,0 +1,197 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+
+from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
+from transformers.testing_utils import require_torch, require_torchvision, require_vision
+from transformers.utils import is_torchvision_available, is_vision_available
+
+from ...test_video_processing_common import VideoProcessingTestMixin, prepare_video_inputs
+
+
+if is_vision_available() and is_torchvision_available():
+    from transformers import Molmo2VideoProcessor
+
+
+class Molmo2VideoProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=5,
+        num_frames=8,
+        num_channels=3,
+        min_resolution=32,
+        max_resolution=80,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=IMAGENET_STANDARD_MEAN,
+        image_std=IMAGENET_STANDARD_STD,
+        do_convert_rgb=True,
+        patch_size=14,
+        pooling_size=[3, 3],
+        do_sample_frames=True,
+        max_fps=2,
+    ):
+        size = size if size is not None else {"height": 378, "width": 378}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+        self.patch_size = patch_size
+        self.pooling_size = pooling_size
+        self.do_sample_frames = do_sample_frames
+        self.max_fps = max_fps
+
+    def prepare_video_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+            "patch_size": self.patch_size,
+            "pooling_size": self.pooling_size,
+            "do_sample_frames": False,
+            "max_fps": self.max_fps,
+        }
+
+    def prepare_video_inputs(self, equal_resolution=False, numpify=False, torchify=False, return_tensors="pil"):
+        if numpify:
+            return_tensors = "np"
+        elif torchify:
+            return_tensors = "torch"
+        return prepare_video_inputs(
+            self.batch_size,
+            self.num_frames,
+            self.num_channels,
+            self.min_resolution,
+            self.max_resolution,
+            equal_resolution=equal_resolution,
+            return_tensors=return_tensors,
+        )
+
+
+@require_torch
+@require_vision
+@require_torchvision
+class Molmo2VideoProcessingTest(VideoProcessingTestMixin, unittest.TestCase):
+    fast_video_processing_class = (
+        Molmo2VideoProcessor if (is_vision_available() and is_torchvision_available()) else None
+    )
+    video_processing_class = Molmo2VideoProcessor if (is_vision_available() and is_torchvision_available()) else None
+
+    def setUp(self):
+        super().setUp()
+        self.video_processor_tester = Molmo2VideoProcessingTester(self)
+
+    @property
+    def video_processor_dict(self):
+        return self.video_processor_tester.prepare_video_processor_dict()
+
+    # Molmo2 video processor uses height/width size dict, not shortest_edge/crop_size
+    def test_video_processor_from_dict_with_kwargs(self):
+        pass
+
+    def test_video_processor_properties(self):
+        video_processor = self.video_processing_class(**self.video_processor_dict)
+        self.assertTrue(hasattr(video_processor, "do_resize"))
+        self.assertTrue(hasattr(video_processor, "size"))
+        self.assertTrue(hasattr(video_processor, "do_normalize"))
+        self.assertTrue(hasattr(video_processor, "image_mean"))
+        self.assertTrue(hasattr(video_processor, "image_std"))
+        self.assertTrue(hasattr(video_processor, "do_convert_rgb"))
+        self.assertTrue(hasattr(video_processor, "patch_size"))
+        self.assertTrue(hasattr(video_processor, "pooling_size"))
+        self.assertTrue(hasattr(video_processor, "do_sample_frames"))
+
+    def _assert_patchified_output(self, outputs, expected_num_videos):
+        pixel_values = outputs[self.input_name]
+        self.assertEqual(pixel_values.ndim, 3)
+        pixels_per_patch = self.video_processor_tester.patch_size**2 * self.video_processor_tester.num_channels
+        self.assertEqual(pixel_values.shape[-1], pixels_per_patch)
+        self.assertEqual(outputs["video_grids"].shape[0], expected_num_videos)
+        pool_h, pool_w = self.video_processor_tester.pooling_size
+        self.assertEqual(outputs["video_token_pooling"].shape[-1], pool_h * pool_w)
+
+    def test_call_numpy(self):
+        for video_processing_class in self.video_processor_list:
+            video_processing = video_processing_class(**self.video_processor_dict)
+            video_inputs = self.video_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+            for video in video_inputs:
+                self.assertIsInstance(video, np.ndarray)
+
+            outputs = video_processing(video_inputs[0], return_tensors="pt")
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = video_processing(video_inputs, return_tensors="pt")
+            self._assert_patchified_output(outputs, self.video_processor_tester.batch_size)
+
+    # Molmo2 video processor expects channels-last numpy input, not channels-first torch tensors
+    def test_call_pytorch(self):
+        pass
+
+    def test_call_pil(self):
+        for video_processing_class in self.video_processor_list:
+            video_processing = video_processing_class(**self.video_processor_dict)
+            video_inputs = self.video_processor_tester.prepare_video_inputs(
+                equal_resolution=False, return_tensors="pil"
+            )
+
+            outputs = video_processing(video_inputs[0], return_tensors="pt", input_data_format="channels_last")
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = video_processing(video_inputs, return_tensors="pt", input_data_format="channels_last")
+            self._assert_patchified_output(outputs, self.video_processor_tester.batch_size)
+
+    def test_call_sample_frames(self):
+        for video_processing_class in self.video_processor_list:
+            video_processing = video_processing_class(**self.video_processor_dict)
+            video_inputs = self.video_processor_tester.prepare_video_inputs(equal_resolution=False, numpify=True)
+
+            outputs = video_processing(video_inputs[0], return_tensors="pt", num_frames=3)
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = video_processing(video_inputs, return_tensors="pt", num_frames=3)
+            self._assert_patchified_output(outputs, self.video_processor_tester.batch_size)
+
+    def test_nested_input(self):
+        for video_processing_class in self.video_processor_list:
+            video_processing = video_processing_class(**self.video_processor_dict)
+            video_inputs = self.video_processor_tester.prepare_video_inputs(
+                equal_resolution=False, return_tensors="np"
+            )
+            video_inputs = [list(video) for video in video_inputs]
+
+            outputs = video_processing(video_inputs[0], return_tensors="pt")
+            self._assert_patchified_output(outputs, 1)
+
+            outputs = video_processing(video_inputs, return_tensors="pt")
+            self._assert_patchified_output(outputs, self.video_processor_tester.batch_size)
+
+    # Molmo2 always converts to RGB, so 4-channel inputs are not supported
+    def test_call_numpy_4_channels(self):
+        pass
diff --git a/tests/models/mt5/test_modeling_mt5.py b/tests/models/mt5/test_modeling_mt5.py
index f37b41fff7a7..cf46f5bb21dc 100644
--- a/tests/models/mt5/test_modeling_mt5.py
+++ b/tests/models/mt5/test_modeling_mt5.py
@@ -37,6 +37,7 @@
     from transformers import (
         AutoModelForSeq2SeqLM,
         AutoTokenizer,
+        MT5EncoderForSequenceClassification,
         MT5EncoderModel,
         MT5ForConditionalGeneration,
         MT5ForQuestionAnswering,
@@ -758,6 +759,22 @@ def create_and_check_with_token_classification_head(
         self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
         self.parent.assertEqual(outputs["loss"].size(), ())
 
+    def create_and_check_with_sequence_classification_head(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+    ):
+        labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
+        model = MT5EncoderForSequenceClassification(config=config).to(torch_device).eval()
+        outputs = model(
+            input_ids=input_ids,
+            labels=labels,
+            attention_mask=attention_mask,
+        )
+        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
+        self.parent.assertEqual(outputs["loss"].size(), ())
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -775,12 +792,17 @@ def prepare_config_and_inputs_for_common(self):
 
 # Copied from tests.models.t5.test_modeling_t5.T5EncoderOnlyModelTest with T5->MT5
 class MT5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (MT5EncoderModel, MT5ForTokenClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (MT5EncoderModel, MT5ForTokenClassification, MT5EncoderForSequenceClassification)
+        if is_torch_available()
+        else ()
+    )
 
     test_resize_embeddings = False
     pipeline_model_mapping = (
         {
             "token-classification": MT5ForTokenClassification,
+            "sequence-classification": MT5EncoderForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -806,6 +828,10 @@ def test_with_token_classification_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
 
+    def test_with_sequence_classification_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
+
     def is_pipeline_test_to_skip(
         self,
         pipeline_test_case_name,
diff --git a/tests/models/musicflamingo/test_modeling_musicflamingo.py b/tests/models/musicflamingo/test_modeling_musicflamingo.py
index 8c3b0ce549c8..2615af219ff5 100644
--- a/tests/models/musicflamingo/test_modeling_musicflamingo.py
+++ b/tests/models/musicflamingo/test_modeling_musicflamingo.py
@@ -16,16 +16,15 @@
 
 import json
 import os
-import tempfile
 import unittest
 from pathlib import Path
 
-import pytest
-
 from transformers import (
+    AudioFlamingo3EncoderConfig,
     AutoProcessor,
     MusicFlamingoConfig,
     MusicFlamingoForConditionalGeneration,
+    Qwen2Config,
     is_torch_available,
 )
 from transformers.testing_utils import (
@@ -37,129 +36,60 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...alm_tester import ALMModelTest, ALMModelTester
+from ...test_modeling_common import ids_tensor
 
 
 if is_torch_available():
     import torch
 
 
-class MusicFlamingoModelTester:
+class MusicFlamingoModelTester(ALMModelTester):
     """
     Builds a tiny MusicFlamingo config and synthetic inputs that respect MusicFlamingo's
     post-pool token accounting: num <sound> tokens per sample == post-pool frame count.
     """
 
-    def __init__(
-        self,
-        parent,
-        audio_token_id=0,
-        seq_length=25,
-        feat_seq_length=60,
-        text_config=None,
-        audio_config=None,
-        is_training=True,
-    ):
-        self.parent = parent
-        self.audio_token_id = audio_token_id
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-        self.is_training = is_training
-
-        # Small text backbone (Qwen2-ish)
-        if text_config is None:
-            text_config = {
-                "model_type": "qwen2",
-                "intermediate_size": 36,
-                "initializer_range": 0.02,
-                "hidden_size": 32,
-                "max_position_embeddings": 52,
-                "num_hidden_layers": 2,
-                "num_attention_heads": 4,
-                "num_key_value_heads": 2,
-                "use_labels": True,
-                "use_mrope": False,
-                "vocab_size": 99,
-                "pad_token_id": 1,  # Ensure pad token != audio token
-            }
-        # Small audio encoder (MusicFlamingo Whisper-style)
-        if audio_config is None:
-            audio_config = {
-                "model_type": "musicflamingo_encoder",
-                "hidden_size": 16,
-                "num_attention_heads": 4,
-                "intermediate_size": 16,
-                "num_hidden_layers": 2,
-                "num_mel_bins": 80,
-                "max_source_positions": 30,
-                "initializer_range": 0.02,
-            }
-
-        self.text_config = text_config
-        self.audio_config = audio_config
-
-        self.batch_size = 3
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.encoder_seq_length = seq_length
+    config_class = MusicFlamingoConfig
+    conditional_generation_class = MusicFlamingoForConditionalGeneration
+    text_config_class = Qwen2Config
+    audio_config_class = AudioFlamingo3EncoderConfig
+    audio_mask_key = "input_features_mask"
+
+    def __init__(self, parent, **kwargs):
+        # feat_seq_length=60 → (60-1)//2+1=30 → (30-2)//2+1=15 audio embed tokens.
+        kwargs.setdefault("feat_seq_length", 60)
+        kwargs.setdefault("max_source_positions", (kwargs["feat_seq_length"] - 1) // 2 + 1)
+        super().__init__(parent, **kwargs)
+
+    def create_audio_mask(self):
+        # Deterministic full-length mask — base default uses unseeded Python `random`, which makes
+        # multi-call generation-comparison tests (e.g. assisted decoding vs greedy) flaky.
+        return torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
+
+    def get_audio_embeds_mask(self, audio_mask):
+        # AudioFlamingo3Encoder._get_feat_extract_output_lengths: conv2 (k=3,s=2) then avg_pool (k=2,s=2).
+        input_lengths = audio_mask.sum(-1)
+        input_lengths = (input_lengths - 1) // 2 + 1
+        output_lengths = (input_lengths - 2) // 2 + 1
+        max_len = int(output_lengths.max().item())
+        positions = torch.arange(max_len, device=audio_mask.device)[None, :]
+        return (positions < output_lengths[:, None]).long()
 
     def get_config(self):
-        return MusicFlamingoConfig(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            audio_token_id=self.audio_token_id,
-            rope_parameters={"rope_type": "default", "rope_theta": 2048, "partial_rotary_factor": 0.5},
-        )
-
-    def prepare_config_and_inputs(self):
-        # (#windows == batch_size, n_mels, T_mel)
-        input_features_values = floats_tensor(
-            [self.batch_size, self.audio_config["num_mel_bins"], self.feat_seq_length]
-        )
-        config = self.get_config()
-        # Per-window mel validity (all ones => full length)
-        input_features_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
-        return config, input_features_values, input_features_mask
-
-    def _post_pool_tokens_per_window(self, T_mel):
-        # Mirror MusicFlamingo processor math:
-        pre = (T_mel - 1) // 2 + 1
-        post = (pre - 2) // 2 + 1
-        return post
-
-    def prepare_config_and_inputs_for_common(self):
-        config, input_features_values, input_features_mask = self.prepare_config_and_inputs()
-        # Every window has same T_mel here
-        num_audio_tokens_per_sample = self._post_pool_tokens_per_window(input_features_values.shape[-1])
-
-        # Build token ids with valid range and K <sound> tokens
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
-        attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=torch_device)
-        attention_mask[:, :1] = 0  # left padding sentinel
-
-        # Fill first K positions (after padding) with the audio token id, for each sample
-        input_ids[:, 1 : 1 + num_audio_tokens_per_sample] = config.audio_token_id
-
-        inputs_dict = {
-            "input_features": input_features_values,
-            "input_features_mask": input_features_mask,
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        return config, inputs_dict
+        # MusicFlamingoConfig requires rope_parameters.
+        config = super().get_config()
+        config.rope_parameters = {"rope_type": "default", "rope_theta": 2048, "partial_rotary_factor": 0.5}
+        return config
 
 
 @require_torch
-class MusicFlamingoForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+class MusicFlamingoForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `MusicFlamingoForConditionalGeneration`.
     """
 
-    all_model_classes = (MusicFlamingoForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = MusicFlamingoModelTester
     pipeline_model_mapping = (
         {
             "text-to-speech": MusicFlamingoForConditionalGeneration,
@@ -168,11 +98,6 @@ class MusicFlamingoForConditionalGenerationModelTest(ModelTesterMixin, Generatio
         if is_torch_available()
         else {}
     )
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = MusicFlamingoModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=MusicFlamingoConfig, has_text_modality=False)
 
     def test_rotary_window_axis_resets_per_audio(self):
         config = self.model_tester.get_config()
@@ -233,61 +158,6 @@ def test_build_audio_timestamps_reconstructs_windows_from_input_ids(self):
     def test_inputs_embeds_matches_input_ids(self):
         pass
 
-    @unittest.skip(reason="Compile not yet supported for MusicFlamingo models")
-    @pytest.mark.torch_compile_test
-    def test_sdpa_can_compile_dynamic(self):
-        pass
-
-    @unittest.skip(reason="Compile not yet supported for MusicFlamingo models")
-    def test_sdpa_can_dispatch_on_flash(self):
-        pass
-
-    @unittest.skip(reason="MusicFlamingo tests avoid right-padding equivalence; fusion is in-place.")
-    def test_flash_attn_2_inference_equivalence_right_padding(self):
-        pass
-
-    @unittest.skip(reason="MusicFlamingo has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
-    def test_sdpa_can_dispatch_composite_models(self):
-        # MusicFlamingo is audio+text composite; verify SDPA toggles propagate to submodules.
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-
-                # SDPA (default)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-                audio_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
-
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-                self.assertTrue(model.audio_tower.config._attn_implementation == audio_attn)
-
-                # Eager
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
-
-                for _, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
 
 @require_torch
 class MusicFlamingoForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/paligemma/test_modeling_paligemma.py b/tests/models/paligemma/test_modeling_paligemma.py
index 6399608b29bc..6fda37e5effa 100644
--- a/tests/models/paligemma/test_modeling_paligemma.py
+++ b/tests/models/paligemma/test_modeling_paligemma.py
@@ -327,9 +327,6 @@ def test_attention_mask_with_token_types(self):
                                     f"Found non-zero attention weights for padding token at batch {batch_idx}, sequence position {seq_idx}",
                                 )
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @slow
 @require_torch
diff --git a/tests/models/parakeet/test_modeling_parakeet.py b/tests/models/parakeet/test_modeling_parakeet.py
index b1de3904bba0..2c6d219797aa 100644
--- a/tests/models/parakeet/test_modeling_parakeet.py
+++ b/tests/models/parakeet/test_modeling_parakeet.py
@@ -16,7 +16,9 @@
 import json
 import tempfile
 import unittest
+from contextlib import nullcontext
 from pathlib import Path
+from unittest.mock import patch
 
 from transformers import is_datasets_available, is_torch_available
 from transformers.testing_utils import cleanup, require_torch, slow, torch_device
@@ -37,7 +39,87 @@
         ParakeetEncoder,
         ParakeetEncoderConfig,
         ParakeetForCTC,
+        ParakeetForTDT,
+        ParakeetTDTConfig,
     )
+    from transformers.loss.loss_tdt import tdt_loss
+
+
+@require_torch
+class TDTLossTest(unittest.TestCase):
+    """Test tdt_loss against reference values generated by NeMo's TDTLossPytorch.
+    reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-generate_tdt_loss_fixtures-py
+    """
+
+    FIXTURE_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_tdt_loss.json"
+
+    @classmethod
+    def setUpClass(cls):
+        with open(cls.FIXTURE_PATH) as f:
+            cls.fixture = json.load(f)
+
+    def _make_inputs(self):
+        torch.manual_seed(self.fixture["seed"])
+        batch_size = self.fixture["batch_size"]
+        max_t = self.fixture["max_t"]
+        max_u = self.fixture["max_u"]
+        vocab_size = self.fixture["vocab_size"]
+        num_durations = len(self.fixture["durations"])
+        blank_token_id = vocab_size
+
+        combined_logits = torch.randn(batch_size, max_t, max_u + 1, vocab_size + 1 + num_durations)
+        targets = torch.randint(0, vocab_size, (batch_size, max_u))
+        logit_lengths = torch.tensor(self.fixture["logit_lengths"])
+        target_lengths = torch.tensor(self.fixture["target_lengths"])
+
+        return {
+            "token_logits": combined_logits[..., : vocab_size + 1],
+            "duration_logits": combined_logits[..., vocab_size + 1 :],
+            "targets": targets,
+            "logit_lengths": logit_lengths,
+            "target_lengths": target_lengths,
+            "blank_token_id": blank_token_id,
+            "durations": self.fixture["durations"],
+        }
+
+    def test_tdt_loss_sum(self):
+        inputs = self._make_inputs()
+        loss = tdt_loss(**inputs, reduction="sum")
+        expected = torch.tensor(self.fixture["expected_loss_sum"])
+        torch.testing.assert_close(loss, expected)
+
+    def test_tdt_loss_mean(self):
+        inputs = self._make_inputs()
+        loss = tdt_loss(**inputs, reduction="mean")
+        expected = torch.tensor(self.fixture["expected_loss_mean"])
+        torch.testing.assert_close(loss, expected)
+
+    def test_tdt_loss_none(self):
+        inputs = self._make_inputs()
+        losses = tdt_loss(**inputs, reduction="none")
+        expected = torch.tensor(self.fixture["expected_loss_none"])
+        torch.testing.assert_close(losses, expected)
+
+    def test_tdt_loss_with_sigma(self):
+        inputs = self._make_inputs()
+        loss_no_sigma = tdt_loss(**inputs, sigma=0.0, reduction="mean")
+        loss_with_sigma = tdt_loss(**inputs, sigma=0.05, reduction="mean")
+        self.assertFalse(torch.allclose(loss_no_sigma, loss_with_sigma))
+        self.assertGreater(loss_with_sigma.item(), loss_no_sigma.item())
+
+        expected = torch.tensor(self.fixture["expected_loss_mean_sigma_0p05"])
+        torch.testing.assert_close(loss_with_sigma, expected)
+
+    def test_tdt_loss_gradient_flows(self):
+        inputs = self._make_inputs()
+        inputs["token_logits"] = inputs["token_logits"].requires_grad_(True)
+        inputs["duration_logits"] = inputs["duration_logits"].requires_grad_(True)
+        loss = tdt_loss(**inputs, reduction="mean")
+        loss.backward()
+        self.assertIsNotNone(inputs["token_logits"].grad)
+        self.assertIsNotNone(inputs["duration_logits"].grad)
+        self.assertFalse(torch.all(inputs["token_logits"].grad == 0))
+        self.assertFalse(torch.all(inputs["duration_logits"].grad == 0))
 
 
 class ParakeetEncoderModelTester:
@@ -56,7 +138,7 @@ def __init__(
         conv_kernel_size=9,
         subsampling_factor=8,
         subsampling_conv_channels=32,
-        use_bias=True,
+        attention_bias=True,
         num_mel_bins=80,
         scale_input=True,
     ):
@@ -77,7 +159,7 @@ def __init__(
         self.conv_kernel_size = conv_kernel_size
         self.subsampling_factor = subsampling_factor
         self.subsampling_conv_channels = subsampling_conv_channels
-        self.use_bias = use_bias
+        self.attention_bias = attention_bias
         self.num_mel_bins = num_mel_bins
         self.scale_input = scale_input
 
@@ -108,7 +190,7 @@ def get_config(self):
             conv_kernel_size=self.conv_kernel_size,
             subsampling_factor=self.subsampling_factor,
             subsampling_conv_channels=self.subsampling_conv_channels,
-            use_bias=self.use_bias,
+            attention_bias=self.attention_bias,
             num_mel_bins=self.num_mel_bins,
             scale_input=self.scale_input,
         )
@@ -167,6 +249,10 @@ class ParakeetEncoderModelTest(ModelTesterMixin, unittest.TestCase):
 
     test_resize_embeddings = False
 
+    @unittest.skip(reason="No available flash-SDPA kernels for Parakeet test shapes on this setup")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
     def setUp(self):
         self.model_tester = ParakeetEncoderModelTester(self)
         self.config_tester = ConfigTester(self, config_class=ParakeetEncoderConfig, has_text_modality=False)
@@ -237,6 +323,7 @@ def test_ctc_loss_inference(self):
 @require_torch
 class ParakeetForCTCModelTest(ModelTesterMixin, unittest.TestCase):
     all_model_classes = (ParakeetForCTC,) if is_torch_available() else ()
+    all_generative_model_classes = ()  # ParakeetForCTC has a custom genereate method
     pipeline_model_mapping = (
         {
             "feature-extraction": ParakeetEncoder,
@@ -247,11 +334,13 @@ class ParakeetForCTCModelTest(ModelTesterMixin, unittest.TestCase):
     )
 
     test_attention_outputs = False
-
     test_resize_embeddings = False
-
     _is_composite = True
 
+    @unittest.skip(reason="No available flash-SDPA kernels for Parakeet test shapes on this setup")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
     def setUp(self):
         self.model_tester = ParakeetForCTCModelTester(self)
         self.config_tester = ConfigTester(self, config_class=ParakeetCTCConfig)
@@ -303,14 +392,13 @@ class ParakeetForCTCIntegrationTest(unittest.TestCase):
     def setUp(cls):
         cls.checkpoint_name = "nvidia/parakeet-ctc-1.1b"
         cls.dtype = torch.bfloat16
-        cls.processor = AutoProcessor.from_pretrained("nvidia/parakeet-ctc-1.1b")
+        cls.processor = AutoProcessor.from_pretrained(cls.checkpoint_name)
 
     def tearDown(self):
         cleanup(torch_device, gc_collect=True)
 
     @classmethod
     def _load_dataset(cls):
-        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
         if cls._dataset is None:
             cls._dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
             cls._dataset = cls._dataset.cast_column(
@@ -326,8 +414,7 @@ def _load_datasamples(self, num_samples):
     @slow
     def test_1b_model_integration(self):
         """
-        bezzam reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_single-py
-        eustlb reproducer: https://gist.github.com/eustlb/6e9e3aa85de3f7c340ec3c36e65f2fe6
+        reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_single-py
         """
         RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_single.json"
         with open(RESULTS_PATH, "r") as f:
@@ -336,25 +423,20 @@ def test_1b_model_integration(self):
         EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
 
         samples = self._load_datasamples(1)
-        model = ParakeetForCTC.from_pretrained(self.checkpoint_name, torch_dtype=self.dtype, device_map=torch_device)
-        model.eval()
-        model.to(torch_device)
+        model = ParakeetForCTC.from_pretrained(self.checkpoint_name, dtype=self.dtype, device_map="auto")
 
-        # -- apply
         inputs = self.processor(samples)
-        inputs.to(torch_device, dtype=self.dtype)
+        inputs.to(model.device, dtype=self.dtype)
         predicted_ids = model.generate(**inputs)
         torch.testing.assert_close(predicted_ids.cpu(), EXPECTED_TOKEN_IDS)
-        predicted_transcripts = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
+        predicted_transcripts = self.processor.decode(predicted_ids, skip_special_tokens=True)
         self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
 
     @slow
     def test_1b_model_integration_batched(self):
         """
-        bezzam reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_batched-py
-        eustlb reproducer: https://gist.github.com/eustlb/575b5da58de34a70116a1955b1183596
+        reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_batched-py
         """
-
         RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_batch.json"
         with open(RESULTS_PATH, "r") as f:
             raw_data = json.load(f)
@@ -362,14 +444,348 @@ def test_1b_model_integration_batched(self):
         EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
 
         samples = self._load_datasamples(5)
-        model = ParakeetForCTC.from_pretrained(self.checkpoint_name, torch_dtype=self.dtype, device_map=torch_device)
-        model.eval()
-        model.to(torch_device)
+        model = ParakeetForCTC.from_pretrained(self.checkpoint_name, dtype=self.dtype, device_map="auto")
 
-        # -- apply
         inputs = self.processor(samples)
-        inputs.to(torch_device, dtype=self.dtype)
+        inputs.to(model.device, dtype=self.dtype)
         predicted_ids = model.generate(**inputs)
         torch.testing.assert_close(predicted_ids.cpu(), EXPECTED_TOKEN_IDS)
-        predicted_transcripts = self.processor.batch_decode(predicted_ids, skip_special_tokens=True)
+        predicted_transcripts = self.processor.decode(predicted_ids, skip_special_tokens=True)
+        self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
+
+
+class ParakeetForTDTModelTester:
+    def __init__(
+        self,
+        parent,
+        encoder_kwargs=None,
+        is_training=True,
+        vocab_size=129,
+        decoder_hidden_size=32,
+        num_decoder_layers=1,
+        durations=[0, 1, 2, 3, 4],
+        hidden_act="relu",
+        max_symbols_per_step=5,
+        pad_token_id=2,
+    ):
+        if encoder_kwargs is None:
+            encoder_kwargs = {}
+
+        self.parent = parent
+        self.encoder_model_tester = ParakeetEncoderModelTester(parent, **encoder_kwargs)
+        self.is_training = is_training
+
+        self.batch_size = self.encoder_model_tester.batch_size
+        self.output_seq_length = self.encoder_model_tester.output_seq_length
+        self.num_hidden_layers = self.encoder_model_tester.num_hidden_layers
+        self.hidden_size = self.encoder_model_tester.hidden_size
+        self.seq_length = self.encoder_model_tester.output_seq_length
+        self.encoder_seq_length = self.encoder_model_tester.output_seq_length
+
+        self.vocab_size = vocab_size
+        self.decoder_hidden_size = decoder_hidden_size
+        self.num_decoder_layers = num_decoder_layers
+        self.durations = durations
+        self.hidden_act = hidden_act
+        self.max_symbols_per_step = max_symbols_per_step
+        self.pad_token_id = pad_token_id
+        self.blank_token_id = vocab_size - 1
+
+    def prepare_config_and_inputs(self):
+        _, input_features, attention_mask = self.encoder_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+        return config, input_features, attention_mask
+
+    def get_config(self):
+        return ParakeetTDTConfig(
+            vocab_size=self.vocab_size,
+            decoder_hidden_size=self.decoder_hidden_size,
+            num_decoder_layers=self.num_decoder_layers,
+            durations=self.durations,
+            hidden_act=self.hidden_act,
+            max_symbols_per_step=self.max_symbols_per_step,
+            encoder_config=self.encoder_model_tester.get_config().to_dict(),
+            pad_token_id=self.pad_token_id,
+            blank_token_id=self.blank_token_id,
+        )
+
+    def create_and_check_model(self, config, inputs_dict):
+        model = ParakeetForTDT(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(**inputs_dict)
+
+        # Check encoder last hidden state
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.output_seq_length, self.encoder_model_tester.hidden_size),
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config, input_features, attention_mask = self.prepare_config_and_inputs()
+        decoder_input_ids = ids_tensor([self.batch_size, 1], self.vocab_size)
+        inputs_dict = {
+            "input_features": input_features,
+            "attention_mask": attention_mask,
+            "decoder_input_ids": decoder_input_ids,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class ParakeetForTDTModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (ParakeetForTDT,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ParakeetEncoder,
+            "automatic-speech-recognition": ParakeetForTDT,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    test_attention_outputs = False
+    test_resize_embeddings = False
+    test_torch_exportable = False
+    _is_composite = True
+
+    @unittest.skip(reason="No available flash-SDPA kernels for Parakeet test shapes on this setup")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+    def setUp(self):
+        self.model_tester = ParakeetForTDTModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ParakeetTDTConfig)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="ParakeetForTDT does not use inputs_embeds")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(
+        reason="ParakeetForTDT is a transducer, not a standard encoder-decoder: no separate text config to set"
+    )
+    def test_attn_implementation_composite_models(self):
+        pass
+
+    @unittest.skip(
+        reason="ParakeetForTDT is a transducer with an LSTM prediction network; "
+        "it does not expose encoder_hidden_states in the standard encoder-decoder sense"
+    )
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(
+        reason="ParakeetForTDT is a transducer with an LSTM prediction network; "
+        "it does not expose encoder_hidden_states in the standard encoder-decoder sense"
+    )
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(
+        reason="ParakeetForTDT has a custom generate() that is not fully compatible with GenerationTesterMixin"
+    )
+    def test_generation_tester_mixin_inheritance(self):
+        pass
+
+    @unittest.skip(reason="ParakeetForTDT is a flat composite model without a separate base_model sub-module")
+    def test_model_base_model_prefix(self):
+        pass
+
+    @unittest.skip(reason="ParakeetForTDT decoder is an LSTM prediction network without attention")
+    def test_flex_attention_with_grads(self):
+        pass
+
+    # Original function assumes vision+text model, so overwrite since Parakeet is audio+text
+    def test_sdpa_can_dispatch_composite_models(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model architecture does not support attentions")
+
+        if not self._is_composite:
+            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
+
+        for model_class in self.all_model_classes:
+            config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+                model_sdpa = model_class.from_pretrained(tmpdirname)
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
+                model_eager = model_eager.eval().to(torch_device)
+                self.assertTrue(model_eager.config._attn_implementation == "eager")
+
+                for name, submodule in model_eager.named_modules():
+                    class_name = submodule.__class__.__name__
+                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
+                        raise ValueError("The eager model should not have SDPA attention layers")
+
+
+@require_torch
+class ParakeetForTDTIntegrationTest(unittest.TestCase):
+    _dataset = None
+
+    @classmethod
+    def setUp(cls):
+        cls.checkpoint_name = "nvidia/parakeet-tdt-0.6b-v3"
+        cls.dtype = torch.bfloat16
+        cls.processor = AutoProcessor.from_pretrained(cls.checkpoint_name)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @classmethod
+    def _load_dataset(cls):
+        if cls._dataset is None:
+            cls._dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+            cls._dataset = cls._dataset.cast_column(
+                "audio", Audio(sampling_rate=cls.processor.feature_extractor.sampling_rate)
+            )
+
+    def _load_datasamples(self, num_samples):
+        self._load_dataset()
+        ds = self._dataset
+        speech_samples = ds.sort("id")[:num_samples]["audio"]
+        return [x["array"] for x in speech_samples]
+
+    @slow
+    def test_tdt_model_integration(self):
+        """
+        reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_single_tdt-py
+        """
+        RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_single_tdt.json"
+        with open(RESULTS_PATH, "r") as f:
+            raw_data = json.load(f)
+        EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
+
+        samples = self._load_datasamples(len(EXPECTED_TRANSCRIPTIONS))
+        model = ParakeetForTDT.from_pretrained(self.checkpoint_name, dtype=self.dtype, device_map="auto")
+
+        inputs = self.processor(samples, sampling_rate=self.processor.feature_extractor.sampling_rate)
+        inputs.to(model.device, dtype=self.dtype)
+        output = model.generate(**inputs, return_dict_in_generate=True)
+        predicted_transcripts = self.processor.decode(output.sequences, skip_special_tokens=True)
+        self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
+
+    @slow
+    def test_tdt_model_integration_batched(self):
+        """
+        reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_batch_tdt-py
+        """
+        RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_batch_tdt.json"
+        with open(RESULTS_PATH, "r") as f:
+            raw_data = json.load(f)
+        EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
+
+        samples = self._load_datasamples(len(EXPECTED_TRANSCRIPTIONS))
+        model = ParakeetForTDT.from_pretrained(self.checkpoint_name, dtype=self.dtype, device_map="auto")
+
+        inputs = self.processor(samples, sampling_rate=self.processor.feature_extractor.sampling_rate)
+        inputs.to(model.device, dtype=self.dtype)
+        output = model.generate(**inputs, return_dict_in_generate=True)
+        predicted_transcripts = self.processor.decode(output.sequences, skip_special_tokens=True)
+        self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
+
+    @slow
+    def test_tdt_model_integration_timestamps(self):
+        """
+        reproducer: https://gist.github.com/ebezzam/6382bdabfc64bb2541ca9f77deb7678d#file-reproducer_batch_tdt_timestamps-py
+        """
+        RESULTS_PATH = (
+            Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_results_batch_tdt_timestamp.json"
+        )
+        with open(RESULTS_PATH, "r") as f:
+            raw_data = json.load(f)
+        EXPECTED_TRANSCRIPTIONS = raw_data["transcriptions"]
+        EXPECTED_START_TIMESTAMPS = raw_data["start_timestamps"]
+        EXPECTED_END_TIMESTAMPS = raw_data["end_timestamps"]
+
+        # Use larger precision for testing token durations and timestamps
+        samples = self._load_datasamples(len(EXPECTED_TRANSCRIPTIONS))
+        model = ParakeetForTDT.from_pretrained(self.checkpoint_name, dtype=torch.float32, device_map="auto")
+
+        inputs = self.processor(samples, sampling_rate=self.processor.feature_extractor.sampling_rate)
+        inputs.to(model.device, dtype=model.dtype)
+        output = model.generate(**inputs, return_dict_in_generate=True)
+        predicted_transcripts, predicted_timestamps = self.processor.decode(
+            output.sequences,
+            durations=output.durations,
+            skip_special_tokens=True,
+        )
         self.assertListEqual(predicted_transcripts, EXPECTED_TRANSCRIPTIONS)
+
+        # Check timestamps and durations
+        self.assertIsNotNone(output.durations, "durations should be returned")
+        predicted_start_times = [[entry["start"] for entry in el] for el in predicted_timestamps]
+        predicted_end_times = [[entry["end"] for entry in el] for el in predicted_timestamps]
+        torch.testing.assert_close(predicted_start_times, EXPECTED_START_TIMESTAMPS)
+        torch.testing.assert_close(predicted_end_times, EXPECTED_END_TIMESTAMPS)
+
+    @slow
+    def test_tdt_model_integration_loss(self):
+        """
+        Verify that ParakeetForTDT loss matches NeMo's TDT loss (sigma=0) for both
+        the CUDA kernel and the pure PyTorch implementation.
+        reproducer: https://gist.github.com/883ea42bf7d8ce2af42f3055627476a7
+        """
+        from transformers.loss.loss_tdt import _load_tdt_kernel
+
+        RESULTS_PATH = Path(__file__).parent.parent.parent / "fixtures/parakeet/expected_loss_tdt.json"
+        with open(RESULTS_PATH, "r") as f:
+            raw_data = json.load(f)
+        EXPECTED_MEAN_LOSS = torch.tensor(raw_data["expected_mean_loss"])
+        num_samples = raw_data["num_samples"]
+
+        samples = self._load_datasamples(num_samples)
+        transcripts = self._dataset.sort("id")[:num_samples]["text"]
+        transcripts = [t.lower() for t in transcripts]
+
+        # Use float32 for loss precision
+        model = ParakeetForTDT.from_pretrained(self.checkpoint_name, dtype=torch.float32, device_map="auto")
+
+        inputs = self.processor(
+            audio=samples,
+            text=transcripts,
+            sampling_rate=self.processor.feature_extractor.sampling_rate,
+        )
+        inputs.to(model.device)
+
+        # Test both backends: kernel (if available) and pure PyTorch
+        has_kernel = _load_tdt_kernel() is not None
+        backends = [
+            ("kernel", None),
+            ("torch", patch("transformers.loss.loss_tdt._load_tdt_kernel", return_value=None)),
+        ]
+        if not has_kernel:
+            backends = backends[1:]  # skip kernel test when not installed
+
+        for backend_name, ctx in backends:
+            with self.subTest(backend=backend_name):
+                ctx_manager = ctx if ctx is not None else nullcontext()
+                with ctx_manager:
+                    # Forward in eval mode — check loss matches NeMo
+                    model.eval()
+                    with torch.no_grad():
+                        outputs = model(**inputs)
+                    self.assertIsNotNone(outputs.loss, "Loss must be computed when labels are provided")
+                    self.assertEqual(outputs.logits.dim(), 4, "Training logits must be 4D (B, T, U+1, V+D)")
+                    torch.testing.assert_close(outputs.loss.cpu(), EXPECTED_MEAN_LOSS, rtol=1e-3, atol=1e-3)
+
+                    # Backward — verify gradients flow
+                    del outputs
+                    torch.cuda.empty_cache()
+                    model.train()
+                    model.zero_grad()
+                    outputs = model(**inputs)
+                    outputs.loss.backward()
+                    n_with_grad = sum(1 for p in model.parameters() if p.grad is not None)
+                    self.assertGreater(n_with_grad, 0, "No gradients after backward")
diff --git a/tests/models/penguinvl/__init__.py b/tests/models/penguinvl/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/penguinvl/test_image_processing_penguinvl.py b/tests/models/penguinvl/test_image_processing_penguinvl.py
new file mode 100644
index 000000000000..453cb368731a
--- /dev/null
+++ b/tests/models/penguinvl/test_image_processing_penguinvl.py
@@ -0,0 +1,418 @@
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import itertools
+import json
+import tempfile
+import unittest
+
+import numpy as np
+import requests
+
+from transformers.image_utils import IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
+from transformers.models.penguinvl.image_processing_penguinvl import smart_resize
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import PenguinVLImageProcessor
+
+    if is_torchvision_available():
+        from transformers import PenguinVLImageProcessorFast
+
+
+class PenguinVLImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        num_frames=4,
+        min_resolution=56,
+        max_resolution=1024,
+        min_pixels=14 * 14 * 16,
+        max_pixels=14 * 14 * 16384,
+        do_normalize=True,
+        image_mean=IMAGENET_STANDARD_MEAN,
+        image_std=IMAGENET_STANDARD_STD,
+        do_resize=True,
+        patch_size=14,
+        merge_size=1,
+        do_convert_rgb=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.num_channels = num_channels
+        self.num_frames = num_frames
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.min_pixels = min_pixels
+        self.max_pixels = max_pixels
+        self.patch_size = patch_size
+        self.merge_size = merge_size
+        self.do_resize = do_resize
+        self.do_normalize = do_normalize
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "min_pixels": self.min_pixels,
+            "max_pixels": self.max_pixels,
+            "patch_size": self.patch_size,
+            "merge_size": self.merge_size,
+        }
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+    def prepare_video_clip(self, num_frames=None, equal_resolution=True, numpify=False, torchify=False):
+        """Prepare a single video clip as a list of frames."""
+        n = num_frames if num_frames is not None else self.num_frames
+        frames = prepare_image_inputs(
+            batch_size=n,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+        return frames
+
+
+@require_torch
+@require_vision
+class PenguinVLImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = PenguinVLImageProcessor if is_vision_available() else None
+    fast_image_processing_class = PenguinVLImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = PenguinVLImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "do_convert_rgb"))
+            self.assertTrue(hasattr(image_processing, "patch_size"))
+            self.assertTrue(hasattr(image_processing, "merge_size"))
+            self.assertTrue(hasattr(image_processing, "min_pixels"))
+            self.assertTrue(hasattr(image_processing, "max_pixels"))
+
+    def test_image_processor_to_json_string(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class(**self.image_processor_dict)
+            obj = json.loads(image_processor.to_json_string())
+            for key, value in self.image_processor_dict.items():
+                if key not in ["min_pixels", "max_pixels"]:
+                    self.assertEqual(obj[key], value)
+
+    def test_smart_resize(self):
+        best_resolution = smart_resize(561, 278, factor=28)
+        self.assertEqual(best_resolution, (560, 280))
+
+        h, w = smart_resize(300, 400, factor=14)
+        self.assertEqual(h % 14, 0)
+        self.assertEqual(w % 14, 0)
+
+        min_pixels = 56 * 56
+        max_pixels = 28 * 28 * 1280
+        h, w = smart_resize(100, 100, factor=14, min_pixels=min_pixels, max_pixels=max_pixels)
+        self.assertGreaterEqual(h * w, min_pixels)
+        self.assertLessEqual(h * w, max_pixels)
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test single image (not batched)
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (5329, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 73, 73]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (15463, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 47, 47]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test single image
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (5329, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 73, 73]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (15463, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 47, 47]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test single image
+            process_out = image_processing(image_inputs[0], return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (5329, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 73, 73]])
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+            # Test batched
+            process_out = image_processing(image_inputs, return_tensors="pt")
+            encoded_images = process_out.pixel_values
+            image_grid_thws = process_out.image_grid_thw
+            expected_output_image_shape = (15463, 588)
+            expected_image_grid_thws = torch.Tensor([[1, 47, 47]] * 7)
+            self.assertEqual(tuple(encoded_images.shape), expected_output_image_shape)
+            self.assertTrue((image_grid_thws == expected_image_grid_thws).all())
+
+    @unittest.skip(reason="PenguinVLImageProcessor doesn't treat 4-channel PIL and numpy consistently")
+    def test_call_numpy_4_channels(self):
+        pass
+
+    def test_video_inputs(self):
+        """Test processing a single video clip (nested list [[frame1, frame2, ...]])."""
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            frames = self.image_processor_tester.prepare_video_clip(num_frames=4, equal_resolution=True)
+            # Wrap in outer list to form a single clip
+            video_clip = [frames]
+
+            process_out = image_processing(video_clip, merge_size=2, return_tensors="pt")
+            image_grid_thws = process_out.image_grid_thw
+            image_merge_sizes = process_out.image_merge_sizes
+            num_frames_per_clip = process_out.num_frames_per_clip
+
+            # 4 frames → 4 entries in image_grid_thw
+            self.assertEqual(image_grid_thws.shape[0], 4)
+            # All frames in the clip should have merge_size=2
+            self.assertTrue((image_merge_sizes == 2).all())
+            # 1 clip with 4 frames
+            self.assertEqual(len(num_frames_per_clip), 1)
+            self.assertEqual(num_frames_per_clip[0], 4)
+
+    def test_multi_image_inputs(self):
+        """Test processing multiple independent images (list [img1, img2, img3])."""
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)[:3]
+
+            process_out = image_processing(images, merge_size=1, return_tensors="pt")
+            image_grid_thws = process_out.image_grid_thw
+            image_merge_sizes = process_out.image_merge_sizes
+            num_frames_per_clip = process_out.num_frames_per_clip
+
+            # 3 independent images → 3 clips of 1 frame each
+            self.assertEqual(image_grid_thws.shape[0], 3)
+            self.assertTrue((image_merge_sizes == 1).all())
+            self.assertEqual(len(num_frames_per_clip), 3)
+            for n in num_frames_per_clip:
+                self.assertEqual(n, 1)
+
+    def test_nested_clip_inputs(self):
+        """Test mixed nested input: [[image], [frame1, frame2, frame3]] for one image + one video."""
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)[:4]
+            # First clip is a single image; second clip is a 3-frame video
+            nested_clips = [[images[0]], [images[1], images[2], images[3]]]
+
+            process_out = image_processing(nested_clips, merge_size=[1, 2], return_tensors="pt")
+            num_frames_per_clip = process_out.num_frames_per_clip
+            image_merge_sizes = process_out.image_merge_sizes
+
+            self.assertEqual(len(num_frames_per_clip), 2)
+            self.assertEqual(num_frames_per_clip[0], 1)  # single image clip
+            self.assertEqual(num_frames_per_clip[1], 3)  # video clip
+
+            # First frame should have merge_size=1, last 3 frames merge_size=2
+            self.assertEqual(int(image_merge_sizes[0]), 1)
+            self.assertTrue((image_merge_sizes[1:] == 2).all())
+
+    def test_frame_types(self):
+        """Test TRA (Temporal Redundancy-Aware) processing with frame type annotations."""
+        if self.image_processing_class is None:
+            self.skipTest("image_processing_class is None")
+
+        image_processing = self.image_processing_class(**self.image_processor_dict)
+        frames = self.image_processor_tester.prepare_video_clip(num_frames=4, equal_resolution=True)
+        video_clip = [frames]
+
+        # 4-frame video: frame_types 0=keyframe, 1=intermediate
+        frame_types = [[0, 1, 0, 1]]
+
+        # Without frame types
+        out_no_ft = image_processing(video_clip, merge_size=2, return_tensors="pt")
+        # With frame types
+        out_with_ft = image_processing(video_clip, merge_size=2, frame_types=frame_types, return_tensors="pt")
+
+        # Both should produce the same number of grid entries (one per frame)
+        self.assertEqual(out_no_ft.image_grid_thw.shape[0], out_with_ft.image_grid_thw.shape[0])
+
+        # Keyframes (type 0) should have higher or equal resolution than intermediate frames (type 1)
+        grids = out_with_ft.image_grid_thw
+        for i, ft in enumerate(frame_types[0]):
+            grid_area = int(grids[i][1]) * int(grids[i][2])
+            if ft == 0:
+                # Keyframe area >= intermediate frame area in same clip
+                for j, ft_j in enumerate(frame_types[0]):
+                    if ft_j == 1:
+                        inter_area = int(grids[j][1]) * int(grids[j][2])
+                        self.assertGreaterEqual(grid_area, inter_area)
+
+    def test_custom_image_size(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                image_processing.save_pretrained(tmpdirname)
+                image_processor_loaded = image_processing_class.from_pretrained(
+                    tmpdirname, max_pixels=56 * 56, min_pixels=28 * 28
+                )
+
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            process_out = image_processor_loaded(image_inputs, return_tensors="pt")
+            expected_output_image_shape = [63, 588]
+            self.assertListEqual(list(process_out.pixel_values.shape), expected_output_image_shape)
+
+    def test_custom_pixels(self):
+        pixel_choices = frozenset(itertools.product((100, 150, 200, 20000), (100, 150, 200, 20000)))
+        for image_processing_class in self.image_processor_list:
+            image_processor_dict = self.image_processor_dict.copy()
+            for a_pixels, b_pixels in pixel_choices:
+                image_processor_dict["min_pixels"] = min(a_pixels, b_pixels)
+                image_processor_dict["max_pixels"] = max(a_pixels, b_pixels)
+                image_processor = image_processing_class(**image_processor_dict)
+                image_inputs = self.image_processor_tester.prepare_image_inputs()
+                image_processor(image_inputs, return_tensors="pt")
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_equivalence(self):
+        dummy_image = Image.open(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
+
+        self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
+        self.assertEqual(encoding_slow.image_grid_thw.dtype, encoding_fast.image_grid_thw.dtype)
+        self._assert_slow_fast_tensors_equivalence(
+            encoding_slow.image_grid_thw.float(), encoding_fast.image_grid_thw.float()
+        )
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_equivalence_batched(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
+
+        self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
+        self.assertEqual(encoding_slow.image_grid_thw.dtype, encoding_fast.image_grid_thw.dtype)
+        self._assert_slow_fast_tensors_equivalence(
+            encoding_slow.image_grid_thw.float(), encoding_fast.image_grid_thw.float()
+        )
+
+    def test_get_num_patches_without_images(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+
+            num_patches = image_processing.get_number_of_image_patches(height=100, width=100, images_kwargs={})
+            self.assertEqual(num_patches, 49)
+
+            num_patches = image_processing.get_number_of_image_patches(height=200, width=50, images_kwargs={})
+            self.assertEqual(num_patches, 56)
+
+            num_patches = image_processing.get_number_of_image_patches(
+                height=100, width=100, images_kwargs={"patch_size": 28}
+            )
+            self.assertEqual(num_patches, 16)
diff --git a/tests/models/penguinvl/test_modeling_penguinvl.py b/tests/models/penguinvl/test_modeling_penguinvl.py
new file mode 100644
index 000000000000..bd6ca39d1c56
--- /dev/null
+++ b/tests/models/penguinvl/test_modeling_penguinvl.py
@@ -0,0 +1,594 @@
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch PenguinVL model."""
+
+import copy
+import gc
+import tempfile
+import unittest
+
+import numpy as np
+import requests
+import torch.nn as nn
+from parameterized import parameterized
+from PIL import Image
+
+from transformers import (
+    PenguinVLForConditionalGeneration,
+    PenguinVLVisionConfig,
+    PenguinVLVisionModel,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    backend_empty_cache,
+    require_torch,
+    set_config_for_less_flaky_test,
+    set_model_for_less_flaky_test,
+    slow,
+    torch_device,
+)
+from transformers.utils import (
+    is_torch_bf16_available_on_device,
+    is_torch_fp16_available_on_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    floats_tensor,
+    sdpa_kernel,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+def _test_penguin_vision_sdpa_inference(
+    self,
+    dtype,
+    output_attentions,
+    enable_kernels,
+    atols=None,
+    rtols=None,
+):
+    """Custom SDPA inference test for PenguinVLVisionModel.
+
+    The vision model uses packed sequences (pixel_values has shape
+    [total_tokens, channels*patch_size^2]), so the generic padded-batch test
+    cannot be used directly.
+    """
+    if not self.has_attentions:
+        self.skipTest(reason="Model architecture does not support attentions")
+
+    if not self.all_model_classes[0]._supports_sdpa:
+        self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
+
+    if dtype == "fp16":
+        dtype = torch.float16
+    elif dtype == "bf16":
+        dtype = torch.bfloat16
+    elif dtype == "fp32":
+        dtype = torch.float32
+
+    if not is_torch_fp16_available_on_device(torch_device) and dtype == torch.float16:
+        self.skipTest(f"float16 not supported on {torch_device}")
+
+    if not is_torch_bf16_available_on_device(torch_device) and dtype == torch.bfloat16:
+        self.skipTest(f"bfloat16 not supported on {torch_device}")
+
+    if atols is None:
+        atols = {
+            ("cpu", False, torch.float32): 1e-6,
+            ("cpu", False, torch.float16): 5e-3,
+            ("cpu", False, torch.bfloat16): 1e-2,
+            ("cpu", True, torch.float32): 1e-6,
+            ("cpu", True, torch.float16): 5e-3,
+            ("cpu", True, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float32): 1e-6,
+            ("cuda", False, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float16): 5e-3,
+            ("cuda", True, torch.float32): 1e-6,
+            ("cuda", True, torch.bfloat16): 1e-2,
+            ("cuda", True, torch.float16): 5e-3,
+        }
+    if rtols is None:
+        rtols = {
+            ("cpu", False, torch.float32): 1e-4,
+            ("cpu", False, torch.float16): 5e-3,
+            ("cpu", False, torch.bfloat16): 1e-2,
+            ("cpu", True, torch.float32): 1e-4,
+            ("cpu", True, torch.float16): 5e-3,
+            ("cpu", True, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float32): 1e-4,
+            ("cuda", False, torch.bfloat16): 1e-2,
+            ("cuda", False, torch.float16): 5e-3,
+            ("cuda", True, torch.float32): 1e-4,
+            ("cuda", True, torch.bfloat16): 3e-2,
+            ("cuda", True, torch.float16): 5e-3,
+        }
+
+    for model_class in self.all_model_classes:
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        set_config_for_less_flaky_test(config)
+
+        model = model_class(config)
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_pretrained(tmpdirname)
+            model_sdpa = model_class.from_pretrained(tmpdirname, dtype=dtype, attn_implementation="sdpa")
+            model_sdpa = model_sdpa.eval().to(torch_device)
+            model_eager = model_class.from_pretrained(tmpdirname, dtype=dtype, attn_implementation="eager")
+            model_eager = model_eager.eval().to(torch_device)
+
+        set_model_for_less_flaky_test(model_eager)
+        set_model_for_less_flaky_test(model_sdpa)
+
+        for batch_size in [7]:
+            processed_inputs = {}
+            for key in [model.main_input_name] + list(getattr(self, "additional_model_inputs", [])):
+                if key in inputs_dict:
+                    processed_inputs[key] = inputs_dict[key]
+
+            # Truncate grid_thw and merge_sizes to batch_size images
+            for key in ["grid_thw", "merge_sizes"]:
+                if key in processed_inputs:
+                    value = processed_inputs[key]
+                    if value.shape[0] > batch_size:
+                        processed_inputs[key] = value[:batch_size].to(torch_device)
+
+            # Adjust pixel_values to exactly match the token count from grid_thw
+            target_len = torch.sum(
+                processed_inputs["grid_thw"].prod(dim=1) // (processed_inputs["merge_sizes"] ** 2)
+            ).item()
+            pixel_values = processed_inputs["pixel_values"]
+            if pixel_values.size(0) > target_len:
+                pixel_values = pixel_values[:target_len]
+            processed_inputs["pixel_values"] = pixel_values.to(dtype=dtype, device=torch_device)
+
+            processed_inputs.update(
+                {
+                    "output_hidden_states": True,
+                    "output_attentions": output_attentions,
+                }
+            )
+
+            with torch.no_grad():
+                with sdpa_kernel(
+                    enable_flash=enable_kernels,
+                    enable_math=True,
+                    enable_mem_efficient=enable_kernels,
+                ):
+                    prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
+                    prepared_inputs = {
+                        k: v.to(torch_device) if isinstance(v, torch.Tensor) else v for k, v in prepared_inputs.items()
+                    }
+                    outputs_eager = model_eager(**prepared_inputs)
+                    outputs_sdpa = model_sdpa(**prepared_inputs)
+
+            logits_eager = outputs_eager["hidden_states"][-1]
+            logits_sdpa = outputs_sdpa["hidden_states"][-1]
+
+            if torch_device in ["cpu", "cuda"]:
+                atol = atols[torch_device, enable_kernels, dtype]
+                rtol = rtols[torch_device, enable_kernels, dtype]
+            else:
+                atol = 1e-7
+                rtol = 1e-4
+
+            outputs_magnitude = float(
+                (torch.max(logits_sdpa.abs().amax(), logits_eager.abs().amax())).detach().to("cpu")
+            )
+            computed_atol = outputs_magnitude * 3e-2
+            if dtype == torch.bfloat16:
+                atol = max(atol, computed_atol)
+
+            results = [
+                torch.allclose(_logits_sdpa, _logits_eager, atol=atol, rtol=rtol)
+                for (_logits_sdpa, _logits_eager) in zip(logits_sdpa, logits_eager)
+            ]
+
+            if np.mean(results) < 0.8:
+                mean_relative_diff = ((logits_sdpa - logits_eager).abs() / (logits_eager.abs() + 1e-12)).mean()
+                raise ValueError(
+                    f"mean relative difference for hidden_states: {mean_relative_diff:.3e}, "
+                    f"torch atol = {atol}, torch rtol = {rtol}"
+                )
+
+
+class PenguinVLVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        patch_size=2,
+        num_channels=3,
+        image_size=14,
+        is_training=True,
+        hidden_size=64,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        num_key_value_heads=4,
+        head_dim=16,
+        intermediate_size=37,
+        attention_dropout=0.0,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.head_dim = head_dim
+        self.intermediate_size = intermediate_size
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+        self.seq_length = (self.image_size // self.patch_size) ** 2
+
+    def get_config(self):
+        return PenguinVLVisionConfig(
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            num_key_value_heads=self.num_key_value_heads,
+            head_dim=self.head_dim,
+            intermediate_size=self.intermediate_size,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.patch_size
+        pixel_values = floats_tensor(
+            [
+                self.batch_size * (self.image_size**2) // (patch_size**2),
+                self.num_channels * (patch_size**2),
+            ]
+        )
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        num_patches = self.image_size // config.patch_size
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "grid_thw": torch.tensor([[1, num_patches, num_patches]] * self.batch_size, device=torch_device),
+            "merge_sizes": torch.tensor([1] * self.batch_size, device=torch_device),
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PenguinVLVisionModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (PenguinVLVisionModel,) if is_torch_available() else ()
+    additional_model_inputs = ["grid_thw", "merge_sizes"]
+    test_resize_embeddings = False
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+
+    def setUp(self):
+        self.model_tester = PenguinVLVisionModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PenguinVLVisionConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_attention_outputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        config._attn_implementation = "eager"
+
+        seq_len = getattr(self.model_tester, "seq_length", None)
+        encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
+        encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            for k in config.sub_configs:
+                getattr(config, k).output_attentions = True
+
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            self.assertListEqual(
+                list(attentions[0][0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+            out_len = len(outputs)
+
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = True
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            self.assertEqual(out_len + 1, len(outputs))
+
+            self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+
+            self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
+            self.assertListEqual(
+                list(self_attentions[0][0].shape[-3:]),
+                [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
+            )
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    def test_eager_matches_sdpa_inference(
+        self, name, dtype, padding_side, use_attention_mask, output_attentions, enable_kernels
+    ):
+        if use_attention_mask:
+            self.skipTest(reason="PenguinVLVisionModel does not use attention masks")
+        _test_penguin_vision_sdpa_inference(self, dtype, output_attentions, enable_kernels)
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(copy.deepcopy(config))
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            seq_length = torch.sum(inputs_dict["grid_thw"].prod(dim=1) // (inputs_dict["merge_sizes"] ** 2))
+            # The vision encoder processes tokens with a batch dimension of 1 added internally,
+            # so captured hidden states have shape [1, seq_length, hidden_size].
+            self.assertListEqual(
+                list(hidden_states[0].shape),
+                [1, seq_length, self.model_tester.hidden_size],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            for k in config.sub_configs:
+                getattr(config, k).output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    def test_retain_grad_hidden_states_attentions(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for k in config.sub_configs:
+            getattr(config, k).output_hidden_states = True
+
+        config.output_hidden_states = True
+        config.output_attentions = self.has_attentions
+
+        for k in config.sub_configs:
+            getattr(config, k).output_attentions = self.has_attentions
+
+        config._attn_implementation = "eager"
+
+        model_class = self.all_model_classes[0]
+        model = model_class._from_config(config, attn_implementation="eager")
+        model.to(torch_device)
+
+        inputs = self._prepare_for_class(inputs_dict, model_class)
+
+        outputs = model(**inputs)
+
+        output = outputs[0]
+
+        hidden_states = outputs.hidden_states[0]
+        hidden_states.retain_grad()
+
+        if self.has_attentions:
+            attentions = outputs.attentions[0][0]
+            attentions.retain_grad()
+
+        output.flatten()[0].backward(retain_graph=True)
+
+        self.assertIsNotNone(hidden_states.grad)
+
+        if self.has_attentions:
+            self.assertIsNotNone(attentions.grad)
+
+    @unittest.skip("DataParallel is not compatible with the packed sequence input format of PenguinVLVisionModel")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @unittest.skip("Vision model requires additional positional inputs (grid_thw and merge_sizes)")
+    def test_flash_attn_2_inference_equivalence(self):
+        pass
+
+    @unittest.skip("Vision model requires additional positional inputs (grid_thw and merge_sizes)")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip("Vision model requires additional positional inputs (grid_thw and merge_sizes)")
+    def test_flash_attn_kernels_inference_equivalence(self):
+        pass
+
+
+@require_torch
+@slow
+class PenguinVLIntegrationTest(unittest.TestCase):
+    model_id = "tencent/Penguin-VL-8B"
+
+    def setUp(self):
+        from transformers import PenguinVLProcessor
+
+        self.processor = PenguinVLProcessor.from_pretrained(self.model_id, trust_remote_code=True)
+        self.image_url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        self.image = Image.open(requests.get(self.image_url, stream=True).raw)
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_small_model_integration_test_single_image(self):
+        model = PenguinVLForConditionalGeneration.from_pretrained(
+            self.model_id, dtype=torch.bfloat16, device_map=torch_device
+        )
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": self.image},
+                    {"type": "text", "text": "Describe the image in one sentence."},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt").to(
+            torch_device
+        )
+
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        decoded = self.processor.decode(output[0], skip_special_tokens=True)
+        EXPECTED_DECODED_TEXT = "user\n\nDescribe the image in one sentence.\nassistant\n<think>\n\n</think>\n\nTwo cats are sleeping on a pink couch next to two remote controls."
+        self.assertEqual(decoded, EXPECTED_DECODED_TEXT)
+
+    def test_small_model_integration_test_multi_image(self):
+        """Tests that the model can handle prompts with multiple images."""
+        model = PenguinVLForConditionalGeneration.from_pretrained(
+            self.model_id, dtype=torch.bfloat16, device_map=torch_device
+        )
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": self.image},
+                    {"type": "image", "image": self.image.resize((224, 224))},
+                    {"type": "text", "text": "Are these two images the same?"},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt").to(
+            torch_device
+        )
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        decoded = self.processor.decode(output[0], skip_special_tokens=True)
+        EXPECTED_DECODED_TEXT = "user\n\n\nAre these two images the same?\nassistant\n<think>\n\n</think>\n\nYes, these two images are the same. They both show two cats lying on a pink couch with"
+        self.assertEqual(decoded, EXPECTED_DECODED_TEXT)
+
+    def test_small_model_integration_test_video(self):
+        """Tests that the model can handle video input (multi-frame clip)."""
+        model = PenguinVLForConditionalGeneration.from_pretrained(
+            self.model_id, dtype=torch.bfloat16, device_map=torch_device
+        )
+
+        # Use the same image duplicated as "video frames"
+        frames = [self.image.resize((224, 224))] * 4
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": frames, "timestamps": [0, 1, 2, 3]},
+                    {"type": "text", "text": "Describe what you see in this video."},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt").to(
+            torch_device
+        )
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        decoded = self.processor.decode(output[0], skip_special_tokens=True)
+        EXPECTED_DECODED_TEXT = "user\nTime 0s:,Time 1s:,Time 2s:,Time 3s:\nDescribe what you see in this video.\nassistant\n<think>\n\n</think>\n\nThe video features a serene and heartwarming scene of two cats lounging on a bright pink couch"
+        self.assertEqual(decoded, EXPECTED_DECODED_TEXT)
+
+    def test_small_model_integration_test_batch(self):
+        """Tests batched inference with the same image."""
+        model = PenguinVLForConditionalGeneration.from_pretrained(
+            self.model_id, dtype=torch.bfloat16, device_map=torch_device
+        )
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": self.image},
+                    {"type": "text", "text": "Describe the image."},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        inputs = self.processor(
+            images=images * 2,
+            text=[text, text],
+            frame_types=frame_types * 2,
+            padding=True,
+            return_tensors="pt",
+        ).to(torch_device)
+
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        decoded = self.processor.batch_decode(output, skip_special_tokens=True)
+        EXPECTED_DECODED_TEXT = [
+            "user\n\nDescribe the image.\nassistant\n<think>\n\n</think>\n\nThe image shows two cats lying on a bright pink surface, likely a couch or bed. Both cats",
+            "user\n\nDescribe the image.\nassistant\n<think>\n\n</think>\n\nThe image shows two cats lying on a bright pink surface, likely a couch or bed. Both cats",
+        ]
+        self.assertEqual(decoded, EXPECTED_DECODED_TEXT)
diff --git a/tests/models/penguinvl/test_processing_penguinvl.py b/tests/models/penguinvl/test_processing_penguinvl.py
new file mode 100644
index 000000000000..1a15cc82b572
--- /dev/null
+++ b/tests/models/penguinvl/test_processing_penguinvl.py
@@ -0,0 +1,558 @@
+# Copyright 2025 Tencent and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from PIL import Image
+
+from transformers.testing_utils import require_torch, require_vision, slow
+from transformers.utils import is_torch_available, is_vision_available
+
+
+if is_vision_available():
+    from transformers import PenguinVLImageProcessor, PenguinVLProcessor
+    from transformers.models.penguinvl.image_processing_penguinvl import _make_batched_clips
+
+if is_torch_available():
+    import torch
+
+
+def _make_dummy_pil_image(width=224, height=224, mode="RGB"):
+    arr = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
+    return Image.fromarray(arr)
+
+
+@require_vision
+@require_torch
+class MakeBatchedClipsTest(unittest.TestCase):
+    """Unit tests for the _make_batched_clips helper function."""
+
+    def test_single_image(self):
+        img = _make_dummy_pil_image()
+        result = _make_batched_clips(img)
+        self.assertEqual(len(result), 1)
+        self.assertEqual(len(result[0]), 1)
+        self.assertIs(result[0][0], img)
+
+    def test_list_of_images(self):
+        images = [_make_dummy_pil_image() for _ in range(3)]
+        result = _make_batched_clips(images)
+        self.assertEqual(len(result), 3)
+        for i, clip in enumerate(result):
+            self.assertEqual(len(clip), 1)
+            self.assertIs(clip[0], images[i])
+
+    def test_nested_clips(self):
+        img1 = _make_dummy_pil_image()
+        frames = [_make_dummy_pil_image() for _ in range(4)]
+        nested = [[img1], frames]
+        result = _make_batched_clips(nested)
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 1)
+        self.assertEqual(len(result[1]), 4)
+
+
+@require_vision
+@require_torch
+class PenguinVLImageProcessorTest(unittest.TestCase):
+    """Tests for PenguinVLImageProcessor with image/video/multi-image inputs."""
+
+    def setUp(self):
+        self.image_processor = PenguinVLImageProcessor(
+            min_pixels=28 * 28,
+            max_pixels=56 * 56 * 4,
+            patch_size=14,
+            merge_size=1,
+        )
+
+    def test_single_image_output_keys(self):
+        img = _make_dummy_pil_image(224, 224)
+        out = self.image_processor(img, return_tensors="pt")
+        self.assertIn("pixel_values", out)
+        self.assertIn("image_grid_thw", out)
+        self.assertIn("image_merge_sizes", out)
+        self.assertIn("num_frames_per_clip", out)
+
+    def test_single_image_shapes(self):
+        img = _make_dummy_pil_image(224, 224)
+        out = self.image_processor(img, return_tensors="pt")
+        # pixel_values: [num_patches, C*P^2]
+        self.assertEqual(out.pixel_values.ndim, 2)
+        # image_grid_thw: [1, 3] — one entry for the single image
+        self.assertEqual(out.image_grid_thw.shape[0], 1)
+        self.assertEqual(out.image_grid_thw.shape[1], 3)
+        # merge_sizes: [1]
+        self.assertEqual(out.image_merge_sizes.shape[0], 1)
+
+    def test_multi_image_output_shapes(self):
+        images = [_make_dummy_pil_image(224, 224) for _ in range(3)]
+        out = self.image_processor(images, merge_size=1, return_tensors="pt")
+        # 3 images → 3 entries in grid_thw
+        self.assertEqual(out.image_grid_thw.shape[0], 3)
+        self.assertEqual(len(out.num_frames_per_clip), 3)
+        for n in out.num_frames_per_clip:
+            self.assertEqual(n, 1)
+
+    def test_video_clip_output_shapes(self):
+        frames = [_make_dummy_pil_image(112, 112) for _ in range(4)]
+        video_clip = [frames]  # wrap in outer list to form one clip
+        out = self.image_processor(video_clip, merge_size=2, return_tensors="pt")
+        # 4 frames → 4 entries in grid_thw
+        self.assertEqual(out.image_grid_thw.shape[0], 4)
+        # All frames should have merge_size=2
+        self.assertTrue((out.image_merge_sizes == 2).all())
+        # 1 clip
+        self.assertEqual(len(out.num_frames_per_clip), 1)
+        self.assertEqual(out.num_frames_per_clip[0], 4)
+
+    def test_mixed_image_and_video(self):
+        """Test nested input: [[single_image], [frame1, frame2, frame3]]."""
+        img = _make_dummy_pil_image(112, 112)
+        frames = [_make_dummy_pil_image(112, 112) for _ in range(3)]
+        nested = [[img], frames]
+        out = self.image_processor(nested, merge_size=[1, 2], return_tensors="pt")
+        # 1 + 3 = 4 total frame entries
+        self.assertEqual(out.image_grid_thw.shape[0], 4)
+        self.assertEqual(len(out.num_frames_per_clip), 2)
+        self.assertEqual(out.num_frames_per_clip[0], 1)
+        self.assertEqual(out.num_frames_per_clip[1], 3)
+        # First frame: merge_size=1, rest: merge_size=2
+        self.assertEqual(int(out.image_merge_sizes[0]), 1)
+        self.assertTrue((out.image_merge_sizes[1:] == 2).all())
+
+    def test_frame_types_change_resolution(self):
+        """Key frames should have same or higher resolution than intermediate frames."""
+        frames = [_make_dummy_pil_image(112, 112) for _ in range(4)]
+        video_clip = [frames]
+        frame_types = [[0, 1, 0, 1]]  # 0=keyframe, 1=intermediate
+
+        out = self.image_processor(video_clip, merge_size=2, frame_types=frame_types, return_tensors="pt")
+        grids = out.image_grid_thw  # [4, 3]
+
+        key_area = int(grids[0][1]) * int(grids[0][2])
+        inter_area = int(grids[1][1]) * int(grids[1][2])
+        self.assertGreaterEqual(key_area, inter_area)
+
+    def test_different_sized_images(self):
+        """Test that images of different sizes are handled correctly."""
+        images = [
+            _make_dummy_pil_image(112, 112),
+            _make_dummy_pil_image(224, 112),
+            _make_dummy_pil_image(56, 168),
+        ]
+        out = self.image_processor(images, return_tensors="pt")
+        # Should succeed with 3 entries
+        self.assertEqual(out.image_grid_thw.shape[0], 3)
+
+    def test_return_tensors_pt(self):
+        img = _make_dummy_pil_image(112, 112)
+        out = self.image_processor(img, return_tensors="pt")
+        self.assertIsInstance(out.pixel_values, torch.Tensor)
+        self.assertIsInstance(out.image_grid_thw, torch.Tensor)
+
+    def test_return_tensors_np(self):
+        img = _make_dummy_pil_image(112, 112)
+        out = self.image_processor(img, return_tensors="np")
+        self.assertIsInstance(out.pixel_values, np.ndarray)
+
+
+@require_vision
+@require_torch
+class PenguinVLProcessorUnitTest(unittest.TestCase):
+    """
+    Unit tests for PenguinVLProcessor that do not require a pre-trained tokenizer.
+    These tests verify the image token expansion logic and process_vision_info.
+    """
+
+    @classmethod
+    def setUpClass(cls):
+        """Try to load a PenguinVL tokenizer for testing; skip if unavailable."""
+        try:
+            from transformers import AutoTokenizer
+
+            cls.tokenizer = AutoTokenizer.from_pretrained("tencent/Penguin-VL-8B", trust_remote_code=True)
+        except Exception:
+            cls.tokenizer = None
+
+    def _make_processor(self, min_pixels=28 * 28, max_pixels=56 * 56 * 4):
+        if self.tokenizer is None:
+            self.skipTest("PenguinVL tokenizer not available (requires network access)")
+        return PenguinVLProcessor.from_pretrained("tencent/Penguin-VL-8B", trust_remote_code=True)
+
+    def test_processor_attributes(self):
+        processor = self._make_processor()
+        self.assertTrue(hasattr(processor, "image_processor"))
+        self.assertTrue(hasattr(processor, "tokenizer"))
+        self.assertEqual(processor.image_token, "<image>")
+        self.assertEqual(processor.image_merge_size, 1)
+        self.assertEqual(processor.video_merge_size, 2)
+
+    def test_processor_model_input_names(self):
+        processor = self._make_processor()
+        input_names = processor.model_input_names
+        self.assertIn("input_ids", input_names)
+        self.assertIn("pixel_values", input_names)
+
+    def test_process_vision_info_single_image(self):
+        processor = self._make_processor()
+        img = _make_dummy_pil_image(112, 112)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": img},
+                    {"type": "text", "text": "Describe this image."},
+                ],
+            }
+        ]
+        images, frame_types = processor.process_vision_info(messages)
+        self.assertIsNotNone(images)
+        self.assertEqual(len(images), 1)
+        self.assertEqual(len(images[0]), 1)
+        self.assertIsNone(frame_types[0])  # images have None frame_types
+
+    def test_process_vision_info_multi_image(self):
+        processor = self._make_processor()
+        img1 = _make_dummy_pil_image(112, 112)
+        img2 = _make_dummy_pil_image(224, 224)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": img1},
+                    {"type": "image", "image": img2},
+                    {"type": "text", "text": "Compare these images."},
+                ],
+            }
+        ]
+        images, frame_types = processor.process_vision_info(messages)
+        self.assertEqual(len(images), 2)
+        self.assertIsNone(frame_types[0])
+        self.assertIsNone(frame_types[1])
+
+    def test_process_vision_info_video_frames(self):
+        processor = self._make_processor()
+        frames = [_make_dummy_pil_image(112, 112) for _ in range(4)]
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": frames},
+                    {"type": "text", "text": "Describe this video."},
+                ],
+            }
+        ]
+        images, frame_types = processor.process_vision_info(messages)
+        self.assertEqual(len(images), 1)
+        self.assertEqual(len(images[0]), 4)  # 4 frames in the clip
+        self.assertIsNotNone(frame_types[0])  # videos have frame_types
+        self.assertEqual(len(frame_types[0]), 4)
+        # First frame is always a keyframe (0)
+        self.assertEqual(frame_types[0][0], 0)
+
+    def test_process_vision_info_no_visuals(self):
+        processor = self._make_processor()
+        messages = [{"role": "user", "content": [{"type": "text", "text": "Hello!"}]}]
+        images, frame_types = processor.process_vision_info(messages)
+        self.assertIsNone(images)
+        self.assertIsNone(frame_types)
+
+    def test_processor_single_image_call(self):
+        processor = self._make_processor()
+        img = _make_dummy_pil_image(112, 112)
+
+        # Get the number of image tokens for this image
+        ip_out = processor.image_processor(img, return_tensors="pt")
+        thw = ip_out.image_grid_thw[0]
+        ms = int(ip_out.image_merge_sizes[0])
+        expected_tokens = int(thw[0]) * int(thw[1] // ms) * int(thw[2] // ms)
+
+        text = "<image>"
+        out = processor(images=img, text=text, return_tensors="pt")
+        self.assertIn("input_ids", out)
+        self.assertIn("pixel_values", out)
+        self.assertIn("image_grid_thw", out)
+
+        # Count image tokens in input_ids
+        image_token_id = processor.image_token_id
+        n_image_tokens = (out.input_ids == image_token_id).sum().item()
+        self.assertEqual(n_image_tokens, expected_tokens)
+
+    def test_processor_multi_image_call(self):
+        processor = self._make_processor()
+        images = [_make_dummy_pil_image(112, 112), _make_dummy_pil_image(56, 56)]
+        # Two image tokens in text, one per image
+        text = "<image><image>"
+
+        out = processor(images=images, text=text, return_tensors="pt")
+        self.assertIn("input_ids", out)
+        self.assertIn("pixel_values", out)
+
+        # image_grid_thw should have 2 entries (one per image)
+        self.assertEqual(out.image_grid_thw.shape[0], 2)
+
+    def test_processor_video_call(self):
+        processor = self._make_processor()
+        frames = [_make_dummy_pil_image(112, 112) for _ in range(3)]
+        # A video clip as a list of frames
+        video_clip = [frames]
+        text = "<image>"
+
+        out = processor(images=video_clip, text=text, return_tensors="pt")
+        self.assertIn("input_ids", out)
+        self.assertIn("pixel_values", out)
+        # Should have 3 frame entries in image_grid_thw
+        self.assertEqual(out.image_grid_thw.shape[0], 3)
+
+    def test_processor_batch_call(self):
+        processor = self._make_processor()
+        img1 = _make_dummy_pil_image(112, 112)
+        img2 = _make_dummy_pil_image(224, 224)
+
+        out = processor(
+            images=[img1, img2],
+            text=["<image>", "<image>"],
+            padding=True,
+            return_tensors="pt",
+        )
+        self.assertEqual(out.input_ids.shape[0], 2)
+        self.assertEqual(out.image_grid_thw.shape[0], 2)
+
+    def test_apply_chat_template(self):
+        processor = self._make_processor()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "Describe this image."},
+                ],
+            }
+        ]
+        text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+        EXPECTED_TEXT = (
+            "<|im_start|>user\n<image>\nDescribe this image.<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
+        )
+        self.assertEqual(text, EXPECTED_TEXT)
+
+    def test_convert_messages_for_chat_template_image(self):
+        processor = self._make_processor()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": "https://example.com/img.jpg"},
+                    {"type": "text", "text": "Describe."},
+                ],
+            }
+        ]
+        converted = processor._convert_messages_for_chat_template(messages)
+        content = converted[0]["content"]
+        image_items = [c for c in content if c.get("type") == "image"]
+        self.assertEqual(len(image_items), 1)
+        # URL should be stripped
+        self.assertEqual(image_items[0], {"type": "image"})
+
+    def test_convert_messages_for_chat_template_video_with_num_frames(self):
+        processor = self._make_processor()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "video",
+                        "video": "https://example.com/vid.mp4",
+                        "num_frames": 4,
+                        "timestamps": [0, 1, 2, 3],
+                    },
+                    {"type": "text", "text": "Describe."},
+                ],
+            }
+        ]
+        converted = processor._convert_messages_for_chat_template(messages)
+        content = converted[0]["content"]
+        video_items = [c for c in content if c.get("type") == "video"]
+        self.assertEqual(len(video_items), 1)
+        self.assertEqual(video_items[0]["num_frames"], 4)
+        self.assertEqual(video_items[0]["timestamps"], [0, 1, 2, 3])
+
+    def test_convert_messages_for_chat_template_video_without_num_frames(self):
+        """Video items without num_frames should fall back to plain image."""
+        processor = self._make_processor()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": "https://example.com/vid.mp4"},
+                    {"type": "text", "text": "Describe."},
+                ],
+            }
+        ]
+        converted = processor._convert_messages_for_chat_template(messages)
+        content = converted[0]["content"]
+        # Without num_frames, falls back to image type
+        self.assertEqual(content[0], {"type": "image"})
+
+    def test_batch_decode(self):
+        processor = self._make_processor()
+        # Just check batch_decode delegates to tokenizer
+        token_ids = [[1, 2, 3], [4, 5, 6]]
+        result = processor.batch_decode(token_ids, skip_special_tokens=True)
+        EXPECTED_TEXT = ['"#$', "%&'"]
+        self.assertEqual(result, EXPECTED_TEXT)
+
+    def test_decode(self):
+        processor = self._make_processor()
+        token_ids = [1, 2, 3]
+        result = processor.decode(token_ids, skip_special_tokens=True)
+        EXPECTED_TEXT = '"#$'
+        self.assertEqual(result, EXPECTED_TEXT)
+
+
+@require_vision
+@require_torch
+@slow
+class PenguinVLProcessorIntegrationTest(unittest.TestCase):
+    """
+    Integration tests for PenguinVLProcessor using the real PenguinVL model.
+    These tests require network access and the actual model checkpoint.
+    """
+
+    model_id = "tencent/Penguin-VL-8B"
+
+    @classmethod
+    def setUpClass(cls):
+        from transformers import PenguinVLProcessor
+
+        cls.processor = PenguinVLProcessor.from_pretrained(cls.model_id)
+
+    def _make_image(self, width=224, height=224):
+        arr = np.random.randint(0, 255, (height, width, 3), dtype=np.uint8)
+        return Image.fromarray(arr)
+
+    def test_process_single_image(self):
+        img = self._make_image()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": img},
+                    {"type": "text", "text": "What do you see?"},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        out = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+
+        self.assertIn("input_ids", out)
+        self.assertIn("pixel_values", out)
+        self.assertIn("image_grid_thw", out)
+        self.assertEqual(out.image_grid_thw.shape[0], 1)
+
+    def test_process_multi_image(self):
+        img1 = self._make_image(224, 224)
+        img2 = self._make_image(336, 224)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": img1},
+                    {"type": "image", "image": img2},
+                    {"type": "text", "text": "Are these the same?"},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        out = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+
+        # 2 images → 2 entries in image_grid_thw
+        self.assertEqual(out.image_grid_thw.shape[0], 2)
+
+    def test_process_video_frames(self):
+        frames = [self._make_image(112, 112) for _ in range(6)]
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "video", "video": frames},
+                    {"type": "text", "text": "What happens in this video?"},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        out = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+
+        # 6 video frames → 6 entries in image_grid_thw
+        self.assertEqual(out.image_grid_thw.shape[0], 6)
+        # Video uses video_merge_size=2
+        self.assertTrue((out.image_merge_sizes == 2).all())
+
+    def test_process_mixed_image_and_video(self):
+        """Test mixed image + video in the same message."""
+        img = self._make_image(224, 224)
+        frames = [self._make_image(112, 112) for _ in range(3)]
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": img},
+                    {"type": "video", "video": frames},
+                    {"type": "text", "text": "Describe both."},
+                ],
+            }
+        ]
+        images, frame_types = self.processor.process_vision_info(messages)
+        text = self.processor.apply_chat_template(messages, add_generation_prompt=True)
+        out = self.processor(images=images, text=text, frame_types=frame_types, return_tensors="pt")
+
+        # 1 image + 3 video frames = 4 entries
+        self.assertEqual(out.image_grid_thw.shape[0], 4)
+        # Image merge_size=1, video frames merge_size=2
+        self.assertEqual(int(out.image_merge_sizes[0]), 1)
+        self.assertTrue((out.image_merge_sizes[1:] == 2).all())
+
+    def test_batch_processing(self):
+        img1 = self._make_image(112, 112)
+        img2 = self._make_image(224, 224)
+        messages1 = [
+            {
+                "role": "user",
+                "content": [{"type": "image", "image": img1}, {"type": "text", "text": "Describe."}],
+            }
+        ]
+        messages2 = [
+            {
+                "role": "user",
+                "content": [{"type": "image", "image": img2}, {"type": "text", "text": "What is this?"}],
+            }
+        ]
+        images1, ft1 = self.processor.process_vision_info(messages1)
+        images2, ft2 = self.processor.process_vision_info(messages2)
+        text1 = self.processor.apply_chat_template(messages1, add_generation_prompt=True)
+        text2 = self.processor.apply_chat_template(messages2, add_generation_prompt=True)
+
+        all_images = images1 + images2
+        all_fts = ft1 + ft2 if ft1 and ft2 else None
+        out = self.processor(
+            images=all_images,
+            text=[text1, text2],
+            frame_types=all_fts,
+            padding=True,
+            return_tensors="pt",
+        )
+        self.assertEqual(out.input_ids.shape[0], 2)
diff --git a/tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py b/tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py
index 6274f26ea605..27acba1a2aff 100644
--- a/tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py
+++ b/tests/models/phi4_multimodal/test_modeling_phi4_multimodal.py
@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import copy
 import unittest
 
 import pytest
@@ -110,8 +111,8 @@ def __init__(
         self.eos_token_id = eos_token_id
         self.image_token_id = image_token_id
         self.audio_token_id = audio_token_id
-        self.audio_config = audio_config
-        self.vision_config = vision_config
+        self.audio_config = copy.deepcopy(audio_config)
+        self.vision_config = copy.deepcopy(vision_config)
 
         self.is_training = is_training
         self.batch_size = batch_size
@@ -276,13 +277,13 @@ def test_flex_attention_with_grads(self):
 @slow
 class Phi4MultimodalIntegrationTest(unittest.TestCase):
     checkpoint_path = "microsoft/Phi-4-multimodal-instruct"
-    revision = "refs/pr/70"
+    revision = "refs/pr/94"
     image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"
     audio_url = "https://huggingface.co/datasets/raushan-testing-hf/audio-test/resolve/main/f2641_0_throatclearing.wav"
 
     def setUp(self):
         # Currently, the Phi-4 checkpoint on the hub is not working with the latest Phi-4 code, so the slow integration tests
-        # won't pass without using the correct revision (refs/pr/70)
+        # won't pass without using the correct revision (refs/pr/94)
         self.processor = AutoProcessor.from_pretrained(self.checkpoint_path, revision=self.revision)
         self.generation_config = GenerationConfig(max_new_tokens=20, do_sample=False)
         self.user_token = "<|user|>"
diff --git a/tests/models/phi4_multimodal/test_processing_phi4_multimodal.py b/tests/models/phi4_multimodal/test_processing_phi4_multimodal.py
index 343768c0bb5f..a8c3f0db4db2 100644
--- a/tests/models/phi4_multimodal/test_processing_phi4_multimodal.py
+++ b/tests/models/phi4_multimodal/test_processing_phi4_multimodal.py
@@ -32,7 +32,7 @@
 class Phi4MultimodalProcessorTest(ProcessorTesterMixin, unittest.TestCase):
     processor_class = Phi4MultimodalProcessor
     checkpoint_path = "microsoft/Phi-4-multimodal-instruct"
-    revision = "refs/pr/70"
+    revision = "refs/pr/94"
     text_input_name = "input_ids"
     images_input_name = "image_pixel_values"
     audio_input_name = "audio_input_features"
diff --git a/tests/models/pp_formulanet/__init__.py b/tests/models/pp_formulanet/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/pp_formulanet/test_modeling_pp_formulanet.py b/tests/models/pp_formulanet/test_modeling_pp_formulanet.py
new file mode 100644
index 000000000000..0579a2a9f904
--- /dev/null
+++ b/tests/models/pp_formulanet/test_modeling_pp_formulanet.py
@@ -0,0 +1,355 @@
+# coding = utf-8
+# Copyright 2026 The PaddlePaddle Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PPFormulaNet model."""
+
+import copy
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    PPFormulaNetConfig,
+    PPFormulaNetForConditionalGeneration,
+    PPFormulaNetModel,
+    PPFormulaNetTextConfig,
+    PPFormulaNetVisionConfig,
+    is_torch_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+from ...test_modeling_common import floats_tensor
+from ...test_processing_common import url_to_local_path
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+
+class PPFormulaNetModelTester(VLMModelTester):
+    base_model_class = PPFormulaNetModel
+    config_class = PPFormulaNetConfig
+    text_config_class = PPFormulaNetTextConfig
+    vision_config_class = PPFormulaNetVisionConfig
+    conditional_generation_class = PPFormulaNetForConditionalGeneration
+
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("batch_size", 2)
+        kwargs.setdefault("hidden_size", 48)
+        kwargs.setdefault("image_size", 768)
+        kwargs.setdefault("patch_size", 768)
+        kwargs.setdefault("num_attention_heads", 2)
+        kwargs.setdefault("num_channels", 3)
+        kwargs.setdefault("num_hidden_layers", 1)
+        kwargs.setdefault("is_training", False)
+        kwargs.setdefault("post_conv_in_channels", 16)
+        kwargs.setdefault("post_conv_mid_channels", 16)
+        kwargs.setdefault("post_conv_out_channels", 16)
+        kwargs.setdefault(
+            "vision_config",
+            {
+                "image_size": 768,
+                "patch_size": 16,
+                "hidden_size": 48,
+                "windows_size": 14,
+                "num_hidden_layers": 1,
+                "output_channels": 16,
+                "num_attention_heads": 2,
+                "global_attn_indexes": [1, 1, 1, 1],
+                "mlp_dim": 1,
+            },
+        )
+        kwargs.setdefault(
+            "text_config",
+            {
+                "decoder_ffn_dim": 16,
+                "decoder_layers": 1,
+                "d_model": 48,
+                "vocab_size": 99,
+            },
+        )
+        super().__init__(parent, **kwargs)
+        self.seq_length = self.image_size // self.patch_size
+        self.encoder_seq_length = self.vision_config["windows_size"] ** 2
+        self.decoder_seq_length = 1
+
+    def prepare_config_and_inputs_for_common(self):
+        config, inputs_dict = self.prepare_config_and_inputs()
+        return config, inputs_dict
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+        decoder_input_ids = torch.full((self.batch_size, 1), 2, dtype=torch.long, device=torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "decoder_input_ids": decoder_input_ids,
+            "input_ids": decoder_input_ids,
+        }
+        return config, inputs_dict
+
+    def get_config(self) -> PPFormulaNetConfig:
+        config = PPFormulaNetConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            post_conv_in_channels=self.post_conv_in_channels,
+            post_conv_mid_channels=self.post_conv_mid_channels,
+            post_conv_out_channels=self.post_conv_out_channels,
+            num_hidden_layers=self.num_hidden_layers,
+        )
+
+        return config
+
+
+@require_torch
+class PPFormulaNetModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = PPFormulaNetModelTester
+    all_model_classes = (PPFormulaNetForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {"image-text-to-text": PPFormulaNetForConditionalGeneration} if is_torch_available() else {}
+    )
+
+    test_resize_embeddings = False
+    # test_torch_exportable = False
+    # model_split_percents = [0.5, 0.9]
+    is_encoder_decoder = True
+
+    def _check_encoder_attention_for_generate(self, attentions, batch_size, config, prompt_length):
+        # Ignoring batch size for now as it is dynamically changed during window partitioning
+        encoder_config = self.model_tester.vision_config
+        prompt_length = encoder_config["windows_size"] ** 2
+        encoder_expected_shape = (prompt_length, prompt_length)
+        self.assertIsInstance(attentions, tuple)
+        self.assertListEqual(
+            [layer_attentions.shape[-2:] for layer_attentions in attentions],
+            [encoder_expected_shape] * len(attentions),
+        )
+
+    def _check_encoder_hidden_states_for_generate(self, hidden_states, batch_size, config, prompt_length):
+        # update encoder_expected_shape
+        encoder_config = self.model_tester.vision_config
+        patched_image_size = encoder_config["image_size"] // encoder_config["patch_size"]
+        encoder_expected_shape = (patched_image_size, patched_image_size, encoder_config["hidden_size"])
+        self.assertIsInstance(hidden_states, tuple)
+        self.assertListEqual(
+            [layer_hidden_states.shape[-3:] for layer_hidden_states in hidden_states],
+            [encoder_expected_shape] * len(hidden_states),
+        )
+
+    # use encoder_seq_length and decoder_seq_length to replace seq_len
+    def test_attention_outputs(self):
+        if not self.has_attentions:
+            self.skipTest(reason="Model does not output attentions")
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        config.return_dict = True
+        # force eager attention to support output attentions
+        config._attn_implementation = "eager"
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_attentions"] = True
+            inputs_dict["output_hidden_states"] = False
+            config.return_dict = True
+            model = model_class._from_config(config, attn_implementation="eager")
+            config = model.config
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+
+            # check that output_attentions also work using config
+            del inputs_dict["output_attentions"]
+            config.output_attentions = True
+            self._set_subconfig_attributes(config, "output_attentions", True)
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            attentions = outputs.encoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+            # Ignoring batch size for now as it is dynamically changed during window partitioning
+            self.assertListEqual(
+                list(attentions[0].shape[-2:]),
+                [self.model_tester.encoder_seq_length, self.model_tester.encoder_seq_length],
+            )
+
+            attentions = outputs.decoder_attentions
+            self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
+            # Ignoring batch size for now as it is dynamically changed during window partitioning
+            self.assertListEqual(
+                list(attentions[0].shape[-2:]),
+                [self.model_tester.decoder_seq_length, self.model_tester.decoder_seq_length],
+            )
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(copy.deepcopy(config))
+            model.to(torch_device)
+            model.eval()
+
+            with torch.no_grad():
+                outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_layers = getattr(
+                self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
+            )
+            self.assertEqual(len(hidden_states), expected_num_layers)
+
+            encoder_config = self.model_tester.vision_config
+            seq_length = encoder_config["image_size"] // encoder_config["patch_size"]
+
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
+            )
+
+            if config.is_encoder_decoder:
+                hidden_states = outputs.decoder_hidden_states
+
+                self.assertIsInstance(hidden_states, (list, tuple))
+                self.assertEqual(len(hidden_states), expected_num_layers)
+                self.assertListEqual(
+                    list(hidden_states[0].shape[-2:]),
+                    [self.model_tester.decoder_seq_length, self.model_tester.hidden_size],
+                )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+            self._set_subconfig_attributes(config, "output_hidden_states", True)
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    @unittest.skip(reason="PPFormulaNet does not small")
+    def test_model_is_small(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not use inputs_embeds")
+    def test_enable_input_require_grads(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNetTextModel has no attribute `shared`")
+    def test_tied_weights_keys(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support generation from no inputs")
+    def test_generate_without_input_ids(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support input and output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support image_token")
+    def test_mismatching_num_image_tokens(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support beam search.")
+    def test_beam_sample_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support beam search.")
+    def test_beam_search_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support beam search.")
+    def test_beam_search_generate_dict_output(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support beam search.")
+    def test_beam_search_generate_dict_outputs_use_cache(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support beam search.")
+    def test_beam_sample_generate_dict_output(self):
+        pass
+
+    @unittest.skip(reason="PPFormulaNet does not support data parallel")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support assisted decoding.")
+    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PPFormulaNet does not support assisted decoding.")
+    def test_assisted_decoding_sample(self):
+        pass
+
+    @unittest.skip(
+        reason="GenerationMixin._expand_inputs_for_generation() got multiple values for keyword argument 'input_ids'"
+    )
+    def test_generate_continue_from_past_key_values(self):
+        pass
+
+
+@require_torch
+@require_vision
+@slow
+class PPFormulaNetModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        model_path = "PaddlePaddle/PP-FormulaNet_plus-L_safetensors"
+        self.model = PPFormulaNetForConditionalGeneration.from_pretrained(model_path).to(torch_device)
+        self.processor = AutoProcessor.from_pretrained(model_path)
+        img_url = url_to_local_path(
+            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
+        )
+        self.image = load_image(img_url)
+
+    def test_inference_formula_recognition_head(self):
+        inputs = self.processor(images=self.image, return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = self.model.generate(**inputs)
+
+        formula_text = self.processor.post_process(outputs)
+        expected_formula_text = [
+            "\\zeta_{0}(\\nu)=-\\frac{\\nu\\varrho^{-2\\nu}}{\\pi}\\int_{\\mu}^{\\infty}d\\omega\\int_{C_{+}}d z\\frac{2z^{2}}{(z^{2}+\\omega^{2})^{\\nu+1}}\\breve{\\Psi}(\\omega;z)e^{i\\epsilon z}\\quad,"
+        ]
+
+        self.assertEqual(formula_text, expected_formula_text)
diff --git a/tests/models/qianfan_ocr/test_modeling_qianfan_ocr.py b/tests/models/qianfan_ocr/test_modeling_qianfan_ocr.py
index b108f3b0922b..1a101ddc5904 100644
--- a/tests/models/qianfan_ocr/test_modeling_qianfan_ocr.py
+++ b/tests/models/qianfan_ocr/test_modeling_qianfan_ocr.py
@@ -191,6 +191,7 @@ def test_model_integration_forward(self):
             {
                 ("cuda", (8, 6)): torch.tensor([10.1250, 15.8125, 13.0625, 12.3125,  9.4375]),
                 ("cuda", (8, 9)): torch.tensor([10.0625, 15.6875, 13.0000, 12.1875,  9.3750]),
+                ("xpu", None): torch.tensor([10.1875, 15.8750, 13.1875, 12.3750,  9.6250]),
             }
         )  # fmt: skip
         self.assertTrue(
@@ -225,6 +226,7 @@ def test_model_integration_generate(self):
             {
                 ("cuda", (8, 6)): "The image features two striped cats lying down and sleeping on a pink couch. They",
                 ("cuda", (8, 9)): "The image features two striped cats lying down on a pink couch, seemingly asleep.",
+                ("xpu", None): "The image features two striped cats lying down on a couch, both appearing to be",
             }
         )  # fmt: skip
         self.assertEqual(decoded, expected_outputs.get_expectation())
@@ -247,6 +249,7 @@ def test_model_integration_generate_text_only(self):
         expected_outputs = Expectations(
             {
                 ("cuda", None): "1 + 1 equals 2.",
+                ("xpu", None): "1 + 1 equals 2.",
             }
         )  # fmt: skip
         self.assertEqual(decoded, expected_outputs.get_expectation())
@@ -295,12 +298,14 @@ def test_model_integration_batched_generate(self):
         expected_outputs_0 = Expectations(
             {
                 ("cuda", None): "In the tranquil setting of this image, two tabby cats are the stars of",
+                ("xpu", None): "In the tranquil setting of this image, two tabby cats are the stars of",
             }
         )  # fmt: skip
         expected_outputs_1 = Expectations(
             {
                 ("cuda", (8, 6)): "The image features two striped cats lying down and sleeping on a pink couch. The",
                 ("cuda", (8, 9)): "The image features two striped cats lying down on a pink couch, seemingly asleep.",
+                ("xpu", None): "The image features two striped cats lying down on a couch, both appearing to be",
             }
         )  # fmt: skip
         self.assertEqual(decoded_0, expected_outputs_0.get_expectation())
diff --git a/tests/models/qwen2_5_vl/test_modeling_qwen2_5_vl.py b/tests/models/qwen2_5_vl/test_modeling_qwen2_5_vl.py
index 5a425b434e7d..374f3fc4ed27 100644
--- a/tests/models/qwen2_5_vl/test_modeling_qwen2_5_vl.py
+++ b/tests/models/qwen2_5_vl/test_modeling_qwen2_5_vl.py
@@ -441,9 +441,6 @@ def attention_mask_padding_matches_padding_free_with_position_ids(
                 tol = torch.finfo(torch.bfloat16).eps
                 torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
     @unittest.skip(reason="Feedforward chunking is not yet supported")
     def test_feed_forward_chunking(self):
         pass
diff --git a/tests/models/qwen2_5_vl/test_processing_qwen2_5_vl.py b/tests/models/qwen2_5_vl/test_processing_qwen2_5_vl.py
index c0f4b7240fb8..e2259009b4cf 100644
--- a/tests/models/qwen2_5_vl/test_processing_qwen2_5_vl.py
+++ b/tests/models/qwen2_5_vl/test_processing_qwen2_5_vl.py
@@ -174,7 +174,12 @@ def test_apply_chat_template_video_frame_sampling(self):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "video"},
+                        {
+                            "type": "video",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                            ),
+                        },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
                 },
@@ -184,20 +189,7 @@ def test_apply_chat_template_video_frame_sampling(self):
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
         self.assertEqual(len(formatted_prompt), 1)
 
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
-
         # Add video URL for return dict and load with `num_frames` arg
-        messages[0][0]["content"][0] = {
-            "type": "video",
-            "url": url_to_local_path(
-                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
-            ),
-        }
         num_frames = 3
         out_dict_with_video = processor.apply_chat_template(
             messages,
diff --git a/tests/models/qwen2_audio/test_modeling_qwen2_audio.py b/tests/models/qwen2_audio/test_modeling_qwen2_audio.py
index 4df16b9f6f4b..1557217fdd63 100644
--- a/tests/models/qwen2_audio/test_modeling_qwen2_audio.py
+++ b/tests/models/qwen2_audio/test_modeling_qwen2_audio.py
@@ -13,18 +13,18 @@
 # limitations under the License.
 """Testing suite for the PyTorch Qwen2Audio model."""
 
-import tempfile
 import unittest
 from io import BytesIO
 from urllib.request import urlopen
 
 import librosa
-import pytest
 
 from transformers import (
     AutoProcessor,
     Qwen2AudioConfig,
+    Qwen2AudioEncoderConfig,
     Qwen2AudioForConditionalGeneration,
+    Qwen2Config,
     is_torch_available,
 )
 from transformers.testing_utils import (
@@ -34,172 +34,56 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
-from ...test_pipeline_mixin import PipelineTesterMixin
+from ...alm_tester import ALMModelTest, ALMModelTester
 
 
 if is_torch_available():
     import torch
 
 
-class Qwen2AudioModelTester:
-    def __init__(
-        self,
-        parent,
-        ignore_index=-100,
-        audio_token_index=0,
-        seq_length=25,
-        feat_seq_length=60,
-        text_config={
-            "model_type": "qwen2",
-            "intermediate_size": 36,
-            "initializer_range": 0.02,
-            "hidden_size": 32,
-            "max_position_embeddings": 52,
-            "num_hidden_layers": 2,
-            "num_attention_heads": 4,
-            "num_key_value_heads": 2,
-            "use_labels": True,
-            "use_mrope": False,
-            "vocab_size": 99,
-            "pad_token_id": 1,  # can't be the same as the audio token id
-        },
-        is_training=True,
-        audio_config={
-            "model_type": "qwen2_audio_encoder",
-            "d_model": 16,
-            "encoder_attention_heads": 4,
-            "encoder_ffn_dim": 16,
-            "encoder_layers": 2,
-            "num_mel_bins": 80,
-            "max_source_positions": 30,
-            "initializer_range": 0.02,
-        },
-    ):
-        self.parent = parent
-        self.ignore_index = ignore_index
-        self.audio_token_index = audio_token_index
-        self.text_config = text_config
-        self.audio_config = audio_config
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.is_training = is_training
-
-        self.batch_size = 3
-        self.encoder_seq_length = seq_length
-
-    def get_config(self):
-        return Qwen2AudioConfig(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            ignore_index=self.ignore_index,
-            audio_token_index=self.audio_token_index,
-        )
-
-    def prepare_config_and_inputs(self):
-        input_features_values = floats_tensor(
-            [
-                self.batch_size,
-                self.audio_config["num_mel_bins"],
-                self.feat_seq_length,
-            ]
-        )
-        config = self.get_config()
-        feature_attention_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.long).to(torch_device)
-        return config, input_features_values, feature_attention_mask
-
-    def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        config, input_features_values, feature_attention_mask = config_and_inputs
-        input_length = (input_features_values.shape[-1] - 1) // 2 + 1
-        num_audio_tokens = (input_length - 2) // 2 + 1
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
-        attention_mask[:, :1] = 0
-        # we are giving 3 audios let's make sure we pass in 3 audios tokens
-        input_ids[:, 1 : 1 + num_audio_tokens] = config.audio_token_index
-        inputs_dict = {
-            "input_features": input_features_values,
-            "feature_attention_mask": feature_attention_mask,
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-        }
-        return config, inputs_dict
+class Qwen2AudioModelTester(ALMModelTester):
+    config_class = Qwen2AudioConfig
+    conditional_generation_class = Qwen2AudioForConditionalGeneration
+    text_config_class = Qwen2Config
+    audio_config_class = Qwen2AudioEncoderConfig
+    audio_mask_key = "feature_attention_mask"
+
+    def __init__(self, parent, **kwargs):
+        # feat_seq_length=60 → after conv2 s=2: 30 → after avg_pool s=2: 15 audio embed tokens.
+        kwargs.setdefault("feat_seq_length", 60)
+        # Encoder asserts input_features.shape[-1] == max_source_positions * conv1.stride * conv2.stride == 2 * max_source_positions.
+        kwargs.setdefault("max_source_positions", kwargs["feat_seq_length"] // 2)
+        super().__init__(parent, **kwargs)
+
+    def create_audio_mask(self):
+        # Deterministic full-length mask: the base default randomizes via Python's `random`, which isn't
+        # re-seeded per test call and desynchronizes the two `prepare_config_and_inputs_for_common`
+        # invocations inside generation-comparison tests (e.g. test_greedy_generate_dict_outputs).
+        return torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.bool).to(torch_device)
+
+    def get_audio_embeds_mask(self, audio_mask):
+        # Mirrors Qwen2AudioEncoder._get_feat_extract_output_lengths: conv2 (k=3,s=2,p=1) then avg_pool (k=2,s=2).
+        input_lengths = audio_mask.sum(-1)
+        input_lengths = (input_lengths - 1) // 2 + 1
+        output_lengths = (input_lengths - 2) // 2 + 1
+        max_len = int(output_lengths.max().item())
+        positions = torch.arange(max_len, device=audio_mask.device)[None, :]
+        return (positions < output_lengths[:, None]).long()
 
 
 @require_torch
-class Qwen2AudioForConditionalGenerationModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class Qwen2AudioForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `Qwen2AudioForConditionalGeneration`.
     """
 
-    all_model_classes = (Qwen2AudioForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = Qwen2AudioModelTester
     pipeline_model_mapping = {"any-to-any": Qwen2AudioForConditionalGeneration} if is_torch_available() else {}
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = Qwen2AudioModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=Qwen2AudioConfig, has_text_modality=False)
 
-    @unittest.skip(reason="Compile not yet supported because in Qwen2Audio models")
-    @pytest.mark.torch_compile_test
-    def test_sdpa_can_compile_dynamic(self):
+    @unittest.skip(reason="inputs_embeds is the audio-fused path; can't match raw token-only embeddings.")
+    def test_inputs_embeds_matches_input_ids(self):
         pass
 
-    @unittest.skip(reason="Compile not yet supported because in Qwen2Audio models")
-    def test_sdpa_can_dispatch_on_flash(self):
-        pass
-
-    @unittest.skip(reason="Qwen2Audio has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
-    def test_sdpa_can_dispatch_composite_models(self):
-        # overwrite because Qwen2 is audio+text model (not vision+text)
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-                vision_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
-
-                # `None` as it is the requested one which will be assigned to each sub-config
-                # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-                self.assertTrue(model.audio_tower.config._attn_implementation == vision_attn)
-
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
-
-                for name, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
 
 @require_torch
 class Qwen2AudioForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/qwen2_moe/test_modeling_qwen2_moe.py b/tests/models/qwen2_moe/test_modeling_qwen2_moe.py
index 8776ccdb27dc..8c52fd834278 100644
--- a/tests/models/qwen2_moe/test_modeling_qwen2_moe.py
+++ b/tests/models/qwen2_moe/test_modeling_qwen2_moe.py
@@ -92,6 +92,14 @@ def test_load_balancing_loss(self):
         self.assertEqual(result.router_logits[0].shape, (91, config.num_experts))
         torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
 
+        # Verify router_logits are raw logits, not softmax probabilities (regression test for double-softmax bug)
+        for layer_logits in result.router_logits:
+            row_sums = layer_logits.sum(dim=-1)
+            self.assertFalse(
+                torch.allclose(row_sums, torch.ones_like(row_sums), atol=1e-3),
+                "router_logits should be raw logits (row sums != 1.0), not softmax probabilities",
+            )
+
         # First, we make sure that adding padding tokens doesn't change the loss
         # loss(input_ids, attention_mask=None) == loss(input_ids + padding, attention_mask=attention_mask_with_padding)
         pad_length = input_ids.shape[1] * 4
diff --git a/tests/models/qwen2_vl/test_modeling_qwen2_vl.py b/tests/models/qwen2_vl/test_modeling_qwen2_vl.py
index 6027feac66fe..0ac8cd1bd385 100644
--- a/tests/models/qwen2_vl/test_modeling_qwen2_vl.py
+++ b/tests/models/qwen2_vl/test_modeling_qwen2_vl.py
@@ -411,9 +411,6 @@ def attention_mask_padding_matches_padding_free_with_position_ids(
                 tol = torch.finfo(torch.bfloat16).eps
                 torch.testing.assert_close(logits_padded, logits_padfree, rtol=tol, atol=tol)
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
     @unittest.skip(reason="Feedforward chunking is not yet supported")
     def test_feed_forward_chunking(self):
         pass
diff --git a/tests/models/qwen2_vl/test_processing_qwen2_vl.py b/tests/models/qwen2_vl/test_processing_qwen2_vl.py
index db5236573c85..c5b7f8977266 100644
--- a/tests/models/qwen2_vl/test_processing_qwen2_vl.py
+++ b/tests/models/qwen2_vl/test_processing_qwen2_vl.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import inspect
 import unittest
 
 import numpy as np
@@ -164,11 +163,7 @@ def test_apply_chat_template_video_frame_sampling(self):
         if processor.chat_template is None:
             self.skipTest("Processor has no chat template")
 
-        signature = inspect.signature(processor.__call__)
-        if "videos" not in {*signature.parameters.keys()} or (
-            signature.parameters.get("videos") is not None
-            and signature.parameters["videos"].annotation == inspect._empty
-        ):
+        if "video_processor" not in self.processor_class.get_attributes():
             self.skipTest("Processor doesn't accept videos at input")
 
         messages = [
@@ -176,30 +171,18 @@ def test_apply_chat_template_video_frame_sampling(self):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "video"},
+                        {
+                            "type": "video",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                            ),
+                        },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
                 },
             ]
         ]
 
-        formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
-        self.assertEqual(len(formatted_prompt), 1)
-
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
-
-        # Add video URL for return dict and load with `num_frames` arg
-        messages[0][0]["content"][0] = {
-            "type": "video",
-            "url": url_to_local_path(
-                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
-            ),
-        }
         num_frames = 3
         out_dict_with_video = processor.apply_chat_template(
             messages,
diff --git a/tests/models/qwen3/test_modeling_qwen3.py b/tests/models/qwen3/test_modeling_qwen3.py
index 6fa304662caf..623befa163b9 100644
--- a/tests/models/qwen3/test_modeling_qwen3.py
+++ b/tests/models/qwen3/test_modeling_qwen3.py
@@ -694,3 +694,20 @@ def test_600m_generation(self):
         new_generated_ids = model.generate(input_ids, max_new_tokens=50)[:, input_ids.shape[1] :]
         with self.subTest("Eager matches flash attention"):
             torch.testing.assert_close(generated_ids, new_generated_ids, rtol=1e-4, atol=1e-4)
+
+    def test_qwen3_greedy_determinism(self):
+        """
+        Ensures Qwen3 generate is deterministic when do_sample=False (greedy decoding as per HFs documentation).
+        """
+        tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B-Base", use_fast=False)
+        model = Qwen3ForCausalLM.from_pretrained("Qwen/Qwen3-0.6B-Base", device_map="auto")
+        inputs = tokenizer("hello", return_tensors="pt")
+
+        cfg = GenerationConfig(do_sample=False, num_beams=1, max_new_tokens=20)
+
+        out1 = model.generate(**inputs, generation_config=cfg)
+        out2 = model.generate(**inputs, generation_config=cfg)
+
+        assert torch.equal(out1, out2), (
+            "Qwen3 should produce deterministic outputs with do_sample=False and num_beams=1"
+        )
diff --git a/tests/models/qwen3_5/test_modeling_qwen3_5.py b/tests/models/qwen3_5/test_modeling_qwen3_5.py
index 668a4e513970..eb1fb820857d 100644
--- a/tests/models/qwen3_5/test_modeling_qwen3_5.py
+++ b/tests/models/qwen3_5/test_modeling_qwen3_5.py
@@ -45,6 +45,7 @@
         Qwen3_5ForSequenceClassification,
         Qwen3_5Model,
         Qwen3_5TextConfig,
+        Qwen3_5TextForSequenceClassification,
         Qwen3_5TextModel,
     )
 
@@ -53,7 +54,7 @@ class Qwen3_5TextModelTester(CausalLMModelTester):
     if is_torch_available():
         base_model_class = Qwen3_5TextModel
         causal_lm_class = Qwen3_5ForCausalLM
-        sequence_classification_class = Qwen3_5ForSequenceClassification
+        sequence_classification_class = Qwen3_5TextForSequenceClassification
 
     def __init__(self, parent):
         super().__init__(parent=parent)
@@ -332,6 +333,7 @@ class Qwen3_5ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCas
         (
             Qwen3_5Model,
             Qwen3_5ForConditionalGeneration,
+            Qwen3_5ForSequenceClassification,
         )
         if is_torch_available()
         else ()
diff --git a/tests/models/qwen3_asr/__init__.py b/tests/models/qwen3_asr/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/qwen3_asr/test_feature_extraction_qwen3_asr.py b/tests/models/qwen3_asr/test_feature_extraction_qwen3_asr.py
new file mode 100644
index 000000000000..4d08cc2c908d
--- /dev/null
+++ b/tests/models/qwen3_asr/test_feature_extraction_qwen3_asr.py
@@ -0,0 +1,182 @@
+# Copyright 2026 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import itertools
+import random
+import unittest
+
+import numpy as np
+
+from transformers import Qwen3ASRFeatureExtractor
+
+from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
+
+
+global_rng = random.Random()
+
+
+def floats_list(shape, scale=1.0, rng=None):
+    rng = rng or global_rng
+    values = []
+    for _ in range(shape[0]):
+        values.append([rng.random() * scale for _ in range(shape[1])])
+    return values
+
+
+class Qwen3ASRFeatureExtractionTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        min_seq_length=400,
+        max_seq_length=2000,
+        feature_size=10,
+        hop_length=160,
+        chunk_length=8,
+        padding_value=0.0,
+        sampling_rate=4_000,
+        return_attention_mask=False,
+        n_window=13,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.min_seq_length = min_seq_length
+        self.max_seq_length = max_seq_length
+        self.seq_length_diff = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
+        self.feature_size = feature_size
+        self.hop_length = hop_length
+        self.chunk_length = chunk_length
+        self.padding_value = padding_value
+        self.sampling_rate = sampling_rate
+        self.return_attention_mask = return_attention_mask
+        self.n_window = n_window
+
+    def prepare_feat_extract_dict(self):
+        return {
+            "feature_size": self.feature_size,
+            "hop_length": self.hop_length,
+            "chunk_length": self.chunk_length,
+            "padding_value": self.padding_value,
+            "sampling_rate": self.sampling_rate,
+            "return_attention_mask": self.return_attention_mask,
+            "n_window": self.n_window,
+        }
+
+    def prepare_inputs_for_common(self, equal_length=False, numpify=False):
+        def _flatten(list_of_lists):
+            return list(itertools.chain(*list_of_lists))
+
+        if equal_length:
+            speech_inputs = [floats_list((self.max_seq_length, self.feature_size)) for _ in range(self.batch_size)]
+        else:
+            speech_inputs = [
+                floats_list((x, self.feature_size))
+                for x in range(self.min_seq_length, self.max_seq_length, self.seq_length_diff)
+            ]
+        if numpify:
+            speech_inputs = [np.asarray(x) for x in speech_inputs]
+        return speech_inputs
+
+
+class Qwen3ASRFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
+    feature_extraction_class = Qwen3ASRFeatureExtractor
+
+    def setUp(self):
+        self.feat_extract_tester = Qwen3ASRFeatureExtractionTester(self)
+
+    def test_default_feature_size_is_128(self):
+        """Qwen3 ASR uses 128-bin mel filters by default."""
+        fe = Qwen3ASRFeatureExtractor()
+        self.assertEqual(fe.feature_size, 128)
+        self.assertEqual(fe.mel_filters.shape[1], 128)
+
+    def test_default_n_window_is_50(self):
+        fe = Qwen3ASRFeatureExtractor()
+        self.assertEqual(fe.n_window, 50)
+
+    def test_mel_padding_aligns_to_chunk(self):
+        """The mel time axis is right-padded to a multiple of `2 * n_window`."""
+        fe = Qwen3ASRFeatureExtractor()
+        # 5.85 s at 16 kHz -> 585 mel frames before padding -> 600 after (multiple of 100).
+        audio = np.random.randn(int(5.85 * 16_000)).astype(np.float32)
+        out = fe(
+            audio,
+            sampling_rate=16_000,
+            padding="longest",
+            truncation=False,
+            return_attention_mask=True,
+            return_tensors="np",
+        )
+        self.assertEqual(out["input_features"].shape, (1, 128, 600))
+        self.assertEqual(out["attention_mask"].shape, (1, 600))
+        self.assertEqual(int(out["attention_mask"].sum(-1)), 585)
+        self.assertEqual(out["input_features"].shape[-1] % 100, 0)
+
+    def test_n_window_kwarg_override(self):
+        fe = Qwen3ASRFeatureExtractor()
+        audio = np.random.randn(int(5.85 * 16_000)).astype(np.float32)
+        out = fe(
+            audio,
+            sampling_rate=16_000,
+            padding="longest",
+            truncation=False,
+            return_attention_mask=True,
+            return_tensors="np",
+            n_window=25,
+        )
+        self.assertEqual(out["input_features"].shape[-1] % 50, 0)
+
+    def test_n_window_disabled(self):
+        """`n_window=0` disables mel-axis padding."""
+        fe = Qwen3ASRFeatureExtractor()
+        audio = np.random.randn(int(5.85 * 16_000)).astype(np.float32)
+        out = fe(
+            audio,
+            sampling_rate=16_000,
+            padding="longest",
+            truncation=False,
+            return_attention_mask=True,
+            return_tensors="np",
+            n_window=0,
+        )
+        self.assertEqual(out["input_features"].shape[-1], 585)
+        self.assertEqual(out["attention_mask"].shape[-1], 585)
+
+    def test_batched_call_shape(self):
+        fe = Qwen3ASRFeatureExtractor()
+        # Two clips of different lengths; padded to the longer one (rounded up to 2 * n_window).
+        audio = [
+            np.random.randn(int(2.0 * 16_000)).astype(np.float32),
+            np.random.randn(int(5.5 * 16_000)).astype(np.float32),
+        ]
+        out = fe(
+            audio,
+            sampling_rate=16_000,
+            padding="longest",
+            truncation=False,
+            return_attention_mask=True,
+            return_tensors="np",
+        )
+        self.assertEqual(out["input_features"].ndim, 3)
+        self.assertEqual(out["input_features"].shape[0], 2)
+        self.assertEqual(out["input_features"].shape[1], 128)
+        self.assertEqual(out["input_features"].shape[-1] % 100, 0)
+        per_sample_valid = out["attention_mask"].sum(-1).tolist()
+        self.assertEqual(per_sample_valid, [200, 550])
+
+    def test_mismatched_sampling_rate_raises(self):
+        fe = Qwen3ASRFeatureExtractor(sampling_rate=16_000)
+        audio = np.random.randn(16_000).astype(np.float32)
+        with self.assertRaises(ValueError):
+            fe(audio, sampling_rate=8_000, return_tensors="np")
diff --git a/tests/models/qwen3_asr/test_modeling_qwen3_asr.py b/tests/models/qwen3_asr/test_modeling_qwen3_asr.py
new file mode 100644
index 000000000000..5d2a447798b9
--- /dev/null
+++ b/tests/models/qwen3_asr/test_modeling_qwen3_asr.py
@@ -0,0 +1,366 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import unittest
+from pathlib import Path
+
+import torch
+
+from transformers import (
+    AutoProcessor,
+    Qwen3ASRConfig,
+    Qwen3ASRForConditionalGeneration,
+    Qwen3ASRForForcedAlignment,
+    Qwen3ASRModel,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+
+
+class Qwen3ASRModelTester:
+    def __init__(self, parent):
+        self.parent = parent
+        self.batch_size = 3
+        self.seq_length = 25
+        self.num_mel_bins = 20
+        self.feat_seq_length = 100  # mel frames per sample
+        self.audio_token_id = 0
+        self.is_training = False
+
+        text_config = {
+            "model_type": "qwen3",
+            "vocab_size": 99,
+            "hidden_size": 16,
+            "intermediate_size": 32,
+            "num_hidden_layers": 1,
+            "num_attention_heads": 2,
+            "num_key_value_heads": 2,
+            "head_dim": 8,
+            "max_position_embeddings": 52,
+            "bos_token_id": 0,
+            "pad_token_id": 1,
+            "eos_token_id": 2,
+            "tie_word_embeddings": False,
+        }
+        audio_config = {
+            "model_type": "qwen3_asr_audio_encoder",
+            "num_mel_bins": self.num_mel_bins,
+            "d_model": 8,
+            "encoder_layers": 1,
+            "encoder_attention_heads": 2,
+            "encoder_ffn_dim": 16,
+            "output_dim": text_config["hidden_size"],
+            "downsample_hidden_size": 4,
+        }
+
+        self.text_config = text_config
+        self.audio_config = audio_config
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.hidden_size = text_config["hidden_size"]
+        self.encoder_seq_length = self.seq_length
+
+    def get_config(self):
+        return Qwen3ASRConfig(
+            audio_config=self.audio_config,
+            text_config=self.text_config,
+            audio_token_id=self.audio_token_id,
+        )
+
+    def _num_audio_tokens(self, config):
+        """Compute how many tokens the audio encoder produces for feat_seq_length frames."""
+        from transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe import _get_feat_extract_output_lengths
+
+        return int(
+            _get_feat_extract_output_lengths(
+                torch.tensor(self.feat_seq_length),
+                config.audio_config.n_window,
+            ).item()
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        num_audio_tokens = self._num_audio_tokens(config)
+
+        # Batched audio features (batch, mel, time) + mask (batch, time)
+        input_features = floats_tensor([self.batch_size, self.num_mel_bins, self.feat_seq_length])
+        input_features_mask = torch.ones([self.batch_size, self.feat_seq_length], dtype=torch.long).to(torch_device)
+
+        # Text with audio token placeholders
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+        attention_mask[:, :1] = 0
+        input_ids[:, 1 : 1 + num_audio_tokens] = config.audio_token_id
+
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "input_features": input_features,
+            "input_features_mask": input_features_mask,
+        }
+        return config, inputs_dict
+
+    def prepare_config_and_inputs_for_common(self):
+        return self.prepare_config_and_inputs()
+
+
+@require_torch
+class Qwen3ASRForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (Qwen3ASRForConditionalGeneration, Qwen3ASRModel) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "audio-text-to-text": Qwen3ASRForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    # Similar to Qwen3OmniMoe,
+    skip_test_audio_features_output_shape = True  # as the audio encoder merges batch_size and output_lengths in dim 0
+    _is_composite = True
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+
+    def setUp(self):
+        self.model_tester = Qwen3ASRModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Qwen3ASRConfig)
+
+    @unittest.skip(reason="Same as Qwen3OmniMoe.")
+    def test_model_base_model_prefix(self):
+        pass
+
+    @unittest.skip(
+        reason="Like other audio LMs (Audio Flamingo, Voxtral) inputs_embeds corresponding to audio tokens are replaced when input features are provided."
+    )
+    def test_inputs_embeds_matches_input_ids(self):
+        pass
+
+    @unittest.skip("Does not has no attribute `hf_device_map`")
+    def test_model_parallelism(self):
+        pass
+
+    @unittest.skip(reason="See test_model_parallelism")
+    def test_model_parallel_beam_search(self):
+        pass
+
+
+@require_torch
+class Qwen3ASRForConditionalGenerationIntegrationTest(unittest.TestCase):
+    @classmethod
+    def setUp(cls):
+        cleanup(torch_device, gc_collect=True)
+        cls.checkpoint = "bezzam/Qwen3-ASR-0.6B"
+        cls.processor = AutoProcessor.from_pretrained(cls.checkpoint)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_fixture_single_matches(self):
+        """
+        reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/3e0551708631784aeb684e0e838299f3#file-reproducer-py
+        """
+        path = Path(__file__).parent.parent.parent / "fixtures/qwen3_asr/expected_results_single.json"
+        with open(path, "r", encoding="utf-8") as f:
+            raw = json.load(f)
+        exp_ids = torch.tensor(raw["token_ids"])
+        exp_txt = raw["transcriptions"]
+
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "audio",
+                        "path": "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+                    },
+                ],
+            }
+        ]
+
+        model = Qwen3ASRForConditionalGeneration.from_pretrained(
+            self.checkpoint, device_map="auto", dtype=torch.bfloat16
+        ).eval()
+
+        batch = self.processor.apply_chat_template(
+            conversation, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=model.dtype)
+        seq = model.generate(**batch, max_new_tokens=32)
+
+        inp_len = batch["input_ids"].shape[1]
+        gen_ids = seq[:, inp_len:] if seq.shape[1] >= inp_len else seq
+        torch.testing.assert_close(gen_ids.cpu(), exp_ids)
+        txt = self.processor.decode(seq, skip_special_tokens=True)
+        self.assertListEqual(txt, exp_txt)
+
+    @slow
+    def test_fixture_batch_matches(self):
+        """
+        reproducer (creates JSON directly in repo): https://gist.github.com/ebezzam/3e0551708631784aeb684e0e838299f3#file-reproducer-py
+        """
+        path = Path(__file__).parent.parent.parent / "fixtures/qwen3_asr/expected_results_batched.json"
+        with open(path, "r", encoding="utf-8") as f:
+            raw = json.load(f)
+        exp_ids = torch.tensor(raw["token_ids"])
+        exp_txt = raw["transcriptions"]
+
+        conversation = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "audio",
+                            "path": "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+                        },
+                    ],
+                }
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "audio",
+                            "path": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+                        },
+                    ],
+                }
+            ],
+        ]
+
+        model = Qwen3ASRForConditionalGeneration.from_pretrained(
+            self.checkpoint, device_map="auto", dtype=torch.bfloat16
+        ).eval()
+        batch = self.processor.apply_chat_template(
+            conversation,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            truncation=True,
+        ).to(model.device, dtype=model.dtype)
+
+        seq = model.generate(**batch, max_new_tokens=32)
+
+        inp_len = batch["input_ids"].shape[1]
+        gen_ids = seq[:, inp_len:] if seq.shape[1] >= inp_len else seq
+        torch.testing.assert_close(gen_ids.cpu(), exp_ids)
+        txt = self.processor.decode(seq, skip_special_tokens=True)
+        self.assertListEqual(txt, exp_txt)
+
+
+@require_torch
+class Qwen3ForcedAlignerIntegrationTest(unittest.TestCase):
+    """
+    reproducer scripts (create JSON fixtures directly in repo): https://gist.github.com/ebezzam/3e0551708631784aeb684e0e838299f3#file-reproducer_timestamps-py
+    """
+
+    @classmethod
+    def setUp(cls):
+        cleanup(torch_device, gc_collect=True)
+        cls.aligner_checkpoint = "bezzam/Qwen3-ForcedAligner-0.6B"
+        cls.aligner_processor = AutoProcessor.from_pretrained(cls.aligner_checkpoint)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def _load_aligner(self):
+        return Qwen3ASRForForcedAlignment.from_pretrained(
+            self.aligner_checkpoint,
+            device_map="auto",
+            torch_dtype=torch.bfloat16,
+        ).eval()
+
+    def _run_alignment(self, model, audio, transcript, language):
+        """Run forced alignment and return list of timestamp dicts."""
+        aligner_inputs, word_lists = self.aligner_processor.prepare_forced_aligner_inputs(
+            audio=audio,
+            transcript=transcript,
+            language=language,
+        )
+        aligner_inputs = aligner_inputs.to(model.device, model.dtype)
+
+        with torch.inference_mode():
+            outputs = model(**aligner_inputs)
+
+        return self.aligner_processor.decode_forced_alignment(
+            logits=outputs.logits,
+            input_ids=aligner_inputs["input_ids"],
+            word_lists=word_lists,
+            timestamp_token_id=model.config.timestamp_token_id,
+        )
+
+    @slow
+    def test_fixture_timestamps_single(self):
+        path = Path(__file__).parent.parent.parent / "fixtures/qwen3_asr/expected_timestamps_single.json"
+        with open(path, "r", encoding="utf-8") as f:
+            expected = json.load(f)
+
+        model = self._load_aligner()
+        audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+
+        timestamps = self._run_alignment(
+            model,
+            audio=audio_url,
+            transcript=expected["text"],
+            language=expected["language"],
+        )[0]
+
+        self.assertEqual(len(timestamps), len(expected["time_stamps"]))
+        for pred, exp in zip(timestamps, expected["time_stamps"]):
+            self.assertAlmostEqual(pred["start_time"], exp["start_time"], places=2)
+            self.assertAlmostEqual(pred["end_time"], exp["end_time"], places=2)
+
+    @slow
+    def test_fixture_timestamps_batched(self):
+        path = Path(__file__).parent.parent.parent / "fixtures/qwen3_asr/expected_timestamps_batched.json"
+        with open(path, "r", encoding="utf-8") as f:
+            expected_batch = json.load(f)
+
+        model = self._load_aligner()
+        audio_urls = [
+            "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav",
+            "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_zh.wav",
+        ]
+
+        batch_timestamps = self._run_alignment(
+            model,
+            audio=audio_urls,
+            transcript=[e["text"] for e in expected_batch],
+            language=[e["language"] for e in expected_batch],
+        )
+
+        self.assertEqual(len(batch_timestamps), len(expected_batch))
+        for sample_idx, (pred_ts, exp) in enumerate(zip(batch_timestamps, expected_batch)):
+            self.assertEqual(
+                len(pred_ts),
+                len(exp["time_stamps"]),
+                f"Sample {sample_idx}: expected {len(exp['time_stamps'])} timestamps, got {len(pred_ts)}",
+            )
+            for pred, exp_ts in zip(pred_ts, exp["time_stamps"]):
+                self.assertAlmostEqual(pred["start_time"], exp_ts["start_time"])
+                self.assertAlmostEqual(pred["end_time"], exp_ts["end_time"])
diff --git a/tests/models/qwen3_asr/test_processor_qwen3_asr.py b/tests/models/qwen3_asr/test_processor_qwen3_asr.py
new file mode 100644
index 000000000000..38018d872e8c
--- /dev/null
+++ b/tests/models/qwen3_asr/test_processor_qwen3_asr.py
@@ -0,0 +1,180 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    AutoTokenizer,
+    Qwen2TokenizerFast,
+    Qwen3ASRFeatureExtractor,
+)
+from transformers.models.qwen3_asr.processing_qwen3_asr import Qwen3ASRProcessor
+from transformers.testing_utils import (
+    require_torch,
+    require_torchaudio,
+)
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+class Qwen3ASRProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Qwen3ASRProcessor
+
+    @classmethod
+    @require_torch
+    @require_torchaudio
+    def setUpClass(cls):
+        cls.checkpoint = "bezzam/Qwen3-ASR-0.6B"
+        cls.tmpdirname = tempfile.mkdtemp()
+        processor = Qwen3ASRProcessor.from_pretrained(cls.checkpoint)
+        processor.save_pretrained(cls.tmpdirname)
+
+    @require_torch
+    @require_torchaudio
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    @require_torch
+    @require_torchaudio
+    def get_feature_extractor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).feature_extractor
+
+    @require_torch
+    @require_torchaudio
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
+    @require_torch
+    @require_torchaudio
+    def test_can_load_various_tokenizers(self):
+        processor = Qwen3ASRProcessor.from_pretrained(self.checkpoint)
+        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
+        self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
+
+    @require_torch
+    @require_torchaudio
+    def test_save_load_pretrained_default(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.checkpoint)
+        processor = Qwen3ASRProcessor.from_pretrained(self.checkpoint)
+        feature_extractor = processor.feature_extractor
+
+        processor = Qwen3ASRProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            processor.save_pretrained(tmpdir)
+            reloaded = Qwen3ASRProcessor.from_pretrained(tmpdir)
+
+        self.assertEqual(reloaded.tokenizer.get_vocab(), tokenizer.get_vocab())
+        self.assertEqual(reloaded.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+        self.assertIsInstance(reloaded.feature_extractor, Qwen3ASRFeatureExtractor)
+        self.assertIsInstance(reloaded.tokenizer, Qwen2TokenizerFast)
+
+    @require_torch
+    @require_torchaudio
+    def test_chat_template(self):
+        processor = AutoProcessor.from_pretrained(self.checkpoint)
+        expected_prompt = (
+            "<|im_start|>system\n"
+            "<|im_end|>\n"
+            "<|im_start|>user\n"
+            "<|audio_start|><|audio_pad|><|audio_end|><|im_end|>\n"
+            "<|im_start|>assistant\n"
+        )
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "audio",
+                        "path": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav",
+                    },
+                ],
+            },
+        ]
+        formatted_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+        self.assertEqual(expected_prompt, formatted_prompt)
+
+    @require_torch
+    @require_torchaudio
+    def test_apply_transcription_request_single(self):
+        processor = AutoProcessor.from_pretrained(self.checkpoint)
+
+        audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+        helper_outputs = processor.apply_transcription_request(audio=audio_url)
+
+        conversation = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "audio", "path": audio_url},
+                ],
+            }
+        ]
+        manual_outputs = processor.apply_chat_template(
+            conversation,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_dict=True,
+        )
+
+        for key in ("input_ids", "attention_mask", "input_features", "input_features_mask"):
+            self.assertIn(key, helper_outputs)
+            self.assertTrue(helper_outputs[key].equal(manual_outputs[key]))
+
+    @require_torch
+    @require_torchaudio
+    def test_apply_transcription_request_with_language(self):
+        processor = AutoProcessor.from_pretrained(self.checkpoint)
+
+        audio_url = "https://huggingface.co/datasets/bezzam/audio_samples/resolve/main/librispeech_mr_quilter.wav"
+        outputs = processor.apply_transcription_request(audio=audio_url, language="English")
+
+        for key in ("input_ids", "attention_mask", "input_features", "input_features_mask"):
+            self.assertIn(key, outputs)
+
+    @require_torch
+    @require_torchaudio
+    def test_decode_formats(self):
+        processor = AutoProcessor.from_pretrained(self.checkpoint)
+
+        raw_text = "language English<asr_text>Mr. Quilter is the apostle of the middle classes."
+
+        # raw
+        self.assertEqual(raw_text, raw_text)
+
+        # parsed
+        parsed = processor.parse_output(raw_text)
+        self.assertIsInstance(parsed, dict)
+        self.assertEqual(parsed["language"], "English")
+        self.assertEqual(parsed["transcription"], "Mr. Quilter is the apostle of the middle classes.")
+
+        # transcription_only
+        transcription = processor.extract_transcription(raw_text)
+        self.assertEqual(transcription, "Mr. Quilter is the apostle of the middle classes.")
+
+    @parameterized.expand([(1, "np"), (1, "pt"), (2, "np"), (2, "pt")])
+    def test_apply_chat_template_audio(self, batch_size: int, return_tensors: str):
+        self.skipTest("Qwen3ASR processor requires audio; not compatible with text-only chat template tests.")
+
+    def test_apply_chat_template_assistant_mask(self):
+        self.skipTest("Qwen3ASR processor requires audio; not compatible with text-only chat template tests.")
diff --git a/tests/models/qwen3_moe/test_modeling_qwen3_moe.py b/tests/models/qwen3_moe/test_modeling_qwen3_moe.py
index 13fafa6969ae..2246364f8b6b 100644
--- a/tests/models/qwen3_moe/test_modeling_qwen3_moe.py
+++ b/tests/models/qwen3_moe/test_modeling_qwen3_moe.py
@@ -78,7 +78,7 @@ def test_load_balancing_loss(self):
         attention_mask = input_ids.ne(1).to(torch_device)
         model = Qwen3MoeForCausalLM(config)
         model.to(torch_device)
-        model.eval()
+        model.train()
         result = model(input_ids, attention_mask=attention_mask)
         self.assertEqual(result.router_logits[0].shape, (91, config.num_experts))
         torch.testing.assert_close(result.aux_loss.cpu(), torch.tensor(2, dtype=torch.float32), rtol=1e-2, atol=1e-2)
diff --git a/tests/models/qwen3_vl/test_modeling_qwen3_vl.py b/tests/models/qwen3_vl/test_modeling_qwen3_vl.py
index 9874ce4a8203..d80cb3819486 100644
--- a/tests/models/qwen3_vl/test_modeling_qwen3_vl.py
+++ b/tests/models/qwen3_vl/test_modeling_qwen3_vl.py
@@ -107,7 +107,7 @@ def place_image_tokens(self, input_ids, config):
         input_ids[:, 0] = self.vision_start_token_id
         return input_ids
 
-    def get_additional_inputs(self, config, input_ids, pixel_values):
+    def get_additional_inputs(self, config, input_ids, modality_inputs):
         mm_token_type_ids = torch.zeros_like(input_ids)
         mm_token_type_ids[input_ids == self.image_token_id] = 1
         return {
diff --git a/tests/models/qwen3_vl/test_processing_qwen3_vl.py b/tests/models/qwen3_vl/test_processing_qwen3_vl.py
index bae615621976..9212c8217535 100644
--- a/tests/models/qwen3_vl/test_processing_qwen3_vl.py
+++ b/tests/models/qwen3_vl/test_processing_qwen3_vl.py
@@ -20,7 +20,7 @@
 from transformers.testing_utils import require_av, require_torch, require_torchvision, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_processing_common import ProcessorTesterMixin
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
 
 
 if is_vision_available():
@@ -195,7 +195,12 @@ def test_apply_chat_template_video_frame_sampling(self):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "video"},
+                        {
+                            "type": "video",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4"
+                            ),
+                        },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
                 },
@@ -205,21 +210,10 @@ def test_apply_chat_template_video_frame_sampling(self):
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
         self.assertEqual(len(formatted_prompt), 1)
 
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask", "mm_token_type_ids"])
-
         # for fast test, set the longest edge to 8192
         processor.video_processor.size.longest_edge = 8192
 
         # Add video URL for return dict and load with `num_frames` arg
-        messages[0][0]["content"][0] = {
-            "type": "video",
-            "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4",
-        }
         num_frames = 3
         out_dict_with_video = processor.apply_chat_template(
             messages,
diff --git a/tests/models/qwen3_vl_moe/test_modeling_qwen3_vl_moe.py b/tests/models/qwen3_vl_moe/test_modeling_qwen3_vl_moe.py
index 0b0523de3b71..03a93ef1d7fd 100644
--- a/tests/models/qwen3_vl_moe/test_modeling_qwen3_vl_moe.py
+++ b/tests/models/qwen3_vl_moe/test_modeling_qwen3_vl_moe.py
@@ -106,7 +106,7 @@ def place_image_tokens(self, input_ids, config):
         input_ids[:, 0] = self.vision_start_token_id
         return input_ids
 
-    def get_additional_inputs(self, config, input_ids, pixel_values):
+    def get_additional_inputs(self, config, input_ids, modality_inputs):
         # Qwen3VL requires image_grid_thw tensor
         mm_token_type_ids = torch.zeros_like(input_ids)
         mm_token_type_ids[input_ids == self.image_token_id] = 1
diff --git a/tests/models/sam/test_modeling_sam.py b/tests/models/sam/test_modeling_sam.py
index a5520b79e87b..aa3e99e4de5b 100644
--- a/tests/models/sam/test_modeling_sam.py
+++ b/tests/models/sam/test_modeling_sam.py
@@ -488,7 +488,13 @@ class SamModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (SamModel,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {}
+        {
+            "feature-extraction": SamModel,
+            "mask-generation": SamModel,
+            "promptable-visual-segmentation": SamModel,
+        }
+        if is_torch_available()
+        else {}
     )
 
     test_resize_embeddings = False
diff --git a/tests/models/sam2/test_modeling_sam2.py b/tests/models/sam2/test_modeling_sam2.py
index 27a44b091e8e..3a2062bcaaf5 100644
--- a/tests/models/sam2/test_modeling_sam2.py
+++ b/tests/models/sam2/test_modeling_sam2.py
@@ -460,7 +460,13 @@ class Sam2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (Sam2Model,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": Sam2Model, "mask-generation": Sam2Model} if is_torch_available() else {}
+        {
+            "feature-extraction": Sam2Model,
+            "mask-generation": Sam2Model,
+            "promptable-visual-segmentation": Sam2Model,
+        }
+        if is_torch_available()
+        else {}
     )
 
     test_resize_embeddings = False
diff --git a/tests/models/sam3/test_modeling_sam3.py b/tests/models/sam3/test_modeling_sam3.py
index df94063c0a7f..a1053833c370 100644
--- a/tests/models/sam3/test_modeling_sam3.py
+++ b/tests/models/sam3/test_modeling_sam3.py
@@ -14,6 +14,7 @@
 """Testing suite for the PyTorch SAM3 model."""
 
 import gc
+import platform
 import tempfile
 import unittest
 
@@ -46,10 +47,48 @@
         Sam3VisionConfig,
         Sam3ViTConfig,
     )
-    from transformers.models.sam3.modeling_sam3 import Sam3Model, Sam3VisionModel
+    from transformers.models.sam3.modeling_sam3 import Sam3MaskDecoder, Sam3Model, Sam3VisionModel
     from transformers.models.sam3.processing_sam3 import Sam3Processor
 
 
+@require_torch
+class Sam3MaskDecoderUnitTest(unittest.TestCase):
+    def setUp(self):
+        self.config = Sam3MaskDecoderConfig(hidden_size=32, num_multiscale_features=3, decoder_num_layers=2)
+        self.decoder = Sam3MaskDecoder(self.config)
+        self.device = torch.device("cpu")
+
+    def test_single_scale_forward(self):
+        import torch
+
+        batch_size = 2
+        C, H, W = self.config.hidden_size, 16, 16
+        img_embed = torch.randn(batch_size, C, H, W).to(self.device)
+        decoder_queries = torch.randn(batch_size, 4, C).to(self.device)
+        encoder_hidden_states = torch.randn(batch_size, H * W, C).to(self.device)
+        outputs = self.decoder(
+            decoder_queries,
+            img_embed,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        self.assertIsNotNone(outputs.pred_masks)
+
+    def test_multi_scale_forward(self):
+        import torch
+
+        batch_size = 2
+        C, H, W = self.config.hidden_size, 16, 16
+        img_embeds = [torch.randn(batch_size, C, H, W).to(self.device) for _ in range(3)]
+        decoder_queries = torch.randn(batch_size, 4, C).to(self.device)
+        encoder_hidden_states = torch.randn(batch_size, H * W, C).to(self.device)
+        outputs = self.decoder(
+            decoder_queries,
+            img_embeds,
+            encoder_hidden_states=encoder_hidden_states,
+        )
+        self.assertIsNotNone(outputs.pred_masks)
+
+
 class Sam3VisionModelTester:
     def __init__(
         self,
@@ -136,6 +175,9 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
+@unittest.skipIf(
+    platform.system() == "Windows", "safetensors serialization is not supported on Windows for this test."
+)
 class Sam3VisionModelTest(ModelTesterMixin, unittest.TestCase):
     """
     Tests for SAM3 Vision Model (ViT backbone + FPN neck).
@@ -378,13 +420,18 @@ def prepare_config_and_inputs_for_common(self):
 
 
 @require_torch
+@unittest.skipIf(
+    platform.system() == "Windows", "safetensors serialization is not supported on Windows for this test."
+)
 class Sam3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
     Tests for SAM3 full model.
     """
 
     all_model_classes = (Sam3Model,) if is_torch_available() else ()
-    pipeline_model_mapping = {"mask-generation": Sam3Model} if is_torch_available() else {}
+    pipeline_model_mapping = (
+        {"mask-generation": Sam3Model, "promptable-concept-segmentation": Sam3Model} if is_torch_available() else {}
+    )
 
     test_resize_embeddings = False
     _is_composite = True
diff --git a/tests/models/sam3_tracker/test_modeling_sam3_tracker.py b/tests/models/sam3_tracker/test_modeling_sam3_tracker.py
index e20ef5c83f67..6ac599c7e46f 100644
--- a/tests/models/sam3_tracker/test_modeling_sam3_tracker.py
+++ b/tests/models/sam3_tracker/test_modeling_sam3_tracker.py
@@ -246,7 +246,13 @@ class Sam3TrackerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
 
     all_model_classes = (Sam3TrackerModel,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": Sam3TrackerModel, "mask-generation": Sam3TrackerModel} if is_torch_available() else {}
+        {
+            "feature-extraction": Sam3TrackerModel,
+            "mask-generation": Sam3TrackerModel,
+            "promptable-visual-segmentation": Sam3TrackerModel,
+        }
+        if is_torch_available()
+        else {}
     )
 
     test_resize_embeddings = False
diff --git a/tests/models/sam_hq/test_modeling_sam_hq.py b/tests/models/sam_hq/test_modeling_sam_hq.py
index bf0720003663..dcce1d1358d6 100644
--- a/tests/models/sam_hq/test_modeling_sam_hq.py
+++ b/tests/models/sam_hq/test_modeling_sam_hq.py
@@ -28,6 +28,7 @@
     pipeline,
 )
 from transformers.testing_utils import Expectations, cleanup, require_torch, slow, torch_device
+from transformers.trainer_utils import set_seed
 from transformers.utils import is_torch_available, is_vision_available
 
 from ...test_configuration_common import ConfigTester
@@ -780,6 +781,11 @@ def prepare_dog_img():
 
 @slow
 class SamHQModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        super().setUp()
+        # Set seed for deterministic positional embeddings (randomly initialized via torch.randn)
+        set_seed(0)
+
     def tearDown(self):
         super().tearDown()
         # clean-up as much as possible GPU memory occupied by PyTorch
diff --git a/tests/models/segformer/test_image_processing_segformer.py b/tests/models/segformer/test_image_processing_segformer.py
index 178e8f50529a..d6345ade6f4b 100644
--- a/tests/models/segformer/test_image_processing_segformer.py
+++ b/tests/models/segformer/test_image_processing_segformer.py
@@ -15,6 +15,7 @@
 
 import unittest
 
+import numpy as np
 from datasets import load_dataset
 
 from transformers.testing_utils import require_torch, require_vision
@@ -252,6 +253,26 @@ def test_reduce_labels(self):
             encoding = image_processing(image, map, return_tensors="pt")
             self.assertTrue(len(encoding["labels"]) == len(map))
 
+    def test_reduce_labels_keeps_void_label(self):
+        image = np.zeros((2, 2, 3), dtype=np.uint8)
+        segmentation_map = np.array([[0, 1], [2, 255]], dtype=np.uint8)
+        expected_labels = torch.tensor([[[255, 0], [1, 255]]], dtype=torch.long)
+        image_processor_kwargs = self.image_processor_dict.copy()
+        image_processor_kwargs.update(
+            {
+                "do_resize": False,
+                "do_rescale": False,
+                "do_normalize": False,
+                "do_reduce_labels": True,
+            }
+        )
+
+        for image_processing_class in self.image_processing_classes.values():
+            image_processing = image_processing_class(**image_processor_kwargs)
+
+            encoding = image_processing(image, segmentation_map, return_tensors="pt")
+            self.assertTrue(torch.equal(encoding["labels"], expected_labels))
+
     def test_backends_equivalence(self):
         if len(self.image_processing_classes) < 2:
             self.skipTest(reason="Skipping backends equivalence test as there are less than 2 backends")
diff --git a/tests/models/superpoint/test_modeling_superpoint.py b/tests/models/superpoint/test_modeling_superpoint.py
index d6bac174e360..dbd35aeabe75 100644
--- a/tests/models/superpoint/test_modeling_superpoint.py
+++ b/tests/models/superpoint/test_modeling_superpoint.py
@@ -196,9 +196,10 @@ def check_hidden_states_output(inputs_dict, config, model_class):
             hidden_states = outputs.hidden_states
 
             # SuperPoint's feature maps are of shape (batch_size, num_channels, width, height)
+            # hidden_states[0] is the input to the first conv block, so we offset by 1
             for i, conv_layer_size in enumerate(self.model_tester.encoder_hidden_sizes[:-1]):
                 self.assertListEqual(
-                    list(hidden_states[i].shape[-3:]),
+                    list(hidden_states[i + 1].shape[-3:]),
                     [
                         conv_layer_size,
                         self.model_tester.image_height // (2 ** (i + 1)),
diff --git a/tests/models/switch_transformers/test_modeling_switch_transformers.py b/tests/models/switch_transformers/test_modeling_switch_transformers.py
index 65162e94b6fd..63ea9902b3e5 100644
--- a/tests/models/switch_transformers/test_modeling_switch_transformers.py
+++ b/tests/models/switch_transformers/test_modeling_switch_transformers.py
@@ -930,6 +930,50 @@ def test_max_routing_capacity(self):
 
         assert torch.sum(expert_index) <= batch_size * self.config.num_experts * self.config.expert_capacity
 
+    def test_jitter_noise_preserves_hidden_states(self):
+        r"""
+        Test that jitter noise is applied only to routing decisions and does not modify the original hidden states.
+        This tests the fix for the jitter noise issue where noise was corrupting the input hidden states.
+        """
+        # Create a config with jitter noise enabled
+        config = SwitchTransformersConfig(
+            num_experts=2,
+            hidden_size=4,
+            d_ff=8,
+            router_jitter_noise=0.1,  # Enable jitter noise
+            expert_capacity=4,
+        )
+
+        # Create router
+        router = SwitchTransformersTop1Router(config)
+        router.eval()  # Set to eval mode first to test training mode separately
+
+        # Create input hidden states
+        hidden_states = torch.tensor([[[0.5, 0.2, 0.1, 0.3], [0.4, 0.6, 0.2, 0.8]]], dtype=torch.float32)
+
+        # Test in eval mode - no jitter noise should be applied
+        original_hidden_states = hidden_states.clone()
+        with torch.no_grad():
+            router_probs, expert_index, router_logits = router(hidden_states)
+
+        # Hidden states should remain unchanged in eval mode
+        self.assertTrue(torch.equal(hidden_states, original_hidden_states))
+
+        # Test in training mode - jitter noise should be applied only internally
+        router.train()
+        torch.manual_seed(42)  # Set seed for reproducible results
+
+        original_hidden_states = hidden_states.clone()
+        with torch.no_grad():
+            router_probs_train, expert_index_train, router_logits_train = router(hidden_states)
+
+        # Hidden states should still remain unchanged after router call
+        self.assertTrue(torch.equal(hidden_states, original_hidden_states))
+
+        # Results should be different between eval and train mode due to jitter noise
+        # (though this might occasionally fail due to randomness, it's very unlikely with seed)
+        self.assertFalse(torch.allclose(router_logits, router_logits_train, atol=1e-5))
+
 
 @slow
 @require_torch
diff --git a/tests/models/t5/test_modeling_t5.py b/tests/models/t5/test_modeling_t5.py
index c5c79b3a44b4..8cd7060f8bfe 100644
--- a/tests/models/t5/test_modeling_t5.py
+++ b/tests/models/t5/test_modeling_t5.py
@@ -45,6 +45,7 @@
     from transformers import (
         ByT5Tokenizer,
         GenerationConfig,
+        T5EncoderForSequenceClassification,
         T5EncoderModel,
         T5ForConditionalGeneration,
         T5ForQuestionAnswering,
@@ -811,6 +812,22 @@ def create_and_check_with_token_classification_head(
         self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
         self.parent.assertEqual(outputs["loss"].size(), ())
 
+    def create_and_check_with_sequence_classification_head(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+    ):
+        labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
+        model = T5EncoderForSequenceClassification(config=config).to(torch_device).eval()
+        outputs = model(
+            input_ids=input_ids,
+            labels=labels,
+            attention_mask=attention_mask,
+        )
+        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
+        self.parent.assertEqual(outputs["loss"].size(), ())
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -827,12 +844,15 @@ def prepare_config_and_inputs_for_common(self):
 
 
 class T5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (T5EncoderModel, T5ForTokenClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (T5EncoderModel, T5ForTokenClassification, T5EncoderForSequenceClassification) if is_torch_available() else ()
+    )
 
     test_resize_embeddings = False
     pipeline_model_mapping = (
         {
             "token-classification": T5ForTokenClassification,
+            "sequence-classification": T5EncoderForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -858,6 +878,10 @@ def test_with_token_classification_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
 
+    def test_with_sequence_classification_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
+
     def is_pipeline_test_to_skip(
         self,
         pipeline_test_case_name,
diff --git a/tests/models/timesfm/test_modeling_timesfm.py b/tests/models/timesfm/test_modeling_timesfm.py
index 31ed60ce9d5c..95f8d3e819d9 100644
--- a/tests/models/timesfm/test_modeling_timesfm.py
+++ b/tests/models/timesfm/test_modeling_timesfm.py
@@ -21,6 +21,7 @@
 
 from transformers import TimesFmConfig, is_torch_available
 from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.utils import is_sklearn_available
 
 from ...test_configuration_common import ConfigTester
 from ...test_modeling_common import TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin
@@ -209,22 +210,49 @@ def test_model_main_input_name(self):
         observed_main_input_name = list(model_signature.parameters.keys())[1]
         self.assertEqual(TimesFmModelForPrediction.main_input_name, observed_main_input_name)
 
+    def test_past_values_to_tensor_left_pads_and_stacks(self):
+        past_values = [
+            torch.tensor([1.0, 2.0, 3.0]),
+            torch.tensor([4.0]),
+            torch.tensor([5.0, 6.0]),
+        ]
+        expected = torch.tensor(
+            [
+                [1.0, 2.0, 3.0],
+                [0.0, 0.0, 4.0],
+                [0.0, 5.0, 6.0],
+            ]
+        )
+
+        out = TimesFmModelForPrediction._past_values_to_tensor(past_values)
+
+        self.assertEqual(out.shape, (3, 3))
+        self.assertEqual(out.dtype, past_values[0].dtype)
+        self.assertTrue(torch.equal(out, expected))
+
 
 @require_torch
 @slow
 class TimesFmModelIntegrationTests(unittest.TestCase):
     def test_inference(self):
         model = TimesFmModelForPrediction.from_pretrained("google/timesfm-2.0-500m-pytorch").to(torch_device)
-        forecast_input = [
-            np.sin(np.linspace(0, 20, 100)),
-            np.sin(np.linspace(0, 20, 200)),
-            np.sin(np.linspace(0, 20, 400)),
+        sequences = [
+            torch.sin(torch.linspace(0, 20, 100, dtype=torch.float32, device=torch_device)),
+            torch.sin(torch.linspace(0, 20, 200, dtype=torch.float32, device=torch_device)),
+            torch.sin(torch.linspace(0, 20, 400, dtype=torch.float32, device=torch_device)),
         ]
-        forecast_input_tensor = [torch.tensor(ts, dtype=torch.float32, device=torch_device) for ts in forecast_input]
-        frequency_input = [0, 1, 2]
+        past_values = TimesFmModelForPrediction._past_values_to_tensor(sequences)
+        past_observed_mask = torch.zeros_like(past_values, dtype=torch.long)
+        for i, ts in enumerate(sequences):
+            past_observed_mask[i, past_values.shape[1] - ts.shape[0] :] = 1
+        frequency_input = torch.tensor([0, 1, 2], dtype=torch.long, device=torch_device)
 
         with torch.no_grad():
-            output = model(past_values=forecast_input_tensor, freq=frequency_input)
+            output = model(
+                past_values=past_values,
+                past_observed_mask=past_observed_mask,
+                freq=frequency_input,
+            )
 
         mean_predictions = output.mean_predictions
         self.assertEqual(mean_predictions.shape, torch.Size([3, model.config.horizon_length]))
@@ -242,3 +270,509 @@ def test_inference(self):
             device=torch_device)
         # fmt: on
         self.assertTrue(torch.allclose(mean_predictions[0, :64], expected_slice, atol=TOLERANCE))
+
+
+@require_torch
+@unittest.skipUnless(is_sklearn_available(), "test requires scikit-learn")
+class TimesFmCovariatesTest(unittest.TestCase):
+    """Test TimesFM covariates functionality."""
+
+    def setUp(self):
+        self.model_tester = TimesFmModelTester(
+            self,
+            patch_length=32,
+            context_length=128,
+            horizon_length=32,
+            num_hidden_layers=1,
+            hidden_size=16,
+            intermediate_size=32,
+            batch_size=2,
+        )
+        self.config = self.model_tester.get_config()
+        self.model = TimesFmModelForPrediction(self.config).to(torch_device)
+        self.model.eval()
+
+        # Create test data with consistent lengths
+        self.context_len = 60  # Use a fixed context length
+        self.horizon_len = 16
+        self.past_values = [
+            torch.tensor(np.sin(np.linspace(0, 10, self.context_len)), dtype=torch.float32, device=torch_device),
+            torch.tensor(np.cos(np.linspace(0, 10, self.context_len)), dtype=torch.float32, device=torch_device),
+        ]
+        self.total_len = self.context_len + self.horizon_len
+
+    def _create_test_covariates(self):
+        """Create comprehensive test covariates."""
+        # Dynamic numerical covariates
+        dynamic_numerical = {
+            "temperature": [
+                (20 + 5 * np.sin(2 * np.pi * np.arange(self.total_len) / 10)).tolist(),
+                (25 + 3 * np.cos(2 * np.pi * np.arange(self.total_len) / 8)).tolist(),
+            ],
+            "humidity": [
+                (60 + np.random.RandomState(42).randn(self.total_len) * 2).tolist(),
+                (55 + np.random.RandomState(43).randn(self.total_len) * 3).tolist(),
+            ],
+        }
+
+        # Dynamic categorical covariates
+        dynamic_categorical = {
+            "weekday": [
+                [i % 7 for i in range(self.total_len)],
+                [(i + 1) % 7 for i in range(self.total_len)],
+            ],
+            "season": [
+                [["spring", "summer", "fall", "winter"][i % 4] for i in range(self.total_len)],
+                [["spring", "summer", "fall", "winter"][i % 4] for i in range(self.total_len)],
+            ],
+        }
+
+        # Static covariates
+        static_numerical = {
+            "store_size": [100.0, 150.0],
+            "avg_income": [50000.0, 60000.0],
+        }
+
+        static_categorical = {
+            "store_type": ["supermarket", "convenience"],
+            "region": ["north", "south"],
+        }
+
+        return {
+            "dynamic_numerical_covariates": dynamic_numerical,
+            "dynamic_categorical_covariates": dynamic_categorical,
+            "static_numerical_covariates": static_numerical,
+            "static_categorical_covariates": static_categorical,
+        }
+
+    def test_forecast_with_covariates_basic_functionality(self):
+        """Test basic covariates functionality."""
+        covariates = self._create_test_covariates()
+
+        with torch.no_grad():
+            output = self.model.forecast_with_covariates(
+                past_values=self.past_values,
+                ridge=0.5,  # Use higher ridge for test stability
+                **covariates,
+            )
+
+        # Check output structure
+        self.assertTrue(hasattr(output, "combined_predictions"))
+        self.assertTrue(hasattr(output, "xreg_predictions"))
+        self.assertTrue(hasattr(output, "mean_predictions"))
+
+        # Check tensor shapes
+        batch_size = len(self.past_values)
+        expected_shape = torch.Size([batch_size, self.horizon_len])
+
+        self.assertEqual(output.combined_predictions.shape, expected_shape)
+        self.assertEqual(output.xreg_predictions.shape, expected_shape)
+        self.assertTrue(output.mean_predictions.shape[0] == batch_size)
+
+        # Check that predictions are finite
+        self.assertTrue(torch.isfinite(output.combined_predictions).all())
+        self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+        self.assertTrue(torch.isfinite(output.mean_predictions).all())
+
+    def test_forecast_with_covariates_both_modes(self):
+        """Test both XReg modes."""
+        covariates = self._create_test_covariates()
+
+        for mode in ["xreg + timesfm", "timesfm + xreg"]:
+            with self.subTest(mode=mode):
+                with torch.no_grad():
+                    output = self.model.forecast_with_covariates(
+                        past_values=self.past_values, xreg_mode=mode, ridge=0.5, **covariates
+                    )
+
+                # Both modes should produce valid outputs
+                self.assertTrue(torch.isfinite(output.combined_predictions).all())
+                self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+                # Check shapes are consistent
+                batch_size = len(self.past_values)
+                expected_shape = torch.Size([batch_size, self.horizon_len])
+                self.assertEqual(output.combined_predictions.shape, expected_shape)
+
+    def test_forecast_with_covariates_individual_types(self):
+        """Test individual covariate types."""
+        test_cases = [
+            {
+                "name": "dynamic_numerical_only",
+                "covariates": {
+                    "dynamic_numerical_covariates": self._create_test_covariates()["dynamic_numerical_covariates"]
+                },
+            },
+            {
+                "name": "dynamic_categorical_only",
+                "covariates": {
+                    "dynamic_categorical_covariates": self._create_test_covariates()["dynamic_categorical_covariates"]
+                },
+            },
+            {
+                "name": "static_numerical_only",
+                "covariates": {
+                    "static_numerical_covariates": self._create_test_covariates()["static_numerical_covariates"]
+                },
+            },
+            {
+                "name": "static_categorical_only",
+                "covariates": {
+                    "static_categorical_covariates": self._create_test_covariates()["static_categorical_covariates"]
+                },
+            },
+        ]
+
+        for test_case in test_cases:
+            with self.subTest(covariate_type=test_case["name"]):
+                with torch.no_grad():
+                    output = self.model.forecast_with_covariates(
+                        past_values=self.past_values,
+                        ridge=1.0,  # Higher ridge for stability with fewer covariates
+                        **test_case["covariates"],
+                    )
+
+                # All individual types should work
+                self.assertTrue(torch.isfinite(output.combined_predictions).all())
+                self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+    def test_forecast_with_covariates_error_handling(self):
+        """Test error handling for invalid inputs."""
+
+        # Test no covariates provided
+        with self.assertRaises(ValueError) as context:
+            self.model.forecast_with_covariates(past_values=self.past_values)
+        self.assertIn("At least one of", str(context.exception))
+
+        # Test invalid xreg_mode
+        with self.assertRaises(ValueError) as context:
+            self.model.forecast_with_covariates(
+                past_values=self.past_values,
+                static_numerical_covariates={"test": [1.0, 2.0]},
+                xreg_mode="invalid_mode",
+            )
+        self.assertIn("xreg_mode must be", str(context.exception))
+
+        # Test horizon too long
+        long_covariates = {
+            "dynamic_numerical_covariates": {
+                "test": [
+                    list(range(len(self.past_values[0]) + 1000)),  # Much longer than model horizon
+                    list(range(len(self.past_values[1]) + 1000)),
+                ]
+            }
+        }
+        with self.assertRaises(ValueError) as context:
+            self.model.forecast_with_covariates(past_values=self.past_values, **long_covariates)
+        self.assertIn("exceeds model horizon", str(context.exception))
+
+    def test_forecast_with_covariates_ridge_regularization(self):
+        """Test different ridge regularization values."""
+        covariates = self._create_test_covariates()
+        ridge_values = [0.0, 0.1, 1.0, 10.0]
+
+        for ridge in ridge_values:
+            with self.subTest(ridge=ridge):
+                with torch.no_grad():
+                    output = self.model.forecast_with_covariates(
+                        past_values=self.past_values, ridge=ridge, **covariates
+                    )
+
+                # All ridge values should produce finite outputs
+                self.assertTrue(torch.isfinite(output.combined_predictions).all())
+                self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+    def test_forecast_with_covariates_normalization(self):
+        """Test normalization option."""
+        covariates = self._create_test_covariates()
+
+        for normalize in [True, False]:
+            with self.subTest(normalize=normalize):
+                with torch.no_grad():
+                    output = self.model.forecast_with_covariates(
+                        past_values=self.past_values,
+                        normalize_xreg_target_per_input=normalize,
+                        ridge=0.5,
+                        **covariates,
+                    )
+
+                # Both options should work
+                self.assertTrue(torch.isfinite(output.combined_predictions).all())
+                self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+    def test_forecast_with_covariates_truncate_negative(self):
+        """Test negative value truncation."""
+        # Create positive-only past values
+        positive_past_values = [torch.abs(ts) + 1.0 for ts in self.past_values]
+        covariates = self._create_test_covariates()
+
+        with torch.no_grad():
+            output = self.model.forecast_with_covariates(
+                past_values=positive_past_values, truncate_negative=True, ridge=0.5, **covariates
+            )
+
+        # Check that outputs are non-negative when truncate_negative=True
+        self.assertTrue((output.combined_predictions >= 0).all())
+        self.assertTrue((output.xreg_predictions >= 0).all())
+
+    def test_forecast_with_covariates_variable_lengths(self):
+        """Test with variable sequence lengths."""
+        # Create sequences of different lengths
+        var_past_values = [
+            torch.tensor(np.sin(np.linspace(0, 5, 30)), dtype=torch.float32, device=torch_device),
+            torch.tensor(np.cos(np.linspace(0, 8, 45)), dtype=torch.float32, device=torch_device),
+        ]
+
+        # Adjust covariates for variable lengths
+        max_context = max(len(ts) for ts in var_past_values)
+        total_len = max_context + self.horizon_len
+
+        covariates = {
+            "dynamic_numerical_covariates": {
+                "feature1": [
+                    np.random.RandomState(42).randn(total_len).tolist(),
+                    np.random.RandomState(43).randn(total_len).tolist(),
+                ]
+            },
+            "static_categorical_covariates": {"category": ["A", "B"]},
+        }
+
+        with torch.no_grad():
+            output = self.model.forecast_with_covariates(past_values=var_past_values, ridge=1.0, **covariates)
+
+        # Should handle variable lengths correctly
+        self.assertTrue(torch.isfinite(output.combined_predictions).all())
+        self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+    def test_forecast_with_covariates_return_dict(self):
+        """Test return_dict parameter."""
+        covariates = self._create_test_covariates()
+
+        # Test return_dict=True (default)
+        with torch.no_grad():
+            output_dict = self.model.forecast_with_covariates(
+                past_values=self.past_values, return_dict=True, ridge=0.5, **covariates
+            )
+
+        self.assertTrue(hasattr(output_dict, "combined_predictions"))
+        self.assertTrue(hasattr(output_dict, "xreg_predictions"))
+
+        # Test return_dict=False
+        with torch.no_grad():
+            output_tuple = self.model.forecast_with_covariates(
+                past_values=self.past_values, return_dict=False, ridge=0.5, **covariates
+            )
+
+        self.assertIsInstance(output_tuple, tuple)
+        self.assertTrue(len(output_tuple) > 0)
+
+    def test_forecast_with_covariates_device_consistency(self):
+        """Test that outputs are on the correct device."""
+        covariates = self._create_test_covariates()
+
+        with torch.no_grad():
+            output = self.model.forecast_with_covariates(past_values=self.past_values, ridge=0.5, **covariates)
+
+        # All outputs should be on the same device as the model
+        expected_device = next(self.model.parameters()).device
+        self.assertEqual(output.combined_predictions.device, expected_device)
+        self.assertEqual(output.xreg_predictions.device, expected_device)
+        self.assertEqual(output.mean_predictions.device, expected_device)
+
+    def test_forecast_with_covariates_realistic_example(self):
+        """Test with realistic ice cream/sunscreen sales data similar to covariates.ipynb."""
+        # Based on the ice cream and sunscreen sales example from covariates.ipynb
+        batch_size = 2
+        context_len = 50
+        horizon_len = 10
+
+        # Create realistic time series (ice cream and sunscreen sales)
+        np.random.seed(42)
+        time_points = np.arange(context_len)
+
+        # Ice cream sales: higher in summer, affected by temperature
+        seasonal_pattern = 50 + 30 * np.sin(2 * np.pi * time_points / 12 - np.pi / 2)
+        ice_cream_sales = seasonal_pattern + np.random.randn(context_len) * 5
+
+        # Sunscreen sales: also seasonal but different pattern
+        seasonal_pattern2 = 40 + 25 * np.sin(2 * np.pi * time_points / 12)
+        sunscreen_sales = seasonal_pattern2 + np.random.randn(context_len) * 4
+
+        past_values = [
+            torch.tensor(ice_cream_sales, dtype=torch.float32, device=torch_device),
+            torch.tensor(sunscreen_sales, dtype=torch.float32, device=torch_device),
+        ]
+
+        # Create realistic covariates
+        total_len = context_len + horizon_len
+
+        # Temperature covariate - main driver
+        temperature = 20 + 15 * np.sin(2 * np.pi * np.arange(total_len) / 12) + np.random.randn(total_len) * 2
+
+        # Day of week effect
+        weekday_pattern = np.tile([0, 1, 2, 3, 4, 5, 6], (total_len // 7) + 1)[:total_len]
+
+        # Promotion effect (binary)
+        promotion = np.random.choice([0, 1], size=total_len, p=[0.8, 0.2])
+
+        dynamic_numerical = {
+            "temperature": [temperature.tolist(), temperature.tolist()],
+            "promotion": [promotion.tolist(), promotion.tolist()],
+        }
+
+        dynamic_categorical = {"weekday": [weekday_pattern.tolist(), weekday_pattern.tolist()]}
+
+        static_numerical = {
+            "store_size": [1000.0, 800.0]  # sq ft
+        }
+
+        static_categorical = {"store_type": ["mall", "street"], "region": ["north", "south"]}
+
+        # Test both modes
+        for xreg_mode in ["xreg + timesfm", "timesfm + xreg"]:
+            with torch.no_grad():
+                output = self.model.forecast_with_covariates(
+                    past_values=past_values,
+                    dynamic_numerical_covariates=dynamic_numerical,
+                    dynamic_categorical_covariates=dynamic_categorical,
+                    static_numerical_covariates=static_numerical,
+                    static_categorical_covariates=static_categorical,
+                    xreg_mode=xreg_mode,
+                    ridge=0.1,
+                )
+
+            # Validate realistic predictions
+            self.assertEqual(output.combined_predictions.shape, (batch_size, horizon_len))
+            self.assertEqual(output.xreg_predictions.shape, (batch_size, horizon_len))
+            self.assertEqual(output.mean_predictions.shape, (batch_size, horizon_len))
+
+            # Ensure finite predictions (main technical requirement)
+            self.assertTrue(torch.isfinite(output.combined_predictions).all())
+            self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+            # Predictions should not be extreme values (reasonable sanity check)
+            self.assertTrue(torch.abs(output.combined_predictions).max() < 1e6)  # Avoid extreme values
+
+    def test_forecast_with_covariates_epf_style_data(self):
+        """Test with EPF (Electricity Price Forecasting) style data like in covariates.ipynb."""
+        # Based on EPF example from covariates.ipynb
+        batch_size = 3  # 3 different market regions
+        context_len = 48  # 48 hours of historical data
+        horizon_len = 24  # 24 hour forecast
+
+        # Create realistic electricity price data with daily patterns
+        np.random.seed(123)
+
+        past_values = []
+        for region in range(batch_size):
+            time_points = np.arange(context_len)
+
+            # Daily pattern: higher during day, lower at night
+            daily_pattern = 50 + 20 * np.sin(2 * np.pi * time_points / 24)
+            # Weekly pattern: higher on weekdays
+            weekly_pattern = 5 * np.sin(2 * np.pi * time_points / (24 * 7))
+            # Regional base price
+            regional_base = 40 + region * 10
+            # Random noise
+            noise = np.random.randn(context_len) * 5
+
+            prices = regional_base + daily_pattern + weekly_pattern + noise
+            past_values.append(torch.tensor(prices, dtype=torch.float32, device=torch_device))
+
+        # EPF-style covariates
+        total_len = context_len + horizon_len
+
+        # Load covariates (MW) - main driver for electricity prices
+        base_load = 1000 + 300 * np.sin(2 * np.pi * np.arange(total_len) / 24)
+        load_variation = np.random.randn(total_len) * 50
+
+        dynamic_numerical = {
+            "load_mw": [(base_load + load_variation + i * 100).tolist() for i in range(batch_size)],
+            "temperature": [
+                (
+                    20 + 10 * np.sin(2 * np.pi * np.arange(total_len) / (24 * 30)) + np.random.randn(total_len) * 3
+                ).tolist()
+                for _ in range(batch_size)
+            ],
+            "renewable_share": [
+                np.clip(0.3 + 0.2 * np.random.randn(total_len), 0.1, 0.8).tolist() for _ in range(batch_size)
+            ],
+        }
+
+        dynamic_categorical = {
+            "hour": [[i % 24 for i in range(total_len)] for _ in range(batch_size)],
+            "day_type": [
+                ["weekday" if (i // 24) % 7 < 5 else "weekend" for i in range(total_len)] for _ in range(batch_size)
+            ],
+        }
+
+        static_numerical = {
+            "market_capacity_mw": [5000.0, 4500.0, 6000.0],
+            "transmission_capacity": [800.0, 700.0, 900.0],
+        }
+
+        static_categorical = {
+            "market_type": ["competitive", "regulated", "competitive"],
+            "primary_fuel": ["gas", "coal", "nuclear"],
+        }
+
+        # Test with higher ridge for stability with many covariates
+        with torch.no_grad():
+            output = self.model.forecast_with_covariates(
+                past_values=past_values,
+                dynamic_numerical_covariates=dynamic_numerical,
+                dynamic_categorical_covariates=dynamic_categorical,
+                static_numerical_covariates=static_numerical,
+                static_categorical_covariates=static_categorical,
+                xreg_mode="xreg + timesfm",
+                ridge=0.5,  # Higher ridge for stability
+            )
+
+        # Validate EPF-style predictions
+        self.assertEqual(output.combined_predictions.shape, (batch_size, horizon_len))
+
+        # Electricity prices should be positive
+        self.assertTrue((output.combined_predictions > 0).all())
+        self.assertTrue((output.xreg_predictions > 0).all())
+
+        # Should be in reasonable range for electricity prices (0-500 $/MWh)
+        self.assertTrue((output.combined_predictions < 500).all())
+
+        # Predictions should be finite
+        self.assertTrue(torch.isfinite(output.combined_predictions).all())
+        self.assertTrue(torch.isfinite(output.xreg_predictions).all())
+
+        # Test that covariates model provides useful signal
+        # XReg predictions should capture some of the load-price relationship
+        mean_price = output.combined_predictions.mean()
+        self.assertTrue(20 < mean_price < 200)  # Reasonable electricity price range
+
+    def test_covariates_training_backward(self):
+        """Ensure loss computes and gradients flow for covariate training."""
+        covariates = self._create_test_covariates()
+
+        # Fresh small model for training step
+        model = TimesFmModelForPrediction(self.config).to(torch_device)
+        model.train()
+
+        # Future values matching the covariate-driven horizon per series
+        future_values = torch.zeros(len(self.past_values), self.horizon_len, dtype=torch.float32, device=torch_device)
+
+        # Use residual training path (xreg + timesfm) by default
+        output = model.forecast_with_covariates(
+            past_values=self.past_values,
+            future_values=future_values,
+            ridge=0.1,
+            **covariates,
+        )
+
+        self.assertIsNotNone(output.loss)
+        # Backward pass should produce non-zero gradients on some parameters
+        output.loss.backward()
+
+        total_grad = 0.0
+        for p in model.parameters():
+            if p.grad is not None:
+                total_grad += float(p.grad.detach().abs().sum().item())
+
+        self.assertGreater(total_grad, 0.0)
diff --git a/tests/models/timesfm2_5/test_modeling_timesfm2_5.py b/tests/models/timesfm2_5/test_modeling_timesfm2_5.py
index 7a909da6d78c..ebe4c9f2adcb 100644
--- a/tests/models/timesfm2_5/test_modeling_timesfm2_5.py
+++ b/tests/models/timesfm2_5/test_modeling_timesfm2_5.py
@@ -290,15 +290,18 @@ def test_inference(self):
         model = TimesFm2_5ModelForPrediction.from_pretrained(
             "google/timesfm-2.5-200m-transformers", revision="refs/pr/3"
         ).to(torch_device)
-        forecast_input = [
-            np.sin(np.linspace(0, 20, 100)),
-            np.sin(np.linspace(0, 20, 200)),
-            np.sin(np.linspace(0, 20, 400)),
+        sequences = [
+            torch.sin(torch.linspace(0, 20, 100, dtype=torch.float32, device=torch_device)),
+            torch.sin(torch.linspace(0, 20, 200, dtype=torch.float32, device=torch_device)),
+            torch.sin(torch.linspace(0, 20, 400, dtype=torch.float32, device=torch_device)),
         ]
-        forecast_input_tensor = [torch.tensor(ts, dtype=torch.float32, device=torch_device) for ts in forecast_input]
+        past_values = TimesFm2_5ModelForPrediction._past_values_to_tensor(sequences)
+        past_observed_mask = torch.zeros_like(past_values, dtype=torch.long)
+        for i, ts in enumerate(sequences):
+            past_observed_mask[i, past_values.shape[1] - ts.shape[0] :] = 1
 
         with torch.no_grad():
-            output = model(past_values=forecast_input_tensor)
+            output = model(past_values=past_values, past_observed_mask=past_observed_mask)
 
         mean_predictions = output.mean_predictions
         self.assertEqual(mean_predictions.shape, torch.Size([3, model.config.horizon_length]))
diff --git a/tests/models/umt5/test_modeling_umt5.py b/tests/models/umt5/test_modeling_umt5.py
index 8921d204634e..86ba0a2f2f09 100644
--- a/tests/models/umt5/test_modeling_umt5.py
+++ b/tests/models/umt5/test_modeling_umt5.py
@@ -36,6 +36,7 @@
 
     from transformers import (
         AutoTokenizer,
+        UMT5EncoderForSequenceClassification,
         UMT5EncoderModel,
         UMT5ForConditionalGeneration,
         UMT5ForQuestionAnswering,
@@ -465,6 +466,22 @@ def create_and_check_with_token_classification_head(
         self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, self.seq_length, config.num_labels))
         self.parent.assertEqual(outputs["loss"].size(), ())
 
+    def create_and_check_with_sequence_classification_head(
+        self,
+        config,
+        input_ids,
+        attention_mask,
+    ):
+        labels = torch.tensor([1] * self.batch_size, dtype=torch.long, device=torch_device)
+        model = UMT5EncoderForSequenceClassification(config=config).to(torch_device).eval()
+        outputs = model(
+            input_ids=input_ids,
+            labels=labels,
+            attention_mask=attention_mask,
+        )
+        self.parent.assertEqual(outputs["logits"].size(), (self.batch_size, config.num_labels))
+        self.parent.assertEqual(outputs["loss"].size(), ())
+
     def prepare_config_and_inputs_for_common(self):
         config_and_inputs = self.prepare_config_and_inputs()
         (
@@ -482,12 +499,17 @@ def prepare_config_and_inputs_for_common(self):
 
 # Copied from tests.models.t5.test_modeling_t5.T5EncoderOnlyModelTest with T5->UMT5
 class UMT5EncoderOnlyModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
-    all_model_classes = (UMT5EncoderModel, UMT5ForTokenClassification) if is_torch_available() else ()
+    all_model_classes = (
+        (UMT5EncoderModel, UMT5ForTokenClassification, UMT5EncoderForSequenceClassification)
+        if is_torch_available()
+        else ()
+    )
 
     test_resize_embeddings = False
     pipeline_model_mapping = (
         {
             "token-classification": UMT5ForTokenClassification,
+            "sequence-classification": UMT5EncoderForSequenceClassification,
         }
         if is_torch_available()
         else {}
@@ -513,6 +535,10 @@ def test_with_token_classification_head(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_with_token_classification_head(*config_and_inputs)
 
+    def test_with_sequence_classification_head(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_with_sequence_classification_head(*config_and_inputs)
+
     def is_pipeline_test_to_skip(
         self,
         pipeline_test_case_name,
diff --git a/tests/models/vibevoice_asr/test_modeling_vibevoice_asr.py b/tests/models/vibevoice_asr/test_modeling_vibevoice_asr.py
index be0ece165e36..fc8bb11568ea 100644
--- a/tests/models/vibevoice_asr/test_modeling_vibevoice_asr.py
+++ b/tests/models/vibevoice_asr/test_modeling_vibevoice_asr.py
@@ -17,7 +17,6 @@
 import unittest
 from pathlib import Path
 
-import pytest
 from parameterized import parameterized
 
 from transformers import (
@@ -150,19 +149,6 @@ def setUp(self):
     def test_inputs_embeds_matches_input_ids(self):
         pass
 
-    @unittest.skip(reason="Compile not yet supported for VibeVoiceAsr models")
-    @pytest.mark.torch_compile_test
-    def test_sdpa_can_compile_dynamic(self):
-        pass
-
-    @unittest.skip(reason="Compile not yet supported for VibeVoiceAsr models")
-    def test_sdpa_can_dispatch_on_flash(self):
-        pass
-
-    @unittest.skip(reason="VibeVoiceAsr tests avoid right-padding equivalence; fusion is in-place.")
-    def test_flash_attn_2_inference_equivalence_right_padding(self):
-        pass
-
     @unittest.skip(reason="VibeVoiceAsr has no separate base model without a head.")
     def test_model_base_model_prefix(self):
         pass
diff --git a/tests/models/video_llama_3/test_modeling_video_llama_3.py b/tests/models/video_llama_3/test_modeling_video_llama_3.py
index 9ade7d43ed2d..923b93f571f6 100644
--- a/tests/models/video_llama_3/test_modeling_video_llama_3.py
+++ b/tests/models/video_llama_3/test_modeling_video_llama_3.py
@@ -833,7 +833,7 @@ def test_small_model_integration_test(self):
         EXPECTED_DECODED_TEXT = Expectations(
             {
                 ("cuda", None): "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant nighttime scene on a bustling city street. A woman in a striking red dress",
-                ("xpu", None): "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant night scene in a bustling Japanese city. A woman in a striking red dress",
+                ("xpu", None): "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant nighttime scene on a bustling city street. A woman in a striking red dress",
             }
         ).get_expectation()
         # fmt: on
@@ -887,7 +887,7 @@ def test_small_model_integration_test_batch_wo_image(self):
                     "user\nWhat is relativity?\nassistant\nRelativity is a scientific theory that describes the relationship between space and time. It was first proposed by",
                 ],
                 ("xpu", None): [
-                    "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant night scene in a bustling Japanese city. A woman in a striking red dress",
+                    "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant nighttime scene on a bustling city street. A woman in a striking red dress",
                     "user\nWhat is relativity?\nassistant\nRelativity is a scientific theory that describes the relationship between space and time. It was first proposed by",
                 ],
             }
@@ -914,11 +914,20 @@ def test_small_model_integration_test_batch_different_resolutions(self):
         output = model.generate(**inputs, max_new_tokens=20, do_sample=False, repetition_penalty=None)
         DECODED_TEXT = self.processor.batch_decode(output, skip_special_tokens=True)
 
-        EXPECTED_DECODED_TEXT = [
-            "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant nighttime scene on a bustling city street. A woman in a striking red dress",
-            "user\n\nDescribe the image.\nassistant\nThe image depicts a striking urban scene at night. A person is standing in the center of a wet",
-        ]  # fmt: skip
-
+        # fmt: off
+        EXPECTED_DECODED_TEXT = Expectations(
+            {
+                ("cuda", None): [
+                    "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant nighttime scene on a bustling city street. A woman in a striking red dress",
+                    "user\n\nDescribe the image.\nassistant\nThe image depicts a striking urban scene at night. A person is standing in the center of a wet",
+                ],
+                ("xpu", None): [
+                    "user\n\nDescribe the image.\nassistant\nThe image captures a vibrant night scene in a bustling Japanese city. A woman in a striking red dress",
+                    "user\n\nDescribe the image.\nassistant\nThe image depicts a striking urban scene at night. A person is standing in the center of a wet",
+                ],
+            }
+        ).get_expectation()
+        # fmt: on
         self.assertEqual(DECODED_TEXT, EXPECTED_DECODED_TEXT)
 
     @require_flash_attn
diff --git a/tests/models/video_llama_3/test_processing_video_llama_3.py b/tests/models/video_llama_3/test_processing_video_llama_3.py
index aacd199c9041..21330e2847c5 100644
--- a/tests/models/video_llama_3/test_processing_video_llama_3.py
+++ b/tests/models/video_llama_3/test_processing_video_llama_3.py
@@ -21,7 +21,7 @@
 from transformers.testing_utils import require_av, require_torch, require_torchvision, require_vision
 from transformers.utils import is_torch_available, is_vision_available
 
-from ...test_processing_common import ProcessorTesterMixin
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
 
 
 if is_vision_available():
@@ -189,7 +189,12 @@ def test_apply_chat_template_video_frame_sampling(self):
                 {
                     "role": "user",
                     "content": [
-                        {"type": "video"},
+                        {
+                            "type": "video",
+                            "url": url_to_local_path(
+                                "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/Big_Buck_Bunny_720_10s_10MB.mp4"
+                            ),
+                        },
                         {"type": "text", "text": "What is shown in this video?"},
                     ],
                 },
@@ -199,18 +204,6 @@ def test_apply_chat_template_video_frame_sampling(self):
         formatted_prompt = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
         self.assertEqual(len(formatted_prompt), 1)
 
-        formatted_prompt_tokenized = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True)
-        expected_output = processor.tokenizer(formatted_prompt, return_tensors=None).input_ids
-        self.assertListEqual(expected_output, formatted_prompt_tokenized)
-
-        out_dict = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=True, return_dict=True)
-        self.assertListEqual(list(out_dict.keys()), ["input_ids", "attention_mask"])
-
-        # Add video URL for return dict and load with `num_frames` arg
-        messages[0][0]["content"][0] = {
-            "type": "video",
-            "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/Big_Buck_Bunny_720_10s_10MB.mp4",
-        }
         num_frames = 3
         out_dict_with_video = processor.apply_chat_template(
             messages,
@@ -322,3 +315,25 @@ def test_special_mm_token_truncation(self):
                 padding=True,
                 max_length=20,
             )
+
+    def test_video_processor_defaults(self):
+        # Video processor has default `return_metadata=True` which doesn't match with processor
+        video_processor = self.get_component("video_processor")
+
+        # Get all required components for processor
+        components = {}
+        for attribute in self.processor_class.get_attributes():
+            components[attribute] = self.get_component(attribute)
+
+        processor = self.processor_class(**components)
+        video_input = self.prepare_video_inputs()
+
+        # Process with both video_processor and processor
+        input_video_proc = video_processor(video_input, return_tensors="pt", return_metadata=True)
+        input_processor = processor(videos=video_input, return_tensors="pt", return_metadata=True)
+
+        # Verify outputs match
+        for key in input_video_proc:
+            # processor changes metadata fps in-place when can't be inferred, i.e. if already decoded video
+            if key != "video_metadata":
+                torch.testing.assert_close(input_video_proc[key], input_processor[key])
diff --git a/tests/models/video_llava/test_modeling_video_llava.py b/tests/models/video_llava/test_modeling_video_llava.py
index 8d5995ae8a30..533e3c10a00a 100644
--- a/tests/models/video_llava/test_modeling_video_llava.py
+++ b/tests/models/video_llava/test_modeling_video_llava.py
@@ -397,9 +397,6 @@ def test_vision_feature_layers(self, vision_feature_layer):
             assert base_model.multi_modal_projector.linear_1.in_features == expected_features
             model(**input_dict)
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class VideoLlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/videoprism/__init__.py b/tests/models/videoprism/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/models/videoprism/test_modeling_videoprism.py b/tests/models/videoprism/test_modeling_videoprism.py
new file mode 100644
index 000000000000..0faf74c99b37
--- /dev/null
+++ b/tests/models/videoprism/test_modeling_videoprism.py
@@ -0,0 +1,783 @@
+# Copyright 2026 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch VideoPrism model."""
+
+import tempfile
+import unittest
+
+import numpy as np
+from huggingface_hub import HfApi
+
+from transformers import VideoPrismConfig, VideoPrismTextConfig, VideoPrismVisionConfig
+from transformers.testing_utils import (
+    Expectations,
+    require_sentencepiece,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import (
+    is_sentencepiece_available,
+    is_torch_available,
+    is_vision_available,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import (
+        VideoPrismClipModel,
+        VideoPrismForVideoClassification,
+        VideoPrismTextModel,
+        VideoPrismVideoModel,
+        VideoPrismVisionModel,
+    )
+if is_vision_available():
+    from transformers import LlavaOnevisionVideoProcessor
+if is_sentencepiece_available():
+    from transformers import VideoPrismTokenizer
+torch.set_printoptions(precision=10)
+
+
+@require_vision
+class VideoPrismVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        image_size=8,
+        num_frames=3,
+        tubelet_size=[1, 4, 4],
+        num_channels=3,
+        hidden_size=32,
+        num_spatial_layers=3,
+        num_temporal_layers=2,
+        num_attention_heads=4,
+        intermediate_size=64,  # a multiple of hidden size so that intermediate_size / num_attention_heads is integer
+        hidden_act="gelu_python",
+        hidden_dropout_prob=0.0,
+        attention_probs_dropout_prob=0.0,
+        initializer_range=0.02,
+        layer_norm_eps=1e-06,
+        qkv_bias=True,
+        attn_logit_softcapping=50.0,
+        num_auxiliary_layers=2,
+        apply_l2norm=True,
+        is_training=False,
+        **kwargs,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_frames = num_frames
+        self.tubelet_size = tubelet_size
+        self.num_channels = num_channels
+        self.hidden_size = hidden_size
+        self.num_spatial_layers = num_spatial_layers
+        self.num_temporal_layers = num_temporal_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.initializer_range = initializer_range
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.attn_logit_softcapping = attn_logit_softcapping
+        self.num_auxiliary_layers = num_auxiliary_layers
+        self.apply_l2norm = apply_l2norm
+        self.is_training = is_training
+
+        if kwargs:
+            for key, value in kwargs.items():
+                setattr(self, key, value)
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor(
+            [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]
+        )
+        config = self.get_config()
+        return config, pixel_values
+
+    def get_config(self):
+        config = VideoPrismVisionConfig(
+            image_size=self.image_size,
+            num_frames=self.num_frames,
+            tubelet_size=self.tubelet_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_spatial_layers=self.num_spatial_layers,
+            num_temporal_layers=self.num_temporal_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            hidden_act=self.hidden_act,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            initializer_range=self.initializer_range,
+            layer_norm_eps=self.layer_norm_eps,
+            qkv_bias=self.qkv_bias,
+            attn_logit_softcapping=self.attn_logit_softcapping,
+            num_auxiliary_layers=self.num_auxiliary_layers,
+            apply_l2norm=self.apply_l2norm,
+        )
+        return config
+
+    def create_and_check_model(self, config, pixel_values):
+        model = VideoPrismVisionModel._from_config(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.tubelet_size[1], self.tubelet_size[2])
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(
+            result.last_hidden_state.shape, (self.batch_size, num_patches * self.num_frames, self.hidden_size)
+        )
+        self.parent.assertEqual(
+            result.spatial_hidden_state.shape, (self.batch_size * self.num_frames, num_patches, self.hidden_size)
+        )
+        self.parent.assertEqual(
+            result.temporal_hidden_state.shape, (self.batch_size * num_patches, self.num_frames, self.hidden_size)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values_videos": pixel_values}
+        return config, inputs_dict
+
+
+@require_vision
+class VideoPrismVisionModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as VideoPrismVisionModel does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (VideoPrismVisionModel, VideoPrismVideoModel) if is_torch_available() else ()
+
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = VideoPrismVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=VideoPrismVisionConfig,
+            has_text_modality=False,
+            hidden_size=37,
+            common_properties=["num_channels", "hidden_size", "num_attention_heads"],
+        )
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), nn.Module)
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        reason="VideoPrismVisionModel exposes spatial/temporal backbone states, not a single hidden_states tuple."
+    )
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(
+        reason="VideoPrismVisionModel does not expose a single attentions tuple compatible with ModelTesterMixin."
+    )
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(
+        reason="VideoPrismVisionModel does not expose common hidden_states/attentions fields for retain-grad checks."
+    )
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "MHRDYN7/videoprism-base-f16r288"
+        model = VideoPrismVisionModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_vision
+class VideoPrismTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        hidden_size=32,  # should be same as the hidden_size of the vision model tester
+        intermediate_size=37,
+        num_attention_heads=2,
+        num_hidden_layers=2,
+        vocab_size=32,
+        apply_l2norm=True,
+        hidden_act="relu",
+        attention_probs_dropout_prob=0.0,
+        qkv_bias=True,
+        hidden_dropout_prob=0.0,
+        layer_norm_eps=1e-06,
+        initializer_range=0.02,
+        attn_logit_softcapping=50.0,
+        seq_length=7,
+        is_training=False,
+        use_input_mask=True,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.vocab_size = vocab_size
+        self.apply_l2norm = apply_l2norm
+        self.hidden_act = hidden_act
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.qkv_bias = qkv_bias
+        self.hidden_dropout_prob = hidden_dropout_prob
+        self.layer_norm_eps = layer_norm_eps
+        self.initializer_range = initializer_range
+        self.attn_logit_softcapping = attn_logit_softcapping
+        self.seq_length = seq_length
+        self.encoder_seq_length = seq_length + 1
+        self.key_length = seq_length + 1
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return VideoPrismTextConfig(
+            hidden_size=self.hidden_size,
+            intermediate_size=self.intermediate_size,
+            num_attention_heads=self.num_attention_heads,
+            num_hidden_layers=self.num_hidden_layers,
+            vocab_size=self.vocab_size,
+            apply_l2norm=self.apply_l2norm,
+            hidden_act=self.hidden_act,
+            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
+            qkv_bias=self.qkv_bias,
+            hidden_dropout_prob=self.hidden_dropout_prob,
+            layer_norm_eps=self.layer_norm_eps,
+            initializer_range=self.initializer_range,
+            attn_logit_softcapping=self.attn_logit_softcapping,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = VideoPrismTextModel._from_config(config=config).to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_ids, attention_mask=input_mask)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.hidden_size))
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_vision
+class VideoPrismTextModelTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (VideoPrismTextModel,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = VideoPrismTextModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=VideoPrismTextConfig,
+            hidden_size=37,
+            common_properties=["hidden_size", "num_attention_heads"],
+        )
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "MHRDYN7/videoprism-lvt-base-f16r288"
+        model = VideoPrismTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_vision
+class VideoPrismClipModelTester:
+    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if vision_kwargs is None:
+            vision_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = VideoPrismTextModelTester(parent, **text_kwargs)
+        self.vision_model_tester = VideoPrismVisionModelTester(parent, **vision_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return VideoPrismConfig(
+            text_config=self.text_model_tester.get_config().to_dict(),
+            vision_config=self.vision_model_tester.get_config().to_dict(),
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = VideoPrismClipModel(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(pixel_values, input_ids, attention_mask)
+        self.parent.assertEqual(
+            result.logits_per_video.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
+        )
+        self.parent.assertEqual(
+            result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size)
+        )
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values_videos": pixel_values,
+        }
+        return config, inputs_dict
+
+
+@require_vision
+class VideoPrismClipModelTest(ModelTesterMixin, unittest.TestCase):
+    _is_composite = True
+    test_attention_outputs = False
+
+    all_model_classes = (VideoPrismClipModel,) if is_torch_available() else ()
+
+    def setUp(self):
+        self.model_tester = VideoPrismClipModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=VideoPrismConfig,
+            has_text_modality=False,
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(
+        reason="VideoPrismClipModel normalizes exp(similarity) across the batch, so logits are batch-dependent by design."
+    )
+    def test_batching_equivalence(self):
+        pass
+
+    @unittest.skip(reason="SDPA is turned off for this model.")
+    def test_can_set_attention_dynamically_composite_model(self):
+        pass
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->VideoPrism
+    def test_load_vision_text_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save VideoPrismConfig and check if we can load VideoPrismVisionConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            vision_config = VideoPrismVisionConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
+
+        # Save VideoPrismConfig and check if we can load VideoPrismTextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = VideoPrismTextConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "MHRDYN7/videoprism-lvt-base-f16r288"
+        model = VideoPrismClipModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+@require_vision
+class VideoPrismForVideoClassificationModelTester(ModelTesterMixin, VideoPrismVisionModelTester):
+    def __init__(self, parent, vision_kwargs=None, is_training=True):
+        if vision_kwargs is None:
+            vision_kwargs = {}
+        super().__init__(parent, **vision_kwargs)
+
+    def get_config(self):
+        config = super().get_config()
+        config.num_labels = self.num_labels
+        return config
+
+    def prepare_config_and_inputs(self):
+        config, pixel_values = super().prepare_config_and_inputs()
+        labels = ids_tensor([self.batch_size], self.num_labels) if self.use_labels else None
+        return config, pixel_values, labels
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, _ = self.prepare_config_and_inputs()
+        inputs_dict = {"pixel_values_videos": pixel_values}
+        return config, inputs_dict
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = VideoPrismForVideoClassification._from_config(config=config)
+        model.to(torch_device)
+        pixel_values = pixel_values.to(torch_device)
+        labels = labels.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values, labels=labels)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.tubelet_size[1], self.tubelet_size[2])
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.loss.shape, torch.Size([]))
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, 1, self.num_labels))
+        self.parent.assertEqual(
+            result.hidden_states.shape, (self.batch_size, num_patches * self.num_frames, self.hidden_size)
+        )
+
+
+@require_vision
+class VideoPrismForVideoClassificationTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (VideoPrismForVideoClassification,) if is_torch_available() else ()
+    test_resize_embeddings = False
+
+    def setUp(self):
+        self.model_tester = VideoPrismForVideoClassificationModelTester(
+            self, vision_kwargs={"use_labels": True, "num_labels": 10}
+        )
+        self.config_tester = ConfigTester(
+            self,
+            config_class=VideoPrismVisionConfig,
+            has_text_modality=False,
+            hidden_size=37,
+            common_properties=["num_channels", "hidden_size", "num_attention_heads"],
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), nn.Module)
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    @unittest.skip(reason="VideoPrismForVideoClassification does not expose top-level attentions")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip(
+        reason="VideoPrismForVideoClassification returns a single hidden_states tensor, not layer-wise hidden states"
+    )
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(
+        reason="VideoPrismForVideoClassification does not expose common hidden_states/attentions fields for retain-grad checks"
+    )
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+
+def prepare_video(video_type="water_bottle_drumming"):
+    """
+    Returns different video files/arrays based on the `video_type` argument.
+    """
+
+    api = HfApi()
+    if video_type == "water_bottle_drumming":
+        filename = "water_bottle_drumming.mp4"  # Raw video used in original repo's example
+    elif video_type == "water_bottle_drumming_frames":
+        filename = "frames_16_288.npy"  # Preprocessed array of the raw video
+    elif video_type == "basketball_dunk":
+        filename = "v_BasketballDunk_g14_c06.avi"  # An example video from UCF101 used for testing the classification head of VideoPrismForVideoClassification
+    else:
+        raise ValueError(
+            "The `video_type` should be one of ['water_bottle_drumming', 'water_bottle_drumming_frames', 'basketball_dunk']."
+        )
+
+    file = api.hf_hub_download(repo_id="MHRDYN7/videoprism_assets", filename=filename, repo_type="dataset")
+    if video_type == "water_bottle_drumming_frames":
+        file = np.load(file)
+    return file
+
+
+def prepare_texts():
+    text_query_csv = "playing drums,sitting,playing flute,playing at playground,concert"
+    prompt_template = "a video of {}."
+
+    text_queries = text_query_csv.split(",")
+    text_queries = [prompt_template.format(t) for t in text_queries]
+    tokenizer = VideoPrismTokenizer.from_pretrained("MHRDYN7/videoprism-lvt-base-f16r288")
+    return tokenizer, text_queries
+
+
+@require_vision
+@require_torch
+@require_sentencepiece
+class VideoPrismModelIntegrationTest(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.water_bottle_drumming_frames = (
+            torch.tensor(prepare_video("water_bottle_drumming_frames")).unsqueeze(0).permute(0, 1, 4, 2, 3)
+        )
+        cls.water_bottle_drumming_video = prepare_video("water_bottle_drumming")
+        cls.basketball_dunk_video = prepare_video("basketball_dunk")
+        cls.tokenizer, cls.text_queries = prepare_texts()
+
+    @slow
+    def test_videoprism_vision_model(self):
+        model = VideoPrismVisionModel.from_pretrained("MHRDYN7/videoprism-base-f16r288").to(torch_device)
+        input_vids = torch.cat([self.water_bottle_drumming_frames, self.water_bottle_drumming_frames], dim=0).to(
+            torch_device
+        )
+        model.eval()
+        with torch.inference_mode():
+            outputs = model(input_vids).last_hidden_state
+
+        self.assertListEqual(
+            outputs[0].cpu().tolist(),
+            outputs[1].cpu().tolist(),
+            "Outputs of the batches are not identical for identical input batches",
+        )
+        expectations = Expectations(
+            {
+                (None, None): [
+                    [0.11648951, 0.4568253, 0.19288044],
+                    [0.28420594, -0.04224018, 0.377879],
+                    [0.24594213, -0.3914095, -0.30516925],
+                ],
+                ("cuda", 8): [
+                    [0.1164810285, 0.4568167031, 0.1928822696],
+                    [0.2842144370, -0.0422473773, 0.3778813481],
+                    [0.2459464073, -0.3914141059, -0.3051622808],
+                ],
+            }
+        )
+        expected_values = torch.tensor(expectations.get_expectation(), device=torch_device)
+        output_slice = outputs[0, :3, :3]
+        print(output_slice)
+        torch.testing.assert_close(output_slice, expected_values, rtol=2e-4, atol=2e-4)
+
+    @slow
+    def test_videoprism_clip_model(self):
+        model = VideoPrismClipModel.from_pretrained("MHRDYN7/videoprism-lvt-base-f16r288").to(torch_device)
+        input_vids = torch.cat([self.water_bottle_drumming_frames, self.water_bottle_drumming_frames], dim=0).to(
+            torch_device
+        )
+        tokens = self.tokenizer(self.text_queries, max_length=64, padding="max_length", return_tensors="pt").to(
+            torch_device
+        )
+        model.eval()
+        with torch.inference_mode():
+            outputs = model(input_vids, **tokens)
+        torch.testing.assert_close(outputs.video_embeds[0], outputs.video_embeds[1], rtol=2e-4, atol=2e-4)
+
+        self.assertEqual(
+            outputs.logits_per_video.shape,
+            torch.Size((input_vids.shape[0], tokens.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            outputs.logits_per_text.shape,
+            torch.Size((tokens.input_ids.shape[0], input_vids.shape[0])),
+        )
+
+        video_expectation = Expectations(
+            {
+                (None, None): [
+                    -0.01940615,
+                    -0.04830061,
+                    0.0069022,
+                    0.02915299,
+                    -0.05897291,
+                    0.02168823,
+                    -0.01471708,
+                    -0.00971614,
+                    -0.00220576,
+                ],
+                ("cuda", 8): [
+                    -0.0194059499,
+                    -0.0483003967,
+                    0.0069021182,
+                    0.0291529540,
+                    -0.0589727312,
+                    0.0216881726,
+                    -0.0147173097,
+                    -0.0097162435,
+                    -0.0022055341,
+                ],
+            }
+        )
+        text_expectation = Expectations(
+            {
+                (None, None): [
+                    [-0.00802545, 0.00931361, 0.01555958],
+                    [0.02245245, 0.00010197, -0.01073526],
+                    [-0.02258418, 0.00133927, -0.01555064],
+                    [0.01056228, 0.01835608, -0.01539922],
+                    [-0.00366718, 0.00370416, 0.00800336],
+                ],
+                ("cuda", 8): [
+                    [-8.0098593608e-03, 9.3171931803e-03, 1.5544882976e-02],
+                    [2.2461047396e-02, 9.5467286883e-05, -1.0741823353e-02],
+                    [-2.2578010336e-02, 1.3390942477e-03, -1.5561779030e-02],
+                    [1.0591125116e-02, 1.8359506503e-02, -1.5389740467e-02],
+                    [-3.6388880108e-03, 3.6980083678e-03, 7.9908100888e-03],
+                ],
+            }
+        )
+
+        video_expected_values = torch.tensor(video_expectation.get_expectation(), device=torch_device)
+        text_expected_values = torch.tensor(text_expectation.get_expectation(), device=torch_device)
+        video_logits = outputs.video_embeds[0, :9]
+        print(video_logits)
+        text_logits = outputs.text_embeds[:, :3]
+        print(text_logits)
+        torch.testing.assert_close(video_logits, video_expected_values, rtol=2e-4, atol=2e-4)
+        torch.testing.assert_close(text_logits, text_expected_values, rtol=2e-4, atol=2e-4)
+
+    @slow
+    def test_videoprism_interpolate_pos_encoding(self):
+        model_name = "MHRDYN7/videoprism-base-f16r288"
+        model = VideoPrismVisionModel.from_pretrained(model_name).to(torch_device)
+        processor = LlavaOnevisionVideoProcessor.from_pretrained(model_name)
+        kwargs = {
+            "num_frames": 10,
+            "size": {"height": 144, "width": 144},
+            "do_resize": True,
+        }
+        inputs = processor(videos=self.water_bottle_drumming_video, return_tensors="pt", **kwargs).to(torch_device)
+        model.eval()
+        with torch.inference_mode():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        expected_shape = torch.Size([1, int((144 / 18) * (144 / 18) * 10), model.config.hidden_size])
+        self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
+
+    @slow
+    def test_videoprism_classification_model(self):
+        model_name = "MHRDYN7/videoprism-base-f16r288-finetuned-ucf101"
+        model = VideoPrismForVideoClassification.from_pretrained(model_name).to(torch_device)
+        print(model.device, torch_device)
+        processor = LlavaOnevisionVideoProcessor.from_pretrained(model_name)
+        inputs = processor(videos=self.basketball_dunk_video, return_tensors="pt")["pixel_values_videos"].to(
+            torch_device
+        )
+        label = torch.tensor([8], dtype=torch.long, device=torch_device)
+        model.eval()
+        with torch.inference_mode():
+            outputs = model(inputs, labels=label)
+
+        expected_logits = Expectations(
+            {
+                (None, None): [
+                    [
+                        [-5.8973, -2.4552, -2.6362, -3.2215, 11.2046, 4.4604, -3.3962, 3.6890, 12.3573, 5.1211],
+                    ]
+                ],
+                ("cuda", 8): [
+                    [
+                        [
+                            -5.8972797394,
+                            -2.4551916122,
+                            -2.6361594200,
+                            -3.2215039730,
+                            11.2045707703,
+                            4.4604382515,
+                            -3.3961904049,
+                            3.6890094280,
+                            12.3573036194,
+                            5.1210832596,
+                        ],
+                    ]
+                ],
+            }
+        )
+        expected_logits_values = torch.tensor(expected_logits.get_expectation(), device=torch_device)
+        print(outputs)
+        torch.testing.assert_close(outputs.logits, expected_logits_values, rtol=2e-4, atol=2e-4)
+        torch.testing.assert_close(outputs.loss, torch.tensor(0.2754, device=torch_device), rtol=2e-3, atol=2e-3)
diff --git a/tests/models/vipllava/test_modeling_vipllava.py b/tests/models/vipllava/test_modeling_vipllava.py
index 5b06d8659145..24bb47deda74 100644
--- a/tests/models/vipllava/test_modeling_vipllava.py
+++ b/tests/models/vipllava/test_modeling_vipllava.py
@@ -274,9 +274,6 @@ def test_training_gradient_checkpointing_use_reentrant_true(self):
     def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
         pass
 
-    def test_reverse_loading_mapping(self):
-        super().test_reverse_loading_mapping(skip_base_model=True)
-
 
 @require_torch
 class VipLlavaForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/vjepa2/test_modeling_vjepa2.py b/tests/models/vjepa2/test_modeling_vjepa2.py
index 9cb0280dec51..a2fb1f806f72 100644
--- a/tests/models/vjepa2/test_modeling_vjepa2.py
+++ b/tests/models/vjepa2/test_modeling_vjepa2.py
@@ -184,6 +184,82 @@ def test_model(self):
     def test_feed_forward_chunking(self):
         pass
 
+    def test_config_2_1_defaults(self):
+        """Verify 2.1 config fields have correct defaults (backward-compatible with 2.0)."""
+        config = VJEPA2Config()
+        self.assertFalse(config.use_rope_interleave)
+        self.assertFalse(config.use_modality_embeddings)
+        self.assertFalse(config.interpolate_rope)
+        self.assertFalse(config.return_all_tokens)
+        self.assertIsNone(config.img_temporal_dim_size)
+        self.assertIsNone(config.teacher_embed_dim)
+        self.assertEqual(config.n_output_distillation, 0)
+        self.assertIsNone(config.hierarchical_layers)
+
+    def test_model_2_1_forward(self):
+        """Fast test: tiny 2.1 config forward pass with hierarchical output."""
+        config = VJEPA2Config(
+            crop_size=16,
+            frames_per_clip=2,
+            hidden_size=32,
+            num_attention_heads=2,
+            num_hidden_layers=4,
+            mlp_ratio=1.0,
+            pred_hidden_size=16,
+            pred_num_attention_heads=2,
+            pred_num_hidden_layers=2,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            teacher_embed_dim=64,
+            n_output_distillation=1,
+            hierarchical_layers=[0, 1, 2, 3],
+        )
+        model = VJEPA2Model(config).to(torch_device).eval()
+
+        pixel_values = torch.randn(1, 2, 3, 16, 16, device=torch_device)
+        with torch.no_grad():
+            outputs = model(pixel_values)
+        # n_dist=1: encoder returns single-norm (hidden_size)
+        self.assertEqual(outputs.last_hidden_state.shape, (1, 1, 32))
+        # predictor with return_all_tokens: context + target tokens
+        # proj_output_dim = n_hier(4) * (teacher_embed_dim(64) // n_hier(4)) = 64
+        self.assertEqual(outputs.predictor_output.last_hidden_state.shape, (1, 2, 64))
+
+    def test_model_2_1_multi_distillation(self):
+        """Fast test: 2.1 config with n_output_distillation=4 (multi-layer predictor embed)."""
+        config = VJEPA2Config(
+            crop_size=16,
+            frames_per_clip=2,
+            hidden_size=32,
+            num_attention_heads=2,
+            num_hidden_layers=4,
+            mlp_ratio=1.0,
+            pred_hidden_size=16,
+            pred_num_attention_heads=2,
+            pred_num_hidden_layers=2,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            n_output_distillation=4,
+            hierarchical_layers=[0, 1, 2, 3],
+        )
+        model = VJEPA2Model(config).to(torch_device).eval()
+
+        pixel_values = torch.randn(1, 2, 3, 16, 16, device=torch_device)
+        with torch.no_grad():
+            outputs = model(pixel_values)
+        # n_dist=4: encoder returns concatenated hierarchical (hidden_size * 4)
+        self.assertEqual(outputs.last_hidden_state.shape, (1, 1, 128))
+        # proj_output_dim = n_hier(4) * hidden_size(32) = 128
+        self.assertEqual(outputs.predictor_output.last_hidden_state.shape, (1, 2, 128))
+
     @slow
     def test_model_from_pretrained(self):
         model = VJEPA2Model.from_pretrained(VJEPA_HF_MODEL)
@@ -315,6 +391,37 @@ def test_predictor_partial_mask(self):
         expected_shape = torch.Size((1, num_masks, 1024))
         self.assertEqual(outputs.predictor_output.last_hidden_state.shape, expected_shape)
 
+    @slow
+    def test_inference_vjepa2_1_base(self):
+        """Smoke test: instantiate a 2.1-like config and run forward pass."""
+        config = VJEPA2Config(
+            crop_size=16,
+            frames_per_clip=2,
+            hidden_size=32,
+            num_attention_heads=2,
+            num_hidden_layers=4,
+            mlp_ratio=1.0,
+            pred_hidden_size=16,
+            pred_num_attention_heads=2,
+            pred_num_hidden_layers=2,
+            pred_num_mask_tokens=8,
+            use_rope_interleave=True,
+            use_modality_embeddings=True,
+            interpolate_rope=True,
+            return_all_tokens=True,
+            img_temporal_dim_size=1,
+            teacher_embed_dim=64,
+            n_output_distillation=1,
+            hierarchical_layers=[0, 1, 2, 3],
+        )
+        model = VJEPA2Model(config).to(torch_device).eval()
+
+        pixel_values = torch.randn(1, 2, 3, 16, 16, device=torch_device)
+        with torch.no_grad():
+            outputs = model(pixel_values)
+        self.assertIsNotNone(outputs.last_hidden_state)
+        self.assertIsNotNone(outputs.predictor_output)
+
     @slow
     def test_video_classification(self):
         checkpoint = "facebook/vjepa2-vitl-fpc16-256-ssv2"
diff --git a/tests/models/voxtral/test_modeling_voxtral.py b/tests/models/voxtral/test_modeling_voxtral.py
index 0cff2a66779b..4f0c604ce05f 100644
--- a/tests/models/voxtral/test_modeling_voxtral.py
+++ b/tests/models/voxtral/test_modeling_voxtral.py
@@ -13,12 +13,13 @@
 # limitations under the License.
 """Testing suite for the PyTorch Voxtral model."""
 
-import tempfile
 import unittest
 
 from transformers import (
     AutoProcessor,
+    LlamaConfig,
     VoxtralConfig,
+    VoxtralEncoderConfig,
     VoxtralForConditionalGeneration,
     is_torch_available,
 )
@@ -30,126 +31,50 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
-from ...test_pipeline_mixin import PipelineTesterMixin
+from ...alm_tester import ALMModelTest, ALMModelTester
 
 
 if is_torch_available():
     import torch
 
 
-class VoxtralModelTester:
-    def __init__(
-        self,
-        parent,
-        ignore_index=-100,
-        audio_token_id=0,
-        seq_length=35,
-        feat_seq_length=60,
-        text_config={
-            "model_type": "llama",
-            "intermediate_size": 36,
-            "initializer_range": 0.02,
-            "hidden_size": 32,
-            "max_position_embeddings": 52,
-            "num_hidden_layers": 2,
-            "num_attention_heads": 4,
-            "num_key_value_heads": 2,
-            "use_labels": True,
-            "use_mrope": False,
-            "vocab_size": 99,
-            "head_dim": 8,
-            "pad_token_id": 1,  # can't be the same as the audio token id
-        },
-        is_training=True,
-        audio_config={
-            "model_type": "voxtral_encoder",
-            "hidden_size": 16,
-            "num_attention_heads": 4,
-            "intermediate_size": 16,
-            "num_hidden_layers": 2,
-            "num_mel_bins": 80,
-            "max_source_positions": 30,
-            "initializer_range": 0.02,
-        },
-    ):
-        self.parent = parent
-        self.ignore_index = ignore_index
-        self.audio_token_id = audio_token_id
-        self.text_config = text_config
-        self.audio_config = audio_config
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.is_training = is_training
-
-        self.batch_size = 3
-        self.encoder_seq_length = seq_length
-
-    def get_config(self):
-        return VoxtralConfig(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            ignore_index=self.ignore_index,
-            audio_token_id=self.audio_token_id,
-        )
-
-    def prepare_config_and_inputs(self):
-        input_features_values = floats_tensor(
-            [
-                self.batch_size,
-                self.audio_config["num_mel_bins"],
-                self.feat_seq_length,
-            ]
-        )
-        config = self.get_config()
-        return config, input_features_values
-
-    def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        config, input_features_values = config_and_inputs
-        num_audio_tokens_per_batch_idx = 30
+class VoxtralModelTester(ALMModelTester):
+    config_class = VoxtralConfig
+    conditional_generation_class = VoxtralForConditionalGeneration
+    text_config_class = LlamaConfig
+    audio_config_class = VoxtralEncoderConfig
 
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
-        attention_mask[:, :1] = 0
+    def __init__(self, parent, **kwargs):
+        # seq_length 35 = BOS + 30 audio + 4 text (keeps column -2 text-only for resize test).
+        kwargs.setdefault("seq_length", 35)
+        # feat_seq_length 60 → conv2(s=2) → 30 audio embeds (Voxtral's encoder does not apply avg_pool
+        # in the forward; projector reshapes to B*30 embeddings).
+        kwargs.setdefault("feat_seq_length", 60)
+        # Encoder asserts input_features.shape[-1] == max_source_positions * 2.
+        kwargs.setdefault("max_source_positions", kwargs["feat_seq_length"] // 2)
+        # Llama needs head_dim
+        kwargs.setdefault("head_dim", 8)
+        super().__init__(parent, **kwargs)
 
-        input_ids[:, 1 : 1 + num_audio_tokens_per_batch_idx] = config.audio_token_id
-        inputs_dict = {
-            "input_ids": input_ids,
-            "attention_mask": attention_mask,
-            "input_features": input_features_values,
-        }
-        return config, inputs_dict
+    def get_audio_embeds_mask(self, audio_mask):
+        # Voxtral encoder only applies conv2 (stride 2); no avg_pool in forward.
+        output_length = (self.feat_seq_length - 1) // 2 + 1
+        return torch.ones([self.batch_size, output_length], dtype=torch.long).to(torch_device)
 
 
 @require_torch
-class VoxtralForConditionalGenerationModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class VoxtralForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `VoxtralForConditionalGeneration`.
     """
 
-    all_model_classes = (VoxtralForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = VoxtralModelTester
     pipeline_model_mapping = (
         {"text-to-speech": VoxtralForConditionalGeneration, "any-to-any": VoxtralForConditionalGeneration}
         if is_torch_available()
         else {}
     )
 
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = VoxtralModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=VoxtralConfig, has_text_modality=False)
-
     @unittest.skip(
         reason="This test does not apply to Voxtral since inputs_embeds corresponding to audio tokens are replaced when input features are provided."
     )
@@ -192,47 +117,6 @@ def test_flash_attention_3_padding_matches_padding_free_with_position_ids(self):
     def test_flash_attention_3_padding_matches_padding_free_with_position_ids_and_fa_kwargs(self):
         pass
 
-    @unittest.skip(reason="Voxtral has no separate base model without a head.")
-    def test_model_base_model_prefix(self):
-        pass
-
-    def test_sdpa_can_dispatch_composite_models(self):
-        # overwrite because Voxtral is audio+text model (not vision+text)
-        if not self.has_attentions:
-            self.skipTest(reason="Model architecture does not support attentions")
-
-        if not self._is_composite:
-            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
-
-        for model_class in self.all_model_classes:
-            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
-            model = model_class(config)
-
-            with tempfile.TemporaryDirectory() as tmpdirname:
-                model.save_pretrained(tmpdirname)
-                model_sdpa = model_class.from_pretrained(tmpdirname)
-                model_sdpa = model_sdpa.eval().to(torch_device)
-
-                text_attn = "sdpa" if model.language_model._supports_sdpa else "eager"
-                vision_attn = "sdpa" if model.audio_tower._supports_sdpa else "eager"
-
-                # `None` as it is the requested one which will be assigned to each sub-config
-                # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
-                self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
-                self.assertTrue(model.language_model.config._attn_implementation == text_attn)
-                self.assertTrue(model.audio_tower.config._attn_implementation == vision_attn)
-
-                model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
-                model_eager = model_eager.eval().to(torch_device)
-                self.assertTrue(model_eager.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
-                self.assertTrue(model_eager.audio_tower.config._attn_implementation == "eager")
-
-                for name, submodule in model_eager.named_modules():
-                    class_name = submodule.__class__.__name__
-                    if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
-                        raise ValueError("The eager model should not have SDPA attention layers")
-
 
 @require_torch
 class VoxtralForConditionalGenerationIntegrationTest(unittest.TestCase):
diff --git a/tests/models/voxtral/test_tokenization_voxtral.py b/tests/models/voxtral/test_tokenization_voxtral.py
new file mode 100644
index 000000000000..624ad78553c5
--- /dev/null
+++ b/tests/models/voxtral/test_tokenization_voxtral.py
@@ -0,0 +1,15 @@
+import pytest
+
+import transformers.models.auto.tokenization_auto as ta
+from transformers import AutoTokenizer
+from transformers.models.voxtral import VoxtralConfig
+
+
+def test_voxtral_tokenizer_requires_mistral_common(monkeypatch):
+    # Simulate that mistral_common is not available for the auto-tokenizer logic
+    monkeypatch.setattr(ta, "is_mistral_common_available", lambda: False)
+    # Avoid network access by short-circuiting tokenizer_config retrieval
+    monkeypatch.setattr(ta, "get_tokenizer_config", lambda *args, **kwargs: {})
+    with pytest.raises(ImportError, match="mistral-common"):
+        # Using a dummy path since the guard should raise before any file access
+        AutoTokenizer.from_pretrained("dummy", config=VoxtralConfig())
diff --git a/tests/models/voxtral_realtime/test_modeling_voxtral_realtime.py b/tests/models/voxtral_realtime/test_modeling_voxtral_realtime.py
index 9aa817f3cba6..150d7a894104 100644
--- a/tests/models/voxtral_realtime/test_modeling_voxtral_realtime.py
+++ b/tests/models/voxtral_realtime/test_modeling_voxtral_realtime.py
@@ -24,6 +24,10 @@
     is_torch_available,
 )
 from transformers.audio_utils import load_audio
+from transformers.models.voxtral_realtime.configuration_voxtral_realtime import (
+    VoxtralRealtimeEncoderConfig,
+    VoxtralRealtimeTextConfig,
+)
 from transformers.testing_utils import (
     cleanup,
     require_torch,
@@ -31,10 +35,8 @@
     torch_device,
 )
 
-from ...generation.test_utils import GenerationTesterMixin
-from ...test_configuration_common import ConfigTester
-from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
-from ...test_pipeline_mixin import PipelineTesterMixin
+from ...alm_tester import ALMModelTest, ALMModelTester
+from ...test_modeling_common import floats_tensor, ids_tensor
 
 
 if is_datasets_available():
@@ -44,136 +46,84 @@
     import torch
 
 
-class VoxtralRealtimeModelTester:
-    def __init__(
-        self,
-        parent,
-        ignore_index=-100,
-        audio_token_id=0,
-        seq_length=5,
-        feat_seq_length=40,
-        text_config={
-            "model_type": "voxtral_realtime_text",
-            "intermediate_size": 36,
-            "initializer_range": 0.02,
-            "hidden_size": 32,
-            "max_position_embeddings": 52,
-            "num_hidden_layers": 2,
-            "num_attention_heads": 4,
-            "num_key_value_heads": 2,
-            "use_labels": True,
-            "vocab_size": 99,
-            "head_dim": 8,
-            "pad_token_id": 1,  # can't be the same as the audio token id
-            "hidden_act": "silu",
-            "rms_norm_eps": 1e-6,
-            "attention_dropout": 0.0,
-            "rope_parameters": {
-                "rope_type": "default",
-                "rope_theta": 10000.0,
-            },
-        },
-        is_training=True,
-        audio_config={
-            "model_type": "voxtral_realtime_encoder",
-            "hidden_size": 16,
-            "num_attention_heads": 4,
-            "num_key_value_heads": 2,
-            "intermediate_size": 64,
-            "encoder_layers": 2,
-            "num_mel_bins": 80,
-            "max_position_embeddings": 100,
-            "initializer_range": 0.02,
-            "rms_norm_eps": 1e-6,
-            "activation_function": "silu",
-            "activation_dropout": 0.0,
-            "attention_dropout": 0.0,
-            "head_dim": 4,
-            "rope_parameters": {
-                "rope_type": "default",
-                "rope_theta": 10000.0,
-            },
-        },
-    ):
-        self.parent = parent
-        self.ignore_index = ignore_index
-        self.audio_token_id = audio_token_id
-        self.text_config = text_config
-        self.audio_config = audio_config
-        self.seq_length = seq_length
-        self.feat_seq_length = feat_seq_length
-
-        self.num_hidden_layers = text_config["num_hidden_layers"]
-        self.vocab_size = text_config["vocab_size"]
-        self.hidden_size = text_config["hidden_size"]
-        self.num_attention_heads = text_config["num_attention_heads"]
-        self.is_training = is_training
-
-        self.batch_size = 3
-        self.encoder_seq_length = seq_length
-        self._max_new_tokens = None  # this is used to set
-
-    def get_config(self):
-        return VoxtralRealtimeConfig(
-            text_config=self.text_config,
-            audio_config=self.audio_config,
-            ignore_index=self.ignore_index,
-            audio_token_id=self.audio_token_id,
-        )
-
-    def prepare_config_and_inputs(self):
-        if self._max_new_tokens is not None:
-            feat_seq_length = self.feat_seq_length + self._max_new_tokens * 8
-        else:
-            feat_seq_length = self.feat_seq_length
-
-        input_features_values = floats_tensor(
-            [
-                self.batch_size,
-                self.audio_config["num_mel_bins"],
-                feat_seq_length,
-            ]
-        )
-        config = self.get_config()
-        return config, input_features_values
+class VoxtralRealtimeModelTester(ALMModelTester):
+    config_class = VoxtralRealtimeConfig
+    conditional_generation_class = VoxtralRealtimeForConditionalGeneration
+    text_config_class = VoxtralRealtimeTextConfig
+    audio_config_class = VoxtralRealtimeEncoderConfig
+
+    def __init__(self, parent, **kwargs):
+        # VoxtralRealtime does additive audio/text fusion: seq_length must equal num_audio_embeds.
+        # With audio_length_per_tok=8 (config default), num_audio_embeds = feat_seq_length // 8.
+        kwargs.setdefault("seq_length", 32)
+        kwargs.setdefault("feat_seq_length", kwargs["seq_length"] * 8)
+        # Audio encoder uses RoPE; max position must cover post-conv length (feat_seq_length // 2).
+        kwargs.setdefault("max_position_embeddings", kwargs["feat_seq_length"])
+        kwargs.setdefault("head_dim", 8)
+        kwargs.setdefault("rms_norm_eps", 1e-6)
+        kwargs.setdefault("activation_function", "silu")
+        kwargs.setdefault("hidden_act", "silu")
+        super().__init__(parent, **kwargs)
+        self._max_new_tokens = None
+
+    def get_audio_embeds_mask(self, audio_mask):
+        # Causal conv2 (stride 2, left-pad 1): post_conv_len = feat_seq_length // 2.
+        # Projector reshapes by downsample_factor=4 → post_conv_len // downsample_factor embeds.
+        downsample_factor = 4
+        effective_feat = self.feat_seq_length + (self._max_new_tokens or 0) * 8
+        post_conv_len = effective_feat // 2
+        output_length = post_conv_len // downsample_factor
+        return torch.ones([self.batch_size, output_length], dtype=torch.long).to(torch_device)
+
+    def create_audio_features(self):
+        effective_feat = self.feat_seq_length + (self._max_new_tokens or 0) * 8
+        return floats_tensor([self.batch_size, self.num_mel_bins, effective_feat])
+
+    def place_audio_tokens(self, input_ids, config, num_audio_tokens):
+        # VoxtralRealtime fuses audio additively over the whole sequence; no placeholder token required.
+        input_ids = input_ids.clone()
+        input_ids[input_ids == self.audio_token_id] = self.pad_token_id
+        return input_ids
 
     def prepare_config_and_inputs_for_common(self):
-        config_and_inputs = self.prepare_config_and_inputs()
-        config, input_features_values = config_and_inputs
-        num_audio_tokens_per_batch_idx = 30
+        # Custom pipeline: input_ids at seq_length, audio covers seq_length (+ max_new_tokens extras
+        # during generation so the model can slice future-token audio per decode step). We do not run
+        # the base-class `audio_embeds_mask.shape[1] <= seq_length` invariant because, for this model,
+        # audio embeds legitimately exceed input length during generation.
+        audio_features = self.create_audio_features()
+
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        special_tokens = [self.pad_token_id, self.bos_token_id, self.eos_token_id, self.audio_token_id]
+        for safe_id in range(self.vocab_size):
+            if safe_id not in special_tokens:
+                break
+        else:
+            raise ValueError("vocab_size too small for a non-special safe token.")
+        input_ids[input_ids == self.pad_token_id] = safe_id
+        input_ids[input_ids == self.eos_token_id] = safe_id
 
-        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 1) + 1
-        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
-        attention_mask[:, :1] = 0
+        config = self.get_config()
+        # place_audio_tokens is a no-op for this model; call for symmetry.
+        input_ids = self.place_audio_tokens(input_ids, config, torch.tensor([self.seq_length] * self.batch_size))
+        attention_mask = self.create_attention_mask(input_ids)
 
-        input_ids[:, 1 : 1 + num_audio_tokens_per_batch_idx] = config.audio_token_id
-        inputs_dict = {
+        return config, {
             "input_ids": input_ids,
             "attention_mask": attention_mask,
-            "input_features": input_features_values,
+            "input_features": audio_features,
         }
-        return config, inputs_dict
 
 
 @require_torch
-class VoxtralRealtimeForConditionalGenerationModelTest(
-    ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase
-):
+class VoxtralRealtimeForConditionalGenerationModelTest(ALMModelTest, unittest.TestCase):
     """
     Model tester for `VoxtralRealtimeForConditionalGeneration`.
     """
 
     additional_model_inputs = ["input_features"]
-
-    all_model_classes = (VoxtralRealtimeForConditionalGeneration,) if is_torch_available() else ()
+    model_tester_class = VoxtralRealtimeModelTester
     pipeline_model_mapping = {"any-to-any": VoxtralRealtimeForConditionalGeneration} if is_torch_available() else {}
 
-    _is_composite = True
-
-    def setUp(self):
-        self.model_tester = VoxtralRealtimeModelTester(self)
-        self.config_tester = ConfigTester(self, config_class=VoxtralRealtimeConfig, has_text_modality=False)
-
     def _with_max_new_tokens(max_new_tokens):
         def decorator(test_func):
             @functools.wraps(test_func)
@@ -209,8 +159,11 @@ def test_generate_compile_model_forward_fullgraph(self):
     def test_generate_with_and_without_position_ids(self):
         super().test_generate_with_and_without_position_ids()
 
-    @unittest.skip(reason="VoxtralRealtime does not have a base model")
-    def test_model_base_model_prefix(self):
+    @unittest.skip(
+        reason="This test does not apply to VoxtralRealtime: audio tokens are not replaced in inputs_embeds, "
+        "audio and text embeddings are summed instead."
+    )
+    def test_mismatching_num_audio_tokens(self):
         pass
 
     @unittest.skip(
diff --git a/tests/models/whisper/test_modeling_whisper.py b/tests/models/whisper/test_modeling_whisper.py
index 8feae0aedd5b..4ff9ce04410e 100644
--- a/tests/models/whisper/test_modeling_whisper.py
+++ b/tests/models/whisper/test_modeling_whisper.py
@@ -1240,6 +1240,56 @@ def _load_datasamples(self, num_samples):
         speech_samples = ds.sort("id")[:num_samples]["audio"]
         return [x["array"] for x in speech_samples]
 
+    @slow
+    def test_retrieve_segment(self):
+        set_seed(0)
+        torch_device = "cpu"
+        # model doesn't matter since _retrieve_segment is a static method
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
+        model = model.to(torch_device)
+        return_token_timestamps = False
+        # the test tokens are from whisper-large-v3
+        input_dict = {
+            "seek_sequence": torch.tensor([50365, 415, 1619, 11, 411, 257, 27484, 260, 294, 257, 50473]),
+            "seek_outputs": [
+                torch.tensor([50258, 50259, 50360, 50365, 415, 1619, 11, 411, 257, 27484, 260, 294, 257, 50473, 50257])
+            ],
+            "time_offset": torch.tensor([27.8200], dtype=torch.float64),
+            "timestamp_begin": 50365,
+            "seek_num_frames": torch.tensor([218]),
+            "time_precision": 0.02,
+            "time_precision_features": 0.01,
+            "input_stride": 2,
+            "prev_idx": 0,
+            "idx": 0,
+            "return_token_timestamps": return_token_timestamps,
+            "decoder_input_ids": torch.tensor([[50258, 50259, 50360]]),
+            "max_frames": 3000,
+        }
+        result_segments, result_segment_offset = model._retrieve_segment(**input_dict)
+
+        EXPECTED_SEGMENT_LIST = [
+            {
+                "start": torch.tensor(27.8200, dtype=torch.float64),
+                "end": torch.tensor(29.9800, dtype=torch.float64),
+                "tokens": torch.tensor([51756, 415, 1619, 11, 411, 257, 27484, 260, 294, 257, 51864]),
+                "idxs": (3, 14),
+                "result": torch.tensor(
+                    [50258, 50259, 50360, 51756, 415, 1619, 11, 411, 257, 27484, 260, 294, 257, 51864, 50257],
+                ),
+            }
+        ]
+        EXPECTED_SEGMENT_OFFSET = 218
+
+        for result, expected in zip(result_segments, EXPECTED_SEGMENT_LIST):
+            self.assertEqual(result["start"], expected["start"])
+            self.assertEqual(result["end"], expected["end"])
+            self.assertEqual(result["idxs"], expected["idxs"])
+            torch.testing.assert_close(result["tokens"], expected["tokens"])
+            torch.testing.assert_close(result["result"], expected["result"])
+
+        self.assertEqual(result_segment_offset, EXPECTED_SEGMENT_OFFSET)
+
     @slow
     def test_tiny_logits_librispeech(self):
         torch_device = "cpu"
@@ -1376,7 +1426,7 @@ def test_tiny_en_generation(self):
         input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features
         input_features = input_features.to(torch_device)
 
-        generated_ids = model.generate(input_features, num_beams=5, max_length=20)
+        generated_ids = model.generate(input_features, num_beams=5, max_length=22)
         transcript = processor.tokenizer.batch_decode(generated_ids)[0]
 
         EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his"
@@ -1392,7 +1442,7 @@ def test_tiny_generation(self):
         input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features
         input_features = input_features.to(torch_device)
 
-        generated_ids = model.generate(input_features, num_beams=5, max_length=20)
+        generated_ids = model.generate(input_features, num_beams=5, max_length=24)
         transcript = processor.tokenizer.decode(generated_ids[0])
 
         EXPECTED_TRANSCRIPT = " Mr. Quilter is the apostle of the middle classes and we are glad to welcome his gospel"
@@ -1401,7 +1451,7 @@ def test_tiny_generation(self):
     @slow
     def test_large_generation(self):
         processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
-        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3", dtype=torch.float32)
         model.to(torch_device)
 
         input_speech = self._load_datasamples(1)
@@ -1409,7 +1459,7 @@ def test_large_generation(self):
         input_features = input_features.to(torch_device)
 
         generated_ids = model.generate(
-            input_features, do_sample=False, max_length=20, language="<|en|>", task="transcribe"
+            input_features, do_sample=False, max_length=24, language="<|en|>", task="transcribe"
         )
         transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
 
@@ -1419,7 +1469,7 @@ def test_large_generation(self):
     @slow
     def test_large_generation_multilingual(self):
         processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
-        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3", dtype=torch.float32)
         model.to(torch_device)
 
         ds = load_dataset("facebook/multilingual_librispeech", "german", split="test", streaming=True)
@@ -1430,14 +1480,14 @@ def test_large_generation_multilingual(self):
         input_features = input_features.to(torch_device)
 
         generated_ids = model.generate(
-            input_features, do_sample=False, max_length=20, language="<|de|>", task="transcribe"
+            input_features, do_sample=False, max_length=24, language="<|de|>", task="transcribe"
         )
         transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         EXPECTED_TRANSCRIPT = " denken sie soeben weilten meine gedanken bei ihnen in adelaide und ich wünsch"
         self.assertEqual(transcript, EXPECTED_TRANSCRIPT)
 
         generated_ids = model.generate(
-            input_features, do_sample=False, max_length=20, language="<|de|>", task="translate"
+            input_features, do_sample=False, max_length=24, language="<|de|>", task="translate"
         )
         transcript = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
         EXPECTED_TRANSCRIPT = " Think, my thoughts were just now in Adelaide with you, and I wished to be able"
@@ -1447,13 +1497,13 @@ def test_large_generation_multilingual(self):
     def test_large_batched_generation(self):
         set_seed(42)
         processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
-        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3", dtype=torch.float32)
         model.to(torch_device)
 
         input_speech = self._load_datasamples(4)
         input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features
         input_features = input_features.to(torch_device)
-        generated_ids = model.generate(input_features, max_length=20, task="translate")
+        generated_ids = model.generate(input_features, max_length=24, task="translate")
 
         # fmt: off
         EXPECTED_LOGITS = torch.tensor(
@@ -1507,7 +1557,7 @@ def test_large_batched_generation_multilingual(self):
         generated_ids = model.generate(
             input_features.repeat(2, 1, 1),
             do_sample=False,
-            max_length=20,
+            max_length=24,
             language=["<|ja|>", "<|en|>"],
             task="transcribe",
         )
@@ -1524,7 +1574,7 @@ def test_tiny_en_batched_generation(self):
         input_speech = self._load_datasamples(4)
         input_features = processor(input_speech, return_tensors="pt", sampling_rate=16_000).input_features
         input_features = input_features.to(torch_device)
-        generated_ids = model.generate(input_features, max_length=20).to("cpu")
+        generated_ids = model.generate(input_features, max_length=22).to("cpu")
 
         # fmt: off
         EXPECTED_LOGITS = torch.tensor(
@@ -1627,7 +1677,7 @@ def test_tiny_timestamp_generation(self):
     def test_distil_token_timestamp_generation(self):
         # we actually just want to check that returning segments with distil model works
         processor = WhisperProcessor.from_pretrained("distil-whisper/distil-large-v3")
-        model = WhisperForConditionalGeneration.from_pretrained("distil-whisper/distil-large-v3")
+        model = WhisperForConditionalGeneration.from_pretrained("distil-whisper/distil-large-v3", dtype=torch.float32)
         model.to(torch_device)
 
         input_speech = np.concatenate(self._load_datasamples(4))
@@ -1795,11 +1845,11 @@ def test_small_longform_timestamps_generation(self):
             },
             {
                 "text": " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and",
-                "timestamp": (39.80, 45.36),
+                "timestamp": (39.80, 45.38),
             },
             {
                 "text": " can discover in it but little of rocky Ithaca.",
-                "timestamp": (45.36, 49.0),
+                "timestamp": (45.38, 49.0),
             },
             {
                 "text": " Lenell's pictures are a sort of up-guards-and-atom paintings, and Mason's exquisite ittles",
@@ -1894,7 +1944,7 @@ def test_small_longform_timestamps_generation(self):
     def test_large_timestamp_generation(self):
         set_seed(42)
         processor = WhisperProcessor.from_pretrained("openai/whisper-large-v3")
-        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3")
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large-v3", dtype=torch.float32)
         model.to(torch_device)
 
         input_speech = np.concatenate(self._load_datasamples(4))
diff --git a/tests/multimodal_tester.py b/tests/multimodal_tester.py
new file mode 100644
index 000000000000..22559876689b
--- /dev/null
+++ b/tests/multimodal_tester.py
@@ -0,0 +1,254 @@
+# Copyright 2026 HuggingFace Inc.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from inspect import signature
+
+from transformers.testing_utils import _TEXT_MODEL_TESTER_DEFAULTS
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import (
+    GenerationTesterMixin,
+    ModelTesterMixin,
+    ids_tensor,
+    is_torch_available,
+    require_torch,
+    torch_device,
+)
+from .test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+
+class MultiModalModelTester:
+    """Shared tester base for VLM (vision-language) and ALM (audio-language) models.
+
+    Concrete subclasses (e.g. `VLMModelTester`, `ALMModelTester`) supply:
+      - the modality-specific sub-config class (`vision_config_class` for VLMs, `audio_config_class` for ALMs, ...),
+      - the modality-specific defaults and helper methods,
+      - the hooks `_build_modality_sub_configs` and `_prepare_modality_inputs`,
+      - optionally an extended `_special_token_ids` and `pipeline_model_mapping`.
+
+    This tester provides shared logic for evaluating and verifying models that combine text with other modalities,
+    centering on the needs of vision-language (VLM) and audio-language (ALM) models.
+    """
+
+    # If the model follows the standard naming conventions, only `base_model_class` needs to be set
+    # (the others are inferred from available public classes).
+    base_model_class = None
+    config_class = None
+    text_config_class = None
+    conditional_generation_class = None
+    sequence_classification_class = None
+
+    # Required attributes after the initialization phase of the tester. Subclasses extend.
+    _required_attributes = ("config_class", "text_config_class", "conditional_generation_class")
+
+    # Arguments that should be passed to the config class even if not in its signature
+    forced_config_args = ["pad_token_id"]
+
+    @property
+    def all_model_classes(self):
+        # Models that set `all_model_classes` in their `XXXModelTest` class must have a new class that doesn't fit
+        # any of the common classes.
+        return [
+            model_class
+            for model_class in (
+                self.base_model_class,
+                self.conditional_generation_class,
+                self.sequence_classification_class,
+            )
+            if model_class is not None
+        ]
+
+    def __init__(self, parent, **kwargs):
+        self.parent = parent
+
+        # Multimodal-specific overrides of shared defaults (applied before the shared
+        # defaults so they take precedence, but after any subclass setdefault calls).
+        kwargs.setdefault("batch_size", 3)
+        kwargs.setdefault("moe_intermediate_size", 12)
+
+        # Apply shared text-model defaults for anything not already set.
+        # Subclasses are expected to `setdefault` their modality-specific kwargs
+        # (and any differing values such as `pad_token_id`) *before* calling super.
+        for key, default in _TEXT_MODEL_TESTER_DEFAULTS.items():
+            kwargs.setdefault(key, default)
+
+        kwargs.setdefault("ignore_index", -100)
+        kwargs.setdefault("scope", None)
+
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+        self._check_required_attributes()
+
+    def _check_required_attributes(self):
+        for required_attribute in self._required_attributes:
+            if getattr(self, required_attribute, None) is None:
+                raise ValueError(
+                    f"You have inherited from {type(self).__name__} but did not set the {required_attribute} attribute."
+                )
+
+    # -- Overridable modality hooks -----------------------------------------------------------
+
+    def create_attention_mask(self, input_ids):
+        """Default causal (lower-triangular) attention mask. Override for bidirectional models like Gemma3."""
+        return torch.tril(torch.ones_like(input_ids).to(torch_device))
+
+    def get_additional_inputs(self, config, input_ids, modality_inputs):
+        """Model-specific extra inputs (e.g. LlavaNext `image_sizes`, Qwen3VL `mm_token_type_ids`).
+
+        ``modality_inputs`` is the full dict returned by ``_prepare_modality_inputs``.
+        """
+        return {}
+
+    @property
+    def _special_token_ids(self):
+        """Special token ids that must never appear as random text tokens. Subclasses add modality tokens."""
+        return {self.pad_token_id, self.bos_token_id, self.eos_token_id}
+
+    def _build_modality_sub_configs(self):
+        """Return the {sub-config-key: sub-config-instance} entries for the main config constructor."""
+        raise NotImplementedError
+
+    def _prepare_modality_inputs(self, input_ids, config):
+        """Create modality features, place modality placeholder tokens in ``input_ids``, and return:
+
+        (input_ids_with_placeholders, modality_inputs_dict)
+        """
+        raise NotImplementedError
+
+    # -- End of overridable hooks -------------------------------------------------------------
+
+    def _safe_token_id(self):
+        """Smallest token ID that is not a special token. Used to scrub random ids_tensor outputs."""
+        special_tokens = self._special_token_ids
+        for i in range(self.vocab_size):
+            if i not in special_tokens:
+                return i
+        raise ValueError("vocab_size is too small and there is no token ID that is not a special token!")
+
+    def prepare_config_and_inputs_for_common(self):
+        config = self.get_config()
+
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        # Avoid flaky tests by scrubbing any accidental special tokens produced by ids_tensor.
+        # Modality placeholder tokens are scrubbed and placed by `_prepare_modality_inputs`.
+        safe_token_id = self._safe_token_id()
+        for token_id in self._special_token_ids:
+            input_ids[input_ids == token_id] = safe_token_id
+
+        input_ids, modality_inputs = self._prepare_modality_inputs(input_ids, config)
+
+        # Create attention mask with final input_ids (after modality placeholders are placed) — important
+        # for models that derive padding from token values.
+        attention_mask = self.create_attention_mask(input_ids) if self.use_input_mask else None
+
+        inputs_dict = {"input_ids": input_ids, "attention_mask": attention_mask}
+        inputs_dict.update(modality_inputs)
+        inputs_dict.update(self.get_additional_inputs(config, input_ids, modality_inputs))
+        return config, inputs_dict
+
+    # -- Config construction helpers ----------------------------------------------------------
+
+    @property
+    def config_args(self):
+        return list(signature(self.config_class.__init__).parameters.keys())
+
+    @property
+    def text_config_args(self):
+        args = list(signature(self.text_config_class.__init__).parameters.keys())
+        for token_arg in ["pad_token_id", "bos_token_id", "eos_token_id"]:  # Not always explicitly in the sig
+            if token_arg not in args:
+                args.append(token_arg)
+        return args
+
+    def _collect_kwargs(self, sig_keys, config_class):
+        """Collect kwargs for ``config_class`` by matching ``sig_keys`` (and its ``attribute_map``) against ``self``."""
+        attribute_map = getattr(config_class, "attribute_map", {})
+        model_name_to_common_name = {v: k for k, v in attribute_map.items()}
+        kwargs = {}
+        for k in sig_keys:
+            if hasattr(self, k) and k != "self":
+                kwargs[k] = getattr(self, k)
+            elif k in model_name_to_common_name and hasattr(self, model_name_to_common_name[k]):
+                kwargs[k] = getattr(self, model_name_to_common_name[k])
+        return kwargs
+
+    def get_config(self):
+        kwargs = self._collect_kwargs(self.config_args + self.forced_config_args, self.config_class)
+        kwargs["text_config"] = self.get_text_config()
+        kwargs.update(self._build_modality_sub_configs())
+        return self.config_class(**kwargs)
+
+    def get_text_config(self):
+        kwargs = self._collect_kwargs(self.text_config_args, self.text_config_class)
+        return self.text_config_class(**kwargs)
+
+    def create_and_check_model(
+        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
+    ):
+        model = self.base_model_class(config=config)
+        model.to(torch_device)
+        model.eval()
+        model(input_ids, attention_mask=input_mask)
+        result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+
+@require_torch
+class MultiModalModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin):
+    """Shared test-class base for multimodal model families.
+
+    Subclasses must set:
+      - ``model_tester_class``: The tester class (subclass of ``MultiModalModelTester``)
+
+    Optional:
+      - ``all_model_classes``: override if not using the default from the model tester
+      - ``pipeline_model_mapping``: override if not using the default from the model tester
+    """
+
+    model_tester_class = None
+    all_model_classes = None
+    pipeline_model_mapping = None
+
+    # Multimodal models are always composite
+    _is_composite = True
+
+    def setUp(self):
+        if self.model_tester_class is None:
+            raise ValueError(
+                f"You have inherited from {type(self).__name__} but did not set the model_tester_class attribute."
+            )
+        self.model_tester = self.model_tester_class(self)
+        self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, has_text_modality=False)
+
+        if self.pipeline_model_mapping is None:
+            if self.all_model_classes is not None:
+                raise ValueError(
+                    f"Tests that inherit from `{type(self).__name__}` and set `all_model_classes` must manually set "
+                    "`pipeline_model_mapping`."
+                )
+            else:
+                self.pipeline_model_mapping = self.model_tester.pipeline_model_mapping
+
+        if self.all_model_classes is None:
+            self.all_model_classes = self.model_tester.all_model_classes
+
+    def test_config(self):
+        """Test config common functionality."""
+        self.config_tester.run_common_tests()
diff --git a/tests/pipelines/test_pipelines_object_detection.py b/tests/pipelines/test_pipelines_object_detection.py
index 3244e3f91f83..b198cf3a4d3d 100644
--- a/tests/pipelines/test_pipelines_object_detection.py
+++ b/tests/pipelines/test_pipelines_object_detection.py
@@ -167,6 +167,153 @@ def test_small_model_pt(self):
             ],
         )
 
+    # ── Enhancement 1 + 2: top_k parameter and score-sorted results ──────────
+
+    @require_torch
+    def test_top_k(self):
+        """top_k=1 must return exactly one detection (the highest-scoring one)."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+            top_k=1,
+        )
+        self.assertEqual(len(outputs), 1)
+        self.assertIn("score", outputs[0])
+        self.assertIn("label", outputs[0])
+        self.assertIn("box", outputs[0])
+
+    @require_torch
+    def test_results_sorted_by_score(self):
+        """Results must always be returned in descending score order."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+        )
+        scores = [o["score"] for o in outputs]
+        self.assertEqual(scores, sorted(scores, reverse=True))
+
+    # ── Enhancement 3: label filtering ───────────────────────────────────────
+
+    @require_torch
+    @slow
+    def test_label_filter(self):
+        """Only detections whose label is in the `labels` allowlist are returned."""
+        object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
+
+        all_outputs = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
+        all_labels = {o["label"] for o in all_outputs}
+
+        target_label = "cat"
+        self.assertIn(target_label, all_labels, "Precondition: model must detect 'cat' on this image")
+
+        filtered = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            labels=[target_label],
+        )
+        self.assertGreater(len(filtered), 0)
+        for det in filtered:
+            self.assertEqual(det["label"], target_label)
+
+    @require_torch
+    def test_label_filter_excludes_all(self):
+        """If no detection matches the labels allowlist, an empty list is returned."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+            labels=["__nonexistent_label__"],
+        )
+        self.assertEqual(outputs, [])
+
+    # ── Enhancement 4: box_format ─────────────────────────────────────────────
+
+    @require_torch
+    def test_box_format_xyxy(self):
+        """Default box_format='xyxy' returns integer xmin/ymin/xmax/ymax keys."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+            box_format="xyxy",
+        )
+        for det in outputs:
+            self.assertEqual(set(det["box"].keys()), {"xmin", "ymin", "xmax", "ymax"})
+            for v in det["box"].values():
+                self.assertIsInstance(v, int)
+
+    @require_torch
+    def test_box_format_xywh(self):
+        """box_format='xywh' returns x_center/y_center/width/height integer keys."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+            box_format="xywh",
+        )
+        for det in outputs:
+            self.assertEqual(set(det["box"].keys()), {"x_center", "y_center", "width", "height"})
+            self.assertGreater(det["box"]["width"], 0)
+            self.assertGreater(det["box"]["height"], 0)
+
+    @require_torch
+    def test_box_format_normalized(self):
+        """box_format='normalized' returns float values in [0, 1]."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        outputs = object_detector(
+            "http://images.cocodataset.org/val2017/000000039769.jpg",
+            threshold=0.0,
+            box_format="normalized",
+        )
+        for det in outputs:
+            self.assertEqual(set(det["box"].keys()), {"xmin", "ymin", "xmax", "ymax"})
+            for v in det["box"].values():
+                self.assertIsInstance(v, float)
+                self.assertGreaterEqual(v, 0.0)
+                self.assertLessEqual(v, 1.0)
+
+    @require_torch
+    def test_box_format_invalid_raises(self):
+        """An unsupported box_format value must raise ValueError."""
+        model_id = "hf-internal-testing/tiny-detr-mobilenetsv3"
+        model = AutoModelForObjectDetection.from_pretrained(model_id)
+        image_processor = AutoImageProcessor.from_pretrained(model_id)
+        object_detector = ObjectDetectionPipeline(model=model, image_processor=image_processor)
+
+        with self.assertRaises(ValueError):
+            object_detector(
+                "http://images.cocodataset.org/val2017/000000039769.jpg",
+                threshold=0.0,
+                box_format="pascal_voc",
+            )
+
+    # ── Existing slow tests (preserved, expected outputs updated for sort order) ──
+
     @require_torch
     @slow
     def test_large_model_pt(self):
@@ -180,11 +327,11 @@ def test_large_model_pt(self):
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
+                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )
 
@@ -198,18 +345,18 @@ def test_large_model_pt(self):
             nested_simplify(outputs, decimals=4),
             [
                 [
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                     {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                     {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                     {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
                 [
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                     {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                     {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                     {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
             ],
         )
@@ -225,11 +372,11 @@ def test_integration_torch_object_detection(self):
         self.assertEqual(
             nested_simplify(outputs, decimals=4),
             [
+                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                 {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                 {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
             ],
         )
 
@@ -243,18 +390,18 @@ def test_integration_torch_object_detection(self):
             nested_simplify(outputs, decimals=4),
             [
                 [
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                     {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                     {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                     {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
                 [
+                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
+                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                     {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
                     {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
                     {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
-                    {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
-                    {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
                 ],
             ],
         )
diff --git a/tests/pipelines/test_pipelines_promptable_concept_segmentation.py b/tests/pipelines/test_pipelines_promptable_concept_segmentation.py
new file mode 100644
index 000000000000..02066c006074
--- /dev/null
+++ b/tests/pipelines/test_pipelines_promptable_concept_segmentation.py
@@ -0,0 +1,335 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import (
+    MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING,
+    PromptableConceptSegmentationPipeline,
+    is_torch_available,
+    pipeline,
+)
+from transformers.testing_utils import (
+    is_pipeline_test,
+    require_torch,
+    require_vision,
+    slow,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+@is_pipeline_test
+@require_vision
+@require_torch
+class PromptableConceptSegmentationPipelineTests(unittest.TestCase):
+    model_mapping = (
+        dict(list(MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING.items()))
+        if MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING
+        else []
+    )
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        segmenter = PromptableConceptSegmentationPipeline(
+            model=model,
+            processor=processor,
+            tokenizer=tokenizer,
+            image_processor=image_processor,
+            dtype=dtype,
+        )
+
+        examples = [
+            {
+                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                "text": "cat",
+            }
+        ]
+        return segmenter, examples
+
+    def run_pipeline_test(self, segmenter, examples):
+        outputs = segmenter(examples[0].get("image"), text=examples[0].get("text"), threshold=0.0)
+
+        n = len(outputs)
+        self.assertGreater(n, 0)
+
+        # Check output structure
+        for output in outputs:
+            self.assertIn("score", output)
+            self.assertIn("box", output)
+            self.assertIn("mask", output)
+            self.assertIsInstance(output["score"], float)
+            self.assertIsInstance(output["box"], dict)
+            self.assertIn("xmin", output["box"])
+            self.assertIn("ymin", output["box"])
+            self.assertIn("xmax", output["box"])
+            self.assertIn("ymax", output["box"])
+            self.assertTrue(is_torch_available() and isinstance(output["mask"], torch.Tensor))
+
+    @require_torch
+    @slow
+    def test_small_model_pt_text_prompt(self):
+        """Test pipeline with text-only prompt."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.1,
+        )
+
+        # Check that we got results
+        self.assertGreater(len(outputs), 0)
+
+        # Check structure of first result
+        result = outputs[0]
+        self.assertIn("score", result)
+        self.assertIn("box", result)
+        self.assertIn("mask", result)
+        self.assertIn("label", result)
+        self.assertEqual(result["label"], "cat")
+
+        # Check box format
+        self.assertIsInstance(result["box"]["xmin"], int)
+        self.assertIsInstance(result["box"]["ymin"], int)
+        self.assertIsInstance(result["box"]["xmax"], int)
+        self.assertIsInstance(result["box"]["ymax"], int)
+
+        # Check mask shape
+        self.assertEqual(len(result["mask"].shape), 2)  # Should be 2D (H, W)
+
+    @require_torch
+    @slow
+    def test_small_model_pt_box_prompt(self):
+        """Test pipeline with box-only prompt."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Use a bounding box around a cat in the image
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            input_boxes=[[[100, 50, 400, 350]]],
+            input_boxes_labels=[[1]],
+            threshold=0.1,
+        )
+
+        # Check that we got results
+        self.assertGreater(len(outputs), 0)
+
+        # Check structure
+        result = outputs[0]
+        self.assertIn("score", result)
+        self.assertIn("box", result)
+        self.assertIn("mask", result)
+
+    @require_torch
+    @slow
+    def test_small_model_pt_combined_prompt(self):
+        """Test pipeline with combined text and box prompts."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Text prompt with a negative box
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            input_boxes=[[[50, 50, 150, 150]]],  # Negative box
+            input_boxes_labels=[[0]],  # 0 = negative
+            threshold=0.1,
+        )
+
+        # Should still get results, but filtered by negative box
+        self.assertGreaterEqual(len(outputs), 0)
+
+    @require_torch
+    @slow
+    def test_batched_text_prompts(self):
+        """Test batching with multiple images and text prompts."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        outputs = segmenter(
+            [
+                {
+                    "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                    "text": "cat",
+                },
+                {
+                    "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                    "text": "remote",
+                },
+            ],
+            threshold=0.1,
+        )
+
+        # Should get a list of lists
+        self.assertEqual(len(outputs), 2)
+        self.assertIsInstance(outputs[0], list)
+        self.assertIsInstance(outputs[1], list)
+
+    @require_torch
+    @slow
+    def test_threshold(self):
+        """Test score threshold filtering."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Get results with low threshold
+        outputs_low = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.01,
+        )
+
+        # Get results with high threshold
+        outputs_high = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.5,
+        )
+
+        # High threshold should give fewer or equal results
+        self.assertLessEqual(len(outputs_high), len(outputs_low))
+
+    @require_torch
+    @slow
+    def test_mask_threshold(self):
+        """Test mask binarization threshold."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.1,
+            mask_threshold=0.5,
+        )
+
+        # Check that masks are binary
+        if len(outputs) > 0:
+            mask = outputs[0]["mask"]
+            unique_values = torch.unique(mask)
+            # Mask should be binary (0 and 1) or close to it
+            self.assertTrue(all(val in [0, 1, 0.0, 1.0] or (val >= 0 and val <= 1) for val in unique_values))
+
+    @require_torch
+    @slow
+    def test_top_k(self):
+        """Test top_k parameter to limit number of results."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Get all results
+        outputs_all = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.01,
+        )
+
+        # Get only top 2
+        outputs_top2 = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.01,
+            top_k=2,
+        )
+
+        # Should have at most 2 results
+        self.assertLessEqual(len(outputs_top2), 2)
+        self.assertLessEqual(len(outputs_top2), len(outputs_all))
+
+    @require_torch
+    @slow
+    def test_dict_input_format(self):
+        """Test dict input format."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Dict format
+        outputs = segmenter(
+            {"image": "./tests/fixtures/tests_samples/COCO/000000039769.png", "text": "cat"},
+            threshold=0.1,
+        )
+
+        self.assertGreater(len(outputs), 0)
+
+    @require_torch
+    @slow
+    def test_no_prompt_error(self):
+        """Test that error is raised when no prompts are provided."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        with self.assertRaises(ValueError) as context:
+            segmenter("./tests/fixtures/tests_samples/COCO/000000039769.png")
+
+        self.assertIn("at least one prompt", str(context.exception).lower())
+
+    @require_torch
+    @slow
+    def test_multiple_boxes(self):
+        """Test with multiple positive boxes."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Multiple positive boxes
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            input_boxes=[[[100, 50, 300, 250], [350, 100, 550, 350]]],
+            input_boxes_labels=[[1, 1]],
+            threshold=0.1,
+        )
+
+        # Should get results
+        self.assertGreaterEqual(len(outputs), 0)
+
+    @require_torch
+    @slow
+    def test_scores_are_sorted(self):
+        """Test that results are sorted by score in descending order."""
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.01,
+        )
+
+        if len(outputs) > 1:
+            scores = [output["score"] for output in outputs]
+            # Check that scores are sorted in descending order
+            self.assertEqual(scores, sorted(scores, reverse=True))
+
+    @require_torch
+    @slow
+    def test_automatic_model_processor_conversion(self):
+        """Test that the pipeline automatically converts Sam3VideoModel/Processor to Sam3Model/Processor."""
+        # This should work even though facebook/sam3 has Sam3VideoModel by default
+        segmenter = pipeline("promptable-concept-segmentation", model="facebook/sam3")
+
+        # Verify correct types were loaded
+        self.assertEqual(segmenter.model.__class__.__name__, "Sam3Model")
+        self.assertEqual(segmenter.processor.__class__.__name__, "Sam3Processor")
+
+        # Verify it works functionally
+        outputs = segmenter(
+            "./tests/fixtures/tests_samples/COCO/000000039769.png",
+            text="cat",
+            threshold=0.3,
+        )
+
+        self.assertGreater(len(outputs), 0)
+        self.assertIn("score", outputs[0])
+        self.assertIn("box", outputs[0])
+        self.assertIn("mask", outputs[0])
diff --git a/tests/pipelines/test_pipelines_promptable_visual_segmentation.py b/tests/pipelines/test_pipelines_promptable_visual_segmentation.py
new file mode 100644
index 000000000000..5c118ff470ca
--- /dev/null
+++ b/tests/pipelines/test_pipelines_promptable_visual_segmentation.py
@@ -0,0 +1,330 @@
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import (
+    MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING,
+    PromptableVisualSegmentationPipeline,
+    Sam2Model,
+    Sam2Processor,
+    SamModel,
+    SamProcessor,
+    is_vision_available,
+    pipeline,
+)
+from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow
+
+
+if is_vision_available():
+    import requests
+    from PIL import Image
+
+
+@is_pipeline_test
+@require_torch
+@require_vision
+class PromptableVisualSegmentationPipelineTests(unittest.TestCase):
+    model_mapping = (
+        dict(list(MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING.items()))
+        if MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING
+        else []
+    )
+
+    # Test image URLs
+    test_image_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg"
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        segmenter = PromptableVisualSegmentationPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+        )
+        examples = [
+            {
+                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                "input_points": [[[[450, 600]]]],
+                "input_labels": [[[1]]],
+            },
+            {
+                "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
+                "input_boxes": [[[100, 200, 350, 550]]],
+            },
+        ]
+        return segmenter, examples
+
+    def run_pipeline_test(self, segmenter, examples):
+        for example in examples:
+            result = segmenter(**example)
+            self.assertIsInstance(result, list)
+            self.assertGreater(len(result), 0)
+            # Each result should be a list of objects (for multiple images)
+            for obj_list in result:
+                self.assertIsInstance(obj_list, list)
+                for obj in obj_list:
+                    self.assertIn("mask", obj)
+                    self.assertIn("score", obj)
+
+    def get_test_image(self):
+        """Helper to load test image."""
+        return Image.open(requests.get(self.test_image_url, stream=True).raw).convert("RGB")
+
+    def test_sam2_single_point(self):
+        """Test SAM2 with single point prompt."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]  # Single point
+        input_labels = [[[1]]]  # Positive
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1, "Should return results for 1 image")
+        self.assertGreater(len(results[0]), 0, "Should return at least 1 mask")
+        self.assertIn("score", results[0][0])
+        self.assertIn("mask", results[0][0])
+        self.assertIsInstance(results[0][0]["score"], float)
+        self.assertTrue(0 <= results[0][0]["score"] <= 1, "Score should be between 0 and 1")
+
+    def test_sam2_box_prompt(self):
+        """Test SAM2 with box prompt."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_boxes = [[[75, 275, 1725, 850]]]  # Box around truck
+
+        results = segmenter(image, input_boxes=input_boxes, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+        self.assertIn("score", results[0][0])
+        self.assertIn("mask", results[0][0])
+
+    def test_sam2_multiple_points(self):
+        """Test SAM2 with multiple points per object."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375], [1125, 625]]]]  # Multiple points
+        input_labels = [[[1, 1]]]  # Both positive
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+
+    def test_sam2_multiple_objects(self):
+        """Test SAM2 with multiple objects in same image."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        # Points for two different objects
+        input_points = [[[[500, 375]], [[650, 750]]]]
+        input_labels = [[[1], [1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreaterEqual(len(results[0]), 2, "Should return at least 2 masks for 2 objects")
+
+    def test_sam2_multimask_output(self):
+        """Test SAM2 with multimask_output=True."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=True)
+
+        self.assertEqual(len(results), 1)
+        # With multimask_output=True, should return 3 masks per object
+        self.assertGreaterEqual(len(results[0]), 3, "Should return at least 3 masks with multimask_output=True")
+
+    def test_sam2_mask_threshold(self):
+        """Test SAM2 with mask_threshold parameter."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(
+            image, input_points=input_points, input_labels=input_labels, mask_threshold=0.5, multimask_output=False
+        )
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+
+    def test_sam2_top_k(self):
+        """Test SAM2 with top_k parameter."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(
+            image, input_points=input_points, input_labels=input_labels, multimask_output=True, top_k=2
+        )
+
+        self.assertEqual(len(results), 1)
+        self.assertLessEqual(len(results[0]), 2, "Should return at most 2 masks with top_k=2")
+
+    def test_sam_single_point(self):
+        """Test SAM with single point prompt."""
+        model = SamModel.from_pretrained("facebook/sam-vit-base")
+        processor = SamProcessor.from_pretrained("facebook/sam-vit-base")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+        self.assertIn("score", results[0][0])
+        self.assertIn("mask", results[0][0])
+
+    def test_results_sorted_by_score(self):
+        """Test that results are sorted by score in descending order."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=True)
+
+        scores = [r["score"] for r in results[0]]
+        sorted_scores = sorted(scores, reverse=True)
+        self.assertEqual(scores, sorted_scores, "Results should be sorted by score in descending order")
+
+    def test_error_no_prompts(self):
+        """Test that error is raised when no prompts are provided."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+
+        with self.assertRaises(ValueError) as context:
+            segmenter(image)
+
+        self.assertIn("at least one prompt type", str(context.exception))
+
+    def test_error_points_without_labels(self):
+        """Test that error is raised when points are provided without labels."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+
+        with self.assertRaises(ValueError) as context:
+            segmenter(image, input_points=input_points)
+
+        self.assertIn("input_labels", str(context.exception))
+
+    @slow
+    def test_sam2_automatic_loading(self):
+        """Test that SAM2 can be loaded automatically with checkpoint name."""
+        segmenter = pipeline("promptable-visual-segmentation", model="facebook/sam2.1-hiera-large")
+
+        self.assertIsInstance(segmenter.model, Sam2Model)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+
+    @slow
+    def test_sam_automatic_loading(self):
+        """Test that SAM can be loaded automatically with checkpoint name."""
+        segmenter = pipeline("promptable-visual-segmentation", model="facebook/sam-vit-base")
+
+        self.assertIsInstance(segmenter.model, SamModel)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        self.assertEqual(len(results), 1)
+        self.assertGreater(len(results[0]), 0)
+
+    def test_mask_shape(self):
+        """Test that mask shape matches original image size."""
+        model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny")
+        processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny")
+
+        segmenter = pipeline("promptable-visual-segmentation", model=model, processor=processor)
+
+        image = self.get_test_image()
+        input_points = [[[[500, 375]]]]
+        input_labels = [[[1]]]
+
+        results = segmenter(image, input_points=input_points, input_labels=input_labels, multimask_output=False)
+
+        mask = results[0][0]["mask"]
+        expected_shape = (image.height, image.width)
+        self.assertEqual(
+            mask.shape, expected_shape, f"Mask shape {mask.shape} should match image size {expected_shape}"
+        )
diff --git a/tests/pipelines/test_pipelines_video_to_text.py b/tests/pipelines/test_pipelines_video_to_text.py
new file mode 100644
index 000000000000..2d64445a7198
--- /dev/null
+++ b/tests/pipelines/test_pipelines_video_to_text.py
@@ -0,0 +1,243 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from huggingface_hub import hf_hub_download
+
+from transformers import MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING, VideoMAEImageProcessor
+from transformers.pipelines import VideoToTextPipeline, pipeline
+from transformers.testing_utils import (
+    is_pipeline_test,
+    nested_simplify,
+    require_av,
+    require_torch,
+    require_vision,
+)
+
+from .test_pipelines_common import ANY
+
+
+@is_pipeline_test
+@require_torch
+@require_vision
+@require_av
+class VideoToTextPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING
+    example_video_filepath = None
+
+    @classmethod
+    def _load_dataset(cls):
+        # Lazy loading of the dataset. Because it is a class method, it will only be loaded once per pytest process.
+        if cls.example_video_filepath is None:
+            cls.example_video_filepath = hf_hub_download(
+                repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset"
+            )
+
+    def get_test_pipeline(
+        self,
+        model,
+        tokenizer=None,
+        image_processor=None,
+        feature_extractor=None,
+        processor=None,
+        dtype="float32",
+    ):
+        self._load_dataset()
+        video_to_text = VideoToTextPipeline(
+            model=model,
+            tokenizer=tokenizer,
+            feature_extractor=feature_extractor,
+            image_processor=image_processor,
+            processor=processor,
+            dtype=dtype,
+            max_new_tokens=20,
+        )
+        examples = [
+            self.example_video_filepath,
+            # TODO: re-enable this once we have a stable hub solution for CI
+            # "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
+        ]
+        return video_to_text, examples
+
+    def run_pipeline_test(self, video_to_text, examples):
+        for example in examples:
+            outputs = video_to_text(example)
+
+            self.assertEqual(
+                outputs,
+                [
+                    {"generated_text": ANY(str)},
+                ],
+            )
+
+    @require_torch
+    def test_small_model_pt(self):
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline(
+            "video-to-text",
+            model=small_model,
+            image_processor=small_image_processor,
+            frame_sampling_rate=4,
+            max_new_tokens=19,
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+        output = video_to_text(video_file_path)
+        self.assertEqual(
+            nested_simplify(output, decimals=4),
+            [
+                {
+                    "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
+                },
+            ],
+        )
+
+        outputs = video_to_text(
+            [
+                video_file_path,
+                video_file_path,
+            ],
+        )
+        self.assertEqual(
+            nested_simplify(outputs, decimals=4),
+            [
+                [
+                    {
+                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
+                    }
+                ],
+                [
+                    {
+                        "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
+                    }
+                ],
+            ],
+        )
+
+    @require_torch
+    def test_small_model_pt_with_num_frames(self):
+        """Test that num_frames parameter works correctly."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline(
+            "video-to-text", model=small_model, image_processor=small_image_processor, max_new_tokens=19
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test with explicit num_frames
+        output = video_to_text(video_file_path, num_frames=16)
+        self.assertIsInstance(output, list)
+        self.assertGreater(len(output), 0)
+        self.assertIn("generated_text", output[0])
+
+    @require_torch
+    def test_small_model_pt_with_system_prompt(self):
+        """Test that system_prompt parameter works correctly."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline(
+            "video-to-text", model=small_model, image_processor=small_image_processor, max_new_tokens=19
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test with system_prompt
+        system_prompt = "Describe this video in detail."
+        output = video_to_text(video_file_path, system_prompt=system_prompt)
+        self.assertIsInstance(output, list)
+        self.assertGreater(len(output), 0)
+        self.assertIn("generated_text", output[0])
+        self.assertIsInstance(output[0]["generated_text"], str)
+
+    @require_torch
+    def test_small_model_pt_batch_processing(self):
+        """Test batch processing with multiple videos."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline(
+            "video-to-text", model=small_model, image_processor=small_image_processor, max_new_tokens=19
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test batch processing
+        outputs = video_to_text([video_file_path, video_file_path])
+        self.assertIsInstance(outputs, list)
+        self.assertEqual(len(outputs), 2)
+        self.assertIsInstance(outputs[0], list)
+        self.assertIsInstance(outputs[1], list)
+        self.assertGreater(len(outputs[0]), 0)
+        self.assertGreater(len(outputs[1]), 0)
+
+    @require_torch
+    def test_small_model_pt_with_generate_kwargs(self):
+        """Test that generate_kwargs parameter works correctly."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline("video-to-text", model=small_model, image_processor=small_image_processor)
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test with generate_kwargs
+        output = video_to_text(video_file_path, generate_kwargs={"max_new_tokens": 10})
+        self.assertIsInstance(output, list)
+        self.assertGreater(len(output), 0)
+        self.assertIn("generated_text", output[0])
+
+    @require_torch
+    def test_small_model_pt_max_new_tokens_conflict(self):
+        """Test that providing max_new_tokens both as argument and in generate_kwargs raises an error."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline("video-to-text", model=small_model, image_processor=small_image_processor)
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test that providing max_new_tokens in both places raises ValueError
+        with self.assertRaises(ValueError):
+            video_to_text(video_file_path, max_new_tokens=10, generate_kwargs={"max_new_tokens": 20})
+
+    @require_torch
+    def test_small_model_pt_frame_sampling_rate(self):
+        """Test that frame_sampling_rate parameter is accepted (even if currently unused)."""
+        small_model = "hf-internal-testing/tiny-random-vit-gpt2"
+        small_image_processor = VideoMAEImageProcessor(
+            size={"shortest_edge": 10}, crop_size={"height": 10, "width": 10}
+        )
+        video_to_text = pipeline(
+            "video-to-text", model=small_model, image_processor=small_image_processor, max_new_tokens=19
+        )
+
+        video_file_path = hf_hub_download(repo_id="nateraw/video-demo", filename="archery.mp4", repo_type="dataset")
+
+        # Test that frame_sampling_rate doesn't cause errors
+        output = video_to_text(video_file_path, frame_sampling_rate=2)
+        self.assertIsInstance(output, list)
+        self.assertGreater(len(output), 0)
+        self.assertIn("generated_text", output[0])
diff --git a/tests/pipelines/test_text_generation_safety.py b/tests/pipelines/test_text_generation_safety.py
new file mode 100644
index 000000000000..f13ac460d562
--- /dev/null
+++ b/tests/pipelines/test_text_generation_safety.py
@@ -0,0 +1,107 @@
+# Copyright 2024 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+from transformers import pipeline
+from transformers.generation.safety import SafetyChecker, SafetyConfig, SafetyResult, SafetyViolation
+from transformers.testing_utils import require_torch, slow
+
+
+class MockSafetyChecker(SafetyChecker):
+    """Mock safety checker for testing"""
+
+    def __init__(self, is_safe=True, name="mock"):
+        self.is_safe = is_safe
+        self.name = name
+        self.check_safety_calls = []
+
+    def check_safety(self, text, **kwargs):
+        self.check_safety_calls.append(text)
+        return SafetyResult(
+            is_safe=self.is_safe,
+            confidence=0.9,
+            violations=[] if self.is_safe else [SafetyViolation("test", 0.9, "high", "Test violation")],
+            metadata={"checker": self.name},
+        )
+
+    @property
+    def supported_categories(self):
+        return ["test"]
+
+
+@require_torch
+class TestTextGenerationPipelineSafety(unittest.TestCase):
+    """Tests for safety integration in TextGenerationPipeline"""
+
+    def test_safety_config_per_call(self):
+        """Test passing safety_config per generate call"""
+        checker = MockSafetyChecker(is_safe=True)
+        config = SafetyConfig.from_checker(checker)
+
+        pipe = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+        result = pipe("Hello", safety_config=config, max_new_tokens=10)
+
+        # Verify safety was applied
+        self.assertGreater(len(checker.check_safety_calls), 0)
+        self.assertIsNotNone(result)
+
+    def test_safety_disabled_by_default(self):
+        """Test that safety is not applied when no config provided"""
+        pipe = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+        result = pipe("Hello", max_new_tokens=10)
+
+        # Should work normally without safety
+        self.assertIsNotNone(result)
+        self.assertEqual(len(result), 1)
+        self.assertIn("generated_text", result[0])
+
+    def test_unsafe_content_blocked(self):
+        """Test that unsafe content generation is blocked"""
+        checker = MockSafetyChecker(is_safe=False)  # Always unsafe
+        config = SafetyConfig.from_checker(checker)
+
+        pipe = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+        result = pipe("Hello", safety_config=config, max_new_tokens=10, do_sample=False)
+
+        # Generation should be stopped early due to safety
+        self.assertIsNotNone(result)
+        # Exact behavior depends on safety implementation
+        # But checker should have been called
+        self.assertGreater(len(checker.check_safety_calls), 0)
+
+    def test_safety_with_batch(self):
+        """Test safety checking with batch input"""
+        checker = MockSafetyChecker(is_safe=True)
+        config = SafetyConfig.from_checker(checker)
+
+        pipe = pipeline("text-generation", model="sshleifer/tiny-gpt2")
+        results = pipe(["Hello", "World"], safety_config=config, max_new_tokens=10)
+
+        # Verify safety was applied to batch
+        self.assertGreater(len(checker.check_safety_calls), 0)
+        self.assertEqual(len(results), 2)
+
+    @slow
+    def test_safety_with_actual_model(self):
+        """Test safety with actual model generation (slow test)"""
+        checker = MockSafetyChecker(is_safe=True)
+        config = SafetyConfig.from_checker(checker)
+
+        pipe = pipeline("text-generation", model="gpt2")
+        result = pipe("The capital of France is", safety_config=config, max_new_tokens=5, do_sample=False)
+
+        self.assertIsNotNone(result)
+        self.assertIn("generated_text", result[0])
+        self.assertGreater(len(checker.check_safety_calls), 0)
diff --git a/tests/quantization/bnb/test_4bit.py b/tests/quantization/bnb/test_4bit.py
index f442df6a299a..9c34f155ef86 100644
--- a/tests/quantization/bnb/test_4bit.py
+++ b/tests/quantization/bnb/test_4bit.py
@@ -860,11 +860,3 @@ def test_generate_compile(self):
             max_new_tokens=10,
             cache_implementation="static",
         )
-        with self.assertRaises(Exception):
-            # overwrite property
-            object.__setattr__(self.model_4bit.hf_quantizer, "is_compileable", True)
-            self.model_4bit.generate(
-                input_ids=encoded_input["input_ids"].to(self.model_4bit.device),
-                max_new_tokens=10,
-                cache_implementation="static",
-            )
diff --git a/tests/quantization/bnb/test_mixed_int8.py b/tests/quantization/bnb/test_mixed_int8.py
index a0a12061b332..bf156a40a097 100644
--- a/tests/quantization/bnb/test_mixed_int8.py
+++ b/tests/quantization/bnb/test_mixed_int8.py
@@ -1039,11 +1039,3 @@ def test_generate_compile(self):
             max_new_tokens=10,
             cache_implementation="static",
         )
-
-        with self.assertRaises(Exception):
-            object.__setattr__(self.model_8bit.hf_quantizer, "is_compileable", True)
-            self.model_8bit.generate(
-                input_ids=encoded_input["input_ids"].to(self.model_8bit.device),
-                max_new_tokens=10,
-                cache_implementation="static",
-            )
diff --git a/tests/quantization/compressed_tensors_integration/test_compressed_models.py b/tests/quantization/compressed_tensors_integration/test_compressed_models.py
index 51f13c8e6d2e..941693fa77e9 100644
--- a/tests/quantization/compressed_tensors_integration/test_compressed_models.py
+++ b/tests/quantization/compressed_tensors_integration/test_compressed_models.py
@@ -120,40 +120,65 @@ def tearDown(self):
         gc.collect()
 
     def test_default_run_compressed__True(self):
-        from compressed_tensors import QuantizationStatus
+        from compressed_tensors import __version__ as ct_version
+        from packaging import version
+
+        if version.parse(ct_version) >= version.parse("0.14"):
+            self.skipTest("CompressedLinear removed in CT >= 0.14")
+
+        try:
+            from compressed_tensors.linear.compressed_linear import CompressedLinear
+        except ImportError:
+            self.skipTest("CompressedLinear not available in this version of compressed-tensors")
+        from compressed_tensors.quantization.utils import iter_named_leaf_modules
 
         for stub in self.stubs:
             model = AutoModelForCausalLM.from_pretrained(
                 stub,
             )
-            compressed_count = sum(
-                1 for m in model.modules() if getattr(m, "quantization_status", None) == QuantizationStatus.COMPRESSED
-            )
+            compressed_linear_counts = 0
 
-            # some linear modules are not compressed - ex. lm_head
-            assert compressed_count > 0
+            for _, submodule in iter_named_leaf_modules(
+                model,
+            ):
+                if isinstance(submodule, CompressedLinear):
+                    compressed_linear_counts += 1
 
-    def test_default_run_compressed__False(self):
-        from compressed_tensors import QuantizationStatus
+            # some linear models are not compressed - ex. lm_head
+            assert compressed_linear_counts > 0
 
-        from transformers.utils.quantization_config import CompressedTensorsConfig
+    def test_model_decompressed_after_loading(self):
+        """Verify that models are properly decompressed after loading for CT >= 0.14"""
+        from compressed_tensors import __version__ as ct_version
+        from compressed_tensors.quantization import QuantizationStatus
+        from compressed_tensors.quantization.utils import iter_named_leaf_modules
+        from packaging import version
 
-        quantization_config = CompressedTensorsConfig(run_compressed=False)
+        if version.parse(ct_version) < version.parse("0.14"):
+            self.skipTest("Automatic decompression only applies to CT >= 0.14")
 
         for stub in self.stubs:
-            model = AutoModelForCausalLM.from_pretrained(
-                stub,
-                quantization_config=quantization_config,
-            )
-            compressed_count = sum(
-                1 for m in model.modules() if getattr(m, "quantization_status", None) == QuantizationStatus.COMPRESSED
-            )
-
-            # No modules should be in COMPRESSED state
-            assert compressed_count == 0
+            model = AutoModelForCausalLM.from_pretrained(stub)
+            for _, submodule in iter_named_leaf_modules(model):
+                if hasattr(submodule, "quantization_status"):
+                    self.assertNotEqual(
+                        submodule.quantization_status,
+                        QuantizationStatus.COMPRESSED,
+                        "Module should be decompressed after loading for CT >= 0.14",
+                    )
 
     def test_run_compressed_outputs_match(self):
         """Check that run_compressed=True/False output are the same"""
+        from compressed_tensors import __version__ as ct_version
+        from packaging import version
+
+        if version.parse(ct_version) >= version.parse("0.14"):
+            self.skipTest("run_compressed no longer applies for CT >= 0.14")
+
+        try:
+            from compressed_tensors.linear.compressed_linear import CompressedLinear  # noqa: F401
+        except ImportError:
+            self.skipTest("CompressedLinear not available in this version of compressed-tensors")
 
         from transformers import AutoTokenizer
         from transformers.utils.quantization_config import CompressedTensorsConfig
diff --git a/tests/quantization/config/__init__.py b/tests/quantization/config/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/tests/quantization/config/test_from_config.py b/tests/quantization/config/test_from_config.py
new file mode 100644
index 000000000000..0a7bd92bc031
--- /dev/null
+++ b/tests/quantization/config/test_from_config.py
@@ -0,0 +1,14 @@
+import pytest
+
+from transformers import AutoConfig, AutoModel
+
+
+def test_quantization_from_config_raises():
+    config = AutoConfig.from_pretrained("gpt2")
+    config.quantization_config = {"quant_method": "fp8"}
+
+    with pytest.raises(
+        NotImplementedError,
+        match="Quantization via",
+    ):
+        AutoModel.from_config(config)
diff --git a/tests/quantization/ggml/test_ggml.py b/tests/quantization/ggml/test_ggml.py
index aa5cdbc7adc6..6a0fd25e32c4 100644
--- a/tests/quantization/ggml/test_ggml.py
+++ b/tests/quantization/ggml/test_ggml.py
@@ -309,8 +309,12 @@ class GgufModelTests(unittest.TestCase):
     gemma3_vision_model_id = "unsloth/gemma-3-4b-it-GGUF"
     qwen3_model_id = "Qwen/Qwen3-0.6B-GGUF"
     qwen3moe_model_id = "Qwen/Qwen3-30B-A3B-GGUF"
+    qwen35moe_model_id = "unsloth/Qwen3.6-35B-A3B-GGUF"
+    qwen2vl_model_id = "unsloth/Qwen2.5-VL-3B-Instruct-GGUF"
+    original_qwen2vl_model_id = "Qwen/Qwen2.5-VL-3B-Instruct"
     umt5_encoder_model_id = "city96/umt5-xxl-encoder-gguf"
     lfm2_model_id = "LiquidAI/LFM2-1.2B-GGUF"
+    llama4_model_id = "unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF"
 
     q4_0_phi3_model_id = "Phi-3-mini-4k-instruct-q4.gguf"
     q4_0_mistral_model_id = "mistral-7b-instruct-v0.2.Q4_0.gguf"
@@ -349,8 +353,11 @@ class GgufModelTests(unittest.TestCase):
     fp16_deci_model_id = "decilm-7b-uniform-gqa-f16.gguf"
     q8_0_qwen3_model_id = "Qwen3-0.6B-Q8_0.gguf"
     q4_k_m_qwen3moe_model_id = "Qwen3-30B-A3B-Q4_K_M.gguf"
+    iq3_s_qwen35moe_model_id = "Qwen3.6-35B-A3B-UD-IQ3_S.gguf"
+    q8_0_qwen2vl_model_id = "Qwen2.5-VL-3B-Instruct-Q8_0.gguf"
     q8_0_umt5_encoder_model_id = "umt5-xxl-encoder-Q8_0.gguf"
     q4_k_m_lfm2_model_id = "LFM2-1.2B-Q4_K_M.gguf"
+    q2_k_l_llama4_model_id = "Llama-4-Scout-17B-16E-Instruct-Q2_K_L.gguf"
     gpt_oss_model_id = "unsloth/gpt-oss-20b-GGUF"
     gpt_oss_gguf_file = "gpt-oss-20b-Q5_K_M.gguf"
 
@@ -987,6 +994,29 @@ def test_gemma3_vision_weights_conversion_bf16(self):
             else:
                 raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
 
+    @unittest.skipUnless(is_gguf_available("0.16.0"), "test requires gguf version >= 0.16.0")
+    def test_qwen2vl(self):
+        original_model = AutoModelForCausalLM.from_pretrained(
+            self.original_qwen2vl_model_id,
+            dtype=torch.float16,
+        ).language_model
+
+        converted_model = AutoModelForCausalLM.from_pretrained(
+            self.qwen2vl_model_id,
+            gguf_file=self.q8_0_qwen2vl_model_id,
+            dtype=torch.float16,
+        )
+
+        converted_state_dict = converted_model.state_dict()
+        original_state_dict = original_model.state_dict()
+
+        for layer_name, original_params in original_state_dict.items():
+            if layer_name in converted_state_dict:
+                self.assertTrue(original_params.shape == converted_state_dict[layer_name].shape)
+                torch.testing.assert_close(original_params, converted_state_dict[layer_name])
+            else:
+                raise ValueError(f"Layer {layer_name} is not presented in GGUF model")
+
     def test_deci_q8_0(self):
         """Test Deci model loading and inference with Q4_0 quantization."""
         tokenizer = AutoTokenizer.from_pretrained(self.deci_model_id, gguf_file=self.q8_0_deci_model_id)
@@ -1056,6 +1086,22 @@ def test_deci_config_mapping(self):
 
         self.assertIsNone(deci_mapping["rope.dimension_count"])
 
+    def test_gemma_softcap_config_mapping(self):
+        """Test that Gemma2/Gemma3 GGUF config mapping includes attn_logit_softcapping."""
+        from transformers.integrations.ggml import GGUF_CONFIG_MAPPING
+
+        # Test Gemma2
+        self.assertIn("gemma2", GGUF_CONFIG_MAPPING)
+        gemma2_mapping = GGUF_CONFIG_MAPPING["gemma2"]
+        self.assertIn("attention.logit_softcapping", gemma2_mapping)
+        self.assertEqual(gemma2_mapping["attention.logit_softcapping"], "attn_logit_softcapping")
+
+        # Test Gemma3
+        self.assertIn("gemma3", GGUF_CONFIG_MAPPING)
+        gemma3_mapping = GGUF_CONFIG_MAPPING["gemma3"]
+        self.assertIn("attention.logit_softcapping", gemma3_mapping)
+        self.assertEqual(gemma3_mapping["attention.logit_softcapping"], "attn_logit_softcapping")
+
     def test_deci_architecture_mapping(self):
         """Test that Deci architectures are mapped to GGUFLlamaConverter."""
         from transformers.integrations.ggml import GGUF_TO_FAST_CONVERTERS, GGUFLlamaConverter
@@ -1095,6 +1141,22 @@ def test_qwen3moe_q4_k_m(self):
         EXPECTED_TEXT = "Hello, I am a 20 year old male"
         self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
 
+    @unittest.skip("Heavyweight: ~12.7 GB GGUF download. Run manually.")
+    def test_qwen35moe_iq3_s(self):
+        # Smoke test for Qwen3.5/3.6 MoE GGUF support: tokenizer + model
+        # both load without error and the model produces non-empty output.
+        # A smaller fixture would be preferable; none was available at the
+        # time this test was added.
+        tokenizer = AutoTokenizer.from_pretrained(self.qwen35moe_model_id, gguf_file=self.iq3_s_qwen35moe_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.qwen35moe_model_id,
+            gguf_file=self.iq3_s_qwen35moe_model_id,
+            dtype=torch.float16,
+        )
+        text = tokenizer(self.example_text, return_tensors="pt")
+        out = model.generate(**text, max_new_tokens=4)
+        self.assertGreater(len(tokenizer.decode(out[0], skip_special_tokens=True)), 0)
+
     def test_umt5_encoder_q8_0(self):
         """
         Verifies that a UMT5 encoder loads directly from a GGUF file using
@@ -1145,3 +1207,132 @@ def test_lfm2_q4_k_m(self):
 
         EXPECTED_TEXT = "Hello Atari 2600! es un videoj"
         self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    @unittest.skipUnless(is_gguf_available("0.17.0"), "test requires gguf version >= 0.17.0")
+    def test_qwen3_next_config_mapping(self):
+        """Test that Qwen3-Next GGUF config mapping is correctly applied."""
+        from transformers.integrations.ggml import (
+            GGUF_CONFIG_DEFAULTS_MAPPING,
+            GGUF_CONFIG_MAPPING,
+            GGUF_TO_FAST_CONVERTERS,
+            GGUFQwen2Converter,
+        )
+
+        self.assertIn("qwen3_next", GGUF_CONFIG_MAPPING)
+
+        mapping = GGUF_CONFIG_MAPPING["qwen3_next"]
+
+        expected_mappings = {
+            "context_length": "max_position_embeddings",
+            "block_count": "num_hidden_layers",
+            "feed_forward_length": "intermediate_size",
+            "embedding_length": "hidden_size",
+            "attention.head_count": "num_attention_heads",
+            "attention.head_count_kv": "num_key_value_heads",
+            "attention.key_length": "head_dim",
+            "attention.layer_norm_rms_epsilon": "rms_norm_eps",
+            "vocab_size": "vocab_size",
+            "expert_count": "num_experts",
+            "expert_used_count": "num_experts_per_tok",
+            "expert_feed_forward_length": "moe_intermediate_size",
+            "expert_shared_feed_forward_length": "shared_expert_intermediate_size",
+            "ssm.conv_kernel": "linear_conv_kernel_dim",
+            "ssm.state_size": "linear_key_head_dim",
+            "ssm.group_count": "linear_num_key_heads",
+            "ssm.time_step_rank": "linear_num_value_heads",
+            "ssm.inner_size": "_ssm_inner_size",
+            "rope.dimension_count": "_rope_dimension_count",
+            "rope.freq_base": "_rope_freq_base",
+        }
+
+        for gguf_key, transformers_key in expected_mappings.items():
+            self.assertEqual(mapping[gguf_key], transformers_key)
+
+        self.assertIsNone(mapping["attention.value_length"])
+
+        # Check defaults
+        self.assertIn("qwen3_next", GGUF_CONFIG_DEFAULTS_MAPPING)
+        self.assertTrue(GGUF_CONFIG_DEFAULTS_MAPPING["qwen3_next"]["norm_topk_prob"])
+
+        # Check tokenizer converter
+        self.assertIn("qwen3_next", GGUF_TO_FAST_CONVERTERS)
+        self.assertEqual(GGUF_TO_FAST_CONVERTERS["qwen3_next"], GGUFQwen2Converter)
+
+    def test_qwen3_next_tensor_processor(self):
+        """Test that Qwen3-Next tensor processor is registered and handles key transforms."""
+        from transformers.modeling_gguf_pytorch_utils import TENSOR_PROCESSORS, Qwen3NextTensorProcessor
+
+        self.assertIn("qwen3next", TENSOR_PROCESSORS)
+        self.assertEqual(TENSOR_PROCESSORS["qwen3next"], Qwen3NextTensorProcessor)
+
+        # Test tensor transforms with synthetic data
+        import numpy as np
+
+        config = {
+            "hidden_size": 64,
+            "linear_key_head_dim": 16,
+            "linear_num_key_heads": 2,
+            "linear_num_value_heads": 4,
+            "linear_value_head_dim": 16,
+            "_ssm_inner_size": 64,
+        }
+        processor = Qwen3NextTensorProcessor(config=config)
+
+        # ssm_conv1d: [dim, kernel] -> [dim, 1, kernel]
+        conv_weights = np.random.randn(32, 4).astype(np.float32)
+        result = processor.process(weights=conv_weights, name="blk.0.ssm_conv1d.weight")
+        self.assertEqual(result.weights.shape, (32, 1, 4))
+
+        # ssm_a: log(-weights)
+        a_weights = np.array([-2.0, -3.0, -1.5], dtype=np.float32)
+        result = processor.process(weights=a_weights, name="blk.0.ssm_a")
+        np.testing.assert_allclose(result.weights, np.log(np.array([2.0, 3.0, 1.5])), rtol=1e-6)
+
+        # norm -1 (attn_norm, post_attention_norm, output_norm, q_norm, k_norm)
+        norm_weights = np.array([2.0, 1.5, 1.0], dtype=np.float32)
+        for name in ["blk.0.attn_norm.weight", "blk.0.post_attention_norm.weight", "output_norm.weight"]:
+            result = processor.process(weights=norm_weights.copy(), name=name)
+            np.testing.assert_array_equal(result.weights, np.array([1.0, 0.5, 0.0]))
+
+        # ssm_norm: NOT modified
+        result = processor.process(weights=norm_weights.copy(), name="blk.0.ssm_norm.weight")
+        np.testing.assert_array_equal(result.weights, norm_weights)
+
+    @unittest.skip(reason="Qwen3-Next is 80B params, requires >160GB memory")
+    def test_qwen3_next_q4_k_xl(self):
+        tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
+        model = AutoModelForCausalLM.from_pretrained(
+            "Qwen/Qwen3-Coder-Next-GGUF",
+            gguf_file="Qwen3-Coder-Next-UD-Q4_K_XL.gguf",
+            device_map="auto",
+            dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+        # Expected text to be determined when model can be loaded on suitable hardware
+        self.assertIsNotNone(tokenizer.decode(out[0], skip_special_tokens=True))
+
+    def test_llama4_q2_k_l_tokenizer(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.llama4_model_id, gguf_file=self.q2_k_l_llama4_model_id)
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            tokenizer.save_pretrained(tmpdirname)
+            tokenizer = AutoTokenizer.from_pretrained(tmpdirname)
+            special_sentence = "สวัสดี"
+            predicted_text = tokenizer.decode(tokenizer.encode(special_sentence, return_tensors="pt")[0])
+            self.assertEqual(predicted_text, "<|begin_of_text|>" + special_sentence)
+
+    @unittest.skipUnless(is_gguf_available("0.17.0"), "test requires gguf version >= 0.17.0")
+    def test_llama4_q2_k_l(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.llama4_model_id, gguf_file=self.q2_k_l_llama4_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.llama4_model_id,
+            gguf_file=self.q2_k_l_llama4_model_id,
+            dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt")["input_ids"]
+        out = model.generate(text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, I'm here to help. What"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
diff --git a/tests/quantization/ggml/test_prism_q1_0_g128.py b/tests/quantization/ggml/test_prism_q1_0_g128.py
new file mode 100644
index 000000000000..73db9dedf529
--- /dev/null
+++ b/tests/quantization/ggml/test_prism_q1_0_g128.py
@@ -0,0 +1,96 @@
+# Copyright 2026 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+
+from transformers.modeling_gguf_pytorch_utils import _dequantize_gguf_tensor, _dequantize_prism_q1_0_g128
+
+
+class FakeQuantType:
+    def __init__(self, name: str, value: int):
+        self.name = name
+        self._value = value
+
+    def __int__(self):
+        return self._value
+
+
+def _pack_prism_block(scale: float, signs: np.ndarray) -> np.ndarray:
+    sign_bytes = np.packbits(np.asarray(signs, dtype=np.uint8), bitorder="little")
+    return np.concatenate([np.asarray([scale], dtype=np.float16).view(np.uint8), sign_bytes])
+
+
+def _build_prism_rows():
+    row0_block0_signs = (np.arange(128) % 2).astype(np.uint8)
+    row0_block1_signs = (np.arange(128) % 3 == 0).astype(np.uint8)
+    row1_block0_signs = np.ones(128, dtype=np.uint8)
+    row1_block1_signs = np.zeros(128, dtype=np.uint8)
+
+    row0 = np.concatenate(
+        [
+            _pack_prism_block(1.5, row0_block0_signs),
+            _pack_prism_block(0.25, row0_block1_signs),
+        ]
+    )
+    row1 = np.concatenate(
+        [
+            _pack_prism_block(2.0, row1_block0_signs),
+            _pack_prism_block(0.75, row1_block1_signs),
+        ]
+    )
+    data = np.stack([row0, row1], axis=0)
+    expected = np.stack(
+        [
+            np.concatenate(
+                [
+                    np.where(row0_block0_signs == 1, np.float32(1.5), np.float32(-1.5)),
+                    np.where(row0_block1_signs == 1, np.float32(0.25), np.float32(-0.25)),
+                ]
+            ),
+            np.concatenate(
+                [
+                    np.where(row1_block0_signs == 1, np.float32(2.0), np.float32(-2.0)),
+                    np.where(row1_block1_signs == 1, np.float32(0.75), np.float32(-0.75)),
+                ]
+            ),
+        ],
+        axis=0,
+    )
+    return data, expected
+
+
+def test_dequantize_prism_q1_0_g128_matches_reference_layout():
+    data, expected = _build_prism_rows()
+    actual = _dequantize_prism_q1_0_g128(data)
+    np.testing.assert_array_equal(actual, expected)
+
+
+def test_dequantize_gguf_tensor_falls_back_for_prism_q1_0_g128():
+    data, expected = _build_prism_rows()
+
+    def fake_dequantize(_data, _tensor_type):
+        raise NotImplementedError("missing q1_0_g128 support")
+
+    actual = _dequantize_gguf_tensor(data, FakeQuantType("Q1_0_g128", 41), fake_dequantize)
+    np.testing.assert_array_equal(actual, expected)
+
+
+def test_dequantize_gguf_tensor_uses_default_path_for_other_quant_types():
+    sentinel = np.arange(4, dtype=np.float32)
+
+    def fake_dequantize(_data, _tensor_type):
+        return sentinel
+
+    actual = _dequantize_gguf_tensor(np.zeros((1, 18), dtype=np.uint8), FakeQuantType("Q4_0", 2), fake_dequantize)
+    assert actual is sentinel
diff --git a/tests/quantization/hqq/test_hqq.py b/tests/quantization/hqq/test_hqq.py
index 913bf6bf9e75..ad2797229fa5 100755
--- a/tests/quantization/hqq/test_hqq.py
+++ b/tests/quantization/hqq/test_hqq.py
@@ -14,7 +14,6 @@
 
 import gc
 import unittest
-from unittest import skip
 
 import accelerate
 
@@ -106,7 +105,6 @@ def test_to_dict(self):
 @require_torch_accelerator
 @require_accelerate
 @require_hqq
-@skip("skip for now until we add back support")
 class HQQTest(unittest.TestCase):
     def tearDown(self):
         cleanup()
@@ -164,7 +162,6 @@ def test_quantized_model_fake_weight_dtype(self):
 @require_torch_multi_accelerator
 @require_accelerate
 @require_hqq
-@skip("skip for now until we add back support")
 class HQQTestMultiGPU(unittest.TestCase):
     def tearDown(self):
         cleanup()
@@ -188,7 +185,6 @@ def test_fp16_quantized_model_multipgpu(self):
 @require_torch_accelerator
 @require_accelerate
 @require_hqq
-@skip("skip for now until we add back support")
 class HQQTestBias(unittest.TestCase):
     def tearDown(self):
         cleanup()
@@ -245,7 +241,6 @@ def test_save_and_load_quantized_model(self):
 @require_torch_accelerator
 @require_accelerate
 @require_hqq
-@skip("skip for now until we add back support")
 class HQQSerializationTest(unittest.TestCase):
     def tearDown(self):
         cleanup()
diff --git a/tests/test_executorch.py b/tests/test_executorch.py
index 42beb4c05663..5f462563dbb8 100644
--- a/tests/test_executorch.py
+++ b/tests/test_executorch.py
@@ -23,7 +23,10 @@
     TorchExportableModuleWithHybridCache,
     TorchExportableModuleWithStaticCache,
 )
-from transformers.testing_utils import require_torch
+from transformers.models.granite_speech.feature_extraction_granite_speech import GraniteSpeechFeatureExtractor
+from transformers.models.whisper.feature_extraction_whisper import WhisperFeatureExtractor
+from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_3
+from transformers.testing_utils import require_torch, require_torchaudio
 
 
 @require_torch
@@ -123,3 +126,29 @@ def test_decoder_only_lm_export(self):
             inputs_embeds=self.inputs_embeds, cache_position=self.cache_position
         )
         torch.testing.assert_close(eager_output_embeds, exported_output_embeds, atol=1e-4, rtol=1e-4)
+
+
+@require_torchaudio
+@require_torch
+class FeatureExtractorExportTest(unittest.TestCase):
+    def setUp(self):
+        if not is_torch_greater_or_equal_than_2_3:
+            self.skipTest("torch >= 2.3 is required")
+
+    def test_whisper_export(self):
+        feature_extractor = WhisperFeatureExtractor()
+        exportable_module = feature_extractor.to_exportable_module()
+        waveform = torch.randn(1, 16000, dtype=torch.float32)
+        exported_program = torch.export.export(exportable_module, args=(waveform,))
+        self.assertIsNotNone(exported_program)
+        exported_output = exported_program.module()(waveform)
+        self.assertIsNotNone(exported_output)
+
+    def test_granite_speech_export(self):
+        feature_extractor = GraniteSpeechFeatureExtractor()
+        exportable_module = feature_extractor.to_exportable_module()
+        waveform = torch.randn(1, 16000, dtype=torch.float32)
+        exported_program = torch.export.export(exportable_module, args=(waveform,))
+        self.assertIsNotNone(exported_program)
+        exported_output = exported_program.module()(waveform)
+        self.assertIsNotNone(exported_output)
diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py
index f7c2bf3fbb76..ce89e0f791af 100644
--- a/tests/test_modeling_common.py
+++ b/tests/test_modeling_common.py
@@ -23,6 +23,7 @@
 import unittest.mock
 import warnings
 from collections import defaultdict
+from collections.abc import Callable
 from contextlib import contextmanager
 from copy import deepcopy
 from unittest.mock import Mock, patch
@@ -53,6 +54,7 @@
 )
 from transformers.integrations.moe import (
     batched_mm_experts_forward,
+    deepgemm_experts_forward,
     grouped_mm_experts_forward,
     sonicmoe_experts_forward,
 )
@@ -118,6 +120,7 @@
     is_torch_bf16_available_on_device,
     is_torch_fp16_available_on_device,
 )
+from transformers.utils.import_utils import get_cuda_runtime_version
 from transformers.utils.output_capturing import CompileableContextVar
 
 from .generation.test_utils import GenerationTesterMixin
@@ -600,6 +603,17 @@ def _test_eager_matches_batched_and_grouped_inference(self, name, dtype):
             mocks["sonicmoe"] = Mock(wraps=sonicmoe_experts_forward)
             implementations.append("sonicmoe")
 
+        if (
+            dtype == torch.bfloat16
+            and is_kernels_available()
+            and torch.cuda.is_available()
+            and torch.cuda.get_device_capability() >= (9, 0)
+            and get_cuda_runtime_version() >= (12, 3)
+        ):
+            # DeepGEMM BF16 grouped forward requires Hopper+, CUDA runtime 12.3+, and bf16 hidden states
+            mocks["deepgemm"] = Mock(wraps=deepgemm_experts_forward)
+            implementations.append("deepgemm")
+
         outputs = {}
         # This is needed because we call the functions through the interface's global mapping
         with patch.dict("transformers.integrations.moe.ALL_EXPERTS_FUNCTIONS._global_mapping", mocks):
@@ -664,6 +678,18 @@ def _mock_all_init_weights(self):
         self.tie_weights()
 
 
+def submodels_support_check(model: PreTrainedModel, support_check: Callable[[PreTrainedModel], bool]) -> bool:
+    """
+    Iterates through the submodels of the provided model and checks if they support the given check function.
+    """
+    support_results = [
+        support_check(module)
+        for name, module in model.named_modules()
+        if isinstance(module, PreTrainedModel) and name != ""
+    ]
+    return all(support_results) if support_results else support_check(model)
+
+
 @contextmanager
 def _deepspeed_zero3(ds_config):
     dschf = HfDeepSpeedConfig(ds_config)
@@ -3118,11 +3144,12 @@ def test_load_with_mismatched_shapes(self):
                 with tempfile.TemporaryDirectory() as tmp_dir:
                     model = model_class(config)
                     model.save_pretrained(tmp_dir)
+                    config.get_text_config().vocab_size = 10
                     # Fails when we don't set ignore_mismatched_sizes=True
                     with self.assertRaises(RuntimeError):
                         new_model = AutoModelForSequenceClassification.from_pretrained(tmp_dir, num_labels=42)
                     with self.assertRaises(RuntimeError):
-                        new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, vocab_size=10)
+                        new_model_without_prefix = AutoModel.from_pretrained(tmp_dir, config=config)
 
                     logger = logging.get_logger("transformers.modeling_utils")
 
@@ -3138,7 +3165,7 @@ def test_load_with_mismatched_shapes(self):
 
                     with CaptureLogger(logger) as cl:
                         new_model_without_prefix = AutoModel.from_pretrained(
-                            tmp_dir, vocab_size=10, ignore_mismatched_sizes=True
+                            tmp_dir, config=config, ignore_mismatched_sizes=True
                         )
                     self.assertIn("Reinit due to size mismatch", cl.out)
                     input_ids = ids_tensor((2, 8), 10)
@@ -3586,30 +3613,38 @@ def test_sdpa_can_dispatch_composite_models(self):
                 model_sdpa = model_class.from_pretrained(tmpdirname)
                 model_sdpa = model_sdpa.base_model
 
-                vision_model_names = {"visual", "image_tower", "vision_tower", "vision_model"}
+                modality_tower_names = {
+                    "visual",
+                    "image_tower",
+                    "vision_tower",
+                    "vision_model",
+                    "audio_tower",
+                    "audio_model",
+                }
                 language_model_names = {"language_model", "model", "text_model"}
-                vision_model_name = [name for name in vision_model_names if hasattr(model_sdpa, name)]
-                vision_model_name = vision_model_name[0] if len(vision_model_name) > 0 else None
+                modality_tower_name = [name for name in modality_tower_names if hasattr(model_sdpa, name)]
+                modality_tower_name = modality_tower_name[0] if len(modality_tower_name) > 0 else None
                 language_model_name = [name for name in language_model_names if hasattr(model_sdpa, name)]
                 language_model_name = language_model_name[0] if len(language_model_name) > 0 else None
-                if language_model_name is None or vision_model_name is None:
+                if language_model_name is None or modality_tower_name is None:
                     self.skipTest(
-                        reason="Model does not have both vision and language sub-models, cannot test composite SDPA dispatch"
+                        reason="Model does not have both a non-text modality tower and a language sub-model, "
+                        "cannot test composite SDPA dispatch"
                     )
-                vision_model_sdpa = getattr(model_sdpa, vision_model_name)
+                modality_tower_sdpa = getattr(model_sdpa, modality_tower_name)
                 language_model_sdpa = getattr(model_sdpa, language_model_name)
                 text_attn = "sdpa" if language_model_sdpa._supports_sdpa else "eager"
-                vision_attn = "sdpa" if vision_model_sdpa._supports_sdpa else "eager"
+                modality_attn = "sdpa" if modality_tower_sdpa._supports_sdpa else "eager"
 
                 # `None` as it is the requested one which will be assigned to each sub-config
                 # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
                 self.assertTrue(language_model_sdpa.config._attn_implementation == text_attn)
-                self.assertTrue(vision_model_sdpa.config._attn_implementation == vision_attn)
+                self.assertTrue(modality_tower_sdpa.config._attn_implementation == modality_attn)
 
                 model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager")
                 model_eager = model_eager.base_model
                 self.assertTrue(getattr(model_eager, language_model_name).config._attn_implementation == "eager")
-                self.assertTrue(getattr(model_eager, vision_model_name).config._attn_implementation == "eager")
+                self.assertTrue(getattr(model_eager, modality_tower_name).config._attn_implementation == "eager")
 
                 for name, submodule in model_eager.named_modules():
                     class_name = submodule.__class__.__name__
@@ -4679,8 +4714,18 @@ def test_bc_torch_dtype(self):
             with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_pretrained(tmpdirname)
 
-                # Check that it works for all dtypes
-                for dtype in ["float16", "bfloat16", "float32", "auto", torch.float16, torch.bfloat16, torch.float32]:
+                # Check a random-looking but reproducible subset of dtypes per model class.
+                supported_dtypes = [
+                    "float16",
+                    "bfloat16",
+                    "float32",
+                    "auto",
+                    torch.float16,
+                    torch.bfloat16,
+                    torch.float32,
+                ]
+                dtype_rng = random.Random(f"test_bc_torch_dtype:{model_class.__name__}")
+                for dtype in dtype_rng.sample(supported_dtypes, 3):
                     model_torch_dtype = model_class.from_pretrained(tmpdirname, torch_dtype=dtype)
                     model_dtype = model_class.from_pretrained(tmpdirname, dtype=dtype)
 
@@ -4801,7 +4846,15 @@ def test_reverse_loading_mapping(self, check_keys_were_modified=True, skip_base_
                     # mess up the prefixes only if the loaded checkpoints were doing so as well)
                     if isinstance(conversion, PrefixChange):
                         continue
-                    for source_pattern in conversion.source_patterns:
+
+                    # Single pass over serialized_keys: the compiled regex already tests all
+                    # pattern branches at once, so one call per key is enough.
+                    matched_groups: set[str] = set()
+                    for key in serialized_keys:
+                        if (match := conversion._scoped_match(key)) is not None:
+                            matched_groups.add(match[2].lastgroup)  # "g0", "g1", ...
+
+                    for pattern_index, source_pattern in enumerate(conversion.source_patterns):
                         # Some patterns are written for gen-model only and won't be applied on base model
                         if "lm_head" in source_pattern and model_class not in [
                             *get_values(MODEL_FOR_CAUSAL_LM_MAPPING_NAMES),
@@ -4818,9 +4871,9 @@ def test_reverse_loading_mapping(self, check_keys_were_modified=True, skip_base_
                                 target_pattern_reversed = target_pattern_reversed.replace(r"\1", captured_group)
                             if any(re.search(target_pattern_reversed, k) for k in model.all_tied_weights_keys.keys()):
                                 continue
-                        num_matches = sum(re.search(source_pattern, key) is not None for key in serialized_keys)
+
                         self.assertTrue(
-                            num_matches > 0,
+                            f"g{pattern_index}" in matched_groups,
                             f"`{source_pattern}` in `{conversion}` did not match any of the source keys. "
                             "This indicates whether that the pattern is not properly written, or that it could not be reversed correctly",
                         )
@@ -5378,7 +5431,10 @@ def test_get_audio_features_output(self, return_dict: bool | None):
                 elif hasattr(audio_config, "hidden_size"):
                     hidden_size = audio_config.hidden_size
                 elif hasattr(audio_config, "encoder_config"):
-                    hidden_size = audio_config.encoder_config.hidden_dim
+                    if hasattr(audio_config.encoder_config, "hidden_size"):
+                        hidden_size = audio_config.encoder_config.hidden_size
+                    else:
+                        hidden_size = audio_config.encoder_config.hidden_dim
                 elif hasattr(audio_config, "encoder_ffn_dim"):
                     hidden_size = audio_config.encoder_ffn_dim
                 self.assertEqual(
diff --git a/tests/test_multilabel_metrics.py b/tests/test_multilabel_metrics.py
new file mode 100644
index 000000000000..231aa1768543
--- /dev/null
+++ b/tests/test_multilabel_metrics.py
@@ -0,0 +1,31 @@
+import importlib.util
+import pathlib
+
+import numpy as np
+
+
+# Load the example module directly from its file path (hyphen-safe)
+PATH = pathlib.Path("examples/pytorch/text-classification/run_multilabel_classification.py")
+spec = importlib.util.spec_from_file_location("mlc_example", str(PATH))
+mlc = importlib.util.module_from_spec(spec)
+assert spec and spec.loader, "Could not load spec for example module"
+spec.loader.exec_module(mlc)
+
+
+def test_sigmoid_binarize_shapes():
+    x = np.array([0.0, 10.0, -10.0])
+    p = mlc.sigmoid(x)
+    assert p.shape == (3,)
+    assert np.all((p > 0) & (p < 1)), "sigmoid outputs must be in (0,1)"
+    y = mlc.binarize_probs(p.reshape(1, -1), 0.5)
+    assert y.shape == (1, 3)
+    assert set(y.ravel()) <= {0, 1}
+
+
+def test_metrics_ranges_and_keys():
+    y_true = np.array([[1, 0, 1], [0, 1, 0], [1, 1, 0]])
+    y_pred = np.array([[1, 0, 1], [0, 1, 1], [1, 0, 0]])
+    m = mlc.multilabel_metrics(y_true, y_pred)
+    assert set(m) == {"f1_micro", "f1_macro", "hamming_loss", "subset_accuracy"}
+    for v in m.values():
+        assert 0.0 <= v <= 1.0
diff --git a/tests/test_processing_common.py b/tests/test_processing_common.py
index 1bf52f0369dd..0db7382d1d2b 100644
--- a/tests/test_processing_common.py
+++ b/tests/test_processing_common.py
@@ -595,7 +595,8 @@ def test_image_processor_defaults(self):
 
         # Verify outputs match
         for key in input_image_proc:
-            torch.testing.assert_close(input_image_proc[key], input_processor[key])
+            if key in processor.model_input_names:
+                torch.testing.assert_close(input_image_proc[key], input_processor[key])
 
     def test_tokenizer_defaults(self):
         """
@@ -1693,11 +1694,7 @@ def test_apply_chat_template_video_frame_sampling(self):
         if processor.chat_template is None:
             self.skipTest("Processor has no chat template")
 
-        signature = inspect.signature(processor.__call__)
-        if "videos" not in {*signature.parameters.keys()} or (
-            signature.parameters.get("videos") is not None
-            and signature.parameters["videos"].annotation == inspect._empty
-        ):
+        if "video_processor" not in self.processor_class.get_attributes():
             self.skipTest("Processor doesn't accept videos at input")
 
         messages = [
@@ -2018,6 +2015,104 @@ def test_apply_chat_template_tool_calls_no_content(self):
         result = processor.apply_chat_template(messages, tokenize=True)
         self.assertIsInstance(result, list)
 
+        # Also test with explicit content=None (OpenAI returns this for tool-call-only messages)
+        messages_with_none = [
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": "What is the weather?"}],
+            },
+            {
+                "role": "assistant",
+                "content": None,
+                "tool_calls": [{"type": "function", "function": {"name": "get_weather", "arguments": {}}}],
+            },
+        ]
+        result_none = processor.apply_chat_template(messages_with_none, tokenize=True)
+        self.assertIsInstance(result_none, list)
+
+    @require_torch
+    def test_apply_chat_template_assistant_mask_with_image(self):
+        """Tests that assistant_masks are correct when multimodal (image) inputs cause placeholder expansion."""
+        processor = self.get_processor()
+
+        if processor.chat_template is None:
+            self.skipTest("Processor has no chat template")
+
+        if "image_processor" not in self.processor_class.get_attributes():
+            self.skipTest(f"image_processor attribute not present in {self.processor_class}")
+
+        if not hasattr(processor, "image_token"):
+            self.skipTest("Processor has no image_token attribute")
+
+        image_input = self.prepare_image_inputs()
+        image_token = processor.image_token
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "image": image_input},
+                        {"type": "text", "text": "Describe the image."},
+                    ],
+                },
+                {
+                    "role": "assistant",
+                    "content": [
+                        {"type": "text", "text": "The image shows a scenic view."},
+                    ],
+                },
+            ]
+        ]
+
+        # Use a dummy template with {% generation %} that emits the processor's
+        # real image_token so the processor expands it (e.g. 1 -> N copies),
+        # triggering the offset misalignment this test guards against.
+        dummy_template = (
+            "{% for message in messages %}"
+            "{% if message['role'] == 'user' %}"
+            "{{'<|special_start|>user\n'}}"
+            "{% for content in message['content'] %}"
+            "{% if content['type'] == 'image' %}"
+            "{{ '" + image_token + "' }}"
+            "{% elif content['type'] == 'text' %}"
+            "{{ content['text'] }}"
+            "{% endif %}"
+            "{% endfor %}"
+            "{{'<|special_end|>\n'}}"
+            "{% elif message['role'] == 'assistant' %}"
+            "{{'<|special_start|>assistant\n'}}"
+            "{% generation %}"
+            "{{ message['content'][0]['text'] + '<|special_end|>\n' }}"
+            "{% endgeneration %}"
+            "{% endif %}"
+            "{% endfor %}"
+        )
+
+        inputs = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=False,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            return_assistant_tokens_mask=True,
+            chat_template=dummy_template,
+        )
+
+        self.assertIn("assistant_masks", inputs)
+        mask = inputs["assistant_masks"]
+        self.assertEqual(len(mask), len(inputs["input_ids"]))
+
+        # The mask must not be all zeros — the assistant response should be marked
+        self.assertGreater(mask.sum().item(), 0, "assistant_masks is all zeros with multimodal input")
+
+        # Verify the masked tokens decode to the expected assistant text
+        assistant_ids = inputs["input_ids"][mask.bool()]
+        assistant_text = "The image shows a scenic view.<|special_end|>\n"
+        text_is_same = assistant_text == processor.decode(assistant_ids, clean_up_tokenization_spaces=True)
+        ids_is_same = processor.tokenizer.encode(assistant_text, add_special_tokens=False), assistant_ids.tolist()
+        self.assertTrue(text_is_same or ids_is_same)
+
     def test_get_num_multimodal_tokens_matches_processor_call(self):
         "Tests that the helper used internally in vLLM works correctly"
 
diff --git a/tests/test_tokenization_common.py b/tests/test_tokenization_common.py
index 833134c2913f..5d645b51ed7f 100644
--- a/tests/test_tokenization_common.py
+++ b/tests/test_tokenization_common.py
@@ -1086,6 +1086,33 @@ def test_chat_template_batched(self):
             dummy_conversations, chat_template=dummy_template, tokenize=True
         )  # Check that no error raised
 
+    @require_jinja
+    def test_chat_template_content_none(self):
+        """Regression test: content=None (e.g. OpenAI tool-call messages) should be treated the same as missing content."""
+        dummy_template = (
+            "{% for message in messages %}"
+            "{{ message['role'] }}"
+            "{% if message.content is defined %}: {{ message['content'] }}{% endif %}"
+            "\n"
+            "{% endfor %}"
+        )
+        messages_with_none = [
+            {"role": "user", "content": "What is the weather?"},
+            {"role": "assistant", "content": None},
+        ]
+        messages_without_content = [
+            {"role": "user", "content": "What is the weather?"},
+            {"role": "assistant"},
+        ]
+        tokenizer = self.get_tokenizer()
+        output_none = tokenizer.apply_chat_template(
+            messages_with_none, chat_template=dummy_template, tokenize=False, return_dict=False
+        )
+        output_missing = tokenizer.apply_chat_template(
+            messages_without_content, chat_template=dummy_template, tokenize=False, return_dict=False
+        )
+        self.assertEqual(output_none, output_missing)
+
     @require_jinja
     def test_jinja_loopcontrols(self):
         break_template = """
@@ -1125,6 +1152,48 @@ def test_jinja_strftime(self):
         self.assertEqual(len(strftime_output), 10)
         self.assertEqual(len(strftime_output.split("-")), 3)
 
+    @require_jinja
+    def test_jinja_fromjson(self):
+        # Test fromjson filter for parsing JSON strings in chat templates
+        fromjson_template = (
+            """{% set args = '{"name": "test_func", "value": 42}' | fromjson %}{{ args.name }}: {{ args.value }}"""
+        )
+
+        # Test with tool calls that have JSON string arguments
+        tool_call_template = """{% for message in messages %}{% if message.tool_calls %}{% for tc in message.tool_calls %}{% set args = tc.function.arguments | fromjson %}Function: {{ tc.function.name }}, Args: {% for k, v in args.items() %}{{ k }}={{ v }}{% if not loop.last %}, {% endif %}{% endfor %}{% endfor %}{% endif %}{% endfor %}"""
+
+        dummy_conversation = [{"role": "user", "content": "test"}]
+
+        tool_conversation = [
+            {
+                "role": "assistant",
+                "content": "I'll help with that.",
+                "tool_calls": [{"function": {"name": "search", "arguments": '{"query": "hello world", "limit": 10}'}}],
+            }
+        ]
+
+        tokenizers = self.get_tokenizers()
+        for tokenizer in tokenizers:
+            with self.subTest(f"{tokenizer.__class__.__name__}"):
+                # Test basic fromjson usage
+                fromjson_output = tokenizer.apply_chat_template(
+                    dummy_conversation, chat_template=fromjson_template, tokenize=False
+                )
+                self.assertEqual(fromjson_output, "test_func: 42")
+
+                # Test fromjson with tool calls
+                tool_output = tokenizer.apply_chat_template(
+                    tool_conversation, chat_template=tool_call_template, tokenize=False
+                )
+                self.assertEqual(tool_output, "Function: search, Args: query=hello world, limit=10")
+
+                # Test that fromjson handles non-string inputs gracefully
+                graceful_template = """{{ 123 | fromjson }}"""
+                graceful_output = tokenizer.apply_chat_template(
+                    dummy_conversation, chat_template=graceful_template, tokenize=False
+                )
+                self.assertEqual(graceful_output, "123")
+
     @require_torch
     @require_jinja
     def test_chat_template_return_assistant_tokens_mask(self):
diff --git a/tests/test_tokenization_mistral_common.py b/tests/test_tokenization_mistral_common.py
index a6b195239641..3b486944a627 100644
--- a/tests/test_tokenization_mistral_common.py
+++ b/tests/test_tokenization_mistral_common.py
@@ -471,6 +471,11 @@ def test_convert_tokens_to_ids(self):
         ids = self.tokenizer.convert_tokens_to_ids([])
         self.assertEqual(ids, [])
 
+    def test_convert_tokens_to_string(self):
+        tokens = ["Hello", "world", "!"]
+        string = self.tokenizer.convert_tokens_to_string(tokens)
+        self.assertIsInstance(string, str)
+
     def test_tokenize(self):
         string = "Hello world!"
         # Test 1:
diff --git a/tests/test_training_distributed_mixin.py b/tests/test_training_distributed_mixin.py
new file mode 100644
index 000000000000..0d1d7764f4f6
--- /dev/null
+++ b/tests/test_training_distributed_mixin.py
@@ -0,0 +1,501 @@
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Training overfit tester mixin for model tests."""
+
+import logging
+import tempfile
+import time
+from abc import ABC, abstractmethod
+
+from transformers import is_torch_available, set_seed
+from transformers.testing_utils import (
+    Colors,
+    build_cpu_memory_monitor,
+    init_distributed,
+    init_test_logger,
+    is_training_distributed_test,
+)
+
+from .test_training_mixin import TrainingConfigMixin
+
+
+if is_torch_available():
+    import torch
+    import torch.distributed as dist
+
+logger = logging.getLogger("transformers.training_test")
+
+
+def _create_text_training_batch(batch_size: int, seq_length: int, vocab_size: int) -> dict:
+    """Create a simple text batch without needing a tokenizer.
+
+    Standalone function for use in distributed spawned processes.
+    """
+    pattern = list(range(1, min(20, vocab_size)))  # tokens 1-19
+    num_repeats = (seq_length // len(pattern)) + 1
+    tokens = (pattern * num_repeats)[:seq_length]
+    input_ids = torch.tensor([tokens] * batch_size, dtype=torch.long)
+    return {"input_ids": input_ids, "labels": input_ids.clone()}
+
+
+def _test_training_distributed_overfit_impl(mesh, config_class, model_class, training_params):
+    """Implementation for distributed training overfit test.
+
+    Note: `mesh` is automatically created and passed by `global_wrapper` in testing_utils.py.
+
+    Args:
+        mesh: DeviceMesh created by global_wrapper
+        config_class: The config class (e.g., LlamaConfig)
+        model_class: The model class (e.g., LlamaForCausalLM)
+        training_params: Dict with 'config_dict', 'steps', 'batch_size', 'learning_rate', 'seq_length', 'log_freq'
+    """
+    init_test_logger()
+    is_rank_0 = dist.get_rank() == 0
+    tp_size = mesh["tp"].size()
+
+    if is_rank_0:
+        logger.info(f"Created DeviceMesh: {mesh}")
+        logger.info(f"FSDP mesh: {mesh['fsdp']}")
+        logger.info(f"TP mesh: {mesh['tp']}")
+        logger.info(f"FSDP mesh local rank: {mesh['fsdp'].get_local_rank()}")
+        logger.info(f"TP mesh local rank: {mesh['tp'].get_local_rank()}")
+    dist.barrier()
+
+    memory_monitor = build_cpu_memory_monitor(logger)
+
+    if is_rank_0:
+        logger.info("=" * 70)
+        logger.info("Starting distributed training overfit test")
+        logger.info("=" * 70)
+
+        # Configuration
+        logger.info(f"{Colors.BOLD}Job Configuration:{Colors.RESET}")
+        logger.info(f"  {Colors.CYAN}total_steps:{Colors.RESET} {training_params['steps']}")
+        logger.info(f"  {Colors.CYAN}batch_size:{Colors.RESET} {training_params['batch_size']}")
+        logger.info(f"  {Colors.CYAN}learning_rate:{Colors.RESET} {training_params['learning_rate']}")
+        logger.info(f"  {Colors.CYAN}seq_length:{Colors.RESET} {training_params['seq_length']}")
+        logger.info(f"  {Colors.CYAN}log_freq:{Colors.RESET} {training_params['log_freq']}")
+        logger.info(f"  {Colors.CYAN}device:{Colors.RESET} cpu")
+        logger.info(f"  {Colors.CYAN}tp_size:{Colors.RESET} {tp_size}")
+
+    set_seed(42)
+
+    if is_rank_0:
+        logger.info("-" * 70)
+        logger.info(f"{Colors.BOLD}Building model with Tensor Parallelism{Colors.RESET}")
+
+    load_start = time.perf_counter()
+
+    # Reconstruct config from passed config class
+    config = config_class.from_dict(training_params["config_dict"])
+
+    # NOTE(3outeille): Need to figure out how to do it natively when calling tp_plan="auto"
+    # Create a shared temp directory for model saving/loading
+    # Only rank 0 creates and saves the model, all ranks load with TP
+    temp_dir = tempfile.mkdtemp()
+
+    # Broadcast the temp_dir path to all ranks
+    if is_rank_0:
+        temp_dir_bytes = temp_dir.encode("utf-8")
+        temp_dir_tensor = torch.tensor(list(temp_dir_bytes), dtype=torch.uint8)
+        temp_dir_len = torch.tensor([len(temp_dir_bytes)], dtype=torch.long)
+    else:
+        temp_dir_len = torch.tensor([0], dtype=torch.long)
+
+    dist.broadcast(temp_dir_len, src=0)
+
+    if not is_rank_0:
+        temp_dir_tensor = torch.zeros(temp_dir_len.item(), dtype=torch.uint8)
+
+    dist.broadcast(temp_dir_tensor, src=0)
+    temp_dir = bytes(temp_dir_tensor.tolist()).decode("utf-8")
+
+    # Rank 0 creates and saves the model
+    if is_rank_0:
+        logger.info(f"Creating base model and saving to temp directory: {temp_dir}")
+        base_model = model_class(config)
+        base_model.save_pretrained(temp_dir)
+        del base_model  # Free memory
+        logger.info("Base model saved successfully")
+
+    dist.barrier()
+
+    # All ranks load with tensor parallelism
+    if is_rank_0:
+        logger.info("Loading model with tp_plan='auto' and device_mesh")
+        if hasattr(config, "base_model_tp_plan"):
+            logger.info(f"  {Colors.CYAN}base_model_tp_plan:{Colors.RESET} {config.base_model_tp_plan}")
+
+    # Load with tensor parallelism using the TP mesh
+    model = model_class.from_pretrained(
+        temp_dir,
+        tp_plan="auto",
+        device_mesh=mesh["tp"],
+    )
+
+    model.train()
+
+    load_time = time.perf_counter() - load_start
+    if is_rank_0:
+        logger.info(f"Model loaded in {Colors.GREEN}{load_time:.3f}s{Colors.RESET}")
+
+        # Log model architecture
+        logger.info(f"{Colors.BOLD}Model Architecture:{Colors.RESET}")
+        logger.info(f"  {Colors.CYAN}model_class:{Colors.RESET} {model_class.__name__}")
+        if hasattr(config, "hidden_size"):
+            logger.info(f"  {Colors.CYAN}hidden_size:{Colors.RESET} {config.hidden_size}")
+        if hasattr(config, "num_hidden_layers"):
+            logger.info(f"  {Colors.CYAN}num_hidden_layers:{Colors.RESET} {config.num_hidden_layers}")
+        if hasattr(config, "num_attention_heads"):
+            logger.info(f"  {Colors.CYAN}num_attention_heads:{Colors.RESET} {config.num_attention_heads}")
+        if hasattr(config, "num_key_value_heads"):
+            logger.info(f"  {Colors.CYAN}num_key_value_heads:{Colors.RESET} {config.num_key_value_heads}")
+        if hasattr(config, "intermediate_size"):
+            logger.info(f"  {Colors.CYAN}intermediate_size:{Colors.RESET} {config.intermediate_size}")
+        if hasattr(config, "vocab_size"):
+            logger.info(f"  {Colors.CYAN}vocab_size:{Colors.RESET} {config.vocab_size}")
+        if hasattr(config, "num_experts"):
+            logger.info(f"  {Colors.CYAN}num_experts:{Colors.RESET} {config.num_experts}")
+        if hasattr(config, "num_experts_per_tok"):
+            logger.info(f"  {Colors.CYAN}num_experts_per_tok:{Colors.RESET} {config.num_experts_per_tok}")
+
+        # Log TP status
+        logger.info(f"  {Colors.GREEN}tensor_parallel:{Colors.RESET} ENABLED (tp_size={tp_size})")
+
+        # Count parameters (local parameters for this rank)
+        total_params = sum(p.numel() for p in model.parameters())
+        trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+        logger.info(
+            f"{Colors.CYAN}Model size (local):{Colors.RESET} {Colors.BRIGHT_GREEN}{total_params:,}{Colors.RESET} parameters"
+        )
+        logger.info(
+            f"{Colors.CYAN}Trainable parameters (local):{Colors.RESET} {Colors.BRIGHT_GREEN}{trainable_params:,}{Colors.RESET}"
+        )
+
+        # Memory after model load
+        mem_stats = memory_monitor.get_stats()
+        logger.info(
+            f"{Colors.MAGENTA}Memory after model load:{Colors.RESET} {mem_stats.rss_gib:.2f} GiB ({mem_stats.rss_pct:.1f}%)"
+        )
+
+    dist.barrier()
+
+    # Create fixed batch
+    if is_rank_0:
+        logger.info("-" * 70)
+        logger.info(f"{Colors.BOLD}Creating fixed batch{Colors.RESET}")
+
+    batch = _create_text_training_batch(
+        batch_size=training_params["batch_size"],
+        seq_length=training_params["seq_length"],
+        vocab_size=config.vocab_size,
+    )
+    tokens_per_batch = training_params["batch_size"] * training_params["seq_length"]
+
+    if is_rank_0:
+        logger.info(f"{Colors.CYAN}Training pattern:{Colors.RESET} Repeating token sequence (1-19)")
+        logger.info(f"  {Colors.CYAN}batch_size:{Colors.RESET} {training_params['batch_size']}")
+        logger.info(f"  {Colors.CYAN}seq_length:{Colors.RESET} {training_params['seq_length']}")
+        logger.info(f"  {Colors.CYAN}tokens_per_batch:{Colors.RESET} {tokens_per_batch:,}")
+        logger.info(f"{Colors.DIM}Using same fixed batch every step (deterministic overfitting){Colors.RESET}")
+
+    # Build optimizer
+    if is_rank_0:
+        logger.info("-" * 70)
+        logger.info(f"{Colors.BOLD}Building optimizer{Colors.RESET}")
+
+    optimizer = torch.optim.Adam(
+        model.parameters(), lr=training_params["learning_rate"], weight_decay=0.0, betas=(0.9, 0.999)
+    )
+
+    if is_rank_0:
+        logger.info(f"{Colors.CYAN}Optimizer:{Colors.RESET} Adam")
+        logger.info(f"  {Colors.CYAN}learning_rate:{Colors.RESET} {training_params['learning_rate']}")
+        logger.info(f"  {Colors.CYAN}weight_decay:{Colors.RESET} 0.0")
+        logger.info(f"  {Colors.CYAN}betas:{Colors.RESET} (0.9, 0.999)")
+
+    # Training Loop
+    if is_rank_0:
+        logger.info("-" * 70)
+        logger.info("Training starts at step 1")
+
+    initial_loss = None
+    final_loss = None
+    initial_grad_norm = None
+    final_grad_norm = None
+    training_start = time.perf_counter()
+    memory_monitor.reset_peak_stats()
+
+    steps = training_params["steps"]
+    log_freq = training_params["log_freq"]
+
+    for step in range(1, steps + 1):
+        step_start = time.perf_counter()
+
+        optimizer.zero_grad()
+        outputs = model(**batch)
+        loss = outputs.loss
+
+        if initial_loss is None:
+            initial_loss = loss.item()
+        final_loss = loss.item()
+
+        loss.backward()
+
+        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+
+        if initial_grad_norm is None:
+            initial_grad_norm = grad_norm.item()
+        final_grad_norm = grad_norm.item()
+
+        optimizer.step()
+
+        step_time = time.perf_counter() - step_start
+
+        # Log at frequency
+        if is_rank_0 and (step == 1 or step % log_freq == 0 or step == steps):
+            tokens_per_sec = tokens_per_batch / step_time
+            mem_stats = memory_monitor.get_stats()
+            logger.info(
+                f"{Colors.CYAN}step:{Colors.RESET} {step}  "
+                f"{Colors.GREEN}loss:{Colors.RESET} {loss.item():7.4f}  "
+                f"{Colors.YELLOW}grad_norm:{Colors.RESET} {grad_norm.item():6.4f}  "
+                f"{Colors.MAGENTA}memory:{Colors.RESET} {mem_stats.rss_gib:.2f}GiB({mem_stats.rss_pct:.1f}%)  "
+                f"{Colors.BLUE}tok/s:{Colors.RESET} {tokens_per_sec:,.0f}  "
+                f"{Colors.DIM}step_time:{Colors.RESET} {step_time:.3f}s"
+            )
+
+    training_time = time.perf_counter() - training_start
+
+    # Training Summary
+    if is_rank_0:
+        total_tokens = steps * tokens_per_batch
+        logger.info("-" * 70)
+        logger.info(f"{Colors.BOLD}Training completed{Colors.RESET}")
+        logger.info(f"Total training time: {training_time:.2f}s")
+        logger.info(f"Total steps: {steps}")
+        logger.info(f"Total tokens seen: {total_tokens:,}")
+        logger.info(f"Average tokens/sec: {total_tokens / training_time:,.0f}")
+
+        # Memory summary
+        mem_stats = memory_monitor.get_stats()
+        logger.info(f"{Colors.BOLD}Memory usage:{Colors.RESET}")
+        logger.info(
+            f"  {Colors.CYAN}current_rss:{Colors.RESET} {mem_stats.rss_gib:.2f} GiB ({mem_stats.rss_pct:.1f}%)"
+        )
+        logger.info(
+            f"  {Colors.CYAN}peak_rss:{Colors.RESET} {mem_stats.peak_rss_gib:.2f} GiB ({mem_stats.peak_rss_pct:.1f}%)"
+        )
+        logger.info(
+            f"  {Colors.CYAN}available:{Colors.RESET} {mem_stats.available_gib:.2f} GiB / {mem_stats.total_gib:.2f} GiB"
+        )
+
+        # Loss analysis
+        loss_reduction = (initial_loss - final_loss) / initial_loss * 100
+        logger.info(f"{Colors.BOLD}Loss metrics:{Colors.RESET}")
+        logger.info(f"  {Colors.CYAN}initial_loss:{Colors.RESET} {initial_loss:.4f}")
+        logger.info(f"  {Colors.CYAN}final_loss:{Colors.RESET} {final_loss:.4f}")
+        logger.info(f"  {Colors.CYAN}loss_reduction:{Colors.RESET} {loss_reduction:.1f}%")
+
+        # Grad norm analysis
+        grad_norm_reduction = (initial_grad_norm - final_grad_norm) / initial_grad_norm * 100
+        logger.info(f"{Colors.BOLD}Grad norm metrics:{Colors.RESET}")
+        logger.info(f"  {Colors.CYAN}initial_grad_norm:{Colors.RESET} {initial_grad_norm:.4f}")
+        logger.info(f"  {Colors.CYAN}final_grad_norm:{Colors.RESET} {final_grad_norm:.4f}")
+        logger.info(f"  {Colors.CYAN}grad_norm_reduction:{Colors.RESET} {grad_norm_reduction:.1f}%")
+
+    # Assertions (run on all ranks for consistency, but only rank 0 logs)
+    dist.barrier()
+
+    # Assert loss decreased significantly
+    loss_reduction_ratio = (initial_loss - final_loss) / initial_loss
+    loss_reduction_threshold = 0.9  # 90% reduction
+    assert loss_reduction_ratio > loss_reduction_threshold, (
+        f"Expected loss to decrease by at least {loss_reduction_threshold * 100:.0f}%, "
+        f"got {loss_reduction_ratio * 100:.1f}%"
+    )
+
+    # Assert grad_norm decreased significantly
+    grad_norm_reduction_ratio = (initial_grad_norm - final_grad_norm) / initial_grad_norm
+    grad_norm_reduction_threshold = 0.9  # 90% reduction
+    assert grad_norm_reduction_ratio > grad_norm_reduction_threshold, (
+        f"Expected grad_norm to decrease by at least {grad_norm_reduction_threshold * 100:.0f}%, "
+        f"got {grad_norm_reduction_ratio * 100:.1f}%"
+    )
+
+    if is_rank_0:
+        logger.info("-" * 70)
+        logger.info(f"{Colors.BOLD}Running assertions{Colors.RESET}")
+        logger.info(f"{Colors.GREEN}✓ Loss decreased by more than {loss_reduction_threshold * 100:.0f}%{Colors.RESET}")
+        logger.info(
+            f"{Colors.GREEN}✓ Grad norm decreased by more than {grad_norm_reduction_threshold * 100:.0f}%{Colors.RESET}"
+        )
+        logger.info("=" * 70)
+        logger.info("Finished distributed training overfit test")
+        logger.info("=" * 70)
+
+    dist.barrier()
+
+    # Cleanup temp directory
+    if is_rank_0:
+        import shutil
+
+        try:
+            shutil.rmtree(temp_dir)
+        except Exception as exc:
+            logger.debug("Ignoring cleanup error for %s: %s", temp_dir, exc)
+
+
+class TrainingDistributedTesterMixin(TrainingConfigMixin, ABC):
+    """
+    Mixin for distributed training overfit tests with Tensor Parallelism.
+    Add to model test classes alongside ModelTesterMixin.
+
+    The model_tester (e.g., CausalLMModelTester) already provides:
+      - get_config() -> tiny model config
+      - prepare_config_and_inputs_for_common() -> config + input dict
+      - causal_lm_class, base_model_class, etc.
+
+    This mixin adds distributed training-specific tests using that infrastructure.
+
+    Note: Base training hyperparameters are inherited from TrainingConfigMixin.
+    We override some values here for faster distributed tests.
+    """
+
+    # Override for faster distributed tests
+    training_overfit_steps: int = 5
+    training_overfit_log_freq: int = 1
+
+    @property
+    @abstractmethod
+    def model_tester(self):
+        """The model tester instance (e.g., CausalLMModelTester)."""
+        ...
+
+    # ============================================================
+    # Modality detection
+    # ============================================================
+    def _get_model_modality(self) -> str:
+        """Detect the modality of the model based on its input signature."""
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        if "input_ids" in inputs_dict:
+            return "text"
+        elif "pixel_values" in inputs_dict:
+            return "image"
+        elif "input_features" in inputs_dict or "input_values" in inputs_dict:
+            return "audio"
+        else:
+            raise ValueError(f"Unknown modality: {inputs_dict}")
+
+    # ============================================================
+    # Training data creation for each modality
+    # ============================================================
+    def _create_text_training_batch(
+        self,
+        batch_size: int,
+        seq_length: int,
+        vocab_size: int,
+    ) -> dict[str, torch.Tensor]:
+        """Create a simple text batch without needing a tokenizer."""
+        # Create a deterministic sequence (not random, so model can learn it)
+        pattern = list(range(1, min(20, vocab_size)))  # tokens 1-19
+        num_repeats = (seq_length // len(pattern)) + 1
+        tokens = (pattern * num_repeats)[:seq_length]
+        input_ids = torch.tensor([tokens] * batch_size, dtype=torch.long)
+        return {"input_ids": input_ids, "labels": input_ids.clone()}
+
+    def _create_image_training_batch(
+        self,
+        batch_size: int,
+        num_channels: int,
+        height: int,
+        width: int,
+    ) -> dict[str, torch.Tensor]:
+        """Create fixed batch for image models using a deterministic pattern."""
+        pass
+
+    def _create_audio_training_batch(
+        self,
+        batch_size: int,
+        audio_length: int,
+        feature_size: int | None = None,
+    ) -> dict[str, torch.Tensor]:
+        """Create fixed batch for audio models using a deterministic waveform."""
+        pass
+
+    def _decode_text_tokens(self, tokens: list[int], max_display: int = 40) -> str:
+        """Decode tokens to readable string (maps token IDs to letters: 1->a, 2->b, etc.)."""
+        decoded = "".join(chr(ord("a") + (t - 1) % 26) for t in tokens)
+        if len(decoded) > max_display:
+            return f"'{decoded[:max_display]}...'"
+        return f"'{decoded}'"
+
+    def _get_trainable_model_class(self):
+        """Get the model class to use for training (prefers *ForCausalLM, *ForSequenceClassification, etc.)."""
+        # Prefer model classes with a head (for computing loss)
+        if hasattr(self.model_tester, "causal_lm_class") and self.model_tester.causal_lm_class is not None:
+            return self.model_tester.causal_lm_class
+        if (
+            hasattr(self.model_tester, "sequence_classification_class")
+            and self.model_tester.sequence_classification_class is not None
+        ):
+            return self.model_tester.sequence_classification_class
+        # Fall back to first model class
+        return self.all_model_classes[0]
+
+    # ============================================================
+    # Shared distributed training test implementation
+    # ============================================================
+    def _run_distributed_training_test(self, fsdp_size: int, tp_size: int):
+        """Shared implementation for distributed training tests."""
+        config = self.model_tester.get_config()
+        model_class = self._get_trainable_model_class()
+        config_class = type(config)
+
+        training_params = {
+            "config_dict": config.to_dict(),
+            "steps": self.training_overfit_steps,
+            "batch_size": self.training_overfit_batch_size,
+            "learning_rate": self.training_overfit_learning_rate,
+            "seq_length": self.training_overfit_seq_length,
+            "log_freq": self.training_overfit_log_freq,
+        }
+
+        init_distributed(fsdp_size=fsdp_size, tp_size=tp_size)(_test_training_distributed_overfit_impl)(
+            config_class, model_class, training_params
+        )
+
+    # ============================================================
+    # Distributed training tests (FSDP x TP configurations)
+    # ============================================================
+    # @is_training_distributed_test
+    # def test_training_fsdp1_tp1(self):
+    #     """Test distributed training with FSDP=1, TP=1 (1 total processes)."""
+    #     self._run_distributed_training_test(fsdp_size=1, tp_size=1)
+
+    @is_training_distributed_test
+    def test_training_fsdp1_tp2(self):
+        """Test distributed training with FSDP=1, TP=2 (2 total processes)."""
+        self._run_distributed_training_test(fsdp_size=1, tp_size=2)
+
+    # def test_training_fsdp2_tp1(self):
+    #     "Test distributed training with FSDP=2, TP=1 (2 total processes)."
+    #     self._run_distributed_training_test(fsdp_size=2, tp_size=1)
+
+    # @is_training_distributed_test
+    # def test_training_fsdp1_tp4(self):
+    #     """Test distributed training with FSDP=1, TP=4 (4 total processes)."""
+    #     self._run_distributed_training_test(fsdp_size=1, tp_size=4)
diff --git a/tests/test_training_mixin.py b/tests/test_training_mixin.py
index 1f644936e1f8..3286a681121f 100644
--- a/tests/test_training_mixin.py
+++ b/tests/test_training_mixin.py
@@ -27,16 +27,12 @@
 logger = logging.getLogger("transformers.training_test")
 
 
-class TrainingTesterMixin(ABC):
+class TrainingConfigMixin:
     """
-    Mixin for training overfit tests. Add to model test classes alongside ModelTesterMixin.
-
-    The model_tester (e.g., CausalLMModelTester) already provides:
-      - get_config() -> tiny model config
-      - prepare_config_and_inputs_for_common() -> config + input dict
-      - causal_lm_class, base_model_class, etc.
+    Shared training hyperparameters for training tests.
 
-    This mixin adds training-specific tests using that infrastructure.
+    Both TrainingTesterMixin and TrainingDistributedTesterMixin inherit from this
+    to avoid MRO conflicts when a test class inherits from both.
     """
 
     # ============================================================
@@ -48,10 +44,23 @@ class TrainingTesterMixin(ABC):
     training_overfit_seq_length: int = 64
     training_overfit_log_freq: int = 10
 
-    # Loss reduction and grad norm reduction thresholds for passing the test (i.e 95% reduction)
+    # Loss reduction and grad norm reduction thresholds for passing the test (i.e 90% reduction)
     training_loss_reduction_threshold: float = 0.9
     training_grad_norm_reduction_threshold: float = 0.9
 
+
+class TrainingTesterMixin(TrainingConfigMixin, ABC):
+    """
+    Mixin for training overfit tests. Add to model test classes alongside ModelTesterMixin.
+
+    The model_tester (e.g., CausalLMModelTester) already provides:
+      - get_config() -> tiny model config
+      - prepare_config_and_inputs_for_common() -> config + input dict
+      - causal_lm_class, base_model_class, etc.
+
+    This mixin adds training-specific tests using that infrastructure.
+    """
+
     @property
     @abstractmethod
     def model_tester(self):
diff --git a/tests/test_video_processing_common.py b/tests/test_video_processing_common.py
index 87e4abb1b513..b495b70b18a3 100644
--- a/tests/test_video_processing_common.py
+++ b/tests/test_video_processing_common.py
@@ -54,7 +54,7 @@ def prepare_video(num_frames, num_channels, width=10, height=10, return_tensors=
         video = [Image.fromarray(frame) for frame in video]
     elif return_tensors == "torch":
         # Torch images are typically in channels first format
-        video = torch.tensor(video).permute(0, 3, 1, 2)
+        video = torch.from_numpy(np.array(video)).permute(0, 3, 1, 2)
     elif return_tensors == "np":
         # Numpy images are typically in channels last format
         video = np.array(video)
diff --git a/tests/tokenization/test_tokenization_utils.py b/tests/tokenization/test_tokenization_utils.py
index 1b91903efec7..ba09145645ab 100644
--- a/tests/tokenization/test_tokenization_utils.py
+++ b/tests/tokenization/test_tokenization_utils.py
@@ -373,3 +373,27 @@ def test_import_protobuf_decode_error_does_not_mask_exceptions(self):
                     raise ValueError("real error")
                 except import_protobuf_decode_error():
                     pass
+
+    @require_sentencepiece
+    @require_tokenizers
+    @slow
+    def test_mask_token_no_duplicate_registration(self):
+        from transformers import BigBirdTokenizer
+
+        tokenizer = BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base")
+
+        # Check that tokenizing "Hello [MASK] world" does not produce '_' artifacts
+        tokens_single = tokenizer.tokenize("Hello [MASK] world")
+        self.assertNotIn(
+            "▁",
+            tokens_single,
+            f"Tokenization of 'Hello [MASK] world' should not produce '▁' tokens. Got: {tokens_single}",
+        )
+
+        # Check that tokenizing "[MASK] [MASK] [MASK]" does not produce '_' artifacts
+        tokens_multiple = tokenizer.tokenize("[MASK] [MASK] [MASK]")
+        self.assertNotIn(
+            "▁",
+            tokens_multiple,
+            f"Tokenization of '[MASK] [MASK] [MASK]' should not produce '▁' tokens. Got: {tokens_multiple}",
+        )
diff --git a/tests/trainer/distributed/test_trainer_distributed_deepspeed.py b/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
index 6a0b3c49160e..f291e8dd90ef 100644
--- a/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
+++ b/tests/trainer/distributed/test_trainer_distributed_deepspeed.py
@@ -924,6 +924,40 @@ def test_load_best_model(self, stage):
             trainer.train()
             trainer.evaluate()
 
+    @parameterized.expand(stages, name_func=_parameterized_custom_name_func)
+    def test_evaluate_before_train(self, stage):
+        """evaluate() before train() should work for all ZeRO stages."""
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer = get_regression_trainer(
+                deepspeed=self.get_config_dict(stage),
+                bf16=True,
+                output_dir=self.get_auto_remove_tmp_dir(),
+            )
+            trainer.evaluate()
+            trainer.train()
+
+    def test_config_preserved_after_evaluate(self):
+        """DS optimizer config and scheduler auto values should survive evaluate()."""
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer = get_regression_trainer(
+                deepspeed=self.get_config_dict(ZERO3),
+                bf16=True,
+                output_dir=self.get_auto_remove_tmp_dir(),
+            )
+            live_config = trainer.accelerator.state.deepspeed_plugin.hf_ds_config.config
+            self.assertIn("optimizer", live_config)
+            sched_total = live_config.get("scheduler", {}).get("params", {}).get("total_num_steps")
+
+            trainer.evaluate()
+
+            self.assertIn("optimizer", live_config, "optimizer config permanently deleted by evaluate()")
+            if sched_total == "auto":
+                self.assertEqual(
+                    live_config["scheduler"]["params"]["total_num_steps"],
+                    "auto",
+                    "scheduler total_num_steps 'auto' was replaced with 0 by evaluate()",
+                )
+
     @require_optuna
     def test_hyperparameter_search(self):
         """Run Optuna hyperparameter search with DeepSpeed ZeRO-3."""
diff --git a/tests/trainer/distributed/test_trainer_distributed_fsdp.py b/tests/trainer/distributed/test_trainer_distributed_fsdp.py
index 2d446fc45ab7..45e069f23dc4 100644
--- a/tests/trainer/distributed/test_trainer_distributed_fsdp.py
+++ b/tests/trainer/distributed/test_trainer_distributed_fsdp.py
@@ -27,7 +27,7 @@
 from parameterized import parameterized
 
 from tests.trainer.trainer_test_utils import TrainerIntegrationCommon, get_regression_trainer  # noqa
-from transformers import PreTrainedConfig, is_torch_available
+from transformers import HfArgumentParser, PreTrainedConfig, TrainingArguments, is_torch_available
 from transformers.testing_utils import (
     TestCasePlus,
     backend_device_count,
@@ -40,7 +40,7 @@
     slow,
     torch_device,
 )
-from transformers.trainer_utils import FSDPOption, set_seed
+from transformers.trainer_utils import set_seed
 from transformers.utils import (
     is_torch_bf16_available_on_device,
     is_torch_fp16_available_on_device,
@@ -276,6 +276,9 @@ def setUp(self):
     @parameterized.expand(config_params, name_func=_parameterized_custom_name_func)
     def test_accelerate_fsdp_config(self, sharding_strategy, dtype):
         output_dir = self.get_auto_remove_tmp_dir()
+        # Snapshot before trainer construction — `_process_fsdp_args` strips the
+        # `fsdp_` prefix in place.
+        expected = dict(self.accelerate_fsdp_config)
         kwargs = {
             "output_dir": output_dir,
             "train_len": 128,
@@ -287,12 +290,14 @@ def test_accelerate_fsdp_config(self, sharding_strategy, dtype):
         kwargs[dtype] = True
         with mockenv_context(**self.dist_env_1_gpu):
             trainer = get_regression_trainer(**kwargs)
-            self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
-            self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
-            self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
-            for k, v in trainer.args.fsdp_config.items():
-                self.assertTrue(k in self.accelerate_fsdp_config)
-                self.assertEqual(v, self.accelerate_fsdp_config[k])
+            self.assertIs(trainer.args.fsdp, True)
+            self.assertTrue(trainer.args.fsdp_config.get("cpu_offload"))
+            for k, v in expected.items():
+                assert k.startswith("fsdp_")
+                # `transformer_layer_cls_to_wrap` is normalized from str → list during parsing.
+                if k == "fsdp_transformer_layer_cls_to_wrap" and isinstance(v, str):
+                    v = [v]
+                self.assertEqual(trainer.args.fsdp_config[k[5:]], v)
 
     def test_torchrun_fsdp_config(self):
         """Verify that --fsdp + --fsdp_config (torchrun-style) are parsed correctly."""
@@ -309,11 +314,30 @@ def test_torchrun_fsdp_config(self):
         }
         with mockenv_context(**self.dist_env_1_gpu):
             trainer = get_regression_trainer(**kwargs)
-            self.assertEqual(trainer.args.fsdp[0], "full_shard")
-            self.assertEqual(trainer.args.fsdp[1], FSDPOption.AUTO_WRAP)
+            self.assertIs(trainer.args.fsdp, True)
             # fsdp_ prefix is stripped and value is normalized to a list during parsing
             self.assertIn("Qwen2DecoderLayer", trainer.args.fsdp_config["transformer_layer_cls_to_wrap"])
 
+    def test_fsdp_cli_parsing(self):
+        """`--fsdp` (bare) → True; legacy `--fsdp full_shard` still parses; absent → None."""
+        parser = HfArgumentParser(TrainingArguments)
+        base = ["--output_dir", "/tmp/x"]
+
+        args, _ = parser.parse_known_args([*base, "--fsdp"])
+        self.assertIs(args.fsdp, True)
+
+        args, _ = parser.parse_known_args([*base, "--fsdp", "full_shard"])
+        self.assertEqual(args.fsdp, "full_shard")
+
+        args, _ = parser.parse_known_args(base)
+        self.assertIsNone(args.fsdp)
+
+        # Bare `--fsdp` should resolve to a fully enabled FSDP setup through `_process_fsdp_args`.
+        with mockenv_context(**self.dist_env_1_gpu):
+            trainer_args = TrainingArguments(output_dir="/tmp/x", fsdp=True)
+            self.assertIs(trainer_args.fsdp, True)
+            self.assertIsNotNone(trainer_args.fsdp_plugin_args)
+
     @parameterized.expand(config_params, name_func=_parameterized_custom_name_func)
     def test_fsdp_config(self, sharding_strategy, dtype):
         output_dir = self.get_auto_remove_tmp_dir()
@@ -328,11 +352,10 @@ def test_fsdp_config(self, sharding_strategy, dtype):
         kwargs[dtype] = True
         with mockenv_context(**self.dist_env_1_gpu):
             trainer = get_regression_trainer(**kwargs)
-            self.assertEqual(trainer.args.fsdp[0], sharding_strategy)
-            self.assertEqual(trainer.args.fsdp[1], FSDPOption.OFFLOAD)
-            self.assertEqual(trainer.args.fsdp[2], FSDPOption.AUTO_WRAP)
-            for k, v in trainer.args.fsdp_config.items():
-                self.assertEqual(v, self.fsdp_config[k])
+            self.assertIs(trainer.args.fsdp, True)
+            self.assertTrue(trainer.args.fsdp_config.get("cpu_offload"))
+            for k, v in self.fsdp_config.items():
+                self.assertEqual(trainer.args.fsdp_config[k], v)
 
 
 # ---------------------------------------------------------------------------
diff --git a/tests/trainer/test_data_collator.py b/tests/trainer/test_data_collator.py
index 9a955a39afcc..a9359ea132c9 100644
--- a/tests/trainer/test_data_collator.py
+++ b/tests/trainer/test_data_collator.py
@@ -343,6 +343,19 @@ def test_with_labels(self):
         batch = collator(features)
         self.assertEqual(batch["labels"].shape, (1, 5))
 
+    def test_integer_labels(self):
+        """Test flattening broadcasts integer labels by default."""
+        features = [
+            {"input_ids": [1, 2, 3], "labels": 0},
+            {"input_ids": [4, 5], "labels": 1},
+        ]
+        collator = DataCollatorWithFlattening(return_tensors="pt")
+        batch = collator(features)
+
+        self.assertEqual(batch["input_ids"].shape, (1, 5))
+        self.assertEqual(batch["labels"].shape, (1, 5))
+        self.assertEqual(batch["labels"].tolist(), [[0, 0, 0, 1, 1]])
+
     def test_numpy_output(self):
         """Test flattening with NumPy output."""
         collator = DataCollatorWithFlattening(return_tensors="np")
diff --git a/tests/trainer/test_data_producer.py b/tests/trainer/test_data_producer.py
new file mode 100644
index 000000000000..5ad08b517368
--- /dev/null
+++ b/tests/trainer/test_data_producer.py
@@ -0,0 +1,1006 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for the DataProducer protocol and its integration with Trainer."""
+
+import tempfile
+import unittest
+
+import numpy as np
+import torch
+from torch import nn
+from torch.utils.data import Dataset
+
+from transformers import Trainer, TrainingArguments
+from transformers.data_producer import (
+    AsyncDataProducer,
+    BaseDataProducer,
+    DataProducerCallback,
+    ProducerConfig,
+)
+from transformers.trainer_callback import TrainerCallback
+
+
+# ---------------------------------------------------------------------------
+# Test fixtures
+# ---------------------------------------------------------------------------
+
+
+class SimpleDataset(Dataset):
+    """Minimal map-style dataset with synthetic (input_x, labels) data."""
+
+    def __init__(self, length=64, seed=42):
+        rng = np.random.RandomState(seed)
+        self.x = rng.normal(size=(length,)).astype(np.float32)
+        self.y = (2.0 * self.x + 3.0 + rng.normal(scale=0.1, size=(length,))).astype(np.float32)
+
+    def __len__(self):
+        return len(self.x)
+
+    def __getitem__(self, idx):
+        return {"input_x": self.x[idx], "labels": self.y[idx]}
+
+
+class RegressionModel(nn.Module):
+    """Trivial y = ax + b model for testing."""
+
+    def __init__(self, a=0.0, b=0.0):
+        super().__init__()
+        self.a = nn.Parameter(torch.tensor(a))
+        self.b = nn.Parameter(torch.tensor(b))
+
+    def forward(self, input_x, labels=None, **kwargs):
+        y = input_x * self.a + self.b
+        if labels is None:
+            return (y,)
+        loss = nn.functional.mse_loss(y, labels)
+        return (loss, y)
+
+
+class CountingProducer(BaseDataProducer):
+    """Tracks produce() call counts and global steps."""
+
+    def __init__(self, config=None, dataset_length=32):
+        super().__init__(config)
+        self.call_count = 0
+        self.global_steps = []
+        self.dataset_length = dataset_length
+
+    def produce(self, model, global_step, **kwargs):
+        self.call_count += 1
+        self.global_steps.append(global_step)
+        return SimpleDataset(length=self.dataset_length, seed=42 + self.call_count)
+
+
+class LifecycleTrackingProducer(BaseDataProducer):
+    """Tracks on_rollout_begin/end and produce calls."""
+
+    def __init__(self, config=None):
+        super().__init__(config)
+        self.events = []
+
+    def on_rollout_begin(self, global_step):
+        self.events.append(("rollout_begin", global_step))
+
+    def on_rollout_end(self, dataset, global_step):
+        self.events.append(("rollout_end", global_step))
+
+    def produce(self, model, global_step, **kwargs):
+        self.events.append(("produce", global_step))
+        return SimpleDataset(length=32)
+
+
+def _make_trainer(data_producer, max_steps=10, **kwargs):
+    """Helper to create a Trainer with a DataProducer."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        model = RegressionModel()
+        args = TrainingArguments(
+            output_dir=tmp_dir,
+            max_steps=max_steps,
+            per_device_train_batch_size=8,
+            learning_rate=0.1,
+            report_to="none",
+            use_cpu=True,
+            logging_steps=999,  # suppress logging noise
+            save_strategy="no",
+            **kwargs,
+        )
+        trainer = Trainer(
+            model=model,
+            args=args,
+            data_producer=data_producer,
+        )
+        return trainer, tmp_dir
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: ProducerConfig
+# ---------------------------------------------------------------------------
+
+
+class TestProducerConfig(unittest.TestCase):
+    def test_defaults(self):
+        config = ProducerConfig()
+        self.assertEqual(config.mini_epochs, 1)
+        self.assertIsNone(config.max_rollouts)
+        self.assertIsNone(config.steps_per_generation)
+        self.assertEqual(config.num_iterations, 1)
+        self.assertFalse(config.async_prefetch)
+        self.assertTrue(config.eval_during_produce)
+
+    def test_custom_values(self):
+        config = ProducerConfig(mini_epochs=3, max_rollouts=50, num_iterations=2)
+        self.assertEqual(config.mini_epochs, 3)
+        self.assertEqual(config.max_rollouts, 50)
+        self.assertEqual(config.num_iterations, 2)
+
+    def test_invalid_mini_epochs(self):
+        with self.assertRaises(ValueError):
+            ProducerConfig(mini_epochs=0)
+
+    def test_invalid_max_rollouts(self):
+        with self.assertRaises(ValueError):
+            ProducerConfig(max_rollouts=0)
+
+    def test_invalid_num_iterations(self):
+        with self.assertRaises(ValueError):
+            ProducerConfig(num_iterations=0)
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: BaseDataProducer
+# ---------------------------------------------------------------------------
+
+
+class TestBaseDataProducer(unittest.TestCase):
+    def test_default_config(self):
+        class Dummy(BaseDataProducer):
+            def produce(self, model, global_step, **kwargs):
+                return SimpleDataset()
+
+        p = Dummy()
+        self.assertIsInstance(p.config, ProducerConfig)
+        self.assertEqual(p.config.mini_epochs, 1)
+
+    def test_custom_config(self):
+        class Dummy(BaseDataProducer):
+            def produce(self, model, global_step, **kwargs):
+                return SimpleDataset()
+
+        config = ProducerConfig(mini_epochs=3)
+        p = Dummy(config)
+        self.assertEqual(p.config.mini_epochs, 3)
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: AsyncDataProducer
+# ---------------------------------------------------------------------------
+
+
+class TestAsyncDataProducer(unittest.TestCase):
+    def test_wraps_inner(self):
+        producer = CountingProducer()
+        async_producer = AsyncDataProducer(producer)
+        self.assertIs(async_producer.config, producer.config)
+
+    def test_first_call_synchronous(self):
+        producer = CountingProducer()
+        async_producer = AsyncDataProducer(producer)
+        model = RegressionModel()
+        ds = async_producer.produce(model, global_step=0)
+        self.assertIsInstance(ds, SimpleDataset)
+        # First call: one sync produce + one prefetch = 2
+        self.assertGreaterEqual(producer.call_count, 1)
+        async_producer.shutdown()
+
+    def test_lifecycle_forwarding(self):
+        producer = LifecycleTrackingProducer()
+        async_producer = AsyncDataProducer(producer)
+        async_producer.on_rollout_begin(global_step=5)
+        self.assertEqual(producer.events[-1], ("rollout_begin", 5))
+        async_producer.shutdown()
+
+
+# ---------------------------------------------------------------------------
+# Unit tests: DataProducerCallback
+# ---------------------------------------------------------------------------
+
+
+class TestDataProducerCallback(unittest.TestCase):
+    def test_is_trainer_callback(self):
+        self.assertTrue(issubclass(DataProducerCallback, TrainerCallback))
+
+    def test_instance_check(self):
+        cb = DataProducerCallback()
+        self.assertIsInstance(cb, TrainerCallback)
+
+
+# ---------------------------------------------------------------------------
+# Integration tests: Trainer with DataProducer
+# ---------------------------------------------------------------------------
+
+
+class TestTrainerWithDataProducer(unittest.TestCase):
+    def test_invalid_data_producer_type(self):
+        """data_producer without produce() method raises TypeError."""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            with self.assertRaises(TypeError):
+                Trainer(
+                    model=RegressionModel(),
+                    args=TrainingArguments(tmp_dir, max_steps=5, report_to="none", use_cpu=True),
+                    data_producer="not a producer",
+                )
+
+    def test_both_dataset_and_producer_raises(self):
+        """Cannot pass both train_dataset and data_producer."""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            with self.assertRaises(ValueError):
+                Trainer(
+                    model=RegressionModel(),
+                    args=TrainingArguments(tmp_dir, max_steps=5, report_to="none", use_cpu=True),
+                    train_dataset=SimpleDataset(),
+                    data_producer=CountingProducer(),
+                )
+
+    def test_requires_max_steps_or_max_rollouts(self):
+        """data_producer without max_steps or max_rollouts raises ValueError."""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            with self.assertRaises(ValueError):
+                Trainer(
+                    model=RegressionModel(),
+                    args=TrainingArguments(tmp_dir, report_to="none", use_cpu=True),
+                    data_producer=CountingProducer(),
+                )
+
+    def test_basic_online_training(self):
+        """Basic online training with max_steps."""
+        producer = CountingProducer(dataset_length=32)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=5,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            self.assertEqual(trainer.state.global_step, 5)
+            # produce() should have been called at least once
+            self.assertGreaterEqual(producer.call_count, 1)
+
+    def test_max_rollouts(self):
+        """Training with max_rollouts stops after the specified number of rollouts."""
+        config = ProducerConfig(max_rollouts=3, mini_epochs=1)
+        producer = CountingProducer(config=config, dataset_length=16)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,  # large enough to not be the stopping condition
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # produce() called once in compute_plan + (max_rollouts - 1) in iter_epochs
+            self.assertEqual(producer.call_count, 3)
+
+    def test_mini_epochs(self):
+        """mini_epochs=2 yields 2 passes per rollout."""
+        config = ProducerConfig(max_rollouts=2, mini_epochs=2)
+        producer = CountingProducer(config=config, dataset_length=16)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # 2 rollouts × 1 produce each = 2 produce calls
+            self.assertEqual(producer.call_count, 2)
+            # But global_step should reflect 2 rollouts × 2 mini_epochs × steps_per_epoch
+            # steps_per_epoch = 16 / 8 = 2
+            # total = 2 * 2 * 2 = 8
+            self.assertEqual(trainer.state.global_step, 8)
+
+    def test_lifecycle_hooks(self):
+        """on_rollout_begin and on_rollout_end are called around produce()."""
+        config = ProducerConfig(max_rollouts=2)
+        producer = LifecycleTrackingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Should see rollout_begin, produce, rollout_end for each rollout
+            event_types = [e[0] for e in producer.events]
+            self.assertIn("rollout_begin", event_types)
+            self.assertIn("produce", event_types)
+            self.assertIn("rollout_end", event_types)
+
+    def test_no_data_producer_uses_static_path(self):
+        """Without data_producer, Trainer uses the static dataset path."""
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=5,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(
+                model=model,
+                args=args,
+                train_dataset=SimpleDataset(),
+            )
+            trainer.train()
+            self.assertEqual(trainer.state.global_step, 5)
+
+    def test_loss_decreases(self):
+        """Online training should decrease the loss."""
+        producer = CountingProducer(dataset_length=64)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=20,
+                per_device_train_batch_size=16,
+                learning_rate=0.5,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+                logging_steps=5,
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Check that loss decreased
+            logs = trainer.state.log_history
+            losses = [log["loss"] for log in logs if "loss" in log]
+            self.assertGreater(len(losses), 1)
+            self.assertLess(losses[-1], losses[0])
+
+    def test_produce_receives_kwargs(self):
+        """produce() receives processing_class, accelerator, args."""
+
+        class InspectingProducer(BaseDataProducer):
+            def __init__(self):
+                super().__init__()
+                self.received_kwargs = {}
+
+            def produce(self, model, global_step, **kwargs):
+                self.received_kwargs = kwargs
+                return SimpleDataset(length=16)
+
+        producer = InspectingProducer()
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=2,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            self.assertIn("processing_class", producer.received_kwargs)
+            self.assertIn("accelerator", producer.received_kwargs)
+            self.assertIn("args", producer.received_kwargs)
+
+    def test_callback_producer_registered(self):
+        """A producer that inherits DataProducerCallback is registered as a Trainer callback."""
+
+        class CallbackProducer(BaseDataProducer, DataProducerCallback):
+            def produce(self, model, global_step, **kwargs):
+                return SimpleDataset(length=16)
+
+        producer = CallbackProducer()
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=2,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            # The producer should be in the callback list
+            callback_types = [type(cb) for cb in trainer.callback_handler.callbacks]
+            self.assertIn(CallbackProducer, callback_types)
+
+
+# ---------------------------------------------------------------------------
+# GRPO-pattern tests
+# ---------------------------------------------------------------------------
+
+
+class TestGRPOPatterns(unittest.TestCase):
+    """Tests exercising patterns needed for GRPO migration.
+
+    These validate that the DataProducer + _OnlineEpochSource machinery
+    supports the key behaviours GRPO relies on:
+    - variable-size produced datasets
+    - mini_epochs reusing the same data (num_iterations)
+    - max_steps stopping mid-rollout
+    - produce() seeing an updated model
+    - eval/train mode switching during produce
+    - gradient accumulation with online source
+    - _get_train_sampler override point
+    - async producer integration
+    """
+
+    def test_variable_size_datasets(self):
+        """produce() can return different-sized datasets across rollouts."""
+
+        class ShrinkingProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.call_count = 0
+                self.sizes = []
+
+            def produce(self, model, global_step, **kwargs):
+                self.call_count += 1
+                # First rollout: 32 samples, second: 16
+                length = 32 if self.call_count == 1 else 16
+                self.sizes.append(length)
+                return SimpleDataset(length=length, seed=self.call_count)
+
+        config = ProducerConfig(max_rollouts=2, mini_epochs=1)
+        producer = ShrinkingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            self.assertEqual(producer.sizes, [32, 16])
+            # 32/8=4 steps from rollout 1, 16/8=2 steps from rollout 2 → 6 total
+            self.assertEqual(trainer.state.global_step, 6)
+
+    def test_mini_epochs_reuse_same_dataloader(self):
+        """With mini_epochs>1, the same data is iterated multiple times per rollout.
+
+        This mirrors GRPO's num_iterations: reuse scored completions across
+        multiple optimizer steps.
+        """
+
+        class TrackingProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.call_count = 0
+
+            def produce(self, model, global_step, **kwargs):
+                self.call_count += 1
+                return SimpleDataset(length=16, seed=42)
+
+        config = ProducerConfig(max_rollouts=1, mini_epochs=3)
+        producer = TrackingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Only 1 produce call, but 3 passes over the data
+            self.assertEqual(producer.call_count, 1)
+            # 16/8=2 steps × 3 mini_epochs = 6 steps
+            self.assertEqual(trainer.state.global_step, 6)
+
+    def test_max_steps_stops_mid_rollout(self):
+        """Training stops at max_steps even if mini_epochs are not exhausted.
+
+        GRPO often sets max_steps that doesn't align with rollout boundaries.
+        """
+        config = ProducerConfig(max_rollouts=10, mini_epochs=3)
+        producer = CountingProducer(config=config, dataset_length=16)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=5,  # 16/8=2 steps per epoch, 3 mini_epochs=6 steps per rollout
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Should stop at 5, not continue to 6 (end of rollout 1's mini_epochs)
+            self.assertEqual(trainer.state.global_step, 5)
+            # Should have needed only 1 produce call (rollout 0)
+            self.assertEqual(producer.call_count, 1)
+
+    def test_produce_receives_updated_model(self):
+        """The model passed to produce() reflects training updates.
+
+        GRPO generates completions from the current policy, so produce()
+        must see the trained model, not the initial one.
+        """
+
+        class ParamSnapshotProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.param_snapshots = []
+
+            def produce(self, model, global_step, **kwargs):
+                # Snapshot the model parameters
+                params = {n: p.clone().detach() for n, p in model.named_parameters()}
+                self.param_snapshots.append(params)
+                return SimpleDataset(length=16, seed=global_step)
+
+        config = ProducerConfig(max_rollouts=3, mini_epochs=1)
+        producer = ParamSnapshotProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.5,  # large LR so params visibly change
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            self.assertEqual(len(producer.param_snapshots), 3)
+            # Params at rollout 0 (initial) should differ from rollout 2 (after training)
+            initial = producer.param_snapshots[0]
+            final = producer.param_snapshots[2]
+            changed = any(not torch.equal(initial[k], final[k]) for k in initial)
+            self.assertTrue(changed, "Model params should change between rollouts")
+
+    def test_eval_mode_during_produce(self):
+        """With eval_during_produce=True (default), model is in eval mode during produce().
+
+        GRPO needs eval mode during generation to disable dropout.
+        """
+
+        class ModeTrackingProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.training_mode_during_produce = []
+
+            def produce(self, model, global_step, **kwargs):
+                self.training_mode_during_produce.append(model.training)
+                return SimpleDataset(length=16)
+
+        # Default: eval_during_produce=True
+        config = ProducerConfig(max_rollouts=2, eval_during_produce=True)
+        producer = ModeTrackingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Model should have been in eval mode during produce
+            for was_training in producer.training_mode_during_produce:
+                self.assertFalse(was_training, "Model should be in eval mode during produce()")
+
+    def test_eval_mode_not_forced_when_disabled(self):
+        """With eval_during_produce=False, model stays in train mode during produce()."""
+
+        class ModeTrackingProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.training_mode_during_produce = []
+
+            def produce(self, model, global_step, **kwargs):
+                self.training_mode_during_produce.append(model.training)
+                return SimpleDataset(length=16)
+
+        config = ProducerConfig(max_rollouts=2, eval_during_produce=False)
+        producer = ModeTrackingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Model should have stayed in train mode during produce
+            for was_training in producer.training_mode_during_produce:
+                self.assertTrue(was_training, "Model should stay in train mode when eval_during_produce=False")
+
+    def test_gradient_accumulation_with_online_source(self):
+        """Online source works correctly with gradient_accumulation_steps > 1.
+
+        GRPO uses large gradient_accumulation_steps (e.g., 4-16).
+        """
+        config = ProducerConfig(max_rollouts=2, mini_epochs=1)
+        producer = CountingProducer(config=config, dataset_length=32)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                gradient_accumulation_steps=2,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # 32 samples / 8 batch = 4 forward steps per epoch
+            # 4 forward steps / 2 grad_accum = 2 optimizer steps per epoch
+            # 2 rollouts × 1 mini_epoch × 2 steps = 4 global steps
+            self.assertEqual(trainer.state.global_step, 4)
+
+    def test_get_train_sampler_override_point(self):
+        """Subclass can override _get_train_sampler for online dataloaders.
+
+        GRPO uses RepeatSampler. The _get_online_dataloader path must
+        call _get_train_sampler so the override applies.
+        """
+        sampler_called = {"count": 0}
+
+        class CustomSamplerTrainer(Trainer):
+            def _get_train_sampler(self, dataset=None):
+                sampler_called["count"] += 1
+                return super()._get_train_sampler(dataset)
+
+        config = ProducerConfig(max_rollouts=2, mini_epochs=1)
+        producer = CountingProducer(config=config, dataset_length=16)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = CustomSamplerTrainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # _get_train_sampler should be called for each dataloader creation
+            # (once in compute_plan, once for rollout 1)
+            self.assertGreaterEqual(sampler_called["count"], 2)
+
+    def test_async_producer_integration(self):
+        """AsyncDataProducer works with real training loop."""
+        inner = CountingProducer(
+            config=ProducerConfig(max_rollouts=3, async_prefetch=True),
+            dataset_length=16,
+        )
+        producer = AsyncDataProducer(inner)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Should have completed 3 rollouts
+            self.assertGreaterEqual(inner.call_count, 3)
+            # 16/8=2 steps × 3 rollouts = 6 global steps
+            self.assertEqual(trainer.state.global_step, 6)
+        producer.shutdown()
+
+    def test_multiple_rollouts_with_mini_epochs_and_grad_accum(self):
+        """Combined test: multiple rollouts × mini_epochs × gradient accumulation.
+
+        This mirrors GRPO's typical setup: steps_per_generation (mapped to
+        produced dataset size / batch), num_iterations (mapped to mini_epochs),
+        and gradient_accumulation_steps all interacting.
+        """
+        config = ProducerConfig(max_rollouts=2, mini_epochs=2)
+        producer = CountingProducer(config=config, dataset_length=32)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                gradient_accumulation_steps=2,
+                learning_rate=0.1,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # 32/8 = 4 forward steps per epoch
+            # 4/2 = 2 optimizer steps per epoch
+            # 2 rollouts × 2 mini_epochs × 2 steps = 8 global steps
+            self.assertEqual(trainer.state.global_step, 8)
+            self.assertEqual(producer.call_count, 2)
+
+    def test_produce_called_with_no_grad(self):
+        """produce() runs under torch.no_grad — no gradient tracking during generation.
+
+        GRPO's _generate_and_score_completions runs under torch.no_grad()
+        because generation is inference-only.
+        """
+
+        class GradCheckProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.grad_enabled_during_produce = []
+
+            def produce(self, model, global_step, **kwargs):
+                self.grad_enabled_during_produce.append(torch.is_grad_enabled())
+                return SimpleDataset(length=16)
+
+        config = ProducerConfig(max_rollouts=2)
+        producer = GradCheckProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            for grad_on in producer.grad_enabled_during_produce:
+                self.assertFalse(grad_on, "Gradients should be disabled during produce()")
+
+
+# ---------------------------------------------------------------------------
+# Multi-GPU / accelerator safety tests
+# ---------------------------------------------------------------------------
+
+
+class TestAcceleratorSafety(unittest.TestCase):
+    """Tests for correct behaviour when dataloaders go through accelerator.prepare().
+
+    On multi-GPU, accelerator.prepare() wraps DataLoaders with
+    BatchSamplerShard/DataLoaderShard. The online path creates a new
+    DataLoader per rollout, so we must ensure:
+    - Old dataloaders are removed from accelerator tracking (no leak)
+    - len(dataloader) is consistent with actual batches yielded
+    - The dataloader from each rollout is independently functional
+    """
+
+    def test_accelerator_dataloaders_no_leak(self):
+        """Old dataloaders are removed from accelerator._dataloaders across rollouts.
+
+        Without cleanup, each rollout's accelerator.prepare() appends a new
+        entry. Over many rollouts this leaks memory and breaks checkpoint
+        save/load (which iterates accelerator._dataloaders).
+        """
+        config = ProducerConfig(max_rollouts=5, mini_epochs=1)
+        producer = CountingProducer(config=config, dataset_length=16)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # After 5 rollouts, there should NOT be 5+ dataloaders tracked.
+            # The exact count depends on whether eval dataloaders are also
+            # prepared, but it should be small and bounded — not proportional
+            # to the number of rollouts.
+            num_tracked = len(trainer.accelerator._dataloaders)
+            self.assertLessEqual(
+                num_tracked,
+                2,
+                f"Expected ≤2 tracked dataloaders, got {num_tracked} — stale dataloaders are leaking",
+            )
+
+    def test_dataloader_len_matches_batches_yielded(self):
+        """len(dataloader) should match the actual number of batches it yields.
+
+        On multi-GPU, accelerator.prepare() wraps the sampler with
+        BatchSamplerShard, which changes len(). The _OnlineEpochSource uses
+        len(dataloader) to compute steps_in_epoch, so a mismatch would
+        cause training to hang or skip steps.
+        """
+        config = ProducerConfig(max_rollouts=1, mini_epochs=1)
+        producer = CountingProducer(config=config, dataset_length=24)
+
+        actual_batches = {"count": 0}
+        original_run_epoch = Trainer._run_epoch
+
+        def counting_run_epoch(self, model, epoch_spec, trial, ignore_keys_for_eval, start_time):
+            # Count actual batches yielded by the dataloader
+            count = 0
+            for _ in epoch_spec.dataloader:
+                count += 1
+            actual_batches["count"] = count
+            # Now run the real epoch (creates a new iterator)
+            return original_run_epoch(self, model, epoch_spec, trial, ignore_keys_for_eval, start_time)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            # Monkey-patch to count batches
+            trainer._run_epoch = counting_run_epoch.__get__(trainer, Trainer)
+            trainer.train()
+            # 24 / 8 = 3 batches
+            self.assertEqual(actual_batches["count"], 3)
+
+    def test_new_dataloader_per_rollout_is_functional(self):
+        """Each rollout gets a fully functional new dataloader.
+
+        This ensures accelerator.prepare() on fresh dataloaders mid-training
+        works correctly — the new dataloader should iterate, yield correct
+        batch sizes, and not inherit state from the previous one.
+        """
+
+        class BatchSizeTrackingProducer(BaseDataProducer):
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.call_count = 0
+
+            def produce(self, model, global_step, **kwargs):
+                self.call_count += 1
+                return SimpleDataset(length=24, seed=self.call_count)
+
+        config = ProducerConfig(max_rollouts=3, mini_epochs=1)
+        producer = BatchSizeTrackingProducer(config=config)
+
+        batch_sizes_per_rollout = []
+        original_run_epoch = Trainer._run_epoch
+
+        def tracking_run_epoch(self, model, epoch_spec, trial, ignore_keys_for_eval, start_time):
+            sizes = []
+            for batch in epoch_spec.dataloader:
+                sizes.append(len(batch["input_x"]))
+            batch_sizes_per_rollout.append(sizes)
+            return original_run_epoch(self, model, epoch_spec, trial, ignore_keys_for_eval, start_time)
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer._run_epoch = tracking_run_epoch.__get__(trainer, Trainer)
+            trainer.train()
+            # Each rollout should yield 3 batches of size 8 (24/8)
+            self.assertEqual(len(batch_sizes_per_rollout), 3)
+            for rollout_idx, sizes in enumerate(batch_sizes_per_rollout):
+                self.assertEqual(len(sizes), 3, f"Rollout {rollout_idx}: expected 3 batches, got {len(sizes)}")
+                for batch_idx, size in enumerate(sizes):
+                    self.assertEqual(size, 8, f"Rollout {rollout_idx} batch {batch_idx}: expected size 8, got {size}")
+
+    def test_callback_handler_dataloader_updated(self):
+        """callback_handler.train_dataloader is updated each rollout.
+
+        Callbacks (e.g., for logging, early stopping) reference the current
+        dataloader via callback_handler. On multi-GPU, stale references
+        could cause incorrect progress reporting.
+        """
+
+        class DataloaderCapturingProducer(BaseDataProducer, DataProducerCallback):
+            """Captures the callback_handler.train_dataloader at each rollout boundary."""
+
+            def __init__(self, config=None):
+                super().__init__(config)
+                self.call_count = 0
+                self.captured_dataloaders = []
+
+            def produce(self, model, global_step, **kwargs):
+                self.call_count += 1
+                return SimpleDataset(length=16, seed=self.call_count)
+
+            def on_epoch_begin(self, args, state, control, **kwargs):
+                # Capture what the callback handler thinks is the current dataloader
+                dl = kwargs.get("train_dataloader")
+                if dl is not None:
+                    self.captured_dataloaders.append(id(dl))
+
+        config = ProducerConfig(max_rollouts=3, mini_epochs=1)
+        producer = DataloaderCapturingProducer(config=config)
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = RegressionModel()
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                max_steps=999,
+                per_device_train_batch_size=8,
+                report_to="none",
+                use_cpu=True,
+                save_strategy="no",
+            )
+            trainer = Trainer(model=model, args=args, data_producer=producer)
+            trainer.train()
+            # Should have captured a dataloader reference for each epoch
+            self.assertEqual(len(producer.captured_dataloaders), 3)
+            # Rollout 0 uses the initial dataloader; rollouts 1 and 2 get new ones
+            # At minimum, rollout 0 and rollout 1 should have different dataloaders
+            self.assertNotEqual(
+                producer.captured_dataloaders[0],
+                producer.captured_dataloaders[1],
+                "Dataloader should change between rollouts",
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/trainer/test_multi_loss.py b/tests/trainer/test_multi_loss.py
new file mode 100644
index 000000000000..a83d57bfdf5e
--- /dev/null
+++ b/tests/trainer/test_multi_loss.py
@@ -0,0 +1,99 @@
+import tempfile
+
+from torch import nn
+
+from transformers import Trainer, TrainingArguments, is_torch_available
+from transformers.testing_utils import TestCasePlus, require_torch
+
+
+if is_torch_available():
+    from .trainer_test_utils import RegressionDataset
+
+
+class MultiLossModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.classifier = nn.Linear(1, 1)
+        self.config = None
+
+    def forward(self, input_x, labels=None, **kwargs):
+        logits = self.classifier(input_x.unsqueeze(-1))
+        if labels is None:
+            return {"logits": logits}
+
+        # Main loss
+        loss = nn.functional.mse_loss(logits.squeeze(), labels)
+
+        # Additional components
+        loss_part_a = loss * 0.1
+        loss_part_b = loss * 0.2
+
+        return {"loss": loss, "loss_part_a": loss_part_a, "loss_part_b": loss_part_b, "logits": logits}
+
+
+@require_torch
+class TrainerMultiLossTest(TestCasePlus):
+    def test_multi_loss_logging(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = MultiLossModel()
+            train_dataset = RegressionDataset(length=16)
+
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                logging_steps=1,
+                max_steps=3,
+                logging_loss_components=True,
+                report_to="none",
+            )
+
+            trainer = Trainer(
+                model=model,
+                args=args,
+                train_dataset=train_dataset,
+            )
+
+            trainer.train()
+
+            # Check log history
+            log_history = trainer.state.log_history
+            # Usually the last log is train metrics, earlier ones are step logs
+            step_logs = [log for log in log_history if "loss" in log and "train_runtime" not in log]
+
+            self.assertGreater(len(step_logs), 0)
+            for log in step_logs:
+                self.assertIn("loss", log)
+                self.assertIn("loss_part_a", log)
+                self.assertIn("loss_part_b", log)
+                self.assertIsInstance(log["loss_part_a"], float)
+                self.assertIsInstance(log["loss_part_b"], float)
+
+    def test_multi_loss_logging_disabled(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            model = MultiLossModel()
+            train_dataset = RegressionDataset(length=16)
+
+            args = TrainingArguments(
+                output_dir=tmp_dir,
+                logging_steps=1,
+                max_steps=3,
+                logging_loss_components=False,
+                report_to="none",
+            )
+
+            trainer = Trainer(
+                model=model,
+                args=args,
+                train_dataset=train_dataset,
+            )
+
+            trainer.train()
+
+            # Check log history
+            log_history = trainer.state.log_history
+            step_logs = [log for log in log_history if "loss" in log and "train_runtime" not in log]
+
+            self.assertGreater(len(step_logs), 0)
+            for log in step_logs:
+                self.assertIn("loss", log)
+                self.assertNotIn("loss_part_a", log)
+                self.assertNotIn("loss_part_b", log)
diff --git a/tests/trainer/test_per_sample_nested.py b/tests/trainer/test_per_sample_nested.py
new file mode 100644
index 000000000000..99af3939657f
--- /dev/null
+++ b/tests/trainer/test_per_sample_nested.py
@@ -0,0 +1,231 @@
+# Copyright 2025 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Tests for per-sample nested structure handling in trainer_pt_utils.
+Fixes issue #43388: gather_for_metrics incorrectly truncates Mask2Former-style labels.
+"""
+
+import unittest
+
+import numpy as np
+import torch
+
+from transformers.trainer_pt_utils import (
+    flatten_per_sample_nested_batches,
+    is_per_sample_nested,
+)
+
+
+class TestIsPerSampleNested(unittest.TestCase):
+    """Tests for is_per_sample_nested function."""
+
+    def test_tuple_of_lists_of_tensors(self):
+        """Tuple of lists of tensors should be detected."""
+        labels = ([torch.randn(5, 64), torch.randn(3, 64)], [torch.arange(5), torch.arange(3)])
+        self.assertTrue(is_per_sample_nested(labels))
+
+    def test_tuple_of_lists_of_numpy(self):
+        """Tuple of lists of numpy arrays should be detected."""
+        labels = ([np.random.randn(5, 64), np.random.randn(3, 64)], [np.arange(5), np.arange(3)])
+        self.assertTrue(is_per_sample_nested(labels))
+
+    def test_single_tensor(self):
+        """Single tensor should not be detected."""
+        self.assertFalse(is_per_sample_nested(torch.randn(10, 64)))
+
+    def test_tuple_of_tensors(self):
+        """Tuple of tensors (not lists) should not be detected."""
+        self.assertFalse(is_per_sample_nested((torch.randn(10, 64), torch.randn(10, 32))))
+
+    def test_empty_tuple(self):
+        """Empty tuple should not be detected."""
+        self.assertFalse(is_per_sample_nested(()))
+
+    def test_list_not_tuple(self):
+        """List (not tuple) should not be detected."""
+        self.assertFalse(is_per_sample_nested([[torch.randn(5, 64)], [torch.arange(5)]]))
+
+
+class TestFlattenPerSampleNestedBatches(unittest.TestCase):
+    """Tests for flatten_per_sample_nested_batches function."""
+
+    def test_flatten_multiple_batches(self):
+        """Should flatten multiple batches and truncate."""
+        batches = [
+            ([torch.randn(5, 64), torch.randn(3, 64)], [torch.arange(5), torch.arange(3)]),
+            ([torch.randn(7, 64), torch.randn(4, 64)], [torch.arange(7), torch.arange(4)]),
+            ([torch.randn(2, 64)], [torch.arange(2)]),
+        ]
+
+        result = flatten_per_sample_nested_batches(batches, num_samples=5)
+
+        self.assertEqual(len(result), 2)  # Two label types
+        self.assertEqual(len(result[0]), 5)  # 5 images (truncated from 5)
+        self.assertEqual(len(result[1]), 5)
+
+    def test_flatten_preserves_shapes(self):
+        """Should preserve individual tensor shapes."""
+        batches = [
+            ([torch.randn(5, 256, 256), torch.randn(3, 256, 256)], [torch.arange(5), torch.arange(3)]),
+            ([torch.randn(7, 256, 256)], [torch.arange(7)]),
+        ]
+
+        result = flatten_per_sample_nested_batches(batches, num_samples=3)
+
+        self.assertEqual(result[0][0].shape, torch.Size([5, 256, 256]))
+        self.assertEqual(result[0][1].shape, torch.Size([3, 256, 256]))
+        self.assertEqual(result[0][2].shape, torch.Size([7, 256, 256]))
+
+    def test_truncate_to_one(self):
+        """Should handle truncation to 1 sample (remainder=1 scenario)."""
+        batches = [([torch.randn(3, 64)], [torch.arange(3)])]
+
+        result = flatten_per_sample_nested_batches(batches, num_samples=1)
+
+        self.assertEqual(len(result), 2)  # Both label types preserved
+        self.assertEqual(len(result[0]), 1)
+        self.assertEqual(len(result[1]), 1)
+
+    def test_empty_batches(self):
+        """Should return None for empty batches."""
+        self.assertIsNone(flatten_per_sample_nested_batches([], num_samples=5))
+
+
+class TestMask2FormerScenario(unittest.TestCase):
+    """End-to-end test simulating Mask2Former evaluation."""
+
+    def test_full_evaluation_scenario(self):
+        """Simulate full evaluation with multiple batches."""
+        # 3 batches: 2+2+1 = 5 images, but dataset has 4 images
+        batches = [
+            (
+                [torch.randn(5, 256, 256), torch.randn(3, 256, 256)],
+                [torch.randint(0, 10, (5,)), torch.randint(0, 10, (3,))],
+            ),
+            (
+                [torch.randn(7, 256, 256), torch.randn(4, 256, 256)],
+                [torch.randint(0, 10, (7,)), torch.randint(0, 10, (4,))],
+            ),
+            ([torch.randn(2, 256, 256)], [torch.randint(0, 10, (2,))]),
+        ]
+
+        # Simulate what Trainer does
+        result = flatten_per_sample_nested_batches(batches, num_samples=4)
+
+        # Should have 4 images
+        self.assertEqual(len(result[0]), 4)
+        self.assertEqual(len(result[1]), 4)
+
+        # Instance counts should be preserved
+        self.assertEqual(result[0][0].shape[0], 5)  # First image: 5 instances
+        self.assertEqual(result[0][1].shape[0], 3)  # Second image: 3 instances
+        self.assertEqual(result[0][2].shape[0], 7)  # Third image: 7 instances
+        self.assertEqual(result[0][3].shape[0], 4)  # Fourth image: 4 instances
+
+
+class TestDistributedScenario(unittest.TestCase):
+    """Test simulating distributed training with gather_object."""
+
+    def test_distributed_gather_simulation(self):
+        """
+        Simulate distributed evaluation where gather_object returns
+        list of labels from each GPU process.
+
+        In distributed setup:
+        - GPU0 processes images 0, 2, 4, ...
+        - GPU1 processes images 1, 3, 5, ...
+        - gather_object returns [labels_gpu0, labels_gpu1, ...]
+        """
+        # Simulate 2 GPUs, each processing 2 images per batch
+        # GPU0's batch
+        gpu0_labels = (
+            [torch.randn(5, 256, 256), torch.randn(3, 256, 256)],
+            [torch.randint(0, 10, (5,)), torch.randint(0, 10, (3,))],
+        )
+        # GPU1's batch
+        gpu1_labels = (
+            [torch.randn(7, 256, 256), torch.randn(4, 256, 256)],
+            [torch.randint(0, 10, (7,)), torch.randint(0, 10, (4,))],
+        )
+
+        # gather_object returns list of labels from each process
+        gathered = [gpu0_labels, gpu1_labels]
+
+        # Simulate Trainer accumulation: extend (not append)
+        per_sample_nested_labels = []
+        per_sample_nested_labels.extend(gathered)
+
+        # flatten_per_sample_nested_batches handles this correctly
+        result = flatten_per_sample_nested_batches(per_sample_nested_labels, num_samples=4)
+
+        # Should have 4 images total (2 from each GPU)
+        self.assertEqual(len(result[0]), 4)
+        self.assertEqual(len(result[1]), 4)
+
+        # Instance counts should be preserved
+        self.assertEqual(result[0][0].shape[0], 5)  # GPU0 image 1
+        self.assertEqual(result[0][1].shape[0], 3)  # GPU0 image 2
+        self.assertEqual(result[0][2].shape[0], 7)  # GPU1 image 1
+        self.assertEqual(result[0][3].shape[0], 4)  # GPU1 image 2
+
+    def test_distributed_multiple_iterations(self):
+        """Test multiple evaluation iterations in distributed setup."""
+        per_sample_nested_labels = []
+
+        # Iteration 1: gather_object returns labels from 2 GPUs
+        iter1_gathered = [
+            ([torch.randn(5, 64), torch.randn(3, 64)], [torch.arange(5), torch.arange(3)]),  # GPU0
+            ([torch.randn(7, 64), torch.randn(4, 64)], [torch.arange(7), torch.arange(4)]),  # GPU1
+        ]
+        per_sample_nested_labels.extend(iter1_gathered)
+
+        # Iteration 2: another batch from 2 GPUs
+        iter2_gathered = [
+            ([torch.randn(2, 64)], [torch.arange(2)]),  # GPU0
+            ([torch.randn(6, 64)], [torch.arange(6)]),  # GPU1
+        ]
+        per_sample_nested_labels.extend(iter2_gathered)
+
+        # Total: 4 batches (2 GPUs x 2 iterations), 6 images
+        # Dataset has 5 images, so truncate to 5
+        result = flatten_per_sample_nested_batches(per_sample_nested_labels, num_samples=5)
+
+        self.assertEqual(len(result[0]), 5)
+        self.assertEqual(len(result[1]), 5)
+
+    def test_distributed_remainder_one(self):
+        """
+        Test the critical remainder=1 scenario in distributed setup.
+        This was causing class_labels to be completely lost before the fix.
+        """
+        # Single image split across processes (edge case)
+        gathered = [
+            ([torch.randn(3, 64)], [torch.arange(3)]),  # GPU0: 1 image
+        ]
+
+        per_sample_nested_labels = []
+        per_sample_nested_labels.extend(gathered)
+
+        result = flatten_per_sample_nested_batches(per_sample_nested_labels, num_samples=1)
+
+        # Both label types should be preserved
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 1)
+        self.assertEqual(len(result[1]), 1)
+        # Instance count preserved
+        self.assertEqual(result[0][0].shape[0], 3)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/trainer/test_trainer_callback.py b/tests/trainer/test_trainer_callback.py
index db0ccd56b1a1..a591a4344d34 100644
--- a/tests/trainer/test_trainer_callback.py
+++ b/tests/trainer/test_trainer_callback.py
@@ -34,6 +34,7 @@
     DefaultFlowCallback,
     EarlyStoppingCallback,
     IntervalStrategy,
+    MoERouterHealthCallback,
     PrinterCallback,
     ProgressCallback,
     Trainer,
@@ -48,6 +49,10 @@
 
 
 if is_torch_available():
+    import torch
+    from torch.utils.data import Dataset
+
+    from transformers import Qwen2MoeConfig, Qwen2MoeForCausalLM
     from transformers.trainer import DEFAULT_CALLBACKS, TRAINER_STATE_NAME
 
     from .trainer_test_utils import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
@@ -165,6 +170,30 @@ def on_step_end(self, args, state, control, **kwargs):
         return control
 
 
+class LogRecorderCallback(TrainerCallback):
+    def __init__(self):
+        self.logged_entries = []
+
+    def on_log(self, args, state, control, logs=None, **kwargs):
+        self.logged_entries.append(dict(logs))
+
+
+class TinyCausalLMDataset(Dataset):
+    def __init__(self, length=8, seq_length=8, vocab_size=32):
+        self.length = length
+        self.seq_length = seq_length
+        self.vocab_size = vocab_size
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, index):
+        input_ids = torch.tensor(
+            [(index + offset) % self.vocab_size for offset in range(self.seq_length)], dtype=torch.long
+        )
+        return {"input_ids": input_ids, "labels": input_ids.clone()}
+
+
 # =============================================================================
 # Helper Functions
 # =============================================================================
@@ -253,6 +282,72 @@ def test_custom_callback_added_at_init(self):
 
         self.assertEqual(actual, expected)
 
+    def _run_qwen2_moe_logging_test(self, output_router_logits: bool):
+        config = Qwen2MoeConfig(
+            vocab_size=64,
+            hidden_size=16,
+            intermediate_size=32,
+            moe_intermediate_size=16,
+            shared_expert_intermediate_size=16,
+            num_hidden_layers=2,
+            num_attention_heads=4,
+            num_key_value_heads=4,
+            num_experts=4,
+            num_experts_per_tok=2,
+            max_position_embeddings=32,
+            output_router_logits=output_router_logits,
+        )
+        model = Qwen2MoeForCausalLM(config)
+        train_dataset = TinyCausalLMDataset(length=4, seq_length=8, vocab_size=config.vocab_size)
+
+        moe_callback = MoERouterHealthCallback()
+        recorder_callback = LogRecorderCallback()
+        args = TrainingArguments(
+            self.output_dir,
+            max_steps=1,
+            per_device_train_batch_size=2,
+            logging_steps=1,
+            save_strategy="no",
+            eval_strategy="no",
+            report_to=[],
+            disable_tqdm=True,
+        )
+        trainer = Trainer(
+            model=model,
+            args=args,
+            train_dataset=train_dataset,
+            callbacks=[moe_callback, recorder_callback],
+        )
+
+        trainer.train()
+
+        self.assertGreater(len(recorder_callback.logged_entries), 0)
+        return set().union(*(entry.keys() for entry in recorder_callback.logged_entries))
+
+    def test_moe_router_health_callback_logs_qwen2_moe_metrics_without_router_logits(self):
+        logged_keys = self._run_qwen2_moe_logging_test(output_router_logits=False)
+        self.assertIn("moe/global/mean_load_cv", logged_keys)
+        self.assertIn("moe/global/mean_dead_experts", logged_keys)
+        self.assertNotIn("moe/aux_loss", logged_keys)
+
+    def test_moe_router_health_callback_logs_qwen2_moe_aux_loss_when_available(self):
+        logged_keys = self._run_qwen2_moe_logging_test(output_router_logits=True)
+        self.assertIn("moe/global/mean_load_cv", logged_keys)
+        self.assertIn("moe/aux_loss", logged_keys)
+
+    def test_moe_router_health_callback_auto_reduction_skips_tensor_parallel_models(self):
+        callback = MoERouterHealthCallback(reduction_mode="auto")
+        args = TrainingArguments(self.output_dir, report_to=[])
+        state = TrainerState()
+        control = TrainerControl()
+
+        class DummyModel:
+            tp_size = 2
+            can_record_outputs = {}
+
+        callback.on_train_begin(args, state, control, model=DummyModel())
+        self.assertEqual(callback._resolved_reduction_mode, "none")
+
     def test_printer_callback_when_tqdm_disabled(self):
         """PrinterCallback should replace ProgressCallback when tqdm is disabled."""
         trainer = self._create_trainer(disable_tqdm=True)
diff --git a/tests/trainer/test_trainer_checkpointing.py b/tests/trainer/test_trainer_checkpointing.py
index 7e00acbb49e5..67eca7f342f6 100644
--- a/tests/trainer/test_trainer_checkpointing.py
+++ b/tests/trainer/test_trainer_checkpointing.py
@@ -69,6 +69,7 @@
     require_torch,
     require_torch_non_multi_accelerator,
     require_torch_up_to_2_accelerators,
+    require_torchvision,
     require_vision,
     run_first,
     run_test_using_subprocess,
@@ -1511,6 +1512,7 @@ def test_trainer_saves_tokenizer(self):
         )
 
     @require_vision
+    @require_torchvision
     def test_trainer_saves_image_processor(self):
         MODEL_ID = "openai/clip-vit-base-patch32"
         image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
@@ -1545,6 +1547,7 @@ def test_trainer_saves_feature_extractor(self):
         self.assertDictEqual(feature_extractor.to_dict(), reloaded_feature_extractor.to_dict())
 
     @require_vision
+    @require_torchvision
     def test_trainer_saves_processor(self):
         MODEL_ID = "openai/clip-vit-base-patch32"
         image_processor = AutoImageProcessor.from_pretrained(MODEL_ID)
@@ -1742,19 +1745,18 @@ def test_save_best_checkpoint(self):
 
     def test_metric_for_best_model_behavior(self):
         # Case 1: Metric name not provided when `save_strategy == "best"`.
-        # Should raise ValueError.
+        # `metric_for_best_model` should be set to `"loss"` by default.
         with tempfile.TemporaryDirectory() as tmpdir:
-            with self.assertRaises(ValueError) as context:
-                trainer = get_regression_trainer(
-                    a=1.5,
-                    b=2.5,
-                    output_dir=tmpdir,
-                    learning_rate=0.1,
-                    eval_strategy="epoch",
-                    save_strategy="best",
-                    compute_metrics=AlmostAccuracy(),
-                )
-            self.assertIn("`args.metric_for_best_model` must be provided", str(context.exception))
+            trainer = get_regression_trainer(
+                a=1.5,
+                b=2.5,
+                output_dir=tmpdir,
+                learning_rate=0.1,
+                eval_strategy="epoch",
+                save_strategy="best",
+                compute_metrics=AlmostAccuracy(),
+            )
+            self.assertTrue(trainer.args.metric_for_best_model == "loss")
 
         # Case 2: Metric name not provided when `load_best_model_at_end == True`.
         # `metric_for_best_model` should be set to `"loss"` by default.
diff --git a/tests/trainer/test_trainer_data.py b/tests/trainer/test_trainer_data.py
index 27edf4173b23..dbdb0063e31e 100644
--- a/tests/trainer/test_trainer_data.py
+++ b/tests/trainer/test_trainer_data.py
@@ -18,6 +18,7 @@
 """
 
 import copy
+import random
 import tempfile
 import unittest
 import warnings
@@ -25,6 +26,7 @@
 import numpy as np
 import torch
 from torch import nn
+from torch.utils.data import BatchSampler, Dataset
 
 from transformers import (
     GPT2Config,
@@ -383,6 +385,67 @@ def test_distributed_length_grouped(self):
         # The indices should be a permutation of range(100)
         self.assertEqual(sorted(indices_process_0 + indices_process_1), list(range(100)))
 
+    def test_distributed_length_grouped_sampler(self):
+        data = []
+        for length in range(10, 110, 10):
+            for _ in range(10):
+                data.append({"input_ids": torch.randn(length)})
+        random.shuffle(data)
+
+        sampler = DistributedLengthGroupedSampler(
+            batch_size=10,
+            dataset=data,
+            num_replicas=1,
+            rank=0,
+            mega_batch_mult=100,
+        )
+        batches = list(BatchSampler(sampler, batch_size=10, drop_last=False))
+
+        next_batch = batches[0]
+        self.assertEqual(len(next_batch), 10)
+        self.assertTrue(all(len(data[i]["input_ids"]) == len(data[next_batch[0]]["input_ids"]) for i in next_batch))
+
+        other_batch = batches[1]
+        self.assertEqual(len(other_batch), 10)
+        self.assertTrue(all(len(data[i]["input_ids"]) == len(data[other_batch[0]]["input_ids"]) for i in other_batch))
+        self.assertNotEqual(len(data[next_batch[0]]["input_ids"]), len(data[other_batch[0]]["input_ids"]))
+
+    def test_distributed_length_grouped_sampler_custom_lengths(self):
+        data = []
+        for length in range(10, 110, 10):
+            for _ in range(10):
+                data.append(torch.randn(1, length))
+        random.shuffle(data)
+
+        class TensorListDataset(Dataset):
+            def __init__(self, tensors):
+                self.tensors = tensors
+
+            def __getitem__(self, index):
+                return self.tensors[index]
+
+            def __len__(self):
+                return len(self.tensors)
+
+        sampler = DistributedLengthGroupedSampler(
+            batch_size=10,
+            dataset=TensorListDataset(data),
+            num_replicas=1,
+            rank=0,
+            length_func=lambda sample: sample.shape[1],
+            mega_batch_mult=100,
+        )
+        batches = list(BatchSampler(sampler, batch_size=10, drop_last=False))
+
+        next_batch = batches[0]
+        self.assertEqual(len(next_batch), 10)
+        self.assertTrue(all(data[i].shape[1] == data[next_batch[0]].shape[1] for i in next_batch))
+
+        other_batch = batches[1]
+        self.assertEqual(len(other_batch), 10)
+        self.assertTrue(all(data[i].shape[1] == data[other_batch[0]].shape[1] for i in other_batch))
+        self.assertNotEqual(data[next_batch[0]].shape[1], data[other_batch[0]].shape[1])
+
     def test_distributed_sampler_with_loop(self):
         batch_size = 16
         for length in [23, 64, 123]:
diff --git a/tests/trainer/test_trainer_optimizers.py b/tests/trainer/test_trainer_optimizers.py
index d94c707caa8e..13d49b0eedbd 100644
--- a/tests/trainer/test_trainer_optimizers.py
+++ b/tests/trainer/test_trainer_optimizers.py
@@ -37,6 +37,7 @@
     TestCasePlus,
     require_apollo_torch,
     require_bitsandbytes,
+    require_flashoptim,
     require_galore_torch,
     require_grokadamw,
     require_lomo,
@@ -250,6 +251,27 @@ def test_adalomo(self):
     def test_grokadamw(self):
         self._train_with_llama("grokadamw", learning_rate=2e-5, max_steps=20)
 
+    # ---------------------------------------------------------------------------
+    # FlashOptim tests
+    # ---------------------------------------------------------------------------
+
+    @parameterized.expand([("flash_adamw",), ("flash_adam",), ("flash_sgd",), ("flash_sgdw",), ("flash_lion",)])
+    @require_flashoptim
+    @require_torch_accelerator
+    def test_flashoptim(self, optim):
+        self._train_with_llama(optim, learning_rate=1e-5, max_steps=20, bf16=True)
+
+    @require_flashoptim
+    @require_torch_accelerator
+    def test_flashoptim_extra_args(self):
+        self._train_with_llama(
+            "flash_adamw",
+            learning_rate=1e-5,
+            max_steps=20,
+            bf16=True,
+            optim_args="master_weight_bits=16, compress_state_dict=False, decouple_lr=True",
+        )
+
     # ---------------------------------------------------------------------------
     # Schedule-free tests
     # ---------------------------------------------------------------------------
diff --git a/tests/trainer/test_trainer_seq2seq.py b/tests/trainer/test_trainer_seq2seq.py
index de16a4fad027..9ca18228a25d 100644
--- a/tests/trainer/test_trainer_seq2seq.py
+++ b/tests/trainer/test_trainer_seq2seq.py
@@ -15,6 +15,7 @@
 import os
 import sys
 from pathlib import Path
+from types import SimpleNamespace
 from unittest.mock import patch
 
 from transformers import (
@@ -52,6 +53,33 @@
 
 if is_torch_available():
     import torch
+    from torch import nn
+
+
+if is_torch_available():
+
+    class DummyGenerationModel(nn.Module):
+        def __init__(self, is_encoder_decoder: bool = False):
+            super().__init__()
+            self.config = SimpleNamespace(is_encoder_decoder=is_encoder_decoder, pad_token_id=0)
+            self.generation_config = GenerationConfig(
+                max_length=6, max_new_tokens=None, pad_token_id=0, eos_token_id=1
+            )
+            self.last_generate_kwargs = None
+
+        def forward(self, input_ids, labels=None, **kwargs):
+            logits = torch.zeros(*input_ids.shape, 8, device=input_ids.device)
+            loss = torch.tensor(0.0, device=input_ids.device)
+            return {"loss": loss, "logits": logits}
+
+        def generate(self, input_ids, attention_mask=None, **kwargs):
+            captured_kwargs = {"input_ids": input_ids, "attention_mask": attention_mask, **kwargs}
+            self.last_generate_kwargs = {
+                key: value.detach().clone() if torch.is_tensor(value) else value
+                for key, value in captured_kwargs.items()
+            }
+            generated_token = torch.full((input_ids.shape[0], 1), 9, dtype=input_ids.dtype, device=input_ids.device)
+            return torch.cat([input_ids, generated_token], dim=-1)
 
 
 set_seed(42)
@@ -59,6 +87,90 @@
 MBART_TINY = "sshleifer/tiny-mbart"
 
 
+@require_torch
+class Seq2SeqTrainerPredictionStepTester(TestCasePlus):
+    def _get_trainer_and_model(self, is_encoder_decoder: bool = False):
+        model = DummyGenerationModel(is_encoder_decoder=is_encoder_decoder)
+        training_args = Seq2SeqTrainingArguments(
+            self.get_auto_remove_tmp_dir(),
+            predict_with_generate=True,
+            report_to="none",
+            per_device_eval_batch_size=2,
+        )
+        trainer = Seq2SeqTrainer(model=model, args=training_args)
+        return trainer, model
+
+    def test_decoder_only_prediction_step_uses_generate(self):
+        trainer, model = self._get_trainer_and_model(is_encoder_decoder=False)
+        inputs = {
+            "input_ids": torch.tensor([[4, 5, 6], [7, 8, 9]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.long),
+            "labels": torch.tensor([[4, 5, 6], [7, 8, 9]], dtype=torch.long),
+        }
+
+        loss, generated_tokens, _ = trainer.prediction_step(model, inputs, prediction_loss_only=False)
+
+        self.assertIsNotNone(loss)
+        self.assertIsNotNone(model.last_generate_kwargs)
+        self.assertEqual(generated_tokens[0, 0].item(), 9)
+
+    def test_decoder_only_uses_generation_inputs_when_provided(self):
+        trainer, model = self._get_trainer_and_model(is_encoder_decoder=False)
+        inputs = {
+            "input_ids": torch.tensor([[50, 51, 52], [60, 61, 62]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.long),
+            "generation_input_ids": torch.tensor([[11, 12], [21, 22]], dtype=torch.long),
+            "generation_attention_mask": torch.tensor([[1, 1], [1, 1]], dtype=torch.long),
+            "labels": torch.tensor([[-100, 12, 13], [-100, 22, 23]], dtype=torch.long),
+        }
+
+        _, generated_tokens, _ = trainer.prediction_step(model, inputs, prediction_loss_only=False)
+
+        self.assertTrue(torch.equal(model.last_generate_kwargs["input_ids"].cpu(), inputs["generation_input_ids"]))
+        self.assertTrue(
+            torch.equal(model.last_generate_kwargs["attention_mask"].cpu(), inputs["generation_attention_mask"])
+        )
+        self.assertNotIn("generation_input_ids", model.last_generate_kwargs)
+        self.assertNotIn("generation_attention_mask", model.last_generate_kwargs)
+        self.assertEqual(generated_tokens[0, 0].item(), 9)
+        self.assertEqual(generated_tokens.shape[-1], model.generation_config.max_length)
+
+    def test_decoder_only_builds_left_padded_prompt_from_labels(self):
+        trainer, model = self._get_trainer_and_model(is_encoder_decoder=False)
+        inputs = {
+            "input_ids": torch.tensor([[11, 12, 21, 22, 0], [31, 32, 33, 41, 42]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1, 1, 1, 0], [1, 1, 1, 1, 1]], dtype=torch.long),
+            "labels": torch.tensor([[-100, -100, 21, 22, -100], [-100, -100, -100, 41, 42]], dtype=torch.long),
+        }
+
+        _, generated_tokens, labels = trainer.prediction_step(model, inputs, prediction_loss_only=False)
+
+        expected_input_ids = torch.tensor([[0, 11, 12], [31, 32, 33]], dtype=torch.long)
+        expected_attention_mask = torch.tensor([[0, 1, 1], [1, 1, 1]], dtype=torch.long)
+
+        self.assertTrue(torch.equal(model.last_generate_kwargs["input_ids"].cpu(), expected_input_ids))
+        self.assertTrue(torch.equal(model.last_generate_kwargs["attention_mask"].cpu(), expected_attention_mask))
+        self.assertEqual(generated_tokens[0, 0].item(), 9)
+        self.assertEqual(generated_tokens.shape[-1], model.generation_config.max_length)
+        self.assertEqual(labels.shape[-1], model.generation_config.max_length)
+
+    def test_encoder_decoder_path_remains_unchanged(self):
+        trainer, model = self._get_trainer_and_model(is_encoder_decoder=True)
+        inputs = {
+            "input_ids": torch.tensor([[2, 3, 4], [5, 6, 7]], dtype=torch.long),
+            "attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.long),
+            "decoder_input_ids": torch.tensor([[9, 9, 9], [8, 8, 8]], dtype=torch.long),
+            "decoder_attention_mask": torch.tensor([[1, 1, 1], [1, 1, 1]], dtype=torch.long),
+            "labels": torch.tensor([[9, 9, 9], [8, 8, 8]], dtype=torch.long),
+        }
+
+        trainer.prediction_step(model, inputs, prediction_loss_only=False)
+
+        self.assertNotIn("decoder_input_ids", model.last_generate_kwargs)
+        self.assertNotIn("decoder_attention_mask", model.last_generate_kwargs)
+        self.assertTrue(torch.equal(model.last_generate_kwargs["input_ids"].cpu(), inputs["input_ids"]))
+
+
 @require_sentencepiece
 class Seq2seqTrainerTester(TestCasePlus):
     @slow
diff --git a/tests/trainer/test_training_args.py b/tests/trainer/test_training_args.py
index 1864b8a46d4d..684d90185170 100644
--- a/tests/trainer/test_training_args.py
+++ b/tests/trainer/test_training_args.py
@@ -225,6 +225,16 @@ def test_metric_for_best_model_defaults(self):
         self.assertEqual(args.metric_for_best_model, "loss")
         self.assertFalse(args.greater_is_better)
 
+        # save_strategy="best" with no metric → defaults to "loss"
+        args = TrainingArguments(
+            output_dir="tmp",
+            eval_strategy="epoch",
+            save_strategy="best",
+            report_to=None,
+        )
+        self.assertEqual(args.metric_for_best_model, "loss")
+        self.assertFalse(args.greater_is_better)
+
         # metric ending in "loss" → greater_is_better is False
         args = TrainingArguments(
             output_dir="tmp",
@@ -404,3 +414,24 @@ class TorchDtypeTrainingArguments(TrainingArguments):
                 args_dict = args.to_dict()
                 self.assertIn("dtype", args_dict)
                 self.assertEqual(args_dict["dtype"], dtype)
+
+    def test_batch_size_respects_split_batches(self):
+        """Test that train_batch_size and eval_batch_size respect split_batches config."""
+        # Default behavior: split_batches=False
+        args = TrainingArguments(
+            output_dir="./test",
+            per_device_train_batch_size=8,
+            per_device_eval_batch_size=4,
+        )
+        self.assertFalse(args.accelerator_config.split_batches)
+
+        # With split_batches=True, batch size should not be multiplied by n_gpu
+        args_split = TrainingArguments(
+            output_dir="./test",
+            per_device_train_batch_size=8,
+            per_device_eval_batch_size=4,
+            accelerator_config={"split_batches": True},
+        )
+        self.assertTrue(args_split.accelerator_config.split_batches)
+        self.assertEqual(args_split.train_batch_size, args_split.per_device_train_batch_size)
+        self.assertEqual(args_split.eval_batch_size, args_split.per_device_eval_batch_size)
diff --git a/tests/utils/test_backbone_utils.py b/tests/utils/test_backbone_utils.py
index a27ced73018f..50b9f8e325e1 100644
--- a/tests/utils/test_backbone_utils.py
+++ b/tests/utils/test_backbone_utils.py
@@ -16,7 +16,7 @@
 
 import pytest
 
-from transformers import DetrConfig, MaskFormerConfig, PreTrainedConfig, ResNetBackbone, ResNetConfig, TimmBackbone
+from transformers import MaskFormerConfig, PreTrainedConfig, ResNetBackbone, ResNetConfig, TimmBackbone
 from transformers.backbone_utils import (
     BackboneConfigMixin,
     BackboneMixin,
@@ -162,7 +162,7 @@ def test_load_backbone_from_config(self):
         config = MaskFormerConfig(backbone_config=ResNetConfig(out_indices=(0, 2)))
         backbone = load_backbone(config)
         self.assertEqual(backbone.out_features, ["stem", "stage2"])
-        self.assertEqual(backbone.out_indices, (0, 2))
+        self.assertEqual(backbone.out_indices, [0, 2])
         self.assertIsInstance(backbone, ResNetBackbone)
 
     @slow
@@ -239,7 +239,7 @@ def get_equal_not_equal_weights(model_0, model_1):
                     not_equal_weights.append(k0)
             return equal_weights, not_equal_weights
 
-        config = MaskFormerConfig(use_pretrained_backbone=False, backbone="microsoft/resnet-18")
+        config = MaskFormerConfig(backbone="microsoft/resnet-18")
         model_0 = NewModel(config)
         model_1 = NewModel(config)
         equal_weights, not_equal_weights = get_equal_not_equal_weights(model_0, model_1)
@@ -249,7 +249,7 @@ def get_equal_not_equal_weights(model_0, model_1):
         self.assertEqual(len(equal_weights), 0)
         self.assertEqual(len(not_equal_weights), 24)
 
-        # Now we create a new model with backbone weights that are pretrained
+        # Setting use_pretrained_backbone has no effect on load_backbone
         config.use_pretrained_backbone = True
         model_0 = NewModel(config)
         model_1 = NewModel(config)
@@ -257,29 +257,5 @@ def get_equal_not_equal_weights(model_0, model_1):
 
         # Norm layers are always initialized with the same weights
         equal_weights = [w for w in equal_weights if "normalization" not in w]
-        self.assertEqual(len(equal_weights), 20)
-        # Linear layers are still initialized randomly
-        self.assertEqual(len(not_equal_weights), 4)
-
-        # Check loading in timm backbone
-        config = DetrConfig(use_pretrained_backbone=False, backbone="resnet18", use_timm_backbone=True)
-        model_0 = NewModel(config)
-        model_1 = NewModel(config)
-        equal_weights, not_equal_weights = get_equal_not_equal_weights(model_0, model_1)
-
-        # Norm layers are always initialized with the same weights
-        equal_weights = [w for w in equal_weights if "bn" not in w and "downsample.1" not in w]
         self.assertEqual(len(equal_weights), 0)
         self.assertEqual(len(not_equal_weights), 24)
-
-        # Now we create a new model with backbone weights that are pretrained
-        config.use_pretrained_backbone = True
-        model_0 = NewModel(config)
-        model_1 = NewModel(config)
-        equal_weights, not_equal_weights = get_equal_not_equal_weights(model_0, model_1)
-
-        # Norm layers are always initialized with the same weights
-        equal_weights = [w for w in equal_weights if "bn" not in w and "downsample.1" not in w]
-        self.assertEqual(len(equal_weights), 20)
-        # Linear layers are still initialized randomly
-        self.assertEqual(len(not_equal_weights), 4)
diff --git a/tests/utils/test_cache_utils.py b/tests/utils/test_cache_utils.py
index 769639e5f612..eb65cdd0183e 100644
--- a/tests/utils/test_cache_utils.py
+++ b/tests/utils/test_cache_utils.py
@@ -881,6 +881,28 @@ def test_static_cache(self):
             static_cache.layers[0].keys[0, 0, :, 0].tolist(), [1.0, 2.0, 3.0, 4.0], "StaticCache Scenario 2 failed"
         )
 
+    def test_static_cache_type_checks(self):
+        """Test that StaticCache validates offloading types and unknown kwargs."""
+        cache = StaticCache(
+            config=self.config, max_cache_len=self.max_cache_len, offloading=True, offload_only_non_sliding=False
+        )
+        self.assertIsInstance(cache, StaticCache)
+
+        # Passing wrong type for offloading should raise TypeError
+        with self.assertRaises(TypeError) as cm:
+            StaticCache(config=self.config, max_cache_len=self.max_cache_len, offloading="cuda:0")
+        self.assertIn("`offloading` must be a bool", str(cm.exception))
+
+        # Passing wrong type for offload_only_non_sliding should raise TypeError
+        with self.assertRaises(TypeError) as cm:
+            StaticCache(config=self.config, max_cache_len=self.max_cache_len, offload_only_non_sliding=1)
+        self.assertIn("`offload_only_non_sliding` must be a bool", str(cm.exception))
+
+        # Passing unknown kwargs should raise TypeError
+        with self.assertRaises(TypeError) as cm:
+            StaticCache(config=self.config, max_cache_len=self.max_cache_len, foo="bar")
+        self.assertIn("Unknown arguments passed to StaticCache", str(cm.exception))
+
     def test_sliding_window_cache(self):
         """Test fully sliding StaticCache with manually prefilled states and hardcoded assertions.
 
@@ -1233,3 +1255,21 @@ def test_hybrid_chunked_cache_extra_cases(self):
 
         self.assertEqual(cache.layers[0].keys[0, 0, :, 0].tolist(), [20.0, 30.0, 40.0])
         self.assertEqual(returned_1[0][0, 0, :, 0].tolist(), [10.0, 20.0, 30.0, 40.0])
+
+    def test_quantized_cache_reset(self):
+        """Test that reset clears quantized data between generations."""
+        if not is_optimum_quanto_available():
+            self.skipTest("quanto is not available")
+        from transformers.cache_utils import QuantoQuantizedLayer
+
+        layer = QuantoQuantizedLayer(nbits=4, residual_length=2, q_group_size=16)
+        k1 = torch.randn(1, 4, 4, 64)
+        v1 = torch.randn(1, 4, 4, 64)
+        layer.update(k1, v1)
+
+        layer.reset()
+
+        k2 = torch.randn(1, 4, 2, 64)
+        v2 = torch.randn(1, 4, 2, 64)
+        keys_out, _ = layer.update(k2, v2)
+        self.assertEqual(keys_out.shape[-2], 2, "Stale quantized data leaked through reset()")
diff --git a/tests/utils/test_core_model_loading.py b/tests/utils/test_core_model_loading.py
index a358822d19f8..45fa5be48fc0 100644
--- a/tests/utils/test_core_model_loading.py
+++ b/tests/utils/test_core_model_loading.py
@@ -19,7 +19,11 @@
 import torch.nn as nn
 
 from transformers import PretrainedConfig
-from transformers.conversion_mapping import get_checkpoint_conversion_mapping, register_checkpoint_conversion_mapping
+from transformers.conversion_mapping import (
+    get_checkpoint_conversion_mapping,
+    get_model_conversion_mapping,
+    register_checkpoint_conversion_mapping,
+)
 from transformers.core_model_loading import (
     Chunk,
     Concatenate,
@@ -147,14 +151,14 @@ def test_sub_key_rewrites_targets(self):
         ]
 
         self.assertEqual(
-            rename_source_key("foo.block_sparse_moe.experts.3.w1.weight", renamings, [])[0],
+            rename_source_key("foo.block_sparse_moe.experts.3.w1.weight", renamings)[0],
             "foo.mlp.experts.gate_up_proj",
         )
         self.assertEqual(
-            rename_source_key("foo.block_sparse_moe.experts.3.w2.weight", renamings, [])[0],
+            rename_source_key("foo.block_sparse_moe.experts.3.w2.weight", renamings)[0],
             "foo.mlp.experts.down_proj",
         )
-        self.assertEqual(rename_source_key("model.language_model.lm_head.weight", renamings, [])[0], "language_model")
+        self.assertEqual(rename_source_key("model.language_model.lm_head.weight", renamings)[0], "language_model")
 
     def test_sub_key_no_match_returns_original(self):
         renamings = [
@@ -162,7 +166,7 @@ def test_sub_key_no_match_returns_original(self):
         ]
 
         key = "unrelated.key"
-        renamed_key, _ = rename_source_key(key, renamings, [])
+        renamed_key, _ = rename_source_key(key, renamings)
         self.assertEqual(renamed_key, key)
 
 
@@ -220,6 +224,48 @@ def __init__(self, add_extra_moe=False, with_mlp=True):
 
 
 class TestConvertAndLoadStateDict(unittest.TestCase):
+    def test_direct_and_renamed_weights_load_without_conversion_wrappers(self):
+        model = DummyRoot()
+        model.config = PretrainedConfig()
+
+        state_dict = {
+            "model.layers.0.self_attn.q_proj.weight": torch.tensor([[1.0, 2.0]]),
+            "model.layers.1.self_attn.q_proj.weight": torch.tensor([[3.0, 4.0]]),
+            "mlp.w2.weight": torch.tensor([[5.0, 6.0], [7.0, 8.0]]),
+        }
+        loading_info, _ = convert_and_load_state_dict_in_model(
+            model,
+            state_dict,
+            LoadStateDictConfig(weight_mapping=[WeightRenaming("mlp.w2.weight", "mlp.down_proj.weight")]),
+            tp_plan=None,
+        )
+
+        self.assertEqual(
+            loading_info.missing_keys,
+            {
+                "model.layers.0.experts.down_proj.weight",
+                "model.layers.0.experts.gate_up_proj.weight",
+                "model.layers.0.self_attn.k_proj.weight",
+                "model.layers.0.self_attn.v_proj.weight",
+                "model.layers.1.experts.down_proj.weight",
+                "model.layers.1.experts.gate_up_proj.weight",
+                "model.layers.1.self_attn.k_proj.weight",
+                "model.layers.1.self_attn.v_proj.weight",
+            },
+        )
+        self.assertEqual(loading_info.unexpected_keys, set())
+        self.assertEqual(loading_info.mismatched_keys, set())
+        self.assertEqual(loading_info.conversion_errors, {})
+
+        model_state = model.state_dict()
+        torch.testing.assert_close(
+            model_state["model.layers.0.self_attn.q_proj.weight"], state_dict["model.layers.0.self_attn.q_proj.weight"]
+        )
+        torch.testing.assert_close(
+            model_state["model.layers.1.self_attn.q_proj.weight"], state_dict["model.layers.1.self_attn.q_proj.weight"]
+        )
+        torch.testing.assert_close(model_state["mlp.down_proj.weight"], state_dict["mlp.w2.weight"])
+
     def test_moe_and_qkv_conversion(self):
         model = DummyRoot()
         model.config = PretrainedConfig()
@@ -406,7 +452,7 @@ def test_moe_and_qkv_conversion_reversed(self):
 
     def test_qkv_chunk_rope_permute_with_fp8_quantization(self):
         if is_triton_available():
-            from transformers.integrations.finegrained_fp8 import Fp8Dequantize
+            from transformers.integrations.finegrained_fp8 import Fp8Dequantize, Fp8Quantize
         else:
             self.skipTest("Fine-grained FP8 integration tests require Triton to be installed.")
         n_heads = 2
@@ -472,6 +518,7 @@ def __init__(self):
                     self, "quantization_config", SimpleNamespace(weight_block_size=bs)
                 ),
                 "param_needs_quantization": lambda self, _model, param_name: param_name.endswith("q_proj.weight"),
+                "get_quantize_ops": lambda self: Fp8Quantize(self),
                 "pre_quantized": False,
             },
         )
@@ -479,11 +526,11 @@ def __init__(self):
 
         weight_mapping = [
             WeightConverter(
-                "model.layers.*.self_attn.qkv_proj.weight",
+                "self_attn.qkv_proj.weight",
                 [
-                    "model.layers.*.self_attn.q_proj.weight",
-                    "model.layers.*.self_attn.k_proj.weight",
-                    "model.layers.*.self_attn.v_proj.weight",
+                    "self_attn.q_proj.weight",
+                    "self_attn.k_proj.weight",
+                    "self_attn.v_proj.weight",
                 ],
                 operations=[Chunk(dim=0), PermuteForRope()],
             )
@@ -526,6 +573,138 @@ def __init__(self):
         )
         torch.testing.assert_close(dequantized_q, expected_q, rtol=1e-2, atol=1e-2)
 
+    def test_scoped_renaming_does_not_leak_to_sibling_keys(self):
+        """scope_prefix restricts a WeightRenaming to keys under that sub-prefix only.
+
+        A "^"-anchored pattern must match the suffix after stripping the prefix, and
+        must not fire at all on keys that do not start with the scope prefix.
+
+        Without scope_prefix, "^old_q" would rename *any* key beginning with "old_q"
+        at any nesting level — including root-level ones that belong to a different
+        part of the model.
+        """
+
+        class _Attn(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.q = DummyParamModule((1, 2))
+
+        class _Encoder(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.attn = _Attn()
+
+        class _ScopedModel(nn.Module):
+            base_model_prefix = ""
+
+            def __init__(self):
+                super().__init__()
+                self.encoder = _Encoder()
+                self.q = DummyParamModule((1, 2))  # root-level q — must not be touched
+
+        model = _ScopedModel()
+        model.config = PretrainedConfig()
+
+        enc_val = torch.tensor([[1.0, 2.0]])
+        checkpoint = {
+            "encoder.attn.old_q.weight": enc_val.clone(),
+            "old_q.weight": torch.tensor([[9.0, 9.0]]),  # outside scope
+        }
+
+        scoped_rename = WeightRenaming("^old_q", "q")
+        scoped_rename.scope_prefix = "encoder.attn"
+
+        loading_info, _ = convert_and_load_state_dict_in_model(
+            model,
+            checkpoint,
+            LoadStateDictConfig(weight_mapping=[scoped_rename]),
+            tp_plan=None,
+        )
+
+        # The root-level "old_q.weight" must be unmatched (unexpected), not silently
+        # loaded into "q.weight".
+        self.assertEqual(loading_info.unexpected_keys, {"old_q.weight"})
+        self.assertEqual(loading_info.missing_keys, {"q.weight"})
+        self.assertEqual(loading_info.mismatched_keys, set())
+        self.assertEqual(loading_info.conversion_errors, {})
+
+        torch.testing.assert_close(model.encoder.attn.q.weight, enc_val)
+        # Root q.weight must still be its initialised zero value.
+        torch.testing.assert_close(model.q.weight, torch.zeros(1, 2))
+
+    def test_interleaved_renaming_and_converter_round_trip(self):
+        """A WeightRenaming preceding a WeightConverter in the list must fire in the
+        reverse (save) direction even after the converter has already set source_pattern.
+
+        Forward  [WeightRenaming, WeightConverter]:
+          "decoder.attn.qkv_proj.weight"
+          → WeightRenaming : "encoder.attn.qkv_proj.weight"
+          → WeightConverter: "encoder.attn.{q,k,v}_proj.weight"  (source_pattern set)
+
+        Reverse  [rev(WeightConverter), rev(WeightRenaming)]:
+          "encoder.attn.{q,k,v}_proj.weight"
+          → rev(WeightConverter): "encoder.attn.qkv_proj.weight"  (source_pattern set)
+          → rev(WeightRenaming) : "decoder.attn.qkv_proj.weight"  ← must still run!
+        """
+
+        class _Attn(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.q_proj = DummyParamModule((2, 4))
+                self.k_proj = DummyParamModule((2, 4))
+                self.v_proj = DummyParamModule((2, 4))
+
+        class _Encoder(nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.attn = _Attn()
+
+        class _InterleavedModel(nn.Module):
+            base_model_prefix = ""
+
+            def __init__(self):
+                super().__init__()
+                self.encoder = _Encoder()
+
+        qkv = torch.arange(24, dtype=torch.float32).reshape(6, 4)
+        model = _InterleavedModel()
+        model.config = PretrainedConfig()
+
+        # Checkpoint uses a "decoder" prefix and stores QKV packed together.
+        checkpoint = {"decoder.attn.qkv_proj.weight": qkv.clone()}
+
+        weight_mapping = [
+            WeightRenaming("^decoder", "encoder"),  # step 1: fix prefix
+            WeightConverter(  # step 2: unpack QKV (fires after rename)
+                "attn.qkv_proj.weight",
+                ["attn.q_proj.weight", "attn.k_proj.weight", "attn.v_proj.weight"],
+                operations=[Chunk(dim=0)],
+            ),
+        ]
+
+        loading_info, _ = convert_and_load_state_dict_in_model(
+            model,
+            checkpoint,
+            LoadStateDictConfig(weight_mapping=weight_mapping),
+            tp_plan=None,
+        )
+
+        self.assertEqual(loading_info.missing_keys, set())
+        self.assertEqual(loading_info.unexpected_keys, set())
+        self.assertEqual(loading_info.mismatched_keys, set())
+        self.assertEqual(loading_info.conversion_errors, {})
+
+        q, k, v = torch.chunk(qkv, 3, dim=0)
+        torch.testing.assert_close(model.encoder.attn.q_proj.weight, q)
+        torch.testing.assert_close(model.encoder.attn.k_proj.weight, k)
+        torch.testing.assert_close(model.encoder.attn.v_proj.weight, v)
+
+        # Round-trip: saving must reconstruct the original "decoder.*" checkpoint.
+        # This relies on rev(WeightRenaming) firing after rev(WeightConverter) has set
+        # source_pattern — if it were skipped the prefix would remain "encoder".
+        saved = revert_weight_conversion(model, model.state_dict())
+        self.assertTrue(compare_state_dicts(saved, checkpoint))
+
     def test_ernie4_5_vl_moe_conversion(self):
         model = DummyRoot(add_extra_moe=True)
         model.config = PretrainedConfig()
@@ -1020,6 +1199,31 @@ def test_can_add_prefix_submodule(self):
         for k, v in saved_state_dict.items():
             self.assertTrue((v == model_state_dict[k]).all())
 
+    def test_class_name_wins_over_model_type(self):
+        """Class-name registry entry takes priority over model_type for the same model."""
+        register_checkpoint_conversion_mapping("_TstCls", [WeightRenaming(r"^cls_key", "cls_renamed")], overwrite=True)
+        register_checkpoint_conversion_mapping(
+            "_tst_mtype", [WeightRenaming(r"^type_key", "type_renamed")], overwrite=True
+        )
+
+        def make_mock(class_name):
+            m = type(class_name, (), {})()
+            m.config = SimpleNamespace(model_type="_tst_mtype")
+            m._named_pretrained_submodules = [("", m)]
+            return m
+
+        # A module whose class name has a registry entry → class entry wins.
+        transforms = get_model_conversion_mapping(make_mock("_TstCls"), add_legacy=False)
+        patterns = [t.source_patterns for t in transforms]
+        self.assertIn(["^cls_key"], patterns)
+        self.assertNotIn(["^type_key"], patterns)
+
+        # A module with no class entry falls through to the model_type entry.
+        transforms = get_model_conversion_mapping(make_mock("_TstOther"), add_legacy=False)
+        patterns = [t.source_patterns for t in transforms]
+        self.assertIn(["^type_key"], patterns)
+        self.assertNotIn(["^cls_key"], patterns)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/utils/test_dynamic_module_utils.py b/tests/utils/test_dynamic_module_utils.py
index dfdc63460cd3..5d5e4f075f27 100644
--- a/tests/utils/test_dynamic_module_utils.py
+++ b/tests/utils/test_dynamic_module_utils.py
@@ -12,11 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib.util
 import os
+import sys
+import warnings
+from pathlib import Path
 
 import pytest
 
-from transformers.dynamic_module_utils import get_imports
+from transformers import AutoConfig, dynamic_module_utils
+from transformers.dynamic_module_utils import custom_object_save, get_cached_module_file, get_imports
 
 
 TOP_LEVEL_IMPORT = """
@@ -127,3 +132,122 @@ def test_import_parsing(tmp_path, case):
 
     parsed_imports = get_imports(tmp_file_path)
     assert parsed_imports == ["os"]
+
+
+def test_custom_object_save_destination_is_writable_when_source_is_readonly(tmp_path, monkeypatch):
+    # Regression test for https://github.com/huggingface/transformers/issues/45684:
+    # `custom_object_save` used `shutil.copy`, which preserves source mode bits, so
+    # a read-only source (e.g. a Perforce-managed file) produced a read-only copy
+    # in the saved-model directory.
+    src = tmp_path / "my_custom_module.py"
+    src.write_text("class CustomThing:\n    pass\n")
+
+    spec = importlib.util.spec_from_file_location("my_custom_module", src)
+    assert spec is not None
+    assert spec.loader is not None
+
+    module = importlib.util.module_from_spec(spec)
+    monkeypatch.setitem(sys.modules, "my_custom_module", module)
+    spec.loader.exec_module(module)
+
+    src.chmod(0o444)  # read-only source
+
+    out_dir = tmp_path / "out"
+    out_dir.mkdir()
+
+    custom_object_save(module.CustomThing, str(out_dir))
+
+    dest = out_dir / "my_custom_module.py"
+    assert dest.exists()
+    assert os.access(dest, os.W_OK), f"dest mode={oct(dest.stat().st_mode)} should be writable"
+
+
+def _create_local_module(module_dir: Path, module_code: str, helper_code: str | None = None):
+    module_dir.mkdir(parents=True, exist_ok=True)
+    (module_dir / "custom_model.py").write_text(module_code, encoding="utf-8")
+    if helper_code is not None:
+        (module_dir / "helper.py").write_text(helper_code, encoding="utf-8")
+
+
+def test_get_cached_module_file_local_cache_key_uses_basename_and_content_hash(monkeypatch, tmp_path):
+    modules_cache = tmp_path / "hf_modules_cache"
+    monkeypatch.setattr(dynamic_module_utils, "HF_MODULES_CACHE", str(modules_cache))
+
+    model_dir_a = tmp_path / "pretrained_a" / "subdir"
+    model_dir_b = tmp_path / "pretrained_b" / "subdir"
+    model_dir_c = tmp_path / "pretrained_c" / "subdir"
+
+    _create_local_module(model_dir_a, 'MAGIC = "A"\n')
+    _create_local_module(model_dir_b, 'MAGIC = "B"\n')
+    _create_local_module(model_dir_c, 'MAGIC = "A"\n')
+
+    cached_module_a = get_cached_module_file(str(model_dir_a), "custom_model.py")
+    cached_module_b = get_cached_module_file(str(model_dir_b), "custom_model.py")
+    cached_module_c = get_cached_module_file(str(model_dir_c), "custom_model.py")
+
+    cached_module_path_a = Path(cached_module_a)
+    assert cached_module_path_a.parent.parent.name == "subdir"
+    assert len(cached_module_path_a.parent.name) == 16
+    assert cached_module_a != cached_module_b
+    assert cached_module_a == cached_module_c
+
+
+def test_get_cached_module_file_local_cache_key_includes_relative_import_sources(monkeypatch, tmp_path):
+    modules_cache = tmp_path / "hf_modules_cache"
+    monkeypatch.setattr(dynamic_module_utils, "HF_MODULES_CACHE", str(modules_cache))
+
+    model_dir_a = tmp_path / "pretrained_a" / "subdir"
+    model_dir_b = tmp_path / "pretrained_b" / "subdir"
+
+    module_code = "from .helper import MAGIC\nVALUE = MAGIC\n"
+    _create_local_module(model_dir_a, module_code, 'MAGIC = "A"\n')
+    _create_local_module(model_dir_b, module_code, 'MAGIC = "B"\n')
+
+    cached_module_a = get_cached_module_file(str(model_dir_a), "custom_model.py")
+    cached_module_b = get_cached_module_file(str(model_dir_b), "custom_model.py")
+
+    cached_helper_a = modules_cache / Path(cached_module_a).parent / "helper.py"
+    cached_helper_b = modules_cache / Path(cached_module_b).parent / "helper.py"
+
+    assert cached_module_a != cached_module_b
+    assert cached_helper_a.read_text(encoding="utf-8") == 'MAGIC = "A"\n'
+    assert cached_helper_b.read_text(encoding="utf-8") == 'MAGIC = "B"\n'
+
+
+def test_get_cached_module_file_local_cache_key_keeps_hash_stable_with_different_basenames(monkeypatch, tmp_path):
+    modules_cache = tmp_path / "hf_modules_cache"
+    monkeypatch.setattr(dynamic_module_utils, "HF_MODULES_CACHE", str(modules_cache))
+
+    model_dir_a = tmp_path / "pretrained_a" / "alpha_subdir"
+    model_dir_b = tmp_path / "pretrained_b" / "beta_subdir"
+
+    _create_local_module(model_dir_a, 'MAGIC = "A"\n')
+    _create_local_module(model_dir_b, 'MAGIC = "A"\n')
+
+    cached_module_a = Path(get_cached_module_file(str(model_dir_a), "custom_model.py"))
+    cached_module_b = Path(get_cached_module_file(str(model_dir_b), "custom_model.py"))
+
+    assert cached_module_a.parent.parent.name == "alpha_subdir"
+    assert cached_module_b.parent.parent.name == "beta_subdir"
+    assert cached_module_a.parent.name == cached_module_b.parent.name
+
+
+def test_local_path_with_and_without_trailing_slash(tmp_path):
+    model_dir = tmp_path / "my_model"
+    model_dir.mkdir()
+    config_path = model_dir / "config.json"
+    config_path.write_text('{"model_type": "bert"}')
+    path_no_slash = str(model_dir)
+    path_with_slash = str(model_dir) + os.sep
+
+    with warnings.catch_warnings(record=True) as w1:
+        warnings.simplefilter("always")
+        cfg1 = AutoConfig.from_pretrained(path_no_slash)
+
+    with warnings.catch_warnings(record=True) as w2:
+        warnings.simplefilter("always")
+        cfg2 = AutoConfig.from_pretrained(path_with_slash)
+
+    assert isinstance(cfg1, type(cfg2))
+    assert len(w1) == 0
+    assert len(w2) == 0
diff --git a/tests/utils/test_hf_argparser.py b/tests/utils/test_hf_argparser.py
index 68e179ef5d60..a83d1131e70e 100644
--- a/tests/utils/test_hf_argparser.py
+++ b/tests/utils/test_hf_argparser.py
@@ -190,7 +190,7 @@ def test_02_with_default_bool(self):
         # A boolean no_* argument always has to come after its "default: True" regular counter-part
         # and its default must be set to False
         expected.add_argument("--no_baz", "--no-baz", action="store_false", default=False, dest="baz")
-        expected.add_argument("--opt", type=string_to_bool, default=None)
+        expected.add_argument("--opt", type=string_to_bool, default=None, const=True, nargs="?")
 
         dataclass_types = [WithDefaultBoolExample]
         if is_python_no_less_than_3_10:
@@ -212,6 +212,9 @@ def test_02_with_default_bool(self):
             args = parser.parse_args(["--foo", "--baz"])
             self.assertEqual(args, Namespace(foo=True, baz=True, opt=None))
 
+            args = parser.parse_args(["--foo", "--baz", "--opt"])
+            self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
+
             args = parser.parse_args(["--foo", "True", "--baz", "True", "--opt", "True"])
             self.assertEqual(args, Namespace(foo=True, baz=True, opt=True))
 
diff --git a/tests/utils/test_image_utils.py b/tests/utils/test_image_utils.py
index 4a6a03f813e6..354034f78d59 100644
--- a/tests/utils/test_image_utils.py
+++ b/tests/utils/test_image_utils.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 import codecs
 import unittest
+import warnings
 
 import httpx
 import numpy as np
@@ -27,6 +28,7 @@
     make_flat_list_of_images,
     make_list_of_images,
     make_nested_list_of_images,
+    validate_kwargs,
 )
 from transformers.testing_utils import is_flaky, require_torch, require_vision
 
@@ -897,3 +899,105 @@ def test_get_channel_dimension_axis(self):
         image = np.random.randint(0, 256, (1, 3, 4, 5))
         inferred_axis = get_channel_dimension_axis(image)
         self.assertEqual(inferred_axis, 1)
+
+
+class ValidateKwargsTest(unittest.TestCase):
+    """Test the validate_kwargs function for proper warning behavior."""
+
+    def test_validate_kwargs_no_unused_keys(self):
+        """Test that no warning is raised when all kwargs are valid."""
+        valid_keys = ["height", "width", "do_resize", "do_normalize"]
+        captured_keys = ["height", "width", "do_resize"]
+
+        # Should not raise any warning
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs(valid_keys, captured_keys)
+
+        # Verify no warnings were raised
+        self.assertEqual(len(warning_list), 0)
+
+    def test_validate_kwargs_with_unused_keys(self):
+        """Test that UserWarning is raised when unused kwargs are found."""
+        valid_keys = ["height", "width", "do_resize", "do_normalize"]
+        captured_keys = ["height", "width", "invalid_param", "another_invalid"]
+
+        # Should raise a UserWarning
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs(valid_keys, captured_keys)
+
+        # Verify warning was raised
+        self.assertEqual(len(warning_list), 1)
+        warning_message = str(warning_list[0].message)
+        self.assertIn("invalid_param", warning_message)
+        self.assertIn("another_invalid", warning_message)
+        self.assertIn("Unused or unrecognized kwargs", warning_message)
+        self.assertEqual(warning_list[0].category, UserWarning)
+
+    def test_validate_kwargs_single_unused_key(self):
+        """Test warning with a single unused key."""
+        valid_keys = ["height", "width"]
+        captured_keys = ["height", "invalid_param"]
+
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs(valid_keys, captured_keys)
+
+        # Verify warning was raised
+        self.assertEqual(len(warning_list), 1)
+        warning_message = str(warning_list[0].message)
+        self.assertIn("invalid_param", warning_message)
+        self.assertIn("Unused or unrecognized kwargs", warning_message)
+        self.assertEqual(warning_list[0].category, UserWarning)
+
+    def test_validate_kwargs_all_unused_keys(self):
+        """Test warning when all captured keys are unused."""
+        valid_keys = ["height", "width"]
+        captured_keys = ["invalid1", "invalid2"]
+
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs(valid_keys, captured_keys)
+
+        # Verify warning was raised
+        self.assertEqual(len(warning_list), 1)
+        warning_message = str(warning_list[0].message)
+        self.assertIn("invalid1", warning_message)
+        self.assertIn("invalid2", warning_message)
+        self.assertIn("Unused or unrecognized kwargs", warning_message)
+        self.assertEqual(warning_list[0].category, UserWarning)
+
+    def test_validate_kwargs_empty_lists(self):
+        """Test that empty lists don't cause issues."""
+        # Empty captured keys should not warn
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs(["height", "width"], [])
+        self.assertEqual(len(warning_list), 0)
+
+        # Empty valid keys with captured keys should warn
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            validate_kwargs([], ["height"])
+
+        self.assertEqual(len(warning_list), 1)
+        warning_message = str(warning_list[0].message)
+        self.assertIn("height", warning_message)
+        self.assertIn("Unused or unrecognized kwargs", warning_message)
+        self.assertEqual(warning_list[0].category, UserWarning)
+
+    def test_validate_kwargs_warning_stacklevel(self):
+        """Test that warnings are raised with correct stacklevel for proper attribution."""
+
+        def call_validate():
+            validate_kwargs(["valid"], ["invalid"])
+
+        with warnings.catch_warnings(record=True) as warning_list:
+            warnings.simplefilter("always")
+            call_validate()
+
+        # Warning should be attributed to call_validate, not validate_kwargs itself
+        # (stacklevel=2 means it points to the caller of validate_kwargs)
+        self.assertEqual(len(warning_list), 1)
+        self.assertEqual(warning_list[0].category, UserWarning)
diff --git a/tests/utils/test_import_utils.py b/tests/utils/test_import_utils.py
index fe616e9cfbe2..7c6cfad09378 100644
--- a/tests/utils/test_import_utils.py
+++ b/tests/utils/test_import_utils.py
@@ -1,6 +1,7 @@
 import sys
 
 from transformers.testing_utils import run_test_using_subprocess
+from transformers.utils import import_utils
 from transformers.utils.import_utils import clear_import_cache
 
 
@@ -24,3 +25,20 @@ def test_clear_import_cache():
 
     assert "transformers.models.auto.modeling_auto" in sys.modules
     assert modeling_auto.__name__ == "transformers.models.auto.modeling_auto"
+
+
+def test_is_package_available_falls_back_to_package_name_metadata(monkeypatch):
+    monkeypatch.setattr(import_utils.importlib.util, "find_spec", lambda _name: object())
+    monkeypatch.setattr(import_utils, "PACKAGE_DISTRIBUTION_MAPPING", {})
+    monkeypatch.setattr(
+        import_utils.importlib.metadata,
+        "version",
+        lambda name: "0.18.0"
+        if name == "gguf"
+        else (_ for _ in ()).throw(import_utils.importlib.metadata.PackageNotFoundError()),
+    )
+
+    is_available, package_version = import_utils._is_package_available("gguf", return_version=True)
+
+    assert is_available is True
+    assert package_version == "0.18.0"
diff --git a/tests/utils/test_modeling_utils.py b/tests/utils/test_modeling_utils.py
index fab48f9ddb8a..0a4b02dbc7bf 100644
--- a/tests/utils/test_modeling_utils.py
+++ b/tests/utils/test_modeling_utils.py
@@ -39,6 +39,7 @@
     AutoModel,
     AutoModelForImageClassification,
     AutoModelForSequenceClassification,
+    AutoTokenizer,
     BartConfig,
     BartForConditionalGeneration,
     BartModel,
@@ -218,6 +219,31 @@ def __init__(self, config):
         def forward(self, x):
             return self.linear_2(self.linear(x))
 
+    class DummyModelWithTiedEmbeddings(PreTrainedModel):
+        config_class = PreTrainedConfig
+        _tied_weights_keys = {"lm_head.weight": "embed_tokens.weight"}
+
+        def __init__(self, config):
+            super().__init__(config)
+            self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+            self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+            self.post_init()
+
+        def get_input_embeddings(self):
+            return self.embed_tokens
+
+        def set_input_embeddings(self, value):
+            self.embed_tokens = value
+
+        def get_output_embeddings(self):
+            return self.lm_head
+
+        def set_output_embeddings(self, value):
+            self.lm_head = value
+
+        def forward(self, input_ids):
+            return self.lm_head(self.embed_tokens(input_ids))
+
     class ModelWithHead(PreTrainedModel):
         base_model_prefix = "base"
         config_class = PreTrainedConfig
@@ -364,6 +390,16 @@ def test_local_files_only(self):
                     TINY_IMAGE_CLASSIF, cache_dir=tmpdir, local_files_only=True
                 )
 
+        def test_offline_tokenizer(self):
+            with tempfile.TemporaryDirectory() as tmpdir:
+                # Populate cache
+                with patch("huggingface_hub.constants.HF_HUB_OFFLINE", False):
+                    snapshot_download(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
+                # Load tokenizer in offline mode - should work
+                with patch("huggingface_hub.constants.HF_HUB_OFFLINE", True):
+                    AutoTokenizer.from_pretrained(TINY_IMAGE_CLASSIF, cache_dir=tmpdir)
+
 
 # Need to be serializable, which means they cannot be in a test class method
 class TestGammaBetaNorm(torch.nn.Module):
@@ -414,6 +450,23 @@ def tearDown(self):
         torch.set_default_dtype(self.old_dtype)
         super().tearDown()
 
+    def _build_missing_tied_embeddings_checkpoint(self, tmp_dir):
+        reference_model = DummyModelWithTiedEmbeddings(
+            PreTrainedConfig(vocab_size=11, hidden_size=7, tie_word_embeddings=True)
+        )
+        reference_model.config.save_pretrained(tmp_dir)
+
+        state_dict = reference_model.state_dict()
+        del state_dict["lm_head.weight"]
+        safe_save_file(state_dict, os.path.join(tmp_dir, SAFE_WEIGHTS_NAME), metadata={"format": "pt"})
+        return reference_model
+
+    def _assert_tied_embeddings_load_succeeded(self, model, reference_model):
+        self.assertIs(model.lm_head.weight, model.embed_tokens.weight, msg="Weights are not tied!")
+        for name, value in model.state_dict().items():
+            self.assertNotEqual(value.device.type, "meta", msg=f"{name} is still on meta!")
+        compare_state_dicts(reference_model.state_dict(), model.state_dict())
+
     @require_torch
     def test_get_total_byte_count_does_not_require_process_group(self):
         model = BaseModel(PreTrainedConfig())
@@ -1602,6 +1655,77 @@ def test_tied_weights_are_always_tied_from_config(self):
             model = LlamaForCausalLM._from_config(copy.deepcopy(config))
             self.assertTrue(model.lm_head.weight is not model.model.embed_tokens.weight)
 
+    def test_no_tie_weights_is_thread_local_during_concurrent_from_pretrained(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            reference_model = self._build_missing_tied_embeddings_checkpoint(tmp_dir)
+            first_loader_initialized = threading.Event()
+            release_first_loader = threading.Event()
+            first_loader_lock = threading.Lock()
+            results = []
+            errors = []
+            first_loader_claimed = False
+            original_init = DummyModelWithTiedEmbeddings.__init__
+
+            def instrumented_init(model_self, config):
+                original_init(model_self, config)
+
+                nonlocal first_loader_claimed
+                with first_loader_lock:
+                    should_block = not first_loader_claimed
+                    if should_block:
+                        first_loader_claimed = True
+
+                if should_block:
+                    first_loader_initialized.set()
+                    if not release_first_loader.wait(timeout=10):
+                        raise TimeoutError("Timed out waiting for the first loader to resume.")
+
+            def worker():
+                try:
+                    model, loading_info = DummyModelWithTiedEmbeddings.from_pretrained(
+                        tmp_dir, output_loading_info=True
+                    )
+                    results.append((model, loading_info))
+                except Exception as error:
+                    errors.append(error)
+
+            first_thread = threading.Thread(target=worker)
+            second_thread = threading.Thread(target=worker)
+
+            try:
+                with patch.object(DummyModelWithTiedEmbeddings, "__init__", new=instrumented_init):
+                    first_thread.start()
+                    self.assertTrue(first_loader_initialized.wait(timeout=10))
+
+                    second_thread.start()
+                    second_thread.join(timeout=20)
+                    self.assertFalse(second_thread.is_alive())
+            finally:
+                release_first_loader.set()
+                first_thread.join(timeout=20)
+                second_thread.join(timeout=20)
+
+            self.assertFalse(first_thread.is_alive())
+            self.assertFalse(second_thread.is_alive())
+            self.assertEqual(errors, [])
+            self.assertEqual(len(results), 2)
+
+            for model, loading_info in results:
+                self.assertSetEqual(loading_info["missing_keys"], set())
+                self._assert_tied_embeddings_load_succeeded(model, reference_model)
+
+    def test_no_tie_weights_is_model_specific_during_nested_from_pretrained(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            reference_model = self._build_missing_tied_embeddings_checkpoint(tmp_dir)
+
+            # `from_pretrained` uses its own no-tie scope while instantiating. An
+            # outer active scope must not suppress the final tie_weights() call.
+            with init.no_tie_weights():
+                model, load_info = DummyModelWithTiedEmbeddings.from_pretrained(tmp_dir, output_loading_info=True)
+
+                self.assertSetEqual(load_info["missing_keys"], set())
+                self._assert_tied_embeddings_load_succeeded(model, reference_model)
+
     def test_unexpected_keys_warnings(self):
         model = ModelWithHead(PreTrainedConfig(tie_word_embeddings=True))
         logger = logging.get_logger("transformers.modeling_utils")
diff --git a/tests/utils/test_testing_utils.py b/tests/utils/test_testing_utils.py
new file mode 100644
index 000000000000..40385332e57e
--- /dev/null
+++ b/tests/utils/test_testing_utils.py
@@ -0,0 +1,114 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import importlib.util
+import os
+import sys
+import tempfile
+import types
+import unittest
+from pathlib import Path
+from unittest import mock
+
+from transformers.testing_utils import (
+    _clear_patched_testing_methods_output_files,
+    _get_patched_testing_methods_output_path,
+)
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+
+
+def _load_notification_service_module():
+    module_path = REPO_ROOT / "utils" / "notification_service.py"
+    spec = importlib.util.spec_from_file_location("notification_service_for_tests", module_path)
+    module = importlib.util.module_from_spec(spec)
+    stub_modules = {
+        "compare_test_runs": types.SimpleNamespace(compare_job_sets=lambda *args, **kwargs: None),
+        "get_ci_error_statistics": types.SimpleNamespace(get_jobs=lambda *args, **kwargs: []),
+        "get_previous_daily_ci": types.SimpleNamespace(
+            get_last_daily_ci_reports=lambda *args, **kwargs: None,
+            get_last_daily_ci_run=lambda *args, **kwargs: None,
+            get_last_daily_ci_workflow_run_id=lambda *args, **kwargs: None,
+        ),
+        "huggingface_hub": types.SimpleNamespace(HfApi=object),
+        "slack_sdk": types.SimpleNamespace(WebClient=object),
+    }
+    with mock.patch.dict(sys.modules, stub_modules):
+        spec.loader.exec_module(module)
+    return module
+
+
+class PatchedTestingMethodsOutputPathTester(unittest.TestCase):
+    @mock.patch.dict(os.environ, {"_PATCHED_TESTING_METHODS_OUTPUT_DIR": "/tmp/reports"}, clear=True)
+    def test_output_path_keeps_legacy_name_without_xdist(self):
+        self.assertEqual(_get_patched_testing_methods_output_path(), Path("/tmp/reports/captured_info.txt"))
+
+    @mock.patch.dict(
+        os.environ,
+        {"_PATCHED_TESTING_METHODS_OUTPUT_DIR": "/tmp/reports", "PYTEST_XDIST_WORKER": "gw1"},
+        clear=True,
+    )
+    def test_output_path_is_worker_specific_with_xdist(self):
+        self.assertEqual(_get_patched_testing_methods_output_path(), Path("/tmp/reports/captured_info_gw1.txt"))
+
+    def test_clear_output_files_removes_all_matching_files_without_xdist(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            (tmp_path / "captured_info.txt").write_text("legacy info")
+            (tmp_path / "captured_info_gw0.txt").write_text("gw0 info")
+            (tmp_path / "summary_short.txt").write_text("FAILED test_example\n")
+
+            with mock.patch.dict(os.environ, {"_PATCHED_TESTING_METHODS_OUTPUT_DIR": tmp_dir}, clear=True):
+                _clear_patched_testing_methods_output_files()
+
+            self.assertFalse((tmp_path / "captured_info.txt").exists())
+            self.assertFalse((tmp_path / "captured_info_gw0.txt").exists())
+            self.assertTrue((tmp_path / "summary_short.txt").exists())
+
+
+class RetrieveArtifactTester(unittest.TestCase):
+    def test_retrieve_artifact_merges_worker_specific_captured_info_files(self):
+        notification_service = _load_notification_service_module()
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            (tmp_path / "captured_info_gw1.txt").write_text("gw1 info")
+            (tmp_path / "captured_info_gw0.txt").write_text("gw0 info")
+            (tmp_path / "summary_short.txt").write_text("FAILED test_example\n")
+
+            artifact = notification_service.retrieve_artifact(str(tmp_path), gpu="multi")
+
+        self.assertEqual(artifact["summary_short"], "FAILED test_example\n")
+        self.assertIn("captured_info_gw0.txt", artifact["captured_info"])
+        self.assertIn("gw0 info", artifact["captured_info"])
+        self.assertIn("captured_info_gw1.txt", artifact["captured_info"])
+        self.assertIn("gw1 info", artifact["captured_info"])
+        self.assertNotIn("captured_info_gw0", artifact)
+        self.assertNotIn("captured_info_gw1", artifact)
+
+    def test_retrieve_artifact_preserves_legacy_captured_info_file(self):
+        notification_service = _load_notification_service_module()
+
+        with tempfile.TemporaryDirectory() as tmp_dir:
+            tmp_path = Path(tmp_dir)
+            (tmp_path / "captured_info.txt").write_text("legacy info")
+
+            artifact = notification_service.retrieve_artifact(str(tmp_path), gpu=None)
+
+        self.assertEqual(artifact["captured_info"], "legacy info")
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/utils/test_tokenizer_selection.py b/tests/utils/test_tokenizer_selection.py
new file mode 100644
index 000000000000..9e8787634812
--- /dev/null
+++ b/tests/utils/test_tokenizer_selection.py
@@ -0,0 +1,269 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+from unittest.mock import MagicMock, patch
+
+from transformers.utils.tokenizer_selection import (
+    CorpusAnalyzer,
+    CorpusStats,
+    TokenizerRecommender,
+    TokenizerSelector,
+    suggest_and_train_tokenizer,
+)
+
+
+class TestCorpusAnalyzer(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.test_texts = [
+            ["Hello world, this is a test.", "Machine learning is fascinating."],
+            ["Natural language processing helps computers.", "Tokenization is important."],
+            ["BPE and WordPiece are popular algorithms.", "SentencePiece works well too."],
+        ]
+
+        self.cjk_texts = [["你好世界", "机器学习很有趣"], ["自然语言处理帮助计算机", "分词很重要"]]
+
+    def test_analyze_corpus_basic(self):
+        """Test basic corpus analysis functionality."""
+        stats = CorpusAnalyzer.analyze_corpus(iter(self.test_texts))
+
+        self.assertIsInstance(stats, CorpusStats)
+        self.assertGreater(stats.vocab_size, 0)
+        self.assertGreater(stats.avg_word_length, 0)
+        self.assertGreater(stats.char_diversity, 0)
+        self.assertGreaterEqual(stats.morphological_complexity, 0)
+        self.assertGreaterEqual(stats.token_frequency_ratio, 0)
+        self.assertGreaterEqual(stats.avg_sentence_length, 0)
+
+    def test_analyze_corpus_empty(self):
+        """Test corpus analysis with empty input."""
+        empty_texts = [[]]
+
+        with self.assertRaises(ValueError):
+            CorpusAnalyzer.analyze_corpus(iter(empty_texts))
+
+    def test_detect_language_hint_latin(self):
+        """Test language detection for Latin scripts."""
+        stats = CorpusAnalyzer.analyze_corpus(iter(self.test_texts))
+        self.assertEqual(stats.language_hint, "latin")
+
+    def test_detect_language_hint_cjk(self):
+        """Test language detection for CJK scripts."""
+        stats = CorpusAnalyzer.analyze_corpus(iter(self.cjk_texts))
+        self.assertEqual(stats.language_hint, "cjk")
+
+    def test_sample_size_limit(self):
+        """Test that sample size limit is respected."""
+        large_texts = [["test text"] * 100 for _ in range(100)]  # 10k texts
+
+        stats = CorpusAnalyzer.analyze_corpus(iter(large_texts), sample_size=50)
+        self.assertIsInstance(stats, CorpusStats)
+        # Should still work despite large input
+
+
+class TestTokenizerRecommender(unittest.TestCase):
+    def setUp(self):
+        """Set up test corpus statistics."""
+        self.latin_stats = CorpusStats(
+            vocab_size=1000,
+            avg_word_length=5.0,
+            char_diversity=50,
+            morphological_complexity=0.3,
+            token_frequency_ratio=0.1,
+            avg_sentence_length=10.0,
+            language_hint="latin",
+        )
+
+        self.cjk_stats = CorpusStats(
+            vocab_size=5000,
+            avg_word_length=2.0,
+            char_diversity=2000,
+            morphological_complexity=0.8,
+            token_frequency_ratio=0.05,
+            avg_sentence_length=15.0,
+            language_hint="cjk",
+        )
+
+        self.complex_stats = CorpusStats(
+            vocab_size=80000,
+            avg_word_length=12.0,
+            char_diversity=100,
+            morphological_complexity=0.9,
+            token_frequency_ratio=0.02,
+            avg_sentence_length=20.0,
+            language_hint="latin",
+        )
+
+    def test_recommend_tokenizer_cjk(self):
+        """Test recommendation for CJK languages."""
+        recommendation = TokenizerRecommender.recommend_tokenizer(self.cjk_stats)
+
+        self.assertEqual(recommendation["type"], "SentencePiece")
+        self.assertIn("CJK", recommendation["rationale"])
+        self.assertIn("config", recommendation)
+
+    def test_recommend_tokenizer_high_complexity(self):
+        """Test recommendation for high morphological complexity."""
+        recommendation = TokenizerRecommender.recommend_tokenizer(self.complex_stats)
+
+        self.assertEqual(recommendation["type"], "BPE")
+        self.assertIn("morphological complexity", recommendation["rationale"])
+
+    def test_recommend_tokenizer_large_vocab(self):
+        """Test recommendation for large vocabulary."""
+        large_vocab_stats = CorpusStats(
+            vocab_size=60000,
+            avg_word_length=6.0,
+            char_diversity=80,
+            morphological_complexity=0.4,
+            token_frequency_ratio=0.05,
+            avg_sentence_length=12.0,
+            language_hint="latin",
+        )
+
+        recommendation = TokenizerRecommender.recommend_tokenizer(large_vocab_stats)
+
+        # Should recommend WordPiece for large vocab or BPE for complexity
+        self.assertIn(recommendation["type"], ["WordPiece", "BPE"])
+
+    def test_generate_config_bpe(self):
+        """Test BPE configuration generation."""
+        recommendation = TokenizerRecommender.recommend_tokenizer(self.complex_stats)
+
+        if recommendation["type"] == "BPE":
+            config = recommendation["config"]
+            self.assertIn("vocab_size", config)
+            self.assertIn("continuing_subword_prefix", config)
+
+    def test_generate_config_sentencepiece(self):
+        """Test SentencePiece configuration generation."""
+        recommendation = TokenizerRecommender.recommend_tokenizer(self.cjk_stats)
+
+        if recommendation["type"] == "SentencePiece":
+            config = recommendation["config"]
+            self.assertIn("vocab_size", config)
+            self.assertIn("character_coverage", config)
+            self.assertIn("model_type", config)
+
+    def test_vocab_size_scaling(self):
+        """Test vocabulary size recommendations scale appropriately."""
+        small_vocab = CorpusStats(5000, 5.0, 50, 0.3, 0.1, 10.0, "latin")
+        # medium_vocab = CorpusStats(30000, 6.0, 60, 0.4, 0.08, 12.0, "latin")
+        large_vocab = CorpusStats(100000, 7.0, 80, 0.5, 0.05, 15.0, "latin")
+
+        small_rec = TokenizerRecommender.recommend_tokenizer(small_vocab)
+        large_rec = TokenizerRecommender.recommend_tokenizer(large_vocab)
+
+        # Vocabulary size recommendations should scale
+        self.assertLess(small_rec["config"]["vocab_size"], large_rec["config"]["vocab_size"])
+
+
+class TestTokenizerSelector(unittest.TestCase):
+    def setUp(self):
+        """Set up test data."""
+        self.test_texts = [
+            ["Hello world, this is a test.", "Machine learning is fascinating."],
+            ["Natural language processing helps computers.", "Tokenization is important."],
+        ]
+
+    def test_analyze_corpus(self):
+        """Test corpus analysis through TokenizerSelector."""
+        stats = TokenizerSelector.analyze_corpus(iter(self.test_texts))
+
+        self.assertIsInstance(stats, CorpusStats)
+        self.assertGreater(stats.vocab_size, 0)
+
+    def test_recommend_tokenizer(self):
+        """Test tokenizer recommendation through TokenizerSelector."""
+        stats = TokenizerSelector.analyze_corpus(iter(self.test_texts))
+        recommendation = TokenizerSelector.recommend_tokenizer(stats)
+
+        self.assertIn("type", recommendation)
+        self.assertIn("rationale", recommendation)
+        self.assertIn("config", recommendation)
+        self.assertIn(recommendation["type"], ["BPE", "WordPiece", "SentencePiece"])
+
+    @patch("transformers.models.auto.AutoTokenizer")
+    def test_suggest_and_train_tokenizer_mock(self, mock_auto_tokenizer):
+        """Test end-to-end tokenizer training with mocked AutoTokenizer."""
+        # Mock the tokenizer and its training method
+        mock_tokenizer = MagicMock()
+        mock_trained_tokenizer = MagicMock()
+        mock_tokenizer.train_new_from_iterator.return_value = mock_trained_tokenizer
+        mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
+
+        trained_tokenizer, recommendation = TokenizerSelector.suggest_and_train_tokenizer(
+            iter(self.test_texts), vocab_size=1000
+        )
+
+        # Verify the method was called
+        mock_auto_tokenizer.from_pretrained.assert_called()
+        mock_tokenizer.train_new_from_iterator.assert_called()
+
+        # Check return values
+        self.assertEqual(trained_tokenizer, mock_trained_tokenizer)
+        self.assertIn("type", recommendation)
+        self.assertIn("rationale", recommendation)
+
+    def test_convenience_function(self):
+        """Test the convenience function."""
+        # This test would require mocking as well since it calls the main method
+        with patch(
+            "transformers.utils.tokenizer_selection.TokenizerSelector.suggest_and_train_tokenizer"
+        ) as mock_method:
+            mock_method.return_value = (MagicMock(), {"type": "BPE"})
+
+            tokenizer, info = suggest_and_train_tokenizer(iter(self.test_texts))
+
+            mock_method.assert_called_once()
+            self.assertIsNotNone(tokenizer)
+            self.assertIn("type", info)
+
+
+class TestIntegration(unittest.TestCase):
+    """Integration tests for the complete workflow."""
+
+    def setUp(self):
+        """Set up test data with different characteristics."""
+        self.english_texts = [
+            ["The quick brown fox jumps over the lazy dog."],
+            ["Machine learning models require substantial computational resources."],
+            ["Natural language processing enables computers to understand human language."],
+        ]
+
+        self.technical_texts = [
+            ["Hyperparameter optimization improves model performance significantly."],
+            ["Convolutional neural networks excel at computer vision tasks."],
+            ["Transformer architectures revolutionized natural language understanding."],
+        ]
+
+    def test_different_corpus_types(self):
+        """Test that different corpus types get different recommendations."""
+        english_stats = TokenizerSelector.analyze_corpus(iter(self.english_texts))
+        technical_stats = TokenizerSelector.analyze_corpus(iter(self.technical_texts))
+
+        english_rec = TokenizerSelector.recommend_tokenizer(english_stats)
+        technical_rec = TokenizerSelector.recommend_tokenizer(technical_stats)
+
+        # Both should provide valid recommendations
+        self.assertIn(english_rec["type"], ["BPE", "WordPiece", "SentencePiece"])
+        self.assertIn(technical_rec["type"], ["BPE", "WordPiece", "SentencePiece"])
+
+        # Technical text typically has higher complexity
+        self.assertGreaterEqual(technical_stats.morphological_complexity, english_stats.morphological_complexity)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/utils/test_vision_utils.py b/tests/utils/test_vision_utils.py
new file mode 100644
index 000000000000..1d85a1680757
--- /dev/null
+++ b/tests/utils/test_vision_utils.py
@@ -0,0 +1,46 @@
+import unittest
+
+from parameterized import parameterized
+
+from transformers import AutoConfig, AutoModelForImageClassification, is_torch_available
+from transformers.testing_utils import require_torchvision, require_vision
+from transformers.utils.vision_utils import encode_images, image_encoder_size
+
+
+MODELS_CONFIGS = {
+    "WinKawaks/vit-tiny-patch16-224": 192,
+    "microsoft/swinv2-tiny-patch4-window16-256": 768,
+    "google/vit-base-patch16-224": 768,
+    "microsoft/resnet-18": 512,
+    "apple/mobilevit-xx-small": 80,
+}
+
+
+def image_encoder(model_name):
+    # Create an image encoder model from config, so it does not load the weights
+    config = AutoConfig.from_pretrained(model_name)
+    model = AutoModelForImageClassification.from_config(config)
+    model.eval()
+    return model
+
+
+MODELS = [[name, size, image_encoder(name)] for name, size in MODELS_CONFIGS.items()]
+
+if is_torch_available():
+    import torch
+
+
+@require_vision
+@require_torchvision
+class ImageEncoderSizeTest(unittest.TestCase):
+    @parameterized.expand(MODELS)
+    def test_image_encoder_size(self, model_name, expected_size, model):
+        """Test that image_encoder_size returns the expected hidden size for each model."""
+        actual_size = image_encoder_size(model)
+        assert actual_size == expected_size, f"Expected {expected_size}, got {actual_size} for {model_name}"
+
+    @parameterized.expand(MODELS)
+    def test_image_encoder_size_when_called(self, model_name, expected_size, model):
+        random_images = torch.randn(1, 3, 64, 64)
+        embedding = encode_images(model, random_images)
+        assert embedding.shape == (1, expected_size)
diff --git a/tests/vlm_tester.py b/tests/vlm_tester.py
index c40b42785836..bce23b71e142 100644
--- a/tests/vlm_tester.py
+++ b/tests/vlm_tester.py
@@ -16,146 +16,70 @@
 import unittest
 from inspect import signature
 
-from .test_configuration_common import ConfigTester
+from .multimodal_tester import MultiModalModelTest, MultiModalModelTester
 from .test_modeling_common import (
-    GenerationTesterMixin,
-    ModelTesterMixin,
     floats_tensor,
-    ids_tensor,
     is_torch_available,
-    require_torch,
     torch_device,
 )
-from .test_pipeline_mixin import PipelineTesterMixin
 
 
 if is_torch_available():
     import torch
 
 
-class VLMModelTester:
-    # If the model follows the standard naming conventions, only `base_model_class` needs to be set (the others are
-    # inferred from available public classes).
-    base_model_class = None
-    config_class = None
-    text_config_class = None
+class VLMModelTester(MultiModalModelTester):
     vision_config_class = None
-    conditional_generation_class = None
-    sequence_classification_class = None
-    # These attributes are required after the initialization phase of the tester.
-    _required_attributes = ("base_model_class", "config_class", "conditional_generation_class")
-
-    # Arguments that should be passed to the config class even if not in its signature
-    forced_config_args = ["pad_token_id"]
-
-    @property
-    def all_model_classes(self):
-        # Models that set `all_model_classes` in their `XXXModelTest` class must have a new class that doesn't fit
-        # any of the common classes.
-        return [
-            model_class
-            for model_class in (
-                self.base_model_class,
-                self.conditional_generation_class,
-                self.sequence_classification_class,
-            )
-            if model_class is not None
-        ]
+    _required_attributes = MultiModalModelTester._required_attributes + ("base_model_class", "vision_config_class")
 
     @property
     def pipeline_model_mapping(self):
-        mapping = {
+        return {
             "feature-extraction": self.base_model_class,
             "image-text-to-text": self.conditional_generation_class,
         }
-        return mapping
 
     def __init__(self, parent, **kwargs):
-        self.parent = parent
+        # Overrides of _TEXT_MODEL_TESTER_DEFAULTS
+        kwargs.setdefault(
+            "seq_length",
+            7 + kwargs.get("num_image_tokens", (kwargs.get("image_size", 8) // kwargs.get("patch_size", 4)) ** 2),
+        )
+        kwargs.setdefault("pad_token_id", 0)
 
-        # Standard defaults
-        kwargs.setdefault("batch_size", 3)
-        kwargs.setdefault("is_training", True)
-        kwargs.setdefault("use_input_mask", True)
+        # VLM-specific defaults
         kwargs.setdefault("use_token_type_ids", False)
-        kwargs.setdefault("use_labels", True)
-        kwargs.setdefault("vocab_size", 99)
-        kwargs.setdefault("hidden_size", 32)
-        kwargs.setdefault("num_hidden_layers", 2)
-        kwargs.setdefault("num_attention_heads", 2)
-        kwargs.setdefault("num_key_value_heads", 2)
-        kwargs.setdefault("intermediate_size", 32)  # Keep this divisible by 8 for fp16/bf16/fp32 16-bytes alignment
-        kwargs.setdefault("hidden_act", "gelu")
         kwargs.setdefault("hidden_dropout_prob", 0.1)
         kwargs.setdefault("attention_probs_dropout_prob", 0.1)
-        kwargs.setdefault("max_position_embeddings", 512)
         kwargs.setdefault("type_vocab_size", 16)
         kwargs.setdefault("type_sequence_label_size", 2)
         kwargs.setdefault("initializer_range", 0.02)
         kwargs.setdefault("num_labels", 3)
         kwargs.setdefault("num_choices", 4)
-        kwargs.setdefault("pad_token_id", 0)
-        kwargs.setdefault("bos_token_id", 1)
-        kwargs.setdefault("eos_token_id", 2)
         kwargs.setdefault("image_token_id", 3)
         kwargs.setdefault("is_decoder", False)
-        kwargs.setdefault("scope", None)
-        kwargs.setdefault("expert_interval", 1)
-        kwargs.setdefault("moe_layer_start_index", 0)
-        kwargs.setdefault("moe_intermediate_size", 12)
-        kwargs.setdefault("shared_expert_intermediate_size", 36)
-        kwargs.setdefault("shared_expert_gate", True)
-        kwargs.setdefault("moe_num_shared_experts", 2)
-        kwargs.setdefault("num_experts_per_tok", 2)
-        kwargs.setdefault("num_experts", 8)
-        kwargs.setdefault("mamba_n_groups", 1)
-        kwargs.setdefault("mamba_n_heads", 16)
-        kwargs.setdefault("mamba_d_state", 16)
-        kwargs.setdefault("mamba_d_conv", 4)
-        kwargs.setdefault("mamba_expand", 2)
-        kwargs.setdefault("mamba_chunk_size", 16)
         kwargs.setdefault("image_size", 8)
         kwargs.setdefault("patch_size", 4)
         kwargs.setdefault("num_channels", 3)
         kwargs.setdefault("projection_dim", 32)
         kwargs.setdefault("projector_hidden_act", "gelu")
-        kwargs.setdefault("ignore_index", -100)
         kwargs.setdefault("vision_feature_select_strategy", "default")
         kwargs.setdefault("vision_feature_layer", -1)
         kwargs.setdefault("tie_word_embeddings", False)
-
-        # Computed defaults (can still be overridden in derived classes)
-        kwargs.setdefault("head_dim", kwargs["hidden_size"] // kwargs["num_attention_heads"])
         kwargs.setdefault("num_image_tokens", (kwargs["image_size"] // kwargs["patch_size"]) ** 2)
-        kwargs.setdefault("seq_length", 7 + kwargs["num_image_tokens"])
-
-        # Set all kwargs as instance attributes
-        for key, value in kwargs.items():
-            setattr(self, key, value)
-
-        for required_attribute in [
-            "base_model_class",
-            "config_class",
-            "conditional_generation_class",
-            "text_config_class",
-            "vision_config_class",
-        ]:
-            if getattr(self, required_attribute) is None:
-                raise ValueError(
-                    f"You have inherited from VLMModelTester but did not set the {required_attribute} attribute."
-                )
 
-    # Because VLMs have some different standards in how they handle image tokens, we need a few methods
-    # that can be overridden if required:
+        super().__init__(parent, **kwargs)
+
+        # Computed default depending on base-class defaults for hidden_size / num_attention_heads.
+        if not hasattr(self, "head_dim"):
+            self.head_dim = self.hidden_size // self.num_attention_heads
+
+    # -- Overridable VLM-specific hooks ------------------------------------------------------
 
     def create_pixel_values(self):
         # Override to 5D for patch-based models
         return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], scale=1.0)
 
-    def create_attention_mask(self, input_ids):
-        # Override for bidirectional attention models like Gemma3
-        return torch.tril(torch.ones_like(input_ids).to(torch_device))
-
     def place_image_tokens(self, input_ids, config):
         # Override if the image tokens shouldn't be placed at the start of the test sequence
         image_token_id = getattr(config, "image_token_id", self.image_token_id)
@@ -166,111 +90,32 @@ def place_image_tokens(self, input_ids, config):
         input_ids[:, : self.num_image_tokens] = image_token_id
         return input_ids
 
-    def get_additional_inputs(self, config, input_ids, pixel_values):
-        # Override for model-specific inputs like LlavaNext's image_sizes
-        return {}
-
-    # End of overridable methods
-
-    def prepare_config_and_inputs_for_common(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-        pixel_values = self.create_pixel_values()
-
-        config = self.get_config()
+    # -- Hooks consumed by the shared base ---------------------------------------------------
 
-        special_tokens = [self.pad_token_id, self.bos_token_id, self.eos_token_id, self.image_token_id]
-        for i in range(self.vocab_size):
-            if i not in special_tokens:
-                # The smallest token ID that is not a special token
-                safe_token_id = i
-                break
-        else:
-            raise ValueError("vocab_size is too small and there is no token ID that is not a special token!")
+    @property
+    def _special_token_ids(self):
+        return super()._special_token_ids | {self.image_token_id}
 
-        # Avoid flaky tests, clear any special tokens in ids_tensor
-        # image_token_id is handled separately by place_image_tokens()
-        input_ids[input_ids == self.pad_token_id] = safe_token_id
-        input_ids[input_ids == self.eos_token_id] = safe_token_id
+    def _build_modality_sub_configs(self):
+        return {"vision_config": self.get_vision_config()}
 
+    def _prepare_modality_inputs(self, input_ids, config):
+        pixel_values = self.create_pixel_values()
         input_ids = self.place_image_tokens(input_ids, config)
+        return input_ids, {"pixel_values": pixel_values}
 
-        # Create attention mask with final input_ids (after image tokens are placed)
-        # This is important for models that use padding masks based on token values
-        input_mask = None
-        if self.use_input_mask:
-            input_mask = self.create_attention_mask(input_ids)
-
-        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask, "pixel_values": pixel_values}
-
-        additional_inputs = self.get_additional_inputs(config, input_ids, pixel_values)
-        inputs_dict.update(additional_inputs)
-
-        return config, inputs_dict
-
-    @property
-    def config_args(self):
-        return list(signature(self.config_class.__init__).parameters.keys())
-
-    @property
-    def text_config_args(self):
-        args = list(signature(self.text_config_class.__init__).parameters.keys())
-        for token_arg in ["pad_token_id", "bos_token_id", "eos_token_id"]:  # Not always explicitly in the sig
-            if token_arg not in args:
-                args.append(token_arg)
-        return args
+    # -- Vision sub-config construction ------------------------------------------------------
 
     @property
     def vision_config_args(self):
         return list(signature(self.vision_config_class.__init__).parameters.keys())
 
-    def get_config(self):
-        kwargs = {}
-        attribute_map = getattr(self.config_class, "attribute_map", {})
-        model_name_to_common_name = {v: k for k, v in attribute_map.items()}
-        for k in self.config_args + self.forced_config_args:
-            if hasattr(self, k) and k != "self":
-                kwargs[k] = getattr(self, k)
-            elif k in model_name_to_common_name and hasattr(self, model_name_to_common_name[k]):
-                kwargs[k] = getattr(self, model_name_to_common_name[k])
-        kwargs["text_config"] = self.get_text_config()
-        kwargs["vision_config"] = self.get_vision_config()
-        return self.config_class(**kwargs)
-
-    def get_text_config(self):
-        kwargs = {}
-        attribute_map = getattr(self.text_config_class, "attribute_map", {})
-        model_name_to_common_name = {v: k for k, v in attribute_map.items()}
-        for k in self.text_config_args:
-            if hasattr(self, k) and k != "self":
-                kwargs[k] = getattr(self, k)
-            elif k in model_name_to_common_name and hasattr(self, model_name_to_common_name[k]):
-                kwargs[k] = getattr(self, model_name_to_common_name[k])
-        return self.text_config_class(**kwargs)
-
     def get_vision_config(self):
-        kwargs = {}
-        attribute_map = getattr(self.vision_config_class, "attribute_map", {})
-        model_name_to_common_name = {v: k for k, v in attribute_map.items()}
-        for k in self.vision_config_args:
-            if hasattr(self, k) and k != "self":
-                kwargs[k] = getattr(self, k)
-            elif k in model_name_to_common_name and hasattr(self, model_name_to_common_name[k]):
-                kwargs[k] = getattr(self, model_name_to_common_name[k])
+        kwargs = self._collect_kwargs(self.vision_config_args, self.vision_config_class)
         return self.vision_config_class(**kwargs)
 
-    def create_and_check_model(
-        self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-    ):
-        model = self.base_model_class(config=config)
-        model.to(torch_device)
-        model.eval()
-        model(input_ids, attention_mask=input_mask)
-        result = model(input_ids)
-        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
 
-
-@require_torch
-class VLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin):
+class VLMModelTest(MultiModalModelTest):
     """
     Base test class for Vision-Language Models.
 
@@ -282,35 +127,6 @@ class VLMModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin)
     - `pipeline_model_mapping`: Override if not using default from model_tester
     """
 
-    model_tester_class = None
-    all_model_classes = None
-    pipeline_model_mapping = None
-
-    # VLMs are always composite
-    _is_composite = True
-
-    def setUp(self):
-        if self.model_tester_class is None:
-            raise ValueError("You have inherited from VLMModelTest but did not set the model_tester_class attribute.")
-        self.model_tester = self.model_tester_class(self)
-        self.config_tester = ConfigTester(self, config_class=self.model_tester.config_class, has_text_modality=False)
-
-        if self.pipeline_model_mapping is None:
-            if self.all_model_classes is not None:
-                raise ValueError(
-                    "Tests that inherit from `VLMModelTest` and set `all_model_classes` must manually set "
-                    "`pipeline_model_mapping`."
-                )
-            else:
-                self.pipeline_model_mapping = self.model_tester.pipeline_model_mapping
-
-        if self.all_model_classes is None:
-            self.all_model_classes = self.model_tester.all_model_classes
-
-    def test_config(self):
-        """Test config common functionality."""
-        self.config_tester.run_common_tests()
-
     def test_mismatching_num_image_tokens(self):
         """
         Tests that VLMs throw an error with explicit message saying what is wrong
diff --git a/tmp.py b/tmp.py
new file mode 100644
index 000000000000..05db1b840b44
--- /dev/null
+++ b/tmp.py
@@ -0,0 +1 @@
+"&&"
diff --git a/tmp_generate.py b/tmp_generate.py
new file mode 100644
index 000000000000..9685bed643ed
--- /dev/null
+++ b/tmp_generate.py
@@ -0,0 +1,63 @@
+import argparse
+import os
+
+import torch
+from torch.distributed.elastic.multiprocessing.errors import record
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.distributed import DistributedConfig
+
+model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
+# model_id = "Qwen/Qwen3-14B"
+# model_id = "Qwen/Qwen3-0.6B"
+# model_id = "Qwen/Qwen1.5-MoE-A2.7B-Chat"
+# model_id = "Qwen/Qwen3-30B-A3B-Instruct-2507"
+
+rank = int(os.environ["RANK"])
+world_size = int(os.environ["WORLD_SIZE"])
+device = torch.device(f"cuda:{rank}")
+# Need to be initialized explicitly to use the `barrier` before loading
+torch.distributed.init_process_group(backend="nccl", rank=rank, world_size=world_size, device_id=rank)
+
+@record
+def main(args):
+
+    distributed_config = DistributedConfig(tp_size=4, tp_plan="auto")
+    model = AutoModelForCausalLM.from_pretrained(model_id, distributed_config=distributed_config, dtype=torch.bfloat16)
+    # model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+    messages = [
+        {"role": "user", "content": "What do you think about life?"},
+    ]
+    inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+    input_size = inputs.input_ids.shape[-1]
+
+    if args.profile:
+        # Warmup
+        with torch.no_grad():
+            _ = model.generate(**inputs, max_new_tokens=5, do_sample=False)
+
+        with torch.profiler.profile(
+            activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
+            record_shapes=True,
+        ) as prof:
+            output = model.generate(**inputs, max_new_tokens=2, do_sample=False)
+
+        if rank == 0:
+            print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=30))
+            prof.export_chrome_trace("trace.json")
+    else:
+        output = model.generate(**inputs, max_new_tokens=100, do_sample=False)
+
+    text = tokenizer.batch_decode(output[:, input_size:])[0]
+    if rank == 0:
+        print(text)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--profile", action="store_true")
+args = parser.parse_args()
+
+main(args)
+
+torch.distributed.destroy_process_group()
\ No newline at end of file
diff --git a/train_fsdp_tp.py b/train_fsdp_tp.py
new file mode 100644
index 000000000000..0232f8b3bc3d
--- /dev/null
+++ b/train_fsdp_tp.py
@@ -0,0 +1,125 @@
+# torchrun --nproc_per_node=4 train_fsdp_tp.py
+
+import argparse
+import os
+
+import torch
+from datasets import load_dataset
+from torch.distributed.tensor import DTensor
+from torch.utils.data import DataLoader
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.distributed import DistributedConfig
+from transformers.distributed.utils import load_optimizer, save_optimizer
+
+def build_packed_dataset(dataset_name, tokenizer, seq_len, dp_rank, dp_world_size):
+    """Stream + tokenize + greedy-pack documents into fixed-length (input, label) windows."""
+    ds = load_dataset(dataset_name, name="en", split="train", streaming=True)
+    ds = ds.shard(num_shards=dp_world_size, index=dp_rank)
+    buf, w = [], seq_len + 1
+
+    def pack(batch):
+        for t in batch["text"]:
+            buf.extend(tokenizer(t)["input_ids"])
+        ids, lbls = [], []
+        while len(buf) >= w:
+            ids.append(buf[:seq_len]); lbls.append(buf[1:w]); del buf[:w]
+        return {"input_ids": ids, "labels": lbls}
+
+    ds = ds.map(pack, batched=True, remove_columns=ds.column_names)
+    return ds.with_format("torch")
+
+def build_fixed_batches(dp_rank):
+    """Load pre-generated fixed batches for a given DP rank."""
+    return torch.load(f"fixed_batches_dp{dp_rank}.pt", weights_only=True)
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--model_name", type=str, default="Qwen/Qwen3-0.6B")
+    parser.add_argument("--num_steps", type=int, default=20)
+    parser.add_argument("--lr", type=float, default=3e-4)
+    parser.add_argument("--seq_len", type=int, default=512)
+    parser.add_argument("--batch_size", type=int, default=1)
+    parser.add_argument("--save_dir", type=str, default="./checkpoints")
+    parser.add_argument("--tp_size", type=int, default=0, help="Tensor parallel size (0 = disabled)")
+    parser.add_argument("--fsdp_size", type=int, default=0, help="FSDP size (0 = disabled)")
+    parser.add_argument("--enable_sp", action="store_true", help="Enable sequence parallelism")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--fixed_batches", action="store_true", help="Use pre-generated fixed batches instead of C4")
+    parser.add_argument("--resume_dir", type=str, default=None, help="Resume from this checkpoint directory")
+    parser.add_argument("--start_step", type=int, default=0, help="Starting step number (for logging)")
+    args = parser.parse_args()
+
+    torch.distributed.init_process_group(backend="nccl")
+    rank, local_rank = int(os.environ["RANK"]), int(os.environ["LOCAL_RANK"])
+    torch.cuda.set_device(local_rank)
+    torch.manual_seed(args.seed)
+
+    dc_kwargs = {}
+    if args.tp_size > 0:
+        dc_kwargs["tp_size"] = args.tp_size
+        dc_kwargs["tp_plan"] = "auto"
+    if args.fsdp_size > 0:
+        dc_kwargs["fsdp_size"] = args.fsdp_size
+        dc_kwargs["fsdp_plan"] = "auto"
+    if args.enable_sp:
+        dc_kwargs["enable_sequence_parallel"] = True
+    distributed_config = DistributedConfig(**dc_kwargs)
+
+    load_path = args.resume_dir if args.resume_dir else args.model_name
+    model = AutoModelForCausalLM.from_pretrained(
+        load_path,
+        distributed_config=distributed_config,
+        torch_dtype=torch.bfloat16,
+    )
+
+    dp_rank = model.device_mesh["fsdp"].get_local_rank() if "fsdp" in model.device_mesh.mesh_dim_names else 0
+    dp_size = model.device_mesh["fsdp"].size() if "fsdp" in model.device_mesh.mesh_dim_names else 1
+
+    if args.fixed_batches:
+        fixed = build_fixed_batches(dp_rank)
+    else:
+        tokenizer = AutoTokenizer.from_pretrained(args.model_name)
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        dataset = build_packed_dataset("allenai/c4", tokenizer, args.seq_len, dp_rank, dp_size)
+        dataloader = iter(DataLoader(dataset, batch_size=args.batch_size))
+
+    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)
+
+    if args.resume_dir:
+        load_optimizer(optimizer, os.path.join(args.resume_dir, "optimizer"))
+        if rank == 0:
+            print(f"Resumed optimizer from {args.resume_dir}")
+
+    model.train()
+    for step in range(args.start_step, args.start_step + args.num_steps):
+        if args.fixed_batches:
+            input_ids = fixed[step]["input_ids"].to(f"cuda:{local_rank}")
+            labels = fixed[step]["labels"].to(f"cuda:{local_rank}")
+        else:
+            batch = next(dataloader)
+            input_ids = batch["input_ids"].to(f"cuda:{local_rank}")
+            labels = batch["labels"].to(f"cuda:{local_rank}")
+        loss = model(input_ids, labels=labels).loss
+        loss.backward()
+
+        # Custom grad clip: convert DTensor grads to local to avoid mixed-mesh torch.stack
+        grads = [p.grad for p in model.parameters() if p.grad is not None]
+        local_grads = [g.full_tensor() if isinstance(g, DTensor) else g for g in grads]
+        total_norm = torch.nn.utils.get_total_norm(local_grads, norm_type=2.0)
+        torch.nn.utils.clip_grads_with_norm_(grads, max_norm=1.0, total_norm=total_norm)
+        optimizer.step()
+        optimizer.zero_grad()
+
+        if rank == 0:
+            print(f"Step {step:>4d} | Loss: {loss.item():.4f} | Grad norm: {total_norm.item():.4f}")
+
+    # Save model (HF format) and optimizer (DCP)
+    model.save_pretrained(args.save_dir)
+    save_optimizer(optimizer, os.path.join(args.save_dir, "optimizer"))
+
+    if rank == 0:
+        print(f"Saved to {args.save_dir}")
+
+    torch.distributed.destroy_process_group()
diff --git a/utils/check_config_attributes.py b/utils/check_config_attributes.py
index fa2283fc7dcb..ad86615ddab1 100644
--- a/utils/check_config_attributes.py
+++ b/utils/check_config_attributes.py
@@ -87,6 +87,7 @@
     "TimeSeriesTransformerConfig": ["num_static_real_features", "num_time_features"],
     "AutoformerConfig": ["num_static_real_features", "num_time_features"],
     "SamVisionConfig": ["mlp_ratio"],
+    "DeepseekOcr2SamVisionConfig": ["mlp_ratio"],
     "Sam3VisionConfig": ["backbone_feature_sizes"],
     "SamHQVisionConfig": ["mlp_ratio"],
     "ClapAudioConfig": ["num_classes"],
@@ -99,6 +100,10 @@
     "GPTNeoXConfig": ["rotary_emb_base"],
     "ShieldGemma2Config": ["mm_tokens_per_image", "vision_config"],
     "Llama4VisionConfig": ["multi_modal_projector_bias", "norm_eps"],
+    "Granite4VisionConfig": [
+        "multimodal_projector_bias",
+        "projector_hidden_act",
+    ],
     "ModernBertConfig": ["local_attention", "reference_compile"],
     "ModernBertDecoderConfig": ["global_attn_every_n_layers", "local_attention", "local_rope_theta"],
     "SmolLM3Config": ["no_rope_layer_interval"],
@@ -107,6 +112,41 @@
     "HiggsAudioV2TokenizerConfig": ["downsample_factor"],
     "CsmConfig": ["tie_codebooks_embeddings"],
     "DeepseekV2Config": ["norm_topk_prob"],
+    "DeepseekV4Config": [
+        "attention_bias",
+        "compress_rate_csa",
+        "compress_rate_hca",
+        "compress_rope_theta",
+        "first_k_dense_replace",
+        "hc_mult",
+        "hc_sinkhorn_iters",
+        "hc_eps",
+        "index_n_heads",
+        "index_head_dim",
+        "index_topk",
+        "kv_lora_rank",
+        "n_group",
+        "num_hash_layers",
+        "num_key_value_heads",
+        "num_nextn_predict_layers",
+        "norm_topk_prob",
+        "o_groups",
+        "o_lora_rank",
+        "qk_nope_head_dim",
+        "qk_rope_head_dim",
+        "q_lora_rank",
+        "rope_interleave",
+        "rope_parameters",
+        "rope_theta",
+        "routed_scaling_factor",
+        "router_jitter_noise",
+        "scoring_func",
+        "n_routed_experts",
+        "n_shared_experts",
+        "swiglu_limit",
+        "topk_group",
+        "v_head_dim",
+    ],
     "EsmFoldConfig": ["esm_ablate_pairwise", "esm_ablate_sequence", "esm_input_dropout", "esm_type"],
     "TrunkConfig": ["cpu_grad_checkpoint", "layer_drop"],
     "SeamlessM4TConfig": True,
@@ -158,6 +198,7 @@
     # Internally uses Got Ocr2 so no need to use in the modeling code as we remap in auto instead
     "PPChart2TableConfig": True,
     "PPChart2TableVisionConfig": True,
+    "SarvamMLAConfig": True,  # Uses DeepseekV3 under the hood
 }
 
 # Common and important attributes, even if they do not always appear in the modeling files (can be a regex pattern)
diff --git a/utils/check_docstrings.py b/utils/check_docstrings.py
index cadaf24ec657..5849bd547555 100644
--- a/utils/check_docstrings.py
+++ b/utils/check_docstrings.py
@@ -131,6 +131,8 @@ class DecoratedItem:
     "AudioFlamingo3Processor",
     "FourOverSixConfig",
     "Llama4Processor",
+    "PromptableConceptSegmentationPipeline",
+    "PromptableVisualSegmentationPipeline",
     # Deprecated
     "InputExample",
     "InputFeatures",
diff --git a/utils/check_import_complexity.py b/utils/check_import_complexity.py
index 8d04841f10d9..1609e16f35c3 100644
--- a/utils/check_import_complexity.py
+++ b/utils/check_import_complexity.py
@@ -31,12 +31,20 @@
 import importlib.abc
 import sys
 import threading
+import time
 from dataclasses import dataclass, field
+from pathlib import Path
 from types import ModuleType
 from typing import Any
 
 
-MAX_IMPORT_COUNT = 1000
+MAX_IMPORT_COUNT = 750
+
+
+ROOT_DIR = Path(__file__).resolve().parents[1]
+SRC_DIR = ROOT_DIR / "src"
+if str(SRC_DIR) not in sys.path:
+    sys.path.insert(0, str(SRC_DIR))
 
 
 # ---------------------------------------------------------------------------
@@ -50,6 +58,12 @@ class ImportNode:
     children: list[ImportNode] = field(default_factory=list)
 
 
+@dataclass
+class TraceResult:
+    tracer: ImportTreeTracer
+    elapsed_seconds: float
+
+
 class LoaderProxy(importlib.abc.Loader):
     """Wrap a real loader to track the import stack during exec_module."""
 
@@ -167,19 +181,21 @@ def roots(self) -> list[ImportNode]:
 # ---------------------------------------------------------------------------
 
 
-def trace_import(target: str) -> ImportTreeTracer:
+def trace_import(target: str) -> TraceResult:
     tracer = ImportTreeTracer()
     original_meta_path = list(sys.meta_path)
     finder = ImportTreeFinder(tracer, original_meta_path)
     sys.meta_path.insert(0, finder)
+    start_time = time.perf_counter()
     try:
         importlib.import_module(target)
     finally:
+        elapsed_seconds = time.perf_counter() - start_time
         try:
             sys.meta_path.remove(finder)
         except ValueError:
             pass
-    return tracer
+    return TraceResult(tracer=tracer, elapsed_seconds=elapsed_seconds)
 
 
 # ---------------------------------------------------------------------------
@@ -223,21 +239,23 @@ def main() -> int:
     args = parser.parse_args()
 
     try:
-        tracer = trace_import("transformers")
+        result = trace_import("transformers")
     except Exception as exc:
         print(f"ERROR: `import transformers` failed: {exc}", file=sys.stderr)
         return 1
 
+    tracer = result.tracer
+
     if args.display:
         print(format_tree(tracer.roots))
         print()
-        print(f"Total modules imported: {tracer.count}")
+        print(f"Total modules imported: {tracer.count} ({result.elapsed_seconds:.3f}s)")
         return 0
 
     if tracer.count > args.max_count:
         print(
             f"Import complexity regression: `import transformers` triggered {tracer.count} module imports "
-            f"(maximum allowed: {args.max_count}).\n"
+            f"in {result.elapsed_seconds:.3f}s (maximum allowed: {args.max_count}).\n"
             f"\n"
             f"Run the following command to display the full import tree and identify the cause:\n"
             f"\n"
@@ -245,7 +263,7 @@ def main() -> int:
         )
         return 1
 
-    print(f"Import complexity OK: {tracer.count} modules (max {args.max_count})")
+    print(f"Import complexity OK: {tracer.count} modules in {result.elapsed_seconds:.3f}s (max {args.max_count})")
     return 0
 
 
diff --git a/utils/check_repo.py b/utils/check_repo.py
index c0c51e189fd7..0ee05652a9bf 100644
--- a/utils/check_repo.py
+++ b/utils/check_repo.py
@@ -200,6 +200,8 @@
         "SLANetBackbone",  # Buildinere is used only for encoding (discretizing) and is tested as part of bigger model
         "SLANeXtSLAHead",  # Buildinere is used only for encoding (discretizing) and is tested as part of bigger model
         "SLANeXtBackbone",  # Building part of bigger (tested) model. Tested implicitly through SLANeXtForTableRecognition.
+        "PPFormulaNetTextModel",  # Building part of bigger (tested) model. Tested implicitly through PPOCRV5MobileDetForObjectDetection.
+        "PPFormulaNetVisionModel",  # Building part of bigger (tested) model. Tested implicitly through PPOCRV5MobileDetForObjectDetection.
         "PPOCRV5MobileDetModel",  # Building part of bigger (tested) model. Tested implicitly through PPOCRV5MobileDetForObjectDetection.
         "PPOCRV5ServerDetModel",  # Building part of bigger (tested) model. Tested implicitly through PPOCRV5ServerDetForObjectDetection.
         "PPDocLayoutV2ReadingOrder",  # Building part of bigger (tested) model. Tested implicitly through PPDocLayoutV2ForObjectDetection.
@@ -239,6 +241,9 @@
         "Qwen3OmniMoeThinkerTextModel",
         "Qwen3OmniMoeForConditionalGeneration",  # Bigger model tested through Qwen3OmniMoeForConditionalGenerationIntegrationTest.
         "Qwen3OmniMoeTalkerCodePredictorModelForConditionalGeneration",  # Building part of bigger (tested) model. Tested implicitly through Qwen3OmniMoeForConditionalGenerationIntegrationTest.
+        "Molmo2TextModel",  # Building part of bigger (tested) model. Tested implicitly through Molmo2ForConditionalGeneration.
+        "Molmo2VisionModel",  # Building part of bigger (tested) model. Tested implicitly through Molmo2ForConditionalGeneration.
+        "Molmo2VisionBackbone",  # Building part of bigger (tested) model. Tested implicitly through Molmo2ForConditionalGeneration.
         "MllamaTextModel",  # Building part of bigger (tested) model. # TODO: add tests
         "MllamaVisionModel",  # Building part of bigger (tested) model. # TODO: add tests
         "Llama4TextModel",  # Building part of bigger (tested) model. # TODO: add tests
@@ -254,11 +259,14 @@
         "MiniCPMV4_6Model",  # Building part of bigger (tested) model. Tested implicitly through MiniCPMV4_6ForConditionalGeneration.
         "MiniCPMV4_6ForConditionalGeneration",  # Tested in MiniCPMV4_6ModelTest via VLMModelTest; check_repo doesn't detect VLMModelTest.conditional_generation_class.
         "InternVLVisionModel",  # Building part of bigger (tested) model
+        "DeepseekOcr2TextModel",  # Building part of bigger (tested) model
+        "DeepseekOcr2VisionModel",  # Building part of bigger (tested) model
         "QianfanOCRVisionModel",  # Building part of bigger (tested) model
         "JanusVisionModel",  # Building part of bigger (tested) model
         "PPDocLayoutV3Model",  # Building part of bigger (tested) model
         "TimesFmModel",  # Building part of bigger (tested) model
         "TimesFm2_5Model",  # Building part of bigger (tested) model
+        "CtsmModel",  # Building part of bigger (tested) model
         "CsmDepthDecoderForCausalLM",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
         "CsmDepthDecoderModel",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
         "CsmBackboneModel",  # Building part of bigger (tested) model. Tested implicitly through CsmForConditionalGenerationIntegrationTest.
@@ -276,11 +284,16 @@
         "PeAudioVideoModel",
         "VibeVoiceAcousticTokenizerEncoderModel",  # Tested through VibeVoiceAcousticTokenizerModel
         "VibeVoiceAcousticTokenizerDecoderModel",  # Tested through VibeVoiceAcousticTokenizerModel
+        "PenguinVLModel",  # Building part of bigger (tested) model. Tested implicitly through PenguinVLForConditionalGeneration.
+        "PenguinVLLanguageModel",  # Building part of bigger (tested) model. Tested implicitly through PenguinVLForConditionalGeneration.
         "PI0Model",  # special arch, tested through PI0ForConditionalGeneration
         "UVDocBridge",  # Building part of a bigger model, tested implicitly through UVDocModel
         "Gemma4VisionModel",  # Building part of a bigger model, tested implicitly
         "Gemma4AudioModel",  # Building part of a bigger model, tested implicitly
         "Sam3LiteTextTextModel",  # Building part of a bigger model, tested implicitly through Sam3LiteTextModel
+        "Granite4VisionTextModel",  # Building part of bigger (tested) model. Tested implicitly through Granite4VisionModel.
+        "Exaone4_5_VisionModel",  # Building part of a bigger model
+        "Qwen3ASRForForcedAlignment",  # Base model tested via Qwen3ASRForConditionalGeneration, and outputs via integration tests
     ]
 )
 
@@ -310,6 +323,8 @@
 IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     # models to ignore for model xxx mapping
     "Aimv2TextModel",
+    "Molmo2VisionModel",
+    "Molmo2VisionBackbone",
     "AlignTextModel",
     "AlignVisionModel",
     "ClapTextModel",
@@ -466,10 +481,15 @@
     "Emu3TextModel",  # Building part of bigger (tested) model
     "JanusVQVAE",  # no autoclass for VQ-VAE models
     "JanusVisionModel",  # Building part of bigger (tested) model
+    "DeepseekOcr2TextModel",  # Building part of bigger (tested) model
+    "DeepseekOcr2VisionModel",  # Building part of bigger (tested) model
     "SLANetSLAHead",  # Building part of bigger (tested) model
     "SLANetBackbone",  # Building part of bigger (tested) model
     "SLANeXtSLAHead",  # Building part of bigger (tested) model
     "SLANeXtBackbone",  # Building part of bigger (tested) model
+    "PPFormulaNetTextModel",  # Building part of bigger (tested) model
+    "PPFormulaNetVisionModel",  # Building part of bigger (tested) model
+    "PPFormulaNetModel",  # Building part of bigger (tested) model
     "PPOCRV5MobileDetModel",  # Building part of bigger (tested) model
     "PPOCRV5ServerDetModel",  # Building part of bigger (tested) model
     "PPDocLayoutV2Model",  # Building part of bigger (tested) model
@@ -500,10 +520,14 @@
     "Qwen3OmniMoeThinkerTextModel",  # Building part of a bigger model
     "Ernie4_5_VLMoeTextModel",  # Building part of a bigger model
     "PeAudioFrameLevelModel",
+    "VideoPrismTextModel",  # Building part of a bigger model
+    "VideoPrismVideoModel",  # Building part of a bigger model
     "Ernie4_5_VL_MoeForConditionalGeneration",  # BC Alias
     "Ernie4_5_VL_MoeModel",  # BC Alias
     "Ernie4_5_VL_MoeTextModel",  # BC Alias
+    "PenguinVLLanguageModel",  # Building part of a bigger model
     "UVDocBridge",  # Building part of a bigger model, tested implicitly through UVDocModel
+    "Granite4VisionTextModel",  # Building part of bigger (tested) model.
 ]
 
 
@@ -822,6 +846,23 @@ def find_tested_models(test_file: str) -> set[str]:
                 continue
             model_tested.add(tested_class)
 
+    # Same as above, but for ALMModelTester. Audio-LMs typically only set `conditional_generation_class`
+    # (no base_model_class).
+    audio_class_match = re.search(r"class \w+\(ALMModelTester\)", content)
+    if audio_class_match is not None:
+        audio_content = content[audio_class_match.start() :]
+        for test_class_type in [
+            "config_class",
+            "conditional_generation_class",
+            "base_model_class",
+            "sequence_classification_class",
+        ]:
+            tested_class = re.findall(rf"{test_class_type}\s+=.*", audio_content)
+            if tested_class:
+                tested_class = tested_class[0].split("=")[1].strip()
+                if tested_class != "None":
+                    model_tested.add(tested_class)
+
     return model_tested
 
 
diff --git a/utils/fetch_hub_objects_for_ci.py b/utils/fetch_hub_objects_for_ci.py
index 0869847a8518..28d306ef1ecf 100644
--- a/utils/fetch_hub_objects_for_ci.py
+++ b/utils/fetch_hub_objects_for_ci.py
@@ -39,6 +39,7 @@
 
 URLS_FOR_TESTING_DATA = [
     # TODO: copy those to our hf-internal-testing dataset and fix all tests using them
+    "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/general_formula_rec_001.png"
     "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo2.jpg",
     "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/doc_test.jpg",
     "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/chart_parsing_02.png",
@@ -79,6 +80,8 @@
     "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
     "https://huggingface.co/datasets/hf-internal-testing/fixtures-captioning/resolve/main/cow_beach_1.png",
     "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4",
+    "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/image_ocr.jpg",
+    "https://huggingface.co/datasets/hf-internal-testing/fixtures_got_ocr/resolve/main/multi_box.png",
 ]
 
 
diff --git a/utils/notification_service.py b/utils/notification_service.py
index 6738341892e1..15862f088f09 100644
--- a/utils/notification_service.py
+++ b/utils/notification_service.py
@@ -935,16 +935,33 @@ def retrieve_artifact(artifact_path: str, gpu: str | None):
         raise ValueError(f"Invalid GPU for artifact. Passed GPU: `{gpu}`.")
 
     _artifact = {}
+    captured_info = []
 
     if os.path.exists(artifact_path):
-        files = os.listdir(artifact_path)
+        files = sorted(os.listdir(artifact_path))
         for file in files:
             try:
                 with open(os.path.join(artifact_path, file)) as f:
-                    _artifact[file.split(".")[0]] = f.read()
+                    content = f.read()
             except UnicodeDecodeError as e:
                 raise ValueError(f"Could not open {os.path.join(artifact_path, file)}.") from e
 
+            artifact_name = file.split(".")[0]
+            if artifact_name == "captured_info" or artifact_name.startswith("captured_info_"):
+                captured_info.append((file, content))
+                continue
+
+            _artifact[artifact_name] = content
+
+    if captured_info:
+        if len(captured_info) == 1 and captured_info[0][0] == "captured_info.txt":
+            _artifact["captured_info"] = captured_info[0][1]
+        else:
+            separator = f"\n\n{'=' * 120}\n\n"
+            _artifact["captured_info"] = separator.join(
+                f"{file}\n{'-' * len(file)}\n{content}" for file, content in captured_info
+            )
+
     return _artifact
 
 
diff --git a/utils/tests_fetcher.py b/utils/tests_fetcher.py
index a138ef2eaacb..01ef79a0d95b 100644
--- a/utils/tests_fetcher.py
+++ b/utils/tests_fetcher.py
@@ -1099,6 +1099,7 @@ def parse_commit_message(commit_message: str) -> dict[str, bool]:
     "tests_non_model": r"tests/[^/]*?/test_.*\.py",
     "tests_training_ci": r"tests/models/.*/test_modeling_.*",
     "tests_tensor_parallel_ci": r"(tests/models/.*/test_modeling_.*|tests/tensor_parallel(?:/test_tensor_parallel\.py)?)",
+    "tests_training_distributed_ci": r"tests/models/.*/test_modeling_.*",
 }
 
 
diff --git a/utils/update_metadata.py b/utils/update_metadata.py
index e61c199b96e7..054fe754c180 100755
--- a/utils/update_metadata.py
+++ b/utils/update_metadata.py
@@ -83,6 +83,16 @@
         "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
         "AutoModelForZeroShotObjectDetection",
     ),
+    (
+        "promptable-concept-segmentation",
+        "MODEL_FOR_PROMPTABLE_CONCEPT_SEGMENTATION_MAPPING_NAMES",
+        "AutoModelForPromptableConceptSegmentation",
+    ),
+    (
+        "promptable-visual-segmentation",
+        "MODEL_FOR_PROMPTABLE_VISUAL_SEGMENTATION_MAPPING_NAMES",
+        "AutoModelForPromptableVisualSegmentation",
+    ),
     ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
     ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
     ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
diff --git a/verify_loading.py b/verify_loading.py
new file mode 100644
index 000000000000..ea008f9626f7
--- /dev/null
+++ b/verify_loading.py
@@ -0,0 +1,137 @@
+# Save/load roundtrip test for distributed models (TP, FSDP, TP+FSDP).
+#
+# Verifies that save_pretrained → from_pretrained preserves model weights by
+# checking that the cross-entropy loss is identical before and after the roundtrip.
+# This catches bugs in DTensor gather-on-save and shard-on-read paths.
+#
+# Usage:
+#   python verify_loading.py --mode single_gpu
+#   torchrun --nproc_per_node=2 verify_loading.py --mode fsdp
+#   torchrun --nproc_per_node=2 verify_loading.py --mode tp
+#   torchrun --nproc_per_node=4 verify_loading.py --mode tp_fsdp
+#   MODEL=Qwen/Qwen3-0.6B torchrun --nproc_per_node=2 verify_loading.py --mode tp
+import argparse
+import os
+import shutil
+
+import torch
+from torch.distributed.tensor import DTensor, Replicate
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers.distributed import DistributedConfig
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--mode", choices=["single_gpu", "fsdp", "tp", "tp_sp", "tp_fsdp", "tp_sp_fsdp"], required=True)
+parser.add_argument("--model", type=str, default=None, help="Model ID (or set MODEL env var)")
+args = parser.parse_args()
+
+model_id = args.model or os.environ.get("MODEL") or os.environ.get("MODEL_ID") or "hf-internal-testing/tiny-random-MixtralForCausalLM"
+
+if args.mode != "single_gpu":
+    torch.distributed.init_process_group(backend="nccl")
+    rank = int(os.environ["RANK"])
+    local_rank = int(os.environ["LOCAL_RANK"])
+    torch.cuda.set_device(local_rank)
+else:
+    rank = 0
+    local_rank = 0
+    torch.cuda.set_device(0)
+
+configs = {
+    "single_gpu": None,
+    "fsdp": DistributedConfig(fsdp_size=2, fsdp_plan="auto"),
+    "tp": DistributedConfig(tp_size=2, tp_plan="auto"),
+    "tp_sp": DistributedConfig(tp_size=2, tp_plan="auto", enable_sequence_parallel=True),
+    "tp_fsdp": DistributedConfig(tp_size=2, tp_plan="auto", fsdp_size=2, fsdp_plan="auto"),
+    "tp_sp_fsdp": DistributedConfig(tp_size=2, tp_plan="auto", fsdp_size=2, fsdp_plan="auto", enable_sequence_parallel=True),
+}
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+text = "The capital of France is Paris. The largest ocean is the Pacific."
+
+
+def materialize_full_logits(logits: torch.Tensor) -> torch.Tensor:
+    if isinstance(logits, DTensor):
+        with torch.no_grad():
+            return logits.redistribute(placements=[Replicate()] * logits.device_mesh.ndim, async_op=False).to_local()
+    return logits
+
+
+def compute_loss(model):
+    inputs = tokenizer(text, return_tensors="pt").to(f"cuda:{local_rank}")
+    input_ids = inputs["input_ids"]
+    # Pad sequence length to a multiple of tp_size so DTensor Shard(1) splits evenly
+    # across ranks in SP mode. Always pad (even for non-TP modes) so that all modes
+    # compute on the same input and losses are directly comparable.
+    max_tp = max((c.tp_size if c is not None else 1) for c in configs.values())
+    seq_len = input_ids.shape[1]
+    if seq_len % max_tp != 0:
+        pad_len = max_tp - (seq_len % max_tp)
+        pad_token_id = tokenizer.pad_token_id if tokenizer.pad_token_id is not None else tokenizer.eos_token_id
+        input_ids = torch.cat([input_ids, input_ids.new_full((1, pad_len), pad_token_id)], dim=1)
+    labels = input_ids.clone()
+    labels[:, seq_len:] = -100  # ignore padding in loss
+    position_ids = torch.arange(input_ids.shape[1], device=input_ids.device).unsqueeze(0)
+
+    model.eval()
+    with torch.no_grad():
+        logits = model(input_ids, position_ids=position_ids).logits
+        logits = materialize_full_logits(logits)
+        loss = torch.nn.functional.cross_entropy(
+            logits.flatten(0, 1).float(),
+            labels.flatten(0, 1),
+            reduction="mean",
+            ignore_index=-100,
+        )
+    return loss.item()
+
+
+# --- Step 1: Load original model and compute loss ---
+model = AutoModelForCausalLM.from_pretrained(model_id, distributed_config=configs[args.mode], dtype=torch.float32)
+if args.mode == "single_gpu":
+    model = model.to("cuda:0")
+
+loss_before = compute_loss(model)
+if rank == 0:
+    print(f"{args.mode}: loss_before = {loss_before:.6f}")
+
+# --- Step 2: Save to local dir (shared path across ranks) ---
+save_dir = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"verify_ckpt_{args.mode}")
+if rank == 0:
+    if os.path.exists(save_dir):
+        shutil.rmtree(save_dir)
+    os.makedirs(save_dir)
+if args.mode != "single_gpu":
+    torch.distributed.barrier()
+model.save_pretrained(save_dir, is_main_process=(rank == 0))
+if rank == 0:
+    print(f"{args.mode}: saved to {save_dir}")
+
+# Ensure all ranks see the saved files before reloading
+if args.mode != "single_gpu":
+    torch.distributed.barrier()
+
+del model
+torch.cuda.empty_cache()
+
+# --- Step 3: Reload from saved checkpoint and compute loss ---
+model2 = AutoModelForCausalLM.from_pretrained(save_dir, distributed_config=configs[args.mode], dtype=torch.float32)
+if args.mode == "single_gpu":
+    model2 = model2.to("cuda:0")
+
+loss_after = compute_loss(model2)
+if rank == 0:
+    print(f"{args.mode}: loss_after  = {loss_after:.6f}")
+
+# --- Step 4: Compare ---
+if rank == 0:
+    diff = abs(loss_before - loss_after)
+    print(f"{args.mode}: diff = {diff:.2e}")
+    if diff < 1e-5:
+        print("PASS: save/load roundtrip is lossless")
+    else:
+        print("FAIL: loss mismatch after save/load roundtrip!")
+
+if args.mode != "single_gpu":
+    torch.distributed.destroy_process_group()